From ed7018260ee905a9fd7d9eb86c54be2dcebb3ec1 Mon Sep 17 00:00:00 2001
From: marcelcosta <marcelcosta@protonmail.com>
Date: Fri, 13 Nov 2020 19:56:55 +0100
Subject: [PATCH] error2

---
 invivos/app.R | 592 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 592 insertions(+)
 create mode 100644 invivos/app.R

diff --git a/invivos/app.R b/invivos/app.R
new file mode 100644
index 0000000..d614c87
--- /dev/null
+++ b/invivos/app.R
@@ -0,0 +1,592 @@
+library(shiny)
+library(ggplot2)
+library(reshape2)
+library(openxlsx)
+library(dplyr)
+library(car)
+library(ggbeeswarm)
+library(gtools)
+library(gridExtra)
+source("../../funcions.R")
+library(survminer)
+library(survival)
+
+# Define UI for application
+ui <- fluidPage(
+   
+   #Navbar
+   navbarPage("Seguimiento in vivos",
+              tabPanel("Diseño",
+                sidebarPanel(
+                  fileInput(inputId = "file_sizes", label = "Hoja de tamaños", multiple = F),
+                  selectInput(inputId = "measure_sys", "Sistema de medida", selected = "L-W-D", choices = c("L-W-D","Min-Max","Absorbance")),
+                  uiOutput('ncages'),
+                  uiOutput('lowcut'),
+                  uiOutput('upcut'),
+                  uiOutput('goButton'),
+                  uiOutput('iterations'),
+                  downloadButton("downloadData", "Descargar Excel")
+                  ),
+                mainPanel(
+                  plotOutput("firstPlot"),
+                  plotOutput("distPlot") 
+                )
+              ),
+              tabPanel("Análisis",
+                       sidebarPanel(
+                         fileInput(inputId = "file_analy", label = "Hoja de análisis", multiple = F),
+                         selectInput(inputId = "vacc", "Experimento de Vacunación", selected = "No", choices = c("Sí","No")),
+                         sliderInput("cutoff", "Cutoff para Survival", min=100, max=1500, step=50, value=750),
+                         checkboxInput("filter_stats","Filtrar Estadística")
+                       ),
+                       mainPanel(
+                         h3('Ratones'),
+                         tableOutput('ntable'),
+                         h3('Cinéticas'),
+                         plotOutput('cin_group'),
+                         plotOutput('cin_indiv'),
+                         plotOutput('survival', height="800px"),
+                         h3('Estadística'),
+                         verbatimTextOutput('stats'),
+                         tableOutput('tab_stats')
+                       )),
+              tabPanel("Exportar",
+                       sidebarPanel(width=2,
+                                    h3('Seleccionar figura'),
+                                    selectInput("fig_id", "", selected="", choices=c("Cinética Grupo", "Cinética Individual", "Survival")),
+                                    h3('Formato'),
+                                    sliderInput("width", "Ancho", min=1000, max=20000, step=1000, value=10000),
+                                    sliderInput("height", "Altura", min=1000, max=20000, step=1000, value=6000),
+                                    textInput("colors", label="Colors", value=""),
+                                    sliderInput("errorbar-width", "% Ancho errorbars", min=0.05, max=1, step=0.05, value=0.05),
+                                    sliderInput("point-size", "Tamaño puntos", min=1, max=10, step=1, value=3),
+                                    sliderInput("font-size", "Tamaño textos", min=5, max=30, step=1, value=11),
+                                    checkboxInput(inputId = "legend", label = "Mostrar llegenda", value = T),
+                                    selectInput("theme", "Seleccionar Tema", selected="BW", choices=c("BW", "Default", "Classic")),
+                                    downloadButton("downloadPicture", "Exportar")
+                       ),
+                       mainPanel(
+                         uiOutput("expPlotUI")
+                       )
+              )
+              )
+   
+   )
+
+# Define server logic required to draw a histogram
+server <- function(input, output) {
+  
+  # Diseño
+  dades<-reactiveValues()
+  dades$taula<-NULL
+  dades$groups<-NULL
+  dades$db<-NULL
+  observe({
