From 05c66854c94e913326853b0d0b580c8d0adef64d Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Mon, 6 Apr 2026 20:25:18 +0100
Subject: [PATCH 01/14] feat: add log scale, data point customization, and bean
 plot median/mean options to UI

---
 BoxPlotR_functions.R |  24 +++++++
 MyVioplot.R          |   8 +--
 README.md            |  10 +--
 server.R             | 148 ++++++++++++++++++++++++++-----------------
 ui.R                 | 100 +++++++++++++++++++++--------
 5 files changed, 198 insertions(+), 92 deletions(-)
 create mode 100644 BoxPlotR_functions.R

diff --git a/BoxPlotR_functions.R b/BoxPlotR_functions.R
new file mode 100644
index 0000000..6f34285
--- /dev/null
+++ b/BoxPlotR_functions.R
@@ -0,0 +1,24 @@
+
+# pointType - 0 = normal; 2 = jittered
+# Add data points for specified columns in 'jittered' arrangement
+jittered.points<-function(D, myHorizontal=FALSE, pointType, pointColors, pointTransparency, pointSize){
+	# normal boxplots
+	if(myHorizontal){
+		for (i in c(1:ncol(D))){
+			if(pointType==0){
+				points(D[,i], rep(i, nrow(D)), col=rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100)), pch=16, cex=pointSize)
+			} else {
+				points(D[,i], jitter(rep(i, nrow(D)), amount=0.25), col=rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100)), pch=16, cex=pointSize)
+			}
+		}
+	} else {
+		# horizontal boxplots
+		for (i in c(1:ncol(D))){
+			if(pointType==0){
+				points(rep(i, nrow(D)), D[,i], col=rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100)), pch=16, cex=pointSize)
+			} else {
+				points(jitter(rep(i, nrow(D)), amount=0.25), D[,i], col=rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100)), pch=16, cex=pointSize)
+			}
+		}
+	}
+}
diff --git a/MyVioplot.R b/MyVioplot.R
index f3ac400..4ec7728 100644
--- a/MyVioplot.R
+++ b/MyVioplot.R
@@ -66,8 +66,8 @@ vioplot<-function (x, range = 1.5, h = NULL, ylim = NULL, names = NULL,
         box()
         for (i in 1:n) {
             polygon(c(at[i] - height[[i]], rev(at[i] + height[[i]])), 
-                c(base[[i]], rev(base[[i]])), col = col, border = border, 
-                lty = lty, lwd = lwd)
+                c(base[[i]], rev(base[[i]])), col = ifelse(length(col)>1, col[1+(i-1)%%length(col)], col),
+                border = border, lty = lty, lwd = lwd)
             if (drawRect) {
                 lines(at[c(i, i)], c(lower[i], upper[i]), lwd = lwd, 
                   lty = lty)
@@ -86,8 +86,8 @@ vioplot<-function (x, range = 1.5, h = NULL, ylim = NULL, names = NULL,
         box()
         for (i in 1:n) {
             polygon(c(base[[i]], rev(base[[i]])), c(at[i] - height[[i]], 
-                rev(at[i] + height[[i]])), col = col, border = border, 
-                lty = lty, lwd = lwd)
+                rev(at[i] + height[[i]])), col = ifelse(length(col)>1, col[1+(i-1)%%length(col)], col),
+                border = border, lty = lty, lwd = lwd)
             if (drawRect) {
                 lines(c(lower[i], upper[i]), at[c(i, i)], lwd = lwd, 
                   lty = lty)
diff --git a/README.md b/README.md
index 911253c..7938974 100644
--- a/README.md
+++ b/README.md
@@ -9,13 +9,13 @@ Installation
 You have two options for running shiny-boxplot:
 
 1) Launch directly from R and GitHub:
-  - Before running the app you will need to have R and RStudio installed (tested with R 3.0.2 and RStudio 0.97.449).
+  - Before running the app you will need to have R and RStudio installed (tested with R 3.2.1 and RStudio 0.99.465).
   - Launch the R console
 
 - Please run these lines in R:
   - install.packages("shiny")
   - install.packages("devtools")
-  - devtools::install_github("shiny-incubator","rstudio")
+  - devtools::install_github("rstudio/shiny-incubator")
   - install.packages("beeswarm")
   - install.packages("vioplot")
   - install.packages("beanplot")
@@ -39,10 +39,10 @@ Your web browser will open the web app.
 	- Go to your **SHINY_APP_HOME**
 	  - cd **SHINY_APP_HOME**
 	- Get the latest shiny-boxplot code from github:
-	  - wget https://github.com/jwildenhain/shiny-boxplot/archive/master.zip
+	  - wget https://github.com/VizWizard/BoxPlotR.shiny/archive/master.zip
 	  - unzip master.zip
-	  - mv shiny-boxplot-master shiny-boxplot
+	  - mv BoxPlotR.shiny-master BoxPlotR.shiny
 	- Restart shiny-server service:
 	  - sudo service shiny-server restart
 
-You should now be able to access shiny-boxplot at: http://YOURSITE:**SHINY_PORT**/shiny-boxplot
+You should now be able to access shiny-boxplot at: http://YOURSITE:**SHINY_PORT**/BoxPlotR.shiny
diff --git a/server.R b/server.R
index b16b8ae..e28409f 100644
--- a/server.R
+++ b/server.R
@@ -1,3 +1,5 @@
+options(shiny.maxRequestSize = 200*1024^2)
+
 shinyServer(function(input, output, session) {
 
 	library(RColorBrewer)
@@ -6,6 +8,7 @@ shinyServer(function(input, output, session) {
 	source("MyVioplot.R")
 	library(beanplot)	 
 	source("boxplot_stats_Function.R")
+	source("BoxPlotR_functions.R")	
 	
 	observe({
 		if (input$clearText_button == 0) return()
@@ -15,9 +18,9 @@ shinyServer(function(input, output, session) {
 	dataM <- reactive({
 		if(input$dataInput==1){
 			if(input$sampleData==1){
-				data<-read.table("Boxplot_testData2.csv", sep=",", header=TRUE, fill=TRUE)			
+				data<-read.table("Boxplot_testData2.csv", sep=",", header=TRUE, fill=TRUE, check.names=FALSE)			
 			} else {
-				data<-read.table("Boxplot_testData.txt", sep=",", header=TRUE)		
+				data<-read.table("Boxplot_testData.txt", sep=",", header=TRUE, check.names=FALSE)
 			}
 		} else if(input$dataInput==2){
 			inFile <- input$upload
@@ -25,21 +28,19 @@ shinyServer(function(input, output, session) {
 			if (is.null(input$upload))  {return(NULL)}
 			# Get the separator
 			mySep<-switch(input$fileSepDF, '1'=",",'2'="\t",'3'=";", '4'="") #list("Comma"=1,"Tab"=2,"Semicolon"=3)
-			if(file.info(inFile$datapath)$size<=10485800){
-				data<-read.table(inFile$datapath, sep=mySep, header=TRUE, fill=TRUE)
-			} else print("File is bigger than 10MB and will not be uploaded.")
+				data<-read.table(inFile$datapath, sep=mySep, header=TRUE, fill=TRUE, check.names=FALSE)
 		} else { # To be looked into again - for special case when last column has empty entries in some rows
-			if(is.null(input$myData)) {return(NULL)} 
+			if(is.null(input$myData)) {return(NULL)}
 			tmp<-matrix(strsplit(input$myData, "\n")[[1]])
 			mySep<-switch(input$fileSepP, '1'=",",'2'="\t",'3'=";")
 			myColnames<-strsplit(tmp[1], mySep)[[1]]
-			data<-matrix(0, length(tmp)-1, length(myColnames))
-			colnames(data)<-myColnames
+			data<-matrix(0, length(tmp)-1, length(myColnames), dimnames=list(NULL,myColnames))
 			for(i in 2:length(tmp)){
 				myRow<-as.numeric(strsplit(paste(tmp[i],mySep,mySep,sep=""), mySep)[[1]])
 				data[i-1,]<-myRow[-length(myRow)]
 			}
-			data<-data.frame(data)		
+			data<-data.frame(data)
+			names(data)<-myColnames
 		}
 		return(data)
 	})
@@ -66,24 +67,40 @@ shinyServer(function(input, output, session) {
 	# *** Generate the box plot ***
 	generateBoxPlot<-function(plotData){
 		par(mar=c(5,8,4,2)) # c(bottom, left, top, right)
+
 		myColours<-gsub("\\s","", strsplit(input$myColours,",")[[1]])
 		myColours<-gsub("0x","#", myColours)
 
 		myColours2<-gsub("\\s","", strsplit(input$myOtherPlotColours,",")[[1]])
 		myColours2<-gsub("0x","#", myColours2)
 
+		pointColors<-gsub("\\s","", strsplit(input$pointColors,",")[[1]])
+		pointColors<-gsub("0x","#", pointColors)
+
+		pointTransparency<-100-input$pointTransparency
 
 		nrOfSamples<-ncol(plotData)
-		# generate colour vector
+		# Display median or mean for bean plot?
+		if(input$beanPlotMedianMean==0){ myBeanplotCenter<-"median" } else { myBeanplotCenter<-"mean" }
+		# Generate colour vector
 		if(length(myColours)==1){
 			myColours<-rep(myColours, nrOfSamples)
 		} else if(length(myColours) < nrOfSamples){
 			myColours<-rep(myColours,times=(round(nrOfSamples/length(myColours)))+1)
 		}
+		
+		# Generate colour vector for points
+		if(length(pointColors)==1){
+			pointColors<-rep(pointColors, nrOfSamples)
+		} else if(length(pointColors) < nrOfSamples){
+			pointColors<-rep(pointColors,times=(round(nrOfSamples/length(pointColors)))+1)
+		}
+
 		plotPoints<-c() # vector for indices of samples that are to be plotted as points, not as boxplots
 		notPlotPoints <- seq(1:nrOfSamples) # samples to plot as boxes/violins/beans
 		plotDataM<-plotData
 		# Determine plot range
+		myBuffer<-max(abs(range(plotData,na.rm=TRUE)))*0.1 # add 10% of data maximum at each end of y/x axis
 		if(as.numeric(input$myOrientation)==0){		
 			if(input$ylimit==""){myLim<-range(plotData,na.rm=TRUE)+c(-1,+1)} else {myLim<-as.numeric(strsplit(input$ylimit,",")[[1]])}
 		} else {
@@ -93,7 +110,7 @@ shinyServer(function(input, output, session) {
 		datapointCounts<-apply(!apply(plotData, 2, is.na),2,sum) # Count number of valid data points for each sample
 		# Check if columns with few data points should be plotted as points
 
