Additional file 6 for “Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus” Nikolaos Skourlis, Michael J. Crowther, Therese M-L. Andersson, Paul C. Lambert Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, Stockholm, Sweden. Biostatistics Research Group, Department of Health Sciences, University of Leicester, University Road, Leicester, UK. *Correspondence: nikolaos.skourlis@ki.se; Tel.: +46-7387-30-384 Example JSON files and MSMplus source code 1) Example JSON files: Ypu should copy the 3 json files below (msboxes.json, predictions_stata_merlin.json and predictions_stata_both_approaches.json) as json files json files msboxes.json, ### msboxes.json file ### { "Nstates":3, "Ntransitions":3, "xvalues": [0.10000,0.60000,0.35000], "yvalues": [0.85000,0.85000,0.35000], "boxwidth":0.20000, "boxheight":0.30000, "statenames": ["(1) Transplant","(2) Platelet recovery","(3) Relapse or death"], "transnames": ["h1","h2","h3"], "tmat":[["NA",1,2], ["NA","NA",3], ["NA","NA","NA"]], "frequencies":[{"time_label":1,"(1) Transplant":2204,"(2) Platelet recovery":0,"(3) Relapse or death":0,"h1":0,"h2":0,"h3":0,"timevar":0.00000},{"time_label":2,"(1) Transplant":1284,"(2) Platelet recovery":853,"(3) Relapse or 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} ######################################### #### 2 MSMplus source code ############## ######################################### ## a) Set a working directory file and save the json files msboxes.json, ## predictions_stata_merlin.json and predictions_stata_both_approaches.json in that file # b) Find within the code the setwd() functions and put the name of your directory file # c) Run the MSMplus source code below. You should now have a local version of the MSMplus ### MSMplus source code### if (!require(visNetwork)) install.packages("visNetwork") if (!require(shiny)) install.packages("shinyjs") if (!require(shiny)) install.packages("shiny") if (!require(mstate)) install.packages("mstate") if (!require(tidyverse)) install.packages("tidyverse") if (!require(mstate)) install.packages("mstate") if (!require(tidyr)) install.packages("tidyr") if (!require(ggplot2)) install.packages("ggplot2") if (!require(DiagrammeR)) install.packages("DiagrammeR") if (!require(stringr)) install.packages("stringr") if (!require(dplyr)) install.packages("dplyr") if (!require(RJSONIO)) install.packages("RJSONIO") if (!require(gapminder)) install.packages("gapminder") if (!require(plyr)) install.packages("plyr") if (!require(viridis)) install.packages("viridis") if (!require(cowplot)) install.packages("cowplot") if (!require(magick)) install.packages("magick") if (!require(StatMeasures)) install.packages("StatMeasures") if (!require("processx")) install.packages("processx") if (!require("webshot")) install.packages("webshot") if (!require("htmlwidgets")) install.packages("htmlwidgets") if (!require("raster")) install.packages("raster") if (!require("jsonlite")) install.packages("jsonlite") if (!require("devtools")) install.packages("devtools") if (!require("usethis")) install.packages("usethis") if (!require("githubinstall")) install.packages("githubinstall") if (!require("shinyMatrix")) install.packages("shinyMatrix") if (!require("dlm")) install.packages("dlm") if (!require("rsvg")) install.packages("rsvg") if (!require("miniUI")) install.packages("miniUI") if (!require("htmltools")) install.packages("htmltools") if (!require("webshot")) install.packages("webshot") #if ( !require("orca") ) {install.packages("orca")} #library("usethis") #library("devtools") #library("githubinstall") #if (!require(devtools)) install.packages("devtools") #devtools::install_github("cpsievert/plotcon17") if (!require("plotly")) install.packages("plotly") if (!require("gridExtra")) install.packages("gridExtra") library("orca") library(rsvg) library(miniUI) library(htmltools) library("webshot") library(visNetwork) library(plotly) library(tidyverse) library(jsonlite) library(webshot) library(htmlwidgets) library(raster) library(plyr) library(viridis) library('RJSONIO') library(ggplot2) library(plotly) library(dplyr) library(formattable) library(reshape2) library(ggplot2) library(gridExtra) library(tidyverse) library(DT) library(shiny.semantic) library(magrittr) library(cowplot) library(imager) library(StatMeasures) library(shinyMatrix) library(dlm) library(gapminder) library(gridExtra) library(shinyjs) library(shiny) #readRenviron("~/.Renviron") Sys.setenv("plotly_username" = "niksko") Sys.setenv("plotly_api_key" = "GCDmoehfftRPyu6TCw80") jscode <- " shinyjs.disableTab = function(name) { var tab = $('.nav li a[data-value=' + name + ']'); tab.bind('click.tab', function(e) { e.preventDefault(); return false; }); tab.addClass('disabled'); } shinyjs.enableTab = function(name) { var tab = $('.nav li a[data-value=' + name + ']'); tab.unbind('click.tab'); tab.removeClass('disabled'); } " css <- ' .disabled { background: #eee !important; cursor: default !important; color: black !important; }' appCSS <- " #loading-content { position: absolute; background: #000000; opacity: 0.9; z-index: 100; left: 0; right: 0; height: 100%; text-align: center; color: #FFFFFF; } " ui <- navbarPage(id="tabs_start", h1("MSMplus"), fluid = TRUE, inverse=TRUE,theme = "bootstrap2.css", tabPanel( h1("Intro"), fluidRow( tags$head( tags$style(type = "text/css", ".navbar-nav { display: -webkit-box; display: -ms-flexbox; -webkit-box-orient: horizontal!important; -webkit-box-direction: normal; -ms-flex-direction: column; flex-direction: column; padding-left: 0px; margin-bottom: 0px; list-style: none; }"), tags$style(type = "text/css", ".form-control { display: block; width: 100%; height: calc(1.5em + 0.75rem + 2px); padding: 0.375rem 0.75rem; font-size: 2rem !important; font-weight: 400; line-height: 1.5; color: #495057; background-color: #fff; background-clip: padding-box; border: 1px solid #ced4da; border-radius: 0.25rem; -webkit-transition: border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; transition: border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; }"), tags$style(type = "text/css", ".btn { display: inline-block; font-weight: 400; color: #495057; text-align: center; vertical-align: middle; cursor: pointer; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; background-color: transparent; border: 1px solid transparent; padding: 0.375rem 0.75rem; font-size: 2rem !important; line-height: 1.5; border-radius: 0.25rem; -webkit-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; }"), tags$style('body {font-size: 20px;}'), tags$style(HTML(type='file', "shiny-input-container{font-size: 12pt !important;}")), tags$style("input[type=checkbox] {transform: scale(2);}"), tags$style("input[type=number] {font-size: 20px;}"), tags$style("input[type=file] {font-size: 20px;}"), tags$style(type = "text/css", " .download_this{ height:40px; width:57px;color:blue; padding-top: 15px;}"), tags$style(type = "text/css", " .upload_this{ height:40px; width:57px;color:blue; padding-top: 15px;}"), tags$style('input[type=radio] {border: 1px;width: 80%; height: 1em;}'), tags$style(type="text/css", "select { width: 400px; }"), tags$style(type="text/css", "textarea { max-height: 400px; }"), tags$style(type='text/css', ".well { max-height: 400px; }"), tags$style(type='text/css', ".span4 { max-height: 400px; }"), tags$style(type="text/css", "select.shiny-bound-input { font-size:20px; height:35px !important;}"), tags$style(type="text/css", "input.shiny-bound-input { font-size:20px; height:35px !important;}"), tags$style(type="text/css", "shiny-output-error-myClass { font-size:20px; height:21px;}"), tags$style(HTML(".shiny-output-error-validation {color: green;}" )), tags$style(type="text/css", "select { max-width: 150px; max-height: 100px;}"), tags$style(type="text/css", "textarea { max-width: 150px; max-height: 100px; }"), tags$style(type='text/css', ".well { max-width: 200px; max-height: 100px;}"), tags$style(type='text/css', ".span4 { max-width: 250px; max-height: 100px;}"), tags$style( ".k-numeric-wrap input {height: 40px;}"), tags$style(type = "text/css", ".irs-grid-text {font-family: 'arial'; color: black; font-size: 20px;}"), tags$style(type = "text/css", ".custom-file-input::before { content: 'Select file'; display: inline-block; background: linear-gradient(top, #f9f9f9, #e3e3e3); border: 1px solid #999; border-radius: 3px; padding: 5px 8px; outline: none; white-space: nowrap; -webkit-user-select: none; cursor: pointer; text-shadow: 1px 1px #fff; font-weight: 700; font-size: 15pt !important;}"), ), column(5,style = "text-align: left;", uiOutput("message_rules1"), uiOutput("to_url1") ), column(7, uiOutput("intro_tutorial"), uiOutput("to_url2"), ) ) ), tabPanel( h1("Interpretation"), fluidRow( tags$style(HTML(".radio-inline { margin-left: 25px !important;}")), column(2, h1("Interepretations"), "What are the interpretations of measures? ", checkboxGroupInput(inputId = "measures", label = "Measures", choices = c("Probability"="prob", "Transition intensity"="trans", "Length of stay"="los", "Differences and ratios"="comp","Extra measures"="extram", "Robustness"="robust"), selected= c("prob","trans","los","vis","comp","extram","robust" )) ), column(10, uiOutput("interpret"), uiOutput("message_prob"), uiOutput("message_trans"), uiOutput("message_los"), uiOutput("message_comp"), uiOutput("message_extram"), uiOutput("message_robust") ) ) ), tabPanel( h1("Load"), uiOutput("pageupload") ) , tabPanel( h1("Model Structure"), uiOutput("pageboxbefore"), uiOutput("pagebox"), fluidRow( tags$head( tags$style("#frequency {font-size:20px;}"), tags$style(HTML(type='number',".irs-grid-text { font-size: 12pt !important; }")), ), column(6, ), column(6, tags$head( tags$style(type='text/css', ".slider-animate-button { font-size: 20pt !important; }")), uiOutput("frequency"), uiOutput("framebox") ) ) ), tabPanel( h1("Setttings"), fluidRow( column(12,h2("1.Select starting state and covariate patterns"))), uiOutput("pageinput1"), fluidRow( column(12,h2("2.Naming states and transitions and extra options"))), uiOutput("pageinput2") ), tabPanel( id = "mytab_p", value = "mytab_p", h1("Probabilities"), useShinyjs(), extendShinyjs(text = jscode,functions =c("disableTab","enableTab")), tags$style(type="text/css",".nav li a.disabled { background-color: #aaa !important;color: #333 !important;cursor: not-allowed !important;border-color: #aaa !important;}"), uiOutput("pagep") ), tabPanel(id = "mytab_h", value = "mytab_h", h1("Hazards"), useShinyjs(), extendShinyjs(text = jscode,functions =c("disableTab","enableTab")), tags$style(type="text/css",".nav li a.disabled { background-color: #aaa !important;color: #333 !important;cursor: not-allowed !important;border-color: #aaa !important;}"), uiOutput("pageh") ), tabPanel( h1("Length of stay"), id = "mytab_los", value = "mytab_los", useShinyjs(), extendShinyjs(text = jscode,functions =c("disableTab","enableTab")), tags$style(type="text/css",".nav li a.disabled { background-color: #aaa !important;color: #333 !important;cursor: not-allowed !important;border-color: #aaa !important;}"), useShinyjs(), extendShinyjs(text = jscode,functions =c("disableTab","enableTab")), uiOutput("pagelos") ), tabPanel(id = "mytab_vis", value = "mytab_vis", h1("Visit"), useShinyjs(), extendShinyjs(text = jscode,functions =c("disableTab","enableTab")), tags$style(type="text/css",".nav li a.disabled { background-color: #aaa !important;color: #333 !important;cursor: not-allowed !important;border-color: #aaa !important;}"), uiOutput("pagevisit") ), tabPanel( h1("User"), id = "mytab_user", value = "mytab_user", useShinyjs(), extendShinyjs(text = jscode,functions =c("disableTab","enableTab")), tags$style(type="text/css",".nav li a.disabled { background-color: #aaa !important;color: #333 !important;cursor: not-allowed !important;border-color: #aaa !important;}"), uiOutput("pageuser") ), tabPanel(id = "mytab_extra", value = "mytab_extra", h1("Extra"), useShinyjs(), extendShinyjs(text = jscode,functions =c("disableTab","enableTab")), tags$style(type="text/css",".nav li a.disabled { background-color: #aaa !important;color: #333 !important;cursor: not-allowed !important;border-color: #aaa !important;}"), uiOutput("page_extra") ) ) server <- function(input, output, session) { output$message_rules1<- renderUI ({ message = p(withMathJax( helpText(strong('Introduction')), helpText("Multistate models (MSM) are used in a variety of epidemiological settings, enabling the study of individuals through different disease states. Studying acute or chronic disease progression, recurrent events such as repeated hospitalisations are typical examples of MSM use. When studying such processes, MSM are used in order to portray accurately, with sufficient complexity the real- world issue under study and provide useful and meaningful predictions."), helpText(strong('Aim of MSMplus')), helpText("MSMplus is a usefull tool for presentation of results from a multi-state analysis in an easy, comprehensible and meaningful way. aiding the user to present the research findings to the targeted audience. A secondary use of the application is that it can contrast the results of two different modelling approaches for the same multi-state setting (e.g clock-reset versus clock forward approach.)") ) ) message return(list(message )) }) to_url1 <- a("Click here for guidance", href="https://nskbiostatistics.shinyapps.io/tabs/") output$to_url1 <- renderUI({ tagList(helpText(strong("First time using MSMplus?")), to_url1) }) output$intro_tutorial <- renderUI({ message = p(withMathJax( helpText(strong('How to upload your results')), helpText("The descriptive and analysis results of the multistate analysis can be supplied either as json files that automatically derived for specific packages in Stata and R, or as manually provided csv files of specific structure. A tutorial for the successful generation of the input files required for the application, including examples in Stata and R, plus the R functions code for R, is provided in the following link:") ) ) return(list(message )) }) to_url2 <- a("Tutorial on preparing the input files", href="https://nskbiostatistics.shinyapps.io/supplementary/") output$to_url2 <- renderUI({ tagList("URL link:", to_url2) }) ###### pageupload has 4 different scenarios, upload in csv or json file and aim of presenting results or comparing approaches######## ####the examples follow the aims########### output$pageupload <- renderUI({ fluidRow( tags$style(HTML(".shiny-output-error-validation {color: green;}")), tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), tags$head( tags$style(type = "text/css", ".navbar-nav {display: -webkit-box;display: -ms-flexbox;-webkit-box-orient: horizontal!important; -webkit-box-direction: normal; -ms-flex-direction: column;flex-direction: column;padding-left: 0px;margin-bottom: 0px; list-style: none;}"), tags$style(type = "text/css", ".form-control {display: block;width: 100%;height: calc(1.5em + 0.75rem + 2px);padding: 0.375rem 0.75rem; font-size: 2rem !important;font-weight: 400;line-height: 1.5;color: #495057;background-color: #fff; background-clip: padding-box;border: 1px solid #ced4da;border-radius: 0.25rem;-webkit-transition: border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; transition: border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; }"), tags$style(type = "text/css", ".btn {display: inline-block; font-weight: 400;color: #495057;text-align: center;vertical-align: middle; cursor: pointer; -webkit-user-select: none;-moz-user-select: none;-ms-user-select: none;user-select: none; background-color: transparent; border: 1px solid transparent;padding: 0.375rem 0.75rem; font-size: 2rem !important;line-height: 1.5;border-radius: 0.25rem; -webkit-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; }"), tags$style('body {font-size: 20px;}'), tags$style(HTML(type='file', "shiny-input-container{font-size: 12pt !important;}")), tags$style("input[type=checkbox] {transform: scale(2);}"), tags$style("input[type=number] {font-size: 20px;}"), tags$style("input[type=file] {font-size: 20px;}"), tags$style(type = "text/css", " .download_this{ height:40px; width:57px;color:blue; padding-top: 15px;}"), tags$style(type = "text/css", " .upload_this{ height:40px; width:57px;color:blue; padding-top: 15px;}"), tags$style('input[type=radio] {border: 1px;width: 80%; height: 1em;}'), tags$style(type="text/css", "select { width: 400px; }"), tags$style(type="text/css", "textarea { max-height: 400px; }"), tags$style(type='text/css', ".well { max-height: 400px; }"), tags$style(type='text/css', ".span4 { max-height: 400px; }"), tags$style(type="text/css", "select.shiny-bound-input { font-size:20px; height:35px !important;}"), tags$style(type="text/css", "input.shiny-bound-input { font-size:20px; height:35px !important;}"), tags$style(type="text/css", "shiny-output-error-myClass { font-size:20px; height:21px;}"), tags$style(HTML(".shiny-output-error-validation {color: green;}" )), tags$style(type="text/css", "select { max-width: 150px; max-height: 100px;}"), tags$style(type="text/css", "textarea { max-width: 150px; max-height: 100px; }"), tags$style(type='text/css', ".well { max-width: 200px; max-height: 100px;}"), tags$style(type='text/css', ".span4 { max-width: 250px; max-height: 100px;}"), tags$style( ".k-numeric-wrap input {height: 40px;}"), tags$style(type = "text/css", ".irs-grid-text {font-family: 'arial'; color: black; font-size: 20px;}"), tags$style(type = "text/css", ".custom-file-input::before {content: 'Select file';display: inline-block;background: linear-gradient(top, #f9f9f9, #e3e3e3); border: 1px solid #999; border-radius: 3px; padding: 5px 8px; outline: none; white-space: nowrap; -webkit-user-select: none; cursor: pointer; text-shadow: 1px 1px #fff; font-weight: 700; font-size: 15pt !important;}"), ), column(6, h1("Upload datasets"), radioButtons(inputId="loadtype", label= "File type (csv or json)", choices=c("json","csv"),selected = "json"), div(radioButtons(inputId="aimtype", label= "Aim", choices=c("Single multistate model"="present","Compare 2 multistate models"="compare"),selected = "present", inline=FALSE, width="100%"), style = "text-align: left; margin-right: 3px;"), conditionalPanel(condition="input.loadtype =='json' && input.aimtype =='present'", uiOutput("example_present1") ) , conditionalPanel(condition="input.loadtype =='json' && input.aimtype =='compare'", uiOutput("example_compare1") ), conditionalPanel("input.loadtype=='csv' && input.aimtype=='present'", uiOutput("example_present2") ), conditionalPanel("input.loadtype=='csv' && input.aimtype=='compare'", uiOutput("example_compare2") ) ), column(6, uiOutput("message"), uiOutput("message2"), uiOutput("message3"), conditionalPanel(condition="input.loadtype =='json' && input.aimtype =='present' && input.example=='No'", uiOutput("jsonupload") ) , conditionalPanel(condition="input.loadtype =='json' && input.aimtype =='compare' && input.compare_approach=='No'", uiOutput("twojsonupload") ), conditionalPanel("input.loadtype=='csv' && input.aimtype=='present' && input.example2=='No'", uiOutput("csvupload1"), uiOutput("message4"), uiOutput("csvupload2"), uiOutput("message6"),uiOutput("message8")#,verbatimTextOutput("message10"), ), conditionalPanel("input.loadtype=='csv' && input.aimtype=='compare' && input.compare_approach2=='No'", uiOutput("twocsvupload1"), uiOutput("message5"), uiOutput("twocsvupload2"), uiOutput("message7"),uiOutput("message9") ) , # uiOutput("message5") ) ) }) output$example_present1<- renderUI({ item_list <- list() item_list[[1]] <- radioButtons(inputId="example", label= "Example (EBMT)", choices=c("No","Yes"),selected = "No") do.call(tagList, item_list) }) output$example_present2<- renderUI({ item_list <- list() item_list[[1]] <- radioButtons(inputId="example2", label= "Example (EBMT)", choices=c("No","Yes"),selected = "No") do.call(tagList, item_list) }) output$example_compare1<- renderUI({ item_list <- list() item_list[[1]] <- radioButtons(inputId="compare_approach", label= "Example (EBMT)- Compare approaches", choices=c("No","Yes"),selected = "No") do.call(tagList, item_list) }) output$example_compare2<- renderUI({ item_list <- list() item_list[[1]] <- radioButtons(inputId="compare_approach2", label= "Example (EBMT)- Compare approaches", choices=c("No","Yes"),selected = "No") do.call(tagList, item_list) }) output$jsonupload <- renderUI({ item_list <- list() item_list[[1]] <- helpText("To derive the msboxes graph, a json file with the information on the states and transitions should be provided. msboxes command provides the json file") item_list[[2]] <- fileInput("json1_pr", h1("Upload MSM summary information json file"), accept = c(".json")) item_list[[3]] <- helpText("To derive the graphs from the predictions, a json file with the information from the estimations should be provided. mspredict_adjusted command provides those estimates") item_list[[4]] <- div(fileInput("json2", h1("Upload MSM analysis results json file"), accept = c(".json")), style="font-size:150%; font-family:Arial;" ) do.call(tagList, item_list) }) output$twojsonupload <- renderUI({ item_list <- list() item_list[[1]] <- helpText("To derive the msboxes graph, a json file with the information on the states and transitions should be provided. msboxes command provides the json file") item_list[[2]] <- fileInput("json1_cp", h1("Upload MSM summary information json file"), accept = c(".json")) item_list[[3]] <- helpText("To derive the graphs from the predictions, a json file with the information from the estimations should be provided. First approach") item_list[[4]] <- div(fileInput("json2a", h1("Upload MSM analysis results 1st approach json file"), accept = c(".json")), style="font-size:150%; font-family:Arial;" ) item_list[[5]] <- helpText("To derive the graphs from the predictions, a json file with the information from the estimations should be provided. Second approach") item_list[[6]] <- div(fileInput("json2b", h1("Upload MSM analysis results 2nd approach json file"), accept = c(".json")), style="font-size:150%; font-family:Arial;" ) do.call(tagList, item_list) }) output$csvupload1 <- renderUI({ item_list <- list() item_list[[1]] <- numericInput(inputId="Nstates_pr", label= "Number of states",value=1) item_list[[2]] <- fileInput("csv1_pr", h1("Upload frequencies csv file (optional) (only comma delimited text files)"), accept = c(".csv")) do.call(tagList, item_list) }) output$csvupload2 <- renderUI({ matrixnames=vector() shiny::validate(need(input$Nstates_pr<50 , "Please define a number of states"), need(input$Nstates_pr!=0, "Please define a number of states different than 0")) Nstates=input$Nstates_pr for (i in 1:input$Nstates_pr) { matrixnames[i]= paste0("State ",i) } item_list <- list() item_list[[1]] <-h1("Transition matrix") item_list[[2]] <- matrixInput(inputId="tmat_input_pr", value = matrix(NA, input$Nstates_pr, input$Nstates_pr,dimnames = list(matrixnames, matrixnames)), rows = list(names=TRUE), cols = list(names=TRUE), class = "numeric", paste = FALSE, copy = FALSE) item_list[[3]]<- fileInput("csv2", h1("Upload csv of analysis results following the specific naming instructions"), accept = c(".csv")) do.call(tagList, item_list) }) output$twocsvupload1 <- renderUI({ item_list <- list() item_list[[1]] <- numericInput(inputId="Nstates_cp", label= "Number of states",value=1) item_list[[2]] <- fileInput("csv1_cp", h1("Upload frequencies csv file (optional) (only comma delimited text files)"), accept = c(".csv")) do.call(tagList, item_list) }) output$twocsvupload2 <- renderUI({ matrixnames=vector() shiny::validate(need(input$Nstates_cp<50 , "Please define a number of states"), need(input$Nstates_cp!=0, "Please define a number of states different than 0")) Nstates=input$Nstates_cp for (i in 1:input$Nstates_cp) { matrixnames[i]= paste0("State ",i) } item_list <- list() item_list[[1]] <-h1("Transition matrix") item_list[[2]] <- matrixInput(inputId="tmat_input_cp", value = matrix(NA, input$Nstates_cp, input$Nstates_cp,dimnames = list(matrixnames, matrixnames)), rows = list(names=TRUE), cols = list(names=TRUE), class = "numeric", paste = FALSE, copy = FALSE) item_list[[3]]<- fileInput("csv2a", h1("Upload csv of analysis results of 1st approach"), accept = c(".csv")) item_list[[4]]<- fileInput("csv2b", h1("Upload csv of analysis results of 2nd approach"), accept = c(".csv")) do.call(tagList, item_list) }) json1manual<-reactive ({ #options(scipen = 999) if (input$aimtype=="present") { Nstates=input$Nstates_pr } else if (input$aimtype=="compare") { Nstates=input$Nstates_cp } boxwidth=0.15 boxheight=0.15 if (input$aimtype=="present") {tmatnamed= as.matrix(input$tmat_input_pr)} else if (input$aimtype=="compare") {tmatnamed= as.matrix(input$tmat_input_cp)} names=vector() for (i in 1:Nstates) {names[i]=paste0("State",i)} x=vector() y=vector() r=0.5 slice=360/Nstates for (i in 1:Nstates) { x[i]= r+ 0.4*cos(slice*(i-1)*(pi/180)) y[i]= r+ 0.4*sin(slice*(i-1)*(pi/180)) } # Statenames statenames= names tmatnamed2=tmatnamed tmatnamed2[is.na(tmatnamed2)] <- 0 #Number of transitions Ntransitions=max(tmatnamed2) #### Transitions values u=list() for (i in 1:nrow(tmatnamed2)) { u[[i]]=unique(tmatnamed2[i,][which(!is.na(tmatnamed2[i,]))]) } u_unlist=sort(unlist(u, recursive = TRUE, use.names = TRUE)) transitions=order(u_unlist[u_unlist!=0]) transnames=vector() for (j in 1:Ntransitions) { transnames[j]=paste0("h",j) } # here the csv frequencies will be included in the app list_desc=list() list_desc[[1]]=Nstates list_desc[[2]]= Ntransitions list_desc[[3]]=x list_desc[[4]]=y list_desc[[5]]=boxwidth list_desc[[6]]=boxheight list_desc[[7]]=statenames list_desc[[8]]=transnames list_desc[[9]]=tmatnamed # L <- readLines(input$csv1$datapath, n = 1) # shiny::validate(need(grepl(";", L),"Provide ; delimited csv file")) if (is.null(input$csv1_pr) & is.null(input$csv1_cp) ) { list_desc_final=list(Nstates= list_desc[[1]], Ntransitions= list_desc[[2]], xvalues= list_desc[[3]], yvalues=list_desc[[4]] , boxwidth=list_desc[[5]], boxheight= list_desc[[6]], statenames=list_desc[[7]],transnames=list_desc[[8]], tmat= list_desc[[9]]) } if (!is.null(input$csv1_pr)) { frequencies= as.data.frame(read.table(input$csv1_pr$datapath,header=TRUE, sep=",")) shiny::validate(need(!is.null(frequencies$time_label) , "Please check that the frequency file was uploaded with the correct format and that it has variable time_label"), need(!is.null(frequencies$timevar), "Please check that the frequency file was uploaded with the correct format and that it has variable timevar"), need(ncol(frequencies)==(Nstates+ Ntransitions+2), "Please check that you have specified frequency variables for all states and transitions")) list_desc_final=list(Nstates= list_desc[[1]], Ntransitions= list_desc[[2]], xvalues= list_desc[[3]], yvalues=list_desc[[4]] , boxwidth=list_desc[[5]], boxheight= list_desc[[6]], statenames=list_desc[[7]],transnames=list_desc[[8]], tmat= list_desc[[9]], frequencies= frequencies ) } else if (!is.null(input$csv1_cp)) { frequencies= as.data.frame(read.table(input$csv1_cp$datapath,header=TRUE, sep=",")) shiny::validate(need(!is.null(frequencies$time_label) , "Please check that the frequency file was uploaded with the correct format and that it has variable time_label"), need(!is.null(frequencies$timevar), "Please check that the frequency file was uploaded with the correct format and that it has variable timevar"), need(ncol(frequencies)==(Nstates+ Ntransitions+2), "Please check that you have specified frequency variables for all states and transitions")) list_desc_final=list(Nstates= list_desc[[1]], Ntransitions= list_desc[[2]], xvalues= list_desc[[3]], yvalues=list_desc[[4]] , boxwidth=list_desc[[5]], boxheight= list_desc[[6]], statenames=list_desc[[7]],transnames=list_desc[[8]], tmat= list_desc[[9]], frequencies= frequencies ) } exportJson <- toJSON(list_desc_final, pretty = TRUE,force = TRUE, na='string') exportJson }) json2manual<-reactive ({ if (input$loadtype=="json") {return()} else if (input$aimtype=="present" & input$loadtype=="csv") { if (is.null(input$csv2)) return() else { data<- read.table(input$csv2$datapath,header=TRUE, sep=",") shiny::validate(need(!is.null(data$timevar) , "Please check that you have specified the timevar variable"), need(!is.null(data$atlist), "Please check that you have specified the atlist variable"), need(!is.null(data$Nats), "Please check that you have specified the Nats variable"), need(!is.null(data$Ntransitions),"Please check that you have specified the Ntransitions variable"), need(length(which( !startsWith(names(data),"timevar") & !startsWith(names(data),"atlist") & !startsWith(names(data),"Nats") & !startsWith(names(data),"Ntransitions") & !startsWith(names(data),"P_") & !startsWith(names(data),"Haz_") & !startsWith(names(data),"Los_") & !startsWith(names(data),"Visit_") & !startsWith(names(data),"User_") & !startsWith(names(data),"Number_") & !startsWith(names(data),"Next_") & !startsWith(names(data),"Soj_") & !startsWith(names(data),"First_")))==0, "You may have specified a variable which does not follow the naming rules") ) tmatnamed= as.matrix(input$tmat_input_pr) tmatnamed2=tmatnamed tmatnamed2[is.na(tmatnamed2)] <- 0 Ntransitions_tmat=max(tmatnamed2) shiny::validate(need(Ntransitions_tmat==data$Ntransitions , "The transition matrix you specified has different number of transitions than the ones specified at the csv results file")) final_list=list() final_list[[1]]=c(unique(data$timevar)) final_list[[2]]=c(unique(data$Nats)) final_list[[3]]=c(unique(data$Ntransitions)) final_list[[4]]=c(unique(data$atlist)) names(final_list)[1:4]<-c("timevar", "Nats", "Ntransitions", "atlist") for (i in 5:ncol(data)) { final_list[[i]]=matrix(nrow=unique(data$Nats), ncol=length(unique(data$timevar)),NA) names(final_list)[i] <- colnames(data)[i] for (k in 1:(data$Nats[1])) { final_list[[i]][k,] =data[which(data$atlist==unique(data$atlist)[k]),i] } if (length(grep("diff", names(final_list)[i])) | length(grep("ratio", names(final_list)[i])==1) ) {final_list[[i]]= final_list[[i]][-1,]} } final_list[[ncol(data)+1]]=as.matrix(input$tmat_input_pr) names(final_list)[ncol(data)+1]<-c("tmat") final_list$timevar=as.vector(final_list$timevar) final_list$atlist=as.vector(final_list$atlist) exportjson <- toJSON(final_list, pretty = TRUE,force = TRUE, na='string') exportjson } } else if (input$aimtype=="compare" & input$loadtype=="csv") { if (is.null(input$csv2a) |is.null(input$csv2b) ) {return()} data1<- read.table(input$csv2a$datapath,header=TRUE, sep=",") shiny::validate(need(!is.null(data1$timevar) , "Please check that you have specified the timevar variable"), need(!is.null(data1$atlist), "Please check that you have specified the atlist variable"), need(!is.null(data1$Nats), "Please check that you have specified the Nats variable"), need(!is.null(data1$Ntransitions),"Please check that you have specified the Ntransitions variable"), need(length(which( !startsWith(names(data1),"timevar") & !startsWith(names(data1),"atlist") & !startsWith(names(data1),"Nats") & !startsWith(names(data1),"Ntransitions") & !startsWith(names(data1),"P_") & !startsWith(names(data1),"Haz_") & !startsWith(names(data1),"Los_") & !startsWith(names(data1),"Visit_") & !startsWith(names(data1),"User_") & !startsWith(names(data1),"Number_") & !startsWith(names(data1),"Next_") & !startsWith(names(data1),"Soj_") & !startsWith(names(data1),"First_")))==0, "You may have specified a variable which does not follow the naming rules") ) tmatnamed= as.matrix(input$tmat_input_cp) tmatnamed2=tmatnamed tmatnamed2[is.na(tmatnamed2)] <- 0 Ntransitions_tmat=max(tmatnamed2) shiny::validate(need(Ntransitions_tmat==data1$Ntransitions , "The transition matrix you specified has different number of transitions than the ones specified at the 1st csv results file")) final_list_a=list() final_list_a[[1]]=c(unique(data1$timevar)) final_list_a[[2]]=c(unique(data1$Nats)) final_list_a[[3]]=c(unique(data1$Ntransitions)) final_list_a[[4]]=c(unique(data1$atlist)) names(final_list_a)[1:4]<-c("timevar", "Nats", "Ntransitions", "atlist") for (i in 5:ncol(data1)) { final_list_a[[i]]=matrix(nrow=unique(data1$Nats), ncol=length(unique(data1$timevar)),NA) names(final_list_a)[i] <- colnames(data1)[i] for (k in 1:(data1$Nats[1])) { final_list_a[[i]][k,] =data1[which(data1$atlist==unique(data1$atlist)[k]),i] } if (length(grep("diff", names(final_list_a)[i])) | length(grep("ratio", names(final_list_a)[i])==1) ) {final_list_a[[i]]= final_list_a[[i]][-1,]} } final_list_a[[ncol(data1)+1]]=as.matrix(input$tmat_input_cp) names(final_list_a)[ncol(data1)+1]<-c("tmat") final_list_a$timevar=as.vector(final_list_a$timevar) final_list_a$atlist=as.vector(final_list_a$atlist) list2a <- final_list_a #toJSON(final_list_a, pretty = TRUE,force = TRUE, na='string') data2<- read.table(input$csv2b$datapath,header=TRUE, sep=",") shiny::validate(need(!is.null(data2$timevar) , "Please check that you have specified the timevar variable"), need(!is.null(data2$atlist), "Please check that you have specified the atlist variable"), need(!is.null(data2$Nats), "Please check that you have specified the Nats variable"), need(!is.null(data2$Ntransitions),"Please check that you have specified the Ntransitions variable"), need(length(which(!startsWith(names(data2),"timevar") & !startsWith(names(data2),"atlist") & !startsWith(names(data2),"Nats") & !startsWith(names(data2),"Ntransitions") & !startsWith(names(data2),"P_") & !startsWith(names(data2),"Haz_") & !startsWith(names(data2),"Los_") & !startsWith(names(data2),"Visit_") & !startsWith(names(data2),"User_") & !startsWith(names(data2),"Number_") & !startsWith(names(data2),"Next_") & !startsWith(names(data2),"Soj_") & !startsWith(names(data2),"First_")))==0, "You may have specified a variable which does not follow the naming rules") ) shiny::validate(need(Ntransitions_tmat==data2$Ntransitions , "The transition matrix you specified has different number of transitions than the ones specified at the 2nd csv results file")) final_list_b=list() final_list_b[[1]]=c(unique(data2$timevar)) final_list_b[[2]]=c(unique(data2$Nats)) final_list_b[[3]]=c(unique(data2$Ntransitions)) final_list_b[[4]]=c(unique(data2$atlist)) names(final_list_b)[1:4]<-c("timevar", "Nats", "Ntransitions", "atlist") for (i in 5:ncol(data2)) { final_list_b[[i]]=matrix(nrow=unique(data2$Nats), ncol=length(unique(data2$timevar)),NA) names(final_list_b)[i] <- colnames(data2)[i] for (k in 1:(data2$Nats[1])) { final_list_b[[i]][k,] =data2[which(data2$atlist==unique(data2$atlist)[k]),i] } if (length(grep("diff", names(final_list_b)[i])) | length(grep("ratio", names(final_list_b)[i])==1) ) {final_list_b[[i]]= final_list_b[[i]][-1,]} } final_list_b[[ncol(data2)+1]]=as.matrix(input$tmat_input_cp) names(final_list_b)[ncol(data2)+1]<-c("tmat") final_list_b$timevar=as.vector(final_list_b$timevar) final_list_b$atlist=as.vector(final_list_b$atlist) list2b <- final_list_b Nstates=ncol(list2b$tmat) Ntransitions=max(list2b$tmat[which(!is.na(list2b$tmat))]) y=vector() for (i in 1:Nstates) { for (k in 1:Nstates) { # if (i<=k) { j=i+Nstates g=k+Nstates y=sub(paste0("_to_",k),paste0("_to_",g),names(list2b) ) names(list2b)=y # } } } list2b=list2b[-which(!startsWith(names(list2b),"User") == FALSE)] names(list2b) for (i in 1:Ntransitions) { k= i+ Ntransitions names(list2b)=sub(paste0("h",i),paste0("h",k),names(list2b) ) } ### Create a hypertmatrix#### list2b$tmat2= list2b$tmat+Ntransitions l <- list(list2b$tmat,list2b$tmat2) list2b$hypertmat<- as.matrix(bdiag(l)) list2b$hypertmat[which(list2b$hypertmat==0)]=NA list2b$hypertmat list2a=list2a[names(list2a) %in% "tmat" == FALSE] list2b=list2b[names(list2b) %in% "tmat" == FALSE] list2b=list2b[names(list2b) %in% "tmat2" == FALSE] list2b$tmat=list2b$hypertmat final_list=list() final_list=c(list2a,list2b) exportjson <- toJSON(final_list, pretty = TRUE,force = TRUE, na='string') exportjson } }) output$message<- renderUI ({ if (input$loadtype=="json" & input$aimtype=="present") { if (is.null(input$json1) & input$example=="No") { return("Provide the json file with the msm box details") } } else if (input$loadtype=="json" & input$aimtype=="compare") { if (is.null(input$json1) & input$compare_approach=="No") { return("Provide the json file with the msm box details") } } else if (input$loadtype=="csv" & input$aimtype=="present") { if (length(which(!is.na(input$tmat_input_pr))==TRUE)==0 & input$example2=="No") { return("Provide transition matrix") } } else if (input$loadtype=="csv" & input$aimtype=="compare") { if ( length(which(!is.na(input$tmat_input_cp))==TRUE)==0 & input$compare_approach2=="No") { return("Provide transition matrix") } } else { return("") } }) output$message2<- renderUI ({ if (input$loadtype=="json" & input$aimtype=="present") { if (is.null(input$json2) & input$example=="No") { return("Provide the json file with the predictions") } } else if (input$loadtype=="csv" & input$aimtype=="present" ) { if (is.null(input$csv2) & input$example2=="No") { return("Provide the csv file with the predictions") } } else if (input$loadtype=="json" & input$aimtype=="compare" ) { if ((is.null(input$json2a) | is.null(input$json2b)) & input$compare_approach=="No") { return("Provide the json files with the predictions from the two approaches") } } else if (input$loadtype=="csv" & input$aimtype=="compare" ) { if ((is.null(input$csv2a)| is.null(input$csv2b)) & input$compare_approach2=="No") { return("Provide the csv files with the predictions from the two approaches") } } else {return("")} }) output$message3<- renderUI ({ if ( input$loadtype=="json" & input$aimtype=="present" ) { if (input$example=="Yes") { message = p(withMathJax( helpText(strong('Example dataset: EBMT')), helpText("The data originate from the European Blood and Marrow Transplant registry.\nThe dataset consists of 2204 patients who received bone marrow transplantation.\nThe three states a patient can be in is 1) Post- transplant, 2) Platelet recovery 3) Relapse/Death.\nThe covariate patterns used in this example are the 3 age categories, namely <20 y.old, 20-40y.old and >40 y.old .") )) message } } else if ( input$loadtype=="json" & input$aimtype=="compare") { if (input$compare_approach=="Yes") { message = p(withMathJax( helpText(strong('Example dataset: EBMT')), helpText("The data originate from the European Blood and Marrow Transplant registry.\nThe dataset consists of 2204 patients who received bone marrow transplantation.\nThe three states a patient can be in is 1) Post- transplant, 2) Platelet recovery 3) Relapse/Death.\nThe covariate patterns used in this example are the 3 age categories, namely <20 y.old, 20-40y.old and >40 y.old .") )) message } } else if ( input$loadtype=="csv" & input$aimtype=="present" ) { if (input$example2=="Yes") { message = p(withMathJax( helpText(strong('Example dataset: EBMT')), helpText("The data originate from the European Blood and Marrow Transplant registry.\nThe dataset consists of 2204 patients who received bone marrow transplantation.\nThe three states a patient can be in is 1) Post- transplant, 2) Platelet recovery 3) Relapse/Death.\nThe covariate patterns used in this example are the 3 age categories, namely <20 y.old, 20-40y.old and >40 y.old .") )) message } } else if ( input$loadtype=="csv" & input$aimtype=="compare" ) { if (input$compare_approach2=="Yes") { message = p(withMathJax( helpText(strong('Example dataset: EBMT')), helpText("The data originate from the European Blood and Marrow Transplant registry.\nThe dataset consists of 2204 patients who received bone marrow transplantation.\nThe three states a patient can be in is 1) Post- transplant, 2) Platelet recovery 3) Relapse/Death.\nThe covariate patterns used in this example are the 3 age categories, namely <20 y.old, 20-40y.old and >40 y.old .") )) message } } else {return("")} }) output$message4<- renderUI ({ json1manual= fromJSON(json1manual(), flatten=TRUE) #options(scipen = 999) if (input$aimtype=="present") { Nstates=input$Nstates_pr;tmatnamed= as.matrix(input$tmat_input_pr) } else if (input$aimtype=="compare") {Nstates=input$Nstates_cp;tmatnamed= as.matrix(input$tmat_input_cp)} tmatnamed2=tmatnamed tmatnamed2[is.na(tmatnamed2)] <- 0 #Number of transitions Ntransitions=max(tmatnamed2) if (!is.null(json1manual$frequencies)) { shiny::validate(need(!is.null(json1manual$frequencies$time_label) , "Please check that the frequency file was uploaded with the correct format and that it has variable time_label"), need(!is.null(json1manual$frequencies$timevar), "Please check that the frequency file was uploaded with the correct format and that it has variable timevar"), need(ncol(json1manual$frequencies)==(Nstates+ Ntransitions+2), "Please check that you have specified frequency variables for for the correct number of states and transitions")) } }) output$message5<- renderUI ({ json1manual= fromJSON(json1manual(), flatten=TRUE) #options(scipen = 999) if (input$aimtype=="present") { Nstates=input$Nstates_pr;tmatnamed= as.matrix(input$tmat_input_pr) } else if (input$aimtype=="compare") {Nstates=input$Nstates_cp;tmatnamed= as.matrix(input$tmat_input_cp)} tmatnamed2=tmatnamed tmatnamed2[is.na(tmatnamed2)] <- 0 #Number of transitions Ntransitions=max(tmatnamed2) if (!is.null(json1manual$frequencies)) { shiny::validate(need(!is.null(json1manual$frequencies$time_label) , "Please check that the frequency file was uploaded with the correct format and that it has variable time_label"), need(!is.null(json1manual$frequencies$timevar), "Please check that the frequency file was uploaded with the correct format and that it has variable timevar"), need(ncol(json1manual$frequencies)==(Nstates+ Ntransitions+2), "Please check that you have specified frequency variables for for the correct number of states and transitions")) } }) output$message6<- renderUI ({ #options(scipen = 999) if (!is.null(json2manual() )) { json2manual= fromJSON(json2manual(), flatten=TRUE) shiny::validate(need(!is.null(json2manual$timevar) , "Please check that you have specified the timevar variable"), need(!is.null(json2manual$atlist), "Please check that you have specified the atlist variable"), need(!is.null(json2manual$Nats), "Please check that you have specified the Nats variable"), need(!is.null(json2manual$Ntransitions),"Please check that you have specified the Ntransitions variable") ) } }) output$message7<- renderUI ({ if (!is.null(json2manual() )) { json2manual= fromJSON(json2manual(), flatten=TRUE) shiny::validate(need(!is.null(json2manual$timevar) , "Please check that you have specified the timevar variable"), need(!is.null(json2manual$atlist), "Please check that you have specified the atlist variable"), need(!is.null(json2manual$Nats), "Please check that you have specified the Nats variable"), need(!is.null(json2manual$Ntransitions),"Please check that you have specified the Ntransitions variable"), need(length(which(!startsWith(names(json2manual),"P_") | !startsWith(names(json2manual),"Haz_") | !startsWith(names(json2manual),"Los_") | !startsWith(names(json2manual),"Visit_") | !startsWith(names(json2manual),"User_") | !startsWith(names(json2manual),"Number_") | !startsWith(names(json2manual),"Next_") | !startsWith(names(json2manual),"Soj_") | !startsWith(names(json2manual),"First_")))==0, "You may have specified a variable which does not follow the naming rules") ) } }) output$message8<- renderUI ({ p_def = withMathJax( helpText('If issues when uploading the results csv file persist, check a) That you have used . as the decimal pointer and b) that you have put NA if whenever a variable has a missing value') ) p_def }) output$message9<- renderUI ({ p_def = withMathJax( helpText('If issues when uploading the results csv file persist, check a) That you have used . as the decimal pointer and b) that you have put NA if whenever a variable has a missing value') ) p_def }) #Functions ### Function deriving the frequencies over time freq_func_total <- function(msdata,msid,names_of_ststates, values_ststates, names_of_nastates, values_nastates, names_of_abstates,values_abstates, names_of_transitions,values_of_transitions, time, timevar,scale_inner=1) { library(plyr) library(viridis) library(dplyr) library(reshape2) library(tidyverse) fr=list() for (h in 1:length(timevar)) { # from=dat$from, to=dat$to, trans=dat$trans,time=dat$rfstime, # data<- read.csv("C:/Users/niksko/Desktop/mstate/jsonread/msset_ebmt.csv",header=TRUE, sep=";") options(scipen = 999) data=msdata attach(data) names(data) data$time_tot=time ####################################### dis_id=vector() dis_id=unique(msid) ######################## listid=list() for (i in 1:length(dis_id)) { listid[[i]]=as.data.frame(data[data$id==dis_id[i],]) } ############################################################################ #Frequency of remaining in starting state until a certain time point## ############################################################################ #Names of starting states names_ststates=names_of_ststates ############################################## #Values of non absorbing states v_ststate=values_ststates ################################################## max(v_ststate) v_st=array(dim=c(length(dis_id),1,max(v_ststate)),"NA") for (k in v_ststate) { for (i in 1:length(dis_id)) { if (length(which(listid[[i]]$from==k & listid[[i]]$status==1 & listid[[i]]$time_tot<=timevar[h]*scale_inner))==0) {v_st[i,1,k]=TRUE} else {v_st[i,1,k]=FALSE} } } freq_stay_st=vector() for (k in v_ststate) { freq_stay_st[k]=length(which(v_st[,1,k]=="TRUE")) } freq_stay_st=freq_stay_st[!is.na(freq_stay_st)] freq_stay_st=matrix(nrow=1,ncol=length(freq_stay_st),freq_stay_st) freq_stay_st=freq_stay_st colnames(freq_stay_st)<-names_ststates freq_stay_st ############################################################################ #Frequency of non absorbing intermediate states until a certain time point## ############################################################################ #Names of non absorbing state names_nastates=names_of_nastates #################################### #Values of non absorbing states v_nastate=values_nastates ###################################### max(v_nastate) v_na=array(dim=c(length(dis_id),1,max(v_nastate)),"NA") for (k in v_nastate) { for (i in 1:length(dis_id)) { if (length(which(listid[[i]]$from==k & listid[[i]]$status==1 & listid[[i]]$time_tot<=timevar[h]*scale_inner))==0 & length(which(listid[[i]]$from!=k & listid[[i]]$to==k & listid[[i]]$status==1 & listid[[i]]$time_tot<=timevar[h]*scale_inner))!=0) { v_na[i,1,k]=TRUE} else {v_na[i,1,k]=FALSE } } } freq_stay_na=vector() for (k in v_nastate) { freq_stay_na[k]=length(which(v_na[,1,k]=="TRUE")) } freq_stay_na=freq_stay_na[!is.na(freq_stay_na)] freq_stay_na=matrix(nrow=1,ncol=length(freq_stay_na),freq_stay_na) colnames(freq_stay_na)<-names_nastates freq_stay_na=freq_stay_na freq_stay_na ############################################################################ #Frequency of ending up in absorbing states until a certain time point## ############################################################################ #Names of absorbing state names_abstates=names_of_abstates ##################################### #Values of non absorbing states v_abstate=values_abstates ################################### max(v_abstate) #Frequency of remaining in non absorbing states until a certain time point freq_stay_ab=vector() for (k in v_abstate) { freq_stay_ab[k]= length(which(data$to==k & data$status==1 & data$time_tot<=timevar[h]*scale_inner )) } freq_stay_ab=freq_stay_ab[!is.na(freq_stay_ab)] freq_stay_ab=matrix(nrow=1,ncol=length(freq_stay_ab),freq_stay_ab) freq_stay_ab=freq_stay_ab colnames(freq_stay_ab)<-names_abstates freq_stay_ab ###################################################### frequencies_stay=cbind(freq_stay_st,freq_stay_na,freq_stay_ab) # frequencies_stay[ , order(names(frequencies_stay))] frequencies_stay ######################################################### #Names of transitions names_transitions= names_of_transitions #Values of transitions v_trans=values_of_transitions trans=array(dim=c(length(dis_id),length(v_trans)),"NA") for (k in 1:length(v_trans)) { for (i in 1:length(dis_id)) { if (length(which(listid[[i]]$trans==k & listid[[i]]$status==1 & listid[[i]]$time_tot<=timevar[h]*scale_inner ))==1) {trans[i,k]=TRUE} else {trans[i,k]=FALSE } } } freq_trans=vector() for (k in 1:length(v_trans)) { freq_trans[k]=length(which(trans[,v_trans[k]]=="TRUE")) } freq_trans=matrix(nrow=1,ncol=length(freq_trans),freq_trans) colnames(freq_trans)<-names_transitions freq_trans results=as.data.frame(cbind(frequencies_stay,freq_trans,timevar[h])) colnames(results)[ncol(results)]="timevar" fr[[h]]=results } fr_time=bind_rows(fr, .id = "time_label") fr_time } ### Function that will take the input of x and y's and produce the arrows msboxes_R_nofreq<- function(yb,xb,boxwidth,boxheight,tmat.) { library(plyr) library(viridis) library(dplyr) library(reshape2) library(tidyverse) ##########Read info from transition matrix ############################# ####Number of transitions and number of states############## tmat.[is.na(tmat.)] <- 0 ntransitions=max(as.matrix(tmat.)) nstates=ncol(tmat.) states=c(seq(1:nstates)) #### Transitions values u=list() for (i in 1:nrow(tmat.)) { u[[i]]=unique(tmat.[i,][which(!is.na(tmat.[i,]))]) } u_unlist=sort(unlist(u, recursive = TRUE, use.names = TRUE)) transitions=u_unlist[u_unlist!=0] ######## Categorizing states############### state_kind=vector() for (i in 1:nstates) { if (length(which(tmat.[,i]==0))==nrow(tmat.)) {state_kind[i]="Starting"} else if (length(which(tmat.[i,]==0))==ncol(tmat.)) {state_kind[i]="Absorbing"} else {state_kind[i]="Intermediate"} } st_states=which(state_kind=="Starting") na_states=which(state_kind=="Intermediate") ab_states=which(state_kind=="Absorbing") ############################################################################## tname=vector() for (i in 1: ntransitions) { tname[i]=paste0("h",i) } statename=vector() for (i in 1: nstates) { statename[i]=paste0("State",i) } tr_start_state=vector() for (k in 1:ntransitions) { tr_start_state[k]=which(tmat. == k, arr.ind = TRUE)[1,1] } tr_end_state=vector() for (k in 1:ntransitions) { tr_end_state[k]=which(tmat. == k, arr.ind = TRUE)[1,2] } names_ststates=vector(); names_nastates=vector(); names_abstates=vector();names_transitions=vector(); for (i in st_states) { names_ststates[i]=paste0("State",i) } names_ststates=names_ststates[which(!is.na(names_ststates))] for (i in na_states) { names_nastates[i]=paste0("State",i) } names_nastates=names_nastates[which(!is.na(names_nastates))] for (i in ab_states) { names_abstates[i]=paste0("State",i) } names_abstates=names_abstates[which(!is.na(names_abstates))] for (i in transitions) { names_transitions[i]=paste0("h",i) } ##### y=yb x=xb lefttoright=vector() toptobottom=vector() y1=vector() x1=vector() y2=vector() x2=vector() arrowstexty=vector() arrowstextx=vector() gradient=vector() cutoff=0.5 textleft=vector() for (i in 1:ntransitions) { if (x[tr_start_state[i]]>x[tr_end_state[i]]) {textleft[i]=-1} else {textleft[i]=1} } for (i in 1:ntransitions) { if (x[tr_start_state[i]]y[tr_end_state[i]]) {toptobottom[i]=-1} else {toptobottom[i]=1} } for (i in 1:ntransitions) { gradient[i]=abs(y[tr_start_state[i]]-y[tr_end_state[i]])/abs(x[tr_start_state[i]]-x[tr_end_state[i]]) } for (i in 1:ntransitions) { ##Horizontal if (y[tr_start_state[i]]==y[tr_end_state[i]]) { y1[i]=y[tr_start_state[i]] x1[i]=x[tr_start_state[i]]+lefttoright[i]*boxwidth/2 y2[i]=y[tr_end_state[i]] x2[i]=x[tr_end_state[i]] -lefttoright[i]*boxwidth/2 arrowstexty[i]=y[tr_start_state[i]]+0.01 arrowstextx[i]=(x[tr_start_state[i]]+x[tr_end_state[i]])/2 } ##Vertical else if (x[tr_start_state[i]]==x[tr_end_state[i]]) { y1[i]=y[tr_start_state[i]]+toptobottom[i]*boxheight/2 x1[i]=x[tr_start_state[i]] y2[i]=y[tr_end_state[i]]-toptobottom[i]*boxheight/2 x2[i]=x[tr_end_state[i]] arrowstexty[i]=(y[tr_start_state[i]]+y[tr_end_state[i]])/2 arrowstextx[i]=x[tr_start_state[i]]-0.01 } ##Dradient else{ if (gradient[i]1 & input$showbox=="No") { show("boxinput") show("colourinput") show("boxinput2") show("network1") } }) }) observeEvent(c(input$showbox2,invalidateLater(1000, session)), { if(input$showbox2=="No"){ hide("statesinputbox") hide("transitioninput") } if(input$showbox2=="Yes"){ show("statesinputbox") show("transitioninput") } isolate({ timer(timer()-1) if(timer()>1 & input$showbox2=="No") { show("statesinputbox") show("transitioninput") } }) }) #observeEvent(c(input$showbox3,invalidateLater(1000, session)), { # # if(input$showbox3=="No"){ # # hide("tickinputgraph") # # # } # # if(input$showbox3=="Yes"){ # # show("tickinputgraph") # # } # # isolate({ # # timer(timer()-1) # if(timer()>1 & input$showbox3=="No") # { # show("tickinputgraph") # } # # }) #}) output$is_before <- renderUI({ radioButtons("interactive", "Type of graph", choices = list("Static" = "No", "Interactive" = "Yes"), selected = "No") }) output$pageboxbefore <- renderUI({ fluidRow( column(12, uiOutput("is_before") ) ) }) output$pagebox <- renderUI({ if (input$interactive=="No") { #.irs-bar-edge {background: black; border: 1px solid black; height: 25px; border-radius: 0px; width: 20px !important;;} fluidRow( tags$head( tags$style(HTML(type="text/css", ".jslider { max-width: 200px; max-height: 100px;}")), tags$style(HTML(type='text/css', ".irs-grid-text { font-size: 15pt; }")), tags$style(HTML(type="text/css", "input.shiny-bound-input { font-size:20px; height:35px !important;}")), tags$style(HTML(type="text/css","shiny-html-output{ font-size:20px; height:25px;}")), tags$style("#frequency {font-size:20px;}"), tags$style(HTML(type='number',".irs-grid-text { font-size: 12pt !important; }")), tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), # tags$style(HTML(type='text/css', " .irs-grid {display: none !important;}")), ), column(2, useShinyjs(), uiOutput("boxtitle"), uiOutput("is_showbox"), uiOutput("statesinputbox"), uiOutput("transitioninput"), # uiOutput("tickinputgraph") ), column(2, uiOutput("boxinput"), uiOutput("colourinput"), #uiOutput("framebox") ), column(2, uiOutput("boxinput2") ), column(6, plotOutput("msm_scheme_interactive", width = "100%", height = "700px"), uiOutput("note"), uiOutput("shouldload") ) ) } else if (input$interactive=="Yes") { fluidRow( tags$head( tags$style(HTML(type="text/css", ".jslider { max-width: 200px; max-height: 100px;}")), tags$style(HTML(type='text/css', ".irs-grid-text { font-size: 15pt; }")), tags$style(HTML(type="text/css", "input.shiny-bound-input { font-size:20px; height:35px !important;}")), tags$style(HTML(type="text/css","shiny-html-output{ font-size:20px; height:25px;}")), # tags$style(HTML(type='text/css', " .irs-grid {display: none !important;}")), tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), ), column(2, useShinyjs(), uiOutput("is_showbox"), uiOutput("boxtitle"), uiOutput("statesinputbox"), uiOutput("transitioninput") ), column(2, uiOutput("network1"), #uiOutput("framebox") ), column(8, visNetworkOutput(outputId="msm_scheme_network", width = "100%", height = "700px"), uiOutput("shouldloadnetwork"), ) ) } }) output$fileob3<- renderPrint({ myjson2()$P #json1manual() #json2manual() #read.csv(input$csv2$datapath,header=TRUE, sep=";") }) output$note <- renderUI({ if (is.null(myjson1()$frequencies)) return() else item_list <- list() item_list[[1]] <- withMathJax(helpText(strong("Frequency of individuals in each state")), helpText('The numbers inside the boxes depict the number of individuals in each state for the time point specified.')) item_list[[2]] <- withMathJax(helpText(strong("Cummulative number of transitions")), helpText('The numbers near the arrows depict the cumulative number of each type of transition up to the time point specified.')) do.call(tagList, item_list) }) ##################################################### output$is_showbox <- renderUI({ if (is.null(myjson1())) return() else item_list <- list() item_list[[1]] <-radioButtons("showbox2", "Show state and transition names MSM", choices = list("No" = "No","Yes" = "Yes"), selected = "No") item_list[[2]] <-radioButtons("showbox", "Show customisation adjustments for MSM", choices = list("No" = "No","Yes" = "Yes"), selected = "No") # item_list[[3]] <- radioButtons("showbox3", "Choose scale and resolution of graph", # choices = list("No" = "No", "Yes" = "Yes"), selected = "No") # do.call(tagList, item_list) }) #output$tickinputgraph <- renderUI({ # # # default_choices_scale=c(1,2,3) # # if (is.null(myjson2())) return() # item_list <- list() # # item_list[[1]] <-numericInput("resbox" ,"Graph resolution",value=300,min=100,max=610) # item_list[[2]] <-numericInput("figscalebox" ,"Graph scale (multiple of 800*800px)",value=1,min=1,max=4) # # do.call(tagList, item_list) #}) output$network1 <- renderUI({ if (is.null(myjson1())) return() item_list <- list() default_choices_shape=c("box","square", "triangle", "box", "circle", "dot", "star","ellipse") default_choices_colour=c("orange","white","red", "grey","lightblue") default_choices_colourtext=c("#17202a","#e74c3c","#7d3c98","#f39c12","#f0f3f4","#2874a6") # default_choices_shadow=c(FALSE,TRUE) default_choices_smooth=c("No","discrete","continuous","cubicBezier") default_choices_arrow=c("to", "from", "middle", "middle;to") #default_choices_physics=c(FALSE,TRUE) default_choices_visI=c("Fluid","layout_nicely","layout_in_circle","layout_as_tree","layout_with_sugiyama") ### Defining the default values for boxes if (is.null(myjson1())) return() item_list <- list() item_list[[1]] <- selectInput("shape", "Shape of nodes", default_choices_shape, selected = default_choices_shape[1]) item_list[[2]] <- selectInput("colournet", "Colour of nodes and arrows", default_choices_colour, selected = default_choices_colour[1]) item_list[[3]] <- selectInput("colourtext", "Colour of text" , default_choices_colourtext, selected = default_choices_colourtext[1]) #item_list[[4]] <- selectInput("putshadow", "Shadow", default_choices_shadow, selected = default_choices_shadow[1]) item_list[[4]] <- selectInput("putsmooth", "Arrows smooth connection", default_choices_smooth, selected = default_choices_smooth[1]) item_list[[5]] <- selectInput("arrowposition", "Arrow position", default_choices_arrow, selected = default_choices_arrow[1]) # item_list[[6]] <- selectInput("physics", "Enable physics attribute", default_choices_physics, selected = default_choices_physics[1]) item_list[[6]] <- selectInput("visI", "Layout", default_choices_visI, selected = default_choices_visI[1]) item_list[[7]] <- sliderInput("cexnet", "Size of text",min=0.2,max=2,step=0.1, value=1) do.call(tagList, item_list) }) output$boxtitle <- renderUI({ if (is.null((myjson1()))) return("Provide msboxes json file") # create some text inputs item_list <- list() item_list[[1]] <- textInput("title","Multi-state Graph title","Multi-state Graph") do.call(tagList, item_list) }) #output$boxfill <- renderUI({ # # if (is.null((myjson1()))) return() # # create some text inputs # item_list <- list() # item_list[[1]] <- radioButtons("fill", "Box fill", choices = list("No","Yes")) # # do.call(tagList, item_list) # #}) #output$boxsave <- renderUI({ # # if (is.null((myjson1()))) return() # # create some text inputs # item_list <- list() # item_list[[1]] <- radioButtons("save", "Save scheme as pdf", choices = list("png")) # # do.call(tagList, item_list) # #}) output$shouldload <- renderUI({ if (is.null((myjson1()))) return() downloadButton(outputId = "down", label = h2("Download the MSM plot")) }) output$shouldloadnetwork <- renderUI({ if (is.null((myjson1()))) return() downloadButton(outputId = "downnetwork", label = h2("Download the MSM plot as html")) }) output$down <- downloadHandler( filename= function() { paste(paste0("MSM","_up_to",input$uptime),"png", sep=".") }, content= function(file) { #if (input$save=="png") heightbox= 800#*input$figscalebox widthbox= 800#*input$figscalebox png(file, width = heightbox, height = heightbox, units = "px")#, res=input$resbox) #else pdf(file, width = 700, height = 700, units = "px") ntransitions=myjson1()$Ntransitions nstates= myjson1()$Nstates xvaluesb=labels_x() #+boxwidth/2 yvaluesb=labels_y() #-boxheight/2 boxes=msboxes_R_nofreq(yb=yvaluesb, xb=xvaluesb, boxwidth=input$boxwidth , boxheight=input$boxheight, tmat.= myjson1()$tmat) #Read through json from msboxes or through a new function x1=boxes$arrows$x1 y1=boxes$arrows$y1 x2=boxes$arrows$x2 y2=boxes$arrows$y2 arrowstextx=boxes$arrowstext$x arrowstexty=boxes$arrowstext$y ####################################################### tname=vector() tname=labels_trans_box() statename=vector() statename=labels_states_box() plotit<-function(){ plot(c(0, 1), c(0, 1), type = "n", ylab='',xlab='', xaxt='n', yaxt='n', pch=30) title(main = input$title, line = -1, cex.main =input$cex) text(0.05, 1, paste0("At time"," ",input$uptime),cex = input$cex) ### Call the box function recttext(xcenter=xvaluesb, ycenter=yvaluesb, boxwidth=input$boxwidth, boxheight=input$boxheight,statename=statename, freq_box=myjson1()$frequencies[which(myjson1()$frequencies$timevar==input$uptime),2:(myjson1()$Nstates+1)], rectArgs = list(col = 'white', lty = 'solid'), textArgs_state = list(col = input$boxcolornames, cex = input$cex,pos=3), textArgs_freq = list(col = input$boxcolorfreqs, cex = input$cex,pos=1)) ### Call the arrows function if ( is.null(myjson1()$frequencies) ) { ### Call the arrows function arrows_msm(xstart=x1, ystart=y1,xend=x2, yend=y2,xtext=arrowstextx, ytext=arrowstexty,tname=tname, tfreq=matrix(nrow=1, ncol=ntransitions,""), textArgs_transname =list(col =input$arrowcolornames, cex = input$cex, pos=3), textArgs_transfreq =list(col = input$arrowcolorfreqs, cex = input$cex, pos=1), arrowcol=input$arrowcolour,lty = 1) } if ( !is.null(myjson1()$frequencies) ) { ### Call the arrows function arrows_msm(xstart=x1, ystart=y1,xend=x2, yend=y2,xtext=arrowstextx, ytext=arrowstexty,tname=tname, tfreq=myjson1()$frequencies[which(myjson1()$frequencies$timevar==input$uptime),(myjson1()$Nstates+2):(myjson1()$Nstates+1+myjson1()$Ntransitions)], textArgs_transname =list(col =input$arrowcolornames, cex = input$cex, pos=3), textArgs_transfreq =list(col = input$arrowcolorfreqs, cex = input$cex, pos=1), arrowcol=input$arrowcolour,lty = 1) } # if (input$fill=="Yes") { # tfreq_n=myjson1()$frequencies[which(myjson1()$frequencies$timevar==input$uptime),2:(myjson1()$Nstates+1)] # total=myjson1()$frequencies[1,2] # rect(xleft = xvaluesb-(input$boxwidth/2), ybottom = yvaluesb-(input$boxheight/2), # xright = xvaluesb+(input$boxwidth/2), ytop = yvaluesb-(input$boxheight/2) +(input$boxheight*(tfreq_n/total)), # col= input$fillcolor) # } } z.plot1<-function(){plotit()} par(mar=c(0, 0, 0, 0)) p=z.plot1() dev.off() } ) output$downnetwork <- downloadHandler( filename= function() { paste(paste0("MSM","_up_to",input$uptime,"_network"),"html", sep=".") }, content= function(file) { #if (input$save=="png") #png(file, width = 700, height = 700, units = "px") #else pdf(file, width = 700, height = 700, units = "px") #### Start, end, Ntransitions, Nstates #### tmat=myjson1()$tmat start=which( !is.na(tmat) ,arr.ind = TRUE)[,1] end=which( !is.na(tmat) ,arr.ind = TRUE)[,2] Ntransitions=myjson1()$Ntransitions Nstates=myjson1()$Nstates ####State names##### states=vector() states=labels_states_box() ########################### ###transition names#### trans=vector() trans=labels_trans_box() label_nodes= paste0(states,"\n",myjson1()$frequencies[which(myjson1()$frequencies$timevar==input$uptime) ,2:(2+(Nstates-1))]) if (!is.null(myjson1()$frequencies)) { label_edge=paste0(trans,"\n", myjson1()$frequencies[which(myjson1()$frequencies$timevar==input$uptime) ,(2+Nstates):((2+Nstates)+ Ntransitions-1)]) } if (is.null(myjson1()$frequencies)) { label_edge=paste0(trans,"\n", rep("",Ntransitions)) } ############################### # customization adding more variables (see visNodes and visEdges) nodes <- data.frame(id = 1:Nstates, label =label_nodes, # labels value = rep(3,Nstates), # size shape = rep(input$shape ,Nstates), # shape color = rep(input$colournet,Nstates), # color shadow =rep(TRUE,Nstates), title = paste0("

