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app.R
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app.R
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####################################### Libraries ########################################
source("libraries.R")
####################################### Scripts ##########################################
file.source=list.files("ggseg3d//R",pattern="*.R",full.names = TRUE)
lapply(file.source, source,.GlobalEnv)
file.source=list.files("R",pattern="*.R",full.names = TRUE)
lapply(file.source, source,.GlobalEnv)
load("desterieux_3d.rda")
source("geom_flat_violin.R")
####################################### User Interface ###################################
ui <- fluidPage(
dashboardPage(
### Header ###
dashboardHeader(title="ShinySurfer", titleWidth = 350),
### Sidebar ###
dashboardSidebar( width = 350,
sidebarMenu(id="sideM",
### Start ###
menuItem("Start",tabName = "updata", icon = icon("home"), selected = T,
# checkboxInput("check_old_new_data", label = "Old data format", value = FALSE),
fileInput("data_tablelh","Freesurfer - Left hemisphere:",accept = c("txt")),
fileInput("data_tablerh","Freesurfer - Right hemisphere:",accept = c("txt")),
fileInput("data_table","Upload demographics data:", accept = c("csv","xlsx","xls"))),
# conditionalPanel(
# condition="output.dataFileLoad==true",
# fileInput("name_file","Names correction:", accept = c("xlsx","xls")))),
### Quality Control ###
menuItem("Quality Control",tabName = "qc",icon=icon("chart-bar"), expandedName = "qc",
conditionalPanel(
condition="output.dataFileLoad==true",
uiOutput("fil_qc"),
uiOutput("qc_kon"),
actionButton("dp","Raincloud Plot")
)),
### Descriptive Statistics ###
menuItem("Descriptive Statistics",expandedName = "ds",icon=icon("brain"),
conditionalPanel(
condition="output.dataFileLoad==true",
uiOutput("fil_ui"),
uiOutput("ds_kon"),
checkboxInput("com","Selection of all records",value = TRUE),
conditionalPanel(
condition = "input.com==1",
uiOutput("com_cd")
), hr(),
prettyRadioButtons(inputId = "select_hemisphere",
label = "Choose Hemisphere:",
choices = c("left","right","both"),
selected = "both",inline = TRUE),
checkboxInput("col","Color and Value", value = FALSE),
#checkboxInput("check_download","Download",FALSE),
actionButton("ab","Brain Map")
#conditionalPanel(
# condition = "input.check_download==1",
# textInput(inputId = "name",
# "file name"
# ),
#
# selectInput(inputId = "format",
# "file format",
# choices = c("svg","pdf","png"),
# "pdf"
# ),
# div(style="height:100px;",
# checkboxInput("down_filter","With Filer Label",value = FALSE)
#
# ),
# actionButton("download","Download image"),
# hr()
#)
)
),
### Linear Regression ###
menuItem("Linear Regression",expandedName = "ss",icon=icon("chart-line"),
conditionalPanel(
condition="output.dataFileLoad==true",
uiOutput("fil_ss"),
uiOutput("ss_kon"),
actionButton("rp","Regression Plot")
)
),
### Lasso Regression ###
menuItem("Lasso Regression",expandedName = "ls",icon=icon("chart-line"),
conditionalPanel(
condition="output.dataFileLoad==true",
uiOutput("fil_ls"),
uiOutput("ls_kon"),
actionButton("lp","Lasso Regression")