+    if (!is.null(input$file_sizes)){ 
+      dades$taula<-read.xlsx(input$file_sizes$datapath, sheet = 1)
+      dades$groups<-read.xlsx(input$file_sizes$datapath, sheet = 2)[,1]
+    }
+  })
+  output$firstPlot <- renderPlot({
+    observeEvent(dades$taula, {})
+    if (!is.null(dades$taula)){
+      ggplot(dades$taula, aes(x="1", y=Volumen))+geom_quasirandom(width=0.2)
+    }
+  })
+  
+  output$iterations<-renderUI({
+    if (!is.null(dades$taula)){
+      sliderInput("iterations", "Iteraciones", min=100, max=2000, step=100, value=100) 
+    }
+  })
+  output$lowcut<-renderUI({
+    if (!is.null(dades$taula)){
+      sliderInput("lowcut", "Corte inferior", min=0, max=1000, step=5, value=0)
+    }
+  })
+  output$upcut<-renderUI({
+    if (!is.null(dades$taula)){
+      sliderInput("upcut", "Corte superior", min=0, max=1000, step=5, value=400)
+    }
+  })
+  output$goButton<-renderUI({
+    if (!is.null(dades$taula)){
+      actionButton("goButton", "Selecciona")
+    }
+  })
+  output$ncages<-renderUI({
+    if (is.null(dades$taula)){
+      sliderInput("ncages", "Cajas", min=1, max=10, value=1)
+    }
+  })
+  grafic<-eventReactive(input$goButton,{
+    df<-dades$taula
+    up_cuttof<-input$upcut
+    low_cuttof<-input$lowcut
+    df<-df[df$Volumen < up_cuttof & df$Volumen > low_cuttof,]
+    df["Mouse"]<-gsub("[a-zA-Z]", "", df$MouseID)
+    
+    s<-shapiro.test(df$Volumen)[[2]]
+    
+    ngroup<-length(dades$groups)
+    
+    ind.list<-list()
+    pval.list<-list()
+    lvn.list<-list()
+    test.list<-list()
+    for (data in 1:input$iterations){
+      interr=T
+      while(interr == T){
+        ind<-sample(rep(dades$groups, each=7), length(unique(df$Mouse)))
+        df_temp<-merge(df, data.frame("Mouse"=unique(df$Mouse), "group"=as.factor(ind)))
+        interr<-any(table(df_temp$group) < floor(nrow(df_temp)/5) | table(df_temp$group) > ceiling(nrow(df_temp)/5))
+      }
+      ind.list[[data]]<-df_temp[,c("MouseID","group")]
+      lvn.list[data]<-leveneTest(Volumen ~ group, data = df_temp[,3:4])[[2]][1]
+      if (s < 0.05){
+        k<-kruskal.test(df_temp$Volumen,df_temp$group)
+        test.list[data]<-k[[1]][1]
+        pval.list[data]<-k[[3]][1]
+      }else{
+        res.aov<-aov(Volumen~group, data=df_temp)
+        pval.list[data]<-summary(res.aov)[[1]][[5]][1]
+        test.list[data]<-summary(res.aov)[[1]][[4]][1]
+      }
+    }
+    
+    index<-which(unlist(lvn.list) == min(unlist(lvn.list)[which(unlist(pval.list) %in% sort(unlist(pval.list), decreasing = T)[1:20])]))
+    df_def<-merge(df, ind.list[[index]])
+    dades$db<-df_def
+    
+    ggplot(df_def, aes(group, Volumen))+
+      geom_boxplot(outlier.alpha = F)+
+      geom_jitter(width=0.25)+
+      geom_point(stat="summary", color="blue", size=3)+
+      lims(y=c(0,max(df_def$Volumen)+10))
+  })
+  output$distPlot <- renderPlot({
+    observeEvent(dades$taula, {})
+    if (!is.null(dades$taula)){
+      grafic()
+    }
+  })
+  
+  output$downloadData <- downloadHandler(
+    
+    filename = function() {
+      paste("invivo", ".xlsx", sep="")
+    },
+    content = function(file){
+      ncages<-input$ncages
+      nrat_cage<-5
+      id_tumors<-c("L","R")
+      timepoint<-c(7,10,13,16,19,22,25)
+      
+      if (!is.null(input$file_sizes)){
+        template<-expand.grid(dades$db$MouseID, timepoint)
+        colnames(template)<-c("MouseID", "Timepoint")
+        template<-template[order(template$Timepoint, template$MouseID),]
+        template<-merge(template, dades$db[c("MouseID", "group")])
+        if (input$measure_sys == "L-W-D"){
+          template<-rbind(template, template, template)
+          template<-template[order(template$Timepoint, template$MouseID),]
+          template["TS"]<-rep(c("TS-Length", "TS-Width", "TS-Deep"), nrow(template)/3)
+          dtemplate<-dcast(template, MouseID+group+TS~Timepoint) 
+          dtemplate<-dtemplate[mixedorder(as.character(dtemplate$MouseID)),]
+        }
+        if (input$measure_sys == "Min-Max"){
+          template<-rbind(template, template)
+          template<-template[order(template$Timepoint, template$MouseID),]
+          template["DPV"]<-rep(c("Major", "Minor"), nrow(template)/2)
+          dtemplate<-dcast(template, MouseID+group+DPV~Timepoint)
+          dtemplate<-dtemplate[mixedorder(as.character(dtemplate$MouseID)),]
