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server.R
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server.R
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#### License ####
#Copyright (C) {2014} {Fichou Dimitri}
#{dimitrifichou@laposte.net}
#This program is free software; you can redistribute it and/or modify
#it under the terms of the GNU General Public License as published by
#the Free Software Foundation; either version 2 of the License, or
# any later version.
#This program is distributed in the hope that it will be useful,
#but WITHOUT ANY WARRANTY; without even the implied warranty of
#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
#GNU General Public License for more details.
#You should have received a copy of the GNU General Public License along
#with this program; if not, write to the Free Software Foundation, Inc.,
#51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#### rTLC ######
require("jpeg");require("png");require('tiff');
require('caret');require('klaR');require('readxl');
require("ChemometricsWithR");require("gplots");require("kohonen");
require("devtools");require("chemometrics");require("ggplot2");
require("abind");require("plyr");require('dplyr');
require("prospectr");require("DiscriMiner");require("baseline");
require("knitr");require('rmarkdown');require("xtable");
require("ptw");require("dtw");require('d3heatmap');
require('randomForest');require('kernlab');require('ipred');
require('extraTrees');require('evtree');require('FBN')
# require("threejs");
require('shinydashboard');
options(shiny.maxRequestSize=1000*1024^2)
# options(stringsAsFactors = FALSE)
source("rTLC-function.R")
source("PreProcess.function.R")
source('Var_selection.R')
MAC.inverse=F
shinyServer(function(input, output,session) {
# session$onSessionEnded(function() {
# obs$suspend()
# }
#### demo batch #####
output$download.demo.batch <- downloadHandler(
filename = "rTLC_demobatch.xls",
content = function(file) {
file.copy('www/rTLC_demobatch.xls', file)
}
)
output$download.demo.picture <- downloadHandler(
filename = "rTLC_demopicture.JPG",
content = function(file) {
file.copy('www/rTLC_demopicture.JPG', file)
}
)
output$download.demo.data.bis <- downloadHandler(
filename = "rTLC_demodata_bis.zip",
content = function(file) {
file.copy('www/rTLC_demodata_bis.zip', file)
}
)
output$manual.pdf <- downloadHandler(
filename = "rTLC_manual.pdf",
content = function(file) {
file.copy('www/rTLC_manual.pdf', file)
}
)
output$leiden.zip <- downloadHandler(
filename = "Leiden_rtlc_workshop.zip",
content = function(file) {
file.copy('www/Leiden_rtlc_workshop.zip', file)
}
)
output$help.global.pipeline <- renderImage({
filename <- normalizePath(file.path('./www','Pipeline-total.jpg'))
# Return a list containing the filename and alt text
list(src = filename,
alt = 'Alternate text')
},deleteFile = F)
output$help.predict.pipeline <- renderImage({
filename <- normalizePath(file.path('./www','Pipeline-Prediction.jpg'))
# Return a list containing the filename and alt text
list(src = filename,
alt = 'Alternate text')
},deleteFile = F)
output$checkpoint.1.download <- downloadHandler(
filename = function(x){paste0(input$checkpoint.1.download.text,'.RData')},
content = function(con) {
assign("data",list(chrom = data.mono.2(),
batch = dataX.mono.pre(),
Vertical.dim = c(dim(data.mono.2())[2],input$hauteur.mono,input$Zf.mono,input$dist.bas.mono),
Horizontale.dim = TableDimension()
))
save(list="data", file=con)
}
)
output$checkpoint.1.download.zip <- downloadHandler(
filename = function(x){paste0(input$checkpoint.1.download.zip.text,'.zip')},
content = function(file) {
fs <- c()
channel <- c(red=1,green=2,blue=3,gray=4)
for(i in names(channel)){
path <- paste0(i,'.csv')
fs <- c(fs,path)
truc = data.mono.2()[,dim(data.mono.2())[2]:1,channel[i]]
hauteur = as.numeric(input$hauteur.mono)
dist.bas = as.numeric(input$dist.bas.mono)
Zf = as.numeric(input$Zf.mono)
colnames(truc) = paste0("Rf.",round(seq(-dist.bas/(Zf-dist.bas),(hauteur-dist.bas)/(Zf-dist.bas),length.out=dim(truc)[2]),3))
write.csv(truc,file=path,row.names = F,col.names = F,sep=';')
}
path = paste0('batch','.csv')
fs = c(fs,path)
write.csv(dataX.mono.pre(),file=path)
tempFile <- tempfile(fileext = ".zip")
zip(zipfile=tempFile, files=fs)
file.rename(tempFile, file)
},
contentType = "application/zip"
)
inFile.photo <- reactive({
validate(
need(input$filedemouse != "checkpoint", "Picture and dimension table not available, chromatograms already extracted.")
)
if(input$filedemouse == 'YourOwnData'){
validate(
need(input$filemonop != "", "Upload your chromatogram(s) or choose the demonstration files")
)
inFile<- input$filemonop
}
if(input$filedemouse == 'QC'){
validate(
need(input$filemonop != "", "Upload your chromatogram(s)")
)
inFile<- input$filemonop
}
if(input$filedemouse == 'demo1'){
inFile<- data.frame(name = 'rTLC_demopicture.JPG',size=T,type='JPG',datapath = 'www/rTLC_demopicture.JPG')
}
if(input$filedemouse == 'demo2'){
name = dir('www',patter='_2_')
inFile<- data.frame(name = name,
size=rep(T,length(name)),
type=rep('JPG',length(name)),
datapath = paste0('www/',name)
)
}
if(input$filedemouse == 'demo3'){
name = dir('www',patter='_3_')
inFile<- data.frame(name = name,
size=rep(T,length(name)),
type=rep('JPG',length(name)),
datapath = paste0('www/',name)
)
}
if(input$filedemouse == 'demo4'){
name = dir('www',patter='_4_')
inFile<- data.frame(name = name,
size=rep(T,length(name)),
type=rep('JPG',length(name)),
datapath = paste0('www/',name)
)
}
if(input$filedemouse == 'demoPropolis'){
name = dir('www',patter='propolis-silicate')
inFile<- data.frame(name = name,
size=rep(T,length(name)),
type=rep('jpg',length(name)),
datapath = paste0('www/',name)
)
}
inFile
})
inFile.X <- reactive({
validate(
need(input$filedemouse != "checkpoint", "Picture and dimension table not available, chromatograms already extracted.")
