Eliminar la columna Cage manteniendo la compatibilidad con hojas de cálculo antiguas.
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+16
-14
@@ -127,6 +127,11 @@ server <- function(input, output) {
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table[i,"Volume"]<-sum(sapply(1:length(long), function(x) (long[x]*wide[x]*wide[x])*(pi/6)))
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}
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table$Volume<-as.numeric(table$Volume)
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if ("Cage" %in% colnames(table)){
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table$Animal<-paste0(table$Cage, table$Animal)
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table<-table[,-which(colnames(table) == "Cage")]
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}
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dades$taula<-table
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dades$groups<-read.xlsx(input$file_sizes$datapath, sheet = "Groups", colNames=F)[,1]
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if (readxl::excel_sheets(input$file_sizes$datapath) %>% length > 2){
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@@ -252,7 +257,7 @@ server <- function(input, output) {
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if(!is.null(dades$sex)){merge(dades$taula %>% filter(DayPostInoc == input$day_vol), dades$sex)}else{
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dades$taula %>% filter(DayPostInoc == input$day_vol) %>% add_column(sex="undefined")} %>% select(-Group),
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df_def[,c("Animal", "group")] %>% unique, all=T, by="Animal") %>% rename(Group=group)
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df_def<-select(df_def, Cage, Animal, Date, DayPostInoc, Group, Side, Weight, Long, Wide, Volume, Observations)
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df_def<-select(df_def, Animal, Date, DayPostInoc, Group, Side, Weight, Long, Wide, Volume, Observations)
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# if("DPV" %in% colnames(df_def)){df_def<-select(df_def, c(`ID animal`, `sex`,`ID tumor`, Volume, Cage, Major, Minor, group))}
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# if("TS-Deep" %in% colnames(df_def)){df_def<-select(df_def, c(`ID animal`, `sex`,`ID tumor`, Volume, Cage, `TS-Deep`,`TS-Length`,`TS-Width`, group))}
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@@ -326,7 +331,7 @@ server <- function(input, output) {
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select(Animal, Group) %>% unique()
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dtemplate<-merge(dades$taula %>% select(-Group), template) %>%
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arrange(DayPostInoc, Animal, Side) %>%
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select(Cage, Animal, Date, DayPostInoc, Group, Side,
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select(Animal, Date, DayPostInoc, Group, Side,
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Weight, Long, Wide, Volume, Observations)
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}else{
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@@ -343,6 +348,7 @@ server <- function(input, output) {
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Volume="",
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Observations=""
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)
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template<-template %>% mutate(Animal=paste0(Cage,Animal)) %>% select(-Cage)
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dtemplate<-template
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}
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write.xlsx(list("Measures"=dtemplate, "Groups"=NULL,"Sex"=NULL), file)
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@@ -379,7 +385,10 @@ server <- function(input, output) {
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table[i,"Volume"]<-sum(sapply(1:length(long), function(x) (long[x]*wide[x]*wide[x])*(pi/6)))
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}
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table$Volume<-as.numeric(table$Volume)
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if ("Cage" %in% colnames(table)){
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table$Animal<-paste0(table$Cage, table$Animal)
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table<-table[,-which(colnames(table) == "Cage")]
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}
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## Autocompletado de grupo
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table_group<-merge(
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@@ -391,7 +400,6 @@ server <- function(input, output) {
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table %>% select(-Group),
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table_group
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) %>% relocate(Group, .after = DayPostInoc) %>%
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relocate(Cage, .before = Animal) %>%
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arrange(DayPostInoc, Animal, Side)
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if("Groups" %in% readxl::excel_sheets(input$file_analy$datapath)){
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@@ -425,7 +433,7 @@ server <- function(input, output) {
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table<-table %>% select(-Major, -Minor) %>%
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spread(Timepoint, Volume) %>%
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mutate(across(all_of(timepoints), function(x){(x*100/`0`)-100})) %>%
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gather(Timepoint, Volume, -Cage, -`ID animal`, -`ID tumor`, -Group) %>%
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gather(Timepoint, Volume, -`ID animal`, -`ID tumor`, -Group) %>%
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mutate(Volume=case_when(Volume < 0 ~ 0, T~Volume))
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}
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table %>% group_by(Group, DayPostInoc, Side) %>% count() %>% spread(DayPostInoc, n)
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@@ -436,13 +444,11 @@ server <- function(input, output) {
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observeEvent(analysis$taula, {})
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table<-analysis$taula
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table$Animal<-paste0(table$Cage, table$Animal)
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print(table)
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animals<-unique(table$Animal)
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sides<-unique(table$Side)
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groups<-table %>% select(Animal, Group) %>% unique() %>% pull(Group)
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basal<-data.frame(
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Cage="",
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Animal=rep(animals, each=length(sides)),
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Date="",
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DayPostInoc=0,
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@@ -515,12 +521,10 @@ server <- function(input, output) {
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observeEvent(analysis$taula, {})
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table<-analysis$taula
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table$Animal<-paste0(table$Cage, table$Animal)
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animals<-unique(table$Animal)
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sides<-unique(table$Side)
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groups<-table %>% select(Animal, Group) %>% unique() %>% pull(Group)
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basal<-data.frame(
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Cage="",
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Animal=rep(animals, each=length(sides)),
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Date="",
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DayPostInoc=0,
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@@ -608,8 +612,8 @@ server <- function(input, output) {
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g<-list()
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for (side in c("L","R")){
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tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
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endtime<-dcast(tableR %>% filter(Volume < input$cutoff), Cage+Animal+Side+Group~., value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
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endtime["Dead"]<-dcast(tableR, Cage+Animal+Side+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
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endtime<-dcast(tableR %>% filter(Volume < input$cutoff), Animal+Side+Group~., value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
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endtime["Dead"]<-dcast(tableR, Animal+Side+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
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table_tumor<<-endtime
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g[side]<-ggsurvplot(survfit(Surv(end, Dead) ~ Group, data=table_tumor),
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@@ -676,7 +680,6 @@ server <- function(input, output) {
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tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
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if (length(unique(tableR$Volume)) > 1 & length(unique(tableR$DayPostInoc)) > 1){
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print(paste0("Side: ",side))
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# print(summary(aov(Volume~Group+DayPostInoc+Error(paste0(Animal,Cage)), data=tableR)))
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print(summary(aov(Volume~Group+DayPostInoc+Error(Animal), data=tableR)))
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}
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}
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@@ -761,7 +764,6 @@ server <- function(input, output) {
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sides<-unique(table$Side)
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groups<-table %>% select(Animal, Group) %>% unique() %>% pull(Group)
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basal<-data.frame(
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Cage="",
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Animal=rep(animals, each=length(sides)),
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Date="",
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DayPostInoc=0,
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@@ -951,8 +953,8 @@ server <- function(input, output) {
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count<-1
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for (side in c("L","R")){
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tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
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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"=".")
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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))
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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"=".")
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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))
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table_tumor<-endtime
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if (input$colors != ""){
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col<-strsplit(input$colors, ",")[[1]]
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