","
Frequency of individuals up to the specified time point for state ", 1:Nstates,"

") ) if (input$putsmooth!="No") { edges <- data.frame(from = start, to = end , label = label_edge , # labels length = rep(300,Ntransitions), # length arrows = rep(input$arrowposition,Ntransitions), # arrows dashes = rep(FALSE,Ntransitions), # dashes smooth = list(enabled = TRUE, type = input$putsmooth), shadow = rep(TRUE,Ntransitions), font = list(size=15*input$cexnet, color=input$colourtext), title = paste0("

","
Cummulative events up to the specified time point for transition ", 1:Ntransitions,"

") ) } else if (input$putsmooth=="No") { edges <- data.frame(from = start, to = end , label = label_edge , # labels length = rep(300,Ntransitions), # length arrows = rep(input$arrowposition,Ntransitions), # arrows dashes = rep(FALSE,Ntransitions), # dashes smooth = rep(FALSE,Ntransitions), shadow = rep(TRUE,Ntransitions), title = paste0("

","
Cummulative events up to the specified time point for transition ", 1:Ntransitions,"

") ) } if (input$visI=="Fluid") { set.seed(124) p=visNetwork(nodes, edges,main=input$title,submain= paste0("At time ",input$uptime) ) %>% visEvents(startStabilizing = "function() {this.moveTo({scale:1.2})}") %>% visPhysics(stabilization = TRUE) %>% visLayout( randomSeed = 145) %>% visNodes(shadow = TRUE, x=labels_x()*(-100),y=labels_y()*(100), fixed = FALSE,font = list(size=15*input$cexnet, color=input$colourtext)) %>% visEdges(shadow = TRUE, font = list(size=15*input$cexnet, color=input$colourtext),smooth=TRUE) p } else if (input$visI!="None") { p=visNetwork(nodes, edges,height = "500px", width = "100%", main =input$title,submain= paste0("At time ",input$uptime) ) %>% visIgraphLayout(layout = input$visI, physics = FALSE, smooth = FALSE, type ="full") %>% visLayout( randomSeed = 145) %>% visInteraction(navigationButtons = TRUE, dragNodes = TRUE, dragView = TRUE, zoomView = FALSE,keyboard = TRUE,selectConnectedEdges = TRUE) %>% visEdges(shadow = TRUE,font = list(size=15*input$cexnet, color=input$colourtext)) %>% visNodes(shadow =TRUE,font = list(size=15*input$cexnet, color=input$colourtext),smooth=TRUE) p } p %>% visSave(file = file) } ) output$statesinputbox <- renderUI({ if (is.null((myjson1()))) return() # create some text inputs item_list <- list() default_choices_state=vector() for (i in 1:myjson1()$Nstates) { if (is.null(myjson1()$statenames)==FALSE & length(myjson1()$statenames)==myjson1()$Nstates) { default_choices_state[i]= myjson1()$statenames[i] } else { default_choices_state[i]=paste0('State',i) } item_list[[i]] <- textInput(paste0('statebox',i),default_choices_state[i], default_choices_state[i]) } do.call(tagList, item_list) }) output$transitioninput <- renderUI({ if (is.null((myjson1()))) return() # create some text inputs item_list <- list() default_choices_tran=vector() for (i in 1:myjson1()$Ntransitions) { if (length(which(!is.na(myjson1()$transnames==TRUE)))==myjson1()$Ntransitions) { default_choices_tran[i]= myjson1()$transnames[i] } else { default_choices_tran[i]=paste0('h',i) } item_list[[i]] <- textInput(paste0('h',i),default_choices_tran[i], default_choices_tran[i]) } do.call(tagList, item_list) }) output$frequency <- renderUI({ if (is.null(myjson1()$frequencies)) return() else item_list <- list() item_list[[1]] <- sliderInput("uptime","Frequencies up to time:", min=min(myjson1()$frequencies$timevar), max=max(myjson1()$frequencies$timevar), step=(max(myjson1()$frequencies$timevar)-min(myjson1()$frequencies$timevar))/ (length(myjson1()$frequencies$timevar)-1), value=myjson1()$frequencies$timevar[1], width='100%', animate=animationOptions(interval = (1000/input$speedbox))) do.call(tagList, item_list) }) output$framebox <- renderUI({ if (is.null(myjson1())|is.null(myjson1()$frequencies)) return() item_list <- list() item_list[[1]] <-numericInput("speedbox",h2("Frame speed frequency"),value=2,min=1, max=30 ) do.call(tagList, item_list) }) #Create the reactive input of covariates output$boxinput <- renderUI({ ### Defining the default values for boxes if (is.null(myjson1())) return() item_list <- list() default_boxwidth=myjson1()$boxwidth default_boxheight=myjson1()$boxheight item_list[[1]] <- sliderInput("boxwidth","Boxwidth", min=0.05, max=0.5, step=0.02, value=default_boxwidth) item_list[[2]] <- sliderInput("boxheight","Boxheight", min=0.05, max=0.5, step=0.02, value=default_boxheight) item_list[[3]] <- sliderInput("cex","Size of text", min=0.2, max=3, step=0.2, value=1) do.call(tagList, item_list) }) #Create the reactive input of covariates output$colourinput <- renderUI({ default_choices=c("black","blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen","tan1","lightslateblue","khaki4","gray28", "cyan3","brown4","darkorchid1","goldenrod4","gray63","lightsalmon", "maroon4","palegreen1","royalblue2","red2","sienna4","yellow4","slategray3") ### Defining the default values for boxes if (is.null(myjson1())) return() item_list <- list() item_list[[1]] <- selectInput("boxcolornames", "Colour of box text", default_choices, selected = default_choices[1]) item_list[[2]] <- selectInput("boxcolorfreqs", "Colour of box frequencies", default_choices, selected = default_choices[1]) item_list[[3]] <- selectInput("arrowcolornames","Colour of arrows text", default_choices, selected = default_choices[1]) item_list[[4]] <- selectInput("arrowcolorfreqs","Colour of arrows freq", default_choices, selected = default_choices[1]) item_list[[5]] <- selectInput("arrowcolour", "Colour of arrows colour",c("red",default_choices)) do.call(tagList, item_list) }) #Create the reactive input of covariates #output$fillcolor <- renderUI({ # # if (is.null((myjson1()))) {return()} # # else if (!is.null((myjson1()))) { # # if (input$fill=="No") { return()} # # else if (input$fill=="Yes") { # # default_choices=vector() # # default_choices[1]=rgb(0,0,1.0,alpha=0.1) # default_choices[2]=rgb(0,1.0,0,alpha=0.1) # default_choices[3]=rgb(1.0,0,0,alpha=0.1) # default_choices[4]=rgb(0.5,0.5,0.5,alpha=0.1) # # item_list <- list() # # item_list[[1]] <- selectInput("fillcolor", "Colour of box fill", choices=default_choices, selected =default_choices) # # do.call(tagList, item_list) # } # } #}) output$boxinput2 <- renderUI({ ### Defining the default values for boxes if (is.null(myjson1())) return() item_list <- list() v_x=vector() for (i in 1:length(myjson1()$xvalues)) { v_x[i]=myjson1()$xvalues[i]+myjson1()$boxwidth/2 } default_choices_x=v_x v_y=vector() for (i in 1:length(myjson1()$yvalues)) { v_y[i]=myjson1()$yvalues[i]-myjson1()$boxheight/2 } default_choices_y=v_y p=0 for (i in 1:myjson1()$Nstates) { item_list[[i+p]] <- sliderInput(paste0('x',i),paste0("Select x centre for box ",i), min=input$boxwidth/2, max=1-input$boxwidth/2, step=1/100, value=default_choices_x[i]) item_list[[(i+1)+p]] <- sliderInput(paste0('y',i),paste0("Select y centre for box ",i), min=input$boxheight/2, max=1-input$boxheight/2, step=1/100, value=default_choices_y[i]) p=p+1 } do.call(tagList, item_list) }) labels_states_box<- reactive ({ myList<-vector("list",(myjson1()$Nstates)) for (i in 1:(myjson1()$Nstates)) { myList[[i]]= input[[paste0('statebox',i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) labels_trans_box<- reactive ({ myList<-vector("list",myjson1()$Ntransitions) for (i in 1:myjson1()$Ntransitions) { myList[[i]]= input[[paste0('h',i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) labels_x<- reactive ({ myList<-vector("list",myjson1()$Nstates) for (i in 1:myjson1()$Nstates) { myList[[i]]= input[[paste0('x',i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) labels_y<- reactive ({ myList<-vector("list",myjson1()$Nstates) for (i in 1:myjson1()$Nstates) { myList[[i]]= input[[paste0('y',i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) output$message_1 <- renderText({labels_y}) output$msm_scheme_interactive <- renderPlot ({ if(is.null(myjson1())) {return()} else ntransitions=myjson1()$Ntransitions nstates= myjson1()$Nstates xvaluesb=labels_x() #+boxwidth/2 yvaluesb=labels_y() #-boxheight/2 boxes=msboxes_R_nofreq(yb=yvaluesb, xb=xvaluesb, boxwidth=input$boxwidth , boxheight=input$boxheight, tmat.= myjson1()$tmat) #Read through json from msboxes or through a new function x1=boxes$arrows$x1 y1=boxes$arrows$y1 x2=boxes$arrows$x2 y2=boxes$arrows$y2 arrowstextx=boxes$arrowstext$x arrowstexty=boxes$arrowstext$y ####################################################### tname=vector() tname=labels_trans_box() statename=vector() statename=labels_states_box() if (is.null(myjson1()$frequencies)) { plotit<-function(){ plot(c(0, 1), c(0, 1), type = "n", ylab='',xlab='', xaxt='n', yaxt='n', pch=30) title(main = input$title, line = -1, cex.main =input$cex) #text(0.05, 1, paste0("At time"," ",input$uptime),cex = input$cex) ### Call the box function recttext(xcenter=xvaluesb, ycenter=yvaluesb, boxwidth=input$boxwidth, boxheight=input$boxheight,statename=statename, freq_box=c(rep("",nstates)), rectArgs = list(col = 'white', lty = 'solid'), textArgs_state = list(col = input$boxcolornames, cex = input$cex,pos=3), textArgs_freq = list(col = input$boxcolorfreqs, cex = input$cex,pos=1)) ### Call the arrows function arrows_msm(xstart=x1, ystart=y1,xend=x2, yend=y2,xtext=arrowstextx, ytext=arrowstexty,tname=tname, tfreq=c(rep("",ntransitions)), textArgs_transname =list(col =input$arrowcolornames, cex = input$cex, pos=3), textArgs_transfreq =list(col = input$arrowcolorfreqs, cex = input$cex, pos=1), arrowcol=input$arrowcolour,lty = 1) } } if (!is.null(myjson1()$frequencies)) { ################################################################################### plotit<-function(){ plot(c(0, 1), c(0, 1), type = "n", ylab='',xlab='', xaxt='n', yaxt='n', pch=30) title(main = input$title, line = -1, cex.main =input$cex) text(0.05, 1, paste0("At time"," ",input$uptime),cex = input$cex) ### Call the box function recttext(xcenter=xvaluesb, ycenter=yvaluesb, boxwidth=input$boxwidth, boxheight=input$boxheight,statename=statename, freq_box=myjson1()$frequencies[which(myjson1()$frequencies$timevar==input$uptime),2:(myjson1()$Nstates+1)], rectArgs = list(col = 'white', lty = 'solid'), textArgs_state = list(col = input$boxcolornames, cex = input$cex,pos=3), textArgs_freq = list(col = input$boxcolorfreqs, cex = input$cex,pos=1)) # if ( is.null(myjson1()$frequencies)) { # ### Call the arrows function # arrows_msm(xstart=x1, ystart=y1,xend=x2, yend=y2,xtext=arrowstextx, ytext=arrowstexty,tname=tname, # tfreq=matrix(nrow=1, ncol=ntransitions,""), # textArgs_transname =list(col =input$arrowcolornames, cex = input$cex, pos=3), # textArgs_transfreq =list(col = input$arrowcolorfreqs, cex = input$cex, pos=1), arrowcol=input$arrowcolour,lty = 1) # # } #if ( !is.null(myjson1()$frequencies)) { ### Call the arrows function arrows_msm(xstart=x1, ystart=y1,xend=x2, yend=y2,xtext=arrowstextx, ytext=arrowstexty,tname=tname, tfreq=myjson1()$frequencies[which(myjson1()$frequencies$timevar==input$uptime),(myjson1()$Nstates+2):(myjson1()$Nstates+1+myjson1()$Ntransitions)], textArgs_transname =list(col =input$arrowcolornames, cex = input$cex, pos=3), textArgs_transfreq =list(col = input$arrowcolorfreqs, cex = input$cex, pos=1), arrowcol=input$arrowcolour,lty = 1) # if (input$fill=="Yes") { # tfreq_n=myjson1()$frequencies[which(myjson1()$frequencies$timevar==input$uptime),2:(myjson1()$Nstates+1)] # total=myjson1()$frequencies[1,2] # rect(xleft = xvaluesb-(input$boxwidth/2), ybottom = yvaluesb-(input$boxheight/2), # xright = xvaluesb+(input$boxwidth/2), ytop = yvaluesb-(input$boxheight/2) +(input$boxheight*(tfreq_n/total)), # col= input$fillcolor) # } } } z.plot1<-function(){plotit()} par(mar=c(2, 2, 2, 2)) p=z.plot1() # z.plot_prob<-function(){plotit_prob()} # png("MSM.png", width = 600, height = 600, units = "px") # par(mar=c(0, 0, 0, 0)) # p=z.plot_prob() # dev.off() p }) output$msm_scheme_network <- renderVisNetwork ({ #### Start, end, Ntransitions, Nstates #### tmat=myjson1()$tmat start=which( !is.na(tmat) ,arr.ind = TRUE)[,1] end=which( !is.na(tmat) ,arr.ind = TRUE)[,2] Ntransitions=myjson1()$Ntransitions Nstates=myjson1()$Nstates ####State names##### states=vector() states=labels_states_box() ########################### ###transition names#### trans=vector() trans=labels_trans_box() label_nodes= paste0(states,"\n",myjson1()$frequencies[which(myjson1()$frequencies$timevar==input$uptime) ,2:(2+(Nstates-1))]) if ( !is.null(myjson1()$frequencies)) { label_edge=paste0(trans,"\n", myjson1()$frequencies[which(myjson1()$frequencies$timevar==input$uptime) ,(2+Nstates):((2+Nstates)+ Ntransitions-1)]) } if ( is.null(myjson1()$frequencies)) { label_edge=paste0(trans,"\n", rep("",Ntransitions)) } ############################### # customization adding more variables (see visNodes and visEdges) nodes <- data.frame(id = 1:Nstates, label =label_nodes, # labels value = rep(3,Nstates), # size shape = rep(input$shape ,Nstates), # shape color = rep(input$colournet,Nstates), # color shadow =rep(TRUE,Nstates), title = paste0("

","
Frequency of individuals up to the specified time point for state ", 1:Nstates,"

") ) if (input$putsmooth!="No") { edges <- data.frame(from = start, to = end , label = label_edge , # labels length = rep(300,Ntransitions), # length arrows = rep(input$arrowposition,Ntransitions), # arrows dashes = rep(FALSE,Ntransitions), # dashes smooth = list(enabled = TRUE, type = input$putsmooth), shadow = rep(TRUE,Ntransitions), font = list(size=15*input$cexnet, color=input$colourtext), title = paste0("

","
Cummulative events up to the scecified time point for transition ", 1:Ntransitions,"

") ) } else if (input$putsmooth=="No") { edges <- data.frame(from = start, to = end , label = label_edge , # labels length = rep(300,Ntransitions), # length arrows = rep(input$arrowposition,Ntransitions), # arrows dashes = rep(FALSE,Ntransitions), # dashes smooth = rep(FALSE,Ntransitions), shadow = rep(TRUE,Ntransitions), title = paste0("

","
Cummulative events up to the scecified time point for transition ", 1:Ntransitions,"

") ) } if (input$visI=="Fluid") { set.seed(124) p= visNetwork(nodes, edges,main=input$title,submain= paste0("At time ",input$uptime) ) %>% visEvents(startStabilizing = "function() {this.moveTo({scale:1.2})}") %>% visPhysics(stabilization = TRUE) %>% visLayout( randomSeed = 145) %>% visNodes(shadow = TRUE, x=labels_x()*(-100),y=labels_y()*(100), fixed = FALSE,font = list(size=15*input$cexnet, color=input$colourtext)) %>% visEdges(shadow = TRUE, font = list(size=15*input$cexnet, color=input$colourtext),smooth=TRUE) p } else if (input$visI!="None") { if (input$putsmooth=="No") { p= visNetwork(nodes, edges,height = "500px", width = "100%", main =input$title,submain= paste0("At time ",input$uptime) ) %>% visIgraphLayout(layout = input$visI, physics = FALSE, smooth = FALSE, type ="full") %>% visLayout( randomSeed = 145) %>% visInteraction(navigationButtons = TRUE, dragNodes = TRUE, dragView = TRUE, zoomView = FALSE,keyboard = TRUE,selectConnectedEdges = TRUE) %>% visEdges(shadow = TRUE,font = list(size=15*input$cexnet, color=input$colourtext)) %>% visNodes(shadow =TRUE,font = list(size=15*input$cexnet, color=input$colourtext)) p } else if (input$putsmooth!="No") { p= visNetwork(nodes, edges,height = "500px", width = "100%", main =input$title,submain= paste0("At time ",input$uptime) ) %>% visIgraphLayout(layout = input$visI, physics = FALSE, smooth = FALSE, type ="full") %>% visLayout( randomSeed = 145) %>% visInteraction(navigationButtons = TRUE, dragNodes = TRUE, dragView = TRUE, zoomView = FALSE,keyboard = TRUE,selectConnectedEdges = TRUE) %>% visEdges(shadow = TRUE,font = list(size=15*input$cexnet, color=input$colourtext),smooth=TRUE) %>% visNodes(shadow =TRUE,font = list(size=15*input$cexnet, color=input$colourtext)) p } } }) #### Hide and show colour options #### timerinput <- reactiveVal(1.5) observeEvent(c(input$showcolor,invalidateLater(1000, session)), { if(input$showcolor=="No"){ hide("colourinputcov") hide("colourinputstate") hide("colourinput_trans") } if(input$showcolor=="Yes"){ show("colourinputcov") show("colourinputstate") show("colourinput_trans") } isolate({ timerinput(timerinput()-1) if(timerinput()>1 & input$showcolor=="No") { show("colourinputcov") show("colourinputstate") show("colourinput_trans") } }) }) observeEvent(c(input$showcovar,invalidateLater(1000, session)), { if(input$showcovar=="No"){ hide("covarinput") } if(input$showcovar=="Yes"){ show("covarinput") } isolate({ timerinput(timerinput()-1) if(timerinput()>1 & input$showcovar=="No") { show("covarinput") } }) }) observeEvent(c(input$showstate,invalidateLater(1000, session)), { if(input$showstate=="No"){ hide("statesinput") } if(input$showstate=="Yes"){ show("statesinput") } isolate({ timerinput(timerinput()-1) if(timerinput()>1 & input$showstate=="No") { show("statesinput") } }) }) observeEvent(c(input$istext,invalidateLater(1000, session)), { if(input$istext=="No"){ hide("tickinput") } if(input$istext=="Yes"){ show("tickinput") } isolate({ timerinput(timerinput()-1) if(timerinput()>1 & input$istext=="No") { show("tickinput") } }) }) output$pageinput1 <- renderUI({ fluidRow( column(3, useShinyjs(), uiOutput("select") ), column(9, useShinyjs(), uiOutput("includecov"), uiOutput("selectcov") ) ) }) output$pageinput2 <- renderUI({ fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, useShinyjs(), uiOutput("is_smooth"), verbatimTextOutput("fileob") ), column(2, useShinyjs(), uiOutput("is_textinput"), uiOutput("tickinput") ), column(2, useShinyjs(), uiOutput("is_showcolor"), uiOutput("colourinputcov"), uiOutput("colourinputstate"), uiOutput("colourinput_trans") ), column(3, useShinyjs(), uiOutput("is_showcovar"), uiOutput("covarinput") ), column(3, useShinyjs(), uiOutput("is_showstate"), uiOutput("statesinput") ) ) }) #output$fileob<- renderPrint({ # # json2manual() # #}) output$is_smooth<- renderUI ({ # if (is.null(myjson2())) return() radioButtons("smooth", "Smooth plot transition between covariate patterns", choices = list("No" = "No", "Yes" = "Yes"), selected = "Yes") }) output$is_textinput<- renderUI ({ # if (is.null(myjson2())) return() radioButtons("istext", "Choose size and color of labels", choices = list("No" = "No", "Yes" = "Yes"), selected = "No") }) output$is_showcolor<- renderUI ({ #if (is.null(myjson2())) return() radioButtons("showcolor", "Show colouring options", choices = list("No" = "No","Yes" = "Yes"),selected = "No") }) output$is_showcovar<- renderUI ({ if (is.null(myjson2())) return() radioButtons("showcovar", "Show covariate naming options", choices = list("No" = "No","Yes" = "Yes"),selected = "No") }) output$is_showstate<- renderUI ({ if (is.null(myjson2())) return() radioButtons("showstate", "Show state naming options", choices = list("No" = "No","Yes" = "Yes"),selected = "No") }) output$tickinput <- renderUI({ default_choices=c("black","blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen","tan1","lightslateblue","khaki4","gray28", "cyan3","brown4","darkorchid1","goldenrod4","gray63","lightsalmon", "maroon4","palegreen1","royalblue2","red2","sienna4","yellow4","slategray3") default_choices_scale=c(1,2,3) if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <-numericInput("textsize" ,"Legends size",value=15,min=5,max=30) #item_list[[2]] <-selectInput("textcolour","Legends colour", choices= default_choices, selected =default_choices[1] ) #item_list[[3]] <-selectInput("textfont" ,"Font", choices= default_choices, selected =default_choices[1] ) item_list[[2]] <-selectInput("figscale" ,"Figures scale (multiple of 1200*900px)",choices= default_choices_scale, selected =default_choices_scale[1] ) do.call(tagList, item_list) }) ######### colour input covariate patterns####################### output$colourinputcov<- renderUI ({ if (is.null(myjson2())) return() item_list <- list() item_list[[1]]<- h2("Select colour for each covariate pattern") default_choices_colour=vector() default_choices_colour=c("blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen","tan1","lightslateblue","khaki4","gray28", "cyan3","brown4","darkorchid1","goldenrod4","gray63","lightsalmon", "maroon4","palegreen1","royalblue2","red2","sienna4","yellow4","slategray3") colour_choices_title=vector("character",length =myjson2()$Nats ) for (i in 1:myjson2()$Nats ) { colour_choices_title[i]=paste0("Colour cov. pattern"," ",i) } for (i in 1:myjson2()$Nats) { item_list[[1+i]] <- selectInput(paste0('colourcov',i),label=colour_choices_title[i], choices= default_choices_colour, selected =default_choices_colour[i] ) } do.call(tagList, item_list) }) labels_colour_cov<- reactive ({ if (is.null(myjson2())) return() myList<-vector("list",myjson2()$Nats) for (i in 1:myjson2()$Nats ) { myList[[i]]= input[[paste0('colourcov', i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) ######### colour input states ####################### output$colourinputstate<- renderUI ({ if (is.null(myjson2())) return() item_list <- list() item_list[[1]]<- h2("Select colour for each state") default_choices_colour=vector() default_choices_colour=c("palegreen1","royalblue2","red2","sienna4","yellow4","slategray3", "blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen","tan1","lightslateblue","khaki4","gray28", "cyan3","brown4","darkorchid1","goldenrod4","gray63","lightsalmon", "maroon4") colour_choices_title=vector("character",length = length(myjson2()$P ) ) for (i in 1:length(myjson2()$P)) { colour_choices_title[i]=paste0("Colour state"," ",i) } for (i in 1:length(myjson2()$P)) { item_list[[1+i]] <- selectInput(paste0('colourstate',i),label=colour_choices_title[i], choices= default_choices_colour, selected =default_choices_colour[i] ) } do.call(tagList, item_list) }) labels_colour_state<- reactive ({ if (is.null(myjson2())) return() myList<-vector("list",length(myjson2()$P)) for (i in 1:length(myjson2()$P)) { myList[[i]]= input[[paste0('colourstate', i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) ###################Input covariate patterns########################## #Create the reactive input of covariates output$covarinput <- renderUI({ #if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <- h2("Covariate patterns") v=vector() for (i in 1:myjson2()$Nats) { v[i]=myjson2()$cov$atlist[i] } default_choices_cov=v for (i in 1:myjson2()$Nats) { item_list[[i+1]] <- textInput(paste0('cov', i),default_choices_cov[i], default_choices_cov[i]) } do.call(tagList, item_list) }) labels_cov<- reactive ({ # if (is.null(myjson2())) return() myList<-vector("list",myjson2()$Nats) for (i in 1:myjson2()$Nats) { myList[[i]]= input[[paste0('cov', i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) #values <- reactiveValues() # #observe({ # labels_experimental=labels_cov() # if (!(is.null(labels_experimental))){ # values$data <- labels_experimental # } #}) ############################################################### ######### Input hazard colours ################################ ############################################################### #output$colourinput_trans <- renderUI ({ # # if (is.null(myjson2())) return() # # item_list <- list() # # item_list[[1]]<- h2("Select colour for each transition") # # default_choices_colour=vector() # default_choices_colour=c("tan1","lightslateblue","khaki4","gray28", # "cyan3","brown4","darkorchid1","goldenrod4","gray63","lightsalmon", # "maroon4","palegreen1","royalblue2","red2","sienna4","yellow4","slategray3", # "blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", # "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", # "yellow2","yellowgreen") # # colour_choices_title=vector("character",length = length(myjson2()$h ) ) # # for (i in 1:length(myjson2()$h)) { # colour_choices_title[i]=paste0("Colour transition"," ",i) # } # # # for (i in 1:length(myjson2()$h)) { # # item_list[[1+i]] <- selectInput(paste0('colourtrans',i),label=colour_choices_title[i], # choices= default_choices_colour, selected =default_choices_colour[i] ) # # } # # do.call(tagList, item_list) # #}) # #labels_colour_trans<- reactive ({ # # if (is.null(myjson2())) return() # # # myList<-vector("list",length(myjson2()$h)) # # for (i in 1:length(myjson2()$h)) { # # myList[[i]]= input[[paste0('colourtrans', i)]][1] # # } # # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # final_list #}) # output$colourinput_trans <- renderUI ({ if (is.null(myjson2())) return() item_list <- list() item_list[[1]]<- h2("Select colour for each transition") default_choices_colour=vector() default_choices_colour=c("tan1","lightslateblue","khaki4","gray28", "cyan3","brown4","darkorchid1","goldenrod4","gray63","lightsalmon", "maroon4","palegreen1","royalblue2","red2","sienna4","yellow4","slategray3", "blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen") tmat_temp=myjson2()$tmat[as.numeric(input$select),] colour_choices_title=vector("character",length = length(which(!is.na(tmat_temp))) ) tr_start_state=vector() for (k in 1:length(which(!is.na(tmat_temp))) ) { tr_start_state[k]=as.numeric(input$select) } tr_end_state=vector() for (k in 1:length(which(!is.na(tmat_temp))) ) { tr_end_state[k]=which(!is.na(tmat_temp))[k] } colour_choices_title=vector() for (i in 1:length(which(!is.na(tmat_temp))) ) { colour_choices_title[i]=paste0('Colour for transition'," ", tr_start_state[i],"->",tr_end_state[i]) } for (i in 1:length(which(!is.na(tmat_temp))) ) { item_list[[1+i]] <- selectInput(paste0('colourtrans',i),label=colour_choices_title[i], choices= default_choices_colour, selected =default_choices_colour[i] ) } do.call(tagList, item_list) }) labels_colour_trans<- reactive ({ if (is.null(myjson2()$haz)) return() tmat_temp=myjson2()$tmat[as.numeric(input$select),] myList<-vector("list",length(which(!is.na(tmat_temp))) ) for (i in 1:length(which(!is.na(tmat_temp))) ) { myList[[i]]= input[[paste0('colourtrans', i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) labels_colour_trans<- reactive ({ if (is.null(myjson2()$haz)) return() tmat_temp=myjson2()$tmat[as.numeric(input$select),] myList<-vector("list",length(which(!is.na(tmat_temp))) ) for (i in 1:length(which(!is.na(tmat_temp))) ) { myList[[i]]= input[[paste0('colourtrans', i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) # ############### Input states ############################### output$statesinput <- renderUI({ if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <- h2("To states") title_choices_state=vector() for (i in 1:length(myjson2()$P)) { title_choices_state[i]=paste0("State"," ",input$select,selectend()[i]) } default_choices_state=vector() if (is.null(myjson1() )) { if ( (input$loadtype=="json"|input$loadtype=="csv") & input$aimtype=="present" ) { if (length(myjson2()$statenames)==length(myjson2()$P) ) { for (i in 1:length(myjson2()$P)) { default_choices_state[i]=myjson2()$statenames[i] } } else if ( (length(myjson2()$statenames)!=length(myjson2()$P) ) | is.null(myjson2()$statenames)) { for (i in 1:length(myjson2()$P)) { default_choices_state[i]=paste0("State"," ",input$select,selectend()[i]) } } } if ((input$loadtype=="json"|input$loadtype=="csv") & input$aimtype=="compare" ) { if (length(myjson2()$statenames)==length(myjson2()$P)/2) { for (i in 1:(length(myjson2()$P)/2)) { default_choices_state[i]=myjson2()$statenames[i] default_choices_state[(length(myjson2()$P)/2)+i]=myjson2()$statenames[i] } } else if ( (length(myjson2()$statenames)!=length(myjson2()$P/2) ) | is.null(myjson2()$statenames)) { for (i in 1:length(myjson2()$P)) { default_choices_state[i]=paste0("State"," ",input$select,selectend()[i]) default_choices_state[(length(myjson2()$P)/2)+i]=paste0("State"," ",input$select,selectend()[i],"2nd approach") } } } } if (!is.null(myjson1() )) { if (input$aimtype=="present") { for (i in 1:length(labels_states_box())) { default_choices_state[i]=labels_states_box()[i] } } if (input$aimtype=="compare") { for (i in 1:(length(myjson2()$P)/2)) { default_choices_state[i]=labels_states_box()[i] default_choices_state[(length(myjson2()$P)/2)+i]=paste0(labels_states_box()[i]," 2nd approach") } } } for (i in 1:length(myjson2()$P)) { item_list[[1+i]] <- textInput(paste0('state',i),title_choices_state[i],default_choices_state[i]) } do.call(tagList, item_list) }) labels_state<- reactive ({ if (is.null(myjson2())) return() myList<-vector("list",length(myjson2()$P)) for (i in 1:length(myjson2()$P)) { myList[[i]]= input[[paste0('state', i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) output$select<- renderUI ({ if (is.null(myjson1_5())) return() radioButtons(inputId="select", label="Select the probabilities From state:", choices=unique(sub("_to_.*","", sub("P_","",names(myjson1_5()$select) )) ), selected = unique(sub("_to_.*","", sub("P_","",names(myjson1_5()$select) )) )[1] ) }) ### Recombine the isolated input of from state to define the ending of names specifind the end states selectend<-reactive ({ if (is.null(myjson1_5() )) return() cond_select<-which(startsWith(names(myjson1_5()), paste0('P_',input$select)) & !startsWith(names(myjson1_5()), 'P_diff' ) & !startsWith(names(myjson1_5()), 'P_ratio' ) & !endsWith(names(myjson1_5()), 'uci' ) & !endsWith(names(myjson1_5()), 'lci') ) names=names(myjson1_5()[cond_select]) v=vector() for (i in 1:length(cond_select)) { v[i]=sub("P_[[:digit:]]+","",names[i]) } v }) #### select which of the defined covariate patterns you want output$includecov<- renderUI ({ if (is.null(myjson1_5() )) return() radioButtons(inputId="includecov", label="Manually select covariate patterns", choices=c("Yes","No") , selected = "No" ) }) output$selectcov<- renderUI ({ if (is.null(myjson1_5() )) return("Provide the json file with the predictions") item_list <- list() item_list[[1]] <- paste0("Reference covariate pattern"," : ",myjson1_5()$atlist[1]) if (input$includecov == 'Yes') { if (length(myjson1_5()$atlist)>1) { item_list[[2]] <- checkboxGroupInput("selectcov","Select extra covariate patterns (at least one)", choices= myjson1_5()$atlist[-1] , selected = myjson1_5()$atlist[-1] ) } else return("You have included only one covariate pattern in your analysis") } do.call(tagList, item_list) }) #### Turn the selected covariate patterns into a vector of numbers corresponding #### to the order of the covariate patterns element in the atlist covselect_char<- reactive ({ shiny::validate( need(!is.null(input$selectcov) , "Please select at least 1 covariate pattern") ) final_list=vector() myList<- input[['selectcov']] final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) covselect<- reactive ({ tonumber=vector() shiny::validate( need(!is.null(input$selectcov) , "Please select at least 1 covariate pattern") ) # if (is.null(input$selectcov)) {tonumber=0} # else if (!is.null(input$selectcov)) { for (i in 1:length(covselect_char())) { tonumber[i]= which(myjson1_5()$atlist==covselect_char()[i]) } #} tonumber }) covselectcontr<- reactive ({ tonumber=vector() if (length(covselect_char())!=0) { for (i in 1:length(covselect_char())) { tonumber[i]= which(myjson1_5()$atlist[-1]==covselect_char()[i]) } tonumber=as.integer(tonumber) } tonumber }) myjson1 <- reactive ({ if (input$loadtype=="json") { if (input$aimtype=="present") { if (is.null(input$json1_pr) & input$example=="No") return() else if (!is.null(input$json1_pr) & input$example=="No" ) { data= fromJSON(input$json1_pr$datapath, flatten=TRUE) } else if (input$example=="Yes" & is.null(input$json1_pr) ) { setwd() data= fromJSON("msboxes.json", flatten=TRUE) } data } else if (input$aimtype=="compare") { if (is.null(input$json1_cp) & input$compare_approach=="No") return() else if (!is.null(input$json1_cp) & input$compare_approach=="No" ) { data= fromJSON(input$json1_cp$datapath, flatten=TRUE) } else if (input$compare_approach=="Yes" & is.null(input$json1_cp) ) { setwd() data= fromJSON("msboxes.json", flatten=TRUE) } data } } else if (input$loadtype=="csv") { if (input$aimtype=="present") { if (length(which(!is.na(input$tmat_input_pr))==TRUE)==0 & input$example2=="No") return() else if (input$example2=="No" & length(which(!is.na(input$tmat_input_pr))==TRUE)!=0 ) { data= fromJSON(json1manual(), flatten=TRUE) } else if (input$example2=="Yes" ) { #& is.null(json1manual()) setwd() data= fromJSON("msboxes.json", flatten=TRUE) } else if (input$example2=="Yes" & length(which(!is.na(input$tmat_input_pr))==TRUE)!=0 ) return("Not both") #& !is.null(json1manual()) data } else if (input$aimtype=="compare") { if (is.null(json1manual()) & input$compare_approach2=="No") return() else if (input$compare_approach2=="No" & !is.null(json1manual()) ) { data= fromJSON(json1manual(), flatten=TRUE) } else if (input$compare_approach2=="Yes" ) { #& is.null(json1manual()) setwd() data= fromJSON("msboxes.json", flatten=TRUE) } else if (input$compare_approach2=="Yes" & length(which(!is.na(input$tmat_input_cp))==TRUE)!=0 ) return("Not both") #& !is.null(json1manual()) data } } }) ####Read in the predictions file and identify the elements of the probabilities myjson1_5 <- reactive ({ if (input$loadtype=="json") { if (input$aimtype=="present") { if (is.null(input$json2) & input$example=="No" ) return("Provide the json file with the predictions") else if (!is.null(input$json2) ) { data= fromJSON(input$json2$datapath, flatten=TRUE) } else if ( input$example=="Yes" & is.null(input$json2)) { #is.null(input$json2) & setwd() data= fromJSON("predictions_stata_merlin.json", flatten=TRUE) } } else if (input$aimtype=="compare") { if ( (is.null(input$json2a) | is.null(input$json2b)) & input$compare_approach=="No" ) {return("Provide two json files with the predictions approaches")} else if (!is.null(input$json2a) & !is.null(input$json2b) ) { list2a=fromJSON(input$json2a$datapath, flatten=TRUE) list2b=fromJSON(input$json2b$datapath, flatten=TRUE) Nstates=ncol(list2b$tmat) Ntransitions=max(list2b$tmat[which(!is.na(list2b$tmat))]) y=vector() for (i in 1:Nstates) { for (k in 1:Nstates) { j=i+Nstates g=k+Nstates y=sub(paste0("_to_",k),paste0("_to_",g),names(list2b) ) names(list2b)=y } } is.integer0 <- function(x) { is.integer(x) && length(x) == 0L } if ( !is.integer0(which(startsWith(names(list2b),"User")) ) == TRUE ) { list2b=list2b[-which(startsWith(names(list2b),"User") == TRUE)] names(list2b) } for (i in 1:Ntransitions) { k= i+ Ntransitions names(list2b)=sub(paste0("h",i),paste0("h",k),names(list2b) ) } ### Create a hypertmatrix#### list2b$tmat2= list2b$tmat+Ntransitions l <- list(list2b$tmat,list2b$tmat2) list2b$hypertmat<- as.matrix(bdiag(l)) list2b$hypertmat[which(list2b$hypertmat==0)]=NA list2b$hypertmat list2a=list2a[names(list2a) %in% "tmat" == FALSE] list2b=list2b[names(list2b) %in% "tmat" == FALSE] list2b=list2b[names(list2b) %in% "tmat2" == FALSE] list2b$tmat=list2b$hypertmat data=c(list2a,list2b) } else if ( input$compare_approach=="Yes" ) { #(is.null(input$json2a) | is.null(input$json2b)) & setwd() data= fromJSON("predictions_stata_both_approaches.json", flatten=TRUE) } else if (!is.null(input$json2a) & !is.null(input$json2b) & input$compare_approach=="Yes" ) return("Not both") } } else if (input$loadtype=="csv") { if (input$aimtype=="present") { if (is.null(json2manual()) & input$example2=="No") return("Provide the csv file with the predictions") else if (!is.null(json2manual()) & input$example2=="No") { #& input$example=="No" data= fromJSON(json2manual(), flatten=TRUE) } else if ( input$example2=="Yes" & is.null(json2manual())) { #& is.null(json2manual()) setwd() data= fromJSON("predictions_stata_merlin.json", flatten=TRUE) data } else if ( input$example2=="Yes" & !is.null(json2manual())) return("Not both") } else if (input$aimtype=="compare") { if (is.null(json2manual()) & input$compare_approach2=="No" ) return("Provide the csv file with the predictions") else if (!is.null(json2manual()) & input$compare_approach2=="No" ) { data= fromJSON(json2manual(), flatten=TRUE) data } else if ( input$compare_approach2=="Yes" & is.null(json2manual()) ) { setwd() data= fromJSON("predictions_stata_both_approaches.json", flatten=TRUE) data } else if ( input$compare_approach2=="Yes" & !is.null(json2manual()) ) return("Not both") } } ##Save all probabilities as a separate list cond_P_select<-which(startsWith(names(data), 'P') & !startsWith(names(data), 'P_diff') & !startsWith(names(data),'P_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) data$select=data[cond_P_select] data }) #### Isolate the from state probability fragment so that you can choose the cluster of probabilities you are going to use output$select<- renderUI ({ if (is.null(myjson1_5())) return() radioButtons(inputId="select", label="Select conditional starting state", choices=unique(sub("_to_.*","", sub("P_","",names(myjson1_5()$select) )) ), selected = unique(sub("_to_.*","", sub("P_","",names(myjson1_5()$select) )) )[1] ) }) ### Reconbine the isolated input of from state to define the ending of names specifind the end states selectend<-reactive ({ if (is.null(myjson1_5() )) return() cond_select<-which(startsWith(names(myjson1_5()), paste0('P_',input$select)) & !startsWith(names(myjson1_5()), 'P_diff' ) & !startsWith(names(myjson1_5()), 'P_ratio' ) & !endsWith(names(myjson1_5()), 'uci' ) & !endsWith(names(myjson1_5()), 'lci') ) names=names(myjson1_5()[cond_select]) v=vector() for (i in 1:length(cond_select)) { v[i]=sub("P_[[:digit:]]+","",names[i]) } v }) selectend_h<-reactive ({ if (is.null(myjson1_5() )) return() cond_select<-which(startsWith(names(myjson1_5()), paste0('Haz_',input$select)) & !startsWith(names(myjson1_5()), 'Haz_diff' ) & !startsWith(names(myjson1_5()), 'Haz_ratio' ) & !endsWith(names(myjson1_5()), 'uci' ) & !endsWith(names(myjson1_5()), 'lci') ) names=names(myjson1_5()[cond_select]) v=vector() for (i in 1:length(cond_select)) { v[i]=sub("Haz_[[:digit:]]+","",names[i]) } v }) #### select which of the defined covariate patterns you want output$includecov<- renderUI ({ if (is.null(myjson1_5() )) return() radioButtons(inputId="includecov", label="Manually select covariate patterns", choices=c("Yes","No") , selected = "No" ) }) output$selectcov<- renderUI ({ if (is.null(myjson1_5() )) return("Provide the json file with the predictions") item_list <- list() item_list[[1]] <- paste0("Reference covariate pattern"," : ",myjson1_5()$atlist[1]) if (input$includecov == 'Yes') { if (length(myjson1_5()$atlist)>1) { item_list[[2]] <- checkboxGroupInput("selectcov","Select extra covariate patterns (at least one)", choices= myjson1_5()$atlist[-1] , selected = myjson1_5()$atlist[-1] ) } else return("You have included only one covariate pattern in your analysis") } do.call(tagList, item_list) }) #### Turn the selected covariate patterns into a vector of numbers corresponding #### to the order of the covariate patterns element in the atlist covselect_char<- reactive ({ shiny::validate( need(!is.null(input$selectcov) , "Please select at least 1 covariate pattern") ) final_list=vector() myList<- input[['selectcov']] final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) covselect<- reactive ({ tonumber=vector() shiny::validate( need(!is.null(input$selectcov) , "Please select at least 1 covariate pattern") ) # if (is.null(input$selectcov)) {tonumber=0} # else if (!is.null(input$selectcov)) { for (i in 1:length(covselect_char())) { tonumber[i]= which(myjson1_5()$atlist==covselect_char()[i]) } #} tonumber }) covselectcontr<- reactive ({ tonumber=vector() if (length(covselect_char())!=0) { for (i in 1:length(covselect_char())) { tonumber[i]= which(myjson1_5()$atlist[-1]==covselect_char()[i]) } tonumber=as.integer(tonumber) } tonumber }) myjson2 <- reactive ({ data= myjson1_5() cond_select_P<-which(startsWith(names(data), paste0('P_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_select_P_diff<-which(startsWith(names(data), paste0('P_diff_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_select_P_ratio<-which(startsWith(names(data), paste0('P_ratio_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_exists_P_ci<- which(startsWith(names(data), paste0('P_',input$select)) & (endsWith(names(data), 'uci' ) | endsWith(names(data), 'lci')) ) data$ci_P=cond_exists_P_ci if (length(cond_select_P)==0) { cond_P=vector();cond_P_uci=vector();cond_P_lci=vector() data$P=NULL;data$P_uci=NULL;data$P_lci=NULL } else if (length(cond_select_P)!=0) { if (length(cond_exists_P_ci)!=0) { ##Save all probabilities as a separate list cond_P=vector() for (i in 1:length(cond_select_P)) { cond_P[i]<-which(startsWith(names(data), paste0('P_',input$select,selectend()[i]) ) & !startsWith(names(data), 'P_diff') & !startsWith(names(data),'P_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_P_uci=vector() for (i in 1:length(cond_select_P)) { cond_P_uci[i]<-which(startsWith(names(data), paste0('P_',input$select,selectend()[i])) & !startsWith(names(data), 'P_diff') & !startsWith(names(data),'P_ratio') & endsWith(names(data), 'uci' ))} cond_P_lci=vector() for (i in 1:length(cond_select_P)) { cond_P_lci[i]<-which(startsWith(names(data), paste0('P_',input$select,selectend()[i])) & !startsWith(names(data), 'P_diff') & !startsWith(names(data),'P_ratio') & endsWith(names(data), 'lci' )) } } else if (length(cond_exists_P_ci)==0) { cond_P=vector();cond_P_uci=vector();cond_P_lci=vector() for (i in 1:length(cond_select_P)) { cond_P[i]<-which(startsWith(names(data), paste0('P_',input$select,selectend()[i]) ) & !startsWith(names(data), 'P_diff') & !startsWith(names(data),'P_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } if (length(cond_select_P_diff)==0) { cond_P_diff=vector();cond_P_diff_uci=vector();cond_P_diff_lci=vector() data$Pd=NULL;data$Pd_uci=NULL;data$Pd_lci=NULL } else if (length(cond_select_P_diff)!=0) { if (length(cond_exists_P_ci)!=0) { cond_P_diff=vector() for (i in 1:length(cond_select_P_diff)) { cond_P_diff[i]<- which(startsWith(names(data), paste0('P_diff_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_P_diff_uci=vector() for (i in 1:length(cond_select_P_diff)) { cond_P_diff_uci[i]<- which(startsWith(names(data), paste0('P_diff_',input$select,selectend()[i])) & endsWith(names(data), 'uci' ) ) } cond_P_diff_lci=vector() for (i in 1:length(cond_select_P_diff)) { cond_P_diff_lci[i]<- which(startsWith(names(data), paste0('P_diff_',input$select,selectend()[i])) & endsWith(names(data), 'lci' ) ) } } else if (length(cond_exists_P_ci)==0) { cond_P_diff_uci=vector();cond_P_diff_lci=vector() cond_P_diff=vector() for (i in 1:length(cond_select_P_diff)) { cond_P_diff[i]<- which(startsWith(names(data), paste0('P_diff_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } if (length(cond_select_P_ratio)==0) { cond_P_ratio=vector();cond_P_ratio_uci=vector();cond_P_ratio_lci=vector() data$Pr=NULL;data$Pr_uci=NULL;data$Pr_lci=NULL } else if (length(cond_select_P_ratio)!=0) { if (length(cond_exists_P_ci)!=0) { cond_P_ratio=vector() for (i in 1:length(cond_select_P_ratio)) { cond_P_ratio[i]<- which(startsWith(names(data), paste0('P_ratio_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_P_ratio_uci=vector() for (i in 1:length(cond_select_P_ratio)) { cond_P_ratio_uci[i]<- which(startsWith(names(data), paste0('P_ratio_',input$select,selectend()[i])) & endsWith(names(data), 'uci' ) ) } cond_P_ratio_lci=vector() for (i in 1:length(cond_select_P_ratio)) { cond_P_ratio_lci[i]<- which(startsWith(names(data), paste0('P_ratio_',input$select,selectend()[i])) & endsWith(names(data), 'lci' ) ) } } else if (length(cond_exists_P_ci)==0) { cond_P_ratio_uci=vector();cond_P_ratio_lci=vector() cond_P_ratio=vector() for (i in 1:length(cond_select_P_ratio)) { cond_P_ratio[i]<- which(startsWith(names(data), paste0('P_ratio_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } data$P=data[cond_P] data$P_uci=data[cond_P_uci] data$P_lci=data[cond_P_lci] data$Pd=data[cond_P_diff] data$Pd_uci=data[cond_P_diff_uci] data$Pd_lci=data[cond_P_diff_lci] data$Pr=data[cond_P_ratio] data$Pr_uci=data[cond_P_ratio_uci] data$Pr_lci=data[cond_P_ratio_lci] if (input$includecov == 'Yes') { for (i in 1:length(cond_select_P)) { data$P[[i]] =as.data.frame(as.matrix(data$P[[i]])[c(1,covselect()),]) data$P_uci[[i]] =as.data.frame(as.matrix(data$P_uci[[i]])[c(1,covselect()),]) data$P_lci[[i]] =as.data.frame(as.matrix(data$P_lci[[i]])[c(1,covselect()),]) } for (i in 1:length(cond_select_P_diff)) { data$Pd[[i]] =as.data.frame(as.matrix(data$Pd[[i]])[covselectcontr(),]) data$Pd_uci[[i]]=as.data.frame(as.matrix(data$Pd_uci[[i]])[covselectcontr(),]) data$Pd_lci[[i]]=as.data.frame(as.matrix(data$Pd_lci[[i]])[covselectcontr(),]) } for (i in 1:length(cond_select_P_ratio)) { data$Pr[[i]] =as.data.frame(as.matrix(data$Pr[[i]])[covselectcontr(),]) data$Pr_uci[[i]]=as.data.frame(as.matrix(data$Pr_uci[[i]])[covselectcontr(),]) data$Pr_lci[[i]]=as.data.frame(as.matrix(data$Pr_lci[[i]])[covselectcontr(),]) } } ##Save all los as a separate list cond_haz_all<-which(startsWith(names(data), 'Haz_') & !startsWith(names(data), 'Haz_diff') & !startsWith(names(data),'Haz_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) if (length(cond_haz_all)==0) { data$haz_all=NULL } else { if (input$includecov == 'No') { data$haz_all=data[cond_haz_all] } if (input$includecov == 'Yes') { data$haz_all=data[cond_haz_all] for (i in 1:data$Ntransitions) { data$haz_all[[i]] =as.data.frame(as.matrix(data$haz_all[[i]])[c(1,covselect()),]) } } } cond_select_haz<-which(startsWith(names(data), paste0('Haz_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_select_haz_diff<-which(startsWith(names(data), paste0('Haz_diff_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_select_haz_ratio<-which(startsWith(names(data), paste0('Haz_ratio_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_exists_haz_ci<- which(startsWith(names(data), paste0('Haz_',input$select)) & (endsWith(names(data), 'uci' ) | endsWith(names(data), 'lci')) ) data$ci_haz=cond_exists_haz_ci if (length(cond_select_haz)==0) { cond_haz=vector();cond_haz_uci=vector();cond_haz_lci=vector() data$haz=NULL;data$haz_uci=NULL;data$haz_lci=NULL } else if (length(cond_select_haz)!=0) { if (length(cond_exists_haz_ci)!=0) { ##Save all probabilities as a separate list cond_haz=vector() for (i in 1:length(cond_select_haz)) { cond_haz[i]<-which(startsWith(names(data), paste0('Haz_',input$select,selectend_h()[i]) ) & !startsWith(names(data), 'Haz_diff') & !startsWith(names(data),'Haz_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_haz_uci=vector() for (i in 1:length(cond_select_haz)) { cond_haz_uci[i]<-which(startsWith(names(data), paste0('Haz_',input$select,selectend_h()[i])) & !startsWith(names(data), 'Haz_diff') & !startsWith(names(data),'Haz_ratio') & endsWith(names(data), 'uci' ))} cond_haz_lci=vector() for (i in 1:length(cond_select_haz)) { cond_haz_lci[i]<-which(startsWith(names(data), paste0('Haz_',input$select,selectend_h()[i])) & !startsWith(names(data), 'Haz_diff') & !startsWith(names(data),'Haz_ratio') & endsWith(names(data),'lci')) } } else if (length(cond_exists_haz_ci)==0) { cond_haz=vector();cond_haz_uci=vector();cond_haz_lci=vector() for (i in 1:length(cond_select_haz)) { cond_haz[i]<-which(startsWith(names(data), paste0('Haz_',input$select,selectend_h()[i]) ) & !startsWith(names(data), 'Haz_diff') & !startsWith(names(data),'Haz_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } if (length(cond_select_haz_diff)==0) { cond_haz_diff=vector();cond_haz_diff_uci=vector();cond_haz_diff_lci=vector() data$hazd=NULL;data$hazd_uci=NULL;data$hazd_lci=NULL } else if (length(cond_select_haz_diff)!=0) { if (length(cond_exists_haz_ci)!=0) { cond_haz_diff=vector() for (i in 1:length(cond_select_haz_diff)) { cond_haz_diff[i]<- which(startsWith(names(data), paste0('Haz_diff_',input$select,selectend_h()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_haz_diff_uci=vector() for (i in 1:length(cond_select_haz_diff)) { cond_haz_diff_uci[i]<- which(startsWith(names(data), paste0('Haz_diff_',input$select,selectend_h()[i])) & endsWith(names(data), 'uci' ) ) } cond_haz_diff_lci=vector() for (i in 1:length(cond_select_haz_diff)) { cond_haz_diff_lci[i]<- which(startsWith(names(data), paste0('Haz_diff_',input$select,selectend_h()[i])) & endsWith(names(data), 'lci' ) ) } } else if (length(cond_exists_haz_ci)==0) { cond_haz_diff_uci=vector();cond_haz_diff_lci=vector() cond_haz_diff=vector() for (i in 1:length(cond_select_haz_diff)) { cond_haz_diff[i]<- which(startsWith(names(data), paste0('Haz_diff_',input$select,selectend_h()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } if (length(cond_select_haz_ratio)==0) { cond_haz_ratio=vector();cond_haz_ratio_uci=vector();cond_haz_ratio_lci=vector() data$hazr=NULL;data$hazr_uci=NULL;data$hazr_lci=NULL } else if (length(cond_select_haz_ratio)!=0) { if (length(cond_exists_haz_ci)!=0) { cond_haz_ratio=vector() for (i in 1:length(cond_select_haz_ratio)) { cond_haz_ratio[i]<- which(startsWith(names(data), paste0('Haz_ratio_',input$select,selectend_h()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_haz_ratio_uci=vector() for (i in 1:length(cond_select_haz_ratio)) { cond_haz_ratio_uci[i]<- which(startsWith(names(data), paste0('Haz_ratio_',input$select,selectend_h()[i])) & endsWith(names(data), 'uci' ) ) } cond_haz_ratio_lci=vector() for (i in 1:length(cond_select_haz_ratio)) { cond_haz_ratio_lci[i]<- which(startsWith(names(data), paste0('Haz_ratio_',input$select,selectend_h()[i])) & endsWith(names(data), 'lci' ) ) } } else if (length(cond_exists_haz_ci)==0) { cond_haz_ratio_uci=vector();cond_haz_ratio_lci=vector() cond_haz_ratio=vector() for (i in 1:length(cond_select_haz_ratio)) { cond_haz_ratio[i]<- which(startsWith(names(data), paste0('Haz_ratio_',input$select,selectend_h()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } data$haz=data[cond_haz] data$haz_uci=data[cond_haz_uci] data$haz_lci=data[cond_haz_lci] data$hazd=data[cond_haz_diff] data$hazd_uci=data[cond_haz_diff_uci] data$hazd_lci=data[cond_haz_diff_lci] data$hazr=data[cond_haz_ratio] data$hazr_uci=data[cond_haz_ratio_uci] data$hazr_lci=data[cond_haz_ratio_lci] if (input$includecov == 'Yes') { for (i in 1:length(cond_select_haz)) { data$haz[[i]] =as.data.frame(as.matrix(data$haz[[i]])[c(1,covselect()),]) data$haz_uci[[i]] =as.data.frame(as.matrix(data$haz_uci[[i]])[c(1,covselect()),]) data$haz_lci[[i]] =as.data.frame(as.matrix(data$haz_lci[[i]])[c(1,covselect()),]) } for (i in 1:length(cond_select_haz_diff)) { data$hazd[[i]] =as.data.frame(as.matrix(data$hazd[[i]])[covselectcontr(),]) data$hazd_uci[[i]]=as.data.frame(as.matrix(data$hazd_uci[[i]])[covselectcontr(),]) data$hazd_lci[[i]]=as.data.frame(as.matrix(data$hazd_lci[[i]])[covselectcontr(),]) } for (i in 1:length(cond_select_haz_ratio)) { data$hazr[[i]] =as.data.frame(as.matrix(data$hazr[[i]])[covselectcontr(),]) data$hazr_uci[[i]]=as.data.frame(as.matrix(data$hazr_uci[[i]])[covselectcontr(),]) data$hazr_lci[[i]]=as.data.frame(as.matrix(data$hazr_lci[[i]])[covselectcontr(),]) } } # ##Save all los as a separate list cond_select_los<-which(startsWith(names(data), paste0('Los_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_select_los_diff<-which(startsWith(names(data), paste0('Los_diff_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_select_los_ratio<-which(startsWith(names(data), paste0('Los_ratio_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_exists_los_ci<- which(startsWith(names(data), paste0('Los_',input$select)) & (endsWith(names(data), 'uci' ) | endsWith(names(data), 'lci')) ) data$ci_los=cond_exists_los_ci if (length(cond_select_los)==0) { cond_los=vector();cond_los_uci=vector();cond_los_lci=vector() data$los=NULL;data$los_uci=NULL;data$los_lci=NULL } else if (length(cond_select_los)!=0) { if (length(cond_exists_los_ci)!=0) { ##Save all probabilities as a separate list cond_los=vector() for (i in 1:length(cond_select_los)) { cond_los[i]<-which(startsWith(names(data), paste0('Los_',input$select,selectend()[i]) ) & !startsWith(names(data), 'Los_diff') & !startsWith(names(data),'Los_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_los_uci=vector() for (i in 1:length(cond_select_los)) { cond_los_uci[i]<-which(startsWith(names(data), paste0('Los_',input$select,selectend()[i])) & !startsWith(names(data), 'Los_diff') & !startsWith(names(data),'Los_ratio') & endsWith(names(data), 'uci' ))} cond_los_lci=vector() for (i in 1:length(cond_select_los)) { cond_los_lci[i]<-which(startsWith(names(data), paste0('Los_',input$select,selectend()[i])) & !startsWith(names(data), 'Los_diff') & !startsWith(names(data),'Los_ratio') & endsWith(names(data),'lci')) } } else if (length(cond_exists_los_ci)==0) { cond_los=vector();cond_los_uci=vector();cond_los_lci=vector() for (i in 1:length(cond_select_los)) { cond_los[i]<-which(startsWith(names(data), paste0('Los_',input$select,selectend()[i]) ) & !startsWith(names(data), 'Los_diff') & !startsWith(names(data),'Los_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } if (length(cond_select_los_diff)==0) { cond_los_diff=vector();cond_los_diff_uci=vector();cond_los_diff_lci=vector() data$losd=NULL;data$losd_uci=NULL;data$losd_lci=NULL } else if (length(cond_select_los_diff)!=0) { if (length(cond_exists_los_ci)!=0) { cond_los_diff=vector() for (i in 1:length(cond_select_los_diff)) { cond_los_diff[i]<- which(startsWith(names(data), paste0('Los_diff_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_los_diff_uci=vector() for (i in 1:length(cond_select_los_diff)) { cond_los_diff_uci[i]<- which(startsWith(names(data), paste0('Los_diff_',input$select,selectend()[i])) & endsWith(names(data), 'uci' ) ) } cond_los_diff_lci=vector() for (i in 1:length(cond_select_los_diff)) { cond_los_diff_lci[i]<- which(startsWith(names(data), paste0('Los_diff_',input$select,selectend()[i])) & endsWith(names(data), 'lci' ) ) } } else if (length(cond_exists_los_ci)==0) { cond_los_diff_uci=vector();cond_los_diff_lci=vector() cond_los_diff=vector() for (i in 1:length(cond_select_los_diff)) { cond_los_diff[i]<- which(startsWith(names(data), paste0('Los_diff_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } if (length(cond_select_los_ratio)==0) { cond_los_ratio=vector();cond_los_ratio_uci=vector();cond_los_ratio_lci=vector() data$losr=NULL;data$losr_uci=NULL;data$losr_lci=NULL } else if (length(cond_select_los_ratio)!=0) { if (length(cond_exists_los_ci)!=0) { cond_los_ratio=vector() for (i in 1:length(cond_select_los_ratio)) { cond_los_ratio[i]<- which(startsWith(names(data), paste0('Los_ratio_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_los_ratio_uci=vector() for (i in 1:length(cond_select_los_ratio)) { cond_los_ratio_uci[i]<- which(startsWith(names(data), paste0('Los_ratio_',input$select,selectend()[i])) & endsWith(names(data), 'uci' ) ) } cond_los_ratio_lci=vector() for (i in 1:length(cond_select_los_ratio)) { cond_los_ratio_lci[i]<- which(startsWith(names(data), paste0('Los_ratio_',input$select,selectend()[i])) & endsWith(names(data), 'lci' ) ) } } else if (length(cond_exists_los_ci)==0) { cond_los_ratio_uci=vector();cond_los_ratio_lci=vector() cond_los_ratio=vector() for (i in 1:length(cond_select_los_ratio)) { cond_los_ratio[i]<- which(startsWith(names(data), paste0('Los_ratio_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } data$los=data[cond_los] data$los_uci=data[cond_los_uci] data$los_lci=data[cond_los_lci] data$losd=data[cond_los_diff] data$losd_uci=data[cond_los_diff_uci] data$losd_lci=data[cond_los_diff_lci] data$losr=data[cond_los_ratio] data$losr_uci=data[cond_los_ratio_uci] data$losr_lci=data[cond_los_ratio_lci] if (input$includecov == 'Yes') { for (i in 1:length(cond_select_los)) { data$los[[i]] =as.data.frame(as.matrix(data$los[[i]])[c(1,covselect()),]) data$los_uci[[i]] =as.data.frame(as.matrix(data$los_uci[[i]])[c(1,covselect()),]) data$los_lci[[i]] =as.data.frame(as.matrix(data$los_lci[[i]])[c(1,covselect()),]) } for (i in 1:length(cond_select_los_diff)) { data$losd[[i]] =as.data.frame(as.matrix(data$losd[[i]])[covselectcontr(),]) data$losd_uci[[i]]=as.data.frame(as.matrix(data$losd_uci[[i]])[covselectcontr(),]) data$losd_lci[[i]]=as.data.frame(as.matrix(data$losd_lci[[i]])[covselectcontr(),]) } for (i in 1:length(cond_select_los_ratio)) { data$losr[[i]] =as.data.frame(as.matrix(data$losr[[i]])[covselectcontr(),]) data$losr_uci[[i]]=as.data.frame(as.matrix(data$losr_uci[[i]])[covselectcontr(),]) data$losr_lci[[i]]=as.data.frame(as.matrix(data$losr_lci[[i]])[covselectcontr(),]) } } ##Save all visit as a separate list cond_select_visit<-which(startsWith(names(data), paste0('Visit_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_select_visit_diff<-which(startsWith(names(data), paste0('Visit_diff_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_select_visit_ratio<-which(startsWith(names(data), paste0('Visit_ratio_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_exists_visit_ci<- which(startsWith(names(data), paste0('Visit_',input$select)) & (endsWith(names(data), 'uci' ) | endsWith(names(data), 'lci')) ) data$ci_visit=cond_exists_visit_ci if (length(cond_select_visit)==0) { cond_visit=vector();cond_visit_uci=vector();cond_visit_lci=vector() data$visit=NULL;data$visit_uci=NULL;data$visit_lci=NULL } else if (length(cond_select_visit)!=0) { if (length(cond_exists_visit_ci)!