)
if(input$filedemouse == 'YourOwnData' | input$filedemouse == 'QC'){
if(is.null(input$fileX)){
inFile <- NULL
}else{
inFile <- input$fileX
}
}
if(input$filedemouse == 'demo1'){
inFile <- data.frame(name = 'rTLC_demobatch.xls',size=T,type='xls',datapath = 'www/rTLC_demobatch.xls')
}
if(input$filedemouse == 'demo2'){
inFile <- data.frame(name = 'rTLC_demobatch.xls',size=T,type='xls',datapath = 'www/rTLC_demobatch_2.xls')
}
if(input$filedemouse == 'demo3'){
inFile <- data.frame(name = 'rTLC_demobatch.xls',size=T,type='xls',datapath = 'www/rTLC_demobatch_3.xls')
}
if(input$filedemouse == 'demo4'){
inFile <- data.frame(name = 'rTLC_demobatch.xls',size=T,type='xls',datapath = 'www/rTLC_demobatch_4.xls')
}
if(input$filedemouse == 'demoPropolis'){
inFile <- data.frame(name = 'rTLC_demobatch.xls',size=T,type='xls',datapath = 'www/rTLC_propolis_batch.xlsx')
}
return(inFile)
})
dataX.editable <- reactive({
if(input$filedemouse == 'checkpoint'){
validate(
need(input$checkpoint.1.upload != "", "Please upload your Rdata file")
)
inFile <- input$checkpoint.1.upload
load(inFile[1,4])
data <- data[[2]]
colnames(data)[colnames(data) == "id"] <- "ID"
data$ID <- seq(nrow(data))
}else if(input$filedemouse == 'demoPropolis'){ # special case for the propolis dataset, I didn't saved the dimension table and we wanted the same exact independent variables than in the paper's figures
load("www/Propolis silica_dim_ok.Rdata")
data <- data[[2]]
}else{
inFile <- inFile.X()
if(is.null(inFile)){
data <- data.mono.1.1()
data <- data.frame(ID = seq(dim(data)[1]),class = rep('unknow',dim(data)[1]),ref = rep('unknow',dim(data)[1]),info=rep('unknow',dim(data)[1]))
}else{
data <- as.data.frame(read_excel(as.character(inFile[1,4]),sheet=1))
}
if(colnames(data)[1] != 'ID' & 'ID' %in% colnames(data)){
colnames(data)[which(colnames(data) == 'ID')] <- 'ID2'
}
if(!'ID' %in% colnames(data)){
data <- cbind(ID=seq(nrow(data)),data)
}
}
rownames(data) <- seq(nrow(data))
data
})
dataX.edited<-reactive({
data <- dataX.editable()
for(j in c(2:ncol(data))){
truc <- c();for(i in seq(nrow(data))){truc <- c(truc,input[[paste0(colnames(data)[j],".",i)]])}
data[,j] <- truc
}
validate(
need(length(colnames(data)) >= 2, "Your batch must contain at least 1 columns"),
need(colnames(data)[1] == "ID", "The first column of your batch is not 'ID'"),
need(data[,1] == seq(1:nrow(data)) , "Your ID column is not a sequence of number starting from 1")
)
data
})
dataX.mono.pre<-reactive({
data<-dataX.edited()
return(data[!Not.Use(),])
})
output$table1 <-renderTable({
data <- dataX.editable()
validate(
need(length(colnames(data)) >= 2, "Your batch must contain at least 1 columns"),
need(colnames(data)[1] == "ID", "The first column of your batch is not 'ID'"),
need(data[,1] == seq(1:nrow(data)) , "Your ID column is not a sequence of number starting from 1")
)
Not.Use <- paste0("<input id='Not.Use.", 1:nrow(data), "' class='form-control shiny-bound-input' type='checkbox' value='1'></input>")
for(i in c(2:ncol(data))){
data[,i] <- paste0("<input id='",colnames(data)[i],'.', 1:nrow(data),"' class='form-control shiny-bound-input' type='text' value='",data[,i],"'></input>")
}
data <- data.frame(cbind(Not.Use,data))
colnames(data)[1:2] = c("Exclude","ID")
return(data)
}, sanitize.text.function = function(x) x)
output$batch.Truc.mono <- renderUI({
data <- colnames(dataX.edited())
if(length(data) <= 4){
checkboxGroupInput('batch.Truc.mono','Information to include in the track plot',choices=data[2:length(data)],selected=data[2:length(data)],inline=T)
}else{
checkboxGroupInput('batch.Truc.mono','Information to include in the track plot',choices=data[2:length(data)],selected=data[2:4],inline=T)
}
})
output$batch.filter <- renderUI({
data <- dataX.edited()
truc <- tagList()
for(i in c(2:ncol(data))){
truc <- tagAppendChild(truc,
selectizeInput(paste0('batch.filter.',i),colnames(data)[i],multiple=T,choices=unique(as.character(data[,i])))
)
}
truc
})
Not.Use <- reactive({
validate(
need(input$Not.Use.1 != "", "Please visit the batch tab in Data Input to choose the data you want to Use")
)
Not.Use <- c()
for(i in seq(nrow(dataX.edited()))){
Not.Use <- c(Not.Use, input[[paste0("Not.Use.",i)]])
}
data <- dataX.editable()
for(i in c(2:ncol(data))){
if(length(input[[paste0('batch.filter.',i)]]) != 0){
Not.Use[!data[,i] %in% input[[paste0('batch.filter.',i)]]] <- T
}
}
return(Not.Use)
})
################ data.mono.1 ##########
output$slider.subset.height<-renderUI({
sliderInput('slider.subset.height','Subset to extract',min=0.01,max=input$hauteur.mono,value=c(0,input$hauteur.mono),
step=0.01)
})
output$TableDimensionVerticale <-renderTable({
if(input$filedemouse == 'QC'){Default <- Pred.upload.model()$Vertical.dim}
if(input$filedemouse == 'checkpoint'){Default <- checkpoint.data()$Vertical.dim}
if(input$filedemouse != 'QC' & input$filedemouse != 'checkpoint'){Default <- c(128,100,70,8)}
if(input$filedemouse == 'demoPropolis'){
Default <- c(126,70,70,4)
}
data <- data.frame(Option = c('Pixel width','Plate width','Migration front','Distance to lower edge'),
