Análisis-Randomización. Evitar la desaparición de ratones sin grupo (excluidos).

invivos/app.R

@@ -26,6 +26,7 @@ ui <- fluidPage(
                   uiOutput('ncages'),
                   uiOutput('ntumors'),
                   # selectInput(inputId = "measure_sys", "Sistema de medida", selected = "L-W-D", choices = c("L-W-D","Min-Max","Absorbance")),
+                  uiOutput('day_vol'),
                   uiOutput('lowcut'),
                   uiOutput('upcut'),
                   uiOutput('goButton'),
@@ -46,6 +47,8 @@ ui <- fluidPage(
                          uiOutput('cutoffUI'),
                          checkboxInput("filter_stats","Filtrar Estadística"),
                          checkboxInput("increase_volume","Usar Incremento de Volumen"),
+                         checkboxInput("operated","Cortar al operar", value = TRUE),
+                         checkboxInput("dead","Eliminar ratones muertos", value = FALSE),
                          downloadButton("downloadVolume", "Descargar Volúmenes")
                        ),
                        mainPanel(
@@ -64,6 +67,7 @@ ui <- fluidPage(
                                     h3('Seleccionar figura'),
                                     selectInput("fig_id", "", selected="", choices=c("Cinética Grupo", "Cinética Individual", "Survival")),
                                     h3('Formato'),
+                                    selectInput("unit_fact", "Factor de unidad", choices = c("0.001","1","1000"), selected="1"),
                                     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=""),
@@ -86,9 +90,14 @@ ui <- fluidPage(
    
    )
 
+
+
+
 # Define server logic required to draw a histogram
 server <- function(input, output) {
-  
+
+# Diseño ------------------------------------------------------------------
+
   # Diseño
   dades<-reactiveValues()
   dades$taula<-NULL
@@ -97,22 +106,39 @@ server <- function(input, output) {
   dades$db<-NULL
   observe({
     if (!is.null(input$file_sizes)){ 
-      taula<-read.xlsx(input$file_sizes$datapath, sheet = 1, sep.names = " ")
+      table<-read.xlsx(input$file_sizes$datapath, sheet = 1, check.names = F, sep.names = " ", detectDates = T,cols=1:11)
-      if ("DPV" %in% colnames(taula)){
+      if("ID.animal" %in% colnames(table)){
-        taula<-dcast(taula, Cage+`ID animal`+`ID tumor`+Group~DPV, value.var = "0")
+        table<-table %>%
-        # taula$Major<-taula$Major/1000
+          rename(Animal=`ID.animal`, Side=`ID.tumor`)
-        # taula$Minor<-taula$Minor/1000
+        table<-table %>% gather(DayPostInoc, Value, which(!is.na(as.numeric(colnames(table))))) %>% 
-        # taula["Volume"]<-((taula$Major*taula$Minor*taula$Minor)*(pi/6))*1000
+          relocate(DayPostInoc, .before = Group) %>% spread(DPV, Value) %>% 
-        taula$Major<-taula$Major
+          rename(Long=Major, Wide=Minor) %>% 
-        taula$Minor<-taula$Minor
+          add_column(Date="", .after = "Animal") %>% 
-        taula["Volume"]<-((taula$Major*taula$Minor*taula$Minor)*(pi/6))
+          add_column(Weight="", .after="Group") %>% 
+          add_column(Volume="",Observations="") %>%
+          relocate(Side, .after = "Group")
+        table$DayPostInoc<-as.numeric(table$DayPostInoc)
       }
-      if ("TS" %in% colnames(taula)){
+      table$Date<-format(table$Date, format="%d/%m/%Y")
-        taula<-dcast(taula, Cage+`ID animal`+`ID tumor`+Group~TS, value.var = "0")
+      if ("sex" %in% colnames(table)){table<-select(table, -sex)}
-        taula["Volume"]<-taula$`TS-Deep`*taula$`TS-Length`*taula$`TS-Width`*pi/6
+      if ("Abs" %in% colnames(table)){
+        table<-rename(table, "Volume"="Abs")
+      }else{
+        for (i in 1:nrow(table)){
+          long<-as.numeric(gsub(",",".",strsplit(as.character(table[i,"Long"]),"+", fixed = T)[[1]]))
+          wide<-as.numeric(gsub(",",".",strsplit(as.character(table[i,"Wide"]),"+", fixed = T)[[1]]))
+          table[i,"Volume"]<-sum(sapply(1:length(long), function(x) (long[x]*wide[x]*wide[x])*(pi/6)))
+        }
+        table$Volume<-as.numeric(table$Volume)
       }
-      dades$taula<-taula
+
-      dades$groups<-read.xlsx(input$file_sizes$datapath, sheet = 2, colNames=F)[,1]
+      if ("Cage" %in% colnames(table)){
+        table$Animal<-paste0(table$Cage, table$Animal)
+        table<-table[,-which(colnames(table) == "Cage")]
+      }
+      
+      dades$taula<-table
+      dades$groups<-read.xlsx(input$file_sizes$datapath, sheet = "Groups", colNames=F)[,1]
       if (readxl::excel_sheets(input$file_sizes$datapath) %>% length > 2){
         dades$sex<-read.xlsx(input$file_sizes$datapath, sheet = 3, sep.names = " ")
       }
@@ -121,7 +147,15 @@ server <- function(input, output) {
   output$firstPlot <- renderPlot({
     observeEvent(dades$taula, {})
     if (!is.null(dades$taula)){
-      ggplot(dades$taula, aes(x="1", y=Volume))+geom_hline(yintercept = c(input$lowcut, input$upcut), color="red")+geom_quasirandom(width=0.2)
+      ggplot(dades$taula %>% filter(DayPostInoc == input$day_vol), aes(x="1", y=Volume))+
+        geom_hline(yintercept = c(input$lowcut, input$upcut), color="red")+
+        geom_quasirandom(width=0.2)
+    }
+  })
+  
+  output$day_vol<-renderUI({
+    if (!is.null(dades$taula)){
+      selectInput("day_vol", "Día para volúmenes", choices = sort(unique(dades$taula$DayPostInoc))) 
     }
   })
   