=0) { ##Save all probabilities as a separate list cond_visit=vector() for (i in 1:length(cond_select_visit)) { cond_visit[i]<-which(startsWith(names(data), paste0('Visit_',input$select,selectend()[i]) ) & !startsWith(names(data), 'Visit_diff') & !startsWith(names(data),'Visit_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_visit_uci=vector() for (i in 1:length(cond_select_visit)) { cond_visit_uci[i]<-which(startsWith(names(data), paste0('Visit_',input$select,selectend()[i])) & !startsWith(names(data), 'Visit_diff') & !startsWith(names(data),'Visit_ratio') & endsWith(names(data), 'uci' ))} cond_visit_lci=vector() for (i in 1:length(cond_select_visit)) { cond_visit_lci[i]<-which(startsWith(names(data), paste0('Visit_',input$select,selectend()[i])) & !startsWith(names(data), 'Visit_diff') & !startsWith(names(data),'Visit_ratio') & endsWith(names(data),'lci')) } } else if (length(cond_exists_visit_ci)==0) { cond_visit=vector();cond_visit_uci=vector();cond_visit_lci=vector() for (i in 1:length(cond_select_visit)) { cond_visit[i]<-which(startsWith(names(data), paste0('Visit_',input$select,selectend()[i]) ) & !startsWith(names(data), 'Visit_diff') & !startsWith(names(data),'Visit_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } if (length(cond_select_visit_diff)==0) { cond_visit_diff=vector();cond_visit_diff_uci=vector();cond_visit_diff_lci=vector() data$visitd=NULL;data$visitd_uci=NULL;data$visitd_lci=NULL } else if (length(cond_select_visit_diff)!=0) { if (length(cond_exists_visit_ci)!=0) { cond_visit_diff=vector() for (i in 1:length(cond_select_visit_diff)) { cond_visit_diff[i]<- which(startsWith(names(data), paste0('Visit_diff_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_visit_diff_uci=vector() for (i in 1:length(cond_select_visit_diff)) { cond_visit_diff_uci[i]<- which(startsWith(names(data), paste0('Visit_diff_',input$select,selectend()[i])) & endsWith(names(data), 'uci' ) ) } cond_visit_diff_lci=vector() for (i in 1:length(cond_select_visit_diff)) { cond_visit_diff_lci[i]<- which(startsWith(names(data), paste0('Visit_diff_',input$select,selectend()[i])) & endsWith(names(data), 'lci' ) ) } } else if (length(cond_exists_visit_ci)==0) { cond_visit_diff_uci=vector();cond_visit_diff_lci=vector() cond_visit_diff=vector() for (i in 1:length(cond_select_visit_diff)) { cond_visit_diff[i]<- which(startsWith(names(data), paste0('Visit_diff_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } if (length(cond_select_visit_ratio)==0) { cond_visit_ratio=vector();cond_visit_ratio_uci=vector();cond_visit_ratio_lci=vector() data$visitr=NULL;data$visitr_uci=NULL;data$visitr_lci=NULL } else if (length(cond_select_visit_ratio)!=0) { if (length(cond_exists_visit_ci)!=0) { cond_visit_ratio=vector() for (i in 1:length(cond_select_visit_ratio)) { cond_visit_ratio[i]<- which(startsWith(names(data), paste0('Visit_ratio_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_visit_ratio_uci=vector() for (i in 1:length(cond_select_visit_ratio)) { cond_visit_ratio_uci[i]<- which(startsWith(names(data), paste0('Visit_ratio_',input$select,selectend()[i])) & endsWith(names(data), 'uci' ) ) } cond_visit_ratio_lci=vector() for (i in 1:length(cond_select_visit_ratio)) { cond_visit_ratio_lci[i]<- which(startsWith(names(data), paste0('Visit_ratio_',input$select,selectend()[i])) & endsWith(names(data), 'lci' ) ) } } else if (length(cond_exists_visit_ci)==0) { cond_visit_ratio_uci=vector();cond_visit_ratio_lci=vector() cond_visit_ratio=vector() for (i in 1:length(cond_select_visit_ratio)) { cond_visit_ratio[i]<- which(startsWith(names(data), paste0('Visit_ratio_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } data$visit=data[cond_visit] data$visit_uci=data[cond_visit_uci] data$visit_lci=data[cond_visit_lci] data$visitd=data[cond_visit_diff] data$visitd_uci=data[cond_visit_diff_uci] data$visitd_lci=data[cond_visit_diff_lci] data$visitr=data[cond_visit_ratio] data$visitr_uci=data[cond_visit_ratio_uci] data$visitr_lci=data[cond_visit_ratio_lci] if (input$includecov == 'Yes') { for (i in 1:length(cond_select_visit)) { data$visit[[i]] =as.data.frame(as.matrix(data$visit[[i]])[c(1,covselect()),]) data$visit_uci[[i]] =as.data.frame(as.matrix(data$visit_uci[[i]])[c(1,covselect()),]) data$visit_lci[[i]] =as.data.frame(as.matrix(data$visit_lci[[i]])[c(1,covselect()),]) } for (i in 1:length(cond_select_visit_diff)) { data$visitd[[i]] =as.data.frame(as.matrix(data$visitd[[i]])[covselectcontr(),]) data$visitd_uci[[i]]=as.data.frame(as.matrix(data$visitd_uci[[i]])[covselectcontr(),]) data$visitd_lci[[i]]=as.data.frame(as.matrix(data$visitd_lci[[i]])[covselectcontr(),]) } for (i in 1:length(cond_select_visit_ratio)) { data$visitr[[i]] =as.data.frame(as.matrix(data$visitr[[i]])[covselectcontr(),]) data$visitr_uci[[i]]=as.data.frame(as.matrix(data$visitr_uci[[i]])[covselectcontr(),]) data$visitr_lci[[i]]=as.data.frame(as.matrix(data$visitr_lci[[i]])[covselectcontr(),]) } } ##Save all user as a separate list cond_U<-which(startsWith(names(data), paste0('User_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_U_diff<-which(startsWith(names(data), paste0('User_diff_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_U_ratio<-which(startsWith(names(data), paste0('User_ratio_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_exists_user_ci<- which(startsWith(names(data), paste0('User_',input$select)) & (endsWith(names(data), 'uci' ) | endsWith(names(data), 'lci')) ) data$ci_user=cond_exists_user_ci if (length(cond_U)==0) { cond_user=vector();cond_user_uci=vector();cond_user_lci=vector() data$user=NULL;data$user_uci=NULL;data$user_lci=NULL } else if (length(cond_U)!=0) { if (length(cond_exists_user_ci)!=0) { cond_user=vector() for (i in 1:length(cond_U)) { cond_user[i]<-which(startsWith(names(data), paste0('User_',input$select)) & !startsWith(names(data), 'User_diff') & !startsWith(names(data),'User_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_user_uci=vector() for (i in 1:length(cond_U)) { cond_user_uci[i]<-which(startsWith(names(data), paste0('User_',input$select)) & !startsWith(names(data), 'User_diff') & !startsWith(names(data),'User_ratio') & endsWith(names(data), 'uci' )) } cond_user_lci=vector() for (i in 1:length(cond_U)) { cond_user_lci[i]<-which(startsWith(names(data), paste0('User_',input$select)) & !startsWith(names(data), 'User_diff') & !startsWith(names(data),'User_ratio') & endsWith(names(data), 'lci' )) } } else if (length(cond_exists_user_ci)==0) { cond_user_uci=vector();cond_user_lci=vector() cond_user=vector() for (i in 1:length(cond_U)) { cond_user[i]<-which(startsWith(names(data), paste0('User_',input$select)) & !startsWith(names(data), 'User_diff') & !startsWith(names(data),'User_ratio') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } if (length(cond_U_diff)==0) { cond_user_diff=vector();cond_user_diff_uci=vector();cond_user_diff_lci=vector() data$userd=NULL;data$userd_uci=NULL;data$userd_lci=NULL } else if (length(cond_U_diff)!=0) { if (length(cond_exists_user_ci)!=0) { cond_user_diff=vector() for (i in 1:length(cond_U_diff)) { cond_user_diff[i]<- which(startsWith(names(data), paste0('User_diff_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_user_diff_uci=vector() for (i in 1:length(cond_U_diff)) { cond_user_diff_uci[i]<- which(startsWith(names(data), paste0('User_diff_',input$select)) & endsWith(names(data), 'uci' ) ) } cond_user_diff_lci=vector() for (i in 1:length(cond_U_diff)) { cond_user_diff_lci[i]<- which(startsWith(names(data), paste0('User_diff_',input$select)) & endsWith(names(data), 'lci' ) ) } } else if (length(cond_exists_user_ci)==0) { cond_user_diff_uci=vector(); cond_user_diff_lci=vector() cond_user_diff=vector() for (i in 1:length(cond_U_diff)) { cond_user_diff[i]<- which(startsWith(names(data), paste0('User_diff_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } if (length(cond_U_ratio)==0) { cond_user_ratio=vector();cond_user_ratio_uci=vector();cond_user_ratio_lci=vector() data$userr=NULL;data$userr_uci=NULL;data$userr_lci=NULL } else if (length(cond_U_ratio)!=0) { if (length(cond_exists_user_ci)!=0) { cond_user_ratio=vector() for (i in 1:length(cond_U_ratio)) { cond_user_ratio[i]<- which(startsWith(names(data), paste0('User_ratio_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data),'lci') ) } cond_user_ratio_uci=vector() for (i in 1:length(cond_U_ratio)) { cond_user_ratio_uci[i]<- which(startsWith(names(data), paste0('User_ratio_',input$select)) & endsWith(names(data), 'uci' ) ) } cond_user_ratio_lci=vector() for (i in 1:length(cond_U_ratio)) { cond_user_ratio_lci[i]<- which(startsWith(names(data), paste0('User_ratio_',input$select)) & endsWith(names(data), 'lci' ) ) } } else if (length(cond_exists_user_ci)==0) { cond_user_ratio_uci=vector(); cond_user_ratio_lci=vector() cond_user_ratio=vector() for (i in 1:length(cond_U_ratio)) { cond_user_ratio[i]<- which(startsWith(names(data), paste0('User_ratio_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data),'lci') ) } } } data$user=data[cond_user] data$user_uci=data[cond_user_uci] data$user_lci=data[cond_user_lci] data$userd=data[cond_user_diff] data$userd_uci=data[cond_user_diff_uci] data$userd_lci=data[cond_user_diff_lci] data$userr=data[cond_user_ratio] data$userr_uci=data[cond_user_ratio_uci] data$userr_lci=data[cond_user_ratio_lci] if (input$includecov == 'Yes') { for (i in 1:length(cond_U)) { data$user[[i]] =as.data.frame(as.matrix(data$user[[i]])[c(1,covselect()),]) data$user_uci[[i]] =as.data.frame(as.matrix(data$user_uci[[i]])[c(1,covselect()),]) data$user_lci[[i]] =as.data.frame(as.matrix(data$user_lci[[i]])[c(1,covselect()),]) } for (i in 1:length(cond_U_diff)) { data$userd[[i]] =as.data.frame(as.matrix(data$userd[[i]])[covselectcontr(),]) data$userd_uci[[i]]=as.data.frame(as.matrix(data$userd_uci[[i]])[covselectcontr(),]) data$userd_lci[[i]]=as.data.frame(as.matrix(data$userd_lci[[i]])[covselectcontr(),]) } for (i in 1:length(cond_U_ratio)) { data$userr[[i]] =as.data.frame(as.matrix(data$userr[[i]])[covselectcontr(),]) data$userr_uci[[i]]=as.data.frame(as.matrix(data$userr_uci[[i]])[covselectcontr(),]) data$userr_lci[[i]]=as.data.frame(as.matrix(data$userr_lci[[i]])[covselectcontr(),]) } } #### Number #### ##Save all Number as a separate list cond_N <- which(startsWith(names(data), paste0('Number_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_exists_number_ci<- which(startsWith(names(data), paste0('Number_',input$select)) & (endsWith(names(data), 'uci' ) | endsWith(names(data), 'lci')) ) data$ci_number=cond_exists_number_ci if (length(cond_N)==0) { cond_number=vector();cond_number_uci=vector();cond_number_lci=vector() data$number=NULL;data$number_uci=NULL;data$number_lci=NULL } else if (length(cond_N)!=0) { if (length(cond_exists_number_ci)!=0) { cond_number=vector() for (i in 1:length(cond_N)) { cond_number[i]<-which(startsWith(names(data), paste0('Number_',input$select,selectend()[i]) ) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_number_uci=vector() for (i in 1:length(cond_N)) { cond_number_uci[i]<-which(startsWith(names(data), paste0('Number_',input$select,selectend()[i])) & endsWith(names(data), 'uci' )) } cond_number_lci=vector() for (i in 1:length(cond_N)) { cond_number_lci[i]<-which(startsWith(names(data), paste0('Number_',input$select,selectend()[i])) & endsWith(names(data), 'lci' )) } } else if (length(cond_exists_number_ci)==0) { cond_number_uci=vector();cond_number_lci=vector() cond_number=vector() for (i in 1:length(cond_N)) { cond_number[i]<-which(startsWith(names(data), paste0('Number_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } data$number=data[cond_number] data$number_uci=data[cond_number_uci] data$number_lci=data[cond_number_lci] if (input$includecov == 'Yes') { if (length(data$number)>0) { for (i in 1:length(data$number)) { data$number[[i]] =as.data.frame(as.matrix(data$number[[i]])[c(1,covselect()),]) data$number_uci[[i]] =as.data.frame(as.matrix(data$number_uci[[i]])[c(1,covselect()),]) data$number_lci[[i]] =as.data.frame(as.matrix(data$number_lci[[i]])[c(1,covselect()),]) } } } ### Save Next ### cond_Ne <- which(startsWith(names(data), paste0('Next_',input$select)) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) data$cond_Ne=cond_Ne cond_exists_next_ci<- which(startsWith(names(data), paste0('Next_',input$select)) & (endsWith(names(data), 'uci' ) | endsWith(names(data), 'lci')) ) data$ci_next=cond_exists_next_ci if (length(cond_Ne)==0) { cond_next=vector();cond_next_uci=vector();cond_next_lci=vector() data$nextv=NULL;data$next_uci=NULL;data$next_lci=NULL } else if (length(cond_Ne)!=0) { if (length(cond_exists_next_ci)!=0) { cond_next=vector() for (i in 1:length(cond_Ne)) { cond_next[i]<-which(startsWith(names(data), paste0('Next_',input$select,selectend()[i]) ) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_next_uci=vector() for (i in 1:length(cond_Ne)) { cond_next_uci[i]<-which(startsWith(names(data), paste0('Next_',input$select,selectend()[i])) & endsWith(names(data), 'uci' )) } cond_next_lci=vector() for (i in 1:length(cond_Ne)) { cond_next_lci[i]<-which(startsWith(names(data), paste0('Next_',input$select,selectend()[i])) & endsWith(names(data), 'lci' )) } } else if (length(cond_exists_next_ci)==0) { cond_next_uci=vector();cond_next_lci=vector() cond_next=vector() for (i in 1:length(cond_Ne)) { cond_next[i]<-which(startsWith(names(data), paste0('Next_',input$select,selectend()[i])) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } data$cond_next=cond_next } } data$nextv=data[cond_next] data$next_uci=data[cond_next_uci] data$next_lci=data[cond_next_lci] if (input$includecov == 'Yes') { if (length(data$nextv)>0) { for (i in 1:length(data$nextv)) { data$nextv[[i]] =as.data.frame(as.matrix(data$nextv[[i]])[c(1,covselect()),]) data$next_uci[[i]] =as.data.frame(as.matrix(data$next_uci[[i]])[c(1,covselect()),]) data$next_lci[[i]] =as.data.frame(as.matrix(data$next_lci[[i]])[c(1,covselect()),]) } } } ### Sojourn ### cond_S <- which(startsWith(names(data), 'Soj_') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_exists_soj_ci<- which(startsWith(names(data), "Soj_" ) & (endsWith(names(data), 'uci' ) | endsWith(names(data), 'lci')) ) data$ci_soj=cond_exists_soj_ci if (length(cond_S)==0) { cond_soj=vector();cond_soj_uci=vector();cond_soj_lci=vector() data$soj=NULL;data$soj_uci=NULL;data$soj_lci=NULL } else if (length(cond_S)!=0) { if (length(cond_exists_soj_ci)!=0) { cond_soj=vector() for (i in 1:length(cond_S)) { cond_soj[i]<-which(startsWith(names(data), paste0('Soj_',sub("_to_","", selectend()[i] ) ) ) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_soj_uci=vector() for (i in 1:length(cond_S)) { cond_soj_uci[i]<-which(startsWith(names(data), paste0('Soj_',sub("_to_","", selectend()[i] ) ) ) & endsWith(names(data), 'uci' )) } cond_soj_lci=vector() for (i in 1:length(cond_S)) { cond_soj_lci[i]<-which(startsWith(names(data), paste0('Soj_',sub("_to_","", selectend()[i] ) ) ) & endsWith(names(data), 'lci' )) } } else if (length(cond_exists_soj_ci)==0) { cond_soj_uci=vector();cond_soj_lci=vector() cond_soj=vector() for (i in 1:length(cond_S)) { cond_soj[i]<-which(startsWith(names(data), paste0('Soj_',sub("_to_","", selectend()[i] ) ) ) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } data$soj=data[cond_soj] data$soj_uci=data[cond_soj_uci] data$soj_lci=data[cond_soj_lci] if (input$includecov == 'Yes') { if (length(data$soj)>0) { for (i in 1:length(data$soj)) { data$soj[[i]] =as.data.frame(as.matrix(data$soj[[i]])[c(1,covselect()),]) data$soj_uci[[i]] =as.data.frame(as.matrix(data$soj_uci[[i]])[c(1,covselect()),]) data$soj_lci[[i]] =as.data.frame(as.matrix(data$soj_lci[[i]])[c(1,covselect()),]) } } } ### First ### ### Save First ### cond_F <- which(startsWith(names(data), 'First_') & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) cond_exists_first_ci<- which(startsWith(names(data), "First_" ) & (endsWith(names(data), 'uci' ) | endsWith(names(data), 'lci')) ) data$ci_first=cond_exists_first_ci if (length(cond_F)==0) { cond_first=vector();cond_first_uci=vector();cond_first_lci=vector() data$first=NULL;data$first_uci=NULL;data$first_lci=NULL } else if (length(cond_F)!=0) { if (length(cond_exists_first_ci)!=0) { cond_first=vector() for (i in 1:length(cond_F)) { cond_first[i]<-which(startsWith(names(data), paste0('First_',sub("_to_","", selectend()[i] ) ) ) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } cond_first_uci=vector() for (i in 1:length(cond_F)) { cond_first_uci[i]<-which(startsWith(names(data), paste0('First_',sub("_to_","", selectend()[i] ) ) ) & endsWith(names(data), 'uci' )) } cond_first_lci=vector() for (i in 1:length(cond_F)) { cond_first_lci[i]<-which(startsWith(names(data), paste0('First_',sub("_to_","", selectend()[i] ) ) ) & endsWith(names(data), 'lci' )) } } else if (length(cond_exists_first_ci)==0) { cond_first_uci=vector();cond_first_lci=vector() cond_first=vector() for (i in 1:length(cond_F)) { cond_first[i]<-which(startsWith(names(data), paste0('First_',sub("_to_","", selectend()[i] ) ) ) & !endsWith(names(data), 'uci' ) & !endsWith(names(data), 'lci') ) } } } data$first=data[cond_first] data$first_uci=data[cond_first_uci] data$first_lci=data[cond_first_lci] if (input$includecov == 'Yes') { if (length(data$first)>0) { for (i in 1:length(data$first)) { data$first[[i]] =as.data.frame(as.matrix(data$first[[i]])[c(1,covselect()),]) data$first_uci[[i]] =as.data.frame(as.matrix(data$first_uci[[i]])[c(1,covselect()),]) data$first_lci[[i]] =as.data.frame(as.matrix(data$first_lci[[i]])[c(1,covselect()),]) } } } ##The time variable data$timevar ### Number of different covariate patterns specified for prediction #### List of covariate patterns if (input$includecov == 'Yes') { data$cov$atlist= c(myjson1_5()$atlist[1],covselect_char()) data$atlist= c(myjson1_5()$atlist[1],covselect_char()) data$Nats=length( c(myjson1_5()$atlist[1],covselect_char())) } else if (input$includecov == 'No') { data$cov$atlist= c(myjson1_5()$atlist) data$atlist= c(myjson1_5()$atlist) data$Nats=length( c(myjson1_5()$atlist))} data }) #### output that shows that the previous reactive works # Include the logic (server) for each tab stacked_function_bars<-function(json, data, labels_cov, labels_state ) { stacked_list_long=list() stacked_list=list() ###Outer loop k is the covariate pattern k- we bring all the stacked states probabilities into a common list- ### that list have now the stackes probabilities matrices for all the covariate patterns for (k in 1:length(json$atlist)) { stacked=matrix(nrow=ncol(as.data.frame(json$P[k])),ncol=length(json$P)+2,NA) ##Stack probabilities for state 1 pro_stack=list() pro_stack[[1]]=data[which(data$state==1),k] ### Inner loop to bring probabilities same covariate pattern but different states together if (length(json$P)>1) { for (i in 2:(length(json$P))) { pro_stack[[i]]=cbind(pro_stack[[i-1]],data[which(data$state==i),k]) } } ### Inner loop to stack those probabilities with same covariate pattern but different states pro_stack2=as.data.frame(pro_stack[[length(json$P)]]) pstate=vector() for (j in 1:length(json$P)) { pstate[j]=paste0("P_state",j) } colnames(pro_stack2)[1:length(json$P)]<-pstate pro_stack2$timevar=json$timevar pro_stack2$cov=as.character(rep(json$atlist[k],nrow(pro_stack2))) pro_stack3=matrix(nrow=nrow(pro_stack2),ncol=ncol(pro_stack2)-2,NA) pro_stack3[,1]=pro_stack2[,1] for (i in 1:(length(json$P))) { pro_stack3[,i]=pro_stack2[,i] } pro_stack3=as.data.frame(pro_stack3) colnames(pro_stack3)[1:length(json$P)]<-pstate pro_stack3$timevar=json$timevar pro_stack3$cov=as.character(rep(labels_cov[k],nrow(pro_stack3))) stacked_list[[k]]=pro_stack3 stacked_list_d =list() for (d in 1:length(json$P)) { stack_wide= as.data.frame(stacked_list[[k]]) stacked_list_d[[d]]=cbind.data.frame(stack_wide[,d], stack_wide[,ncol( stack_wide)-1], stack_wide[,ncol( stack_wide)], rep(d,length( stack_wide[,d])) ) colnames( stacked_list_d[[d]]) <- c("P","timevar","cov_factor","state") } stack_long <- bind_rows(stacked_list_d, .id = "column_label") stack_long= as.data.frame(stack_long) for (o in 1:(length(json$P))) { for (g in 1:nrow(stack_long)) { if (stack_long$state[g]==o) {stack_long$state_factor[g]=labels_state[o] } } } stacked_list_long[[k]]=stack_long } stacked_list_long } output$interpret<-renderUI({ intro = withMathJax( helpText(strong("Description of a Multi-state process and definitions.")), helpText("Consider a stochastic process \\(Y(t)\\) \\( (t\\in T) \\) with a finite state space \\(L = {1,...,p}\\) and a history of the process defined as \\( H_{s}= {Y(u); 0 \\leq u \\leq s} \\)") ) intro return(list(intro )) }) output$message_prob<- renderUI ({ #intro = print("Description of a Multi-state process and definitions\nConsider a stochastic process Y(t) (tET) with a finite state space L = {1,...,p} and a history of the process defined as Hs= Y (u); 0<= u <= sg.\n") if (is.null(input$measures) ){return()} prob <- "prob" %in% input$measures trans <- "trans" %in% input$measures los <- "los" %in% input$measures #vis <- "vis" %in% input$measures comp <- "comp" %in% input$measures extram <- "extram" %in% input$measures robust <- "robust" %in% input$measures if (prob){ p_def = withMathJax( helpText(strong("Transition probabilities")), helpText('The transition probabilities can be defined as $$P(Y(t)=b| Y(s)=a, H_{s-})$$ with $$a,b \\in L, s; t \\in T ; s <=t$$'), helpText("The equation above depicts the probability of being in state b at time t given that you were in state a at time s and conditional on the history of the process up to time s.") ) p_ex = helpText("\nEBMT example: For time= 2 years since transplantation an individual <20 years old at transplantation has 39% probability of having experienced platelet recovery (transition from state 1 to state 2). On the other hand, an individual >40 years old at transplantation has 32% probability of platelet recovery\n") } else if (!prob) {p_def=NULL ; p_ex=NULL} return(list(p_def,p_ex)) }) output$message_trans<- renderUI ({ prob <- "prob" %in% input$measures trans <- "trans" %in% input$measures los <- "los" %in% input$measures # vis <- "vis" %in% input$measures comp <- "comp" %in% input$measures extram <- "extram" %in% input$measures robust <- "robust" %in% input$measures if (trans){ trans_def = withMathJax( helpText(strong("Transition intensity")), helpText('For the stochastic process \\(Y(t)\\), there are \\(K\\) potential transitions \\( (k=1,.,K)\\). If the k-th transition is the transition between state \\(a\\) and \\(b\\) \\(a\\rightarrow b)\\) the \\(k-th\\) transition intensity is defined as the derivative of the (\\(a\\rightarrow b)\\) transition probability):'), helpText("$$q_{k}(t)=\\lim_{\\delta t\\to0}\\frac{P(Y(t+\\delta t)=b_{k}| Y(t)=a_{k}, H_{s-})}{\\delta t}$$"), helpText("Which is the instantaneous rate of moving from \\(a\\) to \\(b\\), at time \\(t\\) given that you were in state \\(a\\) at time \\(s\\) and conditional on the history of the process up to time \\(s\\).") ) trans_ex = helpText("EBMT example: For time= 2 years since transplantation, the transition rate from post transplant (state 1) to relapse/death (state 3) is 0.07 for an individual <20 years old at transplantation and 0.09 for an individual >40 years old at transplantation Therefore the hazard ratio for relapse/death among those who had not recovered previously (state 1) between the older and the younger age group is 1.28\n") } else if (!trans) {trans_def=NULL ; trans_ex=NULL} return(list( trans_def, trans_ex)) }) output$message_los<- renderUI ({ prob <- "prob" %in% input$measures trans <- "trans" %in% input$measures los <- "los" %in% input$measures # vis <- "vis" %in% input$measures comp <- "comp" %in% input$measures extram <- "extram" %in% input$measures robust <- "robust" %in% input$measures if (los){ los_def = withMathJax( helpText(strong("Length of stay")), helpText('The residual restricted expected length of stay (or length of stay for simplicity) in state \\(a\\) during the time period from \\(s\\) to \\( \\tau \\) , conditional on the patient being in state \\(a\\) (non-absorbing) at time \\(s\\), is defined as'), helpText("$$e_{ab}(s)=\\int_{s}^{\\tau} P(Y(u)=b| Y(s)=a, H_{s-}) du$$"), helpText("which defines the amount of time spent in state \\(b\\), starting in state \\(a\\) at time \\(s\\), up until time \\(\\tau\\) . If \\(\\tau=\\infty\\) , and state \\(a=b\\) is a healthy state and all possible next states are deaths, then this equation represents life expectancy.") ) los_ex = helpText("EBMT example: For time= 5 years since transplantation, individuals <20 years old at transplantation are expected to have stayed 1.9 years in the recovery state (state 2) while individuals >40 years old at transplantation are expected to have stayed 1.6 years in the recovery state.") } else if (!los) {los_def=NULL ; los_ex=NULL} return(list(los_def,los_ex )) }) #output$message_vis<- renderUI ({ # # # prob <- "prob" %in% input$measures # trans <- "trans" %in% input$measures # los <- "los" %in% input$measures # vis <- "vis" %in% input$measures # comp <- "comp" %in% input$measures # extram <- "extram" %in% input$measures # robust <- "robust" %in% input$measures # # if (vis){ # # vis_def = withMathJax( # helpText(strong("Probability of ever visiting a state")), # helpText('Now we define $$ f_{ab}:= P(\\tau_{b}<\\infty|X_{0}=a)= \\sum_{k=1}^{\\infty} F_{k}(a,b) $$ for all \\(a,b\\in L \\) # which represents the probability of ever visiting state \\(b\\) after given initial state \\(X_{0}=a\\). # \\( F_{k}\\) is the conditional distribution of the first visit to the state \\(b\\in L\\), given the initial state \\(X_{0}=a\\), # and \\(k\\) is index so that \\(X_{k}=b\\). More on the theoritical derivation of the estimation can be found at "Stochastic Processes" by Lothar Breuer.') # ) # # vis_ex = helpText("EBMT example: For time= 5 years since transplantation, the probability that an individual <20 years old at transplantation # will have a relapse/death (state 3) by that time is 39% while the probability that an individual >40 years old at transplantation will have a relapse/death by that time is 52%.") # } # # else if (!vis) {vis_def=NULL ; vis_ex=NULL} # # return(list( vis_def, vis_ex )) # # #}) # output$message_comp<- renderUI ({ prob <- "prob" %in% input$measures trans <- "trans" %in% input$measures los <- "los" %in% input$measures # vis <- "vis" %in% input$measures comp <- "comp" %in% input$measures extram <- "extram" %in% input$measures robust <- "robust" %in% input$measures if (comp){ comp_def = withMathJax( helpText(strong("Differences and ratios of estimated measures")), helpText("In order to illustrate the impact of different covariate levels on the measures of interest, we can derive differences and ratios between the covariate patterns of interest. Given covariate patterns \\(X_{1}\\) and \\(X_{2}\\) "), helpText('Example for differences in probabilities: $$ P(Y(t)=b| Y(s)=a, H_{s-},X_{1}) - P(Y(t)=b| Y(s)=a, H_{s-},X_{2})$$'), helpText('Example for ratios of length of stay : $$ \\frac{e_{ab}(s,(X_{1})}{e_{ab}(s,(X_{2})}= \\frac{\\int_{s}^{\\tau} P(Y(u)=b| Y(s)=a, H_{s-},X_{1}) du}{\\int_{s}^{\\tau} P(Y(u)=b| Y(s)=a, H_{s-},X_{2}) du} $$') ) comp_ex = helpText("EBMT example: For time= 5 years since transplantation, the probability that an individual <20 years old at transplantation will have a relapse/death (state 3) by that time is 39% while the probability that an individual >40 years old at transplantation will have a relapse/death by that time is 52%.") } else if (!comp) {comp_def=NULL ; comp_ex=NULL} return(list( comp_def, comp_ex )) }) output$message_extram<- renderUI ({ prob <- "prob" %in% input$measures trans <- "trans" %in% input$measures los <- "los" %in% input$measures # vis <- "vis" %in% input$measures comp <- "comp" %in% input$measures extram <- "extram" %in% input$measures robust <- "robust" %in% input$measures if (extram){ extram_def = withMathJax( helpText(strong("Extra estimated measures under time homogeneous Markov assumption")), helpText('Under the time homogeneous continuous time Markov approach, the expected single period of occupancy of a state \\(a\\) is given by \\(-1/q_{aa}\\). The probability that an individuals next move from state \\(a\\) to state \\(b\\) is given by \\( -q_{ab}/q_{aa}\\). The expected total number of visits and expected first hitting times to a specific state can also be derived under this assumption via msm package and theoretical background can be found at Stochastic Processes by Lothar Breuer.') ) extram_ex = withMathJax( helpText("If the mean sojourn time of state a = 1 year, that means that each time an individual visits state a, they spend an average of 1 year in that state."), helpText("If the expected number of visits for state a = 2, that means that an average individual is expected to visit state 2 times."), helpText("If the probability that an individual next move from state a to state b is 0.5 means that the probability that an individual who is presently at state a will next move at state b is 50%."), helpText("If the first passage time to state b is 2 years, that means, that for an individual with a specific covariate pattern, the estimated first time that he/she will reach state b is 2 years.") ) } else if (!extram) {extram_def=NULL ; extram_ex=NULL} return(list( extram_def, extram_ex )) }) output$message_robust<- renderUI ({ prob <- "prob" %in% input$measures trans <- "trans" %in% input$measures los <- "los" %in% input$measures # vis <- "vis" %in% input$measures comp <- "comp" %in% input$measures extram <- "extram" %in% input$measures robust <- "robust" %in% input$measures if (robust){ robust_def = withMathJax( helpText(strong("Robustness of measures under different types of models (different timescales)")), helpText('The types of models used for disease processes may depend on the main objectives of the analysis. If we aim to understand individual process dynamics and related factors, then models whose intensities reflect such dynamics are desirable, using a global "age" of the process (Markov models-clock forward approach). However, if the main objective is to assess a treatment or intervention, then models which consider marginal process characteristics such as time of entry to a particular state are preferred (semi-Markov models clock reset approach) [1]. That being said, the main estimates are said to be quite robust to different modelling approaches. However, exploratory comparison of the results of different methods is commendable.'), helpText("Aalen et al. [2] and Datta and Satten [3] showed that with right-censored multistate data the usual Aalen-Johansen estimator of the state occupancy probabilities, motivated by Markov assumptions, are robust and valid for non-Markov models when censoring is completely independent."), helpText(em('1.Richard J Cook and Jerald F Lawless. "Statistical issues in modeling chronic disease in cohort studies". In:Statistics in Biosciences 6.1 (2014), pp. 127-161.')), helpText(em("2.Aalen OO, Borgan O, Fekjaer H (2001) Covariate adjustment of event histories estimated with Markov chains: the additive approach. Biometrics 57:993-1001")), helpText(em("3.Datta S, Satten GA (2001) Validity of the Aalen-Johansen estimators of stage occupation probabilities and Nelson-Aalen estimators of integrated transition hazards for non-Markov models. Stat Probab Lett 55:403-411")) ) } else if (!robust) {robust_def=NULL} return(list( robust_def )) }) output$message_rob1<- renderUI ({ # if (input$int_rob=="No") return() # if (input$int_rob=="Yes") { print("There are different approaches when performing a multistate analysis. When modelling transition intensities, an initial consideration is whether to emphasize age of a process (clock forward) or the duration of time spent in each state visited (clock-reset). Markov models are basic for the former situation and semi-Markov models for the latter (CookRJ).") # } }) output$message_rob2<- renderUI ({ # if (input$int_rob=="No") return() # if (input$int_rob=="Yes") { print("Despite the approach used, Markov (clock forward) or semi Markov (clock reset), we argue that the resulting estimates will be very similar if not identical. With proper manipulation, the user can create a json input file that includes results from both approaches and compare them directly with the app. To check the robustness of these 2 different approaches on the EBMT example click Yes on the robust example.") # } }) output$message_rob3<- renderUI ({ # if (input$int_rob=="No") return() # if (input$int_rob=="Yes") { print("For more details as to how to develop a json file to assess robustness of different MSM approaches, check the supplementary material of the MSMplus publication.") # } }) #observeEvent(input$aimtype, { # if( input$aimtype=="compare" ) { # js$disableTab("#panel7p") # # } #}) observeEvent(input$json2, { if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'P')))==0 ) { js$disableTab("mytab_p") } }) observeEvent(input$csv2, { if( length(which(startsWith(names( read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'P')))==0 ) { js$disableTab("mytab_p") } if( length(which(startsWith(names( read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'P')))!=0 ) { js$enableTab("mytab_p") } }) ##### Hide or show ticks axis #### timerp <- reactiveVal(1.5) ############################################################## observeEvent(c(input$showtickp,invalidateLater(1000, session)), { if(input$showtickp=="No"){ hide("tickinputp") } if(input$showtickp=="Yes"){ show("tickinputp") } isolate({ timerp(timerp()-1) if(timerp()>1 & input$showtickp=="No") { show("tickinputp") } }) }) ##### The main page ###################################### existp<- reactive({ if (length(myjson2()$P) != 0) { x= 1 } else if (length(myjson2()$P) == 0) { x= 0 } }) existpratio <- reactive({ if (length(myjson2()$Pr) != 0) { x= 1 } else if (length(myjson2()$Pr) == 0| myjson2()$Nats==1 ) { x= 0 } }) existpdiff <- reactive({ if (length(myjson2()$Pd) != 0) { x= 1 } else if (length(myjson2()$Pd) == 0 | myjson2()$Nats==1 ) { x= 0 } }) output$pagep <- renderUI({ if (is.null(myjson2())) return("Provide the json file with the predictions") if (existp()==0) { fluidRow( column(12, output$loginpagep <- renderUI({h1("Non applicable")}) ) ) } else if (existp()==1) { if (existpdiff()==1 & existpratio()==1) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Probabilities"), conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel4p' || input.tabsp =='#panel5p' ||input.tabsp =='#panel8p' ||input.tabsp =='#panel9p'", uiOutput("ui_facetp") ) , conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel3p' || input.tabsp =='#panel8p' ||input.tabsp =='#panel9p'", uiOutput("confp") ) , ), column(2, br(), p(""), conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel3p' || input.tabsp =='#panel4p' ||input.tabsp =='#panel8p' || input.tabsp =='#panel9p'", uiOutput("showtickp"), uiOutput("tickinputp") ) , conditionalPanel(condition="input.tabsp =='#panel5p'||input.tabsp =='#panel6p'||input.tabsp =='#panel7p'", uiOutput("framespeed") ) , conditionalPanel(condition="input.tabsp =='#panel5p'|| input.tabsp =='#panel7p'", uiOutput("areaperc"), ) , conditionalPanel(condition="input.tabsp =='#panel5p'||input.tabsp =='#panel6p'|| input.tabsp =='#panel7p'", uiOutput("textsizep_msm") ) , verbatimTextOutput("infinite") ), column(7, tabsetPanel(id = "tabsp", tabPanel(h2("By state"),value = "#panel1p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_state", height="700px", width = "100%"),uiOutput("shouldloadp1"),verbatimTextOutput("states1")) ) ), tabPanel(h2("By covariate pattern"),value = "#panel2p", fluidRow( column(12, useShinyjs(), plotlyOutput("probability_cov", height="700px", width = "100%"),uiOutput("shouldloadp2"),verbatimTextOutput("cov")) ) ), tabPanel(h2("By covariate pattern and state"),value = "#panel3p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_state_cov", height="600px", width = "100%"),uiOutput("shouldloadp3"),verbatimTextOutput("statecov")) ) ), tabPanel(h2("Stacked"),value = "#panel4p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", useShinyjs(),plotlyOutput("probability_both", height="600px", width = "100%"),uiOutput("shouldloadp4"),uiOutput("afterstacked"), ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_stacked") ) ) ) ), tabPanel(h2("Bar plots by covariate pattern"),value = "#panel5p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_bars", height="600px", width = "100%"),uiOutput("shouldloadp5"),uiOutput("afterbp")) ) ), tabPanel(h2("Stacked bar plot"),value = "#panel6p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", plotlyOutput("probability_bars_stacked", height="600px", width = "100%"),uiOutput("shouldloadp6"),uiOutput("afterstackedbp") ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_stackedbp") ) ) ) ), tabPanel(h2("Predictions on MSM"),value = "#panel7p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", plotOutput("probability_msm_box",height="750px",width = "100%"),uiOutput("shouldloadp7"),uiOutput("aftermsm") ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_MSM") ) ) ) ), tabPanel(h2("Differences"),value = "#panel8p", fluidRow( column(12, useShinyjs(), plotlyOutput("P_diff" , height="600px", width = "100%"),uiOutput("shouldloadp8")) ) ), tabPanel(h2("Ratios"),value = "#panel9p", fluidRow( column(12, useShinyjs(), plotlyOutput("P_ratio" , height="600px", width = "100%"),uiOutput("shouldloadp9")) ) ) ) ) ) } else if (existpdiff()==1 & existpratio()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Probabilities"), conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel4p' || input.tabsp =='#panel5p' ||input.tabsp =='#panel8p' ||input.tabsp =='#panel9p'", uiOutput("ui_facetp") ) , conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel3p' || input.tabsp =='#panel9p' ||input.tabsp =='#panel9p'", uiOutput("confp") ) , ), column(2, br(), p(""), conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel3p' || input.tabsp =='#panel4p' ||input.tabsp =='#panel8p' || input.tabsp =='#panel9p'", uiOutput("showtickp"), uiOutput("tickinputp") ) , conditionalPanel(condition="input.tabsp =='#panel5p'||input.tabsp =='#panel6p'||input.tabsp =='#panel7p'", uiOutput("framespeed") ) , conditionalPanel(condition="input.tabsp =='#panel5p'|| input.tabsp =='#panel7p'", uiOutput("areaperc") ) , conditionalPanel(condition="input.tabsp =='#panel5p'||input.tabsp =='#panel6p'|| input.tabsp =='#panel7p'", uiOutput("textsizep_msm") ) , ), column(7, tabsetPanel(id = "tabsp", tabPanel(h2("By state"),value = "#panel1p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_state", height="700px", width = "100%"),uiOutput("shouldloadp1"),verbatimTextOutput("states1")) ) ), tabPanel(h2("By covariate pattern"),value = "#panel2p", fluidRow( column(12, useShinyjs(), plotlyOutput("probability_cov", height="700px", width = "100%"),uiOutput("shouldloadp2"),verbatimTextOutput("cov")) ) ), tabPanel(h2("By covariate pattern and state"),value = "#panel3p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_state_cov", height="600px", width = "100%"),uiOutput("shouldloadp3"),verbatimTextOutput("statecov")) ) ), tabPanel(h2("Stacked"),value = "#panel4p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", useShinyjs(),plotlyOutput("probability_both", height="600px", width = "100%"),uiOutput("shouldloadp4"),uiOutput("afterstacked"), ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_stacked") ) ) ) ), tabPanel(h2("Bar plots by covariate pattern"),value = "#panel5p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_bars", height="600px", width = "100%"),uiOutput("shouldloadp5"),uiOutput("afterbp")) ) ), tabPanel(h2("Stacked bar plot"),value = "#panel6p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", plotlyOutput("probability_bars_stacked", height="600px", width = "100%"),uiOutput("shouldloadp6"),uiOutput("afterstackedbp") ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_stackedbp") ) ) ) ), tabPanel(h2("Predictions on MSM"),value = "#panel7p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", plotOutput("probability_msm_box",height="750px",width = "100%"),uiOutput("shouldloadp7"),uiOutput("aftermsm") ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_MSM") ) ) ) ), tabPanel(h2("Differences"),value = "#panel8p", fluidRow( column(12, useShinyjs(), plotlyOutput("P_diff" , height="600px", width = "100%"),uiOutput("shouldloadp8")) ) ), tabPanel(h2("Ratios"),value = "#panel9p", fluidRow( column(12, useShinyjs(), print("Not Applicable")) ) ) ) ) ) } else if ( existpdiff()==0 & existpratio()==1) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Probabilities"), conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel4p' || input.tabsp =='#panel5p' ||input.tabsp =='#panel8p' ||input.tabsp =='#panel9p'", uiOutput("ui_facetp") ) , conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel3p' || input.tabsp =='#panel9p' ||input.tabsp =='#panel9p'", uiOutput("confp") ) , ), column(2, br(), p(""), conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel3p' || input.tabsp =='#panel4p' ||input.tabsp =='#panel8p' || input.tabsp =='#panel9p'", uiOutput("showtickp"), uiOutput("tickinputp") ) , conditionalPanel(condition="input.tabsp =='#panel5p'||input.tabsp =='#panel6p'||input.tabsp =='#panel7p'", uiOutput("framespeed") ) , conditionalPanel(condition="input.tabsp =='#panel5p'|| input.tabsp =='#panel7p'", uiOutput("areaperc") ) , conditionalPanel(condition="input.tabsp =='#panel5p'||input.tabsp =='#panel6p'|| input.tabsp =='#panel7p'", uiOutput("textsizep_msm") ) ), column(7, tabsetPanel(id = "tabsp", tabPanel(h2("By state"),value = "#panel1p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_state", height="700px", width = "100%"),uiOutput("shouldloadp1"),verbatimTextOutput("states1")) ) ), tabPanel(h2("By covariate pattern"),value = "#panel2p", fluidRow( column(12, useShinyjs(), plotlyOutput("probability_cov", height="700px", width = "100%"),uiOutput("shouldloadp2"),verbatimTextOutput("cov")) ) ), tabPanel(h2("By covariate pattern and state"),value = "#panel3p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_state_cov", height="600px", width = "100%"),uiOutput("shouldloadp3"),verbatimTextOutput("statecov")) ) ), tabPanel(h2("Stacked"),value = "#panel4p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", useShinyjs(),plotlyOutput("probability_both", height="600px", width = "100%"),uiOutput("shouldloadp4"),uiOutput("afterstacked"), ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_stacked") ) ) ) ), tabPanel(h2("Bar plots by covariate pattern"),value = "#panel5p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_bars", height="600px", width = "100%"),uiOutput("shouldloadp5"),uiOutput("afterbp")) ) ), tabPanel(h2("Stacked bar plot"),value = "#panel6p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", plotlyOutput("probability_bars_stacked", height="600px", width = "100%"),uiOutput("shouldloadp6"),uiOutput("afterstackedbp") ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_stackedbp") ) ) ) ), tabPanel(h2("Predictions on MSM"),value = "#panel7p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", plotOutput("probability_msm_box",height="750px",width = "100%"),uiOutput("shouldloadp7"),uiOutput("aftermsm") ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_MSM") ) ) ) ), tabPanel(h2("Differences"),value = "#panel8p", fluidRow( column(12, useShinyjs(), print("Not Applicable")) ) ), tabPanel(h2("Ratios"),value = "#panel9p", fluidRow( column(12, useShinyjs(), plotlyOutput("P_ratio" , height="600px", width = "100%"),uiOutput("shouldloadp9")) ) ) ) ) ) } else if (existpdiff()==0 & existpratio()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Probabilities"), conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel4p' || input.tabsp =='#panel5p' ||input.tabsp =='#panel8p' ||input.tabsp =='#panel9p'", uiOutput("ui_facetp") ) , conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel3p' || input.tabsp =='#panel9p' ||input.tabsp =='#panel9p'", uiOutput("confp") ) , ), column(2, br(), p(""), conditionalPanel(condition="input.tabsp =='#panel1p'||input.tabsp =='#panel2p'||input.tabsp =='#panel3p' || input.tabsp =='#panel4p' ||input.tabsp =='#panel8p' || input.tabsp =='#panel9p'", uiOutput("showtickp"), uiOutput("tickinputp") ) , conditionalPanel(condition="input.tabsp =='#panel5p'||input.tabsp =='#panel6p'||input.tabsp =='#panel7p'", uiOutput("framespeed") ) , conditionalPanel(condition="input.tabsp =='#panel5p'|| input.tabsp =='#panel7p'", uiOutput("areaperc") ) , conditionalPanel(condition="input.tabsp =='#panel5p'||input.tabsp =='#panel6p'|| input.tabsp =='#panel7p'", uiOutput("textsizep_msm") ) ), column(7, tabsetPanel(id = "tabsp", tabPanel(h2("By state"),value = "#panel1p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_state", height="700px", width = "100%"),uiOutput("shouldloadp1"),verbatimTextOutput("states1")) ) ), tabPanel(h2("By covariate pattern"),value = "#panel2p", fluidRow( column(12, useShinyjs(), plotlyOutput("probability_cov", height="700px", width = "100%"),uiOutput("shouldloadp2"),verbatimTextOutput("cov")) ) ), tabPanel(h2("By covariate pattern and state"),value = "#panel3p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_state_cov", height="600px", width = "100%"),uiOutput("shouldloadp3"),verbatimTextOutput("statecov")) ) ), tabPanel(h2("Stacked"),value = "#panel4p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", useShinyjs(),plotlyOutput("probability_both", height="600px", width = "100%"),uiOutput("shouldloadp4"),uiOutput("afterstacked"), ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_stacked") ) ) ) ), tabPanel(h2("Bar plots by covariate pattern"),value = "#panel5p", fluidRow( column(12, useShinyjs(),plotlyOutput("probability_bars", height="600px", width = "100%"),uiOutput("shouldloadp5"),uiOutput("afterbp")) ) ), tabPanel(h2("Stacked bar plot"),value = "#panel6p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", plotlyOutput("probability_bars_stacked", height="600px", width = "100%"),uiOutput("shouldloadp6"),uiOutput("afterstackedbp") ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_stackedbp") ) ) ) ), tabPanel(h2("Predictions on MSM"),value = "#panel7p", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="input.aimtype =='present'", plotOutput("probability_msm_box",height="750px",width = "100%"),uiOutput("shouldloadp7"),uiOutput("aftermsm") ), conditionalPanel(condition="input.aimtype =='compare'", uiOutput("nograph_MSM") ) ) ) ), tabPanel(h2("Differences"),value = "#panel8p", fluidRow( column(12, useShinyjs(), print("Not Applicable")) ) ), tabPanel(h2("Ratios"),value = "#panel9p", fluidRow( column(12, useShinyjs(), print("Not Applicable")) ) ) ) ) ) } } }) #output$infinite <- renderPrint({ #min(data_P_ratio2_lci()$V[which(!is.na(data_P_ratio2_lci()$V))]) #}) #output$shouldloadgridp1 <- renderUI({ # if (input$facet=="No" ) return() # downloadButton(outputId = "downgridp1", label = h2("Download the plot")) #}) # #output$downgridp1 <- downloadHandler( # # filename= function() { # paste("p1","png", sep=".") # }, # content= function(file) { # # ggsave(file,plot=input$probability_state,device = "png") # # } #) # output$nograph_MSM<- renderUI({ helpText("This graph is not available when comparing 2 approaches") }) output$nograph_stacked<- renderUI({ helpText("This graph is not available when comparing 2 approaches") }) output$nograph_stackedbp<- renderUI({ helpText("This graph is not available when comparing 2 approaches") }) output$showtickp<- renderUI({ radioButtons("showtickp", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No") }) output$ui_facetp<- renderUI({ radioButtons(inputId="facet", label= "Display graph in grids", choices=c("No","Yes"),selected = "No") }) output$confp <- renderUI({ if (length(myjson2()$ci_P)!=0) { radioButtons("conf", "Confidence intervals", c("No" = "ci_no", "Yes" ="ci_yes")) } else if (length(myjson2()$ci_P)==0) { item_list <- list() item_list[[1]]<- radioButtons("conf", "Confidence intervals",c("No" = "ci_no")) item_list[[2]]<-print("Confidence interval data were not provided") do.call(tagList, item_list) } }) output$textsizep_msm <- renderUI({ if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <-numericInput("textsizep_msm",h2("Legends size"),value=15,min=5,max=30) do.call(tagList, item_list) }) ################################################## ###### Will appear conditionally################## ################################################### #observeEvent(input$showSidebar, { # shinyjs::removeClass(selector = "body", class = "sidebar-collapse") #}) #observeEvent(input$hideSidebar, { # shinyjs::addClass(selector = "body", class = "sidebar-collapse") #}) #Create the reactive input of covariates #output$covarinputp <- renderUI({ # # #if (is.null(myjson2())) return() # # if (input$displayp=="same") return() # # else if (input$displayp=="change") { # # item_list <- list() # item_list[[1]] <- h2("Covariate patterns") # # v=vector() # for (i in 1:length(myjson2()$atlist)) { # v[i]=myjson2()$atlist[i] # } # # default_choices_cov=v # # for (i in seq(length(myjson2()$atlist))) { # item_list[[i+1]] <- textInput(paste0('covp', i),default_choices_cov[i], labels_cov()[i]) # } # # do.call(tagList, item_list) # } #}) # #labels_covp<- reactive ({ # # if (input$displayp!="change") {as.vector(labels_cov())} # # else if (input$displayp=="change") { # # # myList<-vector("list",length(myjson2()$cov$atlist)) # # for (i in 1:length(myjson2()$cov$atlist)) { # myList[[i]]= input[[paste0('covp', i)]][1] # } # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # as.vector(final_list) # } #}) #Create the reactive input of states #output$statesinputp <- renderUI({ # # if (input$displayp=="same") return() # # else if (input$displayp=="change") { # # item_list <- list() # item_list[[1]] <- h2("States") # default_choices_state=vector() # # title_choices_state=vector() # for (i in 1:length(myjson2()$P)) { # title_choices_state[i]=paste0("State"," ",input$select,selectend()[i]) # } # for (i in 1:length(myjson2()$P)) { # # item_list[[1+i]] <- textInput(paste0('statep',i),title_choices_state[i], labels_state()[i]) # # } # do.call(tagList, item_list) # } #}) # labels_statep<- reactive ({ # # if (input$displayp!="change") labels_state() # # else if (input$displayp=="change") { # # myList<-vector("list",length(myjson2()$P)) # for (i in 1:length(myjson2()$P)) { # # myList[[i]]= input[[paste0('statep', i)]][1] # } # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # final_list # } # }) ################################################################################## ################################################################################### data_P <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar); names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern prob=list() if (length(myjson2()$P)==0) {return()} for(i in 1:length(myjson2()$P)) { prob[[i]]=as.data.frame(t(data.frame(myjson2()$P[i]))) colnames(prob[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$P)) { prob[[i]]=as.data.frame(cbind(prob[[i]], timevar ,state=rep(i,nrow(prob[[i]] )) )) } # Append the probabilities datasets of the different states data_P=list() data_P[[1]]=prob[[1]] if (length(myjson2()$P)>1) { for (u in 2:(length(myjson2()$P))) { data_P[[u]]=rbind(prob[[u]],data_P[[(u-1)]]) } } datap=data_P[[length(myjson2()$P)]] datap }) data_P_uci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern prob_uci=list() if (length(myjson2()$P_uci)==0) {return()} for(i in 1:length(myjson2()$P_uci)) { prob_uci[[i]]=as.data.frame(t(data.frame(myjson2()$P_uci[i]))) colnames(prob_uci[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$P_uci)) { prob_uci[[i]]=as.data.frame(cbind(prob_uci[[i]], timevar ,state=rep(i,nrow(prob_uci[[i]] )) )) } # Append the probabilities datasets of the different states data_P_uci=list() data_P_uci[[1]]=prob_uci[[1]] if (length(myjson2()$P_lci)>1) { for (u in 2:(length(myjson2()$P_uci))) { data_P_uci[[u]]=rbind(prob_uci[[u]],data_P_uci[[(u-1)]]) } } datap_uci=data_P_uci[[length(myjson2()$P_uci)]] datap_uci }) data_P_lci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern prob_lci=list() if (length(myjson2()$P_lci)==0) {return()} for(i in 1:length(myjson2()$P_lci)) { prob_lci[[i]]=as.data.frame(t(data.frame(myjson2()$P_lci[i]))) colnames(prob_lci[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$P_lci)) { prob_lci[[i]]=as.data.frame(cbind(prob_lci[[i]], timevar ,state=rep(i,nrow(prob_lci[[i]] )) )) } # Append the probabilities datasets of the different states data_P_lci=list() data_P_lci[[1]]=prob_lci[[1]] if (length(myjson2()$P_lci)>1) { for (u in 2:(length(myjson2()$P_lci))) { data_P_lci[[u]]=rbind(prob_lci[[u]],data_P_lci[[(u-1)]]) } } datap_lci=data_P_lci[[length(myjson2()$P_lci)]] datap_lci }) output$areaperc <- renderUI({ if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <- sliderInput("perc",h2("Time points (Applicable to the non stacked bar graphs)"), min=min(myjson2()$timevar), max=max(myjson2()$timevar), step=myjson2()$timevar[2]-myjson2()$timevar[1], value=1, width='100%', animate=animationOptions(interval = (1000/input$speed))) do.call(tagList, item_list) }) data_P_st<-reactive ({ datanew=data_P() # if (input$aimtype=="compare") { datanew$state_fac=c(rep("NA",nrow(datanew)))} # else if (input$aimtype=="present") { datanew$state_fac=ordered(c(rep("NA",nrow(datanew))), levels = labels_state() )} datanew$state_fac=ordered(c(rep("NA",nrow(datanew))), levels = labels_state() ) for (o in 1:(length(myjson2()$P))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_P_st_uci<-reactive ({ datanew=data_P_uci() datanew$state_fac=ordered(c(rep("NA",nrow(datanew))), levels = labels_state() ) for (o in 1:(length(myjson2()$P_uci))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_P_st_lci<-reactive ({ datanew=data_P_lci() datanew$state_fac=ordered(c(rep("NA",nrow(datanew))), levels = labels_state() ) for (o in 1:(length(myjson2()$P_lci))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_P_d <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_P_st()[,d],data_P_st()[,ncol(data_P_st())-2],data_P_st()[,ncol(data_P_st())-1],data_P_st()[,ncol(data_P_st())],rep(d,length(data_P_st()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_P_st())[d],length(data_P_st()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_p <- bind_rows(dlist, .id = "column_label") d_all_p }) data_P_d_uci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_P_st_uci()[,d],data_P_st_uci()[,ncol(data_P_st_uci())-2], data_P_st_uci()[,ncol(data_P_st_uci())-1], data_P_st_uci()[,ncol(data_P_st_uci())],rep(d,length(data_P_st_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_P_st_uci())[d],length(data_P_st_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_p_uci <- bind_rows(dlist, .id = "column_label") d_all_p_uci }) data_P_d_lci<- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_P_st_lci()[,d],data_P_st_lci()[,ncol(data_P_st_lci())-2], data_P_st_lci()[,ncol(data_P_st_lci())-1], data_P_st_lci()[,ncol(data_P_st_lci())],rep(d,length(data_P_st_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_P_st_lci())[d],length(data_P_st_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_p_lci <- bind_rows(dlist, .id = "column_label") d_all_p_lci }) output$tickinputp <- renderUI({ default_choices=c("black","blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen","tan1","lightslateblue","khaki4","gray28", "cyan3","brown4","darkorchid1","goldenrod4","gray63","lightsalmon", "maroon4","palegreen1","royalblue2","red2","sienna4","yellow4","slategray3") if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <- h2("Provide x axis range and ticks") item_list[[2]] <-numericInput("startx","Start x axis at:",value=min(data_P_d()$timevar),min=0 ) item_list[[3]] <-numericInput("stepx","Step at x axis:",value=max(data_P_d()$timevar/10),min=0,max=max(data_P_d()$timevar)) item_list[[4]] <-numericInput("endx","End x axis at:",value =max(data_P_d()$timevar),min=0,max=max(data_P_d()$timevar)) item_list[[5]] <-numericInput("stepy","Step at y axis:",value=max(data_P_d()$V)/10,min=max(data_P_d()$V)/1000,max=max(data_P_d()$V)/10) item_list[[6]] <-numericInput("textsizep",h2("Legends size"),value=input$textsize,min=5,max=30) item_list[[7]] <-numericInput("textfacetp",h2("Facet title size"),value=input$textsize-3,min=5,max=30 ) do.call(tagList, item_list) }) output$framespeed <- renderUI({ if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <-numericInput("speed",h2("Time speed"),value=3,min=1, max=20 ) do.call(tagList, item_list) }) data_P_ci<- reactive ({ x=c( data_P_d()[order(data_P_d()$timevar,data_P_d()$state,data_P_d()$cov),]$timevar, data_P_d_lci()[order(-data_P_d()$timevar,data_P_d()$state,data_P_d()$cov),]$timevar ) y_central=c( data_P_d()[order(data_P_d()$timevar,data_P_d()$state,data_P_d()$cov),]$V, data_P_d()[order(-data_P_d()$timevar,data_P_d()$state,data_P_d()$cov),]$V ) y=c( data_P_d_uci()[order(data_P_d_uci()$timevar,data_P_d_uci()$state,data_P_d_uci()$cov),]$V, data_P_d_lci()[order(-data_P_d_uci()$timevar,data_P_d_uci()$state,data_P_d_uci()$cov),]$V ) frameto=c(as.character(data_P_d_uci()[order(-data_P_d_uci()$timevar,data_P_d_uci()$state,data_P_d_uci()$cov),]$state_factor), as.character(data_P_d_lci()[order(-data_P_d_lci()$timevar,data_P_d_lci()$state,data_P_d_lci()$cov),]$state_factor) ) covto=c( data_P_d_uci()[order(-data_P_d_uci()$timevar,data_P_d_uci()$state,data_P_d_uci()$cov),]$cov_factor, data_P_d_lci()[order(-data_P_d_lci()$timevar,data_P_d_lci()$state,data_P_d_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) ######################################################### #output$shouldloadp1 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotp1", label = h2("Download the plot")) #}) datap1_re <- reactive ({ if(is.null(data_P_d())) return() else if (input$conf=="ci_no") { if (input$facet=="No") { p_state= plot_ly(data_P_d(),alpha=0.5) %>% add_lines( x=data_P_d()$timevar,y=data_P_d()$V, frame=factor(as.factor(data_P_d()$state_factor),levels=labels_state()), color=factor(as.factor(data_P_d()$cov_factor) ,levels=labels_cov()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], mode="lines", line=list(simplify=FALSE,color = labels_colour_cov()[1:length(myjson2()$cov$atlist)] ), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) p_state = p_state %>% layout(title=list(text="Probability of state for each covariate pattern over states",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of state occupancy",rangemode = "nonnegative", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ),queueLength=10 ) p_state } if (input$facet=="Yes") { data_plot=data_P_d() p_state = ggplot(data_plot) p_state = ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor),levels=labels_cov()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Probability: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor),levels=labels_cov()) ) ) ) p_state = p_state+geom_line(aes(x=timevar, y=V, color= factor(as.factor(cov_factor),levels=labels_cov()) ))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { p_state = p_state+ facet_wrap(~ factor(as.factor(state_factor), levels=labels_state() ), nrow=2)} else if (input$aimtype=="present") {p_state = p_state+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()))} p_state = p_state + scale_x_continuous(breaks=c(seq(input$startx,input$endx,by=input$stepx))) p_state = p_state + scale_y_continuous(breaks=c(seq(min(data_plot$V[which(!is.na(data_plot$V))]),max(data_plot$V[which(!is.na(data_plot$V))]), by=input$stepy ))) p_state = p_state +labs(title="Probability of state for each covariate pattern over states", x="Time since entry", y="Probability of state") p_state = p_state + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") p_state = p_state +theme(title = element_text(size = input$textsizep-4), strip.text = element_text(size=input$textfacetp), legend.title = element_text(color="black", size= input$textsizep-5), legend.text=element_text(size= input$textsizep-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizep-5), axis.title.x = element_text(size= input$textsizep-5), axis.text.x = element_text( size=input$textsizep-6),axis.text.y = element_text( size=input$textsizep-6) ) p_state = ggplotly(p_state, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) p_state } } else if (input$conf=="ci_yes") { if (input$facet=="No") { p_state <- plot_ly() p_state <- add_trace(p_state, line=list(simplify=FALSE,color = labels_colour_cov()), mode="lines", type = "scatter", x=data_P_ci()$x, y=data_P_ci()$y_central, frame=factor(as.factor(data_P_ci()$frameto),levels=labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=as.factor(data_P_ci()$covto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) p_state <- add_trace(p_state, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_P_ci()$x, y=data_P_ci()$y, frame=factor(as.factor(data_P_ci()$frameto),levels=labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=as.factor(data_P_ci()$covto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