-		# minimum number of points is 4 -> check that nrOfDataPoints is larger than that		
+		# minimum number of points is 4 -> check that nrOfDataPoints is ay least 4		
 		mnp<-max(4,input$nrOfDataPoints)
 		
 		if(input$plotDataPoints==TRUE){
@@ -102,7 +119,8 @@ shinyServer(function(input, output, session) {
 			notPlotPoints <- seq(1:nrOfSamples)[datapointCounts>=mnp] # samples to plot as boxes/violins/beans
 		}
 
-		# Generate plotDataM matrix such that columns that should be plotted as points are filled with data points outside of visible plot area to 'reserve' spot for points
+		# Generate plotDataM matrix such that columns that should be plotted as points are filled with data points 
+		# outside of visible plot area to 'reserve' spot for points
 		for(i in plotPoints){
 			plotDataM[,i]<-c(rep(myLim[2]+10, nrow(plotData)-1),myLim[2]+20)
 		}
@@ -117,36 +135,42 @@ shinyServer(function(input, output, session) {
 		}
 
 		par(mar=c(12.1, 11.1, 4.1, 2.1))
-
+		
+		if(input$showDataPoints==TRUE) {
+			plotOutliers=FALSE
+		} else {
+			plotOutliers=TRUE
+		}
 		# *** 1) Vertical boxplots ***
 		par(las=1)
 		if(as.numeric(input$myOrientation)==0){
+			if(input$logScale==FALSE){ myLog="" } else { myLog="y"} # log scale for y-axis?
 			# *** Generate boxplot ***
 			if(input$plotType=='0'){
-				boxplot(plotDataM, col=myColours, ylab=input$myYlab, xlab=input$myXlab, ylim=myLim,
+				boxplot(plotDataM, col=myColours, ylab=input$myYlab, xlab=input$myXlab, ylim=myLim, log=myLog, outline=plotOutliers,
 					cex.lab=input$cexAxislabel/10, cex.axis=input$cexAxis/10, cex.main=input$cexTitle/10, 
 					main=input$myTitle, sub=input$mySubtitle, horizontal=as.numeric(input$myOrientation), frame=F, 
-					na.rm=TRUE, xaxt="n", range=myRange(), varwidth=input$myVarwidth, notch=input$myNotch) #notch=TRUE
+					na.rm=TRUE, xaxt="n", range=myRange(), varwidth=input$myVarwidth, notch=input$myNotch) #notch=TRUE, outpch=16
 				axis(1,at=c(1:nrOfSamples), labels=FALSE, cex.axis=input$cexAxis/10) #
-				text(x=c(1:nrOfSamples), y=rep(myLim[1]-3,nrOfSamples), labels=colnames(plotData), 
-					pos=labelPos, xpd=TRUE, srt=xaxisLabelAngleNr, cex=input$cexAxis/10)
-				# * Add data points to plot if selected *
+#				mtext(text=colnames(plotData), side=1, at=c(1:nrOfSamples), xpd=TRUE, srt=xaxisLabelAngleNr, cex=input$cexAxis/10)
+				text(x=c(1:nrOfSamples), y=rep(myLim[1]-(myLim[2]-myLim[1])/10,nrOfSamples), labels=colnames(plotData), pos=labelPos, xpd=TRUE, srt=xaxisLabelAngleNr, cex=input$cexAxis/10)
+
+				# *** Add data points to plot if selected ***
 				if(input$showDataPoints==TRUE){
-					if(length(plotPoints)==0){ # all samples are box plots --> add points for all of them
-						if(input$datapointType==0){
-							for(i in c(1:nrOfSamples)){ points(rep(i, nrow(plotData)), plotData[,i], col="black") }
-						} else { beeswarm(plotData, add=TRUE) }
-					} else { # remove the ones that are already plotted as points
-						if(input$datapointType==0){
-							for(i in c(1:nrOfSamples)[-plotPoints]){ points(rep(i, nrow(plotData)), plotData[,i], col="black") }
-						} else { beeswarm(plotData, add=TRUE) }
-#						} else { beeswarm(plotData[,-plotPoints], at=c(1:nrOfSamples)[-plotPoints], add=TRUE) }
-					}
+						if(input$datapointType==1){
+							beePointColors<-c()
+							for(i in 1:length(pointColors)){
+								beePointColors[i]<-rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100))
+							}
+							beeswarm(plotData, add=TRUE, col=beePointColors, cex=input$pointSize/10, pch=16)
+						} else { 
+							jittered.points(plotData, FALSE, input$datapointType, pointColors, pointTransparency, input$pointSize/10)
+						}			
 				}
 			} else { # *** Generate violin or bean plot ***
 				if(input$otherPlotType==0){ # Violin plot
 					vioplot(as.list(data.frame(plotDataM)), col=myColours2, ylim=myLim, cex.axis=input$cexAxis/10, 
-						horizontal=as.numeric(input$myOrientation), range=myRange(), border=input$violinBorder)
+						horizontal=as.numeric(input$myOrientation), border=input$violinBorder)
 					title(main=input$myTitle, ylab=input$myYlab, xlab=input$myXlab, cex.main=input$cexTitle/10, cex.lab=input$cexAxislabel/10)
 #					axis(1,at=c(1:nrOfSamples), labels=colnames(plotData), cex.axis=input$cexAxis/10, sub=input$mySubtitle)
 					axis(1,at=c(1:nrOfSamples), labels=FALSE, cex.axis=input$cexAxis/10) #
@@ -156,7 +180,7 @@ shinyServer(function(input, output, session) {
 				} else {
 					beanplot(data.frame(plotDataM[,notPlotPoints]), at=notPlotPoints, ylim=myLim, 
 						horizontal=as.numeric(input$myOrientation), xlim=c(0.5, ncol(plotDataM)+0.5), 
-						col=myColours2, border=input$beanBorder)
+						col=myColours2, border=input$beanBorder, overallline = myBeanplotCenter)
 					title(main=input$myTitle, ylab=input$myYlab, xlab=input$myXlab, cex.main=input$cexTitle/10, cex.lab=input$cexAxislabel/10)
 	#				axis(1,at=c(1:nrOfSamples), labels=colnames(plotData), cex.axis=input$cexAxis/10)				
 					axis(1,at=c(1:nrOfSamples), labels=FALSE, cex.axis=input$cexAxis/10) #
@@ -164,7 +188,7 @@ shinyServer(function(input, output, session) {
 #					pos=labelPos, xpd=TRUE, srt=xaxisLabelAngleNr, cex=input$cexAxis/10)
 				}
 			}
-			# * Add points for samples with less then mnp data points *
+			# * Add points for samples with less than mnp data points *
 			# replace "white" with "black" otherwise data points will not be visible
 			for(i in plotPoints){
 				if(input$datapointType==0 | input$plotType==1 | (input$datapointType==1 & input$showDataPoints==FALSE)){
@@ -195,52 +219,56 @@ shinyServer(function(input, output, session) {
 			
 		# *** 2) Horizontal boxplots ***
 		} else { 
+			if(input$logScale==FALSE){ myLog="" } else { myLog="x"} # log scale for y-axis?
 			if(input$plotType=='0'){
-				boxplot(plotDataM, col=myColours, ylab=input$myYlab, xlab=input$myXlab, las=1, ylim=myLim,
+				boxplot(plotDataM, col=myColours, ylab=input$myYlab, xlab=input$myXlab, las=1, ylim=myLim, log=myLog, outline=plotOutliers,
 					cex.lab=input$cexAxislabel/10, cex.axis=input$cexAxis/10, cex.main=input$cexTitle/10, 
 					main=input$myTitle, sub=input$mySubtitle, horizontal=as.numeric(input$myOrientation), frame=F, 
-					na.rm=TRUE, yaxt="n", range=myRange(), varwidth=input$myVarwidth, notch=input$myNotch) #notch=TRUE
+					na.rm=TRUE, yaxt="n", range=myRange(), varwidth=input$myVarwidth, notch=input$myNotch) #notch=TRUE, outpch=16
 				axis(2,at=c(1:nrOfSamples), labels=colnames(plotData), cex.axis=input$cexAxis/10)
 				# Add data points if option has been selected
 				if(input$showDataPoints==TRUE){
-					if(length(plotPoints)==0){ # all samples are boxplots --> add points for all of them
-						if(input$datapointType==0){
-							for(i in c(1:nrOfSamples)){ points(plotData[,i], rep(i, nrow(plotData)), col="black") }
-						} else { beeswarm(plotData, add=TRUE, horizontal=TRUE) }			
-					} else { # remove the ones that are already plotted as points
-						if(input$datapointType==0){
-							for(i in c(1:nrOfSamples)[-plotPoints]){ points(plotData[,i], rep(i, nrow(plotData)), col="black") }
-						} else { beeswarm(plotData, add=TRUE, horizontal=TRUE) }
-					}
+						if(input$datapointType==1){
+							beePointColors<-c()
+							for(i in 1:length(pointColors)){
+								beePointColors[i]<-rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100))
+							}
+							beeswarm(plotData, add=TRUE, horizontal=TRUE, col=beePointColors, cex=input$pointSize/10, pch=16)
+						} else {
+							jittered.points(plotData, TRUE, input$datapointType, pointColors, pointTransparency, input$pointSize/10)
+						}			
 				}
 			} else {
 				if(input$otherPlotType==0){ # Violin plot
 					vioplot(as.list(data.frame(plotDataM)), col=myColours2[1], ylim=myLim, cex.axis=input$cexAxis/10, 
-						horizontal=as.numeric(input$myOrientation), range=myRange(), border=input$violinBorder)
+						horizontal=as.numeric(input$myOrientation), border=input$violinBorder)
 					title(main=input$myTitle, ylab=input$myYlab, xlab=input$myXlab, cex.main=input$cexTitle/10, cex.lab=input$cexAxislabel/10)
 					axis(2,at=c(1:nrOfSamples), labels=colnames(plotData), cex.axis=input$cexAxis/10)
 