@@ -132,15 +166,17 @@ server <- function(input, output) {
   })
   output$lowcut<-renderUI({
     if (!is.null(dades$taula)){
-      cut.max<-round(max(dades$taula$Volume, na.rm = T), 2)
+      taula<-dades$taula %>% filter(DayPostInoc == input$day_vol)
-      step<-round(max(dades$taula$Volume, na.rm = T)/100, 2)
+      cut.max<-round(max(taula$Volume, na.rm = T), 2)
+      step<-round(max(taula$Volume, na.rm = T)/100, 2)
       sliderInput("lowcut", "Corte inferior", min=0, max=cut.max, step=step, value=0)
     }
   })
   output$upcut<-renderUI({
     if (!is.null(dades$taula)){
-      cut.max<-round(max(dades$taula$Volume, na.rm = T), 2)+0.01
+      taula<-dades$taula %>% filter(DayPostInoc == input$day_vol)
-      step<-round(max(dades$taula$Volume, na.rm = T)/20, 2)
+      cut.max<-round(max(taula$Volume, na.rm = T), 2)+0.01
+      step<-round(max(taula$Volume, na.rm = T)/20, 2)
       sliderInput("upcut", "Corte superior", min=0, max=cut.max, step=step, value=cut.max)
     }
   })
@@ -161,11 +197,12 @@ server <- function(input, output) {
   })
   
   grafic<-eventReactive(input$goButton,{
-    df<-dades$taula
+    df<-dades$taula %>% filter(DayPostInoc == input$day_vol)
     df<-df[!is.na(df$Volume),]
+    print(df)
     up_cuttof<-input$upcut
     low_cuttof<-input$lowcut
-    # print(up_cuttof)
+
     df<-df[df$Volume < up_cuttof & df$Volume >= low_cuttof,]
     if (is.null(dades$sex)){
       df<-add_column(df, sex="undefined")
@@ -173,16 +210,14 @@ server <- function(input, output) {
       df<-merge(df, dades$sex)
     }
     
-    
     # df["Mouse"]<-gsub("[a-zA-Z]", "", df$MouseID)
-    # print(df$Volume)
+    
     s<-shapiro.test(df$Volume)[[2]]
     
     ngroup<-length(dades$groups)
     df_def<-list()
-    # print(head(df))
+
     for (sex.var in unique(df$sex)){
-      # print(sex.var)
       df_sex<-df %>% filter(`sex` == sex.var)
       ind.list<-list()
       pval.list<-list()
@@ -191,8 +226,8 @@ server <- function(input, output) {
       for (data in 1:input$iterations){
         interr=T
         while(interr == T){
-          ind<-sample(rep(dades$groups, each=ceiling(length(unique(df_sex$`ID animal`))/ngroup)), length(unique(df_sex$`ID animal`)))
+          ind<-sample(rep(dades$groups, each=ceiling(length(unique(df_sex$Animal))/ngroup)), length(unique(df_sex$Animal)))
-          df_sex<-merge(df_sex[,c("ID animal", "ID tumor","Volume")], data.frame("ID animal"=unique(df_sex$`ID animal`), "group"=as.factor(ind),check.names=F))
+          df_sex<-merge(df_sex[,c("Animal", "Side","Volume")], data.frame("Animal"=unique(df_sex$Animal), "group"=as.factor(ind),check.names=F))
           if ((nrow(df_sex)/ngroup) %% 2 == 0){
             interr<-any(table(df_sex$group) < floor(nrow(df_sex)/ngroup) | table(df_sex$group) > ceiling(nrow(df_sex)/ngroup))  
           }else{
@@ -200,7 +235,7 @@ server <- function(input, output) {
             interr<-diff(range(table(ind))) > 1
           }
         }
-        ind.list[[data]]<-df_sex[,c("ID animal","ID tumor","group","Volume")]
+        ind.list[[data]]<-df_sex[,c("Animal","Side","group","Volume")]
         lvn.list[data]<-leveneTest(Volume ~ group, data = df_sex[,3:4])[[2]][1]
         if (s < 0.05){
           k<-kruskal.test(df_sex$Volume,df_sex$group)
@@ -213,33 +248,36 @@ server <- function(input, output) {
         }
       }
       index<-which(unlist(lvn.list) == min(unlist(lvn.list)[which(unlist(pval.list) %in% sort(unlist(pval.list), decreasing = T)[1:20])]))[1]
-      # print(df_sex)
       df_def[[sex.var]]<-merge(df_sex %>% select(-group), ind.list[[index]])
     }
     df_def<-do.call(rbind, c(df_def, make.row.names=F))
-    # lapply(df_def, function(x) x %>% as_tibble %>% print(n=Inf))
+
-    # df_def<-rbind(df_def[[1]], df_def[[2]], make.row.names=F)
     if ("Group" %in% colnames(df_def)){
       df_def<-df_def %>% select(-"Group") 
     }
+
     df_def<-merge(
-      if(!is.null(dades$sex)){merge(dades$taula, dades$sex)}else{dades$taula %>% add_column(sex="undefined")} %>% select(-Group), 
+      if(!is.null(dades$sex)){merge(dades$taula %>% filter(DayPostInoc == input$day_vol), dades$sex)%>% select(-Group)}else{
-      df_def[,c("ID animal", "group")] %>% unique, all=T, by="ID animal")
+        dades$taula %>% filter(DayPostInoc == input$day_vol) %>% add_column(sex="undefined")} %>% select(-Group), 
-    if("DPV" %in% colnames(df_def)){df_def<-select(df_def, c(`ID animal`, `sex`,`ID tumor`, Volume, Cage, Major, Minor, group))}
+      df_def[,c("Animal", "group")] %>% unique, all=T, by="Animal")
-    if("TS-Deep" %in% colnames(df_def)){df_def<-select(df_def, c(`ID animal`, `sex`,`ID tumor`, Volume, Cage, `TS-Deep`,`TS-Length`,`TS-Width`, group))}
+    df_def<-df_def %>% rename(Group=group)
     