Value = c('redim.height','hauteur.mono','Zf.mono','dist.bas.mono'),
Default = Default
)
if(input$filedemouse == 'QC'| input$filedemouse == 'checkpoint'){
data$Value <- paste0("<input id='",data$Value,"' class='shiny-bound-input' type='number' readonly='readonly' value='",data$Default,"'>")
data[,c(1,2)]
}else{
data$Value <- paste0("<input id='",data$Value,"' class='shiny-bound-input' type='number' value='",data$Default,"'>")
data[,c(1,2)]
}
},include.rownames=F,include.colnames=F, sanitize.text.function = function(y) y)
output$TableDimension <-renderTable({
inFile <- inFile.photo()
truc <- nrow(inFile)
if(input$TableDimensionConvention == 'Linomat'){
data <- data.frame(Plate_width = rep(200,truc),
Left_distance = rep(20,truc),
Band_width = rep(6,truc),
Gap_between_band = rep(2,truc),
Tolerance = rep(2,truc)
)
}else{
data <- data.frame(Plate_width = rep(200,truc),
First_appli_position = rep(23,truc),
Band_Length = rep(6,truc),
Distance_between_tracks = rep(8,truc),
Tolerance = rep(2,truc)
)
}
inFile <- input$TableDimensionUpload
if(!is.null(inFile)){
data.saved <- read.csv(as.character(inFile[1,4]))
validate(
need(nrow(data.saved) == truc, "There is not the same number of row in your saved data than the number of pictures")
)
data <- data.saved
}
if(input$filedemouse == 'demoPropolis'){
data <- as.data.frame(read_excel("www/rTLC_propolis_dimension.xlsx",sheet=1))
for(i in c(1:ncol(data))){
data[,i] <- paste0("<input id='",colnames(data)[i],'.', 1:nrow(data),"' class='shiny-bound-input' type='number' readonly='readonly' value='",data[,i],"'>")
}
}else{
for(i in c(1:ncol(data))){
data[,i] <- paste0("<input id='",colnames(data)[i],'.', 1:nrow(data),"' class='shiny-bound-input' type='number' value='",data[,i],"'>")
}
}
colnames(data) = c("Plate length","First application position","Band length","Distance between track","Edge cut")
data
}, sanitize.text.function = function(y) y,rownames = T)
output$TableDimensionSave <- downloadHandler(
filename = function(x){paste0(input$TableDimensionSave.text,'.csv')},
content = function(file) {
write.csv(TableDimension(),file=file)
}
)
TableDimension <- reactive({
# hot.to.df(input$TableDimension)
inFile <- inFile.photo()
truc <- nrow(inFile)
if(input$TableDimensionConvention == 'Linomat'){
data <- data.frame(Plate_width = rep(200,truc),
Left_distance = rep(20,truc),
Band_width = rep(6,truc),
Gap_between_band = rep(2,truc),
Tolerance = rep(2,truc)
)
}else{
data <- data.frame(Plate_width = rep(200,truc),
First_appli_position = rep(23,truc),
Band_Length = rep(6,truc),
Distance_between_tracks = rep(8,truc),
Tolerance = rep(2,truc)
)
}
for(j in c(1:ncol(data))){
truc <- c();for(i in seq(nrow(data))){truc <- c(truc,input[[paste0(colnames(data)[j],".",i)]])}
data[,j] <- truc
}
data
})
output$TableDimensionPlot <- renderImage({
outfile <- tempfile(fileext='.png')
png(outfile, width=1000, height=500)
plot(x=seq(200),y=seq(1,100,length.out = 200),type='n',yaxt='n',xlab='Horizontal dimensions (mm)',ylab='',main='Illustration of the chromatogram extraction')
text(x=50,y=85,labels='Plate length = 200 mm',cex=1,col='black')
text(x=100,y=97,labels='Unnecessary cropping will result in false horizontal dimensions and reproductibility problems',cex=1.5,col='red')
arrows(x0=0, y0=90, x1 =200,col='black',code=3)
# text(x=100,y=30,labels='LINOMAT CONVENTION',cex=1.5)
text(x=100,y=25,labels='Calculation from the exterior of the band',cex=1.5)
segments(x0=-10,x1=160,y0=40)
segments(x0=-10,x1=160,y0=80)
segments(x0=160,y0=0,y1=80)
segments(x0=20,y0=8,y1=8,x1=28,lwd=5)
segments(x0=38,y0=8,y1=8,x1=46,lwd=5)
segments(x0=56,y0=8,y1=8,x1=64,lwd=5)
segments(x0=74,y0=8,y1=8,x1=82,lwd=5)
segments(x0=92,y0=8,y1=8,x1=100,lwd=5)
segments(x0=110,y0=8,y1=8,x1=118,lwd=5)
segments(x0=128,y0=8,y1=8,x1=136,lwd=5)
segments(x0=146,y0=8,y1=8,x1=154,lwd=5)
segments(x0=164,y0=8,y1=8,x1=172,lwd=5)
text(x=20,y=5,labels='First application position = 20 mm',cex=1,col='green')
arrows(x0=0, y0=8, x1 =20,col='green',code=3,length=0.1)
text(x=30,y=15,labels='Band length = 8 mm',cex=1,col='blue')
arrows(x0=20, y0=10, x1 =28,col='blue',code=3,length=0.1)
text(x=120,y=15,labels='Distance between track = 10 mm',cex=1,col='red')
arrows(x0=100, x1=110, y0 =10,col='red',code=3,length=0.1)
# text(x=100,y=70,labels='ATS-4 CONVENTION',cex=1.5)
text(x=100,y=65,labels='Calculation from the middle of the band',cex=1.5)
segments(x0=20,y0=48,y1=48,x1=28,lwd=5)
segments(x0=38,y0=48,y1=48,x1=46,lwd=5)
segments(x0=56,y0=48,y1=48,x1=64,lwd=5)
segments(x0=74,y0=48,y1=48,x1=82,lwd=5)
segments(x0=92,y0=48,y1=48,x1=100,lwd=5)
segments(x0=110,y0=48,y1=48,x1=118,lwd=5)
segments(x0=128,y0=48,y1=48,x1=136,lwd=5)
segments(x0=146,y0=48,y1=48,x1=154,lwd=5)
segments(x0=164,y0=48,y1=48,x1=172,lwd=5)
text(x=30,y=42,labels='First application position = 24 mm',cex=1,col='green')
arrows(x0=0, y0=45, x1 =24,col='green',code=3,length=0.1)
text(x=30,y=55,labels='Band length = 8 mm',cex=1,col='blue')
arrows(x0=20, y0=50, x1 =28,col='blue',code=3,length=0.1)