 				} else { # Bean plot
 					beanplot(data.frame(plotDataM[,notPlotPoints]), at=notPlotPoints, ylim=myLim, 
 					horizontal=as.numeric(input$myOrientation), xlim=c(0.5, ncol(plotDataM)+0.5), 
-					col=myColours2, border=input$beanBorder)
+					col=myColours2, border=input$beanBorder, overallline = myBeanplotCenter)
 					title(main=input$myTitle, ylab=input$myYlab, xlab=input$myXlab, cex.main=input$cexTitle/10, cex.lab=input$cexAxislabel/10)
 					axis(2,at=c(1:nrOfSamples), labels=FALSE, cex.axis=input$cexAxis/10) # labels=colnames(plotData)
 				}
 			}
-
-			# if there are columns with less than x data points, then add the points
-			for(i in plotPoints){
-				if(input$datapointType==0){
-					if(myColours[i]!="white"){
-						points(plotData[,i], rep(i, nrow(plotData)), col=myColours[i])
-					} else {
-						points(plotData[,i], rep(i, nrow(plotData)), col="white")				
+			# *** Add data points for columns with less than x data points ***
+			# Only if violin or bean plots are shown or points are not already added to boxplot (input$showDataPoints==FALSE)
+			if(input$plotType!='0' | input$showDataPoints==FALSE){
+				for(i in plotPoints){
+					if(input$datapointType==0){
+						if(myColours[i]!="white"){
+							points(plotData[,i], rep(i, nrow(plotData)), col=myColours[i])
+						} else {
+							points(plotData[,i], rep(i, nrow(plotData)), col="black")				
+						}
 					}
 				}
 			}
+			# *** Show number of data points for each column ***
 			if(input$showNrOfPoints==TRUE){text(y=1:ncol(dataM()), x=myLim[1], labels=boxplotStats()$n)}
-			# Add mean and CIs for mean
+			# *** Add mean and CIs for mean ***
 			if(input$addMeans==TRUE & input$plotType=='0'){
 				boxplotMeans<-apply(dataM(), 2, mean, na.rm=TRUE)
 				points(y=1:ncol(dataM()), x=boxplotMeans, pch="+", cex=2) 
@@ -257,7 +285,7 @@ shinyServer(function(input, output, session) {
 			}
 			
 		}
-		# Add grid based on option selected
+		# *** Add grid based on option selected ***
 		if(input$addGrid==0){} 
 		else if(input$addGrid==1){grid()} 
 		else if (input$addGrid==2){grid(ny=NA)}
@@ -317,16 +345,18 @@ shinyServer(function(input, output, session) {
 
 	# *** Output boxplot statistics in table below plot ***
 	output$boxplotStatsTable <- renderTable({
+		M<-rbind(as.matrix(boxplotStats()$stats[c(5,4,3,2,1),]),boxplotStats()$n)
 		if(input$addMeans){
-			M<-rbind(boxplotStats()$stats[c(5,4,3,2,1),],boxplotStats()$n)
+			#M<-rbind(boxplotStats()$stats[c(5,4,3,2,1),],boxplotStats()$n)
 			M<-rbind(M, apply(dataM(), 2, mean, na.rm=TRUE))
 			rownames(M)<-c("Upper whisker","3rd quartile","Median","1st quartile","Lower whisker", "Nr. of data points", "Mean")
 			colnames(M)<-colnames(dataM())
 		} else {
-			M<-rbind(boxplotStats()$stats[c(5,4,3,2,1),],boxplotStats()$n)
+			#M<-rbind(boxplotStats()$stats[c(5,4,3,2,1),],boxplotStats()$n)
 			rownames(M)<-c("Upper whisker","3rd quartile","Median","1st quartile","Lower whisker", "Nr. of data points")
 			colnames(M)<-colnames(dataM())
 		}
+		print(M)
 		M
 	})
 	
@@ -370,9 +400,11 @@ shinyServer(function(input, output, session) {
 				whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles; 
 				polygons represent density estimates of data and extend to extreme values.")
 			} else if (input$otherPlotType=='1') { # Bean plot
-				FL<-c("Black lines show the medians; 
+		# Display median or mean for bean plot?
+				if(input$beanPlotMedianMean==0){ myBeanplotCenter<-"median" } else { myBeanplotCenter<-"mean" }
+				FL<-paste("Black lines show the ",myBeanplotCenter,"s; 
 				white lines represent individual data points; 
-				polygons represent the estimated density of the data.")
+				polygons represent the estimated density of the data.", sep="")
 				#if(input$beanplotOverall){FL<-append(FL, c("dotted line represents overall "))}
 			}
 		} # END: other plot types	
diff --git a/ui.R b/ui.R
index f71b3f3..851aad9 100644
--- a/ui.R
+++ b/ui.R
@@ -40,6 +40,7 @@ shinyUI(pageWithSidebar(
 			radioButtons("plotType", "", list("Boxplot"=0, "Other"=1)),
 			conditionalPanel(condition="input.plotType=='1'",
 				radioButtons("otherPlotType", "", list("Violin plot"=0, "Bean plot"=1)),
+				HTML('<p style="color:#808080">Violin plots are generated with the <a href="https://cran.r-project.org/web/packages/vioplot/index.html">vioplot package</a>. Bean plots are generated with the <a href="https://cran.r-project.org/web/packages/beanplot/index.html">beanplot package</a>.</p>'),
 				textInput("myOtherPlotColours", "Colour(s):", value=c("light grey, white")),
 				conditionalPanel(condition="input.otherPlotType=='0'",
 					helpText("Colour of the 'violin area'"),
@@ -47,7 +48,8 @@ shinyUI(pageWithSidebar(
 				),
 				conditionalPanel(condition="input.otherPlotType=='1'",
 					helpText("up to 4 colours can be specified: area of the beans, lines inside the bean, lines outside the bean, and average line per bean"),
-					textInput("beanBorder", "Border colour:", value=c("grey"))			
+					textInput("beanBorder", "Border colour:", value=c("grey")),	
+					radioButtons("beanPlotMedianMean", "Display: ", list("Median"=0, "Mean"=1))
 				)
 			),
 