",""), showlegend = FALSE) #### May need to have an input value here if we eant to deselect CIs p_state = p_state %>% layout(title=list(text="Probability of state for each covariate pattern over states",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of state occupancy",rangemode = "nonnegative", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") )) %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) p_state } if (input$facet=="Yes") { V_lci= data_P_d_lci()$V V_uci= data_P_d_uci()$V data_plot=cbind(data_P_d(),V_lci,V_uci) p_state=ggplot(data_plot) p_state=ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor),levels=labels_cov()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Probability: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor),levels=labels_cov()) ))) p_state=p_state +scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) p_state=p_state+geom_line(aes(x=timevar, y=V, fill=factor(as.factor(cov_factor),levels=labels_cov()) )) p_state=p_state+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill= factor(as.factor(cov_factor),levels=labels_cov()) ),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { p_state = p_state+ facet_wrap(~ factor(as.factor(state_factor), levels=labels_state()), nrow=2)} else if (input$aimtype=="present") {p_state = p_state+ facet_wrap(~ factor(as.factor(state_factor), levels=labels_state()))} p_state = p_state + scale_x_continuous(breaks=c(seq(input$startx,input$endx,by=input$stepx ))) p_state = p_state + scale_y_continuous(breaks=c(seq(min(data_plot$V_lci[which(!is.na(data_plot$V_lci))]),max(data_plot$V_uci[which(!is.na(data_plot$V_uci))]),by=input$stepy ))) p_state = p_state +labs(title="Probability of state for each covariate pattern over states", x="Time since entry", y="Probability of state") p_state = p_state + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") p_state = p_state +theme(title = element_text(size = input$textsizep-4), strip.text = element_text(size=input$textfacetp), legend.title = element_text(color="black", size= input$textsizep-5), legend.text=element_text(size= input$textsizep-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizep-5), axis.title.x = element_text(size= input$textsizep-5), axis.text.x = element_text( size=input$textsizep-6),axis.text.y = element_text( size=input$textsizep-6)) p_state = ggplotly(p_state, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) p_state } } p_state }) output$probability_state <- renderPlotly ({ datap1_re() }) output$downplotp1 <- downloadHandler( filename = function(){paste("p1",'.png',sep='')}, content = function(file){ # if (input$facet=="No") { plotly_IMAGE( datap1_re(),width = 3000, height = 3000, format = "svg", scale = 1, out_file = file ) # } # if (input$facet=="Yes") { # png(filename=file, width=1000, height=1000,units = "px", res=200) # datap1_re() # dev.off() # } } ) ################################################################ ####Statements after plots ####################### output$covs <- renderPrint({ if ( length(myjson2()$cov$atlist)>5 ) { df= data.frame(Covariate_patterns=labels_cov()) df } else return(print("If more than 5 covariate patterns specified, they will be listed here as well")) }) output$states1 <- renderPrint({ if ( length(myjson2()$P)>5) { df= data.frame(States=labels_state()) df } else return(print("If more than 5 states specified, they will be listed here as well")) }) output$states2 <- renderPrint({ if ( length(myjson2()$P)>5 ) { df= data.frame(States=labels_state()) df } else return() }) output$statecov <- renderPrint({ if ( length(myjson2()$P)>5 | length(myjson2()$cov$atlist)>5) { df= data.frame(States=labels_state(), Covariate_patterns=labels_cov()) df } else return(print("If more than 5 states or covariate patterns specified, they will be listed here as well")) }) output$afterstacked <- renderPrint({ helpText("The transition probabilities for each timepoint are given stacked. They will always sum to 1 ") }) output$afterstackedbp <- renderPrint({ helpText("The transition probabilities for each covariate pattern are given stacked over states. They will always sum to 1. The estimates across time are given through a slide bar underneath the graph.") }) output$aftermsm <- renderPrint({ helpText("The transition probabilities for each covariate pattern over states, depicted in boxes accordingly to the multi-state graph. The estimates across time are given through a slide bar underneath the graph.") }) output$afterbp <- renderPrint({ helpText("The transition probabilities for each covariate pattern depicted as bar plots. The estimates for diffent states are presented either in frames or grids. The estimates across time are given through a slide bar beside the graph.") }) ############################################################## #output$shouldloadp2 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotp2", label = h2("Download the plot")) #}) datap2_re <- reactive ({ if (input$conf=="ci_no") { if (input$facet=="No") { p_cov= plot_ly(data_P_d(),alpha=0.5) %>% add_lines( x=data_P_d()$timevar,y=data_P_d()$V, frame=factor(as.factor(data_P_d()$cov_factor),levels=labels_cov()), color=factor(as.factor(data_P_d()$state_factor),levels=labels_state()), colors=labels_colour_state(), mode="lines", line=list(simplify=FALSE,color = labels_colour_state()), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) p_cov = p_cov %>% layout(title=list(text="Probability of each state over covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of state occupancy",rangemode = "nonnegative", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { p_cov= p_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } p_cov } if (input$facet=="Yes") { data_plot=data_P_d() p_cov = ggplot(data_plot) p_cov = ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(state_factor),levels=labels_state()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Probability: ", V, "
State: ", factor(as.factor(state_factor),levels=labels_state()) ))) p_cov = p_cov+geom_line(aes(x=timevar, y=V, color= factor(as.factor(state_factor),levels=labels_state()) ))+ scale_colour_manual( values =labels_colour_state(),labels = labels_state() ) if (input$aimtype=="compare") { p_cov = p_cov+ facet_wrap(~ factor(as.factor(cov_factor), levels=labels_cov()), nrow=2)} else if (input$aimtype=="present") {p_cov = p_cov+ facet_wrap(~ factor(as.factor(cov_factor), levels=labels_cov() ) )} p_cov = p_cov + scale_x_continuous(breaks=c(seq(input$startx,input$endx,by=input$stepx ))) p_cov = p_cov + scale_y_continuous(breaks=c(seq(min(data_plot$V[which(!is.na(data_plot$V))]),max(data_plot$V[which(!is.na(data_plot$V))]), by=input$stepy ))) p_cov = p_cov +labs(title="Probability of each state over covariate patterns", x="Time since entry", y="Probability of state") p_cov = p_cov + labs(color = "States")+ labs(fill = "States") p_cov = p_cov +theme(title = element_text(size = input$textsizep-4), strip.text = element_text(size=input$textfacetp), legend.title = element_text(color="black", size= input$textsizep-5), legend.text=element_text(size= input$textsizep-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizep-5), axis.title.x = element_text(size= input$textsizep-5), axis.text.x = element_text( size=input$textsizep-6),axis.text.y = element_text( size=input$textsizep-6)) p_cov = ggplotly(p_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) p_cov } } else if (input$conf=="ci_yes") { if (input$facet=="No") { p_cov <- plot_ly() p_cov <- add_trace(p_cov, line=list(simplify=FALSE,color = labels_colour_cov()), mode="lines", type = "scatter", x=data_P_ci()$x, y=data_P_ci()$y_central, frame=factor(as.factor(data_P_ci()$covto),levels=labels_cov()), colors=labels_colour_state()[1:length(myjson2()$P)], color=as.factor(data_P_ci()$frameto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) p_cov <- add_trace(p_cov, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_P_ci()$x, y=data_P_ci()$y, frame=factor(as.factor(data_P_ci()$covto),levels=labels_cov()), colors=labels_colour_state()[1:length(myjson2()$P)], color=as.factor(data_P_ci()$frameto) , showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) p_cov = p_cov %>% layout(title=list(text="Probability of each state for each covariate pattern",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of each state over covariate patterns",rangemode = "nonnegative", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { p_cov= p_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } p_cov } if (input$facet=="Yes") { V_lci= data_P_d_lci()$V V_uci= data_P_d_uci()$V data_plot=cbind(data_P_d(),V_lci,V_uci) p_cov=ggplot(data_plot) p_cov=ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(state_factor), levels=labels_state()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Probability: ", V, "
State: ", factor(as.factor(state_factor), levels=labels_state()) )))+ scale_colour_manual( values =labels_colour_state(),labels = labels_state() ) p_cov=p_cov+geom_line(aes(x=timevar, y=V, fill=factor(as.factor(state_factor), levels=labels_state()) )) p_cov=p_cov+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=factor(as.factor(state_factor), levels=labels_state()) ),alpha=0.4)+ scale_fill_manual( values =labels_colour_state(),labels = labels_state() ) if (input$aimtype=="compare") { p_cov = p_cov+ facet_wrap(~ factor(as.factor(cov_factor), levels=labels_cov()), nrow=2) } else if (input$aimtype=="present") {p_cov = p_cov+ facet_wrap(~factor(as.factor(cov_factor), levels=labels_cov() ) ) } p_cov = p_cov + scale_x_continuous(breaks=c(seq(input$startx,input$endx,by=input$stepx ))) p_cov = p_cov + scale_y_continuous(breaks=c(seq(min(data_plot$V_lci[which(!is.na(data_plot$V_lci))]),max(data_plot$V_uci[which(!is.na(data_plot$V_uci))]),by=input$stepy ))) p_cov = p_cov +labs(title="Probability of each state over covariate patterns", x="Time since entry", y="Probability of state") p_cov = p_cov + labs(color = "States")+ labs(fill = "States") p_cov = p_cov +theme(title = element_text(size = input$textsizep-4), strip.text = element_text(size=input$textfacetp), legend.title = element_text(color="black", size= input$textsizep-5), legend.text=element_text(size= input$textsizep-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizep-5), axis.title.x = element_text(size= input$textsizep-5), axis.text.x = element_text( size=input$textsizep-6),axis.text.y = element_text( size=input$textsizep-6)) p_cov = ggplotly(p_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) p_cov } } p_cov }) output$probability_cov <- renderPlotly ({datap2_re() }) output$downplotp2 <- downloadHandler( filename = function(){paste("p2",'.png',sep='')}, content = function(file){ plotly_IMAGE( datap2_re(),width = 3000, height = 3000, format = "png", scale = 1, out_file = file ) } ) ################################################################# #output$shouldloadp3 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotp3", label = h2("Download the plot")) #}) datap3_re <- reactive ({ if (input$conf=="ci_no") { p_state_cov= plot_ly(data_P_d(),alpha=0.5) %>% add_lines( x=data_P_d()$timevar,y=data_P_d()$V, color = factor(as.factor(data_P_d()$cov_factor),levels=labels_cov()), colors=labels_colour_cov(), fill = factor(as.factor(data_P_d()$cov_factor),levels=labels_cov()), linetype= factor(as.factor(data_P_d()$state_factor),levels=labels_state()), mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text="Probability of state occupancy for all covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of state occupancy",rangemode = "nonnegative", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) p_state_cov } else if (input$conf=="ci_yes") { p_state_cov <- plot_ly() p_state_cov <- add_trace(p_state_cov, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_P_ci()$x, y=data_P_ci()$y_central, color=as.factor(data_P_ci()$covto), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], fill=as.factor(data_P_ci()$frameto), linetype=as.factor(data_P_ci()$frameto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) p_state_cov <- add_trace(p_state_cov, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_P_ci()$x, y=data_P_ci()$y, color=as.factor(data_P_ci()$covto), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], fill=as.factor(data_P_ci()$frameto), linetype=as.factor(data_P_ci()$frameto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text="Probability of state occupancy for all covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of state occupancy",rangemode = "nonnegative", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) p_state_cov } }) output$probability_state_cov <- renderPlotly ({datap3_re() }) output$downplotp3 <- downloadHandler( filename = function(){paste("p3",'.png',sep='')}, content = function(file){ plotly_IMAGE( datap3_re(),width = 3000, height = 3000, format = "png", scale = 1, out_file = file ) } ) ################################################################################# data_stacked_bars <- reactive ({ if(is.null(myjson2())) return() stackedl=list() stackedl=stacked_function_bars(json=myjson2(), data=data_P(), labels_cov=labels_cov(), labels_state=labels_state() ) stackedp=bind_rows(stackedl, .id = "column_label2") stackedp=stackedp[order(stackedp$state,stackedp$cov_factor,stackedp$timevar),] stackedp }) #output$shouldloadp4 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotp4", label = h2("Download the plot")) #}) datap4_re <- reactive ({ if (input$aimtype=="compare") {return("Not available when comparing approaches")} else { dfk=list() for(k in 1:length(myjson2()$P)) { dfk[[k]] <- data.frame(prob_st=data_stacked_bars()[c((length(myjson2()$P)*(k-1)*length(myjson2()$timevar)+1): ((length(myjson2()$P)*(k-1)*length(myjson2()$timevar)+1)+length(myjson2()$P)*length(myjson2()$timevar)-1)),3], Time=data_stacked_bars()$timevar[(length(myjson2()$P)*(k-1)*length(myjson2()$timevar)+1): ((length(myjson2()$P)*(k-1)*length(myjson2()$timevar)+1)+length(myjson2()$P)*length(myjson2()$timevar)-1)], Covariate_pattern=factor(as.factor(data_stacked_bars()$cov_factor[(length(myjson2()$P)*(k-1)*length(myjson2()$timevar)+1): ((length(myjson2()$P)*(k-1)*length(myjson2()$timevar)+1)+length(myjson2()$P)*length(myjson2()$timevar)-1)] ), levels=labels_cov()), State=as.factor(data_stacked_bars()$state_factor[(length(myjson2()$P)*(k-1)*length(myjson2()$timevar)+1): ((length(myjson2()$P)*(k-1)*length(myjson2()$timevar)+1)+length(myjson2()$P)*length(myjson2()$timevar)-1)]) ) } if (input$facet=="No") { P <- plot_ly(data = data_stacked_bars(), colors=labels_colour_state(), alpha=0.5, line=list(simplify=FALSE, mode = 'lines', stackgroup = 'one',hoverinfo='x+y'), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) %>% layout(title=list(text="Stacked probabilities of states among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Stacked probability of state occupancies",rangemode = "nonnegative", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { P= P %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } for(k in 1:length(myjson2()$P)) { P <- add_trace( P, y=~prob_st, x=~Time, frame =~Covariate_pattern, data=dfk[[k]], mode = 'lines', stackgroup = 'one',hoverinfo='x+y',fill="tonexty", color=~State) } P } else if (input$facet=="Yes") { df_final=list() df_final[[1]]=as.data.frame(dfk[[1]]) for(k in 2:length(myjson2()$P)) { df_final[[k]]=rbind(as.data.frame(dfk[[k]]),df_final[[k-1]]) } df_final[[k]]$Probability=df_final[[k]]$prob_st P <- ggplot(as.data.frame(df_final[[k]]), aes(x = Time, y =Probability,fill= State, alpha=0.5))+ geom_area()+ facet_wrap(~Covariate_pattern) P = P + scale_x_continuous(breaks=c(seq(input$startx,input$endx,by=input$stepx ))) + scale_y_continuous(breaks=c(seq(0,1,input$stepy))) P = P +labs(title="Probability of each state over covariate patterns", x="Time since entry", y="Probability of state") P = P + labs(fill = "States") P = P + scale_fill_manual(values=labels_colour_state()) P = P +theme(title = element_text(size = input$textsizep-4), strip.text = element_text(size=input$textfacetp), legend.title = element_text(color="black", size= input$textsizep-5), legend.text=element_text(size= input$textsizep-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizep-5), axis.title.x = element_text(size= input$textsizep-5), axis.text.x = element_text( size=input$textsizep-6),axis.text.y = element_text( size=input$textsizep-6)) P = ggplotly(P)%>% onRender("function(el,x){el.on('plotly_legendclick', function(){ return false; })}") P =P%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } }) output$probability_both <- renderPlotly ({datap4_re() }) output$downplotp4 <- downloadHandler( filename = function(){paste("p4",'.png',sep='')}, content = function(file){ plotly_IMAGE( datap4_re(),width = 1200, height = 900, format = "png", scale =2, out_file = file ) } ) ##################################################################################### data_t <- reactive ({ if(is.null(data_P_d())) return() datat=data_P_d()[which(data_P_d()$timevar==input$perc),] datat }) #output$shouldloadp5 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotp5", label = h2("Download the plot")) #}) datap5_re <- reactive ({ if (input$facet=="No") { data_plot=data_t() map=as.vector(levels(as.factor(data_plot$cov_factor))) color_cov_factor=vector() for (k in 1:myjson2()$Nats) { for (n in 1:nrow(data_plot)) { if (data_plot$cov_factor[n]==map[k]) {color_cov_factor[n]=labels_colour_cov()[k]} } } data_plot$color= color_cov_factor p_state= plot_ly(data_plot,alpha=0.5)%>% add_bars(type="bar", x=factor(as.factor(data_plot$cov_factor),levels=labels_cov()),y=data_plot$V, frame=factor(as.factor(data_plot$state_factor),levels=labels_state()), color=factor(as.factor(data_plot$cov_factor),levels=labels_cov()), colors=data_plot$color, mode="bar") %>% layout(title=list(text="Probability of state occupancy across covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizep_msm, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), yaxis =list(title= "Probability of state", rangemode = "nonnegative",range=c(0,1),dtick = 0.1 ,ticklen = 5,tickwidth = 2,tickcolor = toRGB("black")) ) p_state= p_state %>% animation_opts(1000/input$speed, easing = "quad")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) p_state } else if (input$facet=="Yes") { data_plot=data_t() data_plot$Probability=data_t()$V data_plot$State=data_t()$state_factor data_plot$Covariate_pattern=data_t()$cov_factor p_state <- ggplot(data_plot, aes(y =Probability,x= factor(as.factor(data_plot$Covariate_pattern),levels=labels_cov() ),fill=factor(as.factor(data_plot$Covariate_pattern),levels=labels_cov() ) ))+ geom_bar(position="dodge", stat="identity")+ facet_wrap(~State) p_state = p_state+ scale_y_continuous(0,1,0.1) p_state = p_state +labs(title="Probability of each state over covariate patterns", x="Time since entry", y="Probability of state") p_state = p_state + labs(fill = "States") p_state = p_state +scale_fill_manual(values=labels_colour_cov()) p_state = p_state +theme(title = element_text(size = input$textsizep_msm), strip.text = element_text(size=input$textsizep_msm-5), legend.title = element_text(color="black", size= input$textsizep_msm-1), legend.text=element_text(size= input$textsizep_msm-2), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizep_msm-1), axis.title.x = element_text(size= input$textsizep_msm-1)) p_state = ggplotly(p_state) p_state= p_state %>% animation_opts(1000/input$speed, easing = "quad")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) p_state #p_state = ggplotly(p_state) #onRender("function(el,x){el.on('plotly_legendclick', function(){ return false; })}") } }) output$probability_bars <- renderPlotly ({ datap5_re() }) output$downplotp5 <- downloadHandler( filename = function(){paste("p5",'.png',sep='')}, content = function(file){ plotly_IMAGE( datap5_re(),width = 3000, height = 3000, format = "png", scale = 1, out_file = file ) } ) ############################################################################################# #output$shouldloadp6 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotp6", label = h2("Download the plot")) #}) datap6_re <- reactive ({ if (input$aimtype=="compare") {return("Not available when comparing approaches")} else dfk=list() for(k in 1:length(myjson2()$P)) { dfk[[k]] <- data.frame(prob_st=data_stacked_bars()[c((myjson2()$Nats*(k-1)*length(myjson2()$timevar)+1): ((myjson2()$Nats*(k-1)*length(myjson2()$timevar)+1)+myjson2()$Nats*length(myjson2()$timevar)-1)),3], Time=data_stacked_bars()$timevar[(myjson2()$Nats*(k-1)*length(myjson2()$timevar)+1): ((myjson2()$Nats*(k-1)*length(myjson2()$timevar)+1)+myjson2()$Nats*length(myjson2()$timevar)-1)], Covariate_pattern=as.factor(data_stacked_bars()$cov_factor[(myjson2()$Nats*(k-1)*length(myjson2()$timevar)+1): ((myjson2()$Nats*(k-1)*length(myjson2()$timevar)+1)+myjson2()$Nats*length(myjson2()$timevar)-1)] ) , State=as.factor(data_stacked_bars()$state_factor[(myjson2()$Nats*(k-1)*length(myjson2()$timevar)+1): ((myjson2()$Nats*(k-1)*length(myjson2()$timevar)+1)+myjson2()$Nats*length(myjson2()$timevar)-1)]) )} P <- plot_ly(data_stacked_bars(), type = 'bar', colors=labels_colour_state(), text = 'Select or deselect bars by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) %>% layout(title=list(text="Stacked probability of states across covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizep_msm, color = "black"), yaxis = list(title = 'Stacked probabilities',rangemode = "nonnegative",dtick = 0.1 ,ticklen = 5,tickwidth = 2,tickcolor = toRGB("black")), xaxis = list(rangemode = "nonnegative"), margin = list(l = 50, r = 50, b = 30, t = 70), barmode = 'stack', bargap = 0.1) for(k in 1:length(myjson2()$P)) { P <- add_trace( P, y=dfk[[k]]$prob_st, x=factor(as.factor(dfk[[k]]$Covariate_pattern),levels=labels_cov()), color=factor(as.factor(dfk[[k]]$State),levels=labels_state() ), frame =dfk[[k]]$Time, data=dfk[[k]], name=factor(as.factor(dfk[[k]]$State),levels=labels_state() ), width = 0.1 ) } P <- P %>% animation_opts(1000/input$speed, easing ="linear-in") %>% animation_button(enumerated="animate")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) P }) output$probability_bars_stacked <- renderPlotly ({datap6_re() }) output$downplotp6 <- downloadHandler( filename = function(){paste("p6",'.png',sep='')}, content = function(file){ plotly_IMAGE( datap6_re(),width = 3000, height = 3000, format = "png", scale = 1, out_file = file ) } ) ####################################################################################################### ######### Image MSM probabilities #################################################################### ###################################################################################################### output$shouldloadp7 <- renderUI({ if (is.null((myjson2()))) return() downloadButton(outputId = "downplotp7", label = h2("Download the plot")) }) datap7_re <- reactive ({ if (input$aimtype=="compare") {return("Not available when comparing approaches")} else { if(is.null(myjson1())) {return()} else ntransitions=myjson1()$Ntransitions nstates= myjson1()$Nstates nats=length(myjson2()$atlist) xvaluesb=labels_x() #+boxwidth/2 yvaluesb=labels_y() #-boxheight/2 boxes=msboxes_R_nofreq(yb=yvaluesb, xb=xvaluesb, boxwidth=input$boxwidth , boxheight=input$boxheight, tmat.= myjson1()$tmat) #Read through json from msboxes or through a new function x1=boxes$arrows$x1 y1=boxes$arrows$y1 x2=boxes$arrows$x2 y2=boxes$arrows$y2 if (nstates!=length(myjson2()$P)) { return(h2("The number of states are not corresponding between the 2 uploaded files")) } if (nstates==length(myjson2()$P) & nats==length(myjson2()$atlist)) { w1=input$boxwidth/((5*length(myjson2()$atlist))+1) w2=4*input$boxwidth/((5*length(myjson2()$atlist))+1) prob=matrix(nrow=nstates, ncol= nats, NA) xleft=matrix(nrow=nstates, ncol= nats, NA) xright=matrix(nrow=nstates, ncol= nats, NA) ybottom=matrix(nrow=nstates, ncol= nats, NA) ytop=matrix(nrow=nstates, ncol= nats, NA) for (i in 1:nstates ) { for (j in 1:nats ) { prob[i,j]=data_P_d()$V[which(data_P_d()$timevar==input$perc & data_P_d()$state==i & data_P_d()$cov_factor==labels_cov()[j])] xleft[i,j]=(xvaluesb[i]-input$boxwidth/2)+ j*w1 + (j-1)*w2 xright[i,j]=(xvaluesb[i]-input$boxwidth/2)+ j*w1 + j*w2 ybottom[i,j]=(yvaluesb[i]-input$boxheight/2) ytop[i,j]= (yvaluesb[i]-input$boxheight/2)+ prob[i,j]*input$boxheight } } xticks=matrix(nrow=length(myjson2()$P), ncol=length(seq(0,1,by=0.1)),NA ) yticks=matrix(nrow=length(myjson2()$P), ncol=length(seq(0,1,by=0.1)),NA ) for (i in 1:length(myjson2()$P) ) { for (k in 1: length(seq(0,1,by=0.1)) ) { xticks[i,k]= xvaluesb[i]-(input$boxwidth/2)-(input$boxwidth/6) yticks[i,k]= yvaluesb[i]-(input$boxheight/2)+ k*(input$boxheight/10)- input$boxheight/10 } } plotit_prob<-function(){ plot(c(0, 1),c(0, 1), type = "n" , ylab = "", xlab='', xaxt='n', yaxt='n', pch=30) text(0.05, 1, paste0("At time"," ",input$perc),cex = input$textsizep_msm/10) legend(0.85, 1.0, labels_cov(), fill =labels_colour_cov(), cex = input$textsizep_msm/10) ### Call the box function recttext(xcenter=xvaluesb, ycenter=yvaluesb, boxwidth=input$boxwidth, boxheight=input$boxheight,statename=c(rep("",nstates)), freq_box=c(rep("",nstates)), rectArgs = list(col = 'white', lty = 'solid'), textArgs_state = list(col = input$boxcolornames, cex = input$textsizep_msm/10,pos=3), textArgs_freq = list(col = input$boxcolorfreqs, cex = input$textsizep_msm/10,pos=1)) ### Call the arrows function arrows_msm(xstart=x1, ystart=y1,xend=x2, yend=y2,xtext=c(rep("",ntransitions)), ytext=c(rep("",ntransitions)),tname=c(rep("",ntransitions)), tfreq=c(rep("",ntransitions)), textArgs_transname =list(col =input$arrowcolornames, cex = input$textsizep_msm/10, pos=3), textArgs_transfreq =list(col = input$arrowcolorfreqs, cex = input$textsizep_msm/10, pos=1), arrowcol=input$arrowcolour,lty = 1) for (i in 1:length(myjson2()$P) ) { for (j in 1:length(myjson2()$atlist) ) { rect(xleft = xleft[i,j], ybottom = ybottom[i,j], xright = xright[i,j], ytop = ytop[i,j], col= labels_colour_cov()[j]) } } for (i in 1:length(myjson2()$P) ) { for (k in 1: length(seq(0,1,by=0.1)) ) { text(x = xticks[i,k], y=yticks[i,k],label=as.character( (k-1)*(input$boxheight/10)*(1/input$boxheight) ),cex =input$textsizep_msm*0.07) # do.call('text', c(list(x = xticks[i,k], y = yticks[i,k], labels = as.character(k)), "black")) } } } z.plot2<-function(){plotit_prob()} par(mar=c(0, 0, 0, 0)) p=z.plot2() } } }) output$probability_msm_box <- renderPlot ({datap7_re() }) output$downplotp7 <- downloadHandler( filename = function(){paste("p7",'.tiff',sep='')}, content = function(file){ tiff(file, width = 10, height = 10, units = "cm",res=600) ntransitions=myjson1()$Ntransitions nstates= myjson1()$Nstates nats=length(myjson2()$atlist) xvaluesb=labels_x() #+boxwidth/2 yvaluesb=labels_y() #-boxheight/2 boxes=msboxes_R_nofreq(yb=yvaluesb, xb=xvaluesb, boxwidth=input$boxwidth , boxheight=input$boxheight, tmat.= myjson1()$tmat) #Read through json from msboxes or through a new function x1=boxes$arrows$x1 y1=boxes$arrows$y1 x2=boxes$arrows$x2 y2=boxes$arrows$y2 if (nstates!=length(myjson2()$P)) { return(h2("The number of states are not corresponding between the 2 uploaded files")) } if (nstates==length(myjson2()$P) & nats==length(myjson2()$atlist)) { w1=input$boxwidth/((5*length(myjson2()$atlist))+1) w2=4*input$boxwidth/((5*length(myjson2()$atlist))+1) prob=matrix(nrow=nstates, ncol= nats, NA) xleft=matrix(nrow=nstates, ncol= nats, NA) xright=matrix(nrow=nstates, ncol= nats, NA) ybottom=matrix(nrow=nstates, ncol= nats, NA) ytop=matrix(nrow=nstates, ncol= nats, NA) for (i in 1:nstates ) { for (j in 1:nats ) { prob[i,j]=data_P_d()$V[which(data_P_d()$timevar==input$perc & data_P_d()$state==i & data_P_d()$cov_factor==labels_cov()[j])] xleft[i,j]=(xvaluesb[i]-input$boxwidth/2)+ j*w1 + (j-1)*w2 xright[i,j]=(xvaluesb[i]-input$boxwidth/2)+ j*w1 + j*w2 ybottom[i,j]=(yvaluesb[i]-input$boxheight/2) ytop[i,j]= (yvaluesb[i]-input$boxheight/2)+ prob[i,j]*input$boxheight } } xticks=matrix(nrow=length(myjson2()$P), ncol=length(seq(0,1,by=0.1)),NA ) yticks=matrix(nrow=length(myjson2()$P), ncol=length(seq(0,1,by=0.1)),NA ) for (i in 1:length(myjson2()$P) ) { for (k in 1: length(seq(0,1,by=0.1)) ) { xticks[i,k]= xvaluesb[i]-(input$boxwidth/2)-(input$boxwidth/6) yticks[i,k]= yvaluesb[i]-(input$boxheight/2)+ k*(input$boxheight/10)- input$boxheight/10 } } plotit_prob<-function(){ plot(c(0, 1),c(0, 1), type = "n" , ylab = "", xlab='', xaxt='n', yaxt='n', pch=30) text(0.05, 1, paste0("At time"," ",input$perc),cex = input$textsizep_msm/10) legend(0.85, 1.0, labels_cov(), fill =labels_colour_cov(), cex = input$textsizep_msm/10) ### Call the box function recttext(xcenter=xvaluesb, ycenter=yvaluesb, boxwidth=input$boxwidth, boxheight=input$boxheight,statename=c(rep("",nstates)), freq_box=c(rep("",nstates)), rectArgs = list(col = 'white', lty = 'solid'), textArgs_state = list(col = input$boxcolornames, cex = input$textsizep_msm/10,pos=3), textArgs_freq = list(col = input$boxcolorfreqs, cex = input$textsizep_msm/10,pos=1)) ### Call the arrows function arrows_msm(xstart=x1, ystart=y1,xend=x2, yend=y2,xtext=c(rep("",ntransitions)), ytext=c(rep("",ntransitions)),tname=c(rep("",ntransitions)), tfreq=c(rep("",ntransitions)), textArgs_transname =list(col =input$arrowcolornames, cex = input$textsizep_msm/10, pos=3), textArgs_transfreq =list(col = input$arrowcolorfreqs, cex = input$textsizep_msm/10, pos=1), arrowcol=input$arrowcolour,lty = 1) for (i in 1:length(myjson2()$P) ) { for (j in 1:length(myjson2()$atlist) ) { rect(xleft = xleft[i,j], ybottom = ybottom[i,j], xright = xright[i,j], ytop = ytop[i,j], col= labels_colour_cov()[j]) } } for (i in 1:length(myjson2()$P) ) { for (k in 1: length(seq(0,1,by=0.1)) ) { text(x = xticks[i,k], y=yticks[i,k],label=as.character( (k-1)*(input$boxheight/10)*(1/input$boxheight) ),cex =input$textsizep_msm*0.07) # do.call('text', c(list(x = xticks[i,k], y = yticks[i,k], labels = as.character(k)), "black")) } } } z.plot2<-function(){plotit_prob()} par(mar=c(0, 0, 0, 0)) p=z.plot2() dev.off() } } ) ###################################################################################################################################### ###################################################################################################################################### data_P_diff1 <- reactive ({ P_diff=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$Pd)) { P_diff[[i]]=as.data.frame(t(data.frame(myjson2()$Pd[i]))) colnames(P_diff[[i]]) <- v_diff } for(i in 1:length(myjson2()$Pd)) { P_diff[[i]]=as.data.frame(cbind(P_diff[[i]], timevar ,state=rep(i,nrow(P_diff[[i]] )) )) } } else { for (i in 1:length(myjson2()$Pd)) { P_diff[[i]]=as.data.frame(myjson2()$Pd[[i]][,1]) # colnames(P_diff[[i]]) <- v_diff } for (i in 1:length(myjson2()$Pd)) { P_diff[[i]]=as.data.frame(c(P_diff[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$Pd[[i]][,1])) )) ) colnames(P_diff[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_Pd=list() data_Pd[[1]]=P_diff[[1]] if (length(myjson2()$Pd)>1) { for (u in 2:(length(myjson2()$Pd))) { data_Pd[[u]]=rbind(P_diff[[u]],data_Pd[[(u-1)]]) } } dataPd=data_Pd[[length(myjson2()$Pd)]] dataPd$state_fac=c(rep("NA",nrow(dataPd))) for (o in 1:(length(myjson2()$Pd))) { for (g in 1:nrow(dataPd)) { if (dataPd$state[g]==o) {dataPd$state_fac[g]=labels_state()[o]} } } dataPd }) data_P_diff1_uci <- reactive ({ P_diff_uci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$Pd_uci)) { P_diff_uci[[i]]=as.data.frame(t(data.frame(myjson2()$Pd_uci[i]))) colnames(P_diff_uci[[i]]) <- v_diff } for(i in 1:length(myjson2()$Pd_uci)) { P_diff_uci[[i]]=as.data.frame(cbind(P_diff_uci[[i]], timevar ,state=rep(i,nrow(P_diff_uci[[i]] )) )) } } else { for (i in 1:length(myjson2()$Pd_uci)) { P_diff_uci[[i]]=as.data.frame(myjson2()$Pd_uci[[i]][,1]) # colnames(P_diff[[i]]) <- v_diff } for (i in 1:length(myjson2()$Pd_uci)) { P_diff_uci[[i]]=as.data.frame(c(P_diff_uci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$Pd_uci[[i]][,1])) )) ) colnames(P_diff_uci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_Pd_uci=list() data_Pd_uci[[1]]=P_diff_uci[[1]] if (length(myjson2()$Pd_uci)>1) { for (u in 2:(length(myjson2()$Pd_uci))) { data_Pd_uci[[u]]=rbind(P_diff_uci[[u]],data_Pd_uci[[(u-1)]]) } } dataPd_uci=data_Pd_uci[[length(myjson2()$Pd_uci)]] dataPd_uci$state_fac=c(rep("NA",nrow(dataPd_uci))) for (o in 1:(length(myjson2()$Pd_uci))) { for (g in 1:nrow(dataPd_uci)) { if (dataPd_uci$state[g]==o) {dataPd_uci$state_fac[g]=labels_state()[o]} } } dataPd_uci }) data_P_diff1_lci <- reactive ({ P_diff_lci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$Pd_lci)) { P_diff_lci[[i]]=as.data.frame(t(data.frame(myjson2()$Pd_lci[i]))) colnames(P_diff_lci[[i]]) <- v_diff } for(i in 1:length(myjson2()$Pd_lci)) { P_diff_lci[[i]]=as.data.frame(cbind(P_diff_lci[[i]], timevar ,state=rep(i,nrow(P_diff_lci[[i]] )) )) } } else { for (i in 1:length(myjson2()$Pd_lci)) { P_diff_lci[[i]]=as.data.frame(myjson2()$Pd_lci[[i]][,1]) # colnames(P_diff[[i]]) <- v_diff } for (i in 1:length(myjson2()$Pd_lci)) { P_diff_lci[[i]]=as.data.frame(c(P_diff_lci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$Pd_lci[[i]][,1])) )) ) colnames(P_diff_lci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_Pd_lci=list() data_Pd_lci[[1]]=P_diff_lci[[1]] if (length(myjson2()$Pd_lci)>1) { for (u in 2:(length(myjson2()$Pd_lci))) { data_Pd_lci[[u]]=rbind(P_diff_lci[[u]],data_Pd_lci[[(u-1)]]) } } dataPd_lci=data_Pd_lci[[length(myjson2()$Pd_lci)]] dataPd_lci$state_fac=c(rep("NA",nrow(dataPd_lci))) for (o in 1:(length(myjson2()$Pd_lci))) { for (g in 1:nrow(dataPd_lci)) { if (dataPd_lci$state[g]==o) {dataPd_lci$state_fac[g]=labels_state()[o]} } } dataPd_lci }) data_P_diff2<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_P_diff1()[,d],data_P_diff1()[,ncol(data_P_diff1())-2],data_P_diff1()[,ncol(data_P_diff1())-1], data_P_diff1()[,ncol(data_P_diff1())],rep(d,length(data_P_diff1()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_P_diff1())[d],length(data_P_diff1()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Pd <- bind_rows(dlist, .id = "column_label") d_all_Pd }) data_P_diff2_uci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_P_diff1_uci()[,d],data_P_diff1_uci()[,ncol(data_P_diff1_uci())-2],data_P_diff1_uci()[,ncol(data_P_diff1_uci())-1], data_P_diff1_uci()[,ncol(data_P_diff1_uci())],rep(d,length(data_P_diff1_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_P_diff1_uci())[d],length(data_P_diff1_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Pd_uci <- bind_rows(dlist, .id = "column_label") d_all_Pd_uci }) data_P_diff2_lci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_P_diff1_lci()[,d],data_P_diff1_lci()[,ncol(data_P_diff1_lci())-2],data_P_diff1_lci()[,ncol(data_P_diff1_lci())-1], data_P_diff1_lci()[,ncol(data_P_diff1_lci())],rep(d,length(data_P_diff1_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_P_diff1_lci())[d],length(data_P_diff1_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Pd_lci <- bind_rows(dlist, .id = "column_label") d_all_Pd_lci }) data_P_diff_ci<- reactive ({ x=c( data_P_diff2()[order(data_P_diff2()$timevar,data_P_diff2()$state,data_P_diff2()$cov),]$timevar, data_P_diff2()[order(-data_P_diff2()$timevar,data_P_diff2()$state,data_P_diff2()$cov),]$timevar ) y_central=c( data_P_diff2()[order(data_P_diff2()$timevar,data_P_diff2()$state,data_P_diff2()$cov),]$V, data_P_diff2()[order(-data_P_diff2()$timevar,data_P_diff2()$state,data_P_diff2()$cov),]$V ) y=c( data_P_diff2_uci()[order(data_P_diff2_uci()$timevar,data_P_diff2_uci()$state,data_P_diff2_uci()$cov),]$V, data_P_diff2_lci()[order(-data_P_diff2_lci()$timevar,data_P_diff2_lci()$state,data_P_diff2_lci()$cov),]$V ) frameto=c(as.character(data_P_diff2_uci()[order(-data_P_diff2_uci()$timevar,data_P_diff2_uci()$state,data_P_diff2_uci()$cov),]$state_factor), as.character(data_P_diff2_lci()[order(-data_P_diff2_lci()$timevar,data_P_diff2_lci()$state,data_P_diff2_lci()$cov),]$state_factor) ) covto=c( data_P_diff2_uci()[order(-data_P_diff2_uci()$timevar,data_P_diff2_uci()$state,data_P_diff2_uci()$cov),]$cov_factor, data_P_diff2_lci()[order(-data_P_diff2_lci()$timevar,data_P_diff2_lci()$state,data_P_diff2_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) output$table <- renderTable ({ data_P_diff1() }) ####################################### #output$shouldloadp8 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotp8", label = h2("Download the plot")) #}) datap8_re <- reactive ({ ax <- list( title = "",zeroline = FALSE,showline = FALSE, showticklabels = FALSE, showgrid = FALSE) if (length(myjson2()$Pd) == 0| myjson2()$Nats==1 ) { P_state_d= plot_ly() %>% layout(title=list(text="Not applicable- Only one covariate pattern specified",y=0.95),xaxis=ax, yaxis=ax) P_state_d } else { if (input$conf=="ci_no") { if (input$facet=="No") { P_state_d= plot_ly(data_P_diff2(),alpha=0.5) %>% add_lines( x=data_P_diff2()$timevar,y=data_P_diff2()$V, frame=factor(as.factor(data_P_diff2()$state_factor),levels=labels_state()), color=as.factor(data_P_diff2()$cov_factor), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text="Differences of probabilities among covariate patterns (compared to ref. cov pattern)",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Differences of probabilities", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$facet=="Yes") { data_plot=data_P_diff2() P_state_d = ggplot(data_plot) P_state_d = ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(cov_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Difference of probabilities: ", V, "
Covariate pattern: ", as.factor(cov_factor)))) P_state_d = P_state_d+geom_line(aes(x=timevar, y=V, color= as.factor(cov_factor)))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { P_state_d = P_state_d+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()), nrow=2)} else if (input$aimtype=="present") {P_state_d = P_state_d+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()))} P_state_d = P_state_d + scale_x_continuous(breaks=c(seq(input$startx,input$endx,by=input$stepx ))) P_state_d = P_state_d + scale_y_continuous(breaks=c(seq(min(data_plot$V[which(!is.na(data_plot$V))]),max(data_plot$V[which(!is.na(data_plot$V))]), by=input$stepy ))) P_state_d = P_state_d +labs(title="Differences of probabilities among covariate patterns (compared to ref. cov pattern)", x="Time since entry", y="Differences of probabilities") P_state_d = P_state_d + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") P_state_d = P_state_d +theme(title = element_text(size = input$textsizep-4), strip.text = element_text(size=input$textfacetp) , legend.title = element_text(color="black", size= input$textsizep-5), legend.text=element_text(size= input$textsizep-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizep-5), axis.title.x = element_text(size= input$textsizep-5), axis.text.x = element_text( size=input$textsizep-6),axis.text.y = element_text( size=input$textsizep-6)) P_state_d = ggplotly(P_state_d, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) P_state_d } } else if (input$conf=="ci_yes") { if (input$facet=="No") { P_state_d <- plot_ly() P_state_d <- add_trace(P_state_d, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_P_diff_ci()$x, y=data_P_diff_ci()$y_central, frame=factor(as.factor(data_P_diff_ci()$frameto),levels=labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_P_diff_ci()$covto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) P_state_d <- add_trace(P_state_d, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_P_diff_ci()$x, y=data_P_diff_ci()$y, frame=factor(as.factor(data_P_diff_ci()$frameto),levels=labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_P_diff_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) P_state_d= P_state_d %>% layout(title=list(text="Differences of probabilities among covariate patterns (compared to ref. cov pattern)",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Differences of probabilities ", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$facet=="Yes") { V_lci= data_P_diff2_lci()$V V_uci= data_P_diff2_uci()$V data_plot=cbind(data_P_diff2(),V_lci,V_uci) P_state_d=ggplot(data_plot) P_state_d=ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(cov_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Difference of probabilities: ", V, "
Covariate pattern: ", as.factor(cov_factor)))) P_state_d=P_state_d+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) P_state_d=P_state_d+geom_line(aes(x=timevar, y=V, fill= as.factor(cov_factor))) P_state_d=P_state_d+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=as.factor(cov_factor)),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { P_state_d = P_state_d+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()), nrow=2)} else if (input$aimtype=="present") {P_state_d = P_state_d+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()))} P_state_d = P_state_d + scale_x_continuous(breaks=c(seq(input$startx,input$endx,by=input$stepx ))) P_state_d = P_state_d + scale_y_continuous(breaks=c(seq(min(data_plot$V_lci[which(!is.na(data_plot$V_lci))]),max(data_plot$V_uci[which(!is.na(data_plot$V_uci))]),by=input$stepy ))) P_state_d = P_state_d +labs(title="Differences of probabilities among covariate patterns (compared to ref. cov pattern)", x="Time since entry", y="Differences of probabilities") P_state_d = P_state_d + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") P_state_d = P_state_d +theme(title = element_text(size = input$textsizep-4), strip.text = element_text(size=input$textfacetp), legend.title = element_text(color="black", size= input$textsizep-5), legend.text=element_text(size= input$textsizep-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizep-5), axis.title.x = element_text(size= input$textsizep-5), axis.text.x = element_text( size=input$textsizep-6),axis.text.y = element_text( size=input$textsizep-6)) P_state_d = ggplotly(P_state_d, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } P_state_d } }) output$P_diff <- renderPlotly ({ datap8_re() }) output$downplotp8 <- downloadHandler( filename = function(){paste("p8",'.png',sep='')}, content = function(file){ plotly_IMAGE( datap8_re(),width = 3000, height = 3000, format = "png", scale = 1, out_file = file ) } ) ################################################## data_P_ratio1 <- reactive ({ P_ratio=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$Pr)) { P_ratio[[i]]=as.data.frame(t(data.frame(myjson2()$Pr[i]))) colnames(P_ratio[[i]]) <- v_ratio } for(i in 1:length(myjson2()$Pr)) { P_ratio[[i]]=as.data.frame(cbind(P_ratio[[i]], timevar ,state=rep(i,nrow(P_ratio[[i]] )) )) } } else { for (i in 1:length(myjson2()$Pr)) { P_ratio[[i]]=as.data.frame(myjson2()$Pr[[i]][,1]) # colnames(P_ratio[[i]]) <- v_ratio } for (i in 1:length(myjson2()$Pr)) { P_ratio[[i]]=as.data.frame(c(P_ratio[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$Pr[[i]][,1])) )) ) colnames(P_ratio[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the different states data_Pr=list() data_Pr[[1]]=P_ratio[[1]] if (length(myjson2()$Pr)>1) { for (u in 2:(length(myjson2()$Pr))) { data_Pr[[u]]=rbind(P_ratio[[u]],data_Pr[[(u-1)]]) } } dataPr=data_Pr[[length(myjson2()$Pr)]] dataPr$state_fac=c(rep("NA",nrow(dataPr))) for (o in 1:(length(myjson2()$Pr))) { for (g in 1:nrow(dataPr)) { if (dataPr$state[g]==o) {dataPr$state_fac[g]=labels_state()[o]} } } dataPr }) data_P_ratio1_uci <- reactive ({ P_ratio_uci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$Pr_uci)) { P_ratio_uci[[i]]=as.data.frame(t(data.frame(myjson2()$Pr_uci[i]))) colnames(P_ratio_uci[[i]]) <- v_ratio } for(i in 1:length(myjson2()$Pr_uci)) { P_ratio_uci[[i]]=as.data.frame(cbind(P_ratio_uci[[i]], timevar ,state=rep(i,nrow(P_ratio_uci[[i]] )) )) } } else { for (i in 1:length(myjson2()$Pr_uci)) { P_ratio_uci[[i]]=as.data.frame(myjson2()$Pr_uci[[i]][,1]) # colnames(P_ratio[[i]]) <- v_ratio } for (i in 1:length(myjson2()$Pr_uci)) { P_ratio_uci[[i]]=as.data.frame(c(P_ratio_uci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$Pr_uci[[i]][,1])) )) ) colnames(P_ratio_uci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the ratioerent states data_Pr_uci=list() data_Pr_uci[[1]]=P_ratio_uci[[1]] if (length(myjson2()$Pr_uci)>1) { for (u in 2:(length(myjson2()$Pr_uci))) { data_Pr_uci[[u]]=rbind(P_ratio_uci[[u]],data_Pr_uci[[(u-1)]]) } } dataPr_uci=data_Pr_uci[[length(myjson2()$Pr_uci)]] dataPr_uci$state_fac=c(rep("NA",nrow(dataPr_uci))) for (o in 1:(length(myjson2()$Pr_uci))) { for (g in 1:nrow(dataPr_uci)) { if (dataPr_uci$state[g]==o) {dataPr_uci$state_fac[g]=labels_state()[o]} } } dataPr_uci }) data_P_ratio1_lci <- reactive ({ P_ratio_lci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$Pr_lci)) { P_ratio_lci[[i]]=as.data.frame(t(data.frame(myjson2()$Pr_lci[i]))) colnames(P_ratio_lci[[i]]) <- v_ratio } for(i in 1:length(myjson2()$Pr_lci)) { P_ratio_lci[[i]]=as.data.frame(cbind(P_ratio_lci[[i]], timevar ,state=rep(i,nrow(P_ratio_lci[[i]] )) )) } } else { for (i in 1:length(myjson2()$Pr_lci)) { P_ratio_lci[[i]]=as.data.frame(myjson2()$Pr_lci[[i]][,1]) # colnames(P_ratio[[i]]) <- v_ratio } for (i in 1:length(myjson2()$Pr_lci)) { P_ratio_lci[[i]]=as.data.frame(c(P_ratio_lci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$Pr_lci[[i]][,1])) )) ) colnames(P_ratio_lci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the ratioerent states data_Pr_lci=list() data_Pr_lci[[1]]=P_ratio_lci[[1]] if (length(myjson2()$Pr_lci)>1) { for (u in 2:(length(myjson2()$Pr_lci))) { data_Pr_lci[[u]]=rbind(P_ratio_lci[[u]],data_Pr_lci[[(u-1)]]) } } dataPr_lci=data_Pr_lci[[length(myjson2()$Pr_lci)]] dataPr_lci$state_fac=c(rep("NA",nrow(dataPr_lci))) for (o in 1:(length(myjson2()$Pr_lci))) { for (g in 1:nrow(dataPr_lci)) { if (dataPr_lci$state[g]==o) {dataPr_lci$state_fac[g]=labels_state()[o]} } } dataPr_lci }) data_P_ratio2<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_P_ratio1()[,d],data_P_ratio1()[,ncol(data_P_ratio1())-2],data_P_ratio1()[,ncol(data_P_ratio1())-1], data_P_ratio1()[,ncol(data_P_ratio1())],rep(d,length(data_P_ratio1()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_P_ratio1())[d],length(data_P_ratio1()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Pr <- bind_rows(dlist, .id = "column_label") d_all_Pr }) data_P_ratio2_uci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_P_ratio1_uci()[,d],data_P_ratio1_uci()[,ncol(data_P_ratio1_uci())-2], data_P_ratio1_uci()[,ncol(data_P_ratio1_uci())-1], data_P_ratio1_uci()[,ncol(data_P_ratio1_uci())],rep(d,length(data_P_ratio1_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_P_ratio1_uci())[d],length(data_P_ratio1_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Pd_uci <- bind_rows(dlist, .id = "column_label") d_all_Pd_uci }) data_P_ratio2_lci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_P_ratio1_lci()[,d],data_P_ratio1_lci()[,ncol(data_P_ratio1_lci())-2], data_P_ratio1_lci()[,ncol(data_P_ratio1_lci())-1], data_P_ratio1_lci()[,ncol(data_P_ratio1_lci())],rep(d,length(data_P_ratio1_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_P_ratio1_lci())[d],length(data_P_ratio1_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Pd_lci <- bind_rows(dlist, .id = "column_label") d_all_Pd_lci }) data_P_ratio_ci<- reactive ({ x=c( data_P_ratio2()[order(data_P_ratio2()$timevar,data_P_ratio2()$state,data_P_ratio2()$cov),]$timevar, data_P_ratio2()[order(-data_P_ratio2()$timevar,data_P_ratio2()$state,data_P_ratio2()$cov),]$timevar ) y_central=c( data_P_ratio2()[order(data_P_ratio2()$timevar,data_P_ratio2()$state,data_P_ratio2()$cov),]$V, data_P_ratio2()[order(-data_P_ratio2()$timevar,data_P_ratio2()$state,data_P_ratio2()$cov),]$V ) y=c( data_P_ratio2_uci()[order(data_P_ratio2_uci()$timevar,data_P_ratio2_uci()$state,data_P_ratio2_uci()$cov),]$V, data_P_ratio2_lci()[order(-data_P_ratio2_lci()$timevar,data_P_ratio2_lci()$state,data_P_ratio2_lci()$cov),]$V ) frameto=c(as.character(data_P_ratio2_uci()[order(-data_P_ratio2_uci()$timevar,data_P_ratio2_uci()$state,data_P_ratio2_uci()$cov),]$state_factor), as.character(data_P_ratio2_lci()[order(-data_P_ratio2_lci()$timevar,data_P_ratio2_lci()$state,data_P_ratio2_lci()$cov),]$state_factor) ) covto=c( data_P_ratio2_uci()[order(-data_P_ratio2_uci()$timevar,data_P_ratio2_uci()$state,data_P_ratio2_uci()$cov),]$cov_factor, data_P_ratio2_lci()[order(-data_P_ratio2_lci()$timevar,data_P_ratio2_lci()$state,data_P_ratio2_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) #output$shouldloadp9 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotp9", label = h2("Download the plot")) #}) datap9_re <- reactive ({ ax <- list( title = "",zeroline = FALSE,showline = FALSE, showticklabels = FALSE, showgrid = FALSE) if (length(myjson2()$Pr) == 0| myjson2()$Nats==1 ) { P_state_r= plot_ly() %>% layout(title=list(text="Not applicable- Only one covariate pattern specified",y=0.95),xaxis=ax, yaxis=ax) P_state_r } else { if (input$conf=="ci_no") { if (input$facet=="No") { P_state_r= plot_ly(data_P_ratio2(),alpha=0.5) %>% add_lines( x=data_P_ratio2()$timevar,y=data_P_ratio2()$V, frame=factor(as.factor(data_P_ratio2()$state_factor),levels=labels_state()), color=as.factor(data_P_ratio2()$cov_factor), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text="Ratios of probabilities among covariate patterns (compared to ref. cov pattern)",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Ratios in probabilities", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) ) %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$facet=="Yes") { data_plot=data_P_ratio2() P_state_r = ggplot(data_plot) P_state_r = ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(cov_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Ratio of probability: ", V, "
Covariate pattern: ", as.factor(cov_factor)))) P_state_r = P_state_r+geom_line(aes(x=timevar, y=V, color= as.factor(cov_factor)))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { P_state_r = P_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()), nrow=2)} else if (input$aimtype=="present") {P_state_r = P_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()))} P_state_r = P_state_r + scale_x_continuous(breaks=c(seq(input$startx,input$endx,by=input$stepx ))) P_state_r = P_state_r + scale_y_continuous(breaks=c(seq(min(data_plot$V[which(!is.na(data_plot$V))]),max(data_plot$V[which(!is.na(data_plot$V))]), by=input$stepy ))) P_state_r = P_state_r +labs(title="Ratios of probabilities among covariate patterns (compared to ref. cov pattern)", x="Time since entry", y="Ratios of probabilities") P_state_r = P_state_r + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") P_state_r = P_state_r +theme(title = element_text(size = input$textsizep-4), strip.text = element_text(size=input$textfacetp), legend.title = element_text(color="black", size= input$textsizep-5), legend.text=element_text(size= input$textsizep-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizep-5), axis.title.x = element_text(size= input$textsizep-5), axis.text.x = element_text( size=input$textsizep-6),axis.text.y = element_text( size=input$textsizep-6)) P_state_r = ggplotly(P_state_r, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) P_state_r } } else if (input$conf=="ci_yes") { if (input$facet=="No") { P_state_r <- plot_ly() P_state_r <- add_trace(P_state_r, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_P_ratio_ci()$x, y=data_P_ratio_ci()$y_central, frame=factor(as.factor(data_P_ratio_ci()$frameto), levels=labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_P_ratio_ci()$covto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) P_state_r <- add_trace(P_state_r, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_P_ratio_ci()$x, y=data_P_ratio_ci()$y, frame=factor(as.factor(data_P_ratio_ci()$frameto), levels=labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_P_ratio_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) P_state_r= P_state_r %>% layout(title=list(text="Ratios of probabilities among covariate patterns (compared to ref. cov pattern)",y=0.95), font= list(family = "times new roman", size = input$textsizep, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepx, tick0 = input$startx, range=c(input$startx,input$endx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Ratios of probabilities", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$facet=="Yes") { V_lci= data_P_ratio2_lci()$V V_uci= data_P_ratio2_uci()$V data_plot=cbind(data_P_ratio2(),V_lci,V_uci) P_state_r=ggplot(data_plot) P_state_r=ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(cov_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Ratio of probability: ", V, "