-    df_def[!paste0(df_def$`ID animal`, df_def$`ID tumor`) %in% paste0(df$`ID animal`, df$`ID tumor`),"group"]<-NA
+    df_def<-select(df_def, Animal, sex, Date, DayPostInoc, Group, Side, Weight, Long, Wide, Volume, Observations)
+    # if("DPV" %in% colnames(df_def)){df_def<-select(df_def, c(`ID animal`, `sex`,`ID tumor`, Volume, Cage, Major, Minor, group))}
+    # if("TS-Deep" %in% colnames(df_def)){df_def<-select(df_def, c(`ID animal`, `sex`,`ID tumor`, Volume, Cage, `TS-Deep`,`TS-Length`,`TS-Width`, group))}
+    
+    df_def[!paste0(df_def$Animal, df_def$Side) %in% paste0(df$Animal, df$Side),"Group"]<-NA
     dades$db<-df_def
 
     if (is.null(dades$sex)){
-      ggplot(df_def, aes(group, Volume))+
+      ggplot(df_def, aes(Group, Volume))+
         geom_boxplot(outlier.alpha = F)+
         geom_jitter(width=0.25)+
         geom_point(stat="summary", color="blue", size=3)+
         theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))
     }else{
       ggarrange(
-        ggplot(df_def, aes(group, Volume))+
+        ggplot(df_def, aes(Group, Volume))+
           geom_boxplot(outlier.alpha = F)+
           geom_jitter(width=0.25, aes(color=sex))+
           geom_point(stat="summary", color="blue", size=3)+
@@ -249,7 +287,7 @@ server <- function(input, output) {
           ggplot(df_def, aes(sex, Volume))+
             geom_boxplot(outlier.alpha = F)+
             geom_quasirandom(width=0.3),
-          ggplot(df_def, aes(group, fill=sex))+
+          ggplot(df_def, aes(Group, fill=sex))+
             geom_bar(stat="count", color="black", position="dodge")+
             guides(fill="none")+
             theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5)), ncol = 1, heights = c(0.35, 0.65)),
@@ -269,8 +307,8 @@ server <- function(input, output) {
     observeEvent(dades$db, {})
     if (!is.null(dades$db)){
       df<-dades$db
-      df_sum<-dcast(df, group~., value.var = "Volume", fun.aggregate = mean, na.rm=T) %>% rename("Mean"=".")
+      df_sum<-dcast(df, Group~., value.var = "Volume", fun.aggregate = mean, na.rm=T) %>% rename("Mean"=".")
-      df_sum["SEM"]<-dcast(df, group~., value.var = "Volume", fun.aggregate = std.error, na.rm=T) %>% pull(`.`)
+      df_sum["SEM"]<-dcast(df, Group~., value.var = "Volume", fun.aggregate = std.error, na.rm=T) %>% pull(`.`)
       df_sum
     }
   })
@@ -278,7 +316,7 @@ server <- function(input, output) {
     observeEvent(dades$db, {})
     if (!is.null(dades$db)){
       df<-dades$db
-      df %>% arrange(group)
+      df %>% arrange(Group)
     }
   })
   
@@ -294,31 +332,15 @@ server <- function(input, output) {
       # timepoint<-c(7,10,13,16,19,22,25)
       