@@ -60,7 +62,11 @@ shinyUI(pageWithSidebar(
 			conditionalPanel(condition="input.plotType=='0'",
 				checkboxInput("showDataPoints", "Add data points", FALSE),
 				conditionalPanel(condition="input.showDataPoints",
-					radioButtons("datapointType", "", list("Default"=0, "Bee swarm"=1))
+					radioButtons("datapointType", "", list("Default"=0, "Bee swarm"=1, "Jittered"=2)),
+					sliderInput("pointTransparency", "Transparency of data points", min=0, max=99, value=50),
+					sliderInput("pointSize", "Size of data points", min=1, max=20, value=10),
+					HTML('<p style="color:#808080">Using the <a href="http://www.cbs.dtu.dk/~eklund/beeswarm/">beeswarm package</a>.</p>'),
+					textInput("pointColors", "Colour(s):", value=c("black"))					
 				),
 				checkboxInput("whiskerDefinition", "Definition of whisker extent", FALSE),
 				conditionalPanel(condition="input.whiskerDefinition",
@@ -74,7 +80,7 @@ shinyUI(pageWithSidebar(
 #					conditionalPanel(condition="input.whiskerType=='2'",
 #						numericInput("AltmanRange", "Define whisker extent in percentiles (ie, '5' means that whiskers extend to 5th and 95th percentile):", min=0, step=0.5, value=5)
 #					),
-					HTML('<p>Tukey - whiskers extend to data points that are less than 1.5 x <a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a> away from 1st/3rd <a href=:"http://en.wikipedia.org/wiki/Quartile">quartile</a>; 
+					HTML('<p style="color:#808080">Tukey - whiskers extend to data points that are less than 1.5 x <a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a> away from 1st/3rd <a href=:"http://en.wikipedia.org/wiki/Quartile">quartile</a>; 
 					Spear - whiskers extend to minimum and maximum values; 
 					Altman - whiskers extend to 5th and 95th percentile (use only if n>40)</p>')
 				),
@@ -90,7 +96,7 @@ shinyUI(pageWithSidebar(
 				checkboxInput("myVarwidth", "Variable width boxes", FALSE),
 				helpText("Widths of boxes are proportional to square-roots of the number of observations."),
 				checkboxInput("myNotch", "Add notches", FALSE),
-				HTML('<p>+/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) - gives roughly 95% confidence that two medians differ (Chambers et al., 1983)</p>'),
+				HTML('<p style="color:#808080">+/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) - gives roughly 95% confidence that two medians differ (Chambers et al., 1983)</p>'),
 				conditionalPanel(condition="input.myNotch",
 					HTML('<p>The notches are defined as +/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) and represent the 95% <a href="http://en.wikipedia.org/wiki/Confidence_interval">confidence interval</a> for each median. 
 					Non-overlapping notches give roughly 95% confidence that two medians differ, ie, in 19 out of 20 cases the population 
@@ -129,6 +135,7 @@ shinyUI(pageWithSidebar(
 				h5("X-axis range (eg., '0,10'):"),
 				textInput("xlimit", "", value="")
 			),
+			checkboxInput("logScale", "Change to log scale (only for data >0)", FALSE),			
 			h5("Add grid: "),
 			radioButtons("addGrid", "", list("None" = 0, "X & Y"= 1, "X only" = 2, "Y only" = 3))
 #			numericInput("boxWidth", "Width of boxes:", value=1),
@@ -139,13 +146,24 @@ shinyUI(pageWithSidebar(
 		tabsetPanel(
 			# Welcome tab
 			tabPanel("About",
-				HTML('<p>This application allows users to generate customized <a href="http://en.wikipedia.org/wiki/Box_plot">box plots</a> in a number of variants based on their data. A data matrix 
-				can be uploaded as a file or pasted into the application. Basic box plots are generated based on the data and can be modified to include 
-				additional information. Additional features become available when checking that option.  Information about sample sizes can be represented 
-				by the width of each box where the widths are proportional to the square roots of the number of observations n. Notches can be added to the 
-				boxes. These are defined as +/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) which gives roughly 95% confidence that two medians are different. It is also possible to define 
-				the whiskers based on the ideas of Spear and Tukey. Additional options of data visualization (violin and bean plots) reveal more information 
-				about the underlying data distribution. Plots can be labeled, customized (colors, dimensions, orientation) and exported as eps, pdf and svg files.</p>'),
+				HTML('
+				<br>
+				
+				<p>This application was developed with Nature Methods and you can find the publication <a href="http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2811.html">here</a>.
+				The BoxPlotR has also been mentioned in this <a href="http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2837.html">editorial</a> and this
+				<a href="http://blogs.nature.com/methagora/2014/01/bring-on-the-box-plots-boxplotr.html">blog entry</a>. Nature methods also dedicated a
+				<a href="http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2807.html">Points of View</a> and a <a href="http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2813.html">Points of Significance</a>
+				column to box plots. We hope that you find the <a href= "http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2811.html">BoxPlotR</a> useful and we welcome suggestions
+				for additional features by our users. We would like to thank everyone who has made constructive suggestions so far. We will document the addition of new features in the News tab.</p>
+				<p>This application allows users to generate customized <a href="http://en.wikipedia.org/wiki/Box_plot">box plots</a> in a number of variants based on their data. A data matrix
+				can be uploaded as a file or pasted into the application. Basic box plots are generated based on the data and can be modified to include
+				additional information. Additional features become available when checking that option.  Information about sample sizes can be represented
+				by the width of each box where the widths are proportional to the square roots of the number of observations n. Notches can be added to the
+				boxes. These are defined as +/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) which gives roughly 95% confidence that two medians are different. It is also possible to define
+				the whiskers based on the ideas of Spear and Tukey. Additional options of data visualization (violin and bean plots) reveal more information
+				about the underlying data distribution. Plots can be labeled, customized (colors, dimensions, orientation) and exported as eps, pdf and svg files.</p>
+				<p>BoxPlotR code can be run locally via <a href="https://github.com/VizWizard/BoxPlotR.shiny/blob/master/README.md">GitHub</a>. You can also download and install it as a virtual machine (see <a href="https://github.com/VizWizard/BoxPlotR.shiny/blob/master/README.md">GitHub</a> and FAQs for details). </p>'),
+
 				h5("Software references"),
 				HTML('<p>R Development Core Team. <i><a href="http://www.r-project.org/">R</a>:  A Language and Environment for Statistical Computing.</i> R Foundation for Statistical Computing, Vienna (2013) <br>
 				RStudio and Inc. <i><a href="http://www.rstudio.com/shiny/">shiny</a>: Web Application Framework for R.</i> R package version 0.5.0 (2013) <br>
@@ -155,15 +173,15 @@ shinyUI(pageWithSidebar(
 				Neuwirth, E. <i><a href="http://cran.r-project.org/web/packages/RColorBrewer/index.html">RColorBrewer</a>: ColorBrewer palettes.</i> R package version 1.0-5. (2011)</p>'),
 				h5("Further references"),
 				HTML('<p> Hadley Wickham and Lisa Stryjewski: <a href="http://vita.had.co.nz/papers/boxplots.pdf"> 40 years of boxplots </a></p>'),
-				HTML('<p> Kristin Potter: <a href="http://pages.uoregon.edu/kpotter/publications/potter-2006-MPSI.pdf">Methods for Presenting Statistical Information: The Box Plot</a></p>'),
-				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"), 
-				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ", 
+				HTML('<p> Kristin Potter: <a href="http://www.sci.utah.edu/~kpotter/publications/potter-2006-MPSI.pdf">Methods for Presenting Statistical Information: The Box Plot</a></p>'),
+				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"),
+				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
 				a("shiny package from RStudio", href="http://www.rstudio.com/shiny/"), ".")
 			),
 			# Data upload tab
 			tabPanel("Data upload", tableOutput("filetable"),
-				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"), 
-				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ", 
+				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"),
+				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
 				a("shiny package from RStudio", href="http://www.rstudio.com/shiny/"), ".")
 			),
 			# Boxplot tab
@@ -172,27 +190,59 @@ shinyUI(pageWithSidebar(
 				downloadButton("downloadPlotSVG", "Download svg-file"),
 				plotOutput("boxPlot", height='100%', width='100%'),
 				h4("Box plot statistics"), tableOutput("boxplotStatsTable"),
-				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"), 
-				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ", 
+				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"),
+				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
 				a("shiny package from RStudio", href="http://www.rstudio.com/shiny/"), ".")
-			), 
-			# Figure legend 
+			),
+			# Figure legend
 			tabPanel("Figure legend template", h5("Box plot description for figure legend:"), textOutput("FigureLegend"),
 				h5("Further information to be added to the figure legend:"), p("What do the box plots show, explain colours if used."),
 				downloadButton("downloadBoxplotData", "Download box plot data as .CSV file"),
-				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"), 
-				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ", 
+				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"),
+				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
 				a("shiny package from RStudio", href="http://www.rstudio.com/shiny/"), ".")
 			),
+			# News
+			tabPanel("News",
+                h5("January 17, 2021"),
+                HTML('<p>There are several recent updates. The jitter of points is now consistent for all samples. When
+                data points are added to the plot, the size and transparence of the points can now be modified with
+                sliders. The link to one of the boxplot references has been updated.</p>'),
+                h5("June 11, 2020"),
+                HTML('<p>The sample names are now displayed as they are in the input, ie., spaces and special characters
+                are not replaced with underscores.</p>'),
+                h5("January 11, 2017"),
+                HTML('<p>If you experience problems with this boxplot server, there is an alternative BoxPlotR mirror
+                available at <a href="http://boxplot.bio.ed.ac.uk">boxplot.bio.ed.ac.uk</a>.</p>'),
+                h5("July 23, 2014"),
+     			p("Upgrade to R version 3.1 and shiny-server 1.2"),
+				h5("March 18, 2014"),
+				p("The user can now choose the color of the data points. There is also an additional option for data
+				point display: data points can now be randomly jittered.
+				A small bug in label display was fixed. Log scales can now be used.")
+			),			
 			# FAQ 
 			tabPanel("FAQ",
 				h5("Q: I have trouble editing the graphic files."), 
 				p("A: For EPS files make sure to 'ungroup' all objects so they can be edited independently. 
 				In Adobe Illustrator you will also need to use the 'release compound path' command. For PDF 
 				files you should 'release clipping mask'. SVG import appears to have problems in Adobe Illustrator 
-				and Corel Draw and should be avoided. EPS, PDF and SVG import all work with Inkscape http://www.inkscape.org/.")
-#				h5("Further information to be added to the figure legend:"), 
-#				p("What do the box plots show, explain colours if used.")
+				and Corel Draw and should be avoided. EPS, PDF and SVG import all work with Inkscape http://www.inkscape.org/."),
+				h5("Q: I would like to install BoxPlotR as a virtual machine."), 
+				HTML('<p>A: Please download the virtual machine from http://tyerslab.bio.ed.ac.uk/download/shiny.boxplot.7z (1.1GB). 
+				Unzip the file using <a href="http://www.7-zip.org/">7zip</a> or equivalent. The virtual machine is available in the open 
+				virtualization format (OVF) and you can use this file with <a href="https://www.virtualbox.org/">vbox</a> and 
+				<a href="https://my.vmware.com/web/vmware/free#desktop_end_user_computing/vmware_player/6_0">vmware player</a>. 
+				One easy way to use the server is to set the virtual host network environment to NAT. After importing the virtual machine you can 
+				start the server and login as user shiny  with password pk635153Y6jx89r. On the command line use the command ifconfg and record the 
+				IP address of the virtual server. Now going back to the virtual host network environment change the advanced settings of the NAT and 
+				activate port forwarding for the guest network ip address (shiny server) using port 80 to the host ip address using for example port 8080. 
+				Now you should be able to access the shiny server in a browser on port 127.0.0.1:8080 or your localhost:8080.
+				If you are not familiar with the software packages there are detailed examples for 
+				<a href="http://www.howtogeek.com/122641/how-to-forward-ports-to-a-virtual-machine-and-use-it-as-a-server/">vbox</a> and 
+				<a href="http://blog.fardad.com/2012/06/vmware-player-and-custom-nat-port-map.html">vmware</a>.</p>')
+#				h5("Q:"), 
+#				p("A:")
 			),			
 			id="tabs1"
 		)

From d54693021b86bcbbdd72e20278f8a554a80bda48 Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Wed, 8 Apr 2026 22:31:20 +0100
Subject: [PATCH 02/14] Refactor Shiny application: modernize UI framework,
 optimize data loading, vectorize loops, fix row names, and update README
 dependencies

---
 README.md | 26 ++++++++++---------
 server.R  | 74 +++++++++++++++++++++++++------------------------------
 ui.R      | 31 +++++++++++++----------
 3 files changed, 66 insertions(+), 65 deletions(-)

diff --git a/README.md b/README.md
index 7938974..871814f 100644
--- a/README.md
+++ b/README.md
@@ -8,18 +8,20 @@ Installation
 
 You have two options for running shiny-boxplot:
 