+        }
+        dtemplate<-dtemplate %>% add_column(.after="MouseID", "ID tumor"=dtemplate$MouseID)%>% rename(`ID animal`=MouseID)
+        dtemplate["ID tumor"]<-gsub("[[:digit:]]","",dtemplate$`ID tumor`)
+        dtemplate["ID animal"]<-gsub("[LR]","",dtemplate$`ID animal`)
+        dtemplate[,5:ncol(dtemplate)]<-""
+      }else{
+        template<-expand.grid(LETTERS[1:ncages], 1:5, id_tumors, timepoint)[,-2]
+        colnames(template)<-c("Cage", "ID tumor", "Timepoint")
+        nids<-length(id_tumors)*length(timepoint)
+        template[order(template$Cage),"ID animal"]<-rep(1:(nrow(template)/(nids)), each=nids)
+        template<-template[order(template$Timepoint, template$Cage, template$`ID animal`),]
+        template["Group"]<-""
+        if (input$measure_sys == "L-W-D"){
+          template<-rbind(template, template, template)
+          template<-template[order(template$Timepoint, template$Cage, template$`ID animal`, template$`ID tumor`),]
+          template["TS"]<-rep(c("TS-Length", "TS-Width", "TS-Deep"), nrow(template)/3)
+          dtemplate<-dcast(template, Cage+`ID animal`+`ID tumor`+Group+TS~Timepoint) 
+        }
+        if (input$measure_sys == "Min-Max"){
+          template<-rbind(template, template)
+          template<-template[order(template$Timepoint, template$Cage, template$`ID animal`, template$`ID tumor`),]
+          template["DPV"]<-rep(c("Major", "Minor"), nrow(template)/2)
+          dtemplate<-dcast(template, Cage+`ID animal`+`ID tumor`+Group+DPV~Timepoint)  
+        }
+        dtemplate[,6:ncol(dtemplate)]<-""
+      }
+      write.xlsx(dtemplate,file)
+    }
+  )
+  
+  # Análisis
+  analysis<-reactiveValues()
+  analysis$taula<-NULL
+  analysis$taula_def<-NULL
+  observe({
+    if (!is.null(input$file_analy)){ 
+      analysis$taula<-read.xlsx(input$file_analy$datapath, sheet = 1, check.names = F, sep.names = " ")
+    }
+  })
+  output$ntable<-renderTable({
+    if (!is.null(input$file_analy)){
+      observeEvent(analysis$taula, {})
+      stattest<-"dunn"
+      oneside<-""
+      cutoff<-750
+      
+      table<-analysis$taula
+      if ("ID.animal" %in% colnames(table)){table<-rename(table, "ID animal"=`ID.animal`)}
+      if ("ID" %in% colnames(table)){table<-rename(table, "ID animal"=ID)}
+      if ("ID.tumor" %in% colnames(table)){table<-rename(table, "ID tumor"=`ID.tumor`)}
+      # table[table$ID.tumor == "R","0"]<-NA
+      col_nodays<-c("ID", "Cage","Group", "ID.animal","ID animal", "ID.tumor", "ID tumor", "TS","DPV", "Absorbance")
+      if (length(grep(0, colnames(table)[!colnames(table) %in% col_nodays])) == 0){
+        table["0"]<-0
+      }
+      table<-melt(table, id=colnames(table)[colnames(table) %in% col_nodays], variable.name = "Timepoint")
+      table$Timepoint<-gsub("[A-Za-z ]","",table$Timepoint)
+      if ("DPV" %in% colnames(table)){
+        table<-dcast(table, Cage+`ID animal`+`ID tumor`+Group+Timepoint~DPV, value.var = "value")
+        table$Major<-table$Major/1000
+        table$Minor<-table$Minor/1000
+        table["Volume"]<-((table$Major*table$Minor*table$Minor)*(pi/6))*1000
+      }
+      if ("TS" %in% colnames(table)){
+        table<-dcast(table, Cage+`ID animal`+`ID tumor`+Group+Timepoint~TS, value.var = "value")
+        table["Volume"]<-table$`TS-Deep`*table$`TS-Length`*table$`TS-Width`*pi/6
+      }
+      if (!"Volume" %in% colnames(table)){table<-rename(table, "Volume"=value)}
+      table<-table %>% filter(!is.na(Group))
+      table$Timepoint<-factor(table$Timepoint, levels=mixedsort(as.numeric(as.character(unique(table$Timepoint)))))
+      analysis$taula_def<-table
+      table_plot<-dcast(dcast(table %>% filter(!is.na(Volume)), `ID animal`+Group+Timepoint~., value.var = "Volume", fun.aggregate = mean), Group~Timepoint)
+      table_plot
+    }
+  })
+  output$cin_group<-renderPlot({
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      observeEvent(analysis$taula_def, {})
+      table<-analysis$taula_def
+      if (input$vacc == "Sí"){
+        ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=Group))+
+          geom_errorbar(stat="summary", width=0.05)+
+          geom_line(stat="summary")+
+          geom_point(stat="summary")+