)
),
### Restart Button ###
tags$button("Restart", id="restart",
type="button", class="btn btn-danger action-button",
onclick="history.go(0)")
)
),
### MainBar ###
dashboardBody(
### Start ###
conditionalPanel(
condition = "!(input.sidebarItemExpanded=='qc')
&& !(input.sidebarItemExpanded=='ss')
&& !(input.sidebarItemExpanded=='ds')
&& !(input.sidebarItemExpanded=='ls')",
includeMarkdown("www//Home.md")
),
### Quality Control ###
# conditionalPanel(
# condition="output.dataFileLoad==true && input.sidebarItemExpanded=='qc'&&
# output.dp == 1",
# uiOutput("qc_tabs_filter")),
conditionalPanel(
condition="output.dataFileLoad==true && input.sidebarItemExpanded=='qc'",
uiOutput("qc_tabs")),
### Descriptive Statistics ###
conditionalPanel(
condition="output.dataFileLoad==true && input.sidebarItemExpanded=='ds'",
tabsetPanel(
type="tabs",id="ds_tab",
tabPanel("Table",DT::dataTableOutput("ds_table")),
tabPanel("Descriptive Statistics",DT::dataTableOutput("ds_composity")),
tabPanel("Brain Map",plotlyOutput("ggseg3d",height = "700px"))
)
),
### Descriptive Statistics ###
conditionalPanel(condition="input.col==1 && input.sidebarItemExpanded=='ds'",
colourInput("color_obergrenze", "Please select the color of the upper bound", "red"),
numericInput(inputId = 'wert_obergrenze',
label = 'Please choose a value for the upper bound',
4.2),
colourInput("color_untergrenze", "Please select the color of the lower bound", "blue"),
numericInput(inputId = 'wert_untergrenze',
label = 'Please choose a value for the lower bound',
1.5),
actionButton("add_mitte", "Add new values and colors"),
actionButton("remove_mitte","Remove")
),
### Linear Regression ###
conditionalPanel(
condition="output.dataFileLoad==true && input.sidebarItemExpanded=='ss'",
uiOutput("ss_tabs")
# tabsetPanel(
# type="tabs",id="ss_tab",
# tabPanel("Table",DT::dataTableOutput("ss_table")),
# tabPanel("Regression Plots",plotOutput("regression",height ="40000px",width = "1000px"))
# ),
),
### Lasso Regression ###
conditionalPanel(
condition="output.dataFileLoad==true && input.sidebarItemExpanded=='ls'",
tabsetPanel(
type="tabs",id="ls_tab",
tabPanel("Table",DT::dataTableOutput("ls_table")),
tabPanel("Lasso Regression",DT::dataTableOutput("lasso_table"))
)
)
)
)
)
####################################### Server ###########################################
server<-function(input, output,session) {
OASIS <<- NULL
options(shiny.sanitize.errors = TRUE)
### Get Dataset ###
get_data_file <- reactive({
saving <<-
if(!is.null(input$data_tablelh) && !is.null(input$data_tablerh)) {
lh <- read.delim(input$data_tablelh[["datapath"]])
lh$BrainSegVolNotVent <- NULL
lh$eTIV <- NULL
rh <- read.delim(input$data_tablerh[["datapath"]])
rh$BrainSegVolNotVent <- NULL
rh$eTIV <- NULL
if(!is.null(input$data_table)){
demographics <- read.csv(input$data_table[["datapath"]])
OASIS <- cbind(demographics, lh, rh)
OASIS$lh.aparc.a2009s.thickness <- NULL
OASIS$lh_MeanThickness_thickness <- NULL
OASIS$rh.aparc.a2009s.thickness <- NULL
OASIS$rh_MeanThickness_thickness <- NULL
colnames(OASIS)[1] <- "ID"
}
else{
OASIS <- cbind(lh, rh)
OASIS$lh.aparc.a2009s.thickness <- NULL