-1) Launch directly from R and GitHub:
-  - Before running the app you will need to have R and RStudio installed (tested with R 3.2.1 and RStudio 0.99.465).
-  - Launch the R console
-
-- Please run these lines in R:
-  - install.packages("shiny")
-  - install.packages("devtools")
-  - devtools::install_github("rstudio/shiny-incubator")
-  - install.packages("beeswarm")
-  - install.packages("vioplot")
-  - install.packages("beanplot")
-  - install.packages("RColorBrewer")
+1)  Launch directly from R and GitHub:
+
+-   Before running the app you will need to have R and RStudio installed. Please note this application is maintained to be compatible with R 3.3.3 (the latest supported on the host server).
+
+-   Launch the R console
+
+-   Please run these lines in R:
+
+    -   install.packages("shiny")
+    -   install.packages("shinythemes")
+    -   install.packages("beeswarm")
+    -   install.packages("vioplot")
+    -   install.packages("beanplot")
+    -   install.packages("RColorBrewer")
 
 - Then start the app:
   - shiny::runGitHub("BoxPlotR.shiny", "VizWizard")
diff --git a/server.R b/server.R
index e28409f..bf990c0 100644
--- a/server.R
+++ b/server.R
@@ -1,5 +1,16 @@
 options(shiny.maxRequestSize = 200*1024^2)
 
+# Pre-load example datasets so they don't hit the disk constantly
+sampleData1Cache <- read.table("Boxplot_testData2.csv", sep=",", header=TRUE, fill=TRUE, check.names=FALSE)
+sampleData2Cache <- read.table("Boxplot_testData.txt", sep=",", header=TRUE, check.names=FALSE)
+
+# Helper function to prevent redundant color parsing
+parseColours <- function(colStrings){
+  myColours <- gsub("\\s","", strsplit(colStrings,",")[[1]])
+  myColours <- gsub("0x","#", myColours)
+  return(myColours)
+}
+
 shinyServer(function(input, output, session) {
 
 	library(RColorBrewer)
@@ -18,9 +29,9 @@ shinyServer(function(input, output, session) {
 	dataM <- reactive({
 		if(input$dataInput==1){
 			if(input$sampleData==1){
-				data<-read.table("Boxplot_testData2.csv", sep=",", header=TRUE, fill=TRUE, check.names=FALSE)			
+				data <- sampleData1Cache			
 			} else {
-				data<-read.table("Boxplot_testData.txt", sep=",", header=TRUE, check.names=FALSE)
+				data <- sampleData2Cache
 			}
 		} else if(input$dataInput==2){
 			inFile <- input$upload
@@ -30,17 +41,10 @@ shinyServer(function(input, output, session) {
 			mySep<-switch(input$fileSepDF, '1'=",",'2'="\t",'3'=";", '4'="") #list("Comma"=1,"Tab"=2,"Semicolon"=3)
 				data<-read.table(inFile$datapath, sep=mySep, header=TRUE, fill=TRUE, check.names=FALSE)
 		} else { # To be looked into again - for special case when last column has empty entries in some rows
-			if(is.null(input$myData)) {return(NULL)}
-			tmp<-matrix(strsplit(input$myData, "\n")[[1]])
+			req(input$myData)
+			if(input$myData == "") {return(NULL)}
 			mySep<-switch(input$fileSepP, '1'=",",'2'="\t",'3'=";")
-			myColnames<-strsplit(tmp[1], mySep)[[1]]
-			data<-matrix(0, length(tmp)-1, length(myColnames), dimnames=list(NULL,myColnames))
-			for(i in 2:length(tmp)){
-				myRow<-as.numeric(strsplit(paste(tmp[i],mySep,mySep,sep=""), mySep)[[1]])
-				data[i-1,]<-myRow[-length(myRow)]
-			}
-			data<-data.frame(data)
-			names(data)<-myColnames
+			data <- read.table(text = input$myData, sep=mySep, header=TRUE, fill=TRUE, check.names=FALSE)
 		}
 		return(data)
 	})
@@ -68,14 +72,9 @@ shinyServer(function(input, output, session) {
 	generateBoxPlot<-function(plotData){
 		par(mar=c(5,8,4,2)) # c(bottom, left, top, right)
 
-		myColours<-gsub("\\s","", strsplit(input$myColours,",")[[1]])
-		myColours<-gsub("0x","#", myColours)
-
-		myColours2<-gsub("\\s","", strsplit(input$myOtherPlotColours,",")[[1]])
-		myColours2<-gsub("0x","#", myColours2)
-
-		pointColors<-gsub("\\s","", strsplit(input$pointColors,",")[[1]])
-		pointColors<-gsub("0x","#", pointColors)
+		myColours<-parseColours(input$myColours)
+		myColours2<-parseColours(input$myOtherPlotColours)
+		pointColors<-parseColours(input$pointColors)
 
 		pointTransparency<-100-input$pointTransparency
 
@@ -107,7 +106,7 @@ shinyServer(function(input, output, session) {
 			if(input$xlimit==""){myLim<-range(plotData,na.rm=TRUE)+c(-1,+1)} else {myLim<-as.numeric(strsplit(input$xlimit,",")[[1]])}
 		}
 		# Data point count for each sample
-		datapointCounts<-apply(!apply(plotData, 2, is.na),2,sum) # Count number of valid data points for each sample
+		datapointCounts<-colSums(!is.na(plotData)) # Count number of valid data points for each sample
 		# Check if columns with few data points should be plotted as points
 
 		# minimum number of points is 4 -> check that nrOfDataPoints is ay least 4		
@@ -199,20 +198,20 @@ shinyServer(function(input, output, session) {
 					}
 				}
 			}
-			if(input$showNrOfPoints==TRUE){text(x=1:ncol(dataM()), y=myLim[1], labels=boxplotStats()$n)}
+			if(input$showNrOfPoints==TRUE){text(x=1:nrOfSamples, y=myLim[1], labels=boxplotStats()$n)}
 			# Add mean and CIs for mean
 			if(input$addMeans==TRUE & input$plotType=='0'){
-				boxplotMeans<-apply(dataM(), 2, mean, na.rm=TRUE)
-				points(x=1:ncol(dataM()), y=boxplotMeans, pch="+", cex=2) 
+				boxplotMeans<-colMeans(plotData, na.rm=TRUE)
+				points(x=1:nrOfSamples, y=boxplotMeans, pch="+", cex=2) 
 				if(input$addMeanCI==TRUE){ 
 					# Calculate the error using the quartile function * Standard error; SE=sd/sqrt(n)
 					myQuartile<-1-((1-(as.numeric(input$meanCI)/100))/2)
-					myError<-qt(myQuartile, df=(boxplotStats()$n)-1)*(apply(dataM(), 2, sd, na.rm=TRUE)/sapply(boxplotStats()$n, sqrt))
-					for(ii in 1:ncol(dataM())) { 
+					myError<-qt(myQuartile, df=(boxplotStats()$n)-1)*(apply(plotData, 2, sd, na.rm=TRUE)/sapply(boxplotStats()$n, sqrt))
+					for(ii in 1:nrOfSamples) { 
 #						lines(y=c(ii,ii), x=c(boxplotMeans[ii]-myError[ii], boxplotMeans[ii]+myError[ii]), col="red") 
 						rect(ii-0.05, boxplotMeans[ii]-myError[ii], ii+0.05, boxplotMeans[ii]+myError[ii], col="darkgrey", border="darkgrey") 
 					}
-					points(x=1:ncol(dataM()), y=boxplotMeans, pch="+", cex=2) 
+					points(x=1:nrOfSamples, y=boxplotMeans, pch="+", cex=2) 
 				}
 			}
 			
@@ -267,20 +266,20 @@ shinyServer(function(input, output, session) {
 				}
 			}
 			# *** Show number of data points for each column ***
-			if(input$showNrOfPoints==TRUE){text(y=1:ncol(dataM()), x=myLim[1], labels=boxplotStats()$n)}
+			if(input$showNrOfPoints==TRUE){text(y=1:nrOfSamples, x=myLim[1], labels=boxplotStats()$n)}
 			# *** Add mean and CIs for mean ***
 			if(input$addMeans==TRUE & input$plotType=='0'){
-				boxplotMeans<-apply(dataM(), 2, mean, na.rm=TRUE)
-				points(y=1:ncol(dataM()), x=boxplotMeans, pch="+", cex=2) 
+				boxplotMeans<-colMeans(plotData, na.rm=TRUE)
+				points(y=1:nrOfSamples, x=boxplotMeans, pch="+", cex=2) 
 				if(input$addMeanCI==TRUE){ 
 					# Calculate the error using the quartile function * Standard error; SE=sd/sqrt(n)
 					myQuartile<-1-((1-(as.numeric(input$meanCI)/100))/2)
-					myError<-qt(myQuartile, df=(boxplotStats()$n)-1)*(apply(dataM(), 2, sd, na.rm=TRUE)/sapply(boxplotStats()$n, sqrt))
-					for(ii in 1:ncol(dataM())) { 
+					myError<-qt(myQuartile, df=(boxplotStats()$n)-1)*(apply(plotData, 2, sd, na.rm=TRUE)/sapply(boxplotStats()$n, sqrt))
+					for(ii in 1:nrOfSamples) { 
 #						lines(y=c(ii,ii), x=c(boxplotMeans[ii]-myError[ii], boxplotMeans[ii]+myError[ii]), col="red") 
 						rect(boxplotMeans[ii]-myError[ii], ii-0.05, boxplotMeans[ii]+myError[ii], ii+0.05, col="darkgrey", border="darkgrey") 
 					}
-					points(y=1:ncol(dataM()), x=boxplotMeans, pch="+", cex=2) 
+					points(y=1:nrOfSamples, x=boxplotMeans, pch="+", cex=2) 
 				}
 			}
 			
@@ -294,7 +293,6 @@ shinyServer(function(input, output, session) {
 
 	## *** Data in table ***
 	output$filetable <- renderTable({
-		print(nrow(dataM()))
 		if(nrow(dataM())<500){
 			return(dataM())
 		} else {return(dataM()[1:100,])}
@@ -302,7 +300,6 @@ shinyServer(function(input, output, session) {
 