Covariate pattern: ", as.factor(cov_factor))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) P_state_r=P_state_r+geom_line(aes(x=timevar, y=V, fill= as.factor(cov_factor))) P_state_r=P_state_r+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=as.factor(cov_factor)),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { P_state_r = P_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()), nrow=2)} else if (input$aimtype=="present") {P_state_r = P_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()))} P_state_r = P_state_r + scale_x_continuous(breaks=c(seq(input$startx,input$endx,by=input$stepx ))) P_state_r = P_state_r + scale_y_continuous(breaks=c(seq(min(data_plot$V_lci[which(!is.na(data_plot$V_lci))]),max(data_plot$V_uci[which(!is.na(data_plot$V_uci))]),by=input$stepy ))) P_state_r = P_state_r +labs(title="Ratios of probabilities among covariate patterns (compared to ref. cov pattern)", x="Time since entry", y="Ratios of probabilities") P_state_r = P_state_r + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") P_state_r = P_state_r +theme(title = element_text(size = input$textsizep-4), strip.text = element_text(size=input$textfacetp), legend.title = element_text(color="black", size= input$textsizep-5), legend.text=element_text(size= input$textsizep-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizep-5), axis.title.x = element_text(size= input$textsizep-5), axis.text.x = element_text( size=input$textsizep-6),axis.text.y = element_text( size=input$textsizep-6)) P_state_r = ggplotly(P_state_r, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } P_state_r } }) output$P_ratio <- renderPlotly ({datap9_re() }) output$downplotp9 <- downloadHandler( filename = function(){paste("p9",'.png',sep='')}, content = function(file){ plotly_IMAGE( datap9_re(),width = 3000, height = 3000, format = "png", scale = 1, out_file = file ) } ) ###### Show and hide transition names option, and tick inputs #### timerh <- reactiveVal(1.5) observeEvent(c(input$showtransname,invalidateLater(1000, session)), { if(input$showtransname=="No"){ hide("transinputh") } if(input$showtransname=="Yes"){ show("transinputh") } isolate({ timerh(timerh()-1) if(timerh()>1 & input$showtransname=="No") { show("transinputh") } }) }) observeEvent(c(input$showtickh,invalidateLater(1000, session)), { if(input$showtickh=="No"){ hide("tickinputh") hide("tickinputhlog") } if(input$showtickh=="Yes"){ show("tickinputh") show("tickinputhlog") } isolate({ timerh(timerh()-1) if(timerh()>1 & input$showtickh=="No") { show("tickinputh") show("tickinputhlog") } }) }) #observeEvent(input$tabsh, { # # if( input$tabsh=="#panel1h"|input$tabsh=="#panel2h"|input$tabsh=="#panel3h"){ # # hide("faceth") # hide("scaleh") # hide("showtransname") # hide("transinputh") # hide("showtickh") # hide("tickinputh") # hide("tickinputhlog") # show("faceth") # show("scaleh") # show("showtransname") # show("transinputh") # show("showtickh") # show("tickinputh") # show("tickinputhlog") # # } # # # # if( input$tabsh=="#panel4h"){ # # hide("faceth") # hide("scaleh") # hide("showtransname") # hide("transinputh") # hide("showtickh") # hide("tickinputh") # hide("tickinputhlog") # show("faceth") # show("scaleh") # show("transinputh") # show("showtickh") # show("tickinputh") # show("tickinputhlog") # # } # # #}) ############################################################################ existh <- reactive({ if (length(myjson2()$haz) != 0) { x= 1 } else if (length(myjson2()$haz) == 0) { x= 0 } }) output$pageh <- renderUI({ if (input$aimtype=="compare") {return("Non applicable for comparison")} else { if (is.null(myjson2())) return("Provide the json file with the predictions") if (existh()==0) { fluidRow( # tags$style(type="text/css", # ".shiny-output-error { visibility: hidden; }", # ".shiny-output-error:before { visibility: hidden; }" # ), column(12, output$loginpageh <- renderUI({h1("Non applicable")}) ) ) } else if (existh()==1 & myjson2()$Nats>1) { fluidRow( # tags$style(type="text/css", # ".shiny-output-error { visibility: hidden; }", # ".shiny-output-error:before { visibility: hidden; }" # ), column(2, h1("Predictions hazards"), uiOutput("faceth"), conditionalPanel(condition="input.tabsh =='#panel1h'||input.tabsh =='#panel2h'||input.tabsh =='#panel5h'||input.tabsh =='#panel6h'", uiOutput("confhaz"), ) , conditionalPanel(condition="input.tabsh =='#panel1h'||input.tabsh =='#panel2h'", uiOutput("scaleh"), ) , conditionalPanel(condition="input.tabsh =='#panel4h'", uiOutput("scaleh_logdefault") ) , uiOutput("showtransname"), uiOutput("transinputh") #radioButtons("displayh", "Change labels of states and covariate patterns", # c("same" = "same", "change"= "change")), #uiOutput("covarinputh"), ), column(2, br(), p(""), uiOutput("origin"), uiOutput("showtickh"), uiOutput("tickinputh"), uiOutput("tickinputhlog") ), column(8, tabsetPanel(id = "tabsh", tabPanel(h2("By transitions"), value = "#panel1h",plotlyOutput("hazards_trans", height="600px", width = "100%"),uiOutput("shouldloadh1")), tabPanel(h2("By covariate pattern"), value = "#panel2h",plotlyOutput("hazards_cov", height="600px", width = "100%"),uiOutput("shouldloadh4")), tabPanel(h2("Ratios of transition intensities"), value = "#panel4h", plotlyOutput("hr_transitions", height="600px", width = "100%"),uiOutput("shouldloadh5")), tabPanel(h2("Hazard differences between covariate patterns"),value = "#panel5h", plotlyOutput("H_diff", height="600px", width = "100%"),uiOutput("shouldloadh2")), tabPanel(h2("Hazard ratios between covariate patterns"), value = "#panel6h", plotlyOutput("H_ratio", height="600px", width = "100%"),uiOutput("shouldloadh3")) ) ) ) } else if (existh()==1 & myjson2()$Nats==1) { fluidRow( column(2, h1("Predictions hazards"), uiOutput("faceth"), conditionalPanel(condition="input.tabsh =='#panel1h'||input.tabsh =='#panel2h'||input.tabsh =='#panel5h'||input.tabsh =='#panel6h'", uiOutput("confhaz"), ) , conditionalPanel(condition="input.tabsh =='#panel1h'||input.tabsh =='#panel2h'", uiOutput("scaleh"), ) , conditionalPanel(condition="input.tabsh =='#panel4h'", uiOutput("scaleh_logdefault") ) , uiOutput("showtransname"), uiOutput("transinputh") ), column(2, br(), p(""), uiOutput("origin"), uiOutput("showtickh"), uiOutput("tickinputh"), uiOutput("tickinputhlog") ), column(8, tabsetPanel(id = "tabsh", tabPanel(h2("By transitions"), value = "#panel1h", plotlyOutput("hazards_trans", height="600px", width = "100%"),uiOutput("shouldloadh1")), tabPanel(h2("By covariate pattern"), value = "#panel2h", plotlyOutput("hazards_cov", height="600px", width = "100%"),uiOutput("shouldloadh4")), tabPanel(h2("Ratios of transition intensities"),value = "#panel4h", plotlyOutput("hr_transitions", height="600px", width = "100%"),uiOutput("shouldloadh5")) , tabPanel(h2("Hazard differences between covariate patterns"), value = "#panel5h", plotlyOutput("H_diff", height="600px", width = "100%"),uiOutput("shouldloadh2")), tabPanel(h2("Hazard ratios between covariate patterns"), value = "#panel6h", plotlyOutput("H_ratio", height="600px", width = "100%"),uiOutput("shouldloadh3")) ) ) ) } } }) output$origin <- renderUI({ selectInput("origin",label="Time origin", choices= c("Time since study entry"="study","Time since state entry"="state" ), selected ="Time since study entry" ) }) output$confhaz <- renderUI({ if (length(myjson2()$ci_haz)!=0) { radioButtons("confh", "Confidence intervals", c("No" = "ci_no", "Yes" ="ci_yes")) } else if (length(myjson2()$ci_haz)==0) { item_list <- list() item_list[[1]]<- radioButtons("confh", "Confidence intervals",c("No" = "ci_no")) item_list[[2]]<-print("Confidence interval data were not provided") do.call(tagList, item_list) } }) output$showtransname <- renderUI({ radioButtons("showtransname", "Show transitions name options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No") }) output$scaleh <- renderUI({ radioButtons("scaleh", "Scale", c("Normal" = "normal", "Log" = "log")) }) output$scaleh_logdefault <- renderUI({ radioButtons("scaleh_ld", "Scale", c("Normal" = "normal", "Log" = "log"),selected="log") }) output$faceth <- renderUI({ radioButtons(inputId="faceth", label= "Display graph in grids", choices=c("No","Yes"),selected = "No") }) output$showtickh <- renderUI({ radioButtons("showtickh", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No") }) observeEvent(input$json2, { if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Haz')))==0 ) { js$disableTab("mytab_h") } }) observeEvent(input$csv2, { if( length(which(startsWith(names( read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Haz')))==0 ) { js$disableTab("mytab_h") } }) observeEvent(input$aimtype, { if( input$aimtype=="compare" ) { js$disableTab("mytab_h") } else if( input$aimtype=="present" ) { js$enableTab("mytab_h") } }) ############################################################################## ## Transition input ######################################################## ############################################################################## output$transinputh <- renderUI({ if (is.null((myjson2()$haz))) return() tmat_temp=myjson2()$tmat[as.numeric(input$select),] tr_start_state=vector() for (k in 1:length(which(!is.na(tmat_temp))) ) { tr_start_state[k]=as.numeric(input$select) } tr_end_state=vector() for (k in 1:length(which(!is.na(tmat_temp))) ) { tr_end_state[k]=which(!is.na(tmat_temp))[k] } default_choices_trans=vector() for (i in 1:length(which(!is.na(tmat_temp))) ) { default_choices_trans[i]=paste0('Transition'," ", tr_start_state[i],"->",tr_end_state[i]) } item_list <- list() item_list[[1]] <- h2("Transitions specifics") for (i in 1:length(which(!is.na(tmat_temp))) ) { item_list[[i+1]] <- textInput(paste0('trans', i),default_choices_trans[i], default_choices_trans[i]) } do.call(tagList, item_list) }) #Transform the reactive input of covariates into easy to use laber reactive dataset labels_trans<- reactive ({ tmat_temp=myjson2()$tmat[as.numeric(input$select),] myList<-vector("list",length(which(!is.na(tmat_temp))) ) for (i in 1:length(which(!is.na(tmat_temp))) ) { myList[[i]]= input[[paste0('trans', i)]][1] } final_list=unlist(myList, recursive = TRUE, use.names = TRUE) final_list }) ############################################################################### ############################################################################## ## Covariate input ######################################################## ############################################################################## #output$covarinputh <- renderUI({ # # if (is.null(myjson2())) return() # # if (input$displayh=="same") return() # # else { # # item_list <- list() # item_list[[1]] <- h2("Covariate patterns") # # default_choices_cov=vector() # for (i in 1:length(myjson2()$cov$atlist)) { # default_choices_cov[i]=myjson2()$cov$atlist[i] # } # # for (i in seq(length(myjson2()$cov$atlist))) { # item_list[[i+1]] <- textInput(paste0('covh', i),default_choices_cov[i], labels_cov()[i]) # } # # do.call(tagList, item_list) # } #}) #labels_covh<- reactive ({ # # if (input$displayh=="same") {labels_cov()} # # else { # # myList<-vector("list",length(myjson2()$cov$atlist)) # for (i in 1:length(myjson2()$cov$atlist)) { # myList[[i]]= input[[paste0('covh', i)]][1] # } # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # final_list # } #}) data_H <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) { v[i]=myjson2()$cov$atlist[i] } ## Different variable of probabilities for each covariate pattern ## Different variable of probabilities for each covariate pattern haz=list() if (length(myjson2()$haz)==0) {return()} for(i in 1:length(myjson2()$haz)) { haz[[i]]=as.data.frame(t(data.frame(myjson2()$haz[i]))) colnames(haz[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$haz)) { haz[[i]]=as.data.frame(cbind(haz[[i]], timevar ,trans=rep(i,nrow(haz[[i]] )) )) } # Append the probabilities datasets of the different states data_haz=list() data_haz[[1]]=haz[[1]] if (length(myjson2()$haz)>=2 ) { for (u in 2:(length(myjson2()$haz))) { data_haz[[u]]=rbind(haz[[u]],data_haz[[(u-1)]]) } } datah=data_haz[[length(myjson2()$haz)]] datah }) data_H_uci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern haz_uci=list() if (length(myjson2()$haz_uci)==0) {return()} for(i in 1:length(myjson2()$haz_uci)) { haz_uci[[i]]=as.data.frame(t(data.frame(myjson2()$haz_uci[i]))) colnames(haz_uci[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$haz_uci)) { haz_uci[[i]]=as.data.frame(cbind(haz_uci[[i]], timevar ,trans=rep(i,nrow(haz_uci[[i]] )) )) } # Append the probabilities datasets of the different states data_haz_uci=list() data_haz_uci[[1]]=haz_uci[[1]] if (length(myjson2()$haz_uci)>=2 ) { for (u in 2:(length(myjson2()$haz_uci))) { data_haz_uci[[u]]=rbind(haz_uci[[u]],data_haz_uci[[(u-1)]]) } } datah_uci=data_haz_uci[[length(myjson2()$haz_uci)]] datah_uci }) data_H_lci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern haz_lci=list() if (length(myjson2()$haz_lci)==0) {return()} for(i in 1:length(myjson2()$haz_lci)) { haz_lci[[i]]=as.data.frame(t(data.frame(myjson2()$haz_lci[i]))) colnames(haz_lci[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$haz_lci)) { haz_lci[[i]]=as.data.frame(cbind(haz_lci[[i]], timevar ,trans=rep(i,nrow(haz_lci[[i]] )) )) } # Append the probabilities datasets of the different states data_haz_lci=list() data_haz_lci[[1]]=haz_lci[[1]] if (length(myjson2()$haz_lci)>=2 ) { for (u in 2:(length(myjson2()$haz_lci))) { data_haz_lci[[u]]=rbind(haz_lci[[u]],data_haz_lci[[(u-1)]]) } } datah_lci=data_haz_lci[[length(myjson2()$haz_lci)]] datah_lci }) data_H_st<-reactive ({ datanew=data_H() datanew$trans_factor=ordered(c(rep("NA",nrow(datanew))), levels = labels_trans() ) for (o in 1:(length(myjson2()$haz))) { for (g in 1:nrow(datanew)) { if (datanew$trans[g]==o) {datanew$trans_factor[g]=labels_trans()[o] } } } datanew }) data_H_st_uci<-reactive ({ datanew=data_H_uci() datanew$trans_factor=ordered(c(rep("NA",nrow(datanew))), levels = labels_trans() ) for (o in 1:(length(myjson2()$haz_uci))) { for (g in 1:nrow(datanew)) { if (datanew$trans[g]==o) {datanew$trans_factor[g]=labels_trans()[o] } } } datanew }) data_H_st_lci<-reactive ({ datanew=data_H_lci() datanew$trans_factor=ordered(c(rep("NA",nrow(datanew))), levels = labels_trans() ) for (o in 1:(length(myjson2()$haz_lci))) { for (g in 1:nrow(datanew)) { if (datanew$trans[g]==o) {datanew$trans_factor[g]=labels_trans()[o]} } } datanew }) data_H_d <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_H_st()[,d],data_H_st()[,ncol(data_H_st())-2],data_H_st()[,ncol(data_H_st())-1],data_H_st()[,ncol(data_H_st())],rep(d,length(data_H_st()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_H_st())[d],length(data_H_st()[,d])) colnames(dlist[[d]]) <- c("V","timevar","trans","trans_factor","cov","cov_factor") } d_all_h <- bind_rows(dlist, .id = "column_label") d_all_h }) data_H_d_uci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_H_st_uci()[,d],data_H_st_uci()[,ncol(data_H_st_uci())-2], data_H_st_uci()[,ncol(data_H_st_uci())-1], data_H_st_uci()[,ncol(data_H_st_uci())],rep(d,length(data_H_st_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_H_st_uci())[d],length(data_H_st_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","trans","trans_factor","cov","cov_factor") } d_all_h_uci <- bind_rows(dlist, .id = "column_label") d_all_h_uci }) data_H_d_lci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_H_st_lci()[,d],data_H_st_lci()[,ncol(data_H_st_lci())-2], data_H_st_lci()[,ncol(data_H_st_lci())-1], data_H_st_lci()[,ncol(data_H_st_lci())],rep(d,length(data_H_st_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_H_st_lci())[d],length(data_H_st_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","trans","trans_factor","cov","cov_factor") } d_all_h_lci <- bind_rows(dlist, .id = "column_label") d_all_h_lci }) output$tickinputh <- renderUI({ if (is.null(myjson2())) return() default_choices=c("black","blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen","tan1","lightslateblue","khaki4","gray28", "cyan3","brown4","darkorchid1","goldenrod4","gray63","lightsalmon", "maroon4","palegreen1","royalblue2","red2","sienna4","yellow4","slategray3") item_list <- list() item_list[[1]] <- h2("Provide x axis range and ticks") item_list[[2]] <-numericInput("starthx","Start x at:",value=min(data_H_d()$timevar),min=0 ) item_list[[3]] <-numericInput("stephx","step:",value=max(data_H_d()$timevar/10),min=0,max=max(data_H_d()$timevar)) item_list[[4]] <-numericInput("endhx","End x at:",value =max(data_H_d()$timevar),min=0,max=max(data_H_d()$timevar)) item_list[[5]] <-numericInput("starthy","Start y at:",value=0,min=0 ) item_list[[6]] <-numericInput("stephy","step at y axis:",value=data_H_d()$V[10],min=min(data_H_d()$V),max=max(data_H_d()$V)) item_list[[7]] <-numericInput("endhy","end of y axis:",value=max(data_H_d()$V[which(!is.na(data_H_d()$V))]),min=0.0001) item_list[[8]] <-numericInput("textsizeh",h2("Legends size"),value=input$textsize,min=5,max=30) item_list[[9]] <-numericInput("textfaceth",h2("Facet title size"),value=input$textsize-3,min=5,max=30 ) do.call(tagList, item_list) }) output$tickinputhlog <- renderUI({ if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <- h2("Provide y axis range and ticks for log scale") item_list[[2]] <-numericInput("logstarthy","Start log y at:",value=-10) item_list[[3]] <-numericInput("logstephy","step at log y axis:",value=1,min=0.01,max=10) item_list[[4]] <-numericInput("logendhy","End log y axis at:",value=1,max=20,min=-20) do.call(tagList, item_list) }) data_H_ci<- reactive ({ x=c( data_H_d()[order(data_H_d()$timevar,data_H_d()$trans,data_H_d()$cov),]$timevar, data_H_d_lci()[order(-data_H_d()$timevar,data_H_d()$trans,data_H_d()$cov),]$timevar ) y_central=c( data_H_d()[order(data_H_d()$timevar,data_H_d()$trans,data_H_d()$cov),]$V, data_H_d()[order(-data_H_d()$timevar,data_H_d()$trans,data_H_d()$cov),]$V ) y=c( data_H_d_uci()[order(data_H_d_uci()$timevar,data_H_d_uci()$trans,data_H_d_uci()$cov),]$V, data_H_d_lci()[order(-data_H_d_uci()$timevar,data_H_d_uci()$trans,data_H_d_uci()$cov),]$V ) frameto=c(as.character(data_H_d_uci()[order(-data_H_d_uci()$timevar,data_H_d_uci()$trans,data_H_d_uci()$cov),]$trans_factor), as.character(data_H_d_lci()[order(-data_H_d_lci()$timevar,data_H_d_lci()$trans,data_H_d_lci()$cov),]$trans_factor) ) covto=c( data_H_d_uci()[order(-data_H_d_uci()$timevar,data_H_d_uci()$trans,data_H_d_uci()$cov),]$cov_factor, data_H_d_lci()[order(-data_H_d_lci()$timevar,data_H_d_lci()$trans,data_H_d_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) ######################################### #output$shouldloadh1 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downploth1", label = h2("Download the plot")) #}) datah1_re <- reactive ({ if (input$confh=="ci_no") { if (is.null(myjson2()$is.cumhaz)==FALSE) { if (myjson2()$is.cumhaz==1) {hazname= "Cummulative hazards"} else if (myjson2()$is.cumhaz!=1) {hazname= "Transition intensity rate"} } if (is.null(myjson2()$is.cumhaz)==TRUE) {hazname= "Transition intensity rate"} if (input$scaleh=="normal") { if (input$faceth=="No") { h_trans= plot_ly(data_H_d(),alpha=0.5) %>% add_lines( x=data_H_d()$timevar,y=data_H_d()$V, frame=as.factor(data_H_d()$trans_factor), color=factor(as.factor(data_H_d()$cov_factor),levels=labels_cov()), colors=labels_colour_cov(), mode="lines", line=list(simplify=FALSE),color = labels_colour_cov(), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text=paste0(hazname," for each covariate pattern among states"),y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= hazname,rangemode = "nonnegative", dtick = input$stephy, tick0 = input$starthy, range=c(input$starthy,input$endhy), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) h_trans } if (input$faceth=="Yes") { data_plot=data_H_d() h_trans = ggplot(data_plot) h_trans = ggplot(data_plot,aes(x=timevar, y=V, color=factor(as.factor(cov_factor),levels=labels_cov()))) h_trans = h_trans+geom_line(aes(x=timevar, y=V, color=factor(as.factor(cov_factor),levels=labels_cov()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Hazard: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor),levels=labels_cov()))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) h_trans = h_trans + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) + scale_y_continuous(breaks=c(seq(input$starthy,input$endhy,by=input$stephy ))) h_trans = h_trans +labs(title=paste0(hazname," for each covariate pattern among states"), x=paste0("Time since ",input$origin," entry"), y=hazname) h_trans = h_trans + labs(color = "Covariate \n patterns")+ labs(fill = "Covariate \n patterns") h_trans = h_trans +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5)) h_trans = h_trans+ facet_wrap(~trans_factor,nrow = NULL,ncol = NULL) h_trans = ggplotly(h_trans, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) h_trans } } else if (input$scaleh=="log") { if (input$faceth=="No") { h_trans= plot_ly(data_H_d(),alpha=0.5) %>% add_lines( x=data_H_d()$timevar,y=log(data_H_d()$V), frame=as.factor(data_H_d()$trans_factor), color=factor(as.factor(data_H_d()$cov_factor),levels=labels_cov()), colors=labels_colour_cov(), mode="lines", line=list(simplify=FALSE),color=labels_colour_cov(), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text=paste0(hazname," (log scale) for each covariate pattern among states"),y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= paste0(hazname," (log scale)"), dtick = input$logstephy, tick0 = input$logstarthy, range=c(input$logstarthy,input$logendhy), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) h_trans } if (input$faceth=="Yes") { data_plot=data_H_d() h_trans = ggplot(data_plot) h_trans = ggplot(data_plot,aes(x=timevar, y=log(V), color=factor(as.factor(cov_factor),levels=labels_cov()))) h_trans = h_trans+geom_line(aes(x=timevar, y=log(V), color= factor(as.factor(cov_factor),levels=labels_cov()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
log hazard: ", log(V), "
Covariate pattern: ", factor(as.factor(cov_factor),levels=labels_cov()))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) h_trans = h_trans + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) + scale_y_continuous(breaks=c(seq(input$logstarthy,input$logendhy,by=input$logstephy ))) h_trans = h_trans +labs(title=paste0(hazname," for each covariate pattern among states"), x=paste0("Time since ",input$origin," entry"), y=hazname) h_trans = h_trans + labs(color = "Covariate \n patterns")+ labs(fill = "Covariate \n patterns") h_trans = h_trans +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5)) h_trans = h_trans+ facet_wrap(~factor(as.factor(trans_factor),levels=labels_trans()),nrow = NULL,ncol = NULL) h_trans = ggplotly(h_trans, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } #facet } #scale } # ci if (input$confh=="ci_yes") { if (is.null(myjson2()$is.cumhaz)==FALSE) { if (myjson2()$is.cumhaz==1) {hazname= "Cummulative hazards"} else if (myjson2()$is.cumhaz!=1) {hazname= "Transition intensity rate"} } if (is.null(myjson2()$is.cumhaz)==TRUE) {hazname= "Transition intensity rate"} if (input$scaleh=="normal") { if (input$faceth=="No") { h_trans= plot_ly(data_H_d(),alpha=0.5) %>% add_lines( x=data_H_d()$timevar,y=data_H_d()$V, frame=as.factor(data_H_d()$trans_factor), color=factor(as.factor(data_H_d()$cov_factor),levels=labels_cov()), colors=labels_colour_cov(), mode="lines", line=list(simplify=FALSE),color=labels_colour_cov(), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) h_trans <- add_trace(h_trans, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_H_ci()$x, y=data_H_ci()$y, frame=as.factor(data_H_ci()$frameto), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=as.factor(data_H_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) h_trans = h_trans %>% layout(title=list(text=paste0(hazname," for each covariate pattern among states"),y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= hazname,rangemode = "nonnegative", dtick = input$stephy, tick0 = input$starthy, range=c(input$starthy,input$endhy), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) h_trans } if (input$faceth=="Yes") { V_lci= data_H_d_lci()$V V_uci= data_H_d_uci()$V data_plot=cbind(data_H_d(),V_lci,V_uci) h_trans = ggplot(data_plot) h_trans = ggplot(data_plot,aes(x=timevar, y=V, color=factor(as.factor(cov_factor),levels=labels_cov()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Hazard: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor),levels=labels_cov()))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) h_trans = h_trans+geom_line(aes(x=timevar, y=V, fill=factor(as.factor(cov_factor),levels=labels_cov()))) h_trans=h_trans+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=as.factor(cov_factor)),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) h_trans = h_trans + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) + scale_y_continuous(breaks=c(seq(input$starthy,input$endhy,by=input$stephy ))) h_trans = h_trans +labs(title=paste0(hazname," for each covariate pattern among states"), x=paste0("Time since ",input$origin," entry"), y=hazname) h_trans = h_trans + labs(color = "Covariate \n patterns")+ labs(fill = "Covariate \n patterns") h_trans = h_trans +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5)) h_trans = h_trans+ facet_wrap(~trans_factor,nrow = NULL,ncol = NULL) h_trans = ggplotly(h_trans, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) h_trans } } else if (input$scaleh=="log") { if (input$faceth=="No") { h_trans= plot_ly(data_H_d(),alpha=0.5) %>% add_lines( x=data_H_d()$timevar,y=log(data_H_d()$V), frame=as.factor(data_H_d()$trans_factor), color=factor(as.factor(data_H_d()$cov_factor),levels=labels_cov()), colors=labels_colour_cov(), mode="lines", line=list(simplify=FALSE), color=labels_colour_cov(), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) h_trans <- add_trace(h_trans, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_H_ci()$x, y=log(data_H_ci()$y), frame=as.factor(data_H_ci()$frameto), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=as.factor(data_H_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") )%>% layout(title=list(text=paste0(hazname," (log scale) for each covariate pattern among states"),y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= paste0(hazname," (log scale)"), dtick = input$logstephy, tick0 = input$logstarthy, range=c(input$logstarthy,input$logendhy), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) h_trans } if (input$faceth=="Yes") { V_lci= data_H_d_lci()$V V_uci= data_H_d_uci()$V data_plot=cbind(data_H_d(),V_lci,V_uci) h_trans = ggplot(data_plot) h_trans = ggplot(data_plot,aes(x=timevar, y=log(V), color=factor(as.factor(cov_factor),levels=labels_cov()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Hazard: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor),levels=labels_cov()))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) h_trans = h_trans+geom_line(aes(x=timevar, y=log(V), fill=factor(as.factor(cov_factor),levels=labels_cov()))) h_trans=h_trans+ geom_ribbon(aes(ymin = log(V_lci), ymax =log(V_uci),fill=as.factor(cov_factor)),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) h_trans = h_trans + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) + scale_y_continuous(breaks=c(seq(input$logstarthy,input$logendhy,by=input$logstephy ))) h_trans = h_trans +labs(title=paste0(hazname," for each covariate pattern among states"), x=paste0("Time since ",input$origin," entry"), y=hazname) h_trans = h_trans + labs(color = "Covariate \n patterns")+ labs(fill = "Covariate \n patterns") h_trans = h_trans +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5)) h_trans = h_trans+ facet_wrap(~trans_factor,nrow = NULL,ncol = NULL) h_trans = ggplotly(h_trans, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } #facet } #scale } h_trans }) output$hazards_trans <- renderPlotly ({ datah1_re() }) output$downploth1 <- downloadHandler( filename = function(){paste("h1",'.png',sep='')}, content = function(file){ plotly_IMAGE( datah1_re(),width = 1200, height = 900, format = "png", scale = 2, out_file = file ) } ) ######################################################################### ########################################################################## data_H_diff1 <- reactive ({ haz_diff=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$hazd)) { haz_diff[[i]]=as.data.frame(t(data.frame(myjson2()$hazd[i]))) colnames(haz_diff[[i]]) <- v_diff } for(i in 1:length(myjson2()$hazd)) { haz_diff[[i]]=as.data.frame(cbind(haz_diff[[i]], timevar ,trans=rep(i,nrow(haz_diff[[i]] )) )) } } else { for (i in 1:length(myjson2()$hazd)) { haz_diff[[i]]=as.data.frame(myjson2()$hazd[[i]][,1]) # colnames(P_diff[[i]]) <- v_diff } for (i in 1:length(myjson2()$hazd)) { haz_diff[[i]]=as.data.frame(c(haz_diff[[i]], timevar ,trans=rep(i,ncol(as.data.frame(myjson2()$hazd[[i]][,1])) )) ) colnames(haz_diff[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_hazd=list() data_hazd[[1]]=haz_diff[[1]] if (length(myjson2()$hazd)>1) { for (u in 2:(length(myjson2()$hazd))) { data_hazd[[u]]=rbind(haz_diff[[u]],data_hazd[[(u-1)]]) } } datahazd=data_hazd[[length(myjson2()$hazd)]] datahazd$trans_fac=c(rep("NA",nrow(datahazd))) for (o in 1:(length(myjson2()$hazd))) { for (g in 1:nrow(datahazd)) { if (datahazd$trans[g]==o) {datahazd$trans_fac[g]=labels_trans()[o]} } } datahazd }) data_H_diff1_uci <- reactive ({ haz_diff_uci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$hazd_uci)) { haz_diff_uci[[i]]=as.data.frame(t(data.frame(myjson2()$hazd_uci[i]))) colnames(haz_diff_uci[[i]]) <- v_diff } for(i in 1:length(myjson2()$hazd_uci)) { haz_diff_uci[[i]]=as.data.frame(cbind(haz_diff_uci[[i]], timevar ,trans=rep(i,nrow(haz_diff_uci[[i]] )) )) } } else { for (i in 1:length(myjson2()$hazd_uci)) { haz_diff_uci[[i]]=as.data.frame(myjson2()$hazd_uci[[i]][,1]) # colnames(P_diff[[i]]) <- v_diff } for (i in 1:length(myjson2()$hazd_uci)) { haz_diff_uci[[i]]=as.data.frame(c(haz_diff_uci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$hazd_uci[[i]][,1])) )) ) colnames(haz_diff_uci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_hazd_uci=list() data_hazd_uci[[1]]=haz_diff_uci[[1]] if (length(myjson2()$hazd_uci)>1) { for (u in 2:(length(myjson2()$hazd_uci))) { data_hazd_uci[[u]]=rbind(haz_diff_uci[[u]],data_hazd_uci[[(u-1)]]) } } datahazd_uci=data_hazd_uci[[length(myjson2()$hazd_uci)]] datahazd_uci$trans_fac=c(rep("NA",nrow(datahazd_uci))) for (o in 1:(length(myjson2()$hazd_uci))) { for (g in 1:nrow(datahazd_uci)) { if (datahazd_uci$trans[g]==o) {datahazd_uci$trans_fac[g]=labels_trans()[o]} } } datahazd_uci }) data_H_diff1_lci <- reactive ({ haz_diff_lci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$hazd_lci)) { haz_diff_lci[[i]]=as.data.frame(t(data.frame(myjson2()$hazd_lci[i]))) colnames(haz_diff_lci[[i]]) <- v_diff } for(i in 1:length(myjson2()$hazd_lci)) { haz_diff_lci[[i]]=as.data.frame(cbind(haz_diff_lci[[i]], timevar ,trans=rep(i,nrow(haz_diff_lci[[i]] )) )) } } else { for (i in 1:length(myjson2()$hazd_lci)) { haz_diff_lci[[i]]=as.data.frame(myjson2()$hazd_lci[[i]][,1]) # colnames(P_diff[[i]]) <- v_diff } for (i in 1:length(myjson2()$hazd_lci)) { haz_diff_lci[[i]]=as.data.frame(c(haz_diff_lci[[i]], timevar ,trans=rep(i,ncol(as.data.frame(myjson2()$hazd_lci[[i]][,1])) )) ) colnames(haz_diff_lci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_hazd_lci=list() data_hazd_lci[[1]]=haz_diff_lci[[1]] if (length(myjson2()$hazd_lci)>1) { for (u in 2:(length(myjson2()$hazd_lci))) { data_hazd_lci[[u]]=rbind(haz_diff_lci[[u]],data_hazd_lci[[(u-1)]]) } } datahazd_lci=data_hazd_lci[[length(myjson2()$hazd_lci)]] datahazd_lci$trans_fac=c(rep("NA",nrow(datahazd_lci))) for (o in 1:(length(myjson2()$hazd_lci))) { for (g in 1:nrow(datahazd_lci)) { if (datahazd_lci$trans[g]==o) {datahazd_lci$trans_fac[g]=labels_trans()[o]} } } datahazd_lci }) data_H_diff2<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_H_diff1()[,d],data_H_diff1()[,ncol(data_H_diff1())-2],data_H_diff1()[,ncol(data_H_diff1())-1], data_H_diff1()[,ncol(data_H_diff1())],rep(d,length(data_H_diff1()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_H_diff1())[d],length(data_H_diff1()[,d])) colnames(dlist[[d]]) <- c("V","timevar","trans","trans_factor","cov","cov_factor") } d_all_Hd <- bind_rows(dlist, .id = "column_label") d_all_Hd }) data_H_diff2_uci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_H_diff1_uci()[,d],data_H_diff1_uci()[,ncol(data_H_diff1_uci())-2],data_H_diff1_uci()[,ncol(data_H_diff1_uci())-1], data_H_diff1_uci()[,ncol(data_H_diff1_uci())],rep(d,length(data_H_diff1_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_H_diff1_uci())[d],length(data_H_diff1_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","trans","trans_factor","cov","cov_factor") } d_all_Hd_uci <- bind_rows(dlist, .id = "column_label") d_all_Hd_uci }) data_H_diff2_lci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_H_diff1_lci()[,d],data_H_diff1_lci()[,ncol(data_H_diff1_lci())-2],data_H_diff1_lci()[,ncol(data_H_diff1_lci())-1], data_H_diff1_lci()[,ncol(data_H_diff1_lci())],rep(d,length(data_H_diff1_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_H_diff1_lci())[d],length(data_H_diff1_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","trans","trans_factor","cov","cov_factor") } d_all_Hd_lci <- bind_rows(dlist, .id = "column_label") d_all_Hd_lci }) data_H_diff_ci<- reactive ({ x=c( data_H_diff2()[order(data_H_diff2()$timevar,data_H_diff2()$trans,data_H_diff2()$cov),]$timevar, data_H_diff2()[order(-data_H_diff2()$timevar,data_H_diff2()$trans,data_H_diff2()$cov),]$timevar ) y_central=c( data_H_diff2()[order(data_H_diff2()$timevar,data_H_diff2()$trans,data_H_diff2()$cov),]$V, data_H_diff2()[order(-data_H_diff2()$timevar,data_H_diff2()$trans,data_H_diff2()$cov),]$V ) y=c( data_H_diff2_uci()[order(data_H_diff2_uci()$timevar,data_H_diff2_uci()$trans,data_H_diff2_uci()$cov),]$V, data_H_diff2_lci()[order(-data_H_diff2_lci()$timevar,data_H_diff2_lci()$trans,data_H_diff2_lci()$cov),]$V ) frameto=c(as.character(data_H_diff2_uci()[order(-data_H_diff2_uci()$timevar,data_H_diff2_uci()$trans,data_H_diff2_uci()$cov),]$trans_factor), as.character(data_H_diff2_lci()[order(-data_H_diff2_lci()$timevar,data_H_diff2_lci()$trans,data_H_diff2_lci()$cov),]$trans_factor) ) covto=c( data_H_diff2_uci()[order(-data_H_diff2_uci()$timevar,data_H_diff2_uci()$trans,data_H_diff2_uci()$cov),]$cov_factor, data_H_diff2_lci()[order(-data_H_diff2_lci()$timevar,data_H_diff2_lci()$trans,data_H_diff2_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) #output$shouldloadh2 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downploth2", label = h2("Download the plot")) #}) datah2_re <- reactive ({ ax <- list( title = "",zeroline = FALSE,showline = FALSE, showticklabels = FALSE, showgrid = FALSE) if (length(myjson2()$hazd) == 0| myjson2()$Nats==1 ) { haz_trans_d= plot_ly() %>% layout(title=list(text="Not applicable- Only one covariate pattern specified",y=0.95),xaxis=ax, yaxis=ax) haz_trans_d } else { if (input$confh=="ci_no") { if (input$faceth=="No") { haz_trans_d= plot_ly(data_H_diff2(),alpha=0.5) %>% add_lines( x=data_H_diff2()$timevar,y=data_H_diff2()$V, frame=factor(as.factor(data_H_diff2()$trans_factor),levels=labels_trans() ), color=as.factor(data_H_diff2()$cov_factor), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text="Differences in hazards among covariate patterns (compared to reference)",y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Differences of hazards for each transition", dtick = input$stephy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) haz_trans_d } if (input$faceth=="Yes") { data_plot=data_H_diff2() haz_trans_d = ggplot(data_plot) haz_trans_d = ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(cov_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Difference of probabilities: ", V, "
Covariate pattern: ", as.factor(cov_factor)))) haz_trans_d = haz_trans_d+geom_line(aes(x=timevar, y=V, color= as.factor(cov_factor)))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) haz_trans_d = haz_trans_d+ facet_wrap(~factor(as.factor(trans_factor),levels=labels_trans() )) haz_trans_d = haz_trans_d + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) haz_trans_d = haz_trans_d + scale_y_continuous(breaks=c(seq(min(data_plot$V[which(!is.na(data_plot$V))]),max(data_plot$V[which(!is.na(data_plot$V))]), by=input$stephy ))) haz_trans_d = haz_trans_d +labs(title="Differences in hazards among covariate patterns (compared to reference)", x=paste0("Time since ",input$origin," entry"), y="Differences of hazards") haz_trans_d = haz_trans_d + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") haz_trans_d = haz_trans_d +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color=input$textcolourp, size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5)) haz_trans_d = ggplotly(haz_trans_d, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } else if (input$confh=="ci_yes") { if (input$faceth=="No") { haz_trans_d <- plot_ly() haz_trans_d <- add_trace(haz_trans_d, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_H_diff_ci()$x, y=data_H_diff_ci()$y_central, frame=factor(as.factor(data_H_diff_ci()$frameto),levels=labels_trans()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_H_diff_ci()$covto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) haz_trans_d <- add_trace(haz_trans_d, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_H_diff_ci()$x, y=data_H_diff_ci()$y, frame=factor(as.factor(data_H_diff_ci()$frameto),levels=labels_trans()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_H_diff_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) haz_trans_d= haz_trans_d %>% layout(title=list(text="Differences in hazards among covariate patterns (compared to reference)",y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Differences of hazards for each transition", dtick = input$stephy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$faceth=="Yes") { V_lci= data_H_diff2_lci()$V V_uci= data_H_diff2_uci()$V data_plot=cbind(data_H_diff2(),V_lci,V_uci) haz_trans_d=ggplot(data_plot) haz_trans_d=ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(cov_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Difference of hazards for each transition: ", V, "
Covariate pattern: ", as.factor(cov_factor)))) haz_trans_d=haz_trans_d+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) haz_trans_d=haz_trans_d+geom_line(aes(x=timevar, y=V, fill= as.factor(cov_factor))) haz_trans_d=haz_trans_d+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=as.factor(cov_factor)),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) haz_trans_d = haz_trans_d+ facet_wrap(~factor(as.factor(trans_factor),levels=labels_trans()) ) haz_trans_d = haz_trans_d + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) haz_trans_d = haz_trans_d + scale_y_continuous(breaks=c(seq(min(data_plot$V_lci[which(!is.na(data_plot$V_lci))]),max(data_plot$V_uci[which(!is.na(data_plot$V_uci))]),by=input$stephy ))) haz_trans_d = haz_trans_d +labs(title="Differences in hazards among covariate patterns (compared to reference)", x=paste0("Time since ",input$origin," entry"), y="Differences of hazards for each transition") haz_trans_d = haz_trans_d + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") haz_trans_d = haz_trans_d +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5), axis.text.x = element_text( size=input$textsizeh-6),axis.text.y = element_text( size=input$textsizeh-6)) haz_trans_d = ggplotly(haz_trans_d, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } haz_trans_d } }) output$H_diff <- renderPlotly ({datah2_re() }) output$downploth2 <- downloadHandler( filename = function(){paste("h2",'.png',sep='')}, content = function(file){ plotly_IMAGE( datah2_re(),width = 1200, height = 900, format = "png", scale = 2, out_file = file ) } ) ############################################################################################################ ########################################################################################################### ###################################################################################################################################### ###################################################################################################################################### data_H_ratio1 <- reactive ({ haz_ratio=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$hazr)) { haz_ratio[[i]]=as.data.frame(t(data.frame(myjson2()$hazr[i]))) colnames(haz_ratio[[i]]) <- v_ratio } for(i in 1:length(myjson2()$hazr)) { haz_ratio[[i]]=as.data.frame(cbind(haz_ratio[[i]], timevar ,trans=rep(i,nrow(haz_ratio[[i]] )) )) } } else { for (i in 1:length(myjson2()$hazr)) { haz_ratio[[i]]=as.data.frame(myjson2()$hazr[[i]][,1]) # colnames(P_ratio[[i]]) <- v_ratio } for (i in 1:length(myjson2()$hazr)) { haz_ratio[[i]]=as.data.frame(c(haz_ratio[[i]], timevar ,trans=rep(i,ncol(as.data.frame(myjson2()$hazr[[i]][,1])) )) ) colnames(haz_ratio[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the ratioerent states data_hazr=list() data_hazr[[1]]=haz_ratio[[1]] if (length(myjson2()$hazr)>1) { for (u in 2:(length(myjson2()$hazr))) { data_hazr[[u]]=rbind(haz_ratio[[u]],data_hazr[[(u-1)]]) } } datahazr=data_hazr[[length(myjson2()$hazr)]] datahazr$trans_fac=c(rep("NA",nrow(datahazr))) for (o in 1:(length(myjson2()$hazr))) { for (g in 1:nrow(datahazr)) { if (datahazr$trans[g]==o) {datahazr$trans_fac[g]=labels_trans()[o]} } } datahazr }) data_H_ratio1_uci <- reactive ({ haz_ratio_uci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$hazr_uci)) { haz_ratio_uci[[i]]=as.data.frame(t(data.frame(myjson2()$hazr_uci[i]))) colnames(haz_ratio_uci[[i]]) <- v_ratio } for(i in 1:length(myjson2()$hazr_uci)) { haz_ratio_uci[[i]]=as.data.frame(cbind(haz_ratio_uci[[i]], timevar ,trans=rep(i,nrow(haz_ratio_uci[[i]] )) )) } } else { for (i in 1:length(myjson2()$hazr_uci)) { haz_ratio_uci[[i]]=as.data.frame(myjson2()$hazr_uci[[i]][,1]) # colnames(P_ratio[[i]]) <- v_ratio } for (i in 1:length(myjson2()$hazr_uci)) { haz_ratio_uci[[i]]=as.data.frame(c(haz_ratio_uci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$hazr_uci[[i]][,1])) )) ) colnames(haz_ratio_uci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the ratioerent states data_hazr_uci=list() data_hazr_uci[[1]]=haz_ratio_uci[[1]] if (length(myjson2()$hazr_uci)>1) { for (u in 2:(length(myjson2()$hazr_uci))) { data_hazr_uci[[u]]=rbind(haz_ratio_uci[[u]],data_hazr_uci[[(u-1)]]) } } datahazr_uci=data_hazr_uci[[length(myjson2()$hazr_uci)]] datahazr_uci$trans_fac=c(rep("NA",nrow(datahazr_uci))) for (o in 1:(length(myjson2()$hazr_uci))) { for (g in 1:nrow(datahazr_uci)) { if (datahazr_uci$trans[g]==o) {datahazr_uci$trans_fac[g]=labels_trans()[o]} } } datahazr_uci }) data_H_ratio1_lci <- reactive ({ haz_ratio_lci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$hazr_lci)) { haz_ratio_lci[[i]]=as.data.frame(t(data.frame(myjson2()$hazr_lci[i]))) colnames(haz_ratio_lci[[i]]) <- v_ratio } for(i in 1:length(myjson2()$hazr_lci)) { haz_ratio_lci[[i]]=as.data.frame(cbind(haz_ratio_lci[[i]], timevar ,trans=rep(i,nrow(haz_ratio_lci[[i]] )) )) } } else { for (i in 1:length(myjson2()$hazr_lci)) { haz_ratio_lci[[i]]=as.data.frame(myjson2()$hazr_lci[[i]][,1]) # colnames(P_ratio[[i]]) <- v_ratio } for (i in 1:length(myjson2()$hazr_lci)) { haz_ratio_lci[[i]]=as.data.frame(c(haz_ratio_lci[[i]], timevar ,trans=rep(i,ncol(as.data.frame(myjson2()$hazr_lci[[i]][,1])) )) ) colnames(haz_ratio_lci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the ratioerent states data_hazr_lci=list() data_hazr_lci[[1]]=haz_ratio_lci[[1]] if (length(myjson2()$hazr_lci)>1) { for (u in 2:(length(myjson2()$hazr_lci))) { data_hazr_lci[[u]]=rbind(haz_ratio_lci[[u]],data_hazr_lci[[(u-1)]]) } } datahazr_lci=data_hazr_lci[[length(myjson2()$hazr_lci)]] datahazr_lci$trans_fac=c(rep("NA",nrow(datahazr_lci))) for (o in 1:(length(myjson2()$hazr_lci))) { for (g in 1:nrow(datahazr_lci)) { if (datahazr_lci$trans[g]==o) {datahazr_lci$trans_fac[g]=labels_trans()[o]} } } datahazr_lci }) data_H_ratio2<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_H_ratio1()[,d],data_H_ratio1()[,ncol(data_H_ratio1())-2],data_H_ratio1()[,ncol(data_H_ratio1())-1], data_H_ratio1()[,ncol(data_H_ratio1())],rep(d,length(data_H_ratio1()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_H_ratio1())[d],length(data_H_ratio1()[,d])) colnames(dlist[[d]]) <- c("V","timevar","trans","trans_factor","cov","cov_factor") } d_all_Hr <- bind_rows(dlist, .id = "column_label") d_all_Hr }) data_H_ratio2_uci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_H_ratio1_uci()[,d],data_H_ratio1_uci()[,ncol(data_H_ratio1_uci())-2],data_H_ratio1_uci()[,ncol(data_H_ratio1_uci())-1], data_H_ratio1_uci()[,ncol(data_H_ratio1_uci())],rep(d,length(data_H_ratio1_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_H_ratio1_uci())[d],length(data_H_ratio1_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","trans","trans_factor","cov","cov_factor") } d_all_Hr_uci <- bind_rows(dlist, .id = "column_label") d_all_Hr_uci }) data_H_ratio2_lci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_H_ratio1_lci()[,d],data_H_ratio1_lci()[,ncol(data_H_ratio1_lci())-2],data_H_ratio1_lci()[,ncol(data_H_ratio1_lci())-1], data_H_ratio1_lci()[,ncol(data_H_ratio1_lci())],rep(d,length(data_H_ratio1_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_H_ratio1_lci())[d],length(data_H_ratio1_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","trans","trans_factor","cov","cov_factor") } d_all_Hr_lci <- bind_rows(dlist, .id = "column_label") d_all_Hr_lci }) data_H_ratio_ci<- reactive ({ x=c( data_H_ratio2()[order(data_H_ratio2()$timevar,data_H_ratio2()$trans,data_H_ratio2()$cov),]$timevar, data_H_ratio2()[order(-data_H_ratio2()$timevar,data_H_ratio2()$trans,data_H_ratio2()$cov),]$timevar ) y_central=c( data_H_ratio2()[order(data_H_ratio2()$timevar,data_H_ratio2()$trans,data_H_ratio2()$cov),]$V, data_H_ratio2()[order(-data_H_ratio2()$timevar,data_H_ratio2()$trans,data_H_ratio2()$cov),]$V ) y=c( data_H_ratio2_uci()[order(data_H_ratio2_uci()$timevar,data_H_ratio2_uci()$trans,data_H_ratio2_uci()$cov),]$V, data_H_ratio2_lci()[order(-data_H_ratio2_lci()$timevar,data_H_ratio2_lci()$trans,data_H_ratio2_lci()$cov),]$V ) frameto=c(as.character(data_H_ratio2_uci()[order(-data_H_ratio2_uci()$timevar,data_H_ratio2_uci()$trans,data_H_ratio2_uci()$cov),]$trans_factor), as.character(data_H_ratio2_lci()[order(-data_H_ratio2_lci()$timevar,data_H_ratio2_lci()$trans,data_H_ratio2_lci()$cov),]$trans_factor) ) covto=c( data_H_ratio2_uci()[order(-data_H_ratio2_uci()$timevar,data_H_ratio2_uci()$trans,data_H_ratio2_uci()$cov),]$cov_factor, data_H_ratio2_lci()[order(-data_H_ratio2_lci()$timevar,data_H_ratio2_lci()$trans,data_H_ratio2_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) #output$shouldloadh3 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downploth3", label = h2("Download the plot")) #}) datah3_re <- reactive ({ ax <- list( title = "",zeroline = FALSE,showline = FALSE, showticklabels = FALSE, showgrid = FALSE) if (length(myjson2()$hazr) == 0| myjson2()$Nats==1 ) { haz_trans_r= plot_ly() %>% layout(title=list(text="Not applicable- Only one covariate pattern specified",y=0.95),xaxis=ax, yaxis=ax) haz_trans_r } else { if (input$confh=="ci_no") { if (input$faceth=="No") { haz_trans_r= plot_ly(data_H_ratio2(),alpha=0.5) %>% add_lines( x=data_H_ratio2()$timevar,y=data_H_ratio2()$V, frame=factor(as.factor(data_H_ratio2()$trans_factor),levels=labels_trans()), color=as.factor(data_H_ratio2()$cov_factor), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text="Ratio of hazards for each transition among covariate patterns (compared to ref. cov pattern)",y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Ratio of hazards for each transition", dtick = input$stephy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) %>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$faceth=="Yes") { data_plot=data_H_ratio2() haz_trans_r = ggplot(data_plot) haz_trans_r = ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(cov_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Ratio of probabilities: ", V, "
Covariate pattern: ", as.factor(cov_factor)))) haz_trans_r = haz_trans_r+geom_line(aes(x=timevar, y=V, color= as.factor(cov_factor)))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) haz_trans_r = haz_trans_r+ facet_wrap(~factor(as.factor(trans_factor),levels=labels_trans())) haz_trans_r = haz_trans_r + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) + scale_y_continuous(breaks=c(seq(input$starthy,input$endhy,by=input$stephy ))) haz_trans_r = haz_trans_r +labs(title="Ratio of hazards among covariate patterns (compared to ref. cov pattern)", x=paste0("Time since ",input$origin," entry"), y="Ratio of hazards") haz_trans_r = haz_trans_r + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") haz_trans_r = haz_trans_r +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5), axis.text.x = element_text( size=input$textsizeh-6),axis.text.y = element_text( size=input$textsizeh-6)) haz_trans_r = ggplotly(haz_trans_r, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } else if (input$confh=="ci_yes") { if (input$faceth=="No") { haz_trans_r <- plot_ly() haz_trans_r <- add_trace(haz_trans_r, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_H_ratio_ci()$x, y=data_H_ratio_ci()$y_central, frame=factor(as.factor(data_H_ratio_ci()$frameto),levels=labels_trans()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_H_ratio_ci()$covto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) haz_trans_r <- add_trace(haz_trans_r, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_H_ratio_ci()$x, y=data_H_ratio_ci()$y, frame=factor(as.factor(data_H_ratio_ci()$frameto),levels=labels_trans()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_H_ratio_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) haz_trans_r= haz_trans_r %>% layout(title=list(text="Ratio of hazards for each transition among covariate patterns (compared to ref. cov pattern)",y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Ratio of hazards for each transition", dtick = input$stephy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$faceth=="Yes") { V_lci= data_H_ratio2_lci()$V V_uci= data_H_ratio2_uci()$V data_plot=cbind(data_H_ratio2(),V_lci,V_uci) haz_trans_r=ggplot(data_plot) haz_trans_r=ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(cov_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Ratio of hazards for each transition: ", V, "
Covariate pattern: ", as.factor(cov_factor)))) haz_trans_r=haz_trans_r+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) haz_trans_r=haz_trans_r+geom_line(aes(x=timevar, y=V, fill= as.factor(cov_factor))) haz_trans_r=haz_trans_r+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=as.factor(cov_factor)),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) haz_trans_r = haz_trans_r+ facet_wrap(~factor(as.factor(trans_factor),levels=labels_trans())) haz_trans_r = haz_trans_r + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx )))+ scale_y_continuous(breaks=c(seq(input$starthy,input$endhy,by=input$stephy ))) haz_trans_r = haz_trans_r +labs(title="Ratio of hazards for each transition among covariate patterns (compared to ref. cov pattern)", x=paste0("Time since ",input$origin," entry"), y="Ratio of hazards for each transition") haz_trans_r = haz_trans_r + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") haz_trans_r = haz_trans_r +theme(title = element_text(size = input$textsizeh-4),strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5), axis.text.x = element_text( size=input$textsizeh-6),axis.text.y = element_text( size=input$textsizeh-6)) haz_trans_r = ggplotly(haz_trans_r, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } haz_trans_r } }) output$H_ratio <- renderPlotly ({ datah3_re() }) output$downploth3 <- downloadHandler( filename = function(){paste("h3",'.png',sep='')}, content = function(file){ # svg <- plotly_gadget(datah3_re()) # # png_gadget <- tempfile(fileext=".png") # # rsvg_png(charToRaw(svg), png_gadget) # # png_gadget plotly_IMAGE( datah3_re(),width = 1200, height = 900, format = "png", scale = 2, out_file = file ) } ) ########################################################################################################################## ########################################################################################################################## #output$shouldloadh4 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downploth4", label = h2("Download the plot")) #}) datah4_re <- reactive ({ if (input$confh=="ci_no") { if (is.null(myjson2()$is.cumhaz)==FALSE) { if (myjson2()$is.cumhaz==1) {hazname= "Cummulative hazards"} else if (myjson2()$is.cumhaz!=1) {hazname= "Transition intensity rate"} } if (is.null(myjson2()$is.cumhaz)==TRUE) {hazname="Transition intensity rate"} if (input$scaleh=="log") { if (input$faceth=="No") { h_cov= plot_ly(data_H_d(),alpha=0.5) %>% add_lines( x=data_H_d()$timevar,y=log(data_H_d()$V), frame=factor(as.factor(data_H_d()$cov_factor),levels=labels_cov()), color=as.factor(data_H_d()$trans_factor), colors=labels_colour_trans()[1:length(levels(data_H_d()$trans_factor))], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text=paste0(hazname," among covariate patterns"),y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= paste0(hazname," (log scale)"), dtick = input$logstephy, tick0 = input$logstarthy, range=c(input$logstarthy,input$logendhy), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { h_cov= h_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } h_cov } if (input$faceth=="Yes") { data_plot=data_H_d() h_cov = ggplot(data_plot) h_cov = ggplot(data_plot,aes(x=timevar, y=log(V), color= as.factor(trans_factor))) h_cov = h_cov+geom_line(aes(x=timevar, y=log(V), color= as.factor(trans_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Log hazards: ", log(V), "
Transition: ", as.factor(trans_factor)))) h_cov = h_cov + scale_colour_manual( values =labels_colour_trans(),labels = labels_trans() ) h_cov = h_cov+ facet_wrap(~factor(as.factor(cov_factor),levels=labels_cov())) h_cov = h_cov + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) # scale_y_continuous(breaks=c(seq(input$logstarthy,input$logendhy,by=input$logstephy ))) h_cov = h_cov + scale_y_continuous(breaks=c(seq(input$logstarthy,input$logendhy,by=input$logstephy ))) h_cov = h_cov +labs(title=paste0(hazname," for each state among covariate patterns"), x=paste0("Time since ",input$origin," entry"), y=paste0(hazname," (log scale)")) h_cov = h_cov + labs(color = "States")+ labs(fill = "States") h_cov = h_cov +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5), axis.text.x = element_text( size=input$textsizeh-6),axis.text.y = element_text( size=input$textsizeh-6)) h_cov = ggplotly(h_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) h_cov } } if (input$scaleh=="normal") { if (input$faceth=="No") { h_cov= plot_ly(data_H_d(),alpha=0.5) %>% add_lines( x=data_H_d()$timevar,y=data_H_d()$V, frame=factor(as.factor(data_H_d()$cov_factor),levels=labels_cov()), color=as.factor(data_H_d()$trans_factor), colors=labels_colour_trans()[1:length(levels(data_H_d()$trans_factor))], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text=paste0(hazname," for each state among covariate patterns"),y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= hazname, rangemode = "nonnegative", tick0 = input$starthy, dtick = input$stephy, range=c(input$starthy,input$endhy), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { h_cov= h_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } h_cov } if (input$faceth=="Yes") { data_plot=data_H_d() h_cov = ggplot(data_plot) h_cov = ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(trans_factor))) h_cov = h_cov+geom_line(aes(x=timevar, y=V, color= as.factor(trans_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Hazard: ", V, "
Transition: ", as.factor(trans_factor))))+ scale_colour_manual( values =labels_colour_trans(),labels = labels_trans() ) h_cov = h_cov+ facet_wrap(~factor(as.factor(cov_factor),levels=labels_cov()), ncol=2) h_cov = h_cov + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) #scale_y_continuous(breaks=c(seq(input$starthy,input$endhy,by=input$stephy ))) # h_cov = h_cov + scale_y_continuous(breaks=c(seq(min(data_plot$V[which(!is.na(data_plot$V))]),max(data_plot$V[which(!is.na(data_plot$V))]), by=input$stephy ))) h_cov = h_cov + scale_y_continuous(breaks=c(seq(input$starthy,input$endhy,by=input$stephy ))) h_cov = h_cov +labs(title=paste0(hazname," for each state among covariate patterns"), x=paste0("Time since ",input$origin," entry"), y=hazname) h_cov = h_cov + labs(color = "States")+ labs(fill = "States") h_cov = h_cov +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5), axis.text.x = element_text( size=input$textsizeh-6),axis.text.y = element_text( size=input$textsizeh-6)) h_cov = ggplotly(h_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) h_cov } } } if (input$confh=="ci_yes") { if (is.null(myjson2()$is.cumhaz)==FALSE) { if (myjson2()$is.cumhaz==1) {hazname= "Cummulative hazards"} else if (myjson2()$is.cumhaz!=1) {hazname= "Transition intensity rate"} } if (is.null(myjson2()$is.cumhaz)==TRUE) {hazname="Transition intensity rate"} if (input$scaleh=="log") { if (input$faceth=="No") { h_cov= plot_ly(data_H_d(),alpha=0.5) %>% add_lines( x=data_H_d()$timevar,y=log(data_H_d()$V), frame=factor(as.factor(data_H_d()$cov_factor),levels=labels_cov()), color=as.factor(data_H_d()$trans_factor), colors=labels_colour_trans()[1:length(levels(data_H_d()$trans_factor))], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) h_cov <- add_trace(h_cov, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_H_ci()$x, y=log(data_H_ci()$y), frame=factor(as.factor(data_H_ci()$covto),levels=labels_cov()), colors=labels_colour_trans(), color=as.factor(data_H_ci()$frameto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text=paste0(hazname," among covariate patterns"),y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= paste0(hazname," (log scale)"), dtick = input$logstephy, tick0 = input$logstarthy, range=c(input$logstarthy,input$logendhy), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { h_cov= h_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } h_cov } if (input$faceth=="Yes") { V_lci= data_H_d_lci()$V V_uci= data_H_d_uci()$V data_plot=cbind(data_H_d(),V_lci,V_uci) h_cov = ggplot(data_plot) h_cov =ggplot(data_plot,aes(x=timevar, y=log(V), color= as.factor(trans_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Log hazards: ", log(V), "
Transition: ", as.factor(trans_factor))))+ scale_colour_manual( values =labels_colour_trans(),labels = labels_trans() ) h_cov=h_cov+geom_line(aes(x=timevar, y=log(V), fill= as.factor(trans_factor))) h_cov=h_cov+ geom_ribbon(aes(ymin = log(V_lci), ymax =log(V_uci),fill=as.factor(trans_factor)),alpha=0.4)+ scale_fill_manual( values =labels_colour_trans(),labels = labels_trans() ) h_cov = h_cov+ facet_wrap(~factor(as.factor(cov_factor),levels=labels_cov())) h_cov = h_cov + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) #scale_y_continuous(breaks=c(seq(input$logstarthy,input$logendhy,by=input$logstephy ))) # h_cov = h_cov + scale_y_continuous(breaks=c(seq(min(data_plot$V_lci[which(!is.na(data_plot$V_lci))]),max(data_plot$V_uci[which(!is.na(data_plot$V_uci))]),by=input$logstephy ))) h_cov = h_cov + scale_y_continuous(breaks=c(seq(input$logstarthy,input$logendhy,by=input$logstephy ))) h_cov = h_cov +labs(title=paste0(hazname," for each state among covariate patterns"), x=paste0("Time since ",input$origin," entry"), y=paste0(hazname," (log scale)")) h_cov = h_cov + labs(color = "Transitions")+ labs(fill = "Transitions") h_cov = h_cov +theme(title = element_text(size = input$textsizeh-4),strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5), axis.text.x = element_text( size=input$textsizeh-6),axis.text.y = element_text( size=input$textsizeh-6)) h_cov = ggplotly(h_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) h_cov } #facet } #scale if (input$scaleh=="normal") { if (input$faceth=="No") { h_cov= plot_ly(data_H_d(),alpha=0.5) %>% add_lines( x=data_H_d()$timevar,y=data_H_d()$V, frame=factor(as.factor(data_H_d()$cov_factor),levels=labels_cov()), color=as.factor(data_H_d()$trans_factor), colors=labels_colour_trans()[1:length(levels(data_H_d()$trans_factor))], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) h_cov <- add_trace(h_cov, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_H_ci()$x, y=data_H_ci()$y, frame=factor(as.factor(data_H_ci()$covto),levels=labels_cov()), colors=labels_colour_trans(), color=as.factor(data_H_ci()$frameto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text=paste0(hazname," for each state among covariate patterns"),y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= hazname, rangemode = "nonnegative", tick0 = input$starthy, dtick = input$stephy, range=c(input$starthy,input$endhy), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { h_cov= h_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } h_cov } if (input$faceth=="Yes") { V_lci= data_H_d_lci()$V V_uci= data_H_d_uci()$V data_plot=cbind(data_H_d(),V_lci,V_uci) h_cov = ggplot(data_plot) h_cov =ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(trans_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Log hazards: ", V, "
Transition: ", as.factor(trans_factor))))+ scale_colour_manual( values =labels_colour_trans(),labels = labels_trans() ) h_cov=h_cov+geom_line(aes(x=timevar, y=V, fill= as.factor(trans_factor))) h_cov=h_cov+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=as.factor(trans_factor)),alpha=0.4)+ scale_fill_manual( values =labels_colour_trans(),labels = labels_trans() ) h_cov = h_cov+ facet_wrap(~factor(as.factor(cov_factor),levels=labels_cov()), ncol=2) h_cov = h_cov + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) #scale_y_continuous(breaks=c(seq(input$starthy,input$endhy,by=input$stephy ))) # h_cov = h_cov + scale_y_continuous(breaks=c(seq(min(data_plot$V_lci[which(!is.na(data_plot$V_lci))]),max(data_plot$V_uci[which(!is.na(data_plot$V_uci))]),by=input$stephy ))) h_cov = h_cov + scale_y_continuous(breaks=c(seq(input$starthy,input$endhy,by=input$stephy ))) h_cov = h_cov +labs(title=paste0(hazname," for each state among covariate patterns"), x=paste0("Time since ",input$origin," entry"), y=hazname) h_cov = h_cov + labs(color = "Transitions")+ labs(fill = "Transitions") h_cov = h_cov +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5), axis.text.x = element_text( size=input$textsizeh-6),axis.text.y = element_text( size=input$textsizeh-6)) h_cov = ggplotly(h_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) h_cov } } } h_cov }) output$hazards_cov <- renderPlotly ({ datah4_re() }) output$downploth4 <- downloadHandler( filename = function(){paste("h4",'.png',sep='')}, content = function(file){ plotly_IMAGE( datah4_re(),width = 1200, height = 900, format = "png", scale = 2, out_file = file ) } ) ########################################################################################### ############################################################################################ data_H_d_all <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar2=as.data.frame(myjson2()$timevar) names(timevar2)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) { v[i]=myjson2()$cov$atlist[i] } ## Different variable of probabilities for each covariate pattern haz=list() if (length(myjson2()$haz)==0) {return()} for(i in 1:length(myjson2()$haz_all)) { haz[[i]]=as.data.frame(t(data.frame(myjson2()$haz_all[i]))) colnames( haz[[i]]) <- labels_cov() } for(i in 1:length(myjson2()$haz_all)) { haz[[i]]=as.data.frame(cbind( haz[[i]],timevar2 ,trans=rep(i,nrow( haz[[i]] )) )) } # Append the probabilities datasets of the different states datap_H=list() datap_H[[1]]=haz[[1]] if (length(myjson2()$haz_all)>1 ) { for (u in 2:(length(myjson2()$haz_all))) { datap_H[[u]]=rbind( haz[[u]],datap_H[[(u-1)]]) } } datah=datap_H[[length(myjson2()$haz_all)]] datah datanew=datah datanew$trans_factor=c(rep("NA",nrow(datanew))) for (o in 1:(length(myjson2()$haz_all))) { for (g in 1:nrow(datanew)) { if (datanew$trans[g]==o) {datanew$trans_factor[g]=paste0("Trans",o) } } } ### Meke one variable of hazards so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(datanew[,d],datanew[,ncol(datanew)-2],datanew[,ncol(datanew)-1], datanew[,ncol(datanew)],rep(d,length(datanew[,d])) ) dlist[[d]][,6] <- rep(colnames(datanew)[d],length(datanew[,d])) colnames(dlist[[d]]) <- c("V","timevar","trans","trans_factor","cov","cov_factor") } d_all_h <- bind_rows(dlist, .id = "column_label") d_all_h }) ############################################################################################ ########################################################################################### data_H_trans <- reactive ({ hr_trans=list() p=1 for (i in levels(as.factor(data_H_d_all()$cov_factor)) ) { for (k in 1:myjson2()$Ntransitions) { for (j in 1:myjson2()$Ntransitions) { ratio=data_H_d_all()[which(data_H_d_all()$cov_factor==i & data_H_d_all()$trans==k),2]/data_H_d_all()[which(data_H_d_all()$cov_factor==i & data_H_d_all()$trans==j),2] timevar=data_H_d_all()$timevar nominator_trans=rep(k,length(myjson2()$timevar)) denominator_trans=rep(j,length(myjson2()$timevar)) cov_factor=rep(i,length(myjson2()$timevar)) hr_trans[[p]]=cbind.data.frame(ratio,timevar,nominator_trans,denominator_trans,cov_factor) colnames(hr_trans[[p]]) <- c("ratio","timevar","nominator_trans","denominator_trans","cov_factor") p=p+1 } } } hr_trans_d= bind_rows(hr_trans, .id = "column_label") hr_trans_d=hr_trans_d[which(hr_trans_d$nominator_trans!=hr_trans_d$denominator_trans),] hr_trans_d$ratiolab=paste0("Trans", hr_trans_d$nominator_trans," vs ","Trans",hr_trans_d$denominator_trans) hr_trans_d=hr_trans_d[which(hr_trans_d$nominator_trans% add_lines( x=data_H_trans()$timevar,y=data_H_trans()$ratio, frame=factor(as.factor(data_H_trans()$cov_factor),levels=labels_cov()), color=as.factor(data_H_trans()$ratiolab), mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text=paste0(hazname," among covariate patterns"),y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= hazname, rangemode = "nonnegative", range=c(0,max(data_H_trans()$ratio)), dtick = input$stephy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), legend = list(x = 150, y = 1), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) ) %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$faceth=="Yes") { data_plot=data_H_trans() h_trans = ggplot(data_plot) h_trans = ggplot(data_plot,aes(x=timevar, y=ratio, color= as.factor(ratiolab), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Ratio: ", ratio, "
Transition ratios: ", as.factor(ratiolab)))) h_trans = h_trans+geom_line(aes(x=timevar, y=ratio, color= as.factor(ratiolab))) h_trans = h_trans+ facet_wrap(~factor(as.factor(cov_factor),levels=labels_cov())) h_trans = h_trans + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) #scale_y_continuous(breaks=c(seq(0,input$endhy,by=input$stephy ))) # h_trans = h_trans + scale_y_continuous(breaks=c(seq(min(data_plot$V[which(!is.na(data_plot$V))]),max(data_plot$V[which(!is.na(data_plot$V))]), by=input$stephy ))) h_trans = h_trans +labs(title=paste0(hazname," among covariate patterns"), x=paste0("Time since ",input$origin," entry"), y=hazname) h_trans = h_trans + labs(color = "States")+ labs(fill = "States") h_trans = h_trans +theme(title = element_text(size = input$textsizeh-4), strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5), axis.text.x = element_text( size=input$textsizeh-6),axis.text.y = element_text( size=input$textsizeh-6)) h_trans = ggplotly(h_trans, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } if (input$scaleh_ld=="log") { if (input$faceth=="No") { h_trans= plot_ly(data_H_trans(),alpha=0.5) %>% add_lines( x=data_H_trans()$timevar,y=log(data_H_trans()$ratio), frame=factor(as.factor(data_H_trans()$cov_factor),levels=labels_cov()), color=as.factor(data_H_trans()$ratiolab), mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text=paste0(hazname," (log scale) among covariate patterns"),y=0.95), font= list(family = "times new roman", size = input$textsizeh, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text=paste0("Time since ",input$origin," entry"),y=0.2), dtick = input$stephx, tick0 = input$starthx, range=c(input$starthx,input$endhx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= paste0(hazname," (log scale)"),rangemode = "nonnegative", dtick = input$logstephy, tick0 = input$logstarthy, range=c(input$logstarthy,input$logendhy), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), legend = list(x = 150, y = 1), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) ) %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$faceth=="Yes") { data_plot=data_H_trans() h_trans = ggplot(data_plot) h_trans = ggplot(data_plot,aes(x=timevar, y=log(ratio), color= as.factor(ratiolab), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Log Ratio: ", log(ratio), "
