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marcelcosta 3 years ago
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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)

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