+          facet_grid(factor(`ID tumor`, labels = c("Vaccination", "Rechallenge"))~., scale="free_y")+
+          labs(x="Days after tumor challenge")+
+          scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
+          scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
+          theme_bw()  
+      }else{
+        ggplot(table, aes(Timepoint, Volume, color=Group, group=Group))+
+          geom_errorbar(stat="summary",width=0.05)+
+          geom_line(stat="summary")+
+          geom_point(stat="summary")+
+          labs(x="Days after tumor challenge")+
+          scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
+          theme_bw()+
+          theme(axis.text.x=element_text(angle=45, hjust=1))
+      }
+      
+    }
+  })
+  output$cin_indiv<-renderPlot({
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      observeEvent(analysis$taula_def, {})
+      table<-analysis$taula_def
+      if (input$vacc == "Sí"){
+        ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=`ID animal`))+
+          # geom_errorbar(stat="summary", width=0.05)+
+          geom_line()+
+          geom_point()+
+          scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
+          scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
+          facet_grid(factor(`ID tumor`, labels = c("Vaccination", "Rechallenge"))~Group, scale="free_y")+
+          labs(x="Days after tumor challenge")+
+          theme_bw()  
+      }else{
+        ggplot(table, aes(Timepoint, Volume, color=Group, group=`ID animal`))+
+          # geom_errorbar(stat="summary", width=0.05)+
+          geom_line()+
+          geom_point()+
+          scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
+          # scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
+          facet_wrap(.~Group)+
+          labs(x="Days after tumor challenge")+
+          theme_bw()+
+          theme(axis.text.x=element_text(angle=45, hjust=1))
+      }
+    }
+  })
+  
+  output$survival<-renderPlot({
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      observeEvent(analysis$taula_def, {})
+      table<-analysis$taula_def
+      if (input$vacc == "Sí"){
+        g<-list()
+        for (side in c("L","R")){
+          tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
+          endtime<-dcast(tableR %>% filter(Volume < cutoff), Cage+`ID animal`+`ID tumor`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
+          endtime["Dead"]<-dcast(tableR, Cage+`ID animal`+`ID tumor`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
+          table_tumor<<-endtime
+          
+          g[side]<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
+                              pval = T, pval.method = T,
+                              title = side,
+                              # legend.labs = paste(c("< median", ">= median"), "MICA"),
+                              ggtheme=theme_classic(base_size=15)
+                              # conf.int = TRUE,
+                              # Add risk table
+                              # risk.table = TRUE,
+                              # tables.height = 0.2,
+                              # tables.theme = theme_cleantable(),
+                              
+                              # Color palettes. Use custom color: c("#E7B800", "#2E9FDF"),
+                              # or brewer color (e.g.: "Dark2"), or ggsci color (e.g.: "jco")
+                              # palette = c("#E7B800", "#2E9FDF")
+          )
+        }
+        do.call(grid.arrange, g)
+        
+      }else{
+        tableR<-table %>% filter(!is.na(Volume))
+        endtime<-dcast(tableR %>% filter(Volume < cutoff), `ID animal`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
+        endtime["Dead"]<-dcast(tableR, `ID animal`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
+        table_tumor<<-endtime
+        
+        g<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
+                            pval = T, pval.method = T,
+                            # legend.labs = paste(c("< median", ">= median"), "MICA"),
+                            ggtheme=theme_classic(base_size=15)
+                            # palette = c("#E7B800", "#2E9FDF")
+        )
+        g
+      }
+    }
+  })
+  
+  output$stats<-renderPrint({
+    stattest<-"dunn"
+    oneside<-""