text(x=120,y=55,labels='Distance between track = 18 mm',cex=1,col='red')
arrows(x0=96, x1=114, y0 =50,col='red',code=3,length=0.1)
segments(x0=166,y0=0,x1=166,y1=80,col='red')
segments(x0=170,y0=0,x1=170,y1=80,col='green')
text(x=185,y=60,labels='The software will \nextract the mean pixels\n between each red\n and green vertical line\non each \'channel\'\n of the picture')
text(x=185,y=20,labels='Edge cut = 2 mm\nRemove 2 mm from\n the exterior of the band')
dev.off()
list(src = outfile,
contentType = 'image/png',
alt = "This is alternate text")
}, deleteFile = TRUE)
output$select.image.redim.mono<-renderUI({
truc <- paste(seq(nrow(inFile.photo())),inFile.photo()$name,sep=" - ")
selectizeInput("select.image.redim.mono","Plate choice",choices=truc)
})
output$image.redim.mono <- renderImage({
validate(
need(input$Zf.mono != 0, "The retention front could not be 0 mm, please verify the Verticale dimension table")
)
n.pic<-as.numeric(substr(input$select.image.redim.mono,1,3))
inFile <- inFile.photo()
if(input$TableDimensionConvention == 'Linomat'){
largeur<-as.numeric(TableDimension()[n.pic,1])
dist.gauche<-as.numeric(TableDimension()[n.pic,2])
band<-as.numeric(TableDimension()[n.pic,3])
ecart<-as.numeric(TableDimension()[n.pic,4])
tolerance<-as.numeric(TableDimension()[n.pic,5])
}else{
largeur<-as.numeric(TableDimension()[n.pic,1])
band<-as.numeric(TableDimension()[n.pic,3])
dist.gauche<-as.numeric(TableDimension()[n.pic,2])-band/2
ecart<-as.numeric(TableDimension()[n.pic,4])-band
tolerance<-as.numeric(TableDimension()[n.pic,5])
}
nbr.band<-round((largeur-2*dist.gauche)/(band+ecart))
outfile <- tempfile(fileext='.png')
png(outfile, width=600, height=300)
par(mar=c(0,0,0,0))
plot(c(0,largeur),c(0,input$hauteur.mono), type='n',ylab="",xlab="",bty='n')
rasterImage(f.read.image(as.character(inFile[n.pic,4]),native=T,input$mono.Format.type,height=0),
0 , 0, largeur, input$hauteur.mono)
for(i in seq(nbr.band)){
text(x=(dist.gauche+tolerance+(i-1)*(band+ecart)),y=input$hauteur.mono*0.9,labels=i,col="red",cex=1)
abline(v=dist.gauche+tolerance+(i-1)*(band+ecart),col="red")
abline(v=dist.gauche-tolerance+band+(i-1)*(band+ecart),col="green")
abline(h=input$Zf.mono,col='white')
abline(h=input$dist.bas.mono,col='white')
}
dev.off()
list(src = outfile,
contentType = 'image/png',
# width = 600,
# height = 300,
alt = "This is alternate text")
}, deleteFile = TRUE)
output$select.image.reconstruct<-renderUI({
truc <- paste(seq(nrow(inFile.photo())),inFile.photo()$name,sep=" - ")
tagList(
selectizeInput("select.image.reconstruct","Plate choice",choices=truc),
uiOutput('name.band.mono.recon.1')
)
})
output$image.reconstruct <- renderImage({
n.pic<-as.numeric(substr(input$select.image.reconstruct,1,3))
inFile <- inFile.photo()
if(input$TableDimensionConvention == 'Linomat'){
largeur<-as.numeric(TableDimension()[n.pic,1])
dist.gauche<-as.numeric(TableDimension()[n.pic,2])
band<-as.numeric(TableDimension()[n.pic,3])
ecart<-as.numeric(TableDimension()[n.pic,4])
tolerance<-as.numeric(TableDimension()[n.pic,5])
}else{
largeur<-as.numeric(TableDimension()[n.pic,1])
band<-as.numeric(TableDimension()[n.pic,3])
dist.gauche<-as.numeric(TableDimension()[n.pic,2])-band/2
ecart<-as.numeric(TableDimension()[n.pic,4])-band
tolerance<-as.numeric(TableDimension()[n.pic,5])
}
nbr.band<-round((largeur-2*dist.gauche)/(band+ecart))
outfile <- tempfile(fileext='.png')
png(outfile, width=800, height=1000)
par(mar=c(5,4,0,0),mfrow=c(2,1))
plot(c(-largeur*0.3,largeur),c(0,input$hauteur.mono*2), type='n',ylab="",xlab="",bty='n',xaxt="n",yaxt="n")
text(x = c(-largeur*0.3,-largeur*0.3),y=c(input$hauteur.mono*0.8,input$hauteur.mono*1.8),labels=c("Original\nchromatogram","Extracted\nchromatogram"),pos=4,cex=1.5)
rasterImage(f.read.image(as.character(inFile[n.pic,4]),native=T,input$mono.Format.type,height=0),
0 , 0, largeur, input$hauteur.mono)
image <- f.read.image(as.character(inFile[n.pic,4]),native=F,input$mono.Format.type,height=input$redim.height)
data <- f.eat.image(image,largeur,dist.gauche,band,ecart,tolerance)
for(i in seq(nbr.band)){
abline(v=dist.gauche+tolerance+(i-1)*(band+ecart),col="red")
abline(v=dist.gauche-tolerance+band+(i-1)*(band+ecart),col="green")
data2<-f.rebuilt(data[i,,1],data[i,,2],data[i,,3])
rasterImage(data2,dist.gauche+tolerance+(i-1)*(band+ecart),input$hauteur.mono,dist.gauche-tolerance+band+(i-1)*(band+ecart),input$hauteur.mono*2)
}
ID <- as.numeric(input$name.band.mono.recon.1)
f.plot.array(data,id=ID,label=NULL,input$hauteur.mono,input$Zf.mono,input$dist.bas.mono,ylim.raster = 1.7)
text(x = c(0,0),y=c(1.65,1.35),labels=c("Original chromatogram","Extracted chromatogram"),pos=4,cex=1.5)
a<-dim(image)
rasterImage(aperm(image[a[1]:1,(a[2]/largeur*((dist.gauche+tolerance)+(ID-1)*(band+ecart))):(a[2]/largeur*((dist.gauche+band-tolerance)+(ID-1)*(band+ecart))),],c(2,1,3)),
RF.min() , 1.4, RF.max(), 1.6)
dev.off()
list(src = outfile,
contentType = 'image/png',
# width = 600,
# height = 300,
alt = "This is alternate text")
}, deleteFile = TRUE)
################ data.mono.2 ##########