OASIS$lh_MeanThickness_thickness <- NULL
OASIS$rh.aparc.a2009s.thickness <- NULL
OASIS$rh_MeanThickness_thickness <- NULL
id <- rownames(OASIS)
OASIS <- cbind(id=id, OASIS)
colnames(OASIS)[1] <- "ID"
}
OASIS <<- OASIS
return(TRUE)
}
else{
# OASIS <<- read_excel("OASIS_behavioral.xlsx")
# return(TRUE)
return(FALSE)
}
})
### output.dataFileLoad - Constant to check if data are loaded ###
output$dataFileLoad <- reactive({
return(get_data_file())
})
outputOptions(output,'dataFileLoad',suspendWhenHidden=FALSE)
##################################### Data preprocess ##################################
### Transform the names of OASIS and get the data ###
get_oasis <- reactive({
is.null(input$data_tablelh)
if(!is.null(input$name_file)){
area <- read_excel(input$name_file[["datapath"]],col_names = FALSE)
return(oasis.tidy(session,area,OASIS))
}else{
oasis_data <- OASIS
return(oasis_data)
}
})
### Fill User Interface ###
get_fil <- reactive({
input$fil
})
get_fil_com <- reactive({
input$fil_com
})
get_ss_fil <- reactive({
input$ss_fil
})
get_ls_fil <- reactive({
input$ls_fil
})
### load desterieux_3d ###
get_altes <- reactive({
desterieux_neu <- desterieux_3d
oa <- get_oasis()
cols <- ncol(oa)
t_name <- names(oa[(cols-147):cols])
for (j in 1:6) {
if (desterieux_neu[[3]][[j]] == "left") {
for (i in 1:82) {
desterieux_neu[[4]][[j]][[1]][[i]]<-t_name[trunc((i+1)/2)]
}
for (i in 84:149) {
desterieux_neu[[4]][[j]][[1]][[i]]<-t_name[trunc(i/2)]
}
}else{
for (i in 1:82) {
desterieux_neu[[4]][[j]][[1]][[i]]<-t_name[trunc((i+149)/2)]
}
for (i in 84:149) {
desterieux_neu[[4]][[j]][[1]][[i]]<-t_name[trunc((i+148)/2)]
}
}
}
return(desterieux_neu)
})
### ###
output$fil_ui <- renderUI({
if(input$com==0){
tagList(
selectInput("fil",label = "Filter",
choices = names(get_oasis()),multiple = TRUE)
)
}else{
input$name_file
tagList(
selectInput("fil_com",label = "Filter",
choices = names(get_oasis())[-1],multiple = TRUE)
)
}
})
### ###
output$fil_ss <- renderUI({
input$name_file
tagList(
selectInput("ss_fil",label = "Filter",choices = names(get_oasis())[-1],multiple = TRUE)
)
})
### ###
output$fil_ls <- renderUI({
input$name_file
tagList(
selectInput("ls_fil",label = "Filter",choices = names(get_oasis())[-1],multiple = TRUE)
)
})
### get the explan names
get_explan_names <- reactive({
input$data_tablelh
data_type <- "" # data_type = "FreeSurfer"
cols <- ncol(OASIS)
if(data_type=="FreeSurfer"){
explans <- dplyr::select(OASIS,-(cols-147):-cols)
names_explan <- get.regression.col(explans)
}else if(data_type=="CERES"){
explans <- dplyr::select(OASIS,1:(cols-274))
names_explan <- get.regression.col(explans)
}else{
names_explan <- names(OASIS)
}
return(names_explan)
})
##### Filtering #####
### filter the data from according to selected condition ###
get_choice <- reactive({ # get the select col and return the selected date
input$data_tablelh
col_input <- get_fil()
col_com_input <- get_fil_com()
u_oasis <- get_oasis()
if(input$com==0){
for (col in col_input) {
v <- input[[col]]
if(!(is.null(v)|| ""==v)){
if(as.character(col)=='sex'){
if(input$sex!="All"){
u_oasis <- u_oasis[u_oasis$sex==input$sex,]
}}
else{
u_oasis <- u_oasis[u_oasis[[col]]==v,]