       if (!is.null(input$file_sizes)){
-        # dtemplate<-dades$db %>% select(-Volume,-sex)
+        template<-dades$db %>%
-        # dtemplate<-melt(dtemplate, id=c("Cage", "ID animal", "ID tumor", "group"), variable.name = "DPV", value.name = "0") %>% rename("Group"="group")
+          select(Animal, Group) %>% unique()
-        # dtemplate<-dtemplate[,c("Cage", "ID animal", "ID tumor", "Group", "DPV", "0")] %>% arrange(`ID animal`, `ID tumor`)
+        dtemplate<-merge(dades$taula %>% select(-Group), template) %>% 
-        # template<-expand.grid(dades$db$MouseID, timepoint)
+          arrange(DayPostInoc, Animal, Side) %>% 
-        # colnames(template)<-c("MouseID", "Timepoint")
+          select(Animal, Date, DayPostInoc, Group, Side, 
-        # template<-template[order(template$Timepoint, template$MouseID),]
+                 Weight, Long, Wide, Volume, Observations)
-        # template<-merge(template, dades$db[c("MouseID", "group")])
+        if ("Abs" %in% colnames(read.xlsx(input$file_sizes$datapath, sheet = 1, check.names = F, sep.names = " ", detectDates = T,cols=1:11))){
-        # if (input$measure_sys == "L-W-D"){
+          dtemplate<-rename(dtemplate, "Abs"="Volume")
-        #   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<-data.frame(
@@ -334,36 +356,87 @@ server <- function(input, output) {
           Volume="",
           Observations=""
         )
+        template<-template %>% mutate(Animal=paste0(Cage,Animal)) %>% select(-Cage)
         dtemplate<-template
       }
       write.xlsx(list("Measures"=dtemplate, "Groups"=NULL,"Sex"=NULL), file)
     }
   )
-  
+
-  # Análisis
+
+# Análisis ----------------------------------------------------------------
+
   analysis<-reactiveValues()
   analysis$taula<-NULL
   analysis$taula_def<-NULL
   analysis$taula_vol<-NULL
   observe({
     if (!is.null(input$file_analy)){ 
-      table<-read.xlsx(input$file_analy$datapath, sheet = 1, check.names = F, sep.names = " ")
+      table<-read.xlsx(input$file_analy$datapath, sheet = 1, check.names = F, sep.names = " ", detectDates = T,cols=1:11)
+      if("ID.animal" %in% colnames(table)){
+        table<-table %>%
+          rename(Animal=`ID.animal`, Side=`ID.tumor`)
+        table<-table %>% gather(DayPostInoc, Value, which(!is.na(as.numeric(colnames(table))))) %>% 
+          relocate(DayPostInoc, .before = Group) %>% spread(DPV, Value) %>% 
+          rename(Long=Major, Wide=Minor) %>% 
+          add_column(Date="", .after = "Animal") %>% 
+          add_column(Weight="", .after="Group") %>% 
+          add_column(Volume="",Observations="") %>%
+          relocate(Side, .after = "Group")
+        table$DayPostInoc<-as.numeric(table$DayPostInoc)
+      }
+      table$Date<-format(table$Date, format="%d/%m/%Y")
       if ("sex" %in% colnames(table)){table<-select(table, -sex)}
-      table$Long<-gsub(",",".", table$Long)
-      table$Wide<-gsub(",",".", table$Wide)
       for (i in 1:nrow(table)){
-        long<-as.numeric(strsplit(table[i,"Long"],"+", fixed = T)[[1]])
+        long<-as.numeric(gsub(",",".",strsplit(as.character(table[i,"Long"]),"+", fixed = T)[[1]]))
-        wide<-as.numeric(strsplit(table[i,"Wide"],"+", fixed = T)[[1]])
+        wide<-as.numeric(gsub(",",".",strsplit(as.character(table[i,"Wide"]),"+", fixed = T)[[1]]))
         table[i,"Volume"]<-sum(sapply(1:length(long), function(x) (long[x]*wide[x]*wide[x])*(pi/6)))
       }
+      table$Volume<-as.numeric(table$Volume)
+      if ("Cage" %in% colnames(table)){
+        table$Animal<-paste0(table$Cage, table$Animal)
+        table<-table[,-which(colnames(table) == "Cage")]
+      }
+      
+      ## Autocompletado de grupo
+      table_group<-merge(
+        table %>% select(Animal, Group) %>% unique() %>% group_by(Animal) %>% count(),
+        table %>% select(Animal, Group) %>% unique()
+      ) %>% filter(!is.na(Group) | Group != "") %>% select(-n)
+      
+      table<-merge(
+        table %>% select(-Group),
+        table_group
+      ) %>% relocate(Group, .after = DayPostInoc) %>% 
+        arrange(DayPostInoc, Animal, Side)
+      
+      if("Groups" %in% readxl::excel_sheets(input$file_analy$datapath)){
+        levels<-read.xlsx(input$file_analy$datapath, sheet = "Groups", colNames=F)[,1]
+        table$Group<-factor(table$Group, levels=levels)
+        table<-filter(table, !is.na(Group))
+      }else(table$Group<-factor(table$Group))
+      
+      
+      if (input$increase_volume){
+        cols<-colnames(table)
+        timepoints<-unique(table$DayPostInoc)
+        table_old<-select(table, -Volume)
+        table_vols<-table %>% 
+          select(-Weight, -Date, -Long, -Wide,-Observations) %>% 
+          spread(DayPostInoc, Volume) %>% 
+          mutate(across(all_of(as.character(timepoints)), function(x){(x*100/`0`)-100})) %>%
+          gather(DayPostInoc, Volume, all_of(as.character(timepoints))) %>% 
+          mutate(Volume=case_when(Volume < 0 ~ 0, T~Volume))
+        table<-merge(table_old, table_vols)[,cols]
+      }
+      
       analysis$taula<-table
     }
   })
   output$cutoffUI<-renderUI({
-    if (!is.null(analysis$taula_def)){
+    if (!is.null(analysis$taula)){
-      observeEvent(analysis$taula_def, {})
+      observeEvent(analysis$taula, {})
-      max_val<-max(analysis$taula_def$Volume, na.rm = T)
+      max_val<-max(analysis$taula$Volume, na.rm = T)
-      # print(max_val)
       sliderInput("cutoff", "Cutoff para Survival", min=0, max=round(max_val, digits=2), step=round(max_val, digits=2)/200, value=max_val)
     }
   })
@@ -375,17 +448,10 @@ server <- function(input, output) {
 
       table<-analysis$taula
       table<-table %>% filter(!is.na(Group))
-      if (input$increase_volume){
-        timepoints<-unique(table$Timepoint)
-        table<-table %>% select(-Major, -Minor) %>% 
-          spread(Timepoint, Volume) %>% 
-          mutate(across(all_of(timepoints), function(x){(x*100/`0`)-100})) %>%
-          gather(Timepoint, Volume, -Cage, -`ID animal`, -`ID tumor`, -Group) %>% 
-          mutate(Volume=case_when(Volume < 0 ~ 0, T~Volume))
-      }
       table %>% group_by(Group, DayPostInoc, Side) %>% count() %>% spread(DayPostInoc, n)
     }
   })
+  
   output$cin_group<-renderPlot({
     if (!is.null(input$file_analy) & !is.null(analysis$taula)){
       observeEvent(analysis$taula, {})
@@ -395,7 +461,6 @@ server <- function(input, output) {
       sides<-unique(table$Side)
       groups<-table %>% select(Animal, Group) %>% unique() %>% pull(Group)
       basal<-data.frame(
-        Cage="",
         Animal=rep(animals, each=length(sides)),
         Date="",
         DayPostInoc=0,
@@ -407,8 +472,27 @@ server <- function(input, output) {
         Volume=0,
         Observations=""
       )
-      table<-rbind(table, basal)
+      if (!(0 %in% table$DayPostInoc) | sum(table[table$DayPostInoc == 0,"Volume"],na.rm = T) == 0){
+        table<-rbind(table, basal)  
+      }
+      
       if (input$vacc == "Sí"){
+        firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+          select(DayPostInoc, Side) %>% unique() %>% 
+          group_by(Side) %>% summarise(FirstOper=min(DayPostInoc))
+        deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+          pull(Animal)
+
+        if (input$operated == TRUE & nrow(firstoper) > 0){
+          for (i in 1:nrow(firstoper)){
+            table<-table %>% filter(DayPostInoc < firstoper$FirstOper[i] | Side != firstoper$Side[i])
+          } 
+        }else{
+          if(input$dead == TRUE){
+            table<-table %>% filter(!Animal %in% deadmice)
+          }
+        }
+
         ggplot(table, aes(DayPostInoc, Volume, color=Group, group=Group))+
           geom_errorbar(stat="summary", width=0.05)+
           geom_line(stat="summary")+
@@ -417,8 +501,23 @@ server <- function(input, output) {
           labs(x="Days after tumor inoculation")+
           scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
           scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(table$DayPostInoc) / 5)+1)*5))+
-          theme_bw()  
+          theme_bw()
       }else{
+
+        firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+          pull(DayPostInoc) %>% min(na.rm = T)
+        if (firstoper == Inf){firstoper<-c()}
+        
+        if (input$operated == TRUE & length(firstoper) > 0){
+          table<-table %>% filter(DayPostInoc < firstoper)
+        }else{
+          deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+            pull(Animal)
+          if(input$dead == TRUE){
+            table<-table %>% filter(!Animal %in% deadmice)
+          }
+        }
+
         ggplot(table, aes(DayPostInoc, Volume, color=Group, group=Group))+
           geom_errorbar(stat="summary",width=0.05)+
           geom_line(stat="summary")+
@@ -440,7 +539,6 @@ server <- function(input, output) {
       sides<-unique(table$Side)
       groups<-table %>% select(Animal, Group) %>% unique() %>% pull(Group)
       basal<-data.frame(
-        Cage="",
         Animal=rep(animals, each=length(sides)),
         Date="",
         DayPostInoc=0,
@@ -452,9 +550,25 @@ server <- function(input, output) {
         Volume=0,
         Observations=""
       )
-      table<-rbind(table, basal)
+      if (!(0 %in% table$DayPostInoc) | sum(table[table$DayPostInoc == 0,"Volume"],na.rm = T) == 0){
+        table<-rbind(table, basal)  
+      }
       