data.mono.1.1<-reactive({
validate(
need(input$Zf.mono != 0, "The retention front could not be 0 mm, please verify the Verticale dimension table")
)
withProgress(message = "Work in Progress", value=0, {
if(input$filedemouse == 'checkpoint'){
validate(
need(input$checkpoint.1.upload != "", "Please upload your Rdata file")
)
inFile <- input$checkpoint.1.upload
load(inFile$datapath)
data <- data[[1]]
}else if(input$filedemouse == 'demoPropolis'){ # special case for the propolis dataset, I didn't saved the dimension table and we wanted the same exact independent variables than in the paper's figures
load("www/Propolis silica_dim_ok.Rdata")
data <- data[[1]]
}else{
inFile <- inFile.photo()
# height <- min(dim.pictures()[,1])
height <- input$redim.height
for(n.pic in seq(nrow(inFile.photo()))){
if(input$TableDimensionConvention == 'Linomat'){
largeur<-as.numeric(TableDimension()[n.pic,1])
dist.gauche<-as.numeric(TableDimension()[n.pic,2])
band<-as.numeric(TableDimension()[n.pic,3])
ecart<-as.numeric(TableDimension()[n.pic,4])
tolerance<-as.numeric(TableDimension()[n.pic,5])
}else{
largeur<-as.numeric(TableDimension()[n.pic,1])
band<-as.numeric(TableDimension()[n.pic,3])
dist.gauche<-as.numeric(TableDimension()[n.pic,2])-band/2
ecart<-as.numeric(TableDimension()[n.pic,4])-band
tolerance<-as.numeric(TableDimension()[n.pic,5])
}
data.temp<-f.read.image(as.character(inFile[n.pic,4]),native=F,input$mono.Format.type,height=height)
data.temp<-f.eat.image(data.temp,largeur,dist.gauche,band,ecart,tolerance)
if(n.pic == 1){
data <- data.temp
}else{
data <- abind(data,data.temp,along=1)
}
}
}
})
return(data)
})
data.mono.1 <- reactive({
data <- data.mono.1.1()
dataX<-dataX.edited()
validate(
need(dim(data)[1] == nrow(dataX), "The number of chromatograms extracted do not match the number of row in your batch, please check your batch or your dimension table")
)
data
})
data.mono.2 <- reactive({
data.mono.1()[!Not.Use(),,]
})
################ Preprocess ##########
Train.partition <- reactive({
tot <- nrow(data.mono.2())
set.seed(1)
sample(c(T,F),tot,replace=T,prob=c(input$Train.partition,1-input$Train.partition))
})
Preprocess.order <- reactive({
if(input$filedemouse != 'QC'){
input$Preprocess.order
}else{
Pred.upload.model()[[5]]
}
})
output$ptw.warp.ref <- renderUI({
choices <- Truc.mono()[Train.partition()]
selectizeInput('ptw.warp.ref','Reference track',choices=choices)
})
output$dtw.warp.ref <- renderUI({
choices <- Truc.mono()[Train.partition()]
selectizeInput('dtw.warp.ref','Reference track',choices=choices)
})
Preprocess.options <- reactive({
if(input$filedemouse != 'QC'){
data <- data.mono.2()
Smoothing <- list(window.size = input$window.size,poly.order=input$poly.order,diff.order=input$diff.order)
if(input$warpmethod == 'ptw'){
Warping <- list(warpmethod = input$warpmethod,
ptw.warp.ref = as.numeric(input$ptw.warp.ref)
)
}
if(input$warpmethod == 'dtw'){
Warping <- list(warpmethod = input$warpmethod,
dtw.warp.ref = as.numeric(input$dtw.warp.ref),
dtw.split = input$dtw.split
)
}
# Center <- list(colMeans(data[Train.partition(),,1]),colMeans(data[Train.partition(),,2]),colMeans(data[Train.partition(),,3]),colMeans(data[Train.partition(),,4]))
# Scale <- list(apply(data[Train.partition(),,1],2,sd),apply(data[Train.partition(),,2],2,sd),apply(data[Train.partition(),,3],2,sd),apply(data[Train.partition(),,4],2,sd))
if(input$baseline == "als"){Baseline <- list(method=input$baseline,lambda.1=input$lambda.1,p=input$p,maxit.1=input$maxit.1)}
if(input$baseline == "fillPeaks"){Baseline <- list(method=input$baseline,lambda.2=input$lambda.2,hwi=input$hwi,it=input$it,int=input$int)}
if(input$baseline == "irls"){Baseline <- list(method=input$baseline,lambda1=input$lambda1,lambda2=input$lambda2,maxit.2=input$maxit.2,wi=input$wi)}
if(input$baseline == "lowpass"){Baseline <- list(method=input$baseline,steep=input$steep,half=input$half)}
if(input$baseline == "medianWindow"){Baseline <- list(method=input$baseline,hwm=input$hwm,hws=input$hws,end=input$end)}
if(input$baseline == "modpolyfit"){Baseline <- list(method=input$baseline,degree=input$degree,tol=input$tol,rep=input$rep)}
if(input$baseline == "peakDetection"){Baseline <- list(method=input$baseline,left=input$left,right=input$right,lwin=input$lwin,rwin=input$rwin)}
if(input$baseline == "rfBaseline"){Baseline <- list(method=input$baseline)}
if(input$baseline == "rollingBall"){Baseline <- list(method=input$baseline,wm=input$wm,ws=input$ws)}
return(list(Smoothing=Smoothing,Warping=Warping,Baseline.correction=Baseline,
medianFilter=input$preprocess.medianfilter,gammaCorrection=input$preprocess.gammacorrection))
}else{
Pred.upload.model()[[4]]
}
})
data.mono.3<-reactive({
if(input$filedemouse != 'QC'){
validate(
need(input$window.size %% 2 == 1, "The window size must be an odd value"),
need(input$window.size > input$poly.order, "The window size must be greater than the polynomial order"),
need(input$poly.order > input$diff.order, "The polynomial order must be greater than the differential order")
)