}}
}
}else{
for (col in col_com_input) {
u_oasis <- get.choice(col,data.table =u_oasis,seletedValue = input[[paste0(col,"_range")]],col.type = "_range")
}
}
return(u_oasis)
})
get_ss_choice <- reactive({ # get the select col and return the selected date
col_input <- get_ss_fil()
u_oasis <- get_oasis()
for (col in col_input) {
u_oasis <- get.choice(col,data.table =u_oasis,seletedValue = input[[paste0(col,"_ss")]],col.type = "_ss")
}
return(u_oasis)
})
get_ls_choice <- reactive({ # get the select col and return the selected date
col_input <- get_ls_fil()
u_oasis <- get_oasis()
for (col in col_input) {
u_oasis <- get.choice(col,data.table =u_oasis,seletedValue = input[[paste0(col,"_ls")]],col.type = "_ls")
}
return(u_oasis)
})
### ###
output$ds_kon <- renderUI({
x <- vector("list",length=length(get_fil()))
if(input$com==0||is.null(input$com)){
for (ff in get_fil()) {
x <- append(x,list(
if(as.character(ff)=="sex"){
selectInput("sex",
label = "sex",
choices = unique(append("All",get_choice()[["sex"]])),
selected = {
if (is.null(input[[ff]])||"All"==input[[ff]]){
"All"
} else{
input[[ff]]
}
})
}
else{
selectInput(
inputId = paste0(ff),
label = as.character(ff),
choices = c(" "="",sort(unique(get_choice()[[ff]]))),
selected = {
if (is.null(input[[ff]])||""==input[[ff]]){
""
} else{
input[[ff]]
}
}
)}
))}
}else{
for (ff in get_fil_com()) {
x <- append(x,list(
if(as.character(ff)=="ID"){}
else if(as.character(ff)=="sex"){
selectInput("sex_range",
label = "sex",
choices = unique(append("All",c("F","M"))),
selected = {
if (is.null(input[["sex_range"]])||"All"==input[["sex_range"]]){
"All"
} else{
input[["sex_range"]]
}
})
}else{
sliderInput(paste0(ff,"_range"),paste(ff),
min = min(get_oasis()[[ff]]),max=max(get_oasis()[[ff]]),
value = {if(!is.null(input[[paste0(ff,"_range")]])){
input[[paste0(ff,"_range")]]
}else{
c(min(get_oasis()[[ff]]),max(get_oasis()[[ff]]))
}
}
)
}
))
}
}
return(x)
})
### ###
observeEvent(input$qc_col,{
cols <- input$qc_col
if("All" %in% cols){
updateSelectInput(session,"qc_col",selected = names(dplyr::select(get_qc_choice(),-c("ID","sex"))))
}
})
### Stastics
output$ss_kon <- renderUI({
x <- NULL
for (ff in get_ss_fil()) {
x <- append(x,list(
render.ui.output(ff,get_oasis()[[ff]],input[[paste0(ff,"_ss")]],ui_type="_ss")
))
}
x <- append(x,list(selectInput("explan",label="Explanatory variable",choices = get_explan_names())))
# if(input$data_type=="Others"){
# x <- append(x,list(selectInput("explan_2",label="Data variable",choices = get_explan_names())))
# }
return(x)
})
### ###
output$ls_kon <- renderUI({
x <- NULL
for (ff in get_ls_fil()) {
x <- append(x,list(
render.ui.output(ff,get_oasis()[[ff]],input[[paste0(ff,"_ls")]],ui_type="_ls")
))
# x <- append(x,list(
# if(as.character(ff)=="sex"){
# selectInput("sex_qc",
# label = "sex",
# choices = unique(append("All",c("F","M"))),
# selected = {
# if (is.null(input[["sex_ls"]])||"All"==input[["sex_ls"]]){
# "All"
# } else{
# input[["sex_qc"]]
# }
# })
# }else{
# sliderInput(paste0(ff,"_ls"),paste(ff),
# min = min(get_oasis()[[ff]]),max=max(get_oasis()[[ff]]),
# value = {if(!is.null(input[[paste0(ff,"_ls")]])){
# input[[paste0(ff,"_ls")]]
# }else{
# c(min(get_oasis()[[ff]]),max(get_oasis()[[ff]]))
# }
# }
# )
# }
# ))
}