       if (input$vacc == "Sí"){
+        firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+          select(DayPostInoc, Side) %>% unique() %>% 
+          group_by(Side) %>% summarise(FirstOper=min(DayPostInoc))
+        if (input$operated == TRUE & nrow(firstoper) > 0){
+          for (i in 1:nrow(firstoper)){
+            table<-table %>% filter(DayPostInoc < firstoper$FirstOper[i] | Side != firstoper$Side[i])
+          } 
+        }else{
+          if(input$dead == TRUE){
+            deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+              pull(Animal)
+            table<-table %>% filter(!Animal %in% deadmice)
+          }
+        }
         ggplot(table, aes(DayPostInoc, Volume, color=Group, group=Animal))+
           # geom_errorbar(stat="summary", width=0.05)+
           geom_line()+
@@ -465,6 +579,19 @@ server <- function(input, output) {
           labs(x="Days after tumor inoculation")+
           theme_bw()  
       }else{
+        firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+          pull(DayPostInoc) %>% min(na.rm = T)
+        if (firstoper == Inf){firstoper<-c()}
+        
+        if (input$operated == TRUE & length(firstoper) > 0){
+          table<-table %>% filter(DayPostInoc < firstoper)
+        }else{
+          deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+            pull(Animal)
+          if(input$dead == TRUE){
+            table<-table %>% filter(!Animal %in% deadmice)
+          }
+        }
         ggplot(table, aes(DayPostInoc, Volume, color=Group, group=paste0(Animal, Side)))+
           # geom_errorbar(stat="summary", width=0.05)+
           geom_line()+
@@ -480,18 +607,33 @@ server <- function(input, output) {
   })
   
   output$survival<-renderPlot({
-    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+    if (!is.null(input$file_analy) & !is.null(analysis$taula)){
-      observeEvent(analysis$taula_def, {})
+      observeEvent(analysis$taula, {})
-      table<-analysis$taula_def
+      table<-analysis$taula
       if (input$vacc == "Sí"){
+        firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+          select(DayPostInoc, Side) %>% unique() %>% 
+        if (firstoper == Inf){firstoper<-c()}
+        
+        if (input$operated == TRUE & length(firstoper) > 0){
+          for (i in 1:nrow(firstoper)){
+            table<-table %>% filter(DayPostInoc < firstoper$FirstOper[i] | Side != firstoper$Side[i])
+          } 
+        }else{
+          if(input$dead == TRUE){
+            deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+              pull(Animal)
+            table<-table %>% filter(!Animal %in% deadmice)
+          }
+        }
         g<-list()
         for (side in c("L","R")){
-          tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
+          tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
-          endtime<-dcast(tableR %>% filter(Volume < input$cutoff), Cage+`ID animal`+`ID tumor`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
+          endtime<-dcast(tableR %>% filter(Volume < input$cutoff), Animal+Side+Group~., value.var = "DayPostInoc", 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$cutoff
+          endtime["Dead"]<-dcast(tableR, Animal+Side+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
           table_tumor<<-endtime
           
-          g[side]<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
+          g[side]<-ggsurvplot(survfit(Surv(end, Dead) ~ Group, data=table_tumor),
                               pval = T, pval.method = T,
                               title = side,
                               # legend.labs = paste(c("< median", ">= median"), "MICA"),
@@ -510,13 +652,27 @@ server <- function(input, output) {
         do.call(grid.arrange, g)
         
       }else{
+        firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+          pull(DayPostInoc) %>% min(na.rm = T)
+        if (firstoper == Inf){firstoper<-c()}
+        
+        if (input$operated == TRUE & length(firstoper) > 0){
+          table<-table %>% filter(DayPostInoc < firstoper)
+        }else{
+          deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+            pull(Animal)
+          if(input$dead == TRUE){
+            table<-table %>% filter(!Animal %in% deadmice)
+          }
+        }
+        
         tableR<-table %>% filter(!is.na(Volume))
-        endtime<-dcast(if(length(unique(tableR$Timepoint)) > 1){tableR %>% filter(Volume < input$cutoff)}else{tableR}, `ID animal`+`ID tumor`+Group~., 
+        endtime<-dcast(if(length(unique(tableR$DayPostInoc)) > 1){tableR %>% filter(Volume < input$cutoff)}else{tableR}, Animal+Side+Group~., 
-                       value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
+                       value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
-        endtime["Dead"]<-dcast(tableR, `ID animal`+`ID tumor`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") >= input$cutoff
+        endtime["Dead"]<-dcast(tableR, Animal+Side+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") >= input$cutoff
         table_tumor<<-endtime
         