Transition ratios: ", as.factor(ratiolab)))) h_trans = h_trans+geom_line(aes(x=timevar, y=log(ratio), color= as.factor(ratiolab))) h_trans = h_trans+ facet_wrap(~factor(as.factor(cov_factor),levels=labels_cov())) h_trans = h_trans + scale_x_continuous(breaks=c(seq(input$starthx,input$endhx,by=input$stephx ))) #scale_y_continuous(breaks=c(seq(input$logstarthy,input$logendhy,by=input$logstephy ))) # h_trans = h_trans + scale_y_continuous(breaks=c(seq(min(V[which(!is.na(V))]),max(V[which(!is.na(V))]), # by=max(V[which(!is.na(V))])-min(V[which(!is.na(V))]) ))) h_trans = h_trans +labs(title=paste0(hazname," among covariate patterns"), x=paste0("Time since ",input$origin," entry"), y=hazname) h_trans = h_trans + labs(color = "States")+ labs(fill = "States") h_trans = h_trans +theme(title = element_text(size = input$textsizeh-4),strip.text = element_text(size=input$textfaceth), legend.title = element_text(color="black", size= input$textsizeh-5), legend.text=element_text(size= input$textsizeh-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizeh-5), axis.title.x = element_text(size= input$textsizeh-5), axis.text.x = element_text( size=input$textsizeh-6),axis.text.y = element_text( size=input$textsizeh-6)) h_trans = ggplotly(h_trans, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } h_trans }) output$hr_transitions <- renderPlotly ({ datah5_re() }) output$downploth5 <- downloadHandler( filename = function(){paste("h5",'.png',sep='')}, content = function(file){ plotly_IMAGE( datah5_re(),width = 1200, height = 900, format = "png", scale = 2, out_file = file ) } ) ####################################################################### data_H_cov <- reactive ({ hr_cov=list() #hr_trans=array(dim=c(length(myjson$timevar),5,length(myjson$cov$atlist)*myjsonbox$Ntransitions^2),"NA") p=1 for (i in 1:myjson2()$Ntransitions ) { for (k in levels(as.factor(data_H_d()$cov_factor))) { for (j in levels(as.factor(data_H_d()$cov_factor))) { ratio=data_H_d()[which(data_H_d()$trans==i & data_H_d()$cov_factor==k),2]/data_H_d()[which(data_H_d()$trans==i & data_H_d()$cov_factor==j),2] timevar=data_H_d()$timevar nominator_cov=rep(k,length(myjson2()$timevar)) denominator_cov=rep(j,length(myjson2()$timevar)) trans=rep(i,length(myjson2()$timevar)) trans_factor=rep(input[[paste0('trans',i)]][1],length(myjson2()$timevar)) ratiolab=paste0("HR ",nominator_cov," vs ",denominator_cov) hr_cov[[p]]=as.data.frame(cbind.data.frame(ratio,timevar,nominator_cov,denominator_cov,trans,trans_factor,ratiolab)) colnames(hr_cov[[p]]) <- c("ratio","timevar","nominator_cov","denominator_cov","trans","trans_factor","ratiolab") p=p+1 } } } hr_cov_d= as.data.frame(bind_rows(hr_cov, .id = "column_label")) hr_cov_d=as.data.frame(hr_cov_d[which(hr_cov_d$nominator_cov!=hr_cov_d$denominator_cov),]) hr_cov_d }) ###### Show and hide and tick inputs #### timerlos <- reactiveVal(1.5) observeEvent(c(input$showticklos,invalidateLater(1000, session)), { if(input$showticklos=="No"){ hide("tickinputlos") } if(input$showticklos=="Yes"){ show("tickinputlos") } isolate({ timerlos(timerlos()-1) if(timerlos()>1 & input$showticklos=="No") { show("tickinputlos") } }) }) ############################################################# existlos <- reactive({ if (length(myjson2()$los) != 0) { x= 1 } else if (length(myjson2()$los) == 0) { x= 0 } }) existlosratio <- reactive({ if (length(myjson2()$losr) != 0) { x= 1 } else if (length(myjson2()$losr) == 0) { x= 0 } }) existlosdiff <- reactive({ if (length(myjson2()$losd) != 0) { x= 1 } else if (length(myjson2()$losd) == 0) { x= 0 } }) output$pagelos <- renderUI({ if (is.null(myjson2())) return("Provide the json file with the predictions") if (existlos()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(12, output$loginpagelos <- renderUI({h1("Non applicable")}) ) ) } else if (existlos()==1) { if (existlosdiff()==1 & existlosratio()==1) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Length of stay"), conditionalPanel(condition="input.tabslos =='#panel1los'||input.tabslos =='#panel2los'||input.tabslos =='#panel4los'||input.tabslos =='#panel5los'", uiOutput("facetlos") ), uiOutput("conflos") ), column(2, br(), p(""), radioButtons("showticklos", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No"), uiOutput("tickinputlos") ), column(8, tabsetPanel(id = "tabslos", tabPanel(h2("By state"), value = "#panel1los", plotlyOutput("los_state" , height="600px", width = "100%"),uiOutput("shouldloadlos1")), tabPanel(h2("By covariate pattern"), value = "#panel2los", plotlyOutput("los_cov" , height="600px", width = "100%"),uiOutput("shouldloadlos2")), tabPanel(h2("By state and covariate pattern"), value = "#panel3los", plotlyOutput("los_both" , height="600px", width = "100%"),uiOutput("shouldloadlos3")), tabPanel(h2("Differences"), value = "#panel4los", plotlyOutput("los_diff" , height="600px", width = "100%"),uiOutput("shouldloadlos4")), tabPanel(h2("Ratios"), value = "#panel5los", plotlyOutput("los_ratio" , height="600px", width = "100%"),uiOutput("shouldloadlos5")) ) ) ) } else if (existlosdiff()==1 & existlosratio()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Length of stay"), conditionalPanel(condition="input.tabslos =='#panel1los'||input.tabslos =='#panel2los'||input.tabslos =='#panel4los'||input.tabslos =='#panel5los'", uiOutput("facetlos") ) , uiOutput("conflos") ), column(2, br(), p(""), radioButtons("showticklos", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No"), uiOutput("tickinputlos"), ), column(8, tabsetPanel(id = "tabslos", tabPanel(h2("By state"), value = "#panel1los", plotlyOutput("los_state" , height="600px", width = "100%"),uiOutput("shouldloadlos1")), tabPanel(h2("By covariate pattern"), value = "#panel2los", plotlyOutput("los_cov" , height="600px", width = "100%"),uiOutput("shouldloadlos2")), tabPanel(h2("By state and covariate pattern"), value = "#panel3los", plotlyOutput("los_both" , height="600px", width = "100%"),uiOutput("shouldloadlos3")), tabPanel(h2("Differences"), value = "#panel4los", plotlyOutput("los_diff" , height="600px", width = "100%"),uiOutput("shouldloadlos4")), tabPanel(h2("Ratios"), value = "#panel5los", print("Not Applicable")) ) ) ) } else if ( existlosdiff()==0 & existlosratio()==1) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Length of stay"), conditionalPanel(condition="input.tabslos =='#panel1los'||input.tabslos =='#panel2los'||input.tabslos =='#panel4los'||input.tabslos =='#panel5los'", uiOutput("facetlos") ) , uiOutput("conflos") ), column(2, br(), p(""), radioButtons("showticklos", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No"), uiOutput("tickinputlos"), ), column(8, tabsetPanel(id = "tabslos", tabPanel(h2("By state"), value = "#panel1los", plotlyOutput("los_state" , height="600px", width = "100%"),uiOutput("shouldloadlos1")), tabPanel(h2("By covariate pattern"), value = "#panel2los", plotlyOutput("los_cov" , height="600px", width = "100%"),uiOutput("shouldloadlos2")), tabPanel(h2("By state and covariate pattern"), value = "#panel3los", plotlyOutput("los_both" , height="600px", width = "100%"),uiOutput("shouldloadlos3")), tabPanel(h2("Differences"), value = "#panel4los", print("Not Applicable")), tabPanel(h2("Ratios"), value = "#panel5los", plotlyOutput("los_ratio" , height="600px", width = "100%"),uiOutput("shouldloadlos5")) ) ) ) } else if (existlosdiff()==0 & existlosratio()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Length of stay"), #uiOutput("displaylos"), #uiOutput("covarinputlos"), # uiOutput("statesinputlos") conditionalPanel(condition="input.tabslos =='#panel1los'||input.tabslos =='#panel2los'||input.tabslos =='#panel4los'||input.tabslos =='#panel5los'", uiOutput("facetlos") ) , uiOutput("conflos") ), column(2, br(), p(""), radioButtons("showticklos", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No"), uiOutput("tickinputlos"), ), column(8, tabsetPanel(id = "tabslos", tabPanel(h2("By state"), value = "#panel1los", plotlyOutput("los_state" , height="600px", width = "100%"),uiOutput("shouldloadlos1")), tabPanel(h2("By covariate pattern"), value = "#panel2los", plotlyOutput("los_cov" , height="600px", width = "100%"),uiOutput("shouldloadlos2")), tabPanel(h2("By state and covariate pattern"), value = "#panel3los", plotlyOutput("los_both" , height="600px", width = "100%"),uiOutput("shouldloadlos3")), tabPanel(h2("Differences"), value = "#panel4los", print("Not Applicable")), tabPanel(h2("Ratios"), value = "#panel5los", print("Not Applicable")) ) ) ) } } }) observeEvent(input$json2, { if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Los')))==0 ) { js$disableTab("mytab_los") } }) observeEvent(input$csv2, { if( length(which(startsWith(names( read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Los')))==0 ) { js$disableTab("mytab_los") } }) #output$displaylos <- renderUI({ # radioButtons("displaylos", "Change labels of states and covariate patterns", # c("same" = "same", "change"= "change")) #}) # output$facetlos <- renderUI({ radioButtons(inputId="facetlos", label= "Display graph in grids", choices=c("No","Yes"),selected = "No") }) output$conflos <- renderUI({ if (length(myjson2()$ci_los)!=0) { radioButtons("conflos", "Confidence intervals", c("No" = "ci_no", "Yes" ="ci_yes")) } else if (length(myjson2()$ci_los)==0) { item_list <- list() item_list[[1]]<- radioButtons("conflos", "Confidence intervals",c("No" = "ci_no")) item_list[[2]]<-print("Confidence interval data were not provided") do.call(tagList, item_list) } }) ######################################################################### ######################################################################### #Create the reactive input of covariates ######################################################################### ######################################################################### #output$covarinputlos <- renderUI({ # # if (is.null(myjson2())) return() # # if (input$displaylos=="same") return() # # else { # # item_list <- list() # item_list[[1]] <- h2("Covariate patterns") # # v=vector() # for (i in 1:length(myjson2()$cov$atlist)) { # v[i]=myjson2()$cov$atlist[i] # } # default_choices_cov=v # # for (i in seq(length(myjson2()$cov$atlist))) { # item_list[[i+1]] <- textInput(paste0('covlos', i),default_choices_cov[i], labels_cov()[i]) # } # do.call(tagList, item_list) # } #}) # ##Transform the reactive input of covariates into easy to use laber reactive dataset # #labels_covl<- reactive ({ # # if (input$displaylos=="same") {labels_cov()} # # else { # # myList<-vector("list",length(myjson2()$cov$atlist)) # for (i in 1:length(myjson2()$cov$atlist)) { # myList[[i]]= input[[paste0('covlos', i)]][1] # } # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # final_list # } #}) # ##################################### #Create the reactive input of states# ###################################### #output$statesinputlos <- renderUI({ # # if (input$displaylos=="same") return() # # else { # # item_list <- list() # item_list[[1]] <- h2("States") # default_choices_state=vector() # # title_choices_state=vector() # for (i in 1:length(myjson2()$P)) { # title_choices_state[i]=paste0("State"," ",input$select,selectend()[i]) # } # for (i in 1:length(myjson2()$P)) { # item_list[[1+i]] <- textInput(paste0('statel',i),title_choices_state[i], labels_state()[i]) # } # do.call(tagList, item_list) #} # }) # #labels_statel<- reactive ({ # # if (input$displaylos=="same") {labels_state()} # # else { # # myList<-vector("list",length(myjson2()$P)) # for (i in 1:length(myjson2()$P)) { # # myList[[i]]= input[[paste0('statel', i)]][1] # # } # # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # final_list # } #}) # ######################################################################### ######################################################################### ######################################################################### data_L <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) { v[i]=myjson2()$cov$atlist[i] } ## Different variable of probabilities for each covariate pattern ## Different variable of probabilities for each covariate pattern los=list() if (length(myjson2()$los)==0) {return()} for(i in 1:length(myjson2()$los)) { los[[i]]=as.data.frame(t(data.frame(myjson2()$los[i]))) colnames(los[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$los)) { los[[i]]=as.data.frame(cbind(los[[i]], timevar ,state=rep(i,nrow(los[[i]] )) )) } # Append the probabilities datasets of the different states data_los=list() data_los[[1]]=los[[1]] for (u in 2:(length(myjson2()$los))) { data_los[[u]]=rbind(los[[u]],data_los[[(u-1)]]) } datal=data_los[[length(myjson2()$los)]] datal }) data_L_uci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern los_uci=list() if (length(myjson2()$los_uci)==0) {return()} for(i in 1:length(myjson2()$los_uci)) { los_uci[[i]]=as.data.frame(t(data.frame(myjson2()$los_uci[i]))) colnames(los_uci[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$los_uci)) { los_uci[[i]]=as.data.frame(cbind(los_uci[[i]], timevar ,state=rep(i,nrow(los_uci[[i]] )) )) } # Append the probabilities datasets of the different states data_los_uci=list() data_los_uci[[1]]=los_uci[[1]] for (u in 2:(length(myjson2()$los_uci))) { data_los_uci[[u]]=rbind(los_uci[[u]],data_los_uci[[(u-1)]]) } datal_uci=data_los_uci[[length(myjson2()$los_uci)]] datal_uci }) data_L_lci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern los_lci=list() if (length(myjson2()$los_lci)==0) {return()} for(i in 1:length(myjson2()$los_lci)) { los_lci[[i]]=as.data.frame(t(data.frame(myjson2()$los_lci[i]))) colnames(los_lci[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$los_lci)) { los_lci[[i]]=as.data.frame(cbind(los_lci[[i]], timevar ,state=rep(i,nrow(los_lci[[i]] )) )) } # Append the probabilities datasets of the different states data_los_lci=list() data_los_lci[[1]]=los_lci[[1]] for (u in 2:(length(myjson2()$los_lci))) { data_los_lci[[u]]=rbind(los_lci[[u]],data_los_lci[[(u-1)]]) } datal_lci=data_los_lci[[length(myjson2()$los_lci)]] datal_lci }) data_L_st<-reactive ({ datanew=data_L() datanew$state_fac=ordered(c(rep("NA",nrow(datanew))), levels = labels_state() ) for (o in 1:(length(myjson2()$P))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_L_st_uci<-reactive ({ datanew=data_L_uci() datanew$state_fac=ordered(c(rep("NA",nrow(datanew))), levels = labels_state() ) for (o in 1:(length(myjson2()$los_uci))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_L_st_lci<-reactive ({ datanew=data_L_lci() datanew$state_fac=ordered(c(rep("NA",nrow(datanew))), levels = labels_state() ) for (o in 1:(length(myjson2()$los_lci))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o]} } } datanew }) data_L_d <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_L_st()[,d],data_L_st()[,ncol(data_L_st())-2],data_L_st()[,ncol(data_L_st())-1],data_L_st()[,ncol(data_L_st())],rep(d,length(data_L_st()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_L_st())[d],length(data_L_st()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_l <- bind_rows(dlist, .id = "column_label") d_all_l }) data_L_d_uci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_L_st_uci()[,d],data_L_st_uci()[,ncol(data_L_st_uci())-2], data_L_st_uci()[,ncol(data_L_st_uci())-1], data_L_st_uci()[,ncol(data_L_st_uci())],rep(d,length(data_L_st_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_L_st_uci())[d],length(data_L_st_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_l_uci <- bind_rows(dlist, .id = "column_label") d_all_l_uci }) data_L_d_lci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_L_st_lci()[,d],data_L_st_lci()[,ncol(data_L_st_lci())-2], data_L_st_lci()[,ncol(data_L_st_lci())-1], data_L_st_lci()[,ncol(data_L_st_lci())],rep(d,length(data_L_st_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_L_st_lci())[d],length(data_L_st_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_l_lci <- bind_rows(dlist, .id = "column_label") d_all_l_lci }) output$tickinputlos <- renderUI({ default_choices=c("black","blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen","tan1","lightslateblue","khaki4") if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <- h2("Provide x axis range and ticks") item_list[[2]] <-numericInput("startlosx","Start x at:",value=min(data_L_d()$timevar),min=0 ) item_list[[3]] <-numericInput("steplosx","step:",value=max(data_L_d()$timevar/10),min=0,max=max(data_L_d()$timevar)) item_list[[4]] <-numericInput("endlosx","End x at:",value =max(data_L_d()$timevar),min=0,max=max(data_L_d()$timevar)) item_list[[5]] <-numericInput("steplosy","step at y axis:",value=max(data_L_d()$V)/10,min=max(data_L_d()$V)/100,max=max(data_L_d()$V)/2) item_list[[6]] <-numericInput("endlosy","end of y axis:",value=max(data_L_d()$V),min=max(data_L_d()$V)/10,max=max(data_L_d()$V)*10) item_list[[7]] <-numericInput("textsizelos",h2("Legends size"),value=input$textsize,min=5,max=30) item_list[[8]] <-numericInput("textfacetlos",h2("Facet title size"),value=input$textsize-3,min=5,max=30 ) do.call(tagList, item_list) }) data_L_ci<- reactive ({ x=c( data_L_d()[order(data_L_d()$timevar,data_L_d()$state,data_L_d()$cov),]$timevar, data_L_d_lci()[order(-data_L_d()$timevar,data_L_d()$state,data_L_d()$cov),]$timevar ) y_central=c( data_L_d()[order(data_L_d()$timevar,data_L_d()$state,data_L_d()$cov),]$V, data_L_d()[order(-data_L_d()$timevar,data_L_d()$state,data_L_d()$cov),]$V ) y=c( data_L_d_uci()[order(data_L_d_uci()$timevar,data_L_d_uci()$state,data_L_d_uci()$cov),]$V, data_L_d_lci()[order(-data_L_d_uci()$timevar,data_L_d_uci()$state,data_L_d_uci()$cov),]$V ) frameto=c(as.character(data_L_d_uci()[order(-data_L_d_uci()$timevar,data_L_d_uci()$state,data_L_d_uci()$cov),]$state_factor), as.character(data_L_d_lci()[order(-data_L_d_lci()$timevar,data_L_d_lci()$state,data_L_d_lci()$cov),]$state_factor) ) covto=c( data_L_d_uci()[order(-data_L_d_uci()$timevar,data_L_d_uci()$state,data_L_d_uci()$cov),]$cov_factor, data_L_d_lci()[order(-data_L_d_lci()$timevar,data_L_d_lci()$state,data_L_d_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) ###################################### #output$shouldloadlos1 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotlos1", label = h2("Download the plot")) #}) datalos1_re <- reactive ({ ####### Plot 1 frame is state, factor is cov ######################## if (input$conflos=="ci_no") { if (input$facetlos=="No") { los_state= plot_ly(data_L_d(),alpha=0.5) %>% add_lines( x=data_L_d()$timevar,y=data_L_d()$V, frame=factor(as.factor(data_L_d()$state_factor),levels = labels_state()), color=factor(as.factor(data_L_d()$cov_factor),levels = labels_cov()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], mode="lines", line=list(simplify=FALSE,color = labels_colour_cov()) , text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) los_state = los_state %>% layout(title=list(text="Length of stay of each covariate pattern among states",y=0.95), font= list(family = "times new roman", size = input$textsizelos, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$steplosx, tick0 = input$startlosx, range=c(input$startlosx,input$endlosx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Length of stay",rangemode = "nonnegative", range=c(0,input$endlosy), dtick = input$steplosy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) los_state } if (input$facetlos=="Yes") { data_plot=data_L_d() los_state = ggplot(data_plot) los_state = ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor),levels=labels_cov()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Length of stay: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor),levels=labels_cov())))) los_state = los_state+geom_line(aes(x=timevar, y=V, color=factor(as.factor(cov_factor),levels=labels_cov())))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { los_state = los_state+ facet_wrap(~ factor(as.factor(state_factor),levels = labels_state()), nrow=2) } else if (input$aimtype=="present") {los_state = los_state+ facet_wrap(~factor(as.factor(state_factor),levels = labels_state() ))} los_state = los_state + scale_x_continuous(breaks=c(seq(input$startlosx,input$endlosx,by=input$steplosx ))) + scale_y_continuous(breaks=c(seq(0,input$endlosy,by=input$steplosy ))) los_state = los_state +labs(title="Length of stay of each covariate pattern among states", x="Time since entry", y="Length of stay") los_state = los_state + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") los_state = los_state +theme(title = element_text(size = input$textsizelos-4), strip.text = element_text(size=input$textfacetlos), legend.title = element_text(color="black", size= input$textsizelos-5), legend.text=element_text(size= input$textsizelos-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizelos-5), axis.title.x = element_text(size= input$textsizelos-5), axis.text.x = element_text( size=input$textsizelos-6),axis.text.y = element_text( size=input$textsizelos-6)) los_state = ggplotly(los_state, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) los_state } } else if (input$conflos=="ci_yes") { if (input$facetlos=="No") { los_state <- plot_ly() los_state <- add_trace(los_state, line=list(simplify=FALSE,color = labels_colour_cov()), mode="lines", type = "scatter", x=data_L_ci()$x, y=data_L_ci()$y_central, frame=factor(as.factor(data_L_ci()$frameto),levels= labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=factor(as.factor(data_L_ci()$covto),levels = labels_cov()), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) los_state <- add_trace(los_state, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_L_ci()$x, y=data_L_ci()$y, frame=factor(as.factor(data_L_ci()$frameto),levels= labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=factor(as.factor(data_L_ci()$covto),levels = labels_cov()), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) los_state = los_state %>% layout(title=list(text="Length of stay of each covariate pattern among states",y=0.95), font= list(family = "times new roman", size = input$textsizelos, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$steplosx, tick0 = input$startlosx, range=c(input$startlosx,input$endlosx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Length of stay",rangemode = "nonnegative", range=c(0,input$endlosy), dtick = input$steplosy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) los_state } if (input$facetlos=="Yes") { V_lci= data_L_d_lci()$V V_uci= data_L_d_uci()$V data_plot=cbind(data_L_d(),V_lci,V_uci) los_state=ggplot(data_plot) los_state=ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor),levels=labels_cov()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Length of stay: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor),levels=labels_cov()))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) los_state=los_state+geom_line(aes(x=timevar, y=V, fill= factor(as.factor(cov_factor),levels=labels_cov()))) los_state=los_state+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=factor(as.factor(cov_factor),levels=labels_cov())),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { los_state = los_state+ facet_wrap(~ factor( as.factor(state_factor), levels = labels_state() ), nrow=2)} else if (input$aimtype=="present") {los_state = los_state+ facet_wrap(~ factor( as.factor(state_factor), levels = labels_state() ) ) } los_state = los_state + scale_x_continuous(breaks=c(seq(input$startlosx,input$endlosx,by=input$steplosx ))) + scale_y_continuous(breaks=c(seq(0,input$endlosy,by=input$steplosy ))) los_state = los_state +labs(title="Length of stay at each state", x="Time since entry", y="Length of stay") los_state = los_state + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") los_state = los_state +theme(title = element_text(size = input$textsizelos-4), strip.text = element_text(size=input$textfacetlos), legend.title = element_text(color="black", size= input$textsizelos-5), legend.text=element_text(size= input$textsizelos-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizelos-5), axis.title.x = element_text(size= input$textsizelos-5), axis.text.x = element_text( size=input$textsizelos-6),axis.text.y = element_text( size=input$textsizelos-6)) los_state = ggplotly(los_state, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) los_state } } los_state }) output$los_state <- renderPlotly ({datalos1_re() }) output$downplotlos1 <- downloadHandler( filename = function(){paste("los1",'.png',sep='')}, content = function(file){ plotly_IMAGE( datalos1_re(),width = 1400, height = 1100, format = "png", scale = 2, out_file = file ) } ) ########################################################## #output$shouldloadlos2 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotlos2", label = h2("Download the plot")) #}) datalos2_re <- reactive({ if (input$conflos=="ci_no") { if (input$facetlos=="No") { los_cov= plot_ly(data_L_d(),alpha=0.5) %>% add_lines( x=data_L_d()$timevar,y=data_L_d()$V, frame=factor(as.factor(data_L_d()$cov_factor), levels = labels_cov() ), color=factor(as.factor(data_L_d()$state_factor),levels = labels_state()), colors=labels_colour_state()[1:length(myjson2()$P)], mode="lines", line=list(simplify=FALSE,color = labels_colour_state()) , text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) los_cov = los_cov %>% layout(title=list(text="Length of stay of each state among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizelos, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$steplosx, tick0 = input$startlosx, range=c(input$startlosx,input$endlosx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Length of stay",rangemode = "nonnegative", range=c(0,input$endlosy), dtick = input$steplosy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { los_cov= los_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } los_cov } if (input$facetlos=="Yes") { data_plot=data_L_d() los_cov = ggplot(data_plot) los_cov = ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(state_factor),levels=labels_state()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Length of stay: ", V, "
State: ", factor(as.factor(state_factor),levels=labels_state())))) los_cov = los_cov+geom_line(aes(x=timevar, y=V, color= factor(as.factor(state_factor),levels=labels_state())))+ scale_colour_manual( values =labels_colour_state(),labels = labels_state() ) if (input$aimtype=="compare") {los_cov = los_cov+ facet_wrap(~factor(as.factor(cov_factor), levels = labels_cov() ), nrow=2)} else if (input$aimtype=="present") {los_cov = los_cov+ facet_wrap(~factor(as.factor(cov_factor), levels = labels_cov() ))} los_cov = los_cov + scale_x_continuous(breaks=c(seq(input$startlosx,input$endlosx,by=input$steplosx ))) + scale_y_continuous(breaks=c(seq(0,input$endlosy,by=input$steplosy ))) los_cov = los_cov +labs(title="Length of stay of each state among covariate patterns", x="Time since entry", y="Length of stay") los_cov = los_cov + labs(color = "States")+ labs(fill = "States") los_cov = los_cov +theme(title = element_text(size = input$textsizelos-4), strip.text = element_text(size=input$textfacetlos), legend.title = element_text(color="black", size= input$textsizelos-5), legend.text=element_text(size= input$textsizelos-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizelos-5), axis.title.x = element_text(size= input$textsizelos-5), axis.text.x = element_text( size=input$textsizelos-6),axis.text.y = element_text( size=input$textsizelos-6)) los_cov = ggplotly(los_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) los_cov } } else if (input$conflos=="ci_yes") { if (input$facetlos=="No") { los_cov <- plot_ly() los_cov <- add_trace(los_cov, line=list(simplify=FALSE,color = labels_colour_cov()), mode="lines", type = "scatter", x=data_L_ci()$x, y=data_L_ci()$y_central, frame= factor( as.factor(data_L_ci()$covto), levels = labels_cov() ), colors=labels_colour_state()[1:length(myjson2()$P)], color=factor(as.factor(data_L_ci()$frameto),levels = labels_state()) , text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) los_cov <- add_trace(los_cov, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_L_ci()$x, y=data_L_ci()$y, frame= factor( as.factor(data_L_ci()$covto), levels = labels_cov() ), colors=labels_colour_state()[1:length(myjson2()$P)], color=factor(as.factor(data_L_ci()$frameto),levels = labels_state()) , showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) los_cov = los_cov %>% layout(title=list(text="Length of stay of each state among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizelos, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$steplosx, tick0 = input$startlosx, range=c(input$startlosx,input$endlosx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Length of stay",rangemode = "nonnegative", dtick = input$steplosy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { los_cov= los_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } los_cov } if (input$facetlos=="Yes") { V_lci= data_L_d_lci()$V V_uci= data_L_d_uci()$V data_plot=cbind(data_L_d(),V_lci,V_uci) los_cov=ggplot(data_plot) los_cov=ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(state_factor),levels=labels_state()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Length of stay: ", V, "
State: ", factor(as.factor(state_factor),levels=labels_state()))))+ scale_colour_manual( values =labels_colour_state(),labels = labels_state() ) los_cov=los_cov+geom_line(aes(x=timevar, y=V, fill= factor(as.factor(state_factor),levels=labels_state()))) + scale_y_continuous(breaks=c(seq(0,input$endlosy,by=input$steplosy ))) los_cov=los_cov+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill= factor(as.factor(state_factor),levels=labels_state())),alpha=0.4)+ scale_fill_manual( values =labels_colour_state(),labels = labels_state() ) if (input$aimtype=="compare") {los_cov = los_cov+ facet_wrap(~factor(as.factor(cov_factor), levels = labels_cov() ), nrow=2)} else if (input$aimtype=="present") {los_cov = los_cov+ facet_wrap(~~factor(as.factor(cov_factor), levels = labels_cov() ) )} los_cov = los_cov + scale_x_continuous(breaks=c(seq(input$startlosx,input$endlosx,by=input$steplosx ))) los_cov = los_cov +labs(title="Length of stay of each state among covariate patterns", x="Time since entry", y="Length of stay") los_cov = los_cov + labs(color = "States")+ labs(fill = "States") los_cov = los_cov +theme(title = element_text(size = input$textsizelos-4), strip.text = element_text(size=input$textfacetlos), legend.title = element_text(color="black", size= input$textsizelos-5), legend.text=element_text(size= input$textsizelos-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizelos-5,hjust=2), axis.title.x = element_text(size= input$textsizelos-5), axis.text.x = element_text( size=input$textsizelos-6),axis.text.y = element_text( size=input$textsizelos-6)) los_cov = ggplotly(los_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) los_cov } } los_cov }) output$los_cov <- renderPlotly ({datalos2_re() }) output$downplotlos2 <- downloadHandler( filename = function(){paste("los2",'.png',sep='')}, content = function(file){ plotly_IMAGE( datalos2_re(),width = 1400, height = 1100, format = "png", scale = 2, out_file = file ) } ) ################################################## #output$shouldloadlos3 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotlos3", label = h2("Download the plot")) #}) datalos3_re <- reactive({ ####### Plot 3 f factor is state and cov ######################## if (input$conflos=="ci_no") { los_state_cov= plot_ly(data_L_d(),alpha=0.5) %>% add_lines( x=data_L_d()$timevar,y=data_L_d()$V, color = as.factor(data_L_d()$cov_factor), fill =data_L_d()$state_factor, linetype=data_L_d()$state_factor, mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) } else if (input$conflos=="ci_yes") { los_state_cov <- plot_ly() los_state_cov <- add_trace(los_state_cov, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_L_ci()$x, y=data_L_ci()$y_central, color=as.factor(data_L_ci()$covto), fill=as.factor(data_L_ci()$frameto), linetype=as.factor(data_L_ci()$frameto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) los_state_cov <- add_trace(los_state_cov, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_L_ci()$x, y=data_L_ci()$y, color=as.factor(data_L_ci()$covto), fill=as.factor(data_L_ci()$frameto), linetype=as.factor(data_L_ci()$frameto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) } los_state_cov=los_state_cov%>% layout(title=list(text="Length of stay in each state for all covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizelos, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$steplosx, tick0 = input$startlosx, range=c(input$startlosx,input$endlosx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of state occupancy",rangemode = "nonnegative", dtick = input$steplosy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) los_state_cov }) output$los_both <- renderPlotly ({ datalos3_re() }) output$downplotlos3 <- downloadHandler( filename = function(){paste("los3",'.png',sep='')}, content = function(file){ plotly_IMAGE( datalos3_re(),width = 1400, height = 1100, format = "png", scale = 2, out_file = file ) } ) ######################################################################################################## ########################################################################################################## data_L_diff1 <- reactive ({ los_diff=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$losd)) { los_diff[[i]]=as.data.frame(t(data.frame(myjson2()$losd[i]))) colnames(los_diff[[i]]) <- v_diff } for(i in 1:length(myjson2()$losd)) { los_diff[[i]]=as.data.frame(cbind(los_diff[[i]], timevar ,state=rep(i,nrow(los_diff[[i]] )) )) } } else { for (i in 1:length(myjson2()$losd)) { los_diff[[i]]=as.data.frame(myjson2()$losd[[i]][,1]) } for (i in 1:length(myjson2()$losd)) { los_diff[[i]]=as.data.frame(c(los_diff[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$losd[[i]][,1])) )) ) colnames(los_diff[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_losd=list() data_losd[[1]]=los_diff[[1]] for (u in 2:(length(myjson2()$losd))) { data_losd[[u]]=rbind(los_diff[[u]],data_losd[[(u-1)]]) } datald=data_losd[[length(myjson2()$losd)]] datald$state_fac=c(rep("NA",nrow(datald))) for (o in 1:(length(myjson2()$losd))) { for (g in 1:nrow(datald)) { if (datald$state[g]==o) {datald$state_fac[g]=labels_state()[o]} } } datald }) data_L_diff1_uci <- reactive ({ L_diff_uci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$losd_uci)) { L_diff_uci[[i]]=as.data.frame(t(data.frame(myjson2()$losd_uci[i]))) colnames(L_diff_uci[[i]]) <- v_diff } for(i in 1:length(myjson2()$losd_uci)) { L_diff_uci[[i]]=as.data.frame(cbind(L_diff_uci[[i]], timevar ,state=rep(i,nrow(L_diff_uci[[i]] )) )) } } else { for (i in 1:length(myjson2()$losd_uci)) { L_diff_uci[[i]]=as.data.frame(myjson2()$losd_uci[[i]][,1]) } for (i in 1:length(myjson2()$losd_uci)) { L_diff_uci[[i]]=as.data.frame(c(L_diff_uci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$losd_uci[[i]][,1])) )) ) colnames(L_diff_uci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_losd_uci=list() data_losd_uci[[1]]=L_diff_uci[[1]] for (u in 2:(length(myjson2()$losd_uci))) { data_losd_uci[[u]]=rbind(L_diff_uci[[u]],data_losd_uci[[(u-1)]]) } datald_uci=data_losd_uci[[length(myjson2()$losd_uci)]] datald_uci$state_fac=c(rep("NA",nrow(datald_uci))) for (o in 1:(length(myjson2()$losd_uci))) { for (g in 1:nrow(datald_uci)) { if (datald_uci$state[g]==o) {datald_uci$state_fac[g]=labels_state()[o]} } } datald_uci }) data_L_diff1_lci <- reactive ({ L_diff_lci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$losd_lci)) { L_diff_lci[[i]]=as.data.frame(t(data.frame(myjson2()$losd_lci[i]))) colnames(L_diff_lci[[i]]) <- v_diff } for(i in 1:length(myjson2()$losd_lci)) { L_diff_lci[[i]]=as.data.frame(cbind(L_diff_lci[[i]], timevar ,state=rep(i,nrow(L_diff_lci[[i]] )) )) } } else { for (i in 1:length(myjson2()$losd_lci)) { L_diff_lci[[i]]=as.data.frame(myjson2()$losd_lci[[i]][,1]) } for (i in 1:length(myjson2()$losd_lci)) { L_diff_lci[[i]]=as.data.frame(c(L_diff_lci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$losd_lci[[i]][,1])) )) ) colnames(L_diff_lci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_losd_lci=list() data_losd_lci[[1]]=L_diff_lci[[1]] for (u in 2:(length(myjson2()$losd_lci))) { data_losd_lci[[u]]=rbind(L_diff_lci[[u]],data_losd_lci[[(u-1)]]) } datald_lci=data_losd_lci[[length(myjson2()$losd_lci)]] datald_lci$state_fac=c(rep("NA",nrow(datald_lci))) for (o in 1:(length(myjson2()$losd_lci))) { for (g in 1:nrow(datald_lci)) { if (datald_lci$state[g]==o) {datald_lci$state_fac[g]=labels_state()[o]} } } datald_lci }) data_L_diff2<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_L_diff1()[,d],data_L_diff1()[,ncol(data_L_diff1())-2],data_L_diff1()[,ncol(data_L_diff1())-1], data_L_diff1()[,ncol(data_L_diff1())],rep(d,length(data_L_diff1()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_L_diff1())[d],length(data_L_diff1()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_ld <- bind_rows(dlist, .id = "column_label") d_all_ld }) data_L_diff2_uci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_L_diff1_uci()[,d],data_L_diff1_uci()[,ncol(data_L_diff1_uci())-2], data_L_diff1_uci()[,ncol(data_L_diff1_uci())-1], data_L_diff1_uci()[,ncol(data_L_diff1_uci())],rep(d,length(data_L_diff1_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_L_diff1_uci())[d],length(data_L_diff1_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_ld_uci <- bind_rows(dlist, .id = "column_label") d_all_ld_uci }) data_L_diff2_lci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_L_diff1_lci()[,d],data_L_diff1_lci()[,ncol(data_L_diff1_lci())-2], data_L_diff1_lci()[,ncol(data_L_diff1_lci())-1], data_L_diff1_lci()[,ncol(data_L_diff1_lci())],rep(d,length(data_L_diff1_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_L_diff1_lci())[d],length(data_L_diff1_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_ld_lci <- bind_rows(dlist, .id = "column_label") d_all_ld_lci }) data_L_diff_ci<- reactive ({ x=c( data_L_diff2()[order(data_L_diff2()$timevar,data_L_diff2()$state,data_L_diff2()$cov),]$timevar, data_L_diff2()[order(-data_L_diff2()$timevar,data_L_diff2()$state,data_L_diff2()$cov),]$timevar ) y_central=c( data_L_diff2()[order(data_L_diff2()$timevar,data_L_diff2()$state,data_L_diff2()$cov),]$V, data_L_diff2()[order(-data_L_diff2()$timevar,data_L_diff2()$state,data_L_diff2()$cov),]$V ) y=c( data_L_diff2_uci()[order(data_L_diff2_uci()$timevar,data_L_diff2_uci()$state,data_L_diff2_uci()$cov),]$V, data_L_diff2_lci()[order(-data_L_diff2_lci()$timevar,data_L_diff2_lci()$state,data_L_diff2_lci()$cov),]$V ) frameto=c(as.character(data_L_diff2_uci()[order(-data_L_diff2_uci()$timevar,data_L_diff2_uci()$state,data_L_diff2_uci()$cov),]$state_factor), as.character(data_L_diff2_lci()[order(-data_L_diff2_lci()$timevar,data_L_diff2_lci()$state,data_L_diff2_lci()$cov),]$state_factor) ) covto=c( data_L_diff2_uci()[order(-data_L_diff2_uci()$timevar,data_L_diff2_uci()$state,data_L_diff2_uci()$cov),]$cov_factor, data_L_diff2_lci()[order(-data_L_diff2_lci()$timevar,data_L_diff2_lci()$state,data_L_diff2_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) #output$shouldloadlos4 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotlos4", label = h2("Download the plot")) #}) datalos4_re <- reactive({ ax <- list( title = "",zeroline = FALSE,showline = FALSE, showticklabels = FALSE, showgrid = FALSE) if (length(myjson2()$losd) == 0 | myjson2()$Nats==1 ) { L_state_d= plot_ly() %>% layout(title=list(text="Not applicable- Only one covariate pattern specified",y=0.95),xaxis=ax, yaxis=ax) L_state_d } else { if (input$conflos=="ci_no") { if (input$facetlos=="No") { L_state_d= plot_ly(data_L_diff2(),alpha=0.5) %>% add_lines( x=data_L_diff2()$timevar,y=data_L_diff2()$V, frame=factor(as.factor(data_L_diff2()$state_factor),levels=labels_state()), color=as.factor(data_L_diff2()$cov_factor), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text="Differences in length of stay among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizelos, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$steplosx, tick0 = input$startlosx, range=c(input$startlosx,input$endlosx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Ratios in length of stay", dtick = input$steplosy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) ) %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) L_state_d } if (input$facetlos=="Yes") { data_plot=data_L_diff2() L_state_d = ggplot(data_plot) L_state_d = ggplot(data_plot,aes(x=timevar, y=V, color=factor(as.factor(cov_factor) ), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Differences in length of stay: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor) )))) L_state_d = L_state_d+geom_line(aes(x=timevar, y=V, color= factor(as.factor(cov_factor) )))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") {L_state_d = L_state_d+ facet_wrap(~factor(as.factor(state_factor),levels= labels_state() ),nrow=2)} else if (input$aimtype=="present") {L_state_d = L_state_d+ facet_wrap(~factor(as.factor(state_factor),levels= labels_state() ) )} L_state_d = L_state_d + scale_x_continuous(breaks=c(seq(input$startlosx,input$endlosx,by=input$steplosx ))) L_state_d = L_state_d +labs(title="Differences in length of stay among covariate patterns", x="Time since entry", y="Differences in length of stay") L_state_d = L_state_d + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") L_state_d = L_state_d +theme(title = element_text(size = input$textsizelos-4), strip.text = element_text(size=input$textfacetlos), legend.title = element_text(color="black", size= input$textsizelos-5), legend.text=element_text(size= input$textsizelos-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizelos-5), axis.title.x = element_text(size= input$textsizelos-5), axis.text.x = element_text( size=input$textsizelos-6),axis.text.y = element_text( size=input$textsizelos-6)) L_state_d = ggplotly(L_state_d, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) L_state_d } } else if (input$conflos=="ci_yes") { if (input$facetlos=="No") { L_state_d <- plot_ly() L_state_d <- add_trace(L_state_d, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_L_diff_ci()$x, y=data_L_diff_ci()$y_central, frame=factor(as.factor(data_L_diff_ci()$frameto),levels= labels_state() ), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_L_diff_ci()$covto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) L_state_d <- add_trace(L_state_d, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_L_diff_ci()$x, y=data_L_diff_ci()$y, frame=factor(as.factor(data_L_diff_ci()$frameto),levels= labels_state() ), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_L_diff_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) L_state_d= L_state_d %>% layout(title=list(text="Differences in length of stay among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizelos, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$steplosx, tick0 = input$startlosx, range=c(input$startlosx,input$endlosx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Differences in length of stay", dtick = input$steplosy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) L_state_d } if (input$facetlos=="Yes") { V_lci= data_L_diff2_lci()$V V_uci= data_L_diff2_uci()$V data_plot=cbind(data_L_diff2(),V_lci,V_uci) L_state_d=ggplot(data_plot) L_state_d=ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor) ), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Differences in length of stay: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor) ))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) L_state_d=L_state_d+geom_line(aes(x=timevar, y=V, fill= factor(as.factor(cov_factor) ))) L_state_d=L_state_d+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill= factor(as.factor(cov_factor) )),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") {L_state_d = L_state_d+ facet_wrap(~factor(as.factor(state_factor),levels= labels_state() ),nrow=2)} else if (input$aimtype=="present") {L_state_d = L_state_d+ facet_wrap(~factor(as.factor(state_factor),levels= labels_state() ))} L_state_d = L_state_d + scale_x_continuous(breaks=c(seq(input$startlosx,input$endlosx,by=input$steplosx ))) L_state_d = L_state_d +labs(title="Differences in length of stay among covariate patterns", x="Time since entry", y="Differences in length of stay") L_state_d = L_state_d + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") L_state_d = L_state_d +theme(title = element_text(size = input$textsizelos-4), strip.text = element_text(size=input$textfacetlos), legend.title = element_text(color="black", size= input$textsizelos-5), legend.text=element_text(size= input$textsizelos-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizelos-5), axis.title.x = element_text(size= input$textsizelos-5), axis.text.x = element_text( size=input$textsizelos-6),axis.text.y = element_text( size=input$textsizelos-6)) L_state_d = ggplotly(L_state_d, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } L_state_d } }) output$los_diff <- renderPlotly ({ datalos4_re() }) output$downplotlos4 <- downloadHandler( filename = function(){paste("los4",'.png',sep='')}, content = function(file){ plotly_IMAGE( datalos4_re(),width = 1400, height = 1100, format = "png", scale = 2, out_file = file ) } ) ###################################################################################### ##################################################################################### data_L_ratio1 <- reactive ({ los_ratio=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$losr)) { los_ratio[[i]]=as.data.frame(t(data.frame(myjson2()$losr[i]))) colnames(los_ratio[[i]]) <- v_ratio } for(i in 1:length(myjson2()$losr)) { los_ratio[[i]]=as.data.frame(cbind(los_ratio[[i]], timevar ,state=rep(i,nrow(los_ratio[[i]] )) )) } } else { for (i in 1:length(myjson2()$losr)) { los_ratio[[i]]=as.data.frame(myjson2()$losr[[i]][,1]) } for (i in 1:length(myjson2()$losr)) { los_ratio[[i]]=as.data.frame(c(los_ratio[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$losr[[i]][,1])) )) ) colnames(los_ratio[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the different states data_losr=list() data_losr[[1]]=los_ratio[[1]] for (u in 2:(length(myjson2()$losr))) { data_losr[[u]]=rbind(los_ratio[[u]],data_losr[[(u-1)]]) } datalr=data_losr[[length(myjson2()$losr)]] datalr$state_fac=c(rep("NA",nrow(datalr))) for (o in 1:(length(myjson2()$losr))) { for (g in 1:nrow(datalr)) { if (datalr$state[g]==o) {datalr$state_fac[g]=labels_state()[o]} } } datalr }) data_L_ratio1_uci <- reactive ({ L_ratio_uci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$losr_uci)) { L_ratio_uci[[i]]=as.data.frame(t(data.frame(myjson2()$losr_uci[i]))) colnames(L_ratio_uci[[i]]) <- v_ratio } for(i in 1:length(myjson2()$losr_uci)) { L_ratio_uci[[i]]=as.data.frame(cbind(L_ratio_uci[[i]], timevar ,state=rep(i,nrow(L_ratio_uci[[i]] )) )) } } else { for (i in 1:length(myjson2()$losr_uci)) { L_ratio_uci[[i]]=as.data.frame(myjson2()$losr_uci[[i]][,1]) } for (i in 1:length(myjson2()$losr_uci)) { L_ratio_uci[[i]]=as.data.frame(c(L_ratio_uci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$losr_uci[[i]][,1])) )) ) colnames(L_ratio_uci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the ratioerent states data_losr_uci=list() data_losr_uci[[1]]=L_ratio_uci[[1]] for (u in 2:(length(myjson2()$losr_uci))) { data_losr_uci[[u]]=rbind(L_ratio_uci[[u]],data_losr_uci[[(u-1)]]) } datalr_uci=data_losr_uci[[length(myjson2()$losr_uci)]] datalr_uci$state_fac=c(rep("NA",nrow(datalr_uci))) for (o in 1:(length(myjson2()$losr_uci))) { for (g in 1:nrow(datalr_uci)) { if (datalr_uci$state[g]==o) {datalr_uci$state_fac[g]=labels_state()[o]} } } datalr_uci }) data_L_ratio1_lci <- reactive ({ L_ratio_lci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$losr_lci)) { L_ratio_lci[[i]]=as.data.frame(t(data.frame(myjson2()$losr_lci[i]))) colnames(L_ratio_lci[[i]]) <- v_ratio } for(i in 1:length(myjson2()$losr_lci)) { L_ratio_lci[[i]]=as.data.frame(cbind(L_ratio_lci[[i]], timevar ,state=rep(i,nrow(L_ratio_lci[[i]] )) )) } } else { for (i in 1:length(myjson2()$losr_lci)) { L_ratio_lci[[i]]=as.data.frame(myjson2()$losr_lci[[i]][,1]) } for (i in 1:length(myjson2()$losr_lci)) { L_ratio_lci[[i]]=as.data.frame(c(L_ratio_lci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$losr_lci[[i]][,1])) )) ) colnames(L_ratio_lci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the ratioerent states data_losr_lci=list() data_losr_lci[[1]]=L_ratio_lci[[1]] for (u in 2:(length(myjson2()$losr_lci))) { data_losr_lci[[u]]=rbind(L_ratio_lci[[u]],data_losr_lci[[(u-1)]]) } datalr_lci=data_losr_lci[[length(myjson2()$losr_lci)]] datalr_lci$state_fac=c(rep("NA",nrow(datalr_lci))) for (o in 1:(length(myjson2()$losr_lci))) { for (g in 1:nrow(datalr_lci)) { if (datalr_lci$state[g]==o) {datalr_lci$state_fac[g]=labels_state()[o]} } } datalr_lci }) data_L_ratio2<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_L_ratio1()[,d],data_L_ratio1()[,ncol(data_L_ratio1())-2],data_L_ratio1()[,ncol(data_L_ratio1())-1], data_L_ratio1()[,ncol(data_L_ratio1())],rep(d,length(data_L_ratio1()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_L_ratio1())[d],length(data_L_ratio1()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_lr <- bind_rows(dlist, .id = "column_label") d_all_lr }) data_L_ratio2_uci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_L_ratio1_uci()[,d],data_L_ratio1_uci()[,ncol(data_L_ratio1_uci())-2], data_L_ratio1_uci()[,ncol(data_L_ratio1_uci())-1], data_L_ratio1_uci()[,ncol(data_L_ratio1_uci())],rep(d,length(data_L_ratio1_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_L_ratio1_uci())[d],length(data_L_ratio1_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_lr_uci <- bind_rows(dlist, .id = "column_label") d_all_lr_uci }) data_L_ratio2_lci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_L_ratio1_lci()[,d],data_L_ratio1_lci()[,ncol(data_L_ratio1_lci())-2], data_L_ratio1_lci()[,ncol(data_L_ratio1_lci())-1], data_L_ratio1_lci()[,ncol(data_L_ratio1_lci())],rep(d,length(data_L_ratio1_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_L_ratio1_lci())[d],length(data_L_ratio1_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_lr_lci <- bind_rows(dlist, .id = "column_label") d_all_lr_lci }) data_L_ratio_ci<- reactive ({ x=c( data_L_ratio2()[order(data_L_ratio2()$timevar,data_L_ratio2()$state,data_L_ratio2()$cov),]$timevar, data_L_ratio2()[order(-data_L_ratio2()$timevar,data_L_ratio2()$state,data_L_ratio2()$cov),]$timevar ) y_central=c( data_L_ratio2()[order(data_L_ratio2()$timevar,data_L_ratio2()$state,data_L_ratio2()$cov),]$V, data_L_ratio2()[order(-data_L_ratio2()$timevar,data_L_ratio2()$state,data_L_ratio2()$cov),]$V ) y=c( data_L_ratio2_uci()[order(data_L_ratio2_uci()$timevar,data_L_ratio2_uci()$state,data_L_ratio2_uci()$cov),]$V, data_L_ratio2_lci()[order(-data_L_ratio2_lci()$timevar,data_L_ratio2_lci()$state,data_L_ratio2_lci()$cov),]$V ) frameto=c(as.character(data_L_ratio2_uci()[order(-data_L_ratio2_uci()$timevar,data_L_ratio2_uci()$state,data_L_ratio2_uci()$cov),]$state_factor), as.character(data_L_ratio2_lci()[order(-data_L_ratio2_lci()$timevar,data_L_ratio2_lci()$state,data_L_ratio2_lci()$cov),]$state_factor) ) covto=c( data_L_ratio2_uci()[order(-data_L_ratio2_uci()$timevar,data_L_ratio2_uci()$state,data_L_ratio2_uci()$cov),]$cov_factor, data_L_ratio2_lci()[order(-data_L_ratio2_lci()$timevar,data_L_ratio2_lci()$state,data_L_ratio2_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) #output$shouldloadlos5 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotlos5", label = h2("Download the plot")) #}) datalos5_re <- reactive({ ax <- list( title = "",zeroline = FALSE,showline = FALSE, showticklabels = FALSE, showgrid = FALSE) if (length(myjson2()$losr) == 0| myjson2()$Nats==1 ) { L_state_r= plot_ly() %>% layout(title=list(text="Not applicable- Only one covariate pattern specified",y=0.95),xaxis=ax, yaxis=ax) L_state_r } else { if (input$conflos=="ci_no") { if (input$facetlos=="No") { L_state_r= plot_ly(data_L_ratio2(),alpha=0.5) %>% add_lines( x=data_L_ratio2()$timevar,y=data_L_ratio2()$V, frame=factor(as.factor(data_L_ratio2()$state_factor),levels=labels_state()), color=as.factor(data_L_ratio2()$cov_factor), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text="Ratios in length of stay among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizelos, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$steplosx, tick0 = input$startlosx, range=c(input$startlosx,input$endlosx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Ratios in length of stay", dtick = input$steplosy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) ) %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) L_state_r } if (input$facetlos=="Yes") { data_plot=data_L_ratio2() L_state_r = ggplot(data_plot) L_state_r = ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor) ), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Ratio of length of stay: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor) )))) L_state_r = L_state_r+geom_line(aes(x=timevar, y=V, color=factor(as.factor(cov_factor) )))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") {L_state_r = L_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()),nrow=2)} else if (input$aimtype=="present") {L_state_r = L_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state() ) )} L_state_r = L_state_r + scale_x_continuous(breaks=c(seq(input$startlosx,input$endlosx,by=input$steplosx ))) L_state_r = L_state_r +labs(title="Ratios in length of stay among covariate patterns", x="Time since entry", y="Ratios in length of stay") L_state_r = L_state_r + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") L_state_r = L_state_r+theme(title = element_text(size = input$textsizelos-4), strip.text = element_text(size=input$textfacetlos) , legend.title = element_text(color="black", size= input$textsizelos-5), legend.text=element_text(size= input$textsizelos-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizelos-5), axis.title.x = element_text(size= input$textsizelos-5), axis.text.x = element_text( size=input$textsizelos-6),axis.text.y = element_text( size=input$textsizelos-6)) L_state_r = ggplotly(L_state_r, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) L_state_r } } else if (input$conflos=="ci_yes") { if (input$facetlos=="No") { L_state_r <- plot_ly() L_state_r <- add_trace(L_state_r, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_L_ratio_ci()$x, y=data_L_ratio_ci()$y_central, frame=factor(as.factor(data_L_ratio_ci()$frameto),levels = labels_state() ), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_L_ratio_ci()$covto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) L_state_r <- add_trace(L_state_r, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_L_ratio_ci()$x, y=data_L_ratio_ci()$y, frame=factor(as.factor(data_L_ratio_ci()$frameto),levels = labels_state() ), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_L_ratio_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) L_state_r= L_state_r %>% layout(title=list(text="Ratios in length of stay among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizelos, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$steplosx, tick0 = input$startlosx, range=c(input$startlosx,input$endlosx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Ratios in length of stay", dtick = input$stepy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) L_state_r } if (input$facetlos=="Yes") { V_lci= data_L_ratio2_lci()$V V_uci= data_L_ratio2_uci()$V data_plot=cbind(data_L_ratio2(),V_lci,V_uci) L_state_r=ggplot(data_plot) L_state_r=ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor) ), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Ratio of length of stay: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor) ))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) L_state_r=L_state_r+geom_line(aes(x=timevar, y=V, fill= factor(as.factor(cov_factor) ))) L_state_r=L_state_r+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=factor(as.factor(cov_factor))),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") {L_state_r = L_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state() ),nrow=2)} else if (input$aimtype=="present") {L_state_r = L_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state() ))} L_state_r = L_state_r + scale_x_continuous(breaks=c(seq(input$startlosx,input$endlosx,by=input$steplosx ))) L_state_r = L_state_r +labs(title="Ratios in length of stay among covariate patterns", x="Time since entry", y="Ratios in length of stay") L_state_r = L_state_r + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") L_state_r = L_state_r +theme(title = element_text(size = input$textsizelos-4), strip.text = element_text(size=input$textfacetlos), legend.title = element_text(color="black", size= input$textsizelos-5), legend.text=element_text(size= input$textsizelos-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizelos-5), axis.title.x = element_text(size= input$textsizelos-5), axis.text.x = element_text( size=input$textsizelos-6),axis.text.y = element_text( size=input$textsizelos-6)) L_state_r = ggplotly(L_state_r, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } L_state_r } }) output$los_ratio <- renderPlotly ({datalos5_re()}) output$downplotlos5 <- downloadHandler( filename = function(){paste("los5",'.png',sep='')}, content = function(file){ plotly_IMAGE( datalos5_re(),width = 1400, height = 1100, format = "png", scale = 2, out_file = file ) } ) ###### Show and hide and tick inputs #### timerv <- reactiveVal(1.5) observeEvent(c(input$showtickvis,invalidateLater(1000, session)), { if(input$showtickvis=="No"){ hide("tickinputvisit") } if(input$showtickvis=="Yes"){ show("tickinputvisit") } isolate({ timerv(timerv()-1) if(timerv()>1 & input$showtickvis=="No") { show("tickinputvisit") } }) }) ################################################ existvisit <- reactive({ if (length(myjson2()$visit) != 0) { x= 1 } else if (length(myjson2()$visit) == 0) { x= 0 } }) existvisitratio <- reactive({ if (length(myjson2()$visitr) != 0) { x= 1 } else if (length(myjson2()$visitr) == 0) { x= 0 } }) existvisitdiff <- reactive({ if (length(myjson2()$visitd) != 0) { x= 1 } else if (length(myjson2()$visitd) == 0) { x= 0 } }) output$pagevisit <- renderUI({ if (is.null(myjson2())) return("Provide the json file with the predictions") if (existvisit()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(12, shinyjs::useShinyjs(), output$loginpagevisit <- renderUI({h1("Non applicable")}) ) ) } else if (existvisit()==1) { if (existvisitdiff()==1 & existvisitratio()==1) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, shinyjs::useShinyjs(), h1("Visit probabilities"), conditionalPanel(condition="input.tabsvis =='#panel1vis'||input.tabsvis =='#panel2vis'||input.tabsvis=='#panel4vis'||input.tabsvis =='#panel5vis'", uiOutput("facetvis") ) , uiOutput("confvis") #radioButtons("displayvis", "Change labels of states and covariate patterns", # c("same" = "same", "change"= "change")), #uiOutput("covarinputvis"), #uiOutput("statesinputvis") ), column(2, br(), p(""), radioButtons("showtickvis", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected ="No"), uiOutput("tickinputvisit") ), column(7, tabsetPanel(id = "tabsvis", tabPanel(h2("By state"), value = "#panel1vis", plotlyOutput("visit_state" , height="600px", width = "100%"),uiOutput("shouldloadvis1")), tabPanel(h2("By covariate pattern"), value = "#panel2vis", plotlyOutput("visit_cov" , height="600px", width = "100%"),uiOutput("shouldloadvis2")), tabPanel(h2("By state and covariate pattern"), value = "#panel3vis", plotlyOutput("visit_both" , height="600px", width = "100%"),uiOutput("shouldloadvis3")), tabPanel(h2("Differences"), value = "#panel4vis", plotlyOutput("visit_diff" , height="600px", width = "100%"),uiOutput("shouldloadvis4")), tabPanel(h2("Ratios"), value = "#panel5vis", plotlyOutput("visit_ratio" , height="600px", width = "100%"),uiOutput("shouldloadvis5")) ) ) ) } else if (existvisitdiff()==1 & existvisitratio()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, shinyjs::useShinyjs(), h1("Visit probabilities"), conditionalPanel(condition="input.tabsvis =='#panel1vis'||input.tabsvis =='#panel2vis'||input.tabsvis=='#panel4vis'||input.tabsvis =='#panel5vis'", uiOutput("facetvis") ) , uiOutput("confvis") #radioButtons("displayvis", "Change labels of states and covariate patterns", # c("same" = "same", "change"= "change")), #uiOutput("covarinputvis"), #uiOutput("statesinputvis") ), column(2, br(), p(""), radioButtons("showtickvis", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No"), uiOutput("tickinputvisit") ), column(7, tabsetPanel(id = "tabsvis", tabPanel(h2("By state"), value = "#panel1vis", plotlyOutput("visit_state" , height="600px", width = "100%"),uiOutput("shouldloadvis1")), tabPanel(h2("By covariate pattern"), value = "#panel2vis", plotlyOutput("visit_cov" , height="600px", width = "100%"),uiOutput("shouldloadvis2")), tabPanel(h2("By state and covariate pattern"),value = "#panel3vis", plotlyOutput("visit_both" , height="600px", width = "100%"),uiOutput("shouldloadvis3")), tabPanel(h2("Differences"), value = "#panel4vis", plotlyOutput("visit_diff" , height="600px", width = "100%"),uiOutput("shouldloadvis4")), tabPanel(h2("Ratios"), value = "#panel5vis", print("Not applicable")) ) ) ) } else if ( existvisitdiff()==0 & existvisitratio()==1) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Visit probabilities"), conditionalPanel(condition="input.tabsvis =='#panel1vis'||input.tabsvis =='#panel2vis'||input.tabsvis=='#panel4vis'||input.tabsvis =='#panel5vis'", uiOutput("facetvis") ) , uiOutput("confvis") #radioButtons("displayvis", "Change labels of states and covariate patterns", # c("same" = "same", "change"= "change")), #uiOutput("covarinputvis"), #uiOutput("statesinputvis") ), column(2, br(), p(""), radioButtons("showtickvis", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No"), uiOutput("tickinputvisit") ), column(7, tabsetPanel(id = "tabsvis", tabPanel(h2("By state"), value = "#panel1vis", plotlyOutput("visit_state" , height="600px", width = "100%"),uiOutput("shouldloadvis1")), tabPanel(h2("By covariate pattern"), value = "#panel2vis", plotlyOutput("visit_cov" , height="600px", width = "100%"),uiOutput("shouldloadvis2")), tabPanel(h2("By state and covariate pattern"), value = "#panel3vis", plotlyOutput("visit_both" , height="600px", width = "100%"),uiOutput("shouldloadvis3")), tabPanel(h2("Differences"), value = "#panel4vis", print("Not applicable")), tabPanel(h2("Ratios"), value = "#panel5vis", plotlyOutput("visit_ratio" , height="600px", width = "100%"),uiOutput("shouldloadvis5")) ) ) ) } else if (existvisitdiff()==0 & existvisitratio()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Visit probabilities"), conditionalPanel(condition="input.tabsvis =='#panel1vis'||input.tabsvis =='#panel2vis'||input.tabsvis=='#panel4vis'||input.tabsvis =='#panel5vis'", uiOutput("facetvis") ) , uiOutput("confvis") #radioButtons("displayvis", "Change labels of states and covariate patterns", # c("same" = "same", "change"= "change")), #uiOutput("covarinputvis"), #uiOutput("statesinputvis") ), column(2, br(), p(""), radioButtons("showtickvis", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No"), uiOutput("tickinputvisit") ), column(7, tabsetPanel(id = "tabsvis", tabPanel(h2("By state"), value = "#panel1vis", plotlyOutput("visit_state" , height="600px", width = "100%"),uiOutput("shouldloadvis1")), tabPanel(h2("By covariate pattern"), value = "#panel2vis", plotlyOutput("visit_cov" , height="600px", width = "100%"),uiOutput("shouldloadvis2")), tabPanel(h2("By state and covariate pattern"),value = "#panel3vis", plotlyOutput("visit_both" , height="600px", width = "100%"),uiOutput("shouldloadvis3")), tabPanel(h2("Differences"), value = "#panel4vis", print("Not applicable")), tabPanel(h2("Ratios"), value = "#panel5vis", print("Not applicable")) ) ) ) } } }) observeEvent(input$json2, { if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Visit')))==0 ) { js$disableTab("mytab_vis") } }) observeEvent(input$csv2, { if( length(which(startsWith(names( read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Visit')))==0 ) { js$disableTab("mytab_vis") } }) #output$displayvisit <- renderUI({ # radioButtons("displayvisit", "Change labels of states and covariate patterns", # c("same" = "same", "change"= "change")) #}) output$facetvis <- renderUI({ radioButtons(inputId="facetvis", label= "Display graph in grids", choices=c("No","Yes"),selected = "No") }) output$confvis <- renderUI({ if (length(myjson2()$ci_visit)!