 	# *** Boxplot (using 'generateBoxPlot'-function) ***
 	output$boxPlot <- renderPlot({
-		print(class(dataM()))
 		generateBoxPlot(dataM())
 	}, height = heightSize, width = widthSize)
 	
@@ -347,18 +344,15 @@ shinyServer(function(input, output, session) {
 	output$boxplotStatsTable <- renderTable({
 		M<-rbind(as.matrix(boxplotStats()$stats[c(5,4,3,2,1),]),boxplotStats()$n)
 		if(input$addMeans){
-			#M<-rbind(boxplotStats()$stats[c(5,4,3,2,1),],boxplotStats()$n)
-			M<-rbind(M, apply(dataM(), 2, mean, na.rm=TRUE))
+			M<-rbind(M, colMeans(dataM(), na.rm=TRUE))
 			rownames(M)<-c("Upper whisker","3rd quartile","Median","1st quartile","Lower whisker", "Nr. of data points", "Mean")
 			colnames(M)<-colnames(dataM())
 		} else {
-			#M<-rbind(boxplotStats()$stats[c(5,4,3,2,1),],boxplotStats()$n)
 			rownames(M)<-c("Upper whisker","3rd quartile","Median","1st quartile","Lower whisker", "Nr. of data points")
 			colnames(M)<-colnames(dataM())
 		}
-		print(M)
 		M
-	})
+	}, rownames = TRUE)
 	
 	# *** Print figure legend ***
 	output$FigureLegend <- renderPrint({
diff --git a/ui.R b/ui.R
index 851aad9..f895ae7 100644
--- a/ui.R
+++ b/ui.R
@@ -1,18 +1,22 @@
 
-shinyUI(pageWithSidebar(
-
-	headerPanel("BoxPlotR: a web-tool for generation of box plots",
-		tags$head(tags$style(type="text/css", "label.radio { display: inline-block; }", ".radio input[type=\"radio\"] { float: none; }"),
-		tags$style(type="text/css", "select { max-width: 200px; }"),
-		tags$style(type="text/css", "textarea { max-width: 185px; }"),
-		tags$style(type="text/css", ".jslider { max-width: 200px; }"),
-		tags$style(type='text/css', ".well { max-width: 330px; }"),
-		tags$style(type='text/css', ".span4 { max-width: 330px; }")) 
+shinyUI(fluidPage(
+	theme = shinythemes::shinytheme("flatly"),
+	titlePanel("BoxPlotR: a web-tool for generation of box plots"),
+	tags$head(
+		tags$style(HTML("
+			label.radio { display: inline-block; }
+			.radio input[type=\"radio\"] { float: none; }
+			select { max-width: 200px; }
+			textarea { max-width: 185px; }
+			.jslider { max-width: 200px; }
+			.well { max-width: 330px; }
+			.span4 { max-width: 330px; }
+		"))
 	),
-  
-	sidebarPanel(
-		conditionalPanel(condition="input.tabs1=='About'",
-			h4("Introduction")
+	sidebarLayout(
+		sidebarPanel(
+			conditionalPanel(condition="input.tabs1=='About'",
+				h4("Introduction")
 		),
 		conditionalPanel(condition="input.tabs1=='Data upload'",
 			h4("Enter data"),
@@ -247,6 +251,7 @@ shinyUI(pageWithSidebar(
 			id="tabs1"
 		)
 	)
+  )
 ))
 
 

From 58b8ce1a53c1e2406d6482f6d15a97b95f714aaa Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Wed, 8 Apr 2026 23:52:35 +0100
Subject: [PATCH 03/14] update with docker

---
 Dockerfile | 30 +++++++++++++++++++
 README.md  | 31 +++++++++++++-------
 ui.R       | 85 +++++++++++++++++++++++++++++++++++++++---------------
 3 files changed, 113 insertions(+), 33 deletions(-)
 create mode 100644 Dockerfile

diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..2ae448e
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,30 @@
+# Use the official lightweight rocker shiny image
+FROM rocker/shiny:latest
+
+# Install system dependencies required for basic R packages
+RUN apt-get update && apt-get install -y \
+    libcurl4-gnutls-dev \
+    libcairo2-dev \
+    libxt-dev \
+    libssl-dev \
+    libssh2-1-dev \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install required R packages
+RUN R -e "install.packages(c('beeswarm', 'vioplot', 'beanplot', 'RColorBrewer'), repos='https://cloud.r-project.org/')"
+
+# Remove default Shiny apps
+RUN rm -rf /srv/shiny-server/*
+
+# Copy the application files to the container
+COPY . /srv/shiny-server/
+
+# Ensure proper ownership
+RUN chown -R shiny:shiny /srv/shiny-server/
+
+# Expose the shiny server port
+EXPOSE 3838
+
+# Run the Shiny server
+CMD ["/usr/bin/shiny-server"]
diff --git a/README.md b/README.md
index 871814f..8d06e6c 100644
--- a/README.md
+++ b/README.md
@@ -10,29 +10,30 @@ You have two options for running shiny-boxplot:
 
 1)  Launch directly from R and GitHub:
 
--   Before running the app you will need to have R and RStudio installed. Please note this application is maintained to be compatible with R 3.3.3 (the latest supported on the host server).
+-   Before running the app you will need to have the latest versions of R and RStudio installed.
 
 -   Launch the R console
 
 -   Please run these lines in R:
 
     -   install.packages("shiny")
-    -   install.packages("shinythemes")
     -   install.packages("beeswarm")
     -   install.packages("vioplot")
     -   install.packages("beanplot")
     -   install.packages("RColorBrewer")
 
 - Then start the app:
-  - shiny::runGitHub("BoxPlotR.shiny", "VizWizard")
+  - shiny::runGitHub("BoxPlotR.shiny", "jwildenhain")
 
 Your web browser will open the web app.
 
-2) Install the shiny-server and implement shiny-boxplot as a web application and service:
-  - In Ubuntu 12.04+
-    - sudo apt-get install gdebi-core
-    - wget http://download3.rstudio.org/ubuntu-12.04/x86_64/shiny-server-1.0.0.42-amd64.deb (may need to change ubuntu or server version number)
-	- sudo gdebi shiny-server-1.0.0.42-amd64.deb
+2)  Install the shiny-server and implement shiny-boxplot as a web application and service:
+
+-   In Ubuntu Modern Versions
+    -   sudo apt-get install gdebi-core
+    -   Visit <https://posit.co/download/shiny-server/> to find the URL for the latest Shiny Server release.
+    -   wget <URL_FOR_LATEST_SHINY_SERVER.deb>
+    -   sudo gdebi <DOWNLOADED_FILE.deb>
 	- edit: /opt/shiny-server/config/default.config in a text editor
 	  - Change these lines to suit your environment
 	  - listen **SHINY_PORT**; (change **SHINY_PORT** to match the port you want)
@@ -40,11 +41,21 @@ Your web browser will open the web app.
 	- make sure **SHINY_PORT** is open on your firewall
 	- Go to your **SHINY_APP_HOME**
 	  - cd **SHINY_APP_HOME**
-	- Get the latest shiny-boxplot code from github:
-	  - wget https://github.com/VizWizard/BoxPlotR.shiny/archive/master.zip
+	    -   Get the latest shiny-boxplot code from github:
+        -   wget <https://github.com/jwildenhain/BoxPlotR.shiny/archive/master.zip>
 	  - unzip master.zip
 	  - mv BoxPlotR.shiny-master BoxPlotR.shiny
 	- Restart shiny-server service:
 	  - sudo service shiny-server restart
 
 You should now be able to access shiny-boxplot at: http://YOURSITE:**SHINY_PORT**/BoxPlotR.shiny
+
+3)  Run using Docker (Recommended isolated deployment):
+
+-   You can deploy the fully-configured Docker version natively without installing anything onto the host running system architecture:
+
+```bash
+docker build -t boxplotr .
+docker run -d -p 3838:3838 boxplotr
+```
+-   Then access the app via a web browser at: `http://localhost:3838`
diff --git a/ui.R b/ui.R
index f895ae7..c845175 100644
--- a/ui.R
+++ b/ui.R
@@ -1,16 +1,55 @@
 