+    cutoff<-750
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      observeEvent(analysis$taula_def, {})
+      table<-analysis$taula_def
+      if (input$vacc == "No"){
+        table<-filter(table, !is.na(Volume))
+        summary(aov(Volume~Group+Timepoint+Error(`ID animal`), data=table))
+      }else{
+        for (side in c("L","R")){
+          tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
+          if (length(unique(tableR$Volume)) > 1 & length(unique(tableR$Timepoint)) > 1){
+            print(paste0("Side: ",side))
+            # print(summary(aov(Volume~Group+Timepoint+Error(paste0(ID animal,Cage)), data=tableR)))
+            print(summary(aov(Volume~Group+Timepoint+Error(`ID animal`), data=tableR)))
+          }
+        }
+      }
+    }
+  })
+  output$tab_stats<-renderTable({
+    stattest<-"dunn"
+    oneside<-""
+    cutoff<-750
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      table<-analysis$taula_def
+      table_stats<-list()
+      if (input$vacc == "No"){
+        table<-table%>%filter(!is.na(Volume))
+        if (length(unique(table$Volume)) > 1){
+          table_stats<-multi_stats(table, "Volume", "Timepoint", "Group", stat.test=stattest)  
+        }
+        table_kw<-as.data.frame(matrix(nrow=0, ncol=2))
+        for (point in unique(table$Timepoint)){
+          len_group<-length(unique(table %>% filter(Timepoint == point) %>% pull(Group)))
+          if (len_group > 1){
+            table_kw<-rbind(table_kw, data.frame(point,kruskal.test(table %>% filter(Timepoint == point) %>% pull(Volume), table %>% filter(Timepoint == point) %>% pull(Group))[3][[1]]))
+          }
+        }
+        colnames(table_kw)<-c("Timepoint", "KW-p.val")
+        table_stats<-merge(table_stats, table_kw)
+        
+      }else{
+        for (side in c("L","R")){
+          tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
+          if (length(unique(tableR$Volume)) > 1){
+            table_stats[[side]]<-multi_stats(tableR, "Volume", "Timepoint", "Group", stat.test=stattest)  
+          }
+          table_kw<-as.data.frame(matrix(nrow=0, ncol=2))
+          for (point in unique(tableR$Timepoint)){
+            len_group<-length(unique(tableR %>% filter(Timepoint == point) %>% pull(Group)))
+            if (len_group > 1){
+              table_kw<-rbind(table_kw, data.frame(point,kruskal.test(tableR %>% filter(Timepoint == point) %>% pull(Volume), tableR %>% filter(Timepoint == point) %>% pull(Group))[3][[1]]))
+            }
+          }
+          colnames(table_kw)<-c("Timepoint", "KW-p.val")
+          table_stats[[side]]<-merge(table_stats[[side]], table_kw)
+        }
+      }
+      table_stats_def<-bind_rows(table_stats, .id = "ID tumor")
+      if (input$filter_stats == T){
+        table_stats_def %>% filter(p.adj < 0.05)
+      }else{
+        table_stats_def 
+      }
+    }
+  })
+  
+  output$expPlotUI<- renderUI({
+    observeEvent(analysis$taula_def, {})
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      plotOutput("expPlot", width=paste0(input$width/10,"px"), height = paste0(input$height/10, "px"))
+    }
+  })
+  
+  output$expPlot <- renderPlot({
+    observeEvent(analysis$taula_def, {})
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      table<-analysis$taula_def
+      
+      if (input$fig_id %in% c("Cinética Grupo", "Cinética Individual")){
+        if (input$fig_id == "Cinética Grupo"){
+          if (input$vacc == "Sí"){
+            g<-ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=Group))+
+              scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
+              facet_grid(factor(`ID tumor`, labels = c("Vaccination", "Rechallenge"))~., scale="free_y")+
+              theme_bw()  
+          }else{
+            g<-ggplot(table, aes(Timepoint, Volume, color=Group, group=Group))+
+              theme_bw()
+          }
+        }
+        if (input$fig_id == "Cinética Individual"){
+          if (input$vacc == "Sí"){
+            g<-ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=Group))+
+              scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
+              facet_grid(factor(`ID tumor`, labels = c("Vaccination", "Rechallenge"))~Group, scale="free_y")+
+              theme_bw()  
+          }else{