withProgress(message = "Work in Progress", value=0, {
data<-data.mono.2()
data <- f.preprocess(data,preprocess.order = Preprocess.order(),preprocess.option = Preprocess.options(),
training.data = data[Train.partition(),,])
})
}else{
withProgress(message = "Work in Progress", value=0, {
data<-data.mono.2()
data <- f.preprocess(data,preprocess.order = Preprocess.order(),preprocess.option = Preprocess.options(),
training.data = Pred.upload.model()[[2]])
})
}
return(data)
})
##### Variable.selection #####
RF.max <- reactive({
hauteur<-input$hauteur.mono
dist.bas<-input$dist.bas.mono
Zf <- input$Zf.mono
round((hauteur-dist.bas)/(Zf-dist.bas),3)
})
RF.min <- reactive({
hauteur<-input$hauteur.mono
dist.bas<-input$dist.bas.mono
Zf <- input$Zf.mono
round(-dist.bas/(Zf-dist.bas),3)
})
output$VS_slider_1 <- renderUI({sliderInput("VS_slider_1", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_2 <- renderUI({sliderInput("VS_slider_2", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_3 <- renderUI({sliderInput("VS_slider_3", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_4 <- renderUI({sliderInput("VS_slider_4", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_5 <- renderUI({sliderInput("VS_slider_5", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_6 <- renderUI({sliderInput("VS_slider_6", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_7 <- renderUI({sliderInput("VS_slider_7", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_8 <- renderUI({sliderInput("VS_slider_8", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_9 <- renderUI({sliderInput("VS_slider_9", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_10 <- renderUI({sliderInput("VS_slider_10", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_11 <- renderUI({sliderInput("VS_slider_11", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_12 <- renderUI({sliderInput("VS_slider_12", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_13 <- renderUI({sliderInput("VS_slider_13", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_14 <- renderUI({sliderInput("VS_slider_14", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_15 <- renderUI({sliderInput("VS_slider_15", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_16 <- renderUI({sliderInput("VS_slider_16", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_17 <- renderUI({sliderInput("VS_slider_17", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_18 <- renderUI({sliderInput("VS_slider_18", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_19 <- renderUI({sliderInput("VS_slider_19", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_20 <- renderUI({sliderInput("VS_slider_20", label = NULL, min=RF.min(),max=RF.max(),value=c(0,1),step = 0.001)})
output$VS_slider_score.loading <- renderUI({sliderInput("VS_slider_score.loading", label = 'Not working yet', min=RF.min(),max=RF.max(),value=c(RF.min(),RF.max()),step = 0.001)})
selection.table <- reactive({
if(input$filedemouse != 'QC'){
n <- 20
validate(
need(!is.null(input[[paste0('VS_slider_',1)]]), "Please visit the variable selection table")
)
use <- c();for(i in seq(n)){use <- c(use,input[[paste0('VS_check_',i)]])}
channel <- c();for(i in seq(n)){channel <- c(channel,input[[paste0('VS_select_',i)]])}
start <- c();for(i in seq(n)){start <- c(start,input[[paste0('VS_slider_',i)]][1])}
stop <- c();for(i in seq(n)){stop <- c(stop,input[[paste0('VS_slider_',i)]][2])}
data.frame(
use = use,
channel = channel,
start = start,
stop = stop
)
}else{
Pred.upload.model()$channel
}
})
output$VS_plot <- renderPlot({
par(mfrow=c(1,2))
selection <- selection.table()
min=RF.min();max=RF.max()
plot(c(min,max),c(1,20),type='n',xlab = expression(italic(R)[F]),ylab = 'chanels',main='Scheme of the variable selection')
selection$channel <- gsub(1,'red',gsub(2,'green',gsub(3,'blue',gsub(4,'gray',selection$channel))))
for(i in seq(20)){
if(selection[i,1] == T){
arrows(x0=selection[i,3], y0=i, x1 =selection[i,4],col=selection[i,2],code=3,length=0.1)
}
}
plot(x=seq(dim(data.mono.4())[2]),data.mono.4()[1,dim(data.mono.4())[2]:1],type='l',main='Result for the first track',xlab=expression( paste( "Combination of ", italic("R"), ""[F], " ranges")),ylab='intensity (AU)')
})
data.mono.4 <- reactive({
data <- data.mono.3()
validate(
need(sum(selection.table()$use) != 0, "At least one channel must be used")
)
var_selection(data,selection.table(),RF.min(),RF.max())
})
################ plot.monovariate before and after and integration ##########
output$plot.v.mono.bef.1 <- renderPlot({
par(mgp=c(2,0.75,0),mar=c(5, 4, 2, 0.5))
validate(
need(input$Not.Use.1 != "", "Please visit the batch tab in Data Input to choose the data you want to Use")
)
data <- data.mono.2()
n.band<-as.numeric(input$name.band.mono.bef.1)
f.plot.array(data,n.band,names(Truc.mono()),input$hauteur.mono,input$Zf.mono,input$dist.bas.mono,inverse=MAC.inverse,cex=1.5)
abline(v=input$z.min,col=5)
abline(v=input$z.max,col=7)
},height = 400,width=700)
output$plot.v.mono.bef.2 <- renderPlot({
par(mgp=c(2,0.75,0),mar=c(5, 4, 2, 0.5),cex.lab=1.5)