names_explan <- get_explan_names()
names_explan <- names_explan[names_explan != "sex"];
names_explan <- names_explan[names_explan != "Sex"];
names_explan <- names_explan[names_explan != "ID"];
x <- append(x,list(selectInput("lasso_variable",label="Explanatory variable",choices = names_explan)))
# if(input$data_type=="Others"){
# x <- append(x,list(selectInput("lasso_variable_2",label="Data variable",choices = get_explan_names(),multiple = TRUE)))
# }
return(x)
})
### get the composite way ###
output$com_cd <- renderUI({
tagList(
prettyRadioButtons("com_way_c",label = "Central tendency:",choices = c("mean","median"),inline = TRUE),
prettyRadioButtons("com_way_d",label = "Dispersion:",choices = c("SD","SEM"),selected = character(0),inline = TRUE)
)
})
### ###
observeEvent(input$com_way_c,{
output$com_cd <- renderUI({
tagList(
prettyRadioButtons("com_way_c",label = "Central tendency",choices = c("mean","median"),selected = input$com_way_c,inline = TRUE),
prettyRadioButtons("com_way_d",label = "Dispersion",choices = c("SD","SEM"),selected = character(0),inline = TRUE)
)
})
com_cd <<- input$com_way_c
})
### ###
observeEvent(input$com_way_d,{
output$com_cd <- renderUI({
tagList(
prettyRadioButtons("com_way_c",label = "Central tendency",choices = c("mean","median"),selected = character(0),inline = TRUE),
prettyRadioButtons("com_way_d",label = "Dispersion",choices = c("SD","SEM"),selected = input$com_way_d,inline = TRUE)
)
})
com_cd <<- input$com_way_d
})
com_cd <<-"mean"
aus_daten <- reactive({
input$com_way_c
input$com_way_d
switch (as.vector(com_cd),
"median" = "median",
"mean" = "mean",
"SD" = "sd",
"SEM" = "sem")
})
### ###
output$ds_table<- DT::renderDataTable({
ausgewaehlte_daten <- get_choice()
DT::datatable(ausgewaehlte_daten,class = "display nowrap",options = list(scrollX=TRUE))
})
### ###
output$ds_composity <- DT::renderDataTable({
data <- get_choice()
cols <- ncol(data)
data <- data[(cols-147):cols]
data_mean <-apply(data, 2, mean)
data_median <- apply(data, 2, median)
data_sd <- apply(data, 2, sd)
data_sem <- apply(data, 2, sem)
frame_statistics <- tibble("thickness"=names(data),"mean"=data_mean,
"median"=data_median,
"SD"=data_sd,"SEM"=data_sem)
frame_statistics[2:4] <- frame_statistics[2:4]%>%mutate_if(is.numeric,round,2)
frame_statistics[5] <- frame_statistics[5]%>%mutate_if(is.numeric,round,4)
DT::datatable(frame_statistics,options = list(scrolly=TRUE))
})
### ###
output$ss_table<- DT::renderDataTable({
ausgewaehlte_daten <- get_ss_choice()
DT::datatable(ausgewaehlte_daten,class = "display nowrap",options = list(scrollX=TRUE))
})
### ###
output$ls_table<- DT::renderDataTable({
ausgewaehlte_daten <- get_ls_choice()
DT::datatable(ausgewaehlte_daten,class = "display nowrap",options = list(scrollX=TRUE))
})
### color update auto ###
observeEvent({
input$com_way_c
input$com_way_d
is.null(input$com_way)
input$sidebarItemExpanded
lapply(get_fil(), function(x){input[[x]]})
lapply(get_fil_com(), function(x){input[[paste0(x,"_range")]]})
input$select_hemisphere
},{
if(is.null(OASIS)){return()}