+            g<-ggplot(table, aes(Timepoint, Volume, color=Group, group=`ID animal`))+
+              facet_wrap(.~Group)+
+              theme_bw()
+          }
+        }
+        
+        g<-g+geom_errorbar(stat="summary", width=input$`errorbar-width`)+
+          geom_line(stat="summary")+
+          geom_point(stat="summary", size=input$`point-size`)+
+          labs(x="Days after tumor challenge")+
+          scale_y_continuous(expand = expansion(mult = c(0,0.05)))
+        
+        if (input$theme == "BW"){
+          g<-g+theme_bw(base_size = input$`font-size`)
+        }
+        if (input$theme == "Classic"){
+          g<-g+theme_classic(base_size = input$`font-size`)  
+        }
+        if (input$theme == "Default"){
+          g<-g+theme_gray(base_size = input$`font-size`)
+        }
+        # g<-g+theme(axis.text.x=element_text(angle=45, hjust=1))
+        if (input$legend == F){
+          g<-g+guides(color=FALSE, fill=FALSE)
+        }
+        if (input$colors != ""){
+          v_col<-strsplit(input$colors, ",")[[1]]
+          g<-g+scale_color_manual(values=v_col)+
+            scale_fill_manual(values=v_col)
+        }
+      }else{
+        gg_color_hue <- function(n, l=65) {
+          hues <- seq(15, 375, length=n+1)
+          hcl(h=hues, l=l, c=100)[1:n]
+        }
+        if (input$vacc == "Sí"){
+          g<-list()
+          count<-1
+          for (side in c("L","R")){
+            tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
+            endtime<-dcast(tableR %>% filter(Volume < cutoff), Cage+`ID animal`+`ID tumor`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
+            endtime["Dead"]<-dcast(tableR, Cage+`ID animal`+`ID tumor`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
+            table_tumor<-endtime
+            if (input$colors != ""){
+              col<-input$colors
+            }else{
+              col<-gg_color_hue(length(unique(endtime$Group)))
+            }
+            g[[count]]<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
+                                pval = T, pval.method = T,
+                                title = side,
+                                # legend.labs = paste(c("< median", ">= median"), "MICA"),
+                                ggtheme=theme_classic(base_size=input$`font-size`),
+                                palette = col
+            )
+            count<-count+1
+          }
+          g_surv_vacc<-g
+          
+        }else{
+          if (input$colors != ""){
+            col<-input$colors
+          }else{
+            col<-gg_color_hue(length(unique(table_tumor$Group)))
+          }
+          tableR<-table %>% filter(!is.na(Volume))
+          endtime<-dcast(tableR %>% filter(Volume < cutoff), `ID animal`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
+          endtime["Dead"]<-dcast(tableR, `ID animal`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
+          table_tumor<-endtime
+          g<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
+                        pval = T, pval.method = T,
+                        # legend.labs = paste(c("< median", ">= median"), "MICA"),
+                        ggtheme=theme_classic(base_size=input$`font-size`),
+                        palette = col
+          )
+        }
+      }
+      
+      if (input$vacc == "Sí" & input$fig_id == "Survival"){
+        dades$plot<<-g_surv_vacc
+        arrange_ggsurvplots(g, nrow=2, ncol=1)
+      }else{
+          dades$plot<<-g
+          g
+      }
+    }
+  }, res=72)
+  
+  output$downloadPicture <- downloadHandler(
+    filename = function() {
+      paste("Figura", ".png", sep="")
+    },
+    content = function(file){
+      # tempReport <- file.path(tempdir(), "elispots.Rmd")
+      # file.copy("elispots.Rmd", tempReport, overwrite = TRUE)
+      png(file, width = input$width, height=input$height, units = "px", res=720)
+      if (input$fig_id == "Survival"){
+        if (input$vacc == "Sí"){
+          arrange_ggsurvplots(dades$plot, nrow=2, ncol=1) 
+        }else{
+          arrange_ggsurvplots(list(dades$plot), ncol=1)  
+        }
+        }else{plot(dades$plot)}
+      dev.off()
+      
+    })
+  
+}
+
+# Run the application 
+shinyApp(ui = ui, server = server)
\ No newline at end of file