-        g<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
+        g<-ggsurvplot(survfit(Surv(end, Dead) ~ Group, data=table_tumor),
                             pval = T, pval.method = T,
                             # legend.labs = paste(c("< median", ">= median"), "MICA"),
                             ggtheme=theme_classic(base_size=15)
@@ -530,19 +686,16 @@ server <- function(input, output) {
   output$stats<-renderPrint({
     stattest<-"dunn"
     oneside<-""
-    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+    if (!is.null(input$file_analy) & !is.null(analysis$taula)){
-      observeEvent(analysis$taula_def, {})
+      observeEvent(analysis$taula, {})
-      table<-analysis$taula_def
+      table<-analysis$taula
       if (input$vacc == "No"){
         table<-filter(table, !is.na(Volume))
-        summary(aov(Volume~Group+Timepoint+Error(`ID animal`+`ID tumor`), data=table))
+        summary(aov(Volume~Group+DayPostInoc+Error(Animal+Side), data=table))
       }else{
         for (side in c("L","R")){
-          tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
+          tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
-          if (length(unique(tableR$Volume)) > 1 & length(unique(tableR$Timepoint)) > 1){
+          if (length(unique(tableR$Volume)) > 1 & length(unique(tableR$DayPostInoc)) > 1){
-            print(paste0("Side: ",side))
-            # print(summary(aov(Volume~Group+Timepoint+Error(paste0(ID animal,Cage)), data=tableR)))
-            print(summary(aov(Volume~Group+Timepoint+Error(`ID animal`), data=tableR)))
           }
         }
       }
@@ -551,42 +704,42 @@ server <- function(input, output) {
   output$tab_stats<-renderTable({
     stattest<-"dunn"
     oneside<-""
-    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+    if (!is.null(input$file_analy) & !is.null(analysis$taula)){
-      table<-analysis$taula_def
+      table<-analysis$taula
       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_stats<-multi_stats(table, "Volume", "DayPostInoc", "Group", stat.test=stattest)  
         }
         table_kw<-as.data.frame(matrix(nrow=0, ncol=2))
-        for (point in unique(table$Timepoint)){
+        for (point in unique(table$DayPostInoc)){
-          len_group<-length(unique(table %>% filter(Timepoint == point) %>% pull(Group)))
+          len_group<-length(unique(table %>% filter(DayPostInoc == 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]]))
+            table_kw<-rbind(table_kw, data.frame(point,kruskal.test(table %>% filter(DayPostInoc == point) %>% pull(Volume), table %>% filter(DayPostInoc == point) %>% pull(Group))[3][[1]]))
           }
         }
-        colnames(table_kw)<-c("Timepoint", "KW-p.val")
+        colnames(table_kw)<-c("DayPostInoc", "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))
+          tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
           if (length(unique(tableR$Volume)) > 1){
-            table_stats[[side]]<-multi_stats(tableR, "Volume", "Timepoint", "Group", stat.test=stattest)  
+            table_stats[[side]]<-multi_stats(tableR, "Volume", "DayPostInoc", "Group", stat.test=stattest)  
           }
           table_kw<-as.data.frame(matrix(nrow=0, ncol=2))
-          for (point in unique(tableR$Timepoint)){
+          for (point in unique(tableR$DayPostInoc)){
-            len_group<-length(unique(tableR %>% filter(Timepoint == point) %>% pull(Group)))
+            len_group<-length(unique(tableR %>% filter(DayPostInoc == point) %>% pull(Group)))
             if (len_group > 1){
-              table_kw<-rbind(table_kw, data.frame(point,kruskal.test(tableR %>% filter(Timepoint == point) %>% pull(Volume), tableR %>% filter(Timepoint == point) %>% pull(Group))[3][[1]]))
+              table_kw<-rbind(table_kw, data.frame(point,kruskal.test(tableR %>% filter(DayPostInoc == point) %>% pull(Volume), tableR %>% filter(DayPostInoc == point) %>% pull(Group))[3][[1]]))
             }
           }
-          colnames(table_kw)<-c("Timepoint", "KW-p.val")
+          colnames(table_kw)<-c("DayPostInoc", "KW-p.val")
           table_stats[[side]]<-merge(table_stats[[side]], table_kw)
         }
       }
-      table_stats_def<-bind_rows(table_stats, .id = "ID tumor")
+      table_stats_def<-bind_rows(table_stats, .id = "Side")
       if (input$filter_stats == T){
         table_stats_def %>% filter(p.adj < 0.05)
       }else{
@@ -602,54 +755,107 @@ server <- function(input, output) {
     },
     content = function(file){
       dtemplate<-analysis$taula
-      write.xlsx(dtemplate,file)
+      write.xlsx(dtemplate,file, )
     }
   )
   
