Eliminar la columna Cage manteniendo la compatibilidad con hojas de cálculo antiguas.

invivos/app.R

@@ -127,6 +127,11 @@ server <- function(input, output) {
         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$Animal)
+        table<-table[,-which(colnames(table) == "Cage")]
+      }
+      
       dades$taula<-table
       dades$groups<-read.xlsx(input$file_sizes$datapath, sheet = "Groups", colNames=F)[,1]
       if (readxl::excel_sheets(input$file_sizes$datapath) %>% length > 2){
@@ -252,7 +257,7 @@ server <- function(input, output) {
       if(!is.null(dades$sex)){merge(dades$taula %>% filter(DayPostInoc == input$day_vol), dades$sex)}else{
         dades$taula %>% filter(DayPostInoc == input$day_vol) %>% add_column(sex="undefined")} %>% select(-Group), 
       df_def[,c("Animal", "group")] %>% unique, all=T, by="Animal") %>% rename(Group=group)
-    df_def<-select(df_def, Cage, Animal, Date, DayPostInoc, Group, Side, Weight, Long, Wide, Volume, Observations)
+    df_def<-select(df_def, Animal, 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))}
     
@@ -326,7 +331,7 @@ server <- function(input, output) {
           select(Animal, Group) %>% unique()
         dtemplate<-merge(dades$taula %>% select(-Group), template) %>% 
           arrange(DayPostInoc, Animal, Side) %>% 
-          select(Cage, Animal, Date, DayPostInoc, Group, Side, 
+          select(Animal, Date, DayPostInoc, Group, Side, 
                  Weight, Long, Wide, Volume, Observations)
       }else{
         
@@ -343,6 +348,7 @@ 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)
@@ -379,7 +385,10 @@ server <- function(input, output) {
         table[i,"Volume"]<-sum(sapply(1:length(long), function(x) (long[x]*wide[x]*wide[x])*(pi/6)))
       }
       table$Volume<-as.numeric(table$Volume)
-      table$Animal<-paste0(table$Cage, table$Animal)
+      if ("Cage" %in% colnames(table)){
+        table$Animal<-paste0(table$Cage, table$Animal)
+        table<-table[,-which(colnames(table) == "Cage")]
+      }
       
       ## Autocompletado de grupo
       table_group<-merge(
@@ -391,7 +400,6 @@ server <- function(input, output) {
         table %>% select(-Group),
         table_group
       ) %>% relocate(Group, .after = DayPostInoc) %>% 
-        relocate(Cage, .before = Animal) %>% 
         arrange(DayPostInoc, Animal, Side)
       
       if("Groups" %in% readxl::excel_sheets(input$file_analy$datapath)){
@@ -425,7 +433,7 @@ server <- function(input, output) {
         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) %>% 
+          gather(Timepoint, Volume, -`ID animal`, -`ID tumor`, -Group) %>% 
           mutate(Volume=case_when(Volume < 0 ~ 0, T~Volume))
       }
       table %>% group_by(Group, DayPostInoc, Side) %>% count() %>% spread(DayPostInoc, n)
@@ -436,13 +444,11 @@ server <- function(input, output) {
       observeEvent(analysis$taula, {})
       table<-analysis$taula
       
-      table$Animal<-paste0(table$Cage, table$Animal)
       print(table)
       animals<-unique(table$Animal)
       sides<-unique(table$Side)
       groups<-table %>% select(Animal, Group) %>% unique() %>% pull(Group)
       basal<-data.frame(
-        Cage="",
         Animal=rep(animals, each=length(sides)),
         Date="",
         DayPostInoc=0,
@@ -515,12 +521,10 @@ server <- function(input, output) {
       observeEvent(analysis$taula, {})
       table<-analysis$taula
       
-      table$Animal<-paste0(table$Cage, table$Animal)
       animals<-unique(table$Animal)
       sides<-unique(table$Side)
       groups<-table %>% select(Animal, Group) %>% unique() %>% pull(Group)
       basal<-data.frame(
-        Cage="",
         Animal=rep(animals, each=length(sides)),
         Date="",
         DayPostInoc=0,
@@ -608,8 +612,8 @@ server <- function(input, output) {
         g<-list()
         for (side in c("L","R")){
           tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
-          endtime<-dcast(tableR %>% filter(Volume < input$cutoff), Cage+Animal+Side+Group~., value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
-          endtime["Dead"]<-dcast(tableR, Cage+Animal+Side+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
+          endtime<-dcast(tableR %>% filter(Volume < input$cutoff), Animal+Side+Group~., value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
+          endtime["Dead"]<-dcast(tableR, 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(end, Dead) ~ Group, data=table_tumor),
@@ -676,7 +680,6 @@ server <- function(input, output) {
           tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
           if (length(unique(tableR$Volume)) > 1 & length(unique(tableR$DayPostInoc)) > 1){
             print(paste0("Side: ",side))
-            # print(summary(aov(Volume~Group+DayPostInoc+Error(paste0(Animal,Cage)), data=tableR)))
             print(summary(aov(Volume~Group+DayPostInoc+Error(Animal), data=tableR)))
           }
         }
@@ -761,7 +764,6 @@ server <- function(input, output) {
       sides<-unique(table$Side)
       groups<-table %>% select(Animal, Group) %>% unique() %>% pull(Group)
       basal<-data.frame(
-        Cage="",
         Animal=rep(animals, each=length(sides)),
         Date="",
         DayPostInoc=0,
@@ -951,8 +953,8 @@ server <- function(input, output) {
           count<-1
           for (side in c("L","R")){
             tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
-            endtime<-dcast(tableR %>% filter(Volume < (input$cutoff*as.numeric(input$unit_fact))), Cage+Animal+Side+Group~., value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
-            endtime["Dead"]<-dcast(tableR, Cage+Animal+Side+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > (input$cutoff*as.numeric(input$unit_fact))
+            endtime<-dcast(tableR %>% filter(Volume < (input$cutoff*as.numeric(input$unit_fact))), Animal+Side+Group~., value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
+            endtime["Dead"]<-dcast(tableR, Animal+Side+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > (input$cutoff*as.numeric(input$unit_fact))
             table_tumor<-endtime
             if (input$colors != ""){
               col<-strsplit(input$colors, ",")[[1]]