ausgewaehlte_daten <- get_choice()
cols <- ncol(ausgewaehlte_daten)
wert<-ausgewaehlte_daten[(cols-147):cols]
wert <- tibble(apply(wert, 2, aus_daten()))
if(aus_daten()!="sem"){
wert <- wert%>%mutate_if(is.numeric,round,2)
}else{
wert <- wert%>%mutate_if(is.numeric,round,4)
}
max_wert<-max(wert)
min_wert<-min(wert)
updateNumericInput(session,"wert_obergrenze",value = max_wert)
updateNumericInput(session, inputId = "wert_untergrenze", value = min_wert)
}
)
### control panel tab ###
observeEvent(input$single_region,{
if(input$single_region==0){
hideTab(inputId ="tab",target = "DistributionPlot")
}
if(input$single_region==1){
showTab(inputId ="tab",target = "DistributionPlot")
}
})
### ###
observeEvent(input$com,{
if(input$com==0){
hideTab(inputId ="tab",target="Quality Control")
}
if(input$com==1){
showTab(inputId ="tab",target="Quality Control")
}
})
### Add new values and colors(UI) ###
index_selection <- reactiveVal(1)
observeEvent(input$add_mitte, {
insertUI(
selector = "#add_mitte",
where = "beforeBegin",
ui = tagList(column(
12,
colourInput(inputId = paste("color_mitte", index_selection(), sep = "_"),
label = paste("new colors", index_selection()),
"black"),
numericInput(inputId = paste("wert_mitte", index_selection(), sep = "_"),
label = paste("new values ", index_selection()),
" ")
)
))
new_index <- index_selection() + 1
index_selection(new_index)
})
observeEvent(input$remove_mitte, {
removeUI(selector = paste0(".col-sm-12:has(#wert_mitte_", index_selection()-1, ")"))
removeUI(selector = paste0(".col-sm-12:has(#color_mitte_", index_selection()-1, ")"))
index_selection(index_selection()-1)
})
### ###
get_auswahl_data <- reactive({
auswahl_area <- get_choice()
cols <- ncol(auswahl_area)
if(nrow(auswahl_area)==0){
showModal(modalDialog(title = "INFORMATION",
"The inputed data is empty",
easyClose = TRUE))
return(NULL)
}
auswahl_area <- auswahl_area[(cols-147):cols]
if (nrow(get_choice())==1) {
}else if(input$com==0){
showModal(modalDialog(title = "INFORMATION",
"If you turn off the Selection of all records-Setting,
you must select an element of your choice under Filter to make
a BrainMap!",
easyClose = TRUE))
return(NULL)
}
else{
auswahl_area <- apply(auswahl_area, 2, aus_daten())
}
auswahl_data <- tibble(
area = as.character(names(auswahl_area)),
wert = as.numeric(auswahl_area),
stringsAsFactors = FALSE
)
if(aus_daten()!="sem"){
auswahl_data <- auswahl_data%>%mutate_if(is.numeric,round,2)
}else{
auswahl_data <- auswahl_data%>%mutate_if(is.numeric,round,4)
}
auswahl_data[[" "]] <- paste(auswahl_data$area,"= ",auswahl_data$wert, " mm")
return(auswahl_data)
})
### Generate 3D brain map ###
output$ggseg3d<- renderPlotly({
input$ab
if(input$ab==0)
return()
isolate({
auswahl_data <- get_auswahl_data()
if (is.null(auswahl_data)) {
return()
}
### new values and colors ###
auswahl_wert <- c(input$wert_obergrenze,input$wert_untergrenze)
auswahl_color <- c(input$color_obergrenze,input$color_untergrenze)
if (1<index_selection()) {
for (i in 1:(index_selection()-1)) {
auswahl_wert[i+2] <- input[[paste("wert_mitte", i, sep = "_")]]
auswahl_color[i+2] <- input[[paste("color_mitte", i, sep = "_")]]
}
}