=0) { radioButtons("confvis", "Confidence intervals", c("No" = "ci_no", "Yes" ="ci_yes")) } else if (length(myjson2()$ci_visit)==0) { item_list <- list() item_list[[1]]<- radioButtons("confvis", "Confidence intervals",c("No" = "ci_no")) item_list[[2]]<-print("Confidence interval data were not provided") do.call(tagList, item_list) } }) ################################################## ###### Will appear conditionally################## ################################################### #Create the reactive input of covariates #output$covarinputvis <- renderUI({ # # if (is.null(myjson2())) return() # # if (input$displayvis=="same") return() # # else { # # item_list <- list() # item_list[[1]] <- h2("Covariate patterns") # # v=vector() # for (i in 1:length(myjson2()$cov$atlist)) { # v[i]=myjson2()$cov$atlist[i] # } # # default_choices_cov=v # for (i in seq(length(myjson2()$cov$atlist))) { # item_list[[i+1]] <- textInput(paste0('covvis', i),default_choices_cov[i], labels_cov()[i]) # } # # do.call(tagList, item_list) # } #}) # #labels_covvis<- reactive ({ # # if (input$displayvis=="same") {labels_cov()} # # else { # # myList<-vector("list",length(myjson2()$cov$atlist)) # for (i in 1:length(myjson2()$cov$atlist)) { # myList[[i]]= input[[paste0('covvis', i)]][1] # } # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # final_list # } #}) # ##Create the reactive input of states # #output$statesinputvis <- renderUI({ # # if (input$displayvis=="same") return() # # else { # # item_list <- list() # item_list[[1]] <- h2("States") # default_choices_state=vector() # # title_choices_state=vector() # for (i in 1:length(myjson2()$P)) { # title_choices_state[i]=paste0("State"," ",input$select,selectend()[i]) # } # for (i in 1:length(myjson2()$P)) { # # item_list[[1+i]] <- textInput(paste0('statevis',i),title_choices_state[i], labels_state()[i]) # # } # do.call(tagList, item_list) # } #}) # #labels_statevis<- reactive ({ # # if (input$displayvis=="same") {labels_state()} # else { # # myList<-vector("list",length(myjson2()$P)) # for (i in 1:length(myjson2()$P)) { # # myList[[i]]= input[[paste0('statevis', i)]][1] # # } # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # final_list # } #}) ################################################################################## ################################################################################### data_V <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) { v[i]=myjson2()$cov$atlist[i] } ## Different variable of probabilities for each covariate pattern ## Different variable of probabilities for each covariate pattern vis=list() if (length(myjson2()$visit)==0) {return()} for(i in 1:length(myjson2()$visit)) { vis[[i]]=as.data.frame(t(data.frame(myjson2()$visit[i]))) colnames(vis[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$visit)) { vis[[i]]=as.data.frame(cbind(vis[[i]], timevar ,state=rep(i,nrow(vis[[i]] )) )) } # Append the probabilities datasets of the different states data_vis=list() data_vis[[1]]=vis[[1]] for (u in 2:(length(myjson2()$visit))) { data_vis[[u]]=rbind(vis[[u]],data_vis[[(u-1)]]) } datav=data_vis[[length(myjson2()$visit)]] datav }) data_V_uci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern vis_uci=list() if (length(myjson2()$visit_uci)==0) {return()} for(i in 1:length(myjson2()$visit_uci)) { vis_uci[[i]]=as.data.frame(t(data.frame(myjson2()$visit_uci[i]))) colnames(vis_uci[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$visit_uci)) { vis_uci[[i]]=as.data.frame(cbind(vis_uci[[i]], timevar ,state=rep(i,nrow(vis_uci[[i]] )) )) } # Append the probabilities datasets of the different states data_V_uci=list() data_V_uci[[1]]=vis_uci[[1]] for (u in 2:(length(myjson2()$visit_uci))) { data_V_uci[[u]]=rbind(vis_uci[[u]],data_V_uci[[(u-1)]]) } datav_uci=data_V_uci[[length(myjson2()$visit_uci)]] datav_uci }) data_V_lci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern vis_lci=list() if (length(myjson2()$visit_lci)==0) {return()} for(i in 1:length(myjson2()$visit_lci)) { vis_lci[[i]]=as.data.frame(t(data.frame(myjson2()$visit_lci[i]))) colnames(vis_lci[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$visit_lci)) { vis_lci[[i]]=as.data.frame(cbind(vis_lci[[i]], timevar ,state=rep(i,nrow(vis_lci[[i]] )) )) } # Append the probabilities datasets of the different states data_V_lci=list() data_V_lci[[1]]=vis_lci[[1]] for (u in 2:(length(myjson2()$visit_lci))) { data_V_lci[[u]]=rbind(vis_lci[[u]],data_V_lci[[(u-1)]]) } datav_lci=data_V_lci[[length(myjson2()$visit_lci)]] datav_lci }) data_V_st<-reactive ({ datanew=data_V() datanew$state_fac=ordered(c(rep("NA",nrow(datanew))), levels = labels_state() ) for (o in 1:(length(myjson2()$visit))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_V_st_uci<-reactive ({ datanew=data_V_uci() datanew$state_fac=ordered(c(rep("NA",nrow(datanew))), levels = labels_state() ) for (o in 1:(length(myjson2()$visit_uci))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_V_st_lci<-reactive ({ datanew=data_V_lci() datanew$state_fac=ordered(c(rep("NA",nrow(datanew))), levels = labels_state() ) for (o in 1:(length(myjson2()$visit_lci))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_V_d <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_V_st()[,d],data_V_st()[,ncol(data_V_st())-2],data_V_st()[,ncol(data_V_st())-1],data_V_st()[,ncol(data_V_st())],rep(d,length(data_V_st()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_V_st())[d],length(data_V_st()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_v <- bind_rows(dlist, .id = "column_label") d_all_v }) data_V_d_uci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_V_st_uci()[,d],data_V_st_uci()[,ncol(data_V_st_uci())-2], data_V_st_uci()[,ncol(data_V_st_uci())-1], data_V_st_uci()[,ncol(data_V_st_uci())],rep(d,length(data_V_st_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_V_st_uci())[d],length(data_V_st_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_v_uci <- bind_rows(dlist, .id = "column_label") d_all_v_uci }) data_V_d_lci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_V_st_lci()[,d],data_V_st_lci()[,ncol(data_V_st_lci())-2], data_V_st_lci()[,ncol(data_V_st_lci())-1], data_V_st_lci()[,ncol(data_V_st_lci())],rep(d,length(data_V_st_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_V_st_lci())[d],length(data_V_st_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_v_lci <- bind_rows(dlist, .id = "column_label") d_all_v_lci }) output$tickinputvisit <- renderUI({ default_choices=c("black","blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen","tan1","lightslateblue","khaki4") if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <- h2("Provide x axis range and ticks") item_list[[2]] <-numericInput("startvx","Start x at:",value=min(data_V_d()$timevar),min=0,max=max(data_V_d()$timevar) ) item_list[[3]] <-numericInput("stepvx","step:",value=max(data_V_d()$timevar/10),min=0,max=max(data_V_d()$timevar)) item_list[[4]] <-numericInput("endvx","End x at:",value =max(data_V_d()$timevar),min=0,max=max(data_V_d()$timevar)) item_list[[5]] <-numericInput("stepvy","step at y axis:",value=0.2,min=0.001,max=1) item_list[[6]] <-numericInput("endvy","End y at:",value =1,min=0,max=1) item_list[[7]] <-numericInput("textsizevis",h2("Legends size"),value=input$textsize,min=5,max=30) item_list[[8]] <-numericInput("textfacetvis",h2("Facet title size"),value=input$textsize-3,min=5,max=30 ) do.call(tagList, item_list) }) data_V_ci<- reactive ({ x=c( data_V_d()[order(data_V_d()$timevar,data_V_d()$state,data_V_d()$cov),]$timevar, data_V_d_lci()[order(-data_V_d()$timevar,data_V_d()$state,data_V_d()$cov),]$timevar ) y_central=c( data_V_d()[order(data_V_d()$timevar,data_V_d()$state,data_V_d()$cov),]$V, data_V_d()[order(-data_V_d()$timevar,data_V_d()$state,data_V_d()$cov),]$V ) y=c( data_V_d_uci()[order(data_V_d_uci()$timevar,data_V_d_uci()$state,data_V_d_uci()$cov),]$V, data_V_d_lci()[order(-data_V_d_uci()$timevar,data_V_d_uci()$state,data_V_d_uci()$cov),]$V ) frameto=c(as.character(data_V_d_uci()[order(-data_V_d_uci()$timevar,data_V_d_uci()$state,data_V_d_uci()$cov),]$state_factor), as.character(data_V_d_lci()[order(-data_V_d_lci()$timevar,data_V_d_lci()$state,data_V_d_lci()$cov),]$state_factor) ) covto=c( data_V_d_uci()[order(-data_V_d_uci()$timevar,data_V_d_uci()$state,data_V_d_uci()$cov),]$cov_factor, data_V_d_lci()[order(-data_V_d_lci()$timevar,data_V_d_lci()$state,data_V_d_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) ###################################### #output$shouldloadvis1 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotvis1", label = h2("Download the plot")) #}) datavis1_re <- reactive ({ ####### Plot 1 frame is state, factor is cov ######################## if (input$confvis=="ci_no") { if (input$facetvis=="No") { vis_state= plot_ly(data_V_d(),alpha=0.5) %>% add_lines( x=data_V_d()$timevar,y=data_V_d()$V, frame=factor(as.factor(data_V_d()$state_factor),levels = labels_state()), color=factor(as.factor(data_V_d()$cov_factor),levels = labels_cov()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], mode="lines", line=list(simplify=FALSE,color = labels_colour_cov()) , text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) vis_state = vis_state %>% layout(title=list(text="Probability of visit for each covariate pattern among states",y=0.95), font= list(family = "times new roman", size = input$textsizevis, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepvx, tick0 = input$startvx, range=c(input$startvx,input$endvx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of visit",rangemode = "nonnegative", dtick = input$stepvy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$facetvis=="Yes") { data_plot=data_V_d() vis_state = ggplot(data_plot) vis_state = ggplot(data_plot,aes(x=timevar, y=V, color=factor(as.factor(cov_factor),levels=labels_cov()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Probability of visit: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor),levels=labels_cov())))) vis_state = vis_state+geom_line(aes(x=timevar, y=V, color= factor(as.factor(cov_factor),levels=labels_cov())))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { vis_state = vis_state+ facet_wrap(~ factor(as.factor(state_factor),levels = labels_state()), nrow=2)} else if (input$aimtype=="present") {vis_state = vis_state+ facet_wrap(~factor(as.factor(state_factor),levels = labels_state()))} vis_state = vis_state + scale_x_continuous(breaks=c(seq(input$startvx,input$endvx,by=input$stepvx ))) + scale_y_continuous(breaks=c(seq(0,input$endvy,by=input$stepvy ))) vis_state = vis_state +labs(title="Probability of visit for each covariate pattern among states", x="Time since entry", y="Probability of visit") vis_state = vis_state + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") vis_state = vis_state +theme(title = element_text(size = input$textsizevis-4), strip.text = element_text(size=input$textfacetvis), legend.title = element_text(color="black", size= input$textsizevis-5), legend.text=element_text(size= input$textsizevis-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizevis-5), axis.title.x = element_text(size= input$textsizevis-5), axis.text.x = element_text( size=input$textsizevis-6),axis.text.y = element_text( size=input$textsizevis-6)) vis_state = ggplotly(vis_state, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) vis_state } } else if (input$confvis=="ci_yes") { if (input$facetvis=="No") { vis_state <- plot_ly() vis_state <- add_trace(vis_state, line=list(simplify=FALSE,color = labels_colour_cov()), mode="lines", type = "scatter", x=data_V_ci()$x, y=data_V_ci()$y_central, frame=factor(as.factor(data_V_ci()$frameto),levels = labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=factor(as.factor(data_V_ci()$covto) ,levels = labels_cov()), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) vis_state <- add_trace(vis_state, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_V_ci()$x, y=data_V_ci()$y, frame=factor(as.factor(data_V_ci()$frameto),levels = labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=factor(as.factor(data_V_ci()$covto),levels = labels_cov()), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) vis_state = vis_state %>% layout(title=list(text="Probability of visit for each covariate pattern among states",y=0.95), font= list(family = "times new roman", size = input$textsizevis, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepvx, tick0 = input$startvx, range=c(input$startvx,input$endvx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of visit",rangemode = "nonnegative", dtick = input$stepvy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } if (input$facetvis=="Yes") { V_lci= data_V_d_lci()$V V_uci= data_V_d_uci()$V data_plot=cbind(data_V_d(),V_lci,V_uci) vis_state=ggplot(data_plot) vis_state=ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor),levels=labels_cov()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Probability of visit: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor),levels=labels_cov()))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) vis_state=vis_state+geom_line(aes(x=timevar, y=V, fill= factor(as.factor(cov_factor),levels=labels_cov()))) vis_state=vis_state+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=factor(as.factor(cov_factor),levels=labels_cov())),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { vis_state = vis_state+ facet_wrap(~factor(as.factor(state_factor),levels = labels_state()), nrow=2)} else if (input$aimtype=="present") {vis_state = vis_state+ facet_wrap(~factor(as.factor(state_factor),levels = labels_state()))} vis_state = vis_state + scale_x_continuous(breaks=c(seq(input$startvx,input$endvx,by=input$stepvx ))) + scale_y_continuous(breaks=c(seq(0,input$endvy,by=input$stepvy ))) vis_state = vis_state +labs(title="Probability of visit at each state", x="Time since entry", y="Probability of visit") vis_state = vis_state + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") vis_state = vis_state +theme(title = element_text(size = input$textsizevis-4), strip.text = element_text(size=input$textfacetvis), legend.title = element_text(color="black", size= input$textsizevis-5), legend.text=element_text(size= input$textsizevis-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizevis-5), axis.title.x = element_text(size= input$textsizevis-5), axis.text.x = element_text( size=input$textsizevis-6),axis.text.y = element_text( size=input$textsizevis-6)) vis_state = ggplotly(vis_state, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) vis_state } } vis_state }) output$visit_state <- renderPlotly ({ datavis1_re() }) output$downplotvis1 <- downloadHandler( filename = function(){paste("vis1",'.png',sep='')}, content = function(file){ plotly_IMAGE( datavis1_re(),width = 1400, height = 1100, format = "png", scale = 2, out_file = file ) } ) ##################################### #output$shouldloadvis2 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotvis2", label = h2("Download the plot")) #}) datavis2_re <- reactive ({ if (input$confvis=="ci_no") { if (input$facetvis=="No") { vis_cov= plot_ly(data_V_d(),alpha=0.5) %>% add_lines( x=data_V_d()$timevar,y=data_V_d()$V, frame=factor(as.factor(data_V_d()$cov_factor),levels = labels_cov()), color=factor(as.factor(data_V_d()$state_factor),levels = labels_state()), colors=labels_colour_state()[1:length(myjson2()$P)], mode="lines", line=list(simplify=FALSE,color = labels_colour_state()) , text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) vis_cov = vis_cov %>% layout(title=list(text="Probability of visit for each state among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizevis, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepvx, tick0 = input$startvx, range=c(input$startvx,input$endvx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of visit",rangemode = "nonnegative", dtick = input$stepvy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { vis_cov= vis_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } } if (input$facetvis=="Yes") { data_plot=data_V_d() vis_cov = ggplot(data_plot) vis_cov = ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(state_factor),levels=labels_state()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Probability of visit: ", V, "
State: ", factor(as.factor(state_factor),levels=labels_state())))) vis_cov = vis_cov+geom_line(aes(x=timevar, y=V, color= factor(as.factor(state_factor),levels=labels_state())))+ scale_colour_manual( values =labels_colour_state(),labels = labels_state() ) if (input$aimtype=="compare") {vis_cov = vis_cov+ facet_wrap(~factor(as.factor(cov_factor),levels = labels_cov()),nrow=2)} else if (input$aimtype=="present") {vis_cov = vis_cov+ facet_wrap(~factor(as.factor(cov_factor),levels = labels_cov())) } vis_cov = vis_cov + scale_x_continuous(breaks=c(seq(input$startvx,input$endvx,by=input$endvx ))) + scale_y_continuous(breaks=c(seq(0,input$endvy,by=input$stepvy ))) vis_cov = vis_cov +labs(title="Probability of visit for each state among covariate patterns", x="Time since entry", y="Probability of visit") vis_cov = vis_cov + labs(color = "States")+ labs(fill = "States") vis_cov = vis_cov+theme(title = element_text(size = input$textsizevis-4), strip.text = element_text(size=input$textfacetvis), legend.title = element_text(color="black", size= input$textsizevis-5), legend.text=element_text(size= input$textsizevis-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizevis-5), axis.title.x = element_text(size= input$textsizevis-5), axis.text.x = element_text( size=input$textsizevis-6),axis.text.y = element_text( size=input$textsizevis-6)) vis_cov = ggplotly(vis_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) vis_cov } } else if (input$confvis=="ci_yes") { if (input$facetvis=="No") { vis_cov <- plot_ly() vis_cov <- add_trace(vis_cov, line=list(simplify=FALSE,color = labels_colour_cov()), mode="lines", type = "scatter", x=data_V_ci()$x, y=data_V_ci()$y_central, frame=factor(as.factor(data_V_ci()$covto),levels = labels_cov()), colors=labels_colour_state()[1:length(myjson2()$P)], color=factor(as.factor(data_V_ci()$frameto) ,levels = labels_state()), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) vis_cov <- add_trace(vis_cov, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_V_ci()$x, y=data_V_ci()$y, frame=factor(as.factor(data_V_ci()$covto),levels = labels_cov()), colors=labels_colour_state()[1:length(myjson2()$P)], color=factor(as.factor(data_V_ci()$frameto) ,levels = labels_state()), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) vis_cov = vis_cov %>% layout(title=list(text="Probability of visit for each state among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizevis, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepvx, tick0 = input$startvx, range=c(input$startvx,input$endvx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of visit",rangemode = "nonnegative", dtick = input$stepvy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { vis_cov= vis_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } vis_cov } if (input$facetvis=="Yes") { V_lci= data_V_d_lci()$V V_uci= data_V_d_uci()$V data_plot=cbind(data_V_d(),V_lci,V_uci) vis_cov=ggplot(data_plot) vis_cov=ggplot(data_plot,aes(x=timevar, y=V, color=factor(as.factor(state_factor),levels=labels_state()), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Probability of visit: ", V, "
State: ", factor(as.factor(state_factor),levels=labels_state()))))+ scale_colour_manual( values =labels_colour_state(),labels = labels_state() ) vis_cov=vis_cov+geom_line(aes(x=timevar, y=V, fill= factor(as.factor(state_factor),levels=labels_state()))) vis_cov=vis_cov+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=factor(as.factor(state_factor),levels=labels_state())),alpha=0.4)+ scale_fill_manual( values =labels_colour_state(),labels = labels_state() ) if (input$aimtype=="compare") {vis_cov = vis_cov+ facet_wrap(~factor(as.factor(cov_factor),levels = labels_cov()),nrow=2)} else if (input$aimtype=="present") {vis_cov = vis_cov+ facet_wrap(~factor(as.factor(cov_factor),levels = labels_cov())) } vis_cov = vis_cov + scale_x_continuous(breaks=c(seq(input$startvx,input$endvx,by=input$stepvx ))) vis_cov = vis_cov +labs(title="Probability of visit for each state among covariate patterns", x="Time since entry", y="Probability of visit") vis_cov = vis_cov + labs(color = "States")+ labs(fill = "States") vis_cov = vis_cov+theme(title = element_text(size = input$textsizevis-4), strip.text = element_text(size=input$textfacetvis), legend.title = element_text(color="black", size= input$textsizevis-5), legend.text=element_text(size= input$textsizevis-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizevis-5), axis.title.x = element_text(size= input$textsizevis-5), axis.text.x = element_text( size=input$textsizevis-6),axis.text.y = element_text( size=input$textsizevis-6)) vis_cov = ggplotly(vis_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) vis_cov } } vis_cov }) output$visit_cov <- renderPlotly ({ datavis2_re() }) output$downplotvis2 <- downloadHandler( filename = function(){paste("vis2",'.png',sep='')}, content = function(file){ plotly_IMAGE( datavis2_re(),width = 1400, height = 1100, format = "png", scale = 2, out_file = file ) } ) ########################################## #output$shouldloadvis3 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotvis3", label = h2("Download the plot")) #}) datavis3_re <- reactive ({ if (input$confvis=="ci_no") { ####### Plot 3 f factor is state and cov ######################## v_cov_state = plot_ly(data_V_d(),alpha=0.5) %>% add_lines( x=data_V_d()$timevar,y=data_V_d()$V, color = as.factor(data_V_d()$cov_factor), fill =as.factor(data_V_d()$state_factor), linetype=as.factor(data_V_d()$state_factor), mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) } else if (input$confvis=="ci_yes") { v_cov_state <- plot_ly() v_cov_state <- add_trace(v_cov_state, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_V_ci()$x, y=data_V_ci()$y_central, color=as.factor(data_V_ci()$covto), fill=as.factor(data_V_ci()$frameto), linetype=as.factor(data_V_ci()$frameto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) v_cov_state <- add_trace(v_cov_state, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_V_ci()$x, y=data_V_ci()$y, color=as.factor(data_V_ci()$covto), fill=as.factor(data_V_ci()$frameto), linetype=as.factor(data_V_ci()$frameto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) } v_cov_state=v_cov_state %>% layout(title=list(text="Probability of state visit",y=0.95), font= list(family = "times new roman", size = input$textsizevis, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepvx, tick0 = input$startvx, range=c(input$startvx,input$endvx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Probability of visit",rangemode = "nonnegative", dtick = input$stepvy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) }) output$visit_both <- renderPlotly ({ datavis3_re() }) output$downplotvis3 <- downloadHandler( filename = function(){paste("vis3",'.png',sep='')}, content = function(file){ plotly_IMAGE( datavis3_re(),width = 1400, height = 1100, format = "png", scale = 2, out_file = file ) } ) ############################################################################################## ############### Diff ####################################################################### data_V_diff1 <- reactive ({ visit_diff=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$visitd)) { visit_diff[[i]]=as.data.frame(t(data.frame(myjson2()$visitd[i]))) colnames(visit_diff[[i]]) <- v_diff } for(i in 1:length(myjson2()$visitd)) { visit_diff[[i]]=as.data.frame(cbind(visit_diff[[i]], timevar ,state=rep(i,nrow(visit_diff[[i]] )) )) } } else { for (i in 1:length(myjson2()$visitd)) { visit_diff[[i]]=as.data.frame(myjson2()$visitd[[i]][,1]) } for (i in 1:length(myjson2()$visitd)) { visit_diff[[i]]=as.data.frame(c(visit_diff[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$visitd[[i]][,1])) )) ) colnames(visit_diff[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_visitd=list() data_visitd[[1]]=visit_diff[[1]] for (u in 2:(length(myjson2()$visitd))) { data_visitd[[u]]=rbind(visit_diff[[u]],data_visitd[[(u-1)]]) } datavd=data_visitd[[length(myjson2()$visitd)]] datavd$state_fac=c(rep("NA",nrow(datavd))) for (o in 1:(length(myjson2()$visitd))) { for (g in 1:nrow(datavd)) { if (datavd$state[g]==o) {datavd$state_fac[g]=labels_state()[o]} } } datavd }) data_V_diff1_uci <- reactive ({ V_diff_uci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$visitd_uci)) { V_diff_uci[[i]]=as.data.frame(t(data.frame(myjson2()$visitd_uci[i]))) colnames(V_diff_uci[[i]]) <- v_diff } for(i in 1:length(myjson2()$visitd_uci)) { V_diff_uci[[i]]=as.data.frame(cbind(V_diff_uci[[i]], timevar ,state=rep(i,nrow(V_diff_uci[[i]] )) )) } } else { for (i in 1:length(myjson2()$visitd_uci)) { V_diff_uci[[i]]=as.data.frame(myjson2()$visitd_uci[[i]][,1]) } for (i in 1:length(myjson2()$visitd_uci)) { V_diff_uci[[i]]=as.data.frame(c(V_diff_uci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$visitd_uci[[i]][,1])) )) ) colnames(V_diff_uci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_visitd_uci=list() data_visitd_uci[[1]]=V_diff_uci[[1]] for (u in 2:(length(myjson2()$visitd_uci))) { data_visitd_uci[[u]]=rbind(V_diff_uci[[u]],data_visitd_uci[[(u-1)]]) } datavd_uci=data_visitd_uci[[length(myjson2()$visitd_uci)]] datavd_uci$state_fac=c(rep("NA",nrow(datavd_uci))) for (o in 1:(length(myjson2()$visitd_uci))) { for (g in 1:nrow(datavd_uci)) { if (datavd_uci$state[g]==o) {datavd_uci$state_fac[g]=labels_state()[o]} } } datavd_uci }) data_V_diff1_lci <- reactive ({ V_diff_lci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$visitd_lci)) { V_diff_lci[[i]]=as.data.frame(t(data.frame(myjson2()$visitd_lci[i]))) colnames(V_diff_lci[[i]]) <- v_diff } for(i in 1:length(myjson2()$visitd_lci)) { V_diff_lci[[i]]=as.data.frame(cbind(V_diff_lci[[i]], timevar ,state=rep(i,nrow(V_diff_lci[[i]] )) )) } } else { for (i in 1:length(myjson2()$visitd_lci)) { V_diff_lci[[i]]=as.data.frame(myjson2()$visitd_lci[[i]][,1]) } for (i in 1:length(myjson2()$visitd_lci)) { V_diff_lci[[i]]=as.data.frame(c(V_diff_lci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$visitd_lci[[i]][,1])) )) ) colnames(V_diff_lci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_diff } } # Append the probabilities datasets of the different states data_visitd_lci=list() data_visitd_lci[[1]]=V_diff_lci[[1]] for (u in 2:(length(myjson2()$visitd_lci))) { data_visitd_lci[[u]]=rbind(V_diff_lci[[u]],data_visitd_lci[[(u-1)]]) } datavd_lci=data_visitd_lci[[length(myjson2()$visitd_lci)]] datavd_lci$state_fac=c(rep("NA",nrow(datavd_lci))) for (o in 1:(length(myjson2()$visitd_lci))) { for (g in 1:nrow(datavd_lci)) { if (datavd_lci$state[g]==o) {datavd_lci$state_fac[g]=labels_state()[o]} } } datavd_lci }) data_V_diff2<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_V_diff1()[,d],data_V_diff1()[,ncol(data_V_diff1())-2],data_V_diff1()[,ncol(data_V_diff1())-1], data_V_diff1()[,ncol(data_V_diff1())],rep(d,length(data_V_diff1()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_V_diff1())[d],length(data_V_diff1()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Vd <- bind_rows(dlist, .id = "column_Vabel") d_all_Vd }) data_V_diff2_uci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_V_diff1_uci()[,d],data_V_diff1_uci()[,ncol(data_V_diff1_uci())-2], data_V_diff1_uci()[,ncol(data_V_diff1_uci())-1], data_V_diff1_uci()[,ncol(data_V_diff1_uci())],rep(d,length(data_V_diff1_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_V_diff1_uci())[d],length(data_V_diff1_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Vd_uci <- bind_rows(dlist, .id = "column_Vabel") d_all_Vd_uci }) data_V_diff2_lci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_V_diff1_lci()[,d],data_V_diff1_lci()[,ncol(data_V_diff1_lci())-2], data_V_diff1_lci()[,ncol(data_V_diff1_lci())-1], data_V_diff1_lci()[,ncol(data_V_diff1_lci())],rep(d,length(data_V_diff1_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_V_diff1_lci())[d],length(data_V_diff1_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Vd_lci <- bind_rows(dlist, .id = "column_Vabel") d_all_Vd_lci }) data_V_diff_ci<- reactive ({ x=c( data_V_diff2()[order(data_V_diff2()$timevar,data_V_diff2()$state,data_V_diff2()$cov),]$timevar, data_V_diff2()[order(-data_V_diff2()$timevar,data_V_diff2()$state,data_V_diff2()$cov),]$timevar ) y_central=c( data_V_diff2()[order(data_V_diff2()$timevar,data_V_diff2()$state,data_V_diff2()$cov),]$V, data_V_diff2()[order(-data_V_diff2()$timevar,data_V_diff2()$state,data_V_diff2()$cov),]$V ) y=c( data_V_diff2_uci()[order(data_V_diff2_uci()$timevar,data_V_diff2_uci()$state,data_V_diff2_uci()$cov),]$V, data_V_diff2_lci()[order(-data_V_diff2_lci()$timevar,data_V_diff2_lci()$state,data_V_diff2_lci()$cov),]$V ) frameto=c(as.character(data_V_diff2_uci()[order(-data_V_diff2_uci()$timevar,data_V_diff2_uci()$state,data_V_diff2_uci()$cov),]$state_factor), as.character(data_V_diff2_lci()[order(-data_V_diff2_lci()$timevar,data_V_diff2_lci()$state,data_V_diff2_lci()$cov),]$state_factor) ) covto=c( data_V_diff2_uci()[order(-data_V_diff2_uci()$timevar,data_V_diff2_uci()$state,data_V_diff2_uci()$cov),]$cov_factor, data_V_diff2_lci()[order(-data_V_diff2_lci()$timevar,data_V_diff2_lci()$state,data_V_diff2_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) #output$shouldloadvis4 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotvis4", label = h2("Download the plot")) #}) datavis4_re <- reactive ({ ax <- list( title = "",zeroline = FALSE,showline = FALSE, showticklabels = FALSE, showgrid = FALSE) if (length(myjson2()$visitd) == 0| myjson2()$Nats==1 ) { V_state_d= plot_ly() %>% layout(title=list(text="Not applicable- Only one covariate pattern specified",y=0.95),xaxis=ax, yaxis=ax) V_state_d } else { if (input$confvis=="ci_no") { if (input$facetvis=="No") { V_state_d= plot_ly(data_V_diff2(),alpha=0.5) %>% add_lines( x=data_V_diff2()$timevar,y=data_V_diff2()$V, frame=factor(as.factor(data_V_diff2()$state_factor),levels=labels_state()), color=as.factor(data_V_diff2()$cov_factor), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text="Difference in visit probabilities among covariate patterns (compared to reference)",y=0.95), font= list(family = "times new roman", size = input$textsizevis, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepvx, tick0 = input$startvx, range=c(input$startvx,input$endvx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Difference in visit probabilities", dtick = input$stepvy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) ) %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) V_state_d } if (input$facetvis=="Yes") { data_plot=data_V_diff2() V_state_d = ggplot(data_plot) V_state_d = ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor) ))) V_state_d = V_state_d+geom_line(aes(x=timevar, y=V, color= factor(as.factor(cov_factor) ), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Differences in probability of visit: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor) ))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { V_state_d = V_state_d+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()), nrow=2)} else if (input$aimtype=="present") {V_state_d = V_state_d+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()))} V_state_d = V_state_d + scale_x_continuous(breaks=c(seq(input$startvx,input$endvx,by=input$endvx ))) V_state_d = V_state_d +labs(title="Differences in probability of visit among covariate patterns (compared to reference)", x="Time since entry", y="Differences in probability of visit") V_state_d = V_state_d + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") V_state_d = V_state_d +theme(title = element_text(size = input$textsizevis-4), strip.text = element_text(size=input$textfacetvis), legend.title = element_text(color="black", size= input$textsizevis-5), legend.text=element_text(size= input$textsizevis-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizevis-5), axis.title.x = element_text(size= input$textsizevis-5), axis.text.x = element_text( size=input$textsizevis-6),axis.text.y = element_text( size=input$textsizevis-6)) V_state_d = ggplotly(V_state_d, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) V_state_d } } else if (input$confvis=="ci_yes") { if (input$facetvis=="No") { V_state_d <- plot_ly() V_state_d <- add_trace(V_state_d, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_V_diff_ci()$x, y=data_V_diff_ci()$y_central, frame=factor(as.factor(data_V_diff_ci()$frameto),levels=labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_V_diff_ci()$covto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) V_state_d <- add_trace(V_state_d, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_V_diff_ci()$x, y=data_V_diff_ci()$y, frame=factor(as.factor(data_V_diff_ci()$frameto),levels=labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_V_diff_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) V_state_d= V_state_d %>% layout(title=list(text="Differences in probability of visit among covariate patterns (compared to reference)",y=0.95), font= list(family = "times new roman", size = input$textsizevis, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepvx, tick0 = input$startvx, range=c(input$startvx,input$endvx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Differences in probability of visit", dtick = input$stepvy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) V_state_d } if (input$facetvis=="Yes") { V_lci= data_V_diff2_lci()$V V_uci= data_V_diff2_uci()$V data_plot=cbind(data_V_diff2(),V_lci,V_uci) V_state_d=ggplot(data_plot) V_state_d=ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor) )))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) V_state_d=V_state_d+geom_line(aes(x=timevar, y=V, fill= factor(as.factor(cov_factor) ), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Differences in probability of visit: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor) )))) V_state_d=V_state_d+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=factor(as.factor(cov_factor) )),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { V_state_d = V_state_d+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()), nrow=2)} else if (input$aimtype=="present") {V_state_d = V_state_d+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()) )} V_state_d = V_state_d + scale_x_continuous(breaks=c(seq(input$startvx,input$endvx,by=input$endvx ))) V_state_d = V_state_d +labs(title="Differences in probability of visit among covariate patterns (compared to reference)", x="Time since entry", y="Differences in probability of visit") V_state_d = V_state_d + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") V_state_d = V_state_d +theme(title = element_text(size = input$textsizevis-4), strip.text = element_text(size=input$textfacetvis), legend.title = element_text(color="black", size= input$textsizevis-5), legend.text=element_text(size= input$textsizevis-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizevis-5), axis.title.x = element_text(size= input$textsizevis-5), axis.text.x = element_text( size=input$textsizevis-6),axis.text.y = element_text( size=input$textsizevis-6)) V_state_d = ggplotly(V_state_d, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } V_state_d } }) output$visit_diff <- renderPlotly ({ datavis4_re() }) output$downplotvis4 <- downloadHandler( filename = function(){paste("vis4",'.png',sep='')}, content = function(file){ plotly_IMAGE( datavis4_re(),width = 1400, height = 1100, format = "png", scale = 2, out_file = file ) } ) ###################################################################################### ##################################################################################### data_V_ratio1 <- reactive ({ visit_ratio=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$visitr)) { visit_ratio[[i]]=as.data.frame(t(data.frame(myjson2()$visitr[i]))) colnames(visit_ratio[[i]]) <- v_ratio } for(i in 1:length(myjson2()$visitr)) { visit_ratio[[i]]=as.data.frame(cbind(visit_ratio[[i]], timevar ,state=rep(i,nrow(visit_ratio[[i]] )) )) } } else { for (i in 1:length(myjson2()$visitr)) { visit_ratio[[i]]=as.data.frame(myjson2()$visitr[[i]][,1]) } for (i in 1:length(myjson2()$visitr)) { visit_ratio[[i]]=as.data.frame(c(visit_ratio[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$visitr[[i]][,1])) )) ) colnames(visit_ratio[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the different states data_Vosr=list() data_Vosr[[1]]=visit_ratio[[1]] for (u in 2:(length(myjson2()$visitr))) { data_Vosr[[u]]=rbind(visit_ratio[[u]],data_Vosr[[(u-1)]]) } datalr=data_Vosr[[length(myjson2()$visitr)]] datalr$state_fac=c(rep("NA",nrow(datalr))) for (o in 1:(length(myjson2()$visitr))) { for (g in 1:nrow(datalr)) { if (datalr$state[g]==o) {datalr$state_fac[g]=labels_state()[o]} } } datalr }) data_V_ratio1_uci <- reactive ({ L_ratio_uci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$visitr_uci)) { L_ratio_uci[[i]]=as.data.frame(t(data.frame(myjson2()$visitr_uci[i]))) colnames(L_ratio_uci[[i]]) <- v_ratio } for(i in 1:length(myjson2()$visitr_uci)) { L_ratio_uci[[i]]=as.data.frame(cbind(L_ratio_uci[[i]], timevar ,state=rep(i,nrow(L_ratio_uci[[i]] )) )) } } else { for (i in 1:length(myjson2()$visitr_uci)) { L_ratio_uci[[i]]=as.data.frame(myjson2()$visitr_uci[[i]][,1]) } for (i in 1:length(myjson2()$visitr_uci)) { L_ratio_uci[[i]]=as.data.frame(c(L_ratio_uci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$visitr_uci[[i]][,1])) )) ) colnames(L_ratio_uci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the ratioerent states data_Vosr_uci=list() data_Vosr_uci[[1]]=L_ratio_uci[[1]] for (u in 2:(length(myjson2()$visitr_uci))) { data_Vosr_uci[[u]]=rbind(L_ratio_uci[[u]],data_Vosr_uci[[(u-1)]]) } datalr_uci=data_Vosr_uci[[length(myjson2()$visitr_uci)]] datalr_uci$state_fac=c(rep("NA",nrow(datalr_uci))) for (o in 1:(length(myjson2()$visitr_uci))) { for (g in 1:nrow(datalr_uci)) { if (datalr_uci$state[g]==o) {datalr_uci$state_fac[g]=labels_state()[o]} } } datalr_uci }) data_V_ratio1_lci <- reactive ({ L_ratio_lci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$atlist)) { v_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$visitr_lci)) { L_ratio_lci[[i]]=as.data.frame(t(data.frame(myjson2()$visitr_lci[i]))) colnames(L_ratio_lci[[i]]) <- v_ratio } for(i in 1:length(myjson2()$visitr_lci)) { L_ratio_lci[[i]]=as.data.frame(cbind(L_ratio_lci[[i]], timevar ,state=rep(i,nrow(L_ratio_lci[[i]] )) )) } } else { for (i in 1:length(myjson2()$visitr_lci)) { L_ratio_lci[[i]]=as.data.frame(myjson2()$visitr_lci[[i]][,1]) } for (i in 1:length(myjson2()$visitr_lci)) { L_ratio_lci[[i]]=as.data.frame(c(L_ratio_lci[[i]], timevar ,state=rep(i,ncol(as.data.frame(myjson2()$visitr_lci[[i]][,1])) )) ) colnames(L_ratio_lci[[i]])[1:(length(myjson2()$atlist)-1)] <- v_ratio } } # Append the probabilities datasets of the ratioerent states data_Vosr_lci=list() data_Vosr_lci[[1]]=L_ratio_lci[[1]] for (u in 2:(length(myjson2()$visitr_lci))) { data_Vosr_lci[[u]]=rbind(L_ratio_lci[[u]],data_Vosr_lci[[(u-1)]]) } datalr_lci=data_Vosr_lci[[length(myjson2()$visitr_lci)]] datalr_lci$state_fac=c(rep("NA",nrow(datalr_lci))) for (o in 1:(length(myjson2()$visitr_lci))) { for (g in 1:nrow(datalr_lci)) { if (datalr_lci$state[g]==o) {datalr_lci$state_fac[g]=labels_state()[o]} } } datalr_lci }) data_V_ratio2<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_V_ratio1()[,d],data_V_ratio1()[,ncol(data_V_ratio1())-2],data_V_ratio1()[,ncol(data_V_ratio1())-1], data_V_ratio1()[,ncol(data_V_ratio1())],rep(d,length(data_V_ratio1()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_V_ratio1())[d],length(data_V_ratio1()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Vr <- bind_rows(dlist, .id = "column_Vabel") d_all_Vr }) data_V_ratio2_uci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_V_ratio1_uci()[,d],data_V_ratio1_uci()[,ncol(data_V_ratio1_uci())-2], data_V_ratio1_uci()[,ncol(data_V_ratio1_uci())-1], data_V_ratio1_uci()[,ncol(data_V_ratio1_uci())],rep(d,length(data_V_ratio1_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_V_ratio1_uci())[d],length(data_V_ratio1_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Vr_uci <- bind_rows(dlist, .id = "column_Vabel") d_all_Vr_uci }) data_V_ratio2_lci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { dlist[[d]]=cbind.data.frame(data_V_ratio1_lci()[,d],data_V_ratio1_lci()[,ncol(data_V_ratio1_lci())-2], data_V_ratio1_lci()[,ncol(data_V_ratio1_lci())-1], data_V_ratio1_lci()[,ncol(data_V_ratio1_lci())],rep(d,length(data_V_ratio1_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_V_ratio1_lci())[d],length(data_V_ratio1_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_Vr_lci <- bind_rows(dlist, .id = "column_Vabel") d_all_Vr_lci }) data_V_ratio_ci<- reactive ({ x=c( data_V_ratio2()[order(data_V_ratio2()$timevar,data_V_ratio2()$state,data_V_ratio2()$cov),]$timevar, data_V_ratio2()[order(-data_V_ratio2()$timevar,data_V_ratio2()$state,data_V_ratio2()$cov),]$timevar ) y_central=c( data_V_ratio2()[order(data_V_ratio2()$timevar,data_V_ratio2()$state,data_V_ratio2()$cov),]$V, data_V_ratio2()[order(-data_V_ratio2()$timevar,data_V_ratio2()$state,data_V_ratio2()$cov),]$V ) y=c( data_V_ratio2_uci()[order(data_V_ratio2_uci()$timevar,data_V_ratio2_uci()$state,data_V_ratio2_uci()$cov),]$V, data_V_ratio2_lci()[order(-data_V_ratio2_lci()$timevar,data_V_ratio2_lci()$state,data_V_ratio2_lci()$cov),]$V ) frameto=c(as.character(data_V_ratio2_uci()[order(-data_V_ratio2_uci()$timevar,data_V_ratio2_uci()$state,data_V_ratio2_uci()$cov),]$state_factor), as.character(data_V_ratio2_lci()[order(-data_V_ratio2_lci()$timevar,data_V_ratio2_lci()$state,data_V_ratio2_lci()$cov),]$state_factor) ) covto=c( data_V_ratio2_uci()[order(-data_V_ratio2_uci()$timevar,data_V_ratio2_uci()$state,data_V_ratio2_uci()$cov),]$cov_factor, data_V_ratio2_lci()[order(-data_V_ratio2_lci()$timevar,data_V_ratio2_lci()$state,data_V_ratio2_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) #output$shouldloadvis5 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotvis5", label = h2("Download the plot")) #}) datavis5_re <- reactive ({ ax <- list( title = "",zeroline = FALSE,showline = FALSE, showticklabels = FALSE, showgrid = FALSE) if (length(myjson2()$visitr) == 0| myjson2()$Nats==1 ) { V_state_r= plot_ly() %>% layout(title=list(text="Not applicable- Only one covariate pattern specified",y=0.95),xaxis=ax, yaxis=ax) V_state_r } else { if (input$confvis=="ci_no") { if (input$facetvis=="No") { V_state_r= plot_ly(data_V_ratio2(),alpha=0.5) %>% add_lines( x=data_V_ratio2()$timevar,y=data_V_ratio2()$V, frame=factor(as.factor(data_V_ratio2()$state_factor),levels=labels_state()), color=as.factor(data_V_ratio2()$cov_factor), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) %>% layout(title=list(text="Ratios in visit probabilities among covariate patterns (compared to reference)",y=0.95), font= list(family = "times new roman", size = input$textsizevis, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepvx, tick0 = input$startvx, range=c(input$startvx,input$endvx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Ratios in visit probabilities", dtick = input$stepvy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) ) %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) V_state_r } if (input$facetvis=="Yes") { data_plot=data_V_ratio2() V_state_r = ggplot(data_plot) V_state_r = ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor) ), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Ratio of length of stay: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor) )))) V_state_r = V_state_r+geom_line(aes(x=timevar, y=V, color= factor(as.factor(cov_factor) )))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { V_state_r = V_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()), nrow=2)} else if (input$aimtype=="present") {V_state_r = V_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()))} V_state_r = V_state_r + scale_x_continuous(breaks=c(seq(input$startvx,input$endvx,by=input$endvx ))) V_state_r = V_state_r +labs(title="Ratios in visit probabilities among covariate patterns (compared to reference)", x="Time since entry", y="Ratios in visit probabilities") V_state_r = V_state_r + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") V_state_r = V_state_r +theme(title = element_text(size = input$textsizevis-4), strip.text = element_text(size=input$textfacetvis), legend.title = element_text(color="black", size= input$textsizevis-5), legend.text=element_text(size= input$textsizevis-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizevis-5), axis.title.x = element_text(size= input$textsizevis-5), axis.text.x = element_text( size=input$textsizevis-6),axis.text.y = element_text( size=input$textsizevis-6)) V_state_r = ggplotly(V_state_r, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) V_state_r } } else if (input$confvis=="ci_yes") { if (input$facetvis=="No") { V_state_r <- plot_ly() V_state_r <- add_trace(V_state_r, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_V_ratio_ci()$x, y=data_V_ratio_ci()$y_central, frame=factor(as.factor(data_V_ratio_ci()$frameto),levels=labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_V_ratio_ci()$covto) , text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) V_state_r <- add_trace(V_state_r, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_V_ratio_ci()$x, y=data_V_ratio_ci()$y, frame=factor(as.factor(data_V_ratio_ci()$frameto),levels=labels_state()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_V_ratio_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) V_state_r= V_state_r %>% layout(title=list(text="Ratios in visit probabilities among covariate patterns (compared to reference)",y=0.95), font= list(family = "times new roman", size = input$textsizevis, color = "black"), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepvx, tick0 = input$startvx, range=c(input$startvx,input$endvx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Ratios in visit probabilities", dtick = input$stepvy, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) V_state_r } if (input$facetvis=="Yes") { V_lci= data_V_ratio2_lci()$V V_uci= data_V_ratio2_uci()$V data_plot=cbind(data_V_ratio2(),V_lci,V_uci) V_state_r=ggplot(data_plot) V_state_r=ggplot(data_plot,aes(x=timevar, y=V, color= factor(as.factor(cov_factor) ), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Ratio of length of stay: ", V, "