-  ##Exportar
+
+# Exportar ----------------------------------------------------------------
+
   output$expPlotUI<- renderUI({
-    observeEvent(analysis$taula_def, {})
+    observeEvent(analysis$taula, {})
-    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+    if (!is.null(input$file_analy) & !is.null(analysis$taula)){
       plotOutput("expPlot", width=paste0(input$width/10,"px"), height = paste0(input$height/10, "px"))
     }
   })
   
   output$expPlot <- renderPlot({
-    observeEvent(analysis$taula_def, {})
+    observeEvent(analysis$taula, {})
-    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+    if (!is.null(input$file_analy) & !is.null(analysis$taula)){
-      table<-analysis$taula_def
+      table<-analysis$taula
-      
+      table$Volume<-table$Volume*as.numeric(input$unit_fact)
+      animals<-unique(table$Animal)
+      sides<-unique(table$Side)
+      groups<-table %>% select(Animal, Group) %>% unique() %>% pull(Group)
+      basal<-data.frame(
+        Animal=rep(animals, each=length(sides)),
+        Date="",
+        DayPostInoc=0,
+        Group=rep(groups, each=length(sides)),
+        Side=rep(sides, length(animals)),
+        Weight="",
+        Long="",
+        Wide="",
+        Volume=0,
+        Observations=""
+      )
+      if (!(0 %in% table$DayPostInoc) | sum(table[table$DayPostInoc == 0,"Volume"],na.rm = T) == 0){
+        table<-rbind(table, basal)  
+      }
       if (input$fig_id %in% c("Cinética Grupo", "Cinética Individual")){
         if (input$fig_id == "Cinética Grupo"){
           if (input$vacc == "Sí"){
+            firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+              select(DayPostInoc, Side) %>% unique() %>% 
+              group_by(Side) %>% summarise(FirstOper=min(DayPostInoc))
+            
+            if (input$operated == TRUE){
+              for (i in 1:nrow(firstoper)){
+                table<-table %>% filter(DayPostInoc < firstoper$FirstOper[i] | Side != firstoper$Side[i])
+              } 
+            }else{
+              if(input$dead == TRUE){
+                deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+                  pull(Animal)
+                table<-table %>% filter(!Animal %in% deadmice)
+              }
+            }
             std<-function(x, na.rm=T){sd(x, na.rm=na.rm)/sqrt(length(x))}
-            errbar<-table %>% group_by(Group,`ID tumor`, Timepoint) %>% 
+            errbar<-table %>% group_by(Group,Side,DayPostInoc) %>% 
               summarise(mean=mean(Volume, na.rm=T), std=std(Volume)) %>% 
-              mutate(Timepoint2=as.numeric(as.character(Timepoint))) %>% 
+              mutate(Timepoint2=as.numeric(as.character(DayPostInoc))) %>% 
               mutate(x=Timepoint2-input$`errorbar-width`, xend=Timepoint2+input$`errorbar-width`)
             
-            g<-ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=Group))+
+            g<-ggplot(table, aes(DayPostInoc, Volume, color=Group, group=Group))+
-              scale_x_continuous(expand = expansion(mult = c(0,0.0)), 
+              geom_errorbar(stat="summary", width=0.05)+
-                                 breaks=sort(unique(errbar$Timepoint2)),
+              geom_line(stat="summary")+
-                                 limits = c(0,max(as.numeric(as.character(table$Timepoint)))*1.1))+
+              geom_point(stat="summary")+
-              facet_grid(factor(`ID tumor`, labels = c("Vaccination", "Rechallenge"))~., scale="free_y")+
+              facet_grid(factor(Side, labels = c("Vaccination", "Rechallenge"))~., scale="free_y")+
+              labs(x="Days after tumor inoculation")+
+              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(table$DayPostInoc) / 5)+1)*5))+
               theme_bw()+
               geom_segment(data=errbar, aes(y=mean, yend=mean+std, x=Timepoint2,  xend=Timepoint2))+
               geom_segment(data=errbar, aes(y=mean+std, yend=mean+std,
                                             x=x,xend=xend))
           }else{
+            firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+              pull(DayPostInoc) %>% min(na.rm = T)
+            
+            if (input$operated == TRUE){
+              table<-table %>% filter(DayPostInoc < firstoper)
+            }else{
+              deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+                pull(Animal)
+              if(input$dead == TRUE){
+                table<-table %>% filter(!Animal %in% deadmice)
+              }
+            }
+            
             std<-function(x, na.rm=T){sd(x, na.rm=na.rm)/sqrt(length(x))}
-            errbar<-table %>% group_by(Group, Timepoint) %>% 
+            errbar<-table %>% group_by(Group, DayPostInoc) %>% 
               summarise(mean=mean(Volume, na.rm=T), std=std(Volume)) %>% 
-              mutate(Timepoint2=as.numeric(as.character(Timepoint))) %>% 
+              mutate(Timepoint2=as.numeric(as.character(DayPostInoc))) %>% 
               mutate(x=Timepoint2-input$`errorbar-width`, xend=Timepoint2+input$`errorbar-width`)
             