names(auswahl_wert) <- auswahl_color
### select_hemisphere ###
auswahl_hemisphere<-input$select_hemisphere
if(auswahl_hemisphere=="both"){auswahl_hemisphere=c("left","right")}
### plot ggseg3d ###
gg <<- ggseg3d(.data = auswahl_data,
atlas = get_altes(),
colour = "wert", text = " ",
surface = "LCBC",
palette = sort(auswahl_wert),
hemisphere = auswahl_hemisphere,
na.alpha= .5,
show.legend = TRUE,
options.legend = (colorbar=list(title=list(text="mm")))) %>%
pan_camera("left lateral") %>%
event_register("plotly_relayout") %>%
layout(scene=list(camera=list(center=list(z=-0.4))))%>%
remove_axes()
filter_data <<- NULL
if(input$com==0){
for (i in input$fil) {
filter_data <<- paste(filter_data,i,":",input[[i]],"\n",sep=" ")
}
}else{
for (i in input$fil_com) {
i_range=paste(i,"_range",sep="")
if(i_range=="sex_range"){
filter_data <<- paste(filter_data,"sex: ",input[[i_range]],"\n",sep = "")
}else{
filter_data <<- paste(filter_data,i,": ","[",min(input[[i_range]]),",",max(input[[i_range]]),"]","\n",sep="")
}
}
}
if(is.null(filter_data)){
gg
}else{
gg%>%layout(annotations=list(visible=TRUE,
text=filter_data,
showarrow=FALSE,
x=0,y=1,
align="left",
font=list(family="Arial",size=13)))
}
})
})
### ###
observeEvent(input$ab,{
updateTabsetPanel(session,"ds_tab",selected = "Brain Map")
})
# get_eye <- reactive({
# d <- event_data("plotly_relayout")
# if(input$ab==0||is.null(d)){
# return(NULL)
# }else {
# return(d$scene.camera[["eye"]])
# }
# })
# #p<-output$ggseg3d
# observeEvent(input$download,{
# if(is.null(get_eye())){
# if(isFALSE(input$down_filter)){
# orca(gg, paste(input$name,input$format, sep = ".", collapse = NULL))
# }else{
# orca(gg%>%layout(annotations=list(visible=TRUE,text=filter_data,showarrow=FALSE,
# x=0,y=1,align="left",font=list(family="Arial",size=13))),
# paste(input$name,input$format, sep = ".", collapse = NULL))
# }
# }else{
# eye <- get_eye()
# scene=list(camera=list(eye=list(x=eye$x,y=eye$y,z=eye$z)))
# # gg_eye <- gg%>%layout(scene=scene)
# if(isFALSE(input$down_filter)){
# orca(gg%>%layout(scene=scene),paste(input$name,input$format, sep = ".", collapse = NULL))
# }else{
# gg_eye <- gg%>%layout(scene=scene,annotations=list(visible=TRUE,text=filter_data,showarrow=FALSE,
# x=0,y=1,align="left",font=list(family="Arial",size=13)))
# orca(gg_eye,paste(input$name,input$format, sep = ".", collapse = NULL))
# }
# }
# })
### distributionPlot when single person ###
output$distributionPlot<-renderPlot({
if(input$single_region==0)
return()
# if(input$ab==0)
# return()
if(is.null(input[["ID"]])){}
isolate({
region<-input$region
auswahl_area <- get_choice()
if(nrow(get_choice())!=1){
return()
}
auswahl_region <- input$region
oasis_data <- get_oasis()[-1:-3]
selectedData <- oasis_data[auswahl_region]
colnames(selectedData) <- c("distributionPlot")
selectedData$distributionPlot<-as.numeric(selectedData$distributionPlot)
save<-auswahl_area[[auswahl_region]]
save<-as.numeric(save)
ggplot(selectedData,
aes(x = distributionPlot)
) + geom_density() + geom_point(aes(save,0),col="red", size=8)
})
})
########################################## Quality Control #############################
### Sidebar for Quality Control ###
output$fil_qc <- renderUI({