Covariate pattern: ", factor(as.factor(cov_factor) ))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) V_state_r=V_state_r+geom_line(aes(x=timevar, y=V, fill= factor(as.factor(cov_factor) ))) V_state_r=V_state_r+ geom_ribbon(aes(ymin = V_lci, ymax =V_uci,fill=factor(as.factor(cov_factor) )),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) if (input$aimtype=="compare") { V_state_r = V_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()), nrow=2)} else if (input$aimtype=="present") {V_state_r = V_state_r+ facet_wrap(~factor(as.factor(state_factor),levels=labels_state()),)} V_state_r = V_state_r + scale_x_continuous(breaks=c(seq(input$startvx,input$endvx,by=input$endvx ))) V_state_r = V_state_r +labs(title="Ratios in visit probabilities among covariate patterns (compared to reference)", x="Time since entry", y="Ratios in visit probabilities") V_state_r = V_state_r + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") V_state_r = V_state_r +theme(title = element_text(size = input$textsizevis-4), strip.text = element_text(size=input$textfacetvis), legend.title = element_text(color="black", size= input$textsizevis-5), legend.text=element_text(size= input$textsizevis-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizevis-5), axis.title.x = element_text(size= input$textsizevis-5), axis.text.x = element_text( size=input$textsizevis-6),axis.text.y = element_text( size=input$textsizevis-6)) V_state_r = ggplotly(V_state_r, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } } V_state_r } }) output$visit_ratio <- renderPlotly ({ datavis5_re() }) output$downplotvis5 <- downloadHandler( filename = function(){paste("vis5",'.png',sep='')}, content = function(file){ plotly_IMAGE( datavis5_re(),width = 1400, height = 1100, format = "png", scale = 2, out_file = file ) } ) ###### Show and hide and tick inputs #### timeruser <- reactiveVal(1.5) observeEvent(c(input$showtickuser,invalidateLater(1000, session)), { if(input$showtickuser=="No"){ hide("tickinputuser") } if(input$showtickuser=="Yes"){ show("tickinputuser") } isolate({ timeruser(timeruser()-1) if(timeruser()>1 & input$showtickuser=="No") { show("tickinputuser") } }) }) ################################################ ########################################## #Create the reactive input of covariates# ########################################## existuser <- reactive({ if (length(myjson2()$user)!= 0) { x= 1 } else if (length(myjson2()$user) == 0) { x= 0 } }) existuserratio <- reactive({ if (length(myjson2()$userr) != 0) { x= 1 } else if (length(myjson2()$userr) == 0) { x= 0 } }) existuserdiff <- reactive({ if (length(myjson2()$userd) != 0) { x= 1 } else if (length(myjson2()$userd) == 0) { x= 0 } }) output$loginpageuser <- renderUI({h1("Non applicable")}) output$pageuser <- renderUI({ if (is.null(myjson2())) return("Provide the json file with the predictions") if (existuser()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(12, uiOutput("loginpageuser"), ) ) } else if (existuser()==1) { if (existuserdiff()==1 & existuserratio()==1) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, uiOutput("userinput"), uiOutput("confuser") #uiOutput("displayu"), #uiOutput("covarinputuser") ), column(2, br(), p(""), radioButtons("showtickuser", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No"), uiOutput("tickinputuser") ), column(8, tabsetPanel( tabPanel(h2("User function by covariate pattern"), plotlyOutput("user", height="700px", width = "100%"),uiOutput("shouldloaduser1")), tabPanel(h2("Differences"), plotlyOutput("U_diff", height="600px", width = "100%"),uiOutput("shouldloaduser2")), tabPanel(h2("Ratios"), plotlyOutput("U_ratio", height="600px", width = "100%"),uiOutput("shouldloaduser3")) ) ) ) } else if (existuserdiff()==1 & existuserratio()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, uiOutput("userinput"), uiOutput("confuser") #uiOutput("displayu"), #uiOutput("covarinputuser") ), column(2, br(), p(""), radioButtons("showtickuser", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected ="No"), uiOutput("tickinputuser") ), column(8, tabsetPanel( tabPanel(h2("By covariate pattern"), plotlyOutput("user", height="700px", width = "100%"),uiOutput("shouldloaduser1")), tabPanel(h2("Differences"), plotlyOutput("U_diff", height="600px", width = "100%"),uiOutput("shouldloaduser2")), tabPanel(h2("Ratios"), print("Not applicable")) ) ) ) } else if (existuserdiff()==0 & existuserratio()==1) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, uiOutput("userinput"), uiOutput("confuser") #uiOutput("displayu"), #uiOutput("covarinputuser") ), column(2, br(), p(""), radioButtons("showtickuser", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No"), uiOutput("tickinputuser") ), column(8, tabsetPanel( tabPanel(h2("By covariate pattern"), plotlyOutput("user", height="700px", width = "100%"),uiOutput("shouldloaduser1")), tabPanel(h2("Differences"), print("Not applicable")), tabPanel(h2("Ratios"), plotlyOutput("U_ratio", height="600px", width = "100%"),uiOutput("shouldloaduser3")) ) ) ) } else if (existuser()==1 & existuserdiff()==0 & existuserratio()==0) { fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, uiOutput("userinput"), uiOutput("confuser") #uiOutput("displayu"), #uiOutput("covarinputuser") ), column(2, br(), p(""), radioButtons("showtickuser", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No"), uiOutput("tickinputuser") ), column(8, tabsetPanel( tabPanel(h2("By covariate pattern"), plotlyOutput("user", height="700px", width = "100%")), tabPanel(h2("Differences"), print("Not applicable")), tabPanel(h2("Ratios"), print("Not applicable")) ) ) ) } } }) observeEvent(input$json2, { if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'User')))==0 ) { js$disableTab("mytab_user") } }) observeEvent(input$csv2, { if( length(which(startsWith(names( read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'User')))==0 ) { js$disableTab("mytab_user") } }) #output$displayu <- renderUI({ # radioButtons("displayuser", "Change labels of states and covariate patterns", # c("same" = "same", "change"= "change")) #}) output$confuser <- renderUI({ if (length(myjson2()$ci_user)!=0) { radioButtons("confu", "Confidence intervals", c("No" = "ci_no", "Yes" ="ci_yes")) } else if (length(myjson2()$ci_user)==0) { item_list <- list() item_list[[1]]<- radioButtons("confu", "Confidence intervals",c("No" = "ci_no")) item_list[[2]]<-print("Confidence interval data were not provided") do.call(tagList, item_list) } }) #################################################################################################### ##################################################################################################### #################################################################################################### #output$covarinputuser <- renderUI({ # # if (is.null(myjson2())) return() # # if (input$displayuser=="same") return() # # else { # # item_list <- list() # item_list[[1]] <- h2("Covariate patterns") # # default_choices_cov=vector() # for (i in 1:length(myjson2()$cov$atlist)) { # default_choices_cov[i]=myjson2()$cov$atlist[i] # } # # for (i in seq(length(myjson2()$cov$atlist))) { # item_list[[i+1]] <- textInput(paste0('covuser', i),default_choices_cov[i], labels_cov()[i]) # } # do.call(tagList, item_list) # } #}) # #labels_cov<- reactive ({ # # if (input$displayuser=="same") {labels_cov()} # # else { # # myList<-vector("list",length(myjson2()$cov$atlist)) # # for (i in 1:length(myjson2()$cov$atlist)) { # myList[[i]]= input[[paste0('covuser', i)]][1] # } # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # final_list # } #}) #################################################################################### #################################################################################### #################################################################################### data_U <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) { v[i]=myjson2()$cov$atlist[i] } ## Different variable of probabilities for each covariate pattern user=list() if (length(myjson2()$user)==0) {return()} for(i in 1:length(myjson2()$user)) { user[[i]]= as.data.frame(t(data.frame(myjson2()$user[i]))) colnames(user[[i]]) <- labels_cov() } for(i in 1:length(myjson2()$user)) { user[[i]]=as.data.frame(cbind(user[[i]], timevar )) } # Append the probabilities datasets of the different states data_U=list() data_U[[1]]=user[[1]] if (length(myjson2()$user)>1) { for (u in 2:(length(myjson2()$user))) { data_U[[u]]=rbind(user[[u]],data_U[[(u-1)]]) } } datau=data_U[[length(myjson2()$user)]] datau }) data_U_uci <- reactive ({ #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern user_uci=list() if (length(myjson2()$user_uci)==0) {return()} for(i in 1:length(myjson2()$user_uci)) { user_uci[[i]]=as.data.frame(t(data.frame(myjson2()$user_uci[i]))) colnames(user_uci[[i]]) <- labels_cov() } for(i in 1:length(myjson2()$user_uci)) { user_uci[[i]]=as.data.frame(cbind(user_uci[[i]], timevar )) } # Append the userabilities datasets of the different states data_U_uci=list() data_U_uci[[1]]=user_uci[[1]] if (length(myjson2()$user_uci)>1) { for (u in 2:(length(myjson2()$user_uci))) { data_U_uci[[u]]=rbind(user_uci[[u]],data_U_uci[[(u-1)]]) } } datau_uci=data_U_uci[[length(myjson2()$user_uci)]] datau_uci }) data_U_lci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern user_lci=list() if (length(myjson2()$user_lci)==0) {return()} for(i in 1:length(myjson2()$user_lci)) { user_lci[[i]]=as.data.frame(t(data.frame(myjson2()$user_lci[i]))) colnames(user_lci[[i]]) <- labels_cov() } for(i in 1:length(myjson2()$user_lci)) { user_lci[[i]]=as.data.frame(cbind(user_lci[[i]], timevar )) } # Append the userabilities datasets of the different states data_U_lci=list() data_U_lci[[1]]=user_lci[[1]] if (length(myjson2()$user_lci)>1) { for (u in 2:(length(myjson2()$user_lci))) { data_U_lci[[u]]=rbind(user_lci[[u]],data_U_lci[[(u-1)]]) } } datau_lci=data_U_lci[[length(myjson2()$user_lci)]] datau_lci }) data_U_d <- reactive ({ dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_U()[,d],data_U()[,ncol(data_U())],rep(d,length(data_U()[,d])) ) dlist[[d]][,4] <- rep(colnames(data_U())[d],length(data_U()[,d])) colnames(dlist[[d]]) <- c("V","timevar","cov","cov_factor") } d_all_u <- bind_rows(dlist, .id = "column_label") d_all_u }) data_U_d_uci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_U_uci()[,d],data_U_uci()[,ncol(data_U_uci())],rep(d,length(data_U_uci()[,d])) ) dlist[[d]][,4] <- rep(colnames(data_U_uci())[d],length(data_U_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","cov","cov_factor") } d_all_u_uci <- bind_rows(dlist, .id = "column_label") d_all_u_uci }) data_U_d_lci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_U_lci()[,d],data_U_lci()[,ncol(data_U_lci())],rep(d,length(data_U_lci()[,d])) ) dlist[[d]][,4] <- rep(colnames(data_U_lci())[d],length(data_U_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","cov","cov_factor") } d_all_u_lci <- bind_rows(dlist, .id = "column_label") d_all_u_lci }) #Create the reactive input of covariates output$userinput <- renderUI({ if (is.null(myjson2())) return() default_choice=vector() default_choice[1]="User specified measure" item_list <- list() item_list[[1]] <- h1("Name of user function") item_list[[2]] <- textInput('usertitle',default_choice,default_choice) do.call(tagList, item_list) }) output$tickinputuser <- renderUI({ default_choices=c("black","blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen","tan1","lightslateblue","khaki4") if (is.null(myjson2()$user)) return() item_list <- list() item_list[[1]] <- h2("Provide x axis range and ticks") item_list[[2]] <-numericInput("startuserx","Start x at:",value=min(data_U_d()$timevar) ) item_list[[3]] <-numericInput("stepuserx","step:",value=max(data_U_d()$timevar)/10,min=0,max=max(data_U_d()$timevar)) item_list[[4]] <-numericInput("enduserx","End x at:",value =max(data_U_d()$timevar),max=max(data_U_d()$timevar)) item_list[[5]] <-numericInput("stepusery","step at y axis:",value=max(data_U_d()$V)/10,min=max(data_U_d()$V)/1000,max=max(data_U_d()$V)) item_list[[6]] <-numericInput("textsizeuser",h2("Legends size"),value=input$textsize,min=5,max=30) item_list[[7]] <-selectInput("textcolouruser",h2("Legends colour"), choices= default_choices, selected =input$textcolour ) do.call(tagList, item_list) }) data_U_ci<- reactive ({ x=c( data_U_d()[order(data_U_d()$timevar,data_U_d()$cov),]$timevar, data_U_d()[order(-data_U_d()$timevar,data_U_d()$cov),]$timevar ) y_central=c( data_U_d()[order(data_U_d()$timevar,data_U_d()$cov),]$V, data_U_d()[order(-data_U_d()$timevar,data_U_d()$cov),]$V ) y=c( data_U_d_uci()[order(data_U_d_uci()$timevar,data_U_d_uci()$cov),]$V, data_U_d_lci()[order(-data_U_d_lci()$timevar,data_U_d_lci()$cov),]$V ) covto=c( data_U_d_uci()[order(-data_U_d_uci()$timevar,data_U_d_uci()$cov),]$cov_factor, data_U_d_uci()[order(-data_U_d_uci()$timevar,data_U_d_uci()$cov),]$cov_factor ) data=data.frame(x,y,covto,y_central) data }) ##################################################################### #output$shouldloaduser1 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotuser1", label = h2("Download the plot")) #}) datauser1_re <- reactive ({ # if (is.null(myjson2()$user)) { # # # js$disableTab("tab1") # return(h2("You have not provided user specified information in the json file")) # } # # else if (!is.null(myjson2()$user)) { ####### Plot 1 frame is state, factor is cov ######################## if (input$confu=="ci_no") { user_cov= plot_ly(data_U_d(),alpha=0.5) %>% add_lines( x=data_U_d()$timevar,y=data_U_d()$V, color=factor(as.factor(data_U_d()$cov_factor) ,levels=labels_cov()), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], mode="lines", line=list(simplify=FALSE,color = labels_colour_cov()) , text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } else if (input$confu=="ci_yes") { user_cov <- plot_ly() user_cov <- add_trace(user_cov, line=list(simplify=FALSE,color = labels_colour_cov()), mode="lines", type = "scatter", x=data_U_ci()$x, y=data_U_ci()$y_central, colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=factor(as.factor(data_U_ci()$covto) ,levels=labels_cov()), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) user_cov <- add_trace(user_cov, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_U_ci()$x, y=data_U_ci()$y, colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=factor(as.factor(data_U_ci()$covto) ,levels=labels_cov()), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) } user_cov=user_cov %>% layout(title=paste0(input$usertitle," ","across covariate patterns"), font= list(family = "times new roman", size = input$textsizeuser, color = input$textcolouruser), margin = list(l = 50, r = 50, b = 50, t = 70), xaxis=list(title=list(text="Time since entry",y=0.25), dtick = input$stepuserx, tick0 = input$startuserx, range=c(input$startuserx,input$enduserx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= input$usertitle,rangemode = "nonnegative", dtick = input$stepusery, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) user_cov # } }) output$user <- renderPlotly ({datauser1_re() }) output$downplotuser1 <- downloadHandler( filename = function(){paste("user1",'.png',sep='')}, content = function(file){ plotly_IMAGE( datauser1_re(),width = 1200, height = 900, format = "png", scale = 2, out_file = file ) } ) ##################################################### data_U_diff1 <- reactive ({ user_diff=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" u_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$cov$atlist)) { u_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$userd)==0) {return()} if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$userd)) { user_diff[[i]]=as.data.frame(t(data.frame(myjson2()$userd[i]))) colnames(user_diff[[i]]) <- u_diff user_diff[[i]]=as.data.frame(cbind(user_diff[[i]], timevar )) } } else if (length(myjson2()$atlist)==2) { for (i in 1:length(myjson2()$userd)) { user_diff[[i]]=as.data.frame(myjson2()$userd[[i]][,1]) user_diff[[i]]=as.data.frame(c(user_diff[[i]], timevar ) ) colnames(user_diff[[i]])[1:2] <- u_diff } } # Append the probabilities datasets of the different states data_userd=list() data_userd[[1]]=user_diff[[1]] dataud=data_userd[[length(myjson2()$userd)]] dataud }) data_U_diff1_uci <- reactive ({ user_diff_uci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" u_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$cov$atlist)) { u_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$userd_uci)==0) {return()} if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$userd_uci)) { user_diff_uci[[i]]=as.data.frame(t(data.frame(myjson2()$userd_uci[i]))) colnames(user_diff_uci[[i]]) <- u_diff user_diff_uci[[i]]=as.data.frame(cbind(user_diff_uci[[i]], timevar )) } } else if (length(myjson2()$atlist)==2) { for (i in 1:length(myjson2()$userd_uci)) { user_diff_uci[[i]]=as.data.frame(myjson2()$userd_uci[[i]][,1]) user_diff_uci[[i]]=as.data.frame(c(user_diff_uci[[i]], timevar ) ) colnames(user_diff_uci[[i]])[1:2] <- u_diff } } # Append the probabilities datasets of the different states data_userd_uci=list() data_userd_uci[[1]]=user_diff_uci[[1]] dataud_uci=data_userd_uci[[length(myjson2()$userd_uci)]] dataud_uci }) data_U_diff1_lci <- reactive ({ user_diff_lci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" u_diff= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$cov$atlist)) { u_diff[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$userd_lci)==0) {return()} if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$userd_lci)) { user_diff_lci[[i]]=as.data.frame(t(data.frame(myjson2()$userd_lci[i]))) colnames(user_diff_lci[[i]]) <- u_diff user_diff_lci[[i]]=as.data.frame(cbind(user_diff_lci[[i]], timevar )) } } else if (length(myjson2()$atlist)==2) { for (i in 1:length(myjson2()$userd_lci)) { user_diff_lci[[i]]=as.data.frame(myjson2()$userd_lci[[i]][,1]) user_diff_lci[[i]]=as.data.frame(c(user_diff_lci[[i]], timevar ) ) colnames(user_diff_lci[[i]])[1:2] <- u_diff } } # Append the probabilities datasets of the different states data_userd_lci=list() data_userd_lci[[1]]=user_diff_lci[[1]] dataud_lci=data_userd_lci[[length(myjson2()$userd_lci)]] dataud_lci }) data_U_diff2<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { data=cbind.data.frame(data_U_diff1()[,d],data_U_diff1()[,ncol(data_U_diff1())],rep(d,length(data_U_diff1()[,d])), rep(colnames(data_U_diff1())[d],length(data_U_diff1()[,d])) ) dlist[[d]]=data colnames(dlist[[d]]) <- c("V","timevar","cov","cov_factor") } d_all_ud <- bind_rows(dlist, .id = "column_label") d_all_ud }) data_U_diff2_uci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { data=cbind.data.frame(data_U_diff1_uci()[,d],data_U_diff1_uci()[,ncol(data_U_diff1_uci())],rep(d,length(data_U_diff1_uci()[,d])), rep(colnames(data_U_diff1_uci())[d],length(data_U_diff1_uci()[,d])) ) dlist[[d]]=data colnames(dlist[[d]]) <- c("V","timevar","cov","cov_factor") } d_all_ud_uci <- bind_rows(dlist, .id = "column_label") d_all_ud_uci }) data_U_diff2_lci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { data=cbind.data.frame(data_U_diff1_lci()[,d],data_U_diff1_lci()[,ncol(data_U_diff1_lci())],rep(d,length(data_U_diff1_lci()[,d])), rep(colnames(data_U_diff1_lci())[d],length(data_U_diff1_lci()[,d])) ) dlist[[d]]=data colnames(dlist[[d]]) <- c("V","timevar","cov","cov_factor") } d_all_ud_lci <- bind_rows(dlist, .id = "column_label") d_all_ud_lci }) data_U_diff_ci<- reactive ({ x=c( data_U_diff2()[order(data_U_diff2()$timevar,data_U_diff2()$cov),]$timevar, data_U_diff2()[order(-data_U_diff2()$timevar,data_U_diff2()$cov),]$timevar ) y_central=c( data_U_diff2()[order(data_U_diff2()$timevar,data_U_diff2()$cov),]$V, data_U_diff2()[order(-data_U_diff2()$timevar,data_U_diff2()$cov),]$V ) y=c( data_U_diff2_uci()[order(data_U_diff2_uci()$timevar,data_U_diff2_uci()$cov),]$V, data_U_diff2_lci()[order(-data_U_diff2_lci()$timevar,data_U_diff2_lci()$cov),]$V ) covto=c( data_U_diff2_uci()[order(-data_U_diff2_uci()$timevar,data_U_diff2_uci()$cov),]$cov_factor, data_U_diff2_lci()[order(-data_U_diff2_lci()$timevar,data_U_diff2_lci()$cov),]$cov_factor ) data=data.frame(x,y,covto,y_central) data }) output$fileob2 <- renderPrint({ data_U_diff1()[50,] }) ############################################## #output$shouldloaduser2 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotuser2", label = h2("Download the plot")) #}) datauser2_re <- reactive ({ ax <- list( title = "",zeroline = FALSE,showline = FALSE, showticklabels = FALSE, showgrid = FALSE) if (length(myjson2()$userd) == 0| myjson2()$Nats==1 ) { u_user_d= plot_ly() %>% layout(title=list(text="Not applicable- Only one covariate pattern specified",y=0.95),xaxis=ax, yaxis=ax) u_user_d } else { if (input$confu=="ci_no") { u_user_d= plot_ly(data_U_diff2(),alpha=0.5) %>% add_lines( x=data_U_diff2()$timevar,y=data_U_diff2()$V, color=as.factor(data_U_diff2()$cov_factor), colors=labels_colour_cov()[2:length(myjson2()$cov$atlist)], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } else if (input$confu=="ci_yes") { u_user_d <- plot_ly() u_user_d <- add_trace(u_user_d, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_U_diff_ci()$x, y=data_U_diff_ci()$y_central, # colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_U_diff_ci()$covto), colors=labels_colour_cov()[2:length(myjson2()$cov$atlist)], text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) u_user_d <- add_trace(u_user_d, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_U_diff_ci()$x, y=data_U_diff_ci()$y, # colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_U_diff_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) } u_user_d=u_user_d %>% layout(title=list(text=paste0("Difference in ",input$usertitle," between covariate patterns"),y=0.95), font= list(family = "times new roman", size = input$textsizeuser, color = input$textcolouruser), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepuserx, tick0 = input$startuserx, range=c(input$startuserx,input$enduserx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= paste0("Difference in ",input$usertitle), dtick = input$stepusery, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) u_user_d } }) output$U_diff <- renderPlotly ({ datauser2_re() }) output$downplotuser2 <- downloadHandler( filename = function(){paste("user2",'.png',sep='')}, content = function(file){ plotly_IMAGE( datauser2_re(),width = 1200, height = 900, format = "png", scale = 2, out_file = file ) } ) ################################################################################################ ###### RAtio ################################################ ##################################################################################### data_U_ratio1 <- reactive ({ user_ratio=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" u_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$cov$atlist)) { u_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$userr)==0) {return()} if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$userr)) { user_ratio[[i]]=as.data.frame(t(data.frame(myjson2()$userr[i]))) colnames(user_ratio[[i]]) <- u_ratio user_ratio[[i]]=as.data.frame(cbind(user_ratio[[i]], timevar )) } } else if (length(myjson2()$atlist)==2) { for (i in 1:length(myjson2()$userr)) { user_ratio[[i]]=as.data.frame(myjson2()$userr[[i]][,1]) user_ratio[[i]]=as.data.frame(c(user_ratio[[i]], timevar ) ) colnames(user_ratio[[i]])[1:2] <- u_ratio } } # Append the probabilities datasets of the ratioerent states data_userr=list() data_userr[[1]]=user_ratio[[1]] dataur=data_userr[[length(myjson2()$userr)]] dataur }) data_U_ratio1_uci <- reactive ({ user_ratio_uci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" u_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$cov$atlist)) { u_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$userr_uci)==0) {return()} if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$userr_uci)) { user_ratio_uci[[i]]=as.data.frame(t(data.frame(myjson2()$userr_uci[i]))) colnames(user_ratio_uci[[i]]) <- u_ratio user_ratio_uci[[i]]=as.data.frame(cbind(user_ratio_uci[[i]], timevar )) } } else if (length(myjson2()$atlist)==2) { for (i in 1:length(myjson2()$userr_uci)) { user_ratio_uci[[i]]=as.data.frame(myjson2()$userr_uci[[i]][,1]) user_ratio_uci[[i]]=as.data.frame(c(user_ratio_uci[[i]], timevar ) ) colnames(user_ratio_uci[[i]])[1:2] <- u_ratio } } # Append the probabilities datasets of the ratioerent states data_userr_uci=list() data_userr_uci[[1]]=user_ratio_uci[[1]] dataur_uci=data_userr_uci[[length(myjson2()$userr_uci)]] dataur_uci }) data_U_ratio1_lci <- reactive ({ user_ratio_lci=list() timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" u_ratio= vector() if (length(myjson2()$atlist)>1) { for (i in 2:length(myjson2()$cov$atlist)) { u_ratio[i-1]= paste0(labels_cov()[i]," vs ",labels_cov()[1]) } } if (length(myjson2()$userr_lci)==0) {return()} if (length(myjson2()$atlist)>2) { for(i in 1:length(myjson2()$userr_lci)) { user_ratio_lci[[i]]=as.data.frame(t(data.frame(myjson2()$userr_lci[i]))) colnames(user_ratio_lci[[i]]) <- u_ratio user_ratio_lci[[i]]=as.data.frame(cbind(user_ratio_lci[[i]], timevar )) } } else if (length(myjson2()$atlist)==2) { for (i in 1:length(myjson2()$userr_lci)) { user_ratio_lci[[i]]=as.data.frame(myjson2()$userr_lci[[i]][,1]) user_ratio_lci[[i]]=as.data.frame(c(user_ratio_lci[[i]], timevar ) ) colnames(user_ratio_lci[[i]])[1:2] <- u_ratio } } # Append the probabilities datasets of the ratioerent states data_userr_lci=list() data_userr_lci[[1]]=user_ratio_lci[[1]] dataur_lci=data_userr_lci[[length(myjson2()$userr_lci)]] dataur_lci }) data_U_ratio2<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { data=cbind.data.frame(data_U_ratio1()[,d],data_U_ratio1()[,ncol(data_U_ratio1())],rep(d,length(data_U_ratio1()[,d])), rep(colnames(data_U_ratio1())[d],length(data_U_ratio1()[,d])) ) dlist[[d]]=data colnames(dlist[[d]]) <- c("V","timevar","cov","cov_factor") } d_all_ur <- bind_rows(dlist, .id = "column_label") d_all_ur }) data_U_ratio2_uci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { data=cbind.data.frame(data_U_ratio1_uci()[,d],data_U_ratio1_uci()[,ncol(data_U_ratio1_uci())],rep(d,length(data_U_ratio1_uci()[,d])), rep(colnames(data_U_ratio1_uci())[d],length(data_U_ratio1_uci()[,d])) ) dlist[[d]]=data colnames(dlist[[d]]) <- c("V","timevar","cov","cov_factor") } d_all_ur_uci <- bind_rows(dlist, .id = "column_label") d_all_ur_uci }) data_U_ratio2_lci<- reactive ({ dlist=list() for (d in 1:(length(myjson2()$cov$atlist)-1)) { data=cbind.data.frame(data_U_ratio1_lci()[,d],data_U_ratio1_lci()[,ncol(data_U_ratio1_lci())],rep(d,length(data_U_ratio1_lci()[,d])), rep(colnames(data_U_ratio1_lci())[d],length(data_U_ratio1_lci()[,d])) ) dlist[[d]]=data colnames(dlist[[d]]) <- c("V","timevar","cov","cov_factor") } d_all_ur_lci <- bind_rows(dlist, .id = "column_label") d_all_ur_lci }) data_U_ratio_ci<- reactive ({ x=c( data_U_ratio2()[order(data_U_ratio2()$timevar,data_U_ratio2()$cov),]$timevar, data_U_ratio2()[order(-data_U_ratio2()$timevar,data_U_ratio2()$cov),]$timevar ) y_central=c( data_U_ratio2()[order(data_U_ratio2()$timevar,data_U_ratio2()$cov),]$V, data_U_ratio2()[order(-data_U_ratio2()$timevar,data_U_ratio2()$cov),]$V ) y=c( data_U_ratio2_uci()[order(data_U_ratio2_uci()$timevar,data_U_ratio2_uci()$cov),]$V, data_U_ratio2_lci()[order(-data_U_ratio2_lci()$timevar,data_U_ratio2_lci()$cov),]$V ) covto=c( data_U_ratio2_uci()[order(-data_U_ratio2_uci()$timevar,data_U_ratio2_uci()$cov),]$cov_factor, data_U_ratio2_lci()[order(-data_U_ratio2_lci()$timevar,data_U_ratio2_lci()$cov),]$cov_factor ) data=data.frame(x,y,covto,y_central) data }) output$fileob2 <- renderPrint({ data_U_ratio1()[50,] }) ########################################## #output$shouldloaduser3 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotuser3", label = h2("Download the plot")) #}) datauser3_re <- reactive ({ ax <- list( title = "",zeroline = FALSE,showline = FALSE, showticklabels = FALSE, showgrid = FALSE) if (length(myjson2()$userr) == 0| myjson2()$Nats==1 ) { u_user_d= plot_ly() %>% layout(title=list(text="Not applicable- Only one covariate pattern specified",y=0.95),xaxis=ax, yaxis=ax) u_user_d } else { if (input$confu=="ci_no") { u_user_d= plot_ly(data_U_ratio2(),alpha=0.5) %>% add_lines( x=data_U_ratio2()$timevar,y=data_U_ratio2()$V, color=as.factor(data_U_ratio2()$cov_factor), colors=labels_colour_cov()[2:length(myjson2()$cov$atlist)], mode="lines", line=list(simplify=FALSE), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) } else if (input$confu=="ci_yes") { u_user_d <- plot_ly() u_user_d <- add_trace(u_user_d, line=list(simplify=FALSE), mode="lines", type = "scatter", x=data_U_ratio_ci()$x, y=data_U_ratio_ci()$y_central, # colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_U_ratio_ci()$covto) , colors=labels_colour_cov()[2:length(myjson2()$cov$atlist)], text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) u_user_d <- add_trace(u_user_d, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_U_ratio_ci()$x, y=data_U_ratio_ci()$y, # colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)-1], color=as.factor(data_U_ratio_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) } u_user_d=u_user_d %>% layout(title=list(text=paste0("Ratio in ",input$usertitle," between covariate pattern"),y=0.95), font= list(family = "times new roman", size = input$textsizeuser, color = input$textcolouruser), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepuserx, tick0 = input$startuserx, range=c(input$startuserx,input$enduserx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= paste0("Ratio of ",input$usertitle), dtick = input$stepusery, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) u_user_d } }) output$U_ratio <- renderPlotly ({ datauser3_re() }) output$downplotuser3 <- downloadHandler( filename = function(){paste("user2",'.png',sep='')}, content = function(file){ plotly_IMAGE( datauser3_re(),width = 1200, height = 900, format = "png", scale = 2, out_file = file ) } ) ###Note: Comparisons are deactivated for the extra tab temporarily observeEvent(input$json2, { if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Number')))==0 & length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Next')))==0 & length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Soj')))==0 & length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'First')))==0 ) { js$disableTab("mytab_extra") } }) observeEvent(input$csv2, { if( length(which(startsWith(names( read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Number')))==0 & length(which(startsWith(names( read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Next')))==0 & length(which(startsWith(names( read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Soj')))==0 & length(which(startsWith(names( read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'First')))==0 ) { js$disableTab("mytab_extra") } }) observeEvent(input$example, { if( input$example=="Yes" ) { js$disableTab("mytab_extra") } }) observeEvent(input$example2, { if( input$example2=="Yes" ) { js$disableTab("mytab_extra") } }) observeEvent(input$compare_approach, { if( input$example=="Yes" ) { js$disableTab("mytab_extra") } }) observeEvent(input$compare_approach2, { if( input$example2=="Yes" ) { js$disableTab("mytab_extra") } }) observeEvent(input$aimtype, { if( input$aimtype=="compare" ) { js$disableTab("mytab_extra") } }) #observeEvent(input$json2, { # if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Soj')))==0 & # length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Next')))==0 & # length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'First')))==0 & # length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Number')))==0) { # js$disableTab("mytab_extra") # # } #}) # #observeEvent(input$csv2, { # if( length(which(startsWith(names(read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Soj')))==0 & # length(which(startsWith(names(read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Next')))==0 & # length(which(startsWith(names(read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'First')))==0 & # length(which(startsWith(names(read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Number')))==0) { # js$disableTab("mytab_extra") # # } # # if( length(which(startsWith(names(read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Soj')))!=0 | # length(which(startsWith(names(read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Next')))!=0 | # length(which(startsWith(names(read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'First')))!=0 | # length(which(startsWith(names(read.table(input$csv2$datapath,header=TRUE, sep=",") ), 'Number')))!=0) { # js$enableTab("mytab_extra") # # } #}) # ###### Show and hide and tick inputs #### timerextra <- reactiveVal(1.5) observeEvent(c(input$showticknumber,invalidateLater(1000, session)), { if(input$showticknumber=="No"){ hide("tickinputnumber") } if(input$showticknumber=="Yes"){ show("tickinputnumber") } isolate({ timerextra(timerextra()-1) if(timerextra()>1 & input$showticknumber=="No") { show("tickinputnumber") } }) }) ############################################ existnumber <- reactive({ if (length(myjson2()$number) != 0) { x= 1 } else if (length(myjson2()$number) == 0) { x= 0 } }) existfirst <- reactive({ if (length(myjson2()$first ) == 0) { x= 0 } else if (length(myjson2()$first ) != 0 & length(myjson2()$ci_first)==0) { x= 1 } else if (length(myjson2()$first ) != 0 & length(myjson2()$ci_first)!=0) { x= 2 } }) existnext <- reactive({ if (length(myjson2()$nextv) == 0 ) { x= 0 } else if (length(myjson2()$nextv) != 0 & length(myjson2()$ci_next)==0) { x= 1 } else if (length(myjson2()$nextv) != 0 & length(myjson2()$ci_next)!=0) { x= 2 } }) existsoj <- reactive({ if (length(myjson2()$soj) == 0) { x= 0 } else if (length(myjson2()$soj) != 0 & length(myjson2()$ci_soj)==0 ) { x= 1 } else if (length(myjson2()$soj)!= 0 & length(myjson2()$ci_soj)!=0) { x= 2 } }) output$page_extra <- renderUI({ if (is.null(myjson2())) return("Provide the json file with the predictions") fluidRow( tags$style(type="text/css", ".shiny-output-error { visibility: hidden; }", ".shiny-output-error:before { visibility: hidden; }" ), column(2, h1("Extra estimates"), conditionalPanel(condition="input.tabsextra =='#panel1extra'||input.tabsextra =='#panel2extra'", uiOutput("facetnum"), uiOutput("confnum") ) , ), column(2, br(), p(""), conditionalPanel(condition="input.tabsextra =='#panel1extra'||input.tabsextra =='#panel2extra'", uiOutput("showticknumber"), uiOutput("tickinputnumber") ) ), column(8, tabsetPanel(id = "tabsextra", # tabPanel(h2("Expected number of visits by state"), id="#panel1extra", value = "#panel1extra", plotlyOutput("number_state" , height="600px", width = "100%"),uiOutput("shouldloadextra1")), tabPanel(h2("Expected number of visits by state"), id="#panel1extra", value = "#panel1extra", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="output.test_number=='1'", plotlyOutput("number_state" , height="600px", width = "100%"),uiOutput("shouldloadextra1"), ), conditionalPanel(condition="output.test_number=='0'", uiOutput("notab_number") ) ) ) ), # tabPanel(h2("Expected number of visits by cov pattern"), value = "#panel2extra", plotlyOutput("number_cov" , height="600px", width = "100%"),uiOutput("shouldloadextra2")), tabPanel(h2("Expected number of visits by cov pattern"), id="#panel2extra", value = "#panel2extra", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="output.test_number=='1'", plotlyOutput("number_cov" , height="600px", width = "100%"),uiOutput("shouldloadextra2"), ), conditionalPanel(condition="output.test_number=='0'", uiOutput("notab_number2") ) ) ) ), # tabPanel(h2("Next state"), textOutput("next1"), value = "#panel3extra", DT:: dataTableOutput("next_state")), tabPanel(h2("Next state"), id="#panel3extra", value = "#panel3extra", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="output.test_next=='1'", DT:: dataTableOutput("next_state"), ), conditionalPanel(condition="output.test_next=='0'", uiOutput("notab_next") ) ) ) ), # tabPanel(h2("Mean sojourn times"), textOutput("soj1"), value = "#panel4extra", DT:: dataTableOutput("soj_state")), tabPanel(h2("Mean sojourn times"), id="#panel4extra", value = "#panel4extra", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="output.test_soj=='1'", DT:: dataTableOutput("soj_state"), ), conditionalPanel(condition="output.test_soj=='0'", uiOutput("notab_soj") ) ) ) ), # tabPanel(h2("Expected first passage times"), textOutput("first1"),value = "#panel5extra", DT:: dataTableOutput("first_state")) tabPanel(h2("Expected first passage times"), id="#panel5extra", value = "#panel5extra", fluidRow( column(12, useShinyjs(), conditionalPanel(condition="output.test_first=='1'", DT:: dataTableOutput("first_state"), ), conditionalPanel(condition="output.test_first=='0'", uiOutput("notab_first") ) ) ) ) ) ) ) }) ################################################################# ### code helping the conditional panel for Number ############## ################################################################ number_tab <- reactive({ if (length(myjson2()$number) != 0) { x= 1 } else if (length(myjson2()$number) == 0) { x= 0 } x }) output$test_number <- renderText({ number_tab() }) outputOptions(output, "test_number", suspendWhenHidden=FALSE) output$notab_number<- renderUI({ helpText("No information on expected number of visits was provided- Tab not available") }) output$notab_number2<- renderUI({ helpText("No information on expected number of visits was provided- Tab not available") }) ################################################################# ################################################################# ################################################################# # ################################################################## #### code helping the conditional panel for Next state ############## ################################################################# next_tab <- reactive({ if (length(myjson2()$nextv) != 0) { x= 1 } else if (length(myjson2()$nextv) == 0) { x= 0 } x }) output$test_next <- renderText({ next_tab() }) outputOptions(output, "test_next", suspendWhenHidden=FALSE) output$notab_next<- renderUI({ helpText("No information on expected next state was provided- Tab not available") }) ######################################################################### # # ################################################################## #### code helping the conditional panel for sojourn ############## ################################################################# soj_tab <- reactive({ if (length(myjson2()$soj) != 0) { x= 1 } else if (length(myjson2()$soj) == 0) { x= 0 } x }) output$test_soj <- renderText({ soj_tab() }) outputOptions(output, "test_soj", suspendWhenHidden=FALSE) output$notab_soj<- renderUI({ helpText("No information on sojourn states was provided- Tab not available") }) ######################################################################## ################################################################################### #### code helping the conditional panel for first passage from states ############## ##################################################################################### first_tab <- reactive({ if (length(myjson2()$first) != 0) { x= 1 } else if (length(myjson2()$first) == 0) { x= 0 } x }) output$test_first <- renderText({ first_tab() }) outputOptions(output, "test_first", suspendWhenHidden=FALSE) output$notab_first<- renderUI({ helpText("No information on first passage times was provided- Tab not available") }) ######################################################################### # #observeEvent(input$json2, { # if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Next')))==0 ) { # js$disableTab("#panel3extra") # # } #}) #observeEvent(input$json2, { # if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Soj')))==0 ) { # js$disableTab("#panel4extra") # # } #}) #observeEvent(input$json2, { # if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'First')))==0 ) { # js$disableTab("#panel5extra") # # } #}) # # #toListenExtra <- reactive({ # list(input$json2,input$example,input$example2,input$compare_approach,input$compare_approach2) #}) # #observeEvent(toListenExtra(), { # if( length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Number')))==0 & # length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Next')))==0 & # length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'Soj')))==0 & # length(which(startsWith(names(fromJSON(input$json2$datapath, flatten=TRUE)), 'First')))==0 ) { # js$disableTab("mytab_extra") # # } # # if (input$example=="Yes"|input$example2=="Yes"|input$compare_approach=="Yes"|input$compare_approach2=="Yes") { # js$disableTab("mytab_extra") # } #}) #else { # shinyjs::enable(id = "mytab_visit") #} #observeEvent(input$json2, { # if (length(fromJSON(input$json2$datapath, flatten=TRUE)$number)==0) { # hideTab(inputId = "tabs_start", target = "mytab_extra") # } #}) #observeEvent(input$json2, { # if (length(fromJSON(input$json2$datapath, flatten=TRUE)$number)==0) { # # js$disableTab("mytab_extra") # } # }) # else { # js$enableTab("mytab_extra") # updateTabsetPanel(session,"tabs_start" ,"mytab_extra") # } # #}) # #observeEvent({ # toggleClass(selector = "#navbar li a[data-value=mytab_extra]", class = "disabled", # condition = length(fromJSON(input$json2$datapath, flatten=TRUE)$number)==0) #}) #output$displaynumber <- renderUI({ # radioButtons("displayvisit", "Change labels of states and covariate patterns", # c("same" = "same", "change"= "change")) #}) output$showticknumber <- renderUI({ radioButtons("showticknumber", "Show axis tick options", choices = list("No" = "No", "Yes" = "Yes"), selected = "No") }) output$facetnum <- renderUI({ radioButtons(inputId="facetnum", label= "Display graph in grids", choices=c("No","Yes"),selected = "No") }) output$confnum <- renderUI({ if (length(myjson2()$ci_number)!=0) { radioButtons("confnum", "Confidence intervals", c("No" = "ci_no", "Yes" ="ci_yes")) } else if (length(myjson2()$ci_number)==0) { item_list <- list() item_list[[1]]<- radioButtons("confnum", "Confidence intervals",c("No" = "ci_no")) item_list[[2]]<-print("Confidence interval data were not provided") do.call(tagList, item_list) } }) output$numbers1 <- renderText({ if ( length(myjson2()$number)>0) {print("Expected number of visits")} else if ( length(myjson2()$number)==0) {print("Non applicable")} }) output$next1 <- renderText({ if ( length(myjson2()$nextv)>0) {print("Probability that each state is next")} else if ( length(myjson2()$nextv)==0) {print("Non applicable")} }) output$first1 <- renderText({ if ( length(myjson2()$first)>0) {print("Expected first passage times")} else if ( length(myjson2()$first)==0) {print("Non applicable")} }) output$soj1 <- renderText({ if ( length(myjson2()$soj)>0) {print("Mean sojourn times")} else if ( length(myjson2()$soj)==0) {print("Non applicable")} }) ################################################## ###### Will appear conditionally################## ################################################### #Create the reactive input of covariates #output$covarinputnum <- renderUI({ # # if (is.null(myjson2())) return() # # if (input$displaynum=="same") return() # # else { # # item_list <- list() # item_list[[1]] <- h2("Covariate patterns") # # v=vector() # for (i in 1:length(myjson2()$cov$atlist)) { # v[i]=myjson2()$cov$atlist[i] # } # # default_choices_cov=v # # for (i in seq(length(myjson2()$cov$atlist))) { # item_list[[i+1]] <- textInput(paste0('covnum', i),default_choices_cov[i], labels_cov()[i]) # } # # do.call(tagList, item_list) # } #}) # #labels_covnum<- reactive ({ # # if (input$displaynum=="same") {labels_cov()} # # else { # # myList<-vector("list",length(myjson2()$cov$atlist)) # for (i in 1:length(myjson2()$cov$atlist)) { # myList[[i]]= input[[paste0('covnum', i)]][1] # } # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # final_list # } #}) #Create the reactive input of states #output$statesinputnum <- renderUI({ # # if (input$displaynum=="same") return() # # else { # # item_list <- list() # item_list[[1]] <- h2("States") # default_choices_state=vector() # # title_choices_state=vector() # for (i in 1:length(myjson2()$P)) { # title_choices_state[i]=paste0("State"," ",input$select,selectend()[i]) # } # for (i in 1:length(myjson2()$P)) { # # item_list[[1+i]] <- textInput(paste0('statenum',i),title_choices_state[i], labels_state()[i]) # # } # do.call(tagList, item_list) # } #}) # #labels_statenum<- reactive ({ # # if (input$displaynum=="same") {labels_state()} # else { # # myList<-vector("list",length(myjson2()$P)) # # for (i in 1:length(myjson2()$P)) { # # myList[[i]]= input[[paste0('statenum', i)]][1] # # } # final_list=unlist(myList, recursive = TRUE, use.names = TRUE) # final_list # } #}) # ################################################################################## ################################################################################### data_N <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) { v[i]=myjson2()$cov$atlist[i] } ## Different variable of probabilities for each covariate pattern ## Different variable of probabilities for each covariate pattern num=list() if (length(myjson2()$number)==0) {return()} for(i in 1:length(myjson2()$number)) { num[[i]]=as.data.frame(t(data.frame(myjson2()$number[i]))) colnames(num[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$number)) { num[[i]]=as.data.frame(cbind(num[[i]], timevar ,state=rep(i,nrow(num[[i]] )) )) } # Append the probabilities datasets of the different states data_num=list() data_num[[1]]=num[[1]] for (u in 2:(length(myjson2()$number))) { data_num[[u]]=rbind(num[[u]],data_num[[(u-1)]]) } datan=data_num[[length(myjson2()$number)]] datan }) data_N_uci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern num_uci=list() if (length(myjson2()$number_uci)==0) {return()} for(i in 1:length(myjson2()$number_uci)) { num_uci[[i]]=as.data.frame(t(data.frame(myjson2()$number_uci[i]))) colnames(num_uci[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$number_uci)) { num_uci[[i]]=as.data.frame(cbind(num_uci[[i]], timevar ,state=rep(i,nrow(num_uci[[i]] )) )) } # Append the probabilities datasets of the different states data_N_uci=list() data_N_uci[[1]]=num_uci[[1]] for (u in 2:(length(myjson2()$number_uci))) { data_N_uci[[u]]=rbind(num_uci[[u]],data_N_uci[[(u-1)]]) } datan_uci=data_N_uci[[length(myjson2()$number_uci)]] datan_uci }) data_N_lci <- reactive ({ if(is.null(myjson2())) return() #Will give a certain shape to the probability data so that we have the #covariate patterns as variables and the states as groups timevar=as.data.frame(myjson2()$timevar) names(timevar)[1]<- "timevar" v=vector() for (i in 1:length(myjson2()$cov$atlist)) {v[i]=myjson2()$cov$atlist[i]} ## Different variable of probabilities for each covariate pattern num_lci=list() if (length(myjson2()$number_lci)==0) {return()} for(i in 1:length(myjson2()$number_lci)) { num_lci[[i]]=as.data.frame(t(data.frame(myjson2()$number_lci[i]))) colnames(num_lci[[i]]) <-labels_cov() } for(i in 1:length(myjson2()$number_lci)) { num_lci[[i]]=as.data.frame(cbind(num_lci[[i]], timevar ,state=rep(i,nrow(num_lci[[i]] )) )) } # Append the probabilities datasets of the different states data_N_lci=list() data_N_lci[[1]]=num_lci[[1]] for (u in 2:(length(myjson2()$number_lci))) { data_N_lci[[u]]=rbind(num_lci[[u]],data_N_lci[[(u-1)]]) } datan_lci=data_N_lci[[length(myjson2()$number_lci)]] datan_lci }) data_N_st<-reactive ({ datanew=data_N() datanew$state_fac=c(rep("NA",nrow(datanew))) for (o in 1:(length(myjson2()$number))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_N_st_uci<-reactive ({ datanew=data_N_uci() datanew$state_fac=c(rep("NA",nrow(datanew))) for (o in 1:(length(myjson2()$number_uci))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_N_st_lci<-reactive ({ datanew=data_N_lci() datanew$state_fac=c(rep("NA",nrow(datanew))) for (o in 1:(length(myjson2()$number_lci))) { for (g in 1:nrow(datanew)) { if (datanew$state[g]==o) {datanew$state_fac[g]=labels_state()[o] } } } datanew }) data_N_d <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_N_st()[,d],data_N_st()[,ncol(data_N_st())-2],data_N_st()[,ncol(data_N_st())-1],data_N_st()[,ncol(data_N_st())],rep(d,length(data_N_st()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_N_st())[d],length(data_N_st()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_n <- bind_rows(dlist, .id = "column_label") d_all_n }) data_N_d_uci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_N_st_uci()[,d],data_N_st_uci()[,ncol(data_N_st_uci())-2], data_N_st_uci()[,ncol(data_N_st_uci())-1], data_N_st_uci()[,ncol(data_N_st_uci())],rep(d,length(data_N_st_uci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_N_st_uci())[d],length(data_N_st_uci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_n_uci <- bind_rows(dlist, .id = "column_label") d_all_n_uci }) data_N_d_lci <- reactive ({ ### Meke one variable of probabilities so now, states and covariate patterns ### define subgroups of the dataset dlist=list() for (d in 1:length(myjson2()$cov$atlist)) { dlist[[d]]=cbind.data.frame(data_N_st_lci()[,d],data_N_st_lci()[,ncol(data_N_st_lci())-2], data_N_st_lci()[,ncol(data_N_st_lci())-1], data_N_st_lci()[,ncol(data_N_st_lci())],rep(d,length(data_N_st_lci()[,d])) ) dlist[[d]][,6] <- rep(colnames(data_N_st_lci())[d],length(data_N_st_lci()[,d])) colnames(dlist[[d]]) <- c("V","timevar","state","state_factor","cov","cov_factor") } d_all_n_lci <- bind_rows(dlist, .id = "column_label") d_all_n_lci }) output$tickinputnumber <- renderUI({ if (existnumber()==0) {return()} else if (existnumber()==1) { default_choices=c("black","blue1","brown1","chartreuse2","cyan1","darkgray","firebrick3", "gold","darkorange2","lightsteelblue4","rosybrow2","violetred2", "yellow2","yellowgreen","tan1","lightslateblue","khaki4") if (is.null(myjson2())) return() item_list <- list() item_list[[1]] <- h4("Provide x axis range and ticks") item_list[[2]] <-numericInput("startnx","Start x at:",value=min(data_N_d()$timevar),min=0,max=max(data_N_d()$timevar) ) item_list[[3]] <-numericInput("stepnx","step:",value=max(data_N_d()$timevar/10),min=0,max=max(data_N_d()$timevar)) item_list[[4]] <-numericInput("endnx","End x at:",value =max(data_N_d()$timevar),min=0,max=max(data_N_d()$timevar)) item_list[[5]] <-numericInput("stepny","step at y axis:",value=0.2,min=0.001,max=1) item_list[[6]] <-numericInput("endny","End y at:",value =1,min=0,max=1) item_list[[7]] <-numericInput("textsizenum",h2("Legends size"),value=input$textsize,min=5,max=30) item_list[[8]] <-selectInput("textcolournum",h2("Legends colour"), choices= default_choices, selected =input$textcolour ) do.call(tagList, item_list) } }) data_N_ci<- reactive ({ x=c( data_N_d()[order(data_N_d()$timevar,data_N_d()$state,data_N_d()$cov),]$timevar, data_N_d_lci()[order(-data_N_d()$timevar,data_N_d()$state,data_N_d()$cov),]$timevar ) y_central=c( data_N_d()[order(data_N_d()$timevar,data_N_d()$state,data_N_d()$cov),]$V, data_N_d()[order(-data_N_d()$timevar,data_N_d()$state,data_N_d()$cov),]$V ) y=c( data_N_d_uci()[order(data_N_d_uci()$timevar,data_N_d_uci()$state,data_N_d_uci()$cov),]$V, data_N_d_lci()[order(-data_N_d_uci()$timevar,data_N_d_uci()$state,data_N_d_uci()$cov),]$V ) frameto=c(as.character(data_N_d_uci()[order(-data_N_d_uci()$timevar,data_N_d_uci()$state,data_N_d_uci()$cov),]$state_factor), as.character(data_N_d_lci()[order(-data_N_d_lci()$timevar,data_N_d_lci()$state,data_N_d_lci()$cov),]$state_factor) ) covto=c( data_N_d_uci()[order(-data_N_d_uci()$timevar,data_N_d_uci()$state,data_N_d_uci()$cov),]$cov_factor, data_N_d_lci()[order(-data_N_d_lci()$timevar,data_N_d_lci()$state,data_N_d_lci()$cov),]$cov_factor ) data=data.frame(x,y,frameto,covto,y_central) data }) #output$shouldloadextra1 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotextra1", label = h2("Download the plot")) #}) dataextra1_re <- reactive ({ ####### Plot 1 frame is state, factor is cov ######################## if (input$confnum=="ci_no") { if (input$facetnum=="No") { num_state= plot_ly(data_N_d(),alpha=0.5) %>% add_lines( x=data_N_d()$timevar,y=data_N_d()$V, frame=as.factor(data_N_d()$state_factor), color=as.factor(data_N_d()$cov_factor), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], mode="lines", line=list(simplify=FALSE,color = labels_colour_cov()) , text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) num_state = num_state %>% layout(title=list(text="Expected number of visits for each covariate pattern among states",y=0.95), font= list(family = "times new roman", size = input$textsizenum, color = input$textcolournum), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepnx, tick0 = input$startnx, range=c(input$startnx,input$endnx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Expected number of visits",rangemode = "nonnegative", dtick = input$stepny, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) num_state } if (input$facetnum=="Yes") { data_plot=data_N_d() num_state = ggplot(data_plot) num_state = ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(cov_factor))) num_state = num_state+geom_line(aes(x=timevar, y=V, color= as.factor(cov_factor)))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) num_state = num_state+ facet_wrap(~state_factor) num_state = num_state + scale_x_continuous(breaks=c(seq(input$startnx,input$endnx,by=input$stepnx ))) + scale_y_continuous(breaks=c(seq(0,input$endny,by=input$stepny ))) num_state = num_state +labs(title="Expected number of visits for each covariate pattern among states", x="Time since entry", y="Expected number of visits") num_state = num_state + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") num_state = num_state +theme(title = element_text(size = input$textsizenum-4), legend.title = element_text(color=input$textcolourvis, size= input$textsizenum-5), legend.text=element_text(size= input$textsizenum-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizenum-5), axis.title.x = element_text(size= input$textsizenum-5), axis.text.x = element_text( size=input$textsizenum-6),axis.text.y = element_text( size=input$textsizenum-6)) num_state = ggplotly(num_state, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) num_state } } else if (input$confnum=="ci_yes") { if (input$facetnum=="No") { num_state <- plot_ly() num_state <- add_trace(num_state, line=list(simplify=FALSE,color = labels_colour_cov()), mode="lines", type = "scatter", x=data_N_ci()$x, y=data_N_ci()$y_central, frame=as.factor(data_N_ci()$frameto), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=as.factor(data_N_ci()$covto) , text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) num_state <- add_trace(num_state, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_N_ci()$x, y=data_N_ci()$y, frame=as.factor(data_N_ci()$frameto), colors=labels_colour_cov()[1:length(myjson2()$cov$atlist)], color=as.factor(data_N_ci()$covto), showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) num_state = num_state %>% layout(title=list(text="Expected number of visits for each covariate pattern among states",y=0.95), font= list(family = "times new roman", size = input$textsizenum, color = input$textcolournum), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepnx, tick0 = input$startnx, range=c(input$startnx,input$endnx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Expected number of visits",rangemode = "nonnegative", dtick = input$stepny, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% animation_opts(frame = 1000, transition = 0, redraw = FALSE)%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) num_state } if (input$facetnum=="Yes") { N_lci= data_N_d_lci()$V N_uci= data_N_d_uci()$V data_plot=cbind(data_N_d(),N_lci,N_uci) num_state=ggplot(data_plot) num_state=ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(cov_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Expected number of visits: ", V, "
Covariate pattern: ", as.factor(cov_factor))))+ scale_colour_manual( values =labels_colour_cov(),labels = labels_cov() ) num_state=num_state+geom_line(aes(x=timevar, y=V, fill= as.factor(cov_factor))) num_state=num_state+ geom_ribbon(aes(ymin = N_lci, ymax =N_uci,fill=as.factor(cov_factor)),alpha=0.4)+ scale_fill_manual( values =labels_colour_cov(),labels = labels_cov() ) num_state = num_state+ facet_wrap(~state_factor) num_state = num_state + scale_x_continuous(breaks=c(seq(input$startnx,input$endnx,by=input$stepnx ))) + scale_y_continuous(breaks=c(seq(0,input$endny,by=input$stepny ))) num_state = num_state +labs(title="Expected number of visits at each state", x="Time since entry", y="Expected number of visits") num_state = num_state + labs(color = "Covariate\npatterns")+ labs(fill = "Covariate\npatterns") num_state = num_state +theme(title = element_text(size = input$textsizenum-4), legend.title = element_text(color=input$textcolourvis, size= input$textsizenum-5), legend.text=element_text(size= input$textsizenum-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizenum-5), axis.title.x = element_text(size= input$textsizenum-5), axis.text.x = element_text( size=input$textsizenum-6),axis.text.y = element_text( size=input$textsizenum-6)) num_state = ggplotly(num_state, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) num_state } } num_state }) output$number_state <- renderPlotly ({ dataextra1_re()}) output$downplotextra1 <- downloadHandler( filename = function(){paste("extra1",'.png',sep='')}, content = function(file){ plotly_IMAGE( dataextra1_re(),width = 1200, height = 900, format = "png", scale = 2, out_file = file ) } ) #################################################### #output$shouldloadextra2 <- renderUI({ # if (is.null((myjson2()))) return() # downloadButton(outputId = "downplotextra2", label = h2("Download the plot")) #}) dataextra2_re <- reactive ({ if (input$confnum=="ci_no") { if (input$facetnum=="No") { num_cov= plot_ly(data_N_d(),alpha=0.5) %>% add_lines( x=data_N_d()$timevar,y=data_N_d()$V, frame=as.factor(data_N_d()$cov_factor), color=as.factor(data_N_d()$state_factor), colors=labels_colour_state()[1:length(myjson2()$P)], mode="lines", line=list(simplify=FALSE,color = labels_colour_state()) , text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) num_cov = num_cov %>% layout(title=list(text="Expected number of visits for each state among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizenum, color = input$textcolournum), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepnx, tick0 = input$startnx, range=c(input$startnx,input$endnx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Expected number of visits",rangemode = "nonnegative", dtick = input$stepny, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =0, xref = "x", y0 = 0, y1 = 1, yref = "y") ) )%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { num_cov= num_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } num_cov } if (input$facetnum=="Yes") { data_plot=data_N_d() num_cov = ggplot(data_plot) num_cov = ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(state_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Expected number of visits: ", V, "
State: ", as.factor(state_factor)))) num_cov = num_cov+geom_line(aes(x=timevar, y=V, color= as.factor(state_factor)))+ scale_colour_manual( values =labels_colour_state(),labels = labels_state() ) num_cov = num_cov+ facet_wrap(~cov_factor) num_cov = num_cov + scale_x_continuous(breaks=c(seq(input$startnx,input$endnx,by=input$endnx ))) + scale_y_continuous(breaks=c(seq(0,input$endny,by=input$stepny ))) num_cov = num_cov +labs(title="Expected number of visits for each state among covariate patterns", x="Time since entry", y="Expected number of visits") num_cov = num_cov + labs(color = "States")+ labs(fill = "States") num_cov = num_cov+theme(title = element_text(size = input$textsizenum-4), legend.title = element_text(color=input$textcolourvis, size= input$textsizenum-5), legend.text=element_text(size= input$textsizenum-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizenum-5), axis.title.x = element_text(size= input$textsizenum-5), axis.text.x = element_text( size=input$textsizenum-6),axis.text.y = element_text( size=input$textsizenum-6)) num_cov = ggplotly(num_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) num_cov } } else if (input$confnum=="ci_yes") { if (input$facetnum=="No") { num_cov <- plot_ly() num_cov <- add_trace(num_cov, line=list(simplify=FALSE,color = labels_colour_cov()), mode="lines", type = "scatter", x=data_N_ci()$x, y=data_N_ci()$y_central, frame=as.factor(data_N_ci()$covto), colors=labels_colour_state()[1:length(myjson2()$P)], color=as.factor(data_N_ci()$frameto), text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","") ) num_cov <- add_trace(num_cov, fill = "tozerox", line=list(dash = "solid", color = "transparent", width = 1.8897637), mode = "lines", type = "scatter", x=data_N_ci()$x, y=data_N_ci()$y, frame=as.factor(data_N_ci()$covto), colors=labels_colour_state()[1:length(myjson2()$P)], color=as.factor(data_N_ci()$frameto) , showlegend = FALSE, text = 'Select or deselect lines by clicking on the legend', hovertemplate = paste("%{text}

", "%{yaxis.title.text}: %{y:,}
", "%{xaxis.title.text}: %{x:,}
","")) num_cov = num_cov %>% layout(title=list(text="Expected number of visits for each state among covariate patterns",y=0.95), font= list(family = "times new roman", size = input$textsizenum, color = input$textcolournum), margin = list(l = 50, r = 50, b = 30, t = 70), xaxis=list(title=list(text="Time since entry",y=0.2), dtick = input$stepnx, tick0 = input$startnx, range=c(input$startnx,input$endnx), ticklen = 5, tickwidth = 2, tickcolor = toRGB("black"), tickmode = "linear"), yaxis =list(title= "Expected number of visits",rangemode = "nonnegative", dtick = input$stepny, ticklen = 5, tickwidth = 2, tickcolor = toRGB("black")), shapes = list( list(type = "rect", fillcolor = "grey", line = list(color = "grey"), opacity = 0.8, x0 = 0, x1 =input$area, xref = "x", y0 = 0, y1 = 1, yref = "y") ))%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) if (input$smooth=="No") { num_cov= num_cov %>% animation_opts(frame = 1000, transition = 0, redraw = FALSE) } num_cov } if (input$facetnum=="Yes") { N_lci= data_N_d_lci()$V N_uci= data_N_d_uci()$V data_plot=cbind(data_N_d(),N_lci,N_uci) num_cov=ggplot(data_plot) num_cov=ggplot(data_plot,aes(x=timevar, y=V, color= as.factor(state_factor), group=1, text=paste("Select or deselect lines by clicking on the legend", "
Time: ", timevar, "
Expected number of visits: ", V, "
State: ", as.factor(state_factor))))+ scale_colour_manual( values =labels_colour_state(),labels = labels_state() ) num_cov=num_cov+geom_line(aes(x=timevar, y=V, fill= as.factor(state_factor))) num_cov=num_cov+ geom_ribbon(aes(ymin = N_lci, ymax =N_uci,fill=as.factor(state_factor)),alpha=0.4)+ scale_fill_manual( values =labels_colour_state(),labels = labels_state() ) num_cov = num_cov+ facet_wrap(~cov_factor) num_cov = num_cov + scale_x_continuous(breaks=c(seq(input$startnx,input$endnx,by=input$stepnx ))) num_cov = num_cov +labs(title="Expected number of visits for each state among covariate patterns", x="Time since entry", y="Expected number of visits") num_cov = num_cov + labs(color = "States")+ labs(fill = "States") num_cov = num_cov +theme(title = element_text(size = input$textsizenum-4), legend.title = element_text(color=input$textcolourvis, size= input$textsizenum-5), legend.text=element_text(size= input$textsizenum-6), plot.margin = unit(x=c(1.5,1.5,1.5,1.5),units="cm"), legend.margin = margin(1.5, 1, 1, 1, "cm"), legend.justification = "center",legend.box.spacing = unit(0.2, "cm"), axis.title.y = element_text(size= input$textsizenum-5), axis.title.x = element_text(size= input$textsizenum-5), axis.text.x = element_text( size=input$textsizenum-6),axis.text.y = element_text( size=input$textsizenum-6)) num_cov = ggplotly(num_cov, tooltip = "text")%>% config( toImageButtonOptions = list( format = "png", width = 1200, height = 900,scale=input$figscale ), edits = list( annotationPosition = TRUE, annotationTail = TRUE, annotationText = TRUE, axisTitleText=TRUE, colorbarTitleText=TRUE, legendPosition=TRUE, legendText=TRUE, shapePosition=TRUE, titleText=TRUE ) ,queueLength=10 ) num_cov } } num_cov }) output$number_cov <- renderPlotly ({ dataextra2_re() }) output$downplotextra2 <- downloadHandler( filename = function(){paste("extra2",'.png',sep='')}, content = function(file){ plotly_IMAGE( dataextra2_re(),width = 1200, height = 900, format = "png", scale =2, out_file = file ) } ) ########################################### df_next <- reactive({ if (existnext()==0) { next_matrix=matrix(nrow=1, ncol=1, "Not applicable") } else if (existnext()==1) { next_matrix=matrix(nrow=length(myjson2()$cov$atlist), ncol=length(myjson2()$nextv)+1, NA) next_matrix=as.data.frame(next_matrix) names(next_matrix)=c("Covariate patterns",labels_state()) next_matrix[,1]<- labels_cov() for (i in 1: length(myjson2()$cov$atlist)) { for (j in 2: (length(myjson2()$nextv)+1)) { next_matrix[i,j]=myjson2()$nextv[[j-1]][i,1] } } } else if (existnext()==2) { next_matrix=matrix(nrow=length(myjson2()$cov$atlist), ncol=3*length(myjson2()$nextv)+1, NA) label_state_next=vector() label_state_next[1]="Covariate patterns" for (j in 1: length(myjson2()$nextv)) { label_state_next[1+(3*(j-1) )+1] = labels_state()[j] label_state_next[1+(3*(j-1) )+2] = paste0(labels_state()[j],"_lci") label_state_next[1+(3*(j-1) )+3] = paste0(labels_state()[j],"_uci") } for (i in 1: length(myjson2()$cov$atlist)) { for (j in 1: length(myjson2()$nextv)) { next_matrix[,1]<- labels_cov() next_matrix[i,1+(3*(j-1) )+1]= myjson2()$nextv[[j]][i,1] next_matrix[i,1+(3*(j-1) )+2]=myjson2()$next_lci[[j]][i,1] next_matrix[i,1+(3*(j-1) )+3]=myjson2()$next_uci[[j]][i,1] } } next_matrix=as.data.frame(next_matrix) names(next_matrix)=label_state_next } next_matrix }) output$next_state<- DT::renderDataTable({ df_next() }) df_soj <- reactive({ if (existsoj()==0) { soj_matrix=matrix(nrow=1, ncol=1, "Not applicable") } else if (existsoj()==1) { soj_matrix=matrix(nrow=length(myjson2()$cov$atlist), ncol=length(myjson2()$soj)+1, NA) soj_matrix=as.data.frame(soj_matrix) labels_state_soj=vector() for (k in 1: length(myjson2()$intermediate_states) ) { labels_state_soj[k]=paste0("Sojourn: State"," ",k) } names(soj_matrix)=c("Covariate patterns",labels_state_soj) soj_matrix[,1]<- labels_cov() for (i in 1: length(myjson2()$cov$atlist)) { for (j in 1: length(myjson2()$intermediate_states) ) { soj_matrix[i,j+1]=myjson2()$soj[[j]][i,1] } } } else if (existsoj()==2) { soj_matrix=matrix(nrow=length(myjson2()$cov$atlist), ncol=3*length(myjson2()$intermediate_states)+1, NA) label_state_soj=vector() label_state_soj[1]="Covariate patterns" label_state=vector() for (j in 1: length(myjson2()$intermediate_states) ) { label_state[j]=paste0("Sojourn: State"," ",myjson2()$intermediate_states[j]) } for (j in 1: length(myjson2()$intermediate_states) ) { label_state_soj[1+(3*(j-1) )+1] = label_state[j] label_state_soj[1+(3*(j-1) )+2] = paste0(label_state[j],"_lci") label_state_soj[1+(3*(j-1) )+3] = paste0(label_state[j],"_uci") } for (i in 1: length(myjson2()$cov$atlist)) { for (j in 1: length(myjson2()$soj)) { soj_matrix[,1]<- labels_cov() soj_matrix[i,1+(3*(j-1) )+1]= myjson2()$soj[[j]][i,1] soj_matrix[i,1+(3*(j-1) )+2]= myjson2()$soj_lci[[j]][i,1] soj_matrix[i,1+(3*(j-1) )+3]= myjson2()$soj_uci[[j]][i,1] } } soj_matrix=as.data.frame(soj_matrix) names(soj_matrix)=label_state_soj } soj_matrix }) output$soj_state<- DT::renderDataTable({ df_soj() }) df_first <- reactive({ if (existfirst()==0) { first_matrix=matrix(nrow=1, ncol=1, "Not applicable") } else if (existfirst()==1) { first_matrix=matrix(nrow=length(myjson2()$cov$atlist), ncol=length(myjson2()$first)+1, NA) first_matrix=as.data.frame(first_matrix) labels_state_first=vector() for (k in 1: length(myjson2()$intermediate_states) ) { labels_state_first[k]=paste0("First: State"," ",k) } names(first_matrix)=c("Covariate patterns",labels_state_first) first_matrix[,1]<- labels_cov() for (i in 1: length(myjson2()$cov$atlist)) { for (j in 1: length(myjson2()$intermediate_states) ) { first_matrix[i,j+1]=myjson2()$first[[j]][i,1] } } } else if (existfirst()==2) { first_matrix=matrix(nrow=length(myjson2()$cov$atlist), ncol=3*length(myjson2()$intermediate_states)+1, NA) label_state_first=vector() label_state_first[1]="Covariate patterns" label_state=vector() for (j in 1: length(myjson2()$intermediate_states) ) { label_state[j]=paste0("First: State"," ",myjson2()$intermediate_states[j]) } for (j in 1: length(myjson2()$intermediate_states) ) { label_state_first[1+(3*(j-1) )+1] = label_state[j] label_state_first[1+(3*(j-1) )+2] = paste0(label_state[j],"_lci") label_state_first[1+(3*(j-1) )+3] = paste0(label_state[j],"_uci") } for (i in 1: length(myjson2()$cov$atlist)) { for (j in 1: length(myjson2()$first)) { first_matrix[,1]<- labels_cov() first_matrix[i,1+(3*(j-1) )+1]= myjson2()$first[[j]][i,1] first_matrix[i,1+(3*(j-1) )+2]= myjson2()$first_lci[[j]][i,1] first_matrix[i,1+(3*(j-1) )+3]= myjson2()$first_uci[[j]][i,1] } } first_matrix=as.data.frame(first_matrix) names(first_matrix)=label_state_first } first_matrix }) output$first_state<- DT::renderDataTable({ df_first() }) } shinyApp(ui = ui, server = server)