validate(
need(input$Not.Use.1 != "", "Please visit the batch tab in Data Input to choose the data you want to Use")
)
data <- data.mono.2()
n.band<-as.numeric(input$name.band.mono.bef.2)
f.plot.array(data,n.band,names(Truc.mono()),input$hauteur.mono,input$Zf.mono,input$dist.bas.mono,inverse=MAC.inverse,cex=1.5)
abline(v=input$z.min,col=5)
abline(v=input$z.max,col=7)
},height = 400,width=700)
output$plot.v.mono.aft.1 <- renderPlot({
par(mgp=c(2,0.75,0),mar=c(5, 4, 2, 0.5),cex.lab=1.5)
data <- data.mono.3()
n.band<-as.numeric(input$name.band.mono.aft.1)
f.plot.array(data,n.band,names(Truc.mono()),input$hauteur.mono,input$Zf.mono,input$dist.bas.mono,reconstruct=F,cex=1.5)
abline(h=0)
},height = 400,width=700)
output$plot.v.mono.aft.2 <- renderPlot({
par(mgp=c(2,0.75,0),mar=c(5, 4, 2, 0.5),cex.lab=1.5)
data <- data.mono.3()
n.band<-as.numeric(input$name.band.mono.aft.2)
f.plot.array(data,n.band,names(Truc.mono()),input$hauteur.mono,input$Zf.mono,input$dist.bas.mono,reconstruct=F,cex=1.5)
abline(h=0)
},height = 400,width=700)
output$plot.v.mono.bef.tot <- renderPlot({
validate(
need(length(input$name.band.mono.bef.tot) > 1,"Select at least 2 tracks for comparison")
)
n.band<-as.numeric(input$name.band.mono.bef.tot)
hauteur<-input$hauteur.mono
dist.bas<-input$dist.bas.mono
Zf <- input$Zf.mono
data <- data.mono.2()
par(mar=c(5,4,4,1), mfrow=c(4,1),cex.lab=1.2,cex.main=1.2,cex=1)
matplot(x=seq((hauteur-dist.bas)/(Zf-dist.bas),-dist.bas/(Zf-dist.bas),length.out=dim(data)[2]),y=t(as.matrix(data[n.band,,1])),
lty=1,type="l",main="Red channel",xlab=expression(italic(R)['F']),ylab="Pixel intensity (AU)", col = seq(length(n.band)))
legend("topright", legend=names(Truc.mono()[n.band]) , col = seq(length(n.band)),pch="*")
matplot(x=seq((hauteur-dist.bas)/(Zf-dist.bas),-dist.bas/(Zf-dist.bas),length.out=dim(data)[2]),y=t(as.matrix(data[n.band,,2])),
lty=1,type="l",main="Green channel",xlab=expression(italic(R)['F']),ylab="Pixel intensity (AU)", col = seq(length(n.band)))
legend("topright", legend=names(Truc.mono()[n.band]) , col = seq(length(n.band)),pch="*")
matplot(x=seq((hauteur-dist.bas)/(Zf-dist.bas),-dist.bas/(Zf-dist.bas),length.out=dim(data)[2]),y=t(as.matrix(data[n.band,,3])),
lty=1,type="l",main="Blue channel",xlab=expression(italic(R)['F']),ylab="Pixel intensity (AU)", col = seq(length(n.band)))
legend("topright", legend=names(Truc.mono()[n.band]) , col = seq(length(n.band)),pch="*")
matplot(x=seq((hauteur-dist.bas)/(Zf-dist.bas),-dist.bas/(Zf-dist.bas),length.out=dim(data)[2]),y=t(as.matrix(data[n.band,,4])),
lty=1,type="l",main="Grayscale",xlab=expression(italic(R)['F']),ylab="Pixel intensity (AU)", col = seq(length(n.band)))
legend("topright", legend=names(Truc.mono()[n.band]) , col = seq(length(n.band)),pch="*")
},height = 1200,width=800)
output$plot.v.mono.aft.tot <- renderPlot({
validate(
need(length(input$name.band.mono.aft.tot) > 1,"Select at least 2 tracks for comparison")
)
n.band<-as.numeric(input$name.band.mono.aft.tot)
hauteur<-input$hauteur.mono
dist.bas<-input$dist.bas.mono
Zf <- input$Zf.mono
data <- data.mono.3()
par(mar=c(5,4,4,1), mfrow=c(4,1),cex.lab=1.2,cex.main=1.2,cex=1)
matplot(x=seq((hauteur-dist.bas)/(Zf-dist.bas),-dist.bas/(Zf-dist.bas),length.out=dim(data)[2]),y=t(as.matrix(data[n.band,,1])),
lty=1,type="l",main="Red channel",xlab=expression(italic(R)['F']),ylab="Pixel intensity (AU)", col = seq(length(n.band)))
legend("topright", legend=names(Truc.mono()[n.band]) , col = seq(length(n.band)),pch="*")
matplot(x=seq((hauteur-dist.bas)/(Zf-dist.bas),-dist.bas/(Zf-dist.bas),length.out=dim(data)[2]),y=t(as.matrix(data[n.band,,2])),
lty=1,type="l",main="Green channel",xlab=expression(italic(R)['F']),ylab="Pixel intensity (AU)", col = seq(length(n.band)))
legend("topright", legend=names(Truc.mono()[n.band]) , col = seq(length(n.band)),pch="*")
matplot(x=seq((hauteur-dist.bas)/(Zf-dist.bas),-dist.bas/(Zf-dist.bas),length.out=dim(data)[2]),y=t(as.matrix(data[n.band,,3])),
lty=1,type="l",main="Blue channel",xlab=expression(italic(R)['F']),ylab="Pixel intensity (AU)", col = seq(length(n.band)))
legend("topright", legend=names(Truc.mono()[n.band]) , col = seq(length(n.band)),pch="*")
matplot(x=seq((hauteur-dist.bas)/(Zf-dist.bas),-dist.bas/(Zf-dist.bas),length.out=dim(data)[2]),y=t(as.matrix(data[n.band,,4])),
lty=1,type="l",main="Grayscale",xlab=expression(italic(R)['F']),ylab="Pixel intensity (AU)", col = seq(length(n.band)))
legend("topright", legend=names(Truc.mono()[n.band]) , col = seq(length(n.band)),pch="*")
},height = 1200,width=800)
output$image.comparaison.1 <- renderPlot({
validate(
need(length(input$name.band.m.com.1) > 1,"Select at least 2 tracks for comparison")
)
data<-data.mono.2()
band<-as.numeric(input$name.band.m.com.1)
plot(c(0,length(band)),c(0,10), type='n',ylab="",xlab="",xaxt = "n",yaxt = "n")
for(i in seq(band)){
data2<-f.rebuilt(data[band[i],,1],data[band[i],,2],data[band[i],,3])
rasterImage(data2,i-1,0,i,10)
text(x=i-0.5,y=9,labels=band[i],col="red",cex=2)
par(new=T)
}
})