input$name_file
tagList(selectInput("qc_fil",label = "Filter", choices = names(get_oasis())[-1], multiple = TRUE))
})
### Filter for the Rainbow Plot ###
output$qc_tabs_filter <- renderUI({
input$name_file
tagList(selectInput("qc_fil",label = "Filter", choices = names(get_oasis())[-1], multiple = TRUE))
})
### Get the selected values
get_qc_fil <- reactive({
input$qc_fil
})
### Sidebar for Quality Control ###
output$qc_kon <- renderUI({
x <- NULL
for (selected_values in get_qc_fil()) {
x <- append(x,list(
render.ui.output(selected_values,get_oasis()[[selected_values]],
input[[paste0(selected_values,"_qc")]],ui_type="_qc")
))
}
# if(input$data_type=="Others"){
# x <- append(x,list(
# selectInput("qc_col","Selected Cols to RainCloud",selected = NULL,multiple = TRUE,
# choices = c("All",names(get_qc_choice())))
# ))
# }
return(x)
})
### Get the selected values and the selected data
get_qc_choice <- reactive({
col_input <- get_qc_fil()
u_oasis <- get_oasis()
for (col in col_input) {
u_oasis <- get.choice(col,data.table = u_oasis,seletedValue = input[[paste0(col,"_qc")]], col.type = "_qc")
}
return(u_oasis)
})
### Update Tabs after Button Click ###
observeEvent(input$dp,{
updateTabsetPanel(session,"qc_tab","Raincloud Plot")
})
### Quality Control - Plot after Clicking the Button ###
output$quality <- renderPlot({
if(is.null(input$dp) || input$dp==0){return(NULL)}
isolate({
data <- get_qc_choice()
cols <- ncol(data)
data_type <- "FreeSurfer"
if(data_type=="FreeSurfer"){
data <- data[(cols-147):cols]
name_level <- names(data)
data <- melt(data)
names(data) <- c("area","thickness")
data$lr <- substr(data$area,1,1)
data[which(data$lr=='l'|data$lr=="L"),]$lr <- "left"
data[which(data$lr=='r'|data$lr=="R"),]$lr <- "right"
data$lr <- as.factor(data$lr)
##format the thickness area names the rearrange the factor levels
if(!is.null(input$name_file)){
name_level <- rev(unique(substring(name_level,2)))
data$area <- factor(x=substring(data$area,2),levels=name_level,ordered = TRUE)
}else{
name_level <- rev(unique(substring(name_level,4)))
data$area <- factor(x=substring(data$area,4),levels=name_level,ordered = TRUE)
}
p <- ggplot(data,aes(x=area,y=thickness,fill=area))+
geom_flat_violin(position=position_nudge(x=0.2,y=0),adjust=1,trim = TRUE)+
geom_point(position = position_jitter(width=.1),size=.2,aes(color=area),show.legend = FALSE)+
geom_boxplot(aes(x=as.numeric(area)+0.2,y=thickness),outlier.shape = NA,alpha=0.3,width=0.1,color="BLACK")+
coord_flip()+
facet_wrap(~lr)+
theme_light()+
guides(fill=FALSE)
}else if(data_type=="CERES"){
data <- get_qc_choice()
qc.cols <- ncol(data)
qc.data <- data[(qc.cols-273):qc.cols]
p <- get.ceres.qc.plot(qc.data)
}#else{
# data <- get_qc_choice()
# cols <- input$qc_col
# p <- get.other.qc.plot(data,cols)
#}
return(p)
})
})
### Quality Control - Table ###
output$qc_table<- DT::renderDataTable({
ausgewaehlte_daten <- get_qc_choice()
DT::datatable(ausgewaehlte_daten,class = "display nowrap",options = list(scrollX=TRUE))
})
### Mainbody Quality Control ###
output$qc_tabs <- renderUI({
data_type <-"FreeSurfer"
tagList(
tabsetPanel(type="tabs",id="qc_tab",