server.R

library(shiny)
library(dfcrm)
library(Epi)

#controle des dates
  data_time = read.table("Z:/Programmation_R/Appli_Shiny/fichier excel/fichier_1.csv",header = T, sep = ";")
  

  data_time$dat_num <- as.Date(data_time$Date_inclusion, format="%d/%m/%Y" )
  day_date = Sys.Date()
  data_time$dat_num = data_time$dat_num - day_date
  data_time$dat_num =abs(data_time$dat_num)
  
  data_control = min(data_time$dat_num)

#preparation des données crm
  data_time$rep_bin[data_time$reponse == "Succes"] = 1
  data_time$rep_bin[data_time$reponse == "Echec"] = 0


#Crm
  prior <- c(0.05, 0.10, 0.20, 0.35, 0.50, 0.70)
  target <- 0.3
  level <- data_time$Dose
  y <- data_time$rep_bin
  foo <- crm(prior, target, y, level)
  ptox <- foo$ptox  # updated estimates of toxicity rates
  
  dose = c(1,2,3,4,5,6)

#2 décimales après la virgule
  prior = round(prior,2)
  ptox = round(ptox,2)

#Construction tableau 1
  data = cbind(dose,prior,ptox)
  data = as.data.frame(data)
  
  data_tableau1 = data
  data_tableau1$dose = as.factor(data_tableau1$dose)
  data_tableau1 = t(data_tableau1)
  data_tableau1 = as.data.frame(data_tableau1)
  names(data_tableau1) <- NULL

#choix de la dose optimale
  data$tox_opt = data$ptox - 0.3
  data$tox_opt = abs(data$tox_opt)
  best_dose = data$dose[data$tox_opt == min(data$tox_opt)]


# Define server logic required to summarize and view the selected dataset
  shinyServer(function(input, output) {
  
    #Liste des tables
    datasetInput <- reactive({
      switch(input$dataset,
             "Observations" = data_tableau1,
             "Historique" = data_time[-c(7,6)],)
    })
    
      
     #affichage du tableau
        output$view <- renderTable({
          head(datasetInput(), n = length(data))
        })
    
    # Show the first "n" observations
        output$view <- renderTable({
          head(datasetInput(), n = input$obs)
        })
    
    #Graphique
        output$plot <- renderPlot({
          
          plot(data$prior ~ data$dose,type = "b",col = "red",ylim =c(0,1),xlab = "Dose", ylab = "Probability of response or Toxicity")
          par(new = T)
          plot(data$ptox ~ data$dose,type ="b",col = "blue",xaxt='n',ylim =c(0,1),
               yaxt='n',ann=FALSE)
          
          legend("topleft", legend = c("Initial toxicity", "Modified toxicity"), col = c("red", "blue"), 
                 pch = 15, bty = "n",lty=c(1,1),
                 horiz = TRUE, inset = c(0.1, 0.1))
          
          abline(h=0.30,lwd=2)
        })
    
   
      })

Ui.r

library(shiny)
library(dfcrm)
library(Epi)

#controle des dates
  data_time = read.table("Z:/Programmation_R/Appli_Shiny/fichier excel/fichier_1.csv",header = T, sep = ";")
  
  data_time$dat_num <- as.Date(data_time$Date_inclusion, format="%d/%m/%Y" )
  day_date = Sys.Date()
  data_time$dat_num = data_time$dat_num - day_date
  data_time$dat_num =abs(data_time$dat_num)
  
  data_control = min(data_time$dat_num)

#preparation des données crm
  data_time$rep_bin[data_time$reponse == "Succes"] = 1
  data_time$rep_bin[data_time$reponse == "Echec"] = 0


#Crm
  prior <- c(0.05, 0.10, 0.20, 0.35, 0.50, 0.70)
  target <- 0.3
  level <- data_time$Dose
  y <- data_time$rep_bin
  foo <- crm(prior, target, y, level)
  ptox <- foo$ptox  # updated estimates of toxicity rates
  
  dose = c(1,2,3,4,5,6)

#2 décimales après la virgule
  prior = round(prior,2)
  ptox = round(ptox,2)

#Construction tableau 1
  data = cbind(dose,prior,ptox)
  data = as.data.frame(data)
  
  data_tableau1 = data
  data_tableau1$dose = as.factor(data_tableau1$dose)
  data_tableau1 = t(data_tableau1)
  data_tableau1 = as.data.frame(data_tableau1)
  names(data_tableau1) <- NULL

#choix de la dose optimale
  data$tox_opt = data$ptox - 0.3
  data$tox_opt = abs(data$tox_opt)
  best_dose = data$dose[data$tox_opt == min(data$tox_opt)]


# Define UI for dataset viewer application
  shinyUI(pageWithSidebar(
  
  #Application title.
    headerPanel("OPTIMUM TRIAL"),
      
    sidebarPanel(
      
    #Menu des tables
    selectInput("dataset", "Choisir une table:", 
                choices = c("Observations", "Historique")),
    
    #Nombre D'observation
    numericInput("obs", "Number of observations to view:", 10),
    
    #Bouton de mise à jours
    submitButton("Update View")
    
        
    ),
    
     mainPanel(    
      h4("Observations"),
      tableOutput("view"),
      
      if ( data_control < 15 )
        h3("ceci est une phrase de test")
      else
        h3(paste("la dose recommandee est la dose ",best_dose)),
    
    #Affichage du graphique
      plotOutput("plot")
      
     )   
      ))