 shinyUI(fluidPage(
-	theme = shinythemes::shinytheme("flatly"),
 	titlePanel("BoxPlotR: a web-tool for generation of box plots"),
 	tags$head(
 		tags$style(HTML("
+			@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
+			body, h1, h2, h3, h4, h5, h6, .shiny-text-output, label { font-family: 'Inter', sans-serif !important; }
+			body { background-color: #f3f4f6; }
 			label.radio { display: inline-block; }
 			.radio input[type=\"radio\"] { float: none; }
-			select { max-width: 200px; }
-			textarea { max-width: 185px; }
+			select { max-width: 200px; border-radius: 6px; border: 1px solid #d1d5db; padding: 4px; }
+			textarea { max-width: 185px; border-radius: 6px; border: 1px solid #d1d5db; }
 			.jslider { max-width: 200px; }
-			.well { max-width: 330px; }
+			
+			/* Glassmorphism Sidebar */
+			.well {
+				max-width: 330px;
+				background: rgba(255, 255, 255, 0.6) !important;
+				backdrop-filter: blur(12px) !important;
+				-webkit-backdrop-filter: blur(12px) !important;
+				border: 1px solid rgba(255, 255, 255, 0.6) !important;
+				border-radius: 16px !important;
+				box-shadow: 0 10px 25px rgba(0, 0, 0, 0.05) !important;
+			}
 			.span4 { max-width: 330px; }
+
+			/* Accent Colors - Ocean Blue */
+			.nav-tabs > li.active > a, .nav-tabs > li.active > a:focus, .nav-tabs > li.active > a:hover {
+				color: #fff !important; background-color: #0c4a6e !important;
+				border-radius: 8px 8px 0 0; border: none !important;
+			}
+			.nav-tabs > li > a { color: #475569 !important; font-weight: 500; }
+			.btn-default {
+				background-color: #e0f2fe !important; color: #0369a1 !important;
+				border: 1px solid #bae6fd !important; border-radius: 6px !important;
+				transition: all 0.2s ease; font-weight: 600; margin-bottom: 5px;
+			}
+			.btn-default:hover {
+				background-color: #0ea5e9 !important; color: white !important;
+				box-shadow: 0 4px 12px rgba(14, 165, 233, 0.3) !important;
+			}
+			
+			/* Card Layout for Plots */
+			.plot-card {
+				background: #ffffff; border-radius: 16px; padding: 20px;
+				box-shadow: 0 10px 25px rgba(0,0,0,0.06); margin-top: 15px; margin-bottom: 25px; border: 1px solid #e5e7eb;
+			}
+			.table-card {
+				background: #ffffff; border-radius: 12px; padding: 15px;
+				box-shadow: 0 4px 12px rgba(0,0,0,0.05); border: 1px solid #e5e7eb;
+			}
+			.controls-row { display: flex; gap: 10px; margin-top: 15px; flex-wrap: wrap; }
 		"))
 	),
 	sidebarLayout(
@@ -166,7 +205,7 @@ shinyUI(fluidPage(
 				boxes. These are defined as +/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) which gives roughly 95% confidence that two medians are different. It is also possible to define
 				the whiskers based on the ideas of Spear and Tukey. Additional options of data visualization (violin and bean plots) reveal more information
 				about the underlying data distribution. Plots can be labeled, customized (colors, dimensions, orientation) and exported as eps, pdf and svg files.</p>
-				<p>BoxPlotR code can be run locally via <a href="https://github.com/VizWizard/BoxPlotR.shiny/blob/master/README.md">GitHub</a>. You can also download and install it as a virtual machine (see <a href="https://github.com/VizWizard/BoxPlotR.shiny/blob/master/README.md">GitHub</a> and FAQs for details). </p>'),
+				<p>BoxPlotR code can be run locally via <a href="https://github.com/jwildenhain/BoxPlotR.shiny/blob/master/README.md">GitHub</a>. You can also download and install it as a virtual machine (see <a href="https://github.com/jwildenhain/BoxPlotR.shiny/blob/master/README.md">GitHub</a> and FAQs for details). </p>'),
 
 				h5("Software references"),
 				HTML('<p>R Development Core Team. <i><a href="http://www.r-project.org/">R</a>:  A Language and Environment for Statistical Computing.</i> R Foundation for Statistical Computing, Vienna (2013) <br>
@@ -189,11 +228,20 @@ shinyUI(fluidPage(
 				a("shiny package from RStudio", href="http://www.rstudio.com/shiny/"), ".")
 			),
 			# Boxplot tab
-			tabPanel("Data visualization", downloadButton("downloadPlotEPS", "Download eps-file"),
-				downloadButton("downloadPlotPDF", "Download pdf-file"),
-				downloadButton("downloadPlotSVG", "Download svg-file"),
-				plotOutput("boxPlot", height='100%', width='100%'),
-				h4("Box plot statistics"), tableOutput("boxplotStatsTable"),
+			tabPanel("Data visualization", 
+				div(class="controls-row",
+					downloadButton("downloadPlotEPS", "Download eps-file"),
+					downloadButton("downloadPlotPDF", "Download pdf-file"),
+					downloadButton("downloadPlotSVG", "Download svg-file")
+				),
+				div(class="plot-card",
+					plotOutput("boxPlot", height='100%', width='100%')
+				),
+				div(class="table-card",
+					h4("Box plot statistics"), 
+					tableOutput("boxplotStatsTable")
+				),
+				br(),
 				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"),
 				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
 				a("shiny package from RStudio", href="http://www.rstudio.com/shiny/"), ".")
@@ -208,6 +256,8 @@ shinyUI(fluidPage(
 			),
 			# News
 			tabPanel("News",
+				h5("April 8, 2026"),
+				HTML('<p>The shiny server backend has been updated. The number of concurrent sessions has been limited to 15 and the session idle timeout set to 10 minutes. We are currently reworking the code to support the latest R and shiny versions, plus preparing a system upgrade of the server.</p>'),
                 h5("January 17, 2021"),
                 HTML('<p>There are several recent updates. The jitter of points is now consistent for all samples. When
                 data points are added to the plot, the size and transparence of the points can now be modified with
@@ -232,19 +282,8 @@ shinyUI(fluidPage(
 				In Adobe Illustrator you will also need to use the 'release compound path' command. For PDF 
 				files you should 'release clipping mask'. SVG import appears to have problems in Adobe Illustrator 
 				and Corel Draw and should be avoided. EPS, PDF and SVG import all work with Inkscape http://www.inkscape.org/."),
-				h5("Q: I would like to install BoxPlotR as a virtual machine."), 
-				HTML('<p>A: Please download the virtual machine from http://tyerslab.bio.ed.ac.uk/download/shiny.boxplot.7z (1.1GB). 
-				Unzip the file using <a href="http://www.7-zip.org/">7zip</a> or equivalent. The virtual machine is available in the open 
-				virtualization format (OVF) and you can use this file with <a href="https://www.virtualbox.org/">vbox</a> and 
-				<a href="https://my.vmware.com/web/vmware/free#desktop_end_user_computing/vmware_player/6_0">vmware player</a>. 
-				One easy way to use the server is to set the virtual host network environment to NAT. After importing the virtual machine you can 
-				start the server and login as user shiny  with password pk635153Y6jx89r. On the command line use the command ifconfg and record the 
-				IP address of the virtual server. Now going back to the virtual host network environment change the advanced settings of the NAT and 
-				activate port forwarding for the guest network ip address (shiny server) using port 80 to the host ip address using for example port 8080. 
-				Now you should be able to access the shiny server in a browser on port 127.0.0.1:8080 or your localhost:8080.
-				If you are not familiar with the software packages there are detailed examples for 
-				<a href="http://www.howtogeek.com/122641/how-to-forward-ports-to-a-virtual-machine-and-use-it-as-a-server/">vbox</a> and 
-				<a href="http://blog.fardad.com/2012/06/vmware-player-and-custom-nat-port-map.html">vmware</a>.</p>')
+				h5("Q: I would like to run BoxPlotR locally in an isolated environment."), 
+				HTML('<p>A: We provide a pre-configured Docker image that automatically installs and configures the environment. First, ensure you have <a href="https://docs.docker.com/get-docker/">Docker installed</a>. Then, clone the repository and run the following terminal commands inside the repository directory: <br><br> <code>docker build -t boxplotr .</code> <br> <code>docker run -d -p 3838:3838 boxplotr</code> <br><br> You can then access the application by navigating your web browser to <strong>http://localhost:3838</strong>.</p>')
 #				h5("Q:"), 
 #				p("A:")
 			),			

From f233ad77a5dfa67a88d779e03dcec982cdb056e3 Mon Sep 17 00:00:00 2001
From: Jan Wildenhain <jan.wildenhain@gmail.com>
Date: Tue, 14 Apr 2026 11:53:47 +0100
Subject: [PATCH 04/14] Add Docker publish workflow

This workflow automates the building and publishing of Docker images to a specified registry. It includes steps for checking out the repository, installing necessary tools, logging into the registry, building the image, and signing the image digest.
---
 .github/workflows/docker-publish.yml | 98 ++++++++++++++++++++++++++++
 1 file changed, 98 insertions(+)
 create mode 100644 .github/workflows/docker-publish.yml

diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml
new file mode 100644
index 0000000..6f252d4
--- /dev/null
+++ b/.github/workflows/docker-publish.yml
@@ -0,0 +1,98 @@
+name: Docker
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+on:
+  schedule:
+    - cron: '24 21 * * *'
+  push:
+    branches: [ "master" ]
+    # Publish semver tags as releases.
+    tags: [ 'v*.*.*' ]
+  pull_request:
+    branches: [ "master" ]
+
+env:
+  # Use docker.io for Docker Hub if empty
+  REGISTRY: ghcr.io
+  # github.repository as <account>/<repo>
+  IMAGE_NAME: ${{ github.repository }}
+
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+      # This is used to complete the identity challenge
+      # with sigstore/fulcio when running outside of PRs.
+      id-token: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      # Install the cosign tool except on PR
+      # https://github.com/sigstore/cosign-installer
+      - name: Install cosign
+        if: github.event_name != 'pull_request'
+        uses: sigstore/cosign-installer@59acb6260d9c0ba8f4a2f9d9b48431a222b68e20 #v3.5.0
+        with:
+          cosign-release: 'v2.2.4'
+
+      # Set up BuildKit Docker container builder to be able to build
+      # multi-platform images and export cache
+      # https://github.com/docker/setup-buildx-action
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@f95db51fddba0c2d1ec667646a06c2ce06100226 # v3.0.0
+
+      # Login against a Docker registry except on PR
+      # https://github.com/docker/login-action
+      - name: Log into registry ${{ env.REGISTRY }}
+        if: github.event_name != 'pull_request'
+        uses: docker/login-action@343f7c4344506bcbf9b4de18042ae17996df046d # v3.0.0
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      # Extract metadata (tags, labels) for Docker
+      # https://github.com/docker/metadata-action
+      - name: Extract Docker metadata
+        id: meta
+        uses: docker/metadata-action@96383f45573cb7f253c731d3b3ab81c87ef81934 # v5.0.0
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      # Build and push Docker image with Buildx (don't push on PR)
+      # https://github.com/docker/build-push-action
+      - name: Build and push Docker image
+        id: build-and-push
+        uses: docker/build-push-action@0565240e2d4ab88bba5387d719585280857ece09 # v5.0.0
+        with:
+          context: .
+          push: ${{ github.event_name != 'pull_request' }}
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+
+      # Sign the resulting Docker image digest except on PRs.
+      # This will only write to the public Rekor transparency log when the Docker
+      # repository is public to avoid leaking data.  If you would like to publish
+      # transparency data even for private images, pass --force to cosign below.
+      # https://github.com/sigstore/cosign
+      - name: Sign the published Docker image
+        if: ${{ github.event_name != 'pull_request' }}
+        env:
+          # https://docs.github.com/en/actions/security-guides/security-hardening-for-github-actions#using-an-intermediate-environment-variable
+          TAGS: ${{ steps.meta.outputs.tags }}
+          DIGEST: ${{ steps.build-and-push.outputs.digest }}
+        # This step uses the identity token to provision an ephemeral certificate
+        # against the sigstore community Fulcio instance.
+        run: echo "${TAGS}" | xargs -I {} cosign sign --yes {}@${DIGEST}