-            g<-ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=Group))+
+            g<-ggplot(table, aes(DayPostInoc, Volume, color=Group, group=Group))+
-              scale_x_continuous(expand = expansion(mult = c(0,0.0)), 
+              geom_errorbar(stat="summary",width=0.05)+
-                                 breaks=sort(unique(errbar$Timepoint2)),
+              geom_line(stat="summary")+
-                                 limits = c(0,max(as.numeric(as.character(table$Timepoint)))*1.1))+
+              geom_point(stat="summary")+
-              # limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
+              labs(x="Days after tumor challenge")+
+              scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
               theme_bw()+
+              theme(axis.text.x=element_text(angle=45, hjust=1))+
               geom_segment(data=errbar, aes(y=mean, yend=mean+std, x=Timepoint2,  xend=Timepoint2))+
               geom_segment(data=errbar, aes(y=mean+std, yend=mean+std,
                                             x=x,xend=xend))
@@ -657,17 +863,46 @@ server <- function(input, output) {
         }
         if (input$fig_id == "Cinética Individual"){
           if (input$vacc == "Sí"){
-            g<-ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=`ID animal`))+
+            firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
-              scale_x_continuous(expand = expansion(mult = c(0,0.0)), 
+              select(DayPostInoc, Side) %>% unique() %>% 
-                                 breaks=sort(unique(as.numeric(as.character(table$Timepoint)))),
+              group_by(Side) %>% summarise(FirstOper=min(DayPostInoc))
-                                 limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
+            
-              facet_grid(factor(`ID tumor`, labels = c("Vaccination", "Rechallenge"))~Group, scale="free_y")+
+            if (input$operated == TRUE){
-              theme_bw()  
+              for (i in 1:nrow(firstoper)){
+                table<-table %>% filter(DayPostInoc < firstoper$FirstOper[i] | Side != firstoper$Side[i])
+              } 
+            }else{
+              if(input$dead == TRUE){
+                deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+                  pull(Animal)
+                table<-table %>% filter(!Animal %in% deadmice)
+              }
+            }
+            
+            g<-ggplot(table, aes(as.numeric(as.character(DayPostInoc)), Volume, color=Group, group=Animal))+
+              scale_x_continuous(expand = expansion(mult = c(0,0.0)),
+                                 breaks=sort(unique(as.numeric(as.character(table$DayPostInoc)))),
+                                 limits = c(0, (round(max(as.numeric(as.character(table$DayPostInoc))) / 5)+1)*5))+
+              facet_grid(factor(Side, labels = c("Vaccination", "Rechallenge"))~Group, scale="free_y")+
+              theme_bw()
           }else{
-            g<-ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=`ID animal`))+
+            firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
-              scale_x_continuous(expand = expansion(mult = c(0,0.0)), 
+              pull(DayPostInoc) %>% min(na.rm = T)
-                                 breaks=sort(unique(as.numeric(as.character(table$Timepoint)))),
+            
-                                 limits = c(0,max(as.numeric(as.character(table$Timepoint)))*1.1))+
+            if (input$operated == TRUE){
+              table<-table %>% filter(DayPostInoc < firstoper)
+            }else{
+              deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+                pull(Animal)
+              if(input$dead == TRUE){
+                table<-table %>% filter(!Animal %in% deadmice)
+              }
+            }
+
+            g<-ggplot(table, aes(as.numeric(as.character(DayPostInoc)), Volume, color=Group, group=paste0(Animal,Side)))+
+              scale_x_continuous(expand = expansion(mult = c(0,0.0)),
+                                 breaks=sort(unique(as.numeric(as.character(table$DayPostInoc)))),
+                                 limits = c(0,max(as.numeric(as.character(table$DayPostInoc)))*1.1))+
               facet_wrap(.~Group)+
               theme_bw()
           }
@@ -712,23 +947,38 @@ server <- function(input, output) {
           hues <- seq(15, 375, length=n+1)
           hcl(h=hues, l=l, c=100)[1:n]
         }
+
         if (input$vacc == "Sí"){
+          firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+            select(DayPostInoc, Side) %>% unique() %>% 
+            group_by(Side) %>% summarise(FirstOper=min(DayPostInoc))
+          if (input$operated == TRUE){
+            for (i in 1:nrow(firstoper)){
+              table<-table %>% filter(DayPostInoc < firstoper$FirstOper[i] | Side != firstoper$Side[i])
+            } 
+          }else{
+            if(input$dead == TRUE){
+              deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+                pull(Animal)
+              table<-table %>% filter(!Animal %in% deadmice)
+            }
+          }
           g<-list()
           count<-1
           for (side in c("L","R")){
-            tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
+            tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
-            endtime<-dcast(tableR %>% filter(Volume < input$cutoff), Cage+`ID animal`+`ID tumor`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
+            endtime<-dcast(tableR %>% filter(Volume < (input$cutoff*as.numeric(input$unit_fact))), Animal+Side+Group~., value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
-            endtime["Dead"]<-dcast(tableR, Cage+`ID animal`+`ID tumor`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
+            endtime["Dead"]<-dcast(tableR, Animal+Side+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > (input$cutoff*as.numeric(input$unit_fact))
             table_tumor<-endtime
             if (input$colors != ""){
-              col<-input$colors
+              col<-strsplit(input$colors, ",")[[1]]
             }else{
               col<-gg_color_hue(length(unique(endtime$Group)))
             }
-            g[[count]]<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
+            table_tumor$Group<-factor(table_tumor$Group, levels = levels(analysis$taula$Group))
+            g[[count]]<-ggsurvplot(survfit(Surv(end, Dead) ~ Group, data=table_tumor),
                                 pval = T, pval.method = T,
                                 title = side,
-                                # legend.labs = paste(c("< median", ">= median"), "MICA"),
                                 ggtheme=theme_classic(base_size=input$`font-size`),
                                 palette = col
             )
@@ -737,19 +987,31 @@ server <- function(input, output) {
           g_surv_vacc<-g
           
         }else{
+          firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+            pull(DayPostInoc) %>% min(na.rm = T)
+          
+          if (input$operated == TRUE){
+            table<-table %>% filter(DayPostInoc < firstoper)
+          }else{
+            deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+              pull(Animal)
+            if(input$dead == TRUE){
+              table<-table %>% filter(!Animal %in% deadmice)
+            }
+          }
           tableR<-table %>% filter(!is.na(Volume))
-          endtime<-dcast(tableR %>% filter(Volume < input$cutoff), `ID animal`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
+          endtime<-dcast(tableR %>% filter(Volume < (input$cutoff*as.numeric(input$unit_fact))), Animal+Group~., value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
-          endtime["Dead"]<-dcast(tableR, `ID animal`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
+          endtime["Dead"]<-dcast(tableR, Animal+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > (input$cutoff*as.numeric(input$unit_fact))
           table_tumor<-endtime
           if (input$colors != ""){
-            col<-input$colors
+            col<-strsplit(input$colors, ",")[[1]]
           }else{
             col<-gg_color_hue(length(unique(table_tumor$Group)))
           }
-          g<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
+          table_tumor$Group<-factor(table_tumor$Group, levels = levels(analysis$taula$Group))
+          g<-ggsurvplot(survfit(Surv(end, Dead) ~ Group, data=table_tumor),
                         pval = T, pval.method = T,
-                        # legend.labs = paste(c("< median", ">= median"), "MICA"),
+                        # ggtheme=theme_classic(base_size=input$`font-size`),
-                        ggtheme=theme_classic(base_size=input$`font-size`),
                         palette = col
           )
         }