# ################ model pca ##########
model.pca<-reactive({
data <- data.mono.4()
PCA(as.matrix(data))
})
# ################# output$pca.plot.1 #################
pca.plot.1<-reactive({
data<-model.pca()
# label.color <- paste(input$col.pca,collapse=', ')
# label.color <- gsub(1,'red',gsub(2,'green',gsub(3,'blue',gsub(4,'gray',label.color))))
xlabel<-paste0(input$PCA.comp.a,' (',round(data$var[as.numeric(substr(input$PCA.comp.a,3,3))]/data$totalvar*100,2),"%)")
ylabel<-paste0(input$PCA.comp.b,' (',round(data$var[as.numeric(substr(input$PCA.comp.b,3,3))]/data$totalvar*100,2),"%)")
data<-scores(data,npc=10)
colnames(data)<-c("PC1","PC2","PC3","PC4","PC5","PC6","PC7","PC8","PC9","PC10")
data<-data[,c(input$PCA.comp.a,input$PCA.comp.b)]
colnames(data)<-c("PC1","PC2")
data<-cbind(dataX.mono.pre(),data)
plot<-ggplot()+geom_point(data=data,aes(x=PC1,y=PC2),size=as.numeric(input$cex.pca))+
labs(x=xlabel, y=ylabel)
if(input$shape.plot.pca != "None"){
validate(
need(length(unique(data[,input$shape.plot.pca])) < 6, "The number of factor in your shape variable must be lower than 6")
)
}
if(input$shape.plot.pca != "None" & input$col.plot.pca == "None"){
if(input$shape.plot.pca != "HCA results"){data$Shape<-data[,input$shape.plot.pca]}else{data$Shape<-Cluster.table.1()$Cluster}
plot<-ggplot()+geom_point(data=data,aes(x=PC1,y=PC2,shape=Shape),size=as.numeric(input$cex.pca))+
labs(x=xlabel, y=ylabel)
}
if(input$col.plot.pca != "None" & input$shape.plot.pca == "None"){
if(input$col.plot.pca != "HCA results"){data$Color<-data[,input$col.plot.pca]}else{data$Color<-Cluster.table.1()$Cluster}
plot<-ggplot()+geom_point(data=data,aes(x=PC1,y=PC2,col=Color),size=as.numeric(input$cex.pca))+
labs(x=xlabel, y=ylabel)
if(input$pca.col.palette != "default"){plot <- plot+scale_color_brewer(palette=input$pca.col.palette)} # add here the color palette ref
}
if(input$col.plot.pca != "None" & input$shape.plot.pca != "None"){
if(input$col.plot.pca != "HCA results"){data$Color<-data[,input$col.plot.pca]}else{data$Color<-Cluster.table.1()$Cluster}
if(input$shape.plot.pca != "HCA results"){data$Shape<-data[,input$shape.plot.pca]}else{data$Shape<-Cluster.table.1()$Cluster}
plot<-ggplot()+geom_point(data=data,aes(x=PC1,y=PC2,col=Color,shape=Shape),size=as.numeric(input$cex.pca))+
labs(x=xlabel, y=ylabel)
if(input$pca.col.palette != "default"){plot <- plot+scale_color_brewer(palette=input$pca.col.palette)} # add here the color palette ref
}
if(input$label.plot.pca != "None"){
if(input$label.plot.pca != "HCA results"){data$Label<-data[,input$label.plot.pca]}else{data$Label<-Cluster.table.1()[,ncol(Cluster.table.1())]}
plot<-plot+geom_text(data=data,aes(x=PC1,y=PC2,label=Label),hjust=as.numeric(input$hjust.pca),vjust=as.numeric(input$vjust.pca))
}
if(input$pca.ellipse == T){
validate(
need(input$col.plot.pca != "None","Select a color to plot the ellipses")
)
plot <- plot+ stat_ellipse(data=data,aes(x=PC1,y=PC2,col=Color),level=input$pca.ellipse.level)
}
plot <- plot +theme(axis.text=element_text(size=18),
axis.title=element_text(size=18),
plot.title = element_text(size=20),
legend.text = element_text(size = 16))
if(input$pca.axis){plot <- plot + geom_vline(xintercept = 0) + geom_hline(yintercept = 0)}
return(plot+ggtitle(input$pca.plot.1.title))
})
output$pca.plot.1<-renderPlot({
truc <- pca.plot.1()
print(truc)
})
output$pca.plot.pair <- renderPlot({
data = scores(model.pca(),npc=input$PCA.pair)
colnames(data) = paste0("PC",seq(input$PCA.pair))
if(input$col.plot.pca != "None"){
data = data.frame(data,col=factor(dataX.mono.pre()[,input$col.plot.pca]))
GGally::ggpairs(data,aes(col=col),
upper=list(continuous = input$PCA.pair.upper.continuous, combo = "box", discrete = "facetbar", na = "na"),
lower=list(continuous = input$PCA.pair.lower.continuous, combo = 'facethist', discrete = "facetbar", na = "na"),
diag = list(continuous = "densityDiag", discrete ="barDiag", na = "naDiag")
)
}else{
GGally::ggpairs(data,
upper=list(continuous = input$PCA.pair.upper.continuous, combo = "box", discrete = "facetbar", na = "na"),
lower=list(continuous = input$PCA.pair.lower.continuous, combo = 'facethist', discrete = "facetbar", na = "na"),
diag = list(continuous = "densityDiag", discrete ="barDiag", na = "naDiag")
)
}
})
############### PCA_3d ############
# output$plot_PCA_3d <- renderScatterplotThree({
# validate(
# need(input$col.plot.pca != "None","Select a color for the points")
# )
# dep = as.factor(dataX.mono.pre()[,input$col.plot.pca])
# col = palette(rainbow(nlevels(dep)))[dep]
# scatterplot3js(model.pca()$scores[,1],model.pca()$scores[,2],model.pca()$scores[,3], color=col, size=1,
# axisLabels=c("PC1","PC3","PC2"),grid = F,height = "400px",width="400px",labels=names(Truc.mono()))
#
# })
# output$PCA_3d <- renderUI({
# scatterplotThreeOutput("plot_PCA_3d")
# })
# ################# output$pca.summary #################
output$pca.summary.1<-renderPrint({
summary(model.pca())
})
# ################# output$pca.table.1 #################
output$pca.table.1<-renderDataTable({
data<-model.pca()
data<-scores(data,npc=4)