From 571a56aa8222a614db64832f6011ed936f4e3f2c Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Thu, 16 Apr 2026 10:39:13 +0100
Subject: [PATCH 05/14] refactor: update UI to fluidPage, optimize data
 processing, and improve server-side performance

---
 server.R |   8 +-
 ui.R     | 479 +++++++++++++++++++++++++++++--------------------------
 2 files changed, 259 insertions(+), 228 deletions(-)

diff --git a/server.R b/server.R
index bf990c0..50ff3bc 100644
--- a/server.R
+++ b/server.R
@@ -41,8 +41,7 @@ shinyServer(function(input, output, session) {
 			mySep<-switch(input$fileSepDF, '1'=",",'2'="\t",'3'=";", '4'="") #list("Comma"=1,"Tab"=2,"Semicolon"=3)
 				data<-read.table(inFile$datapath, sep=mySep, header=TRUE, fill=TRUE, check.names=FALSE)
 		} else { # To be looked into again - for special case when last column has empty entries in some rows
-			req(input$myData)
-			if(input$myData == "") {return(NULL)}
+			if(is.null(input$myData) || input$myData == "") {return(NULL)}
 			mySep<-switch(input$fileSepP, '1'=",",'2'="\t",'3'=";")
 			data <- read.table(text = input$myData, sep=mySep, header=TRUE, fill=TRUE, check.names=FALSE)
 		}
@@ -65,6 +64,7 @@ shinyServer(function(input, output, session) {
 	
 	# *** Get boxplot statistics ***
 	boxplotStats <- reactive({
+		if(is.null(dataM())) return(NULL)
 		return(boxplot(dataM(), na.rm=TRUE, range=myRange(), plot=FALSE))
 	})
 	
@@ -293,6 +293,7 @@ shinyServer(function(input, output, session) {
 
 	## *** Data in table ***
 	output$filetable <- renderTable({
+		if(is.null(dataM())) return(NULL)
 		if(nrow(dataM())<500){
 			return(dataM())
 		} else {return(dataM()[1:100,])}
@@ -300,6 +301,7 @@ shinyServer(function(input, output, session) {
 
 	# *** Boxplot (using 'generateBoxPlot'-function) ***
 	output$boxPlot <- renderPlot({
+		if(is.null(dataM())) return(NULL)
 		generateBoxPlot(dataM())
 	}, height = heightSize, width = widthSize)
 	
@@ -342,6 +344,7 @@ shinyServer(function(input, output, session) {
 
 	# *** Output boxplot statistics in table below plot ***
 	output$boxplotStatsTable <- renderTable({
+		if(is.null(dataM()) || is.null(boxplotStats())) return(NULL)
 		M<-rbind(as.matrix(boxplotStats()$stats[c(5,4,3,2,1),]),boxplotStats()$n)
 		if(input$addMeans){
 			M<-rbind(M, colMeans(dataM(), na.rm=TRUE))
@@ -356,6 +359,7 @@ shinyServer(function(input, output, session) {
 	
 	# *** Print figure legend ***
 	output$FigureLegend <- renderPrint({
+		if(is.null(dataM()) || is.null(boxplotStats())) return(invisible())
 		# Center lines show the medians; box limits indicate the 25th and 75th percentiles as determined by R software; whiskers extend to minimum and maximum values; crosses represent means; bars indicate 95% confidence intervals. n = 100, 76, 16, 76, 41 sample points.
 		# Generate vector with pieces of the legend based on user selections
 		FL<-vector()
diff --git a/ui.R b/ui.R
index c845175..f0eed18 100644
--- a/ui.R
+++ b/ui.R
@@ -1,17 +1,16 @@
-
 shinyUI(fluidPage(
-	titlePanel("BoxPlotR: a web-tool for generation of box plots"),
-	tags$head(
-		tags$style(HTML("
+    titlePanel("BoxPlotR: a web-tool for generation of box plots"),
+    tags$head(
+        tags$style(HTML("
 			@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
 			body, h1, h2, h3, h4, h5, h6, .shiny-text-output, label { font-family: 'Inter', sans-serif !important; }
 			body { background-color: #f3f4f6; }
 			label.radio { display: inline-block; }
 			.radio input[type=\"radio\"] { float: none; }
 			select { max-width: 200px; border-radius: 6px; border: 1px solid #d1d5db; padding: 4px; }
-			textarea { max-width: 185px; border-radius: 6px; border: 1px solid #d1d5db; }
+			textarea { width: 100%; max-width: 100%; display: block; border-radius: 6px; border: 1px solid #d1d5db; box-sizing: border-box; margin-bottom: 10px; font-family: 'Courier New', Courier, monospace; white-space: pre; overflow-x: auto; }
 			.jslider { max-width: 200px; }
-			
+
 			/* Glassmorphism Sidebar */
 			.well {
 				max-width: 330px;
@@ -39,7 +38,7 @@ shinyUI(fluidPage(
 				background-color: #0ea5e9 !important; color: white !important;
 				box-shadow: 0 4px 12px rgba(14, 165, 233, 0.3) !important;
 			}
-			
+
 			/* Card Layout for Plots */
 			.plot-card {
 				background: #ffffff; border-radius: 16px; padding: 20px;
@@ -51,147 +50,163 @@ shinyUI(fluidPage(
 			}
 			.controls-row { display: flex; gap: 10px; margin-top: 15px; flex-wrap: wrap; }
 		"))
-	),
-	sidebarLayout(
-		sidebarPanel(
-			conditionalPanel(condition="input.tabs1=='About'",
-				h4("Introduction")
-		),
-		conditionalPanel(condition="input.tabs1=='Data upload'",
-			h4("Enter data"),
-			radioButtons("dataInput", "", list("Load sample data"=1,"Upload file"=2,"Paste data"=3)),
-			conditionalPanel(condition="input.dataInput=='1'",
-				h5("Load sample data:"),
-				radioButtons("sampleData", "Load sample data", list("Example 1 (100,76,16,76,41 data points)"=1,"Example 2 (3 columns with 100 data points)"=2))
-			),
-			conditionalPanel(condition="input.dataInput=='2'",
-				h5("Upload delimited text file: "),
-				fileInput("upload", "", multiple = FALSE),
-				radioButtons("fileSepDF", "Delimiter:", list("Comma"=1,"Tab"=2,"Semicolon"=3)),#, "Space"=4))
-				HTML('<p>Data in <a href="http://en.wikipedia.org/wiki/Delimiter-separated_values">delimited text files </a> can be separated by comma, tab or semicolon. 
+    ),
+    sidebarLayout(
+        sidebarPanel(
+            conditionalPanel(
+                condition = "input.tabs1=='About'",
+                h4("Introduction")
+            ),
+            conditionalPanel(
+                condition = "input.tabs1=='Data upload'",
+                h4("Enter data"),
+                radioButtons("dataInput", "", list("Load sample data" = 1, "Upload file" = 2, "Paste data" = 3)),
+                conditionalPanel(
+                    condition = "input.dataInput=='1'",
+                    h5("Load sample data:"),
+                    radioButtons("sampleData", "Load sample data", list("Example 1 (100,76,16,76,41 data points)" = 1, "Example 2 (3 columns with 100 data points)" = 2))
+                ),
+                conditionalPanel(
+                    condition = "input.dataInput=='2'",
+                    h5("Upload delimited text file: "),
+                    fileInput("upload", "", multiple = FALSE),
+                    radioButtons("fileSepDF", "Delimiter:", list("Comma" = 1, "Tab" = 2, "Semicolon" = 3)), # , "Space"=4))
+                    HTML('<p>Data in <a href="http://en.wikipedia.org/wiki/Delimiter-separated_values">delimited text files </a> can be separated by comma, tab or semicolon.
 				For example, Excel data can be exported in .csv (comma separated) or .tab (tab separated) format. </p>')
-			),
-			conditionalPanel(condition="input.dataInput=='3'",
-				h5("Paste data below:"),
-				tags$textarea(id="myData", rows=10, cols=5, ""),
-				actionButton('clearText_button','Clear data'),
-				radioButtons("fileSepP", "Separator:", list("Comma"=1,"Tab"=2,"Semicolon"=3))
-			)
-		),
-		conditionalPanel(condition="input.tabs1=='Data visualization'",
-
-			radioButtons("plotType", "", list("Boxplot"=0, "Other"=1)),
-			conditionalPanel(condition="input.plotType=='1'",
-				radioButtons("otherPlotType", "", list("Violin plot"=0, "Bean plot"=1)),
-				HTML('<p style="color:#808080">Violin plots are generated with the <a href="https://cran.r-project.org/web/packages/vioplot/index.html">vioplot package</a>. Bean plots are generated with the <a href="https://cran.r-project.org/web/packages/beanplot/index.html">beanplot package</a>.</p>'),
-				textInput("myOtherPlotColours", "Colour(s):", value=c("light grey, white")),
-				conditionalPanel(condition="input.otherPlotType=='0'",
-					helpText("Colour of the 'violin area'"),
-					textInput("violinBorder", "Border colour:", value=c("grey"))			
-				),
-				conditionalPanel(condition="input.otherPlotType=='1'",
-					helpText("up to 4 colours can be specified: area of the beans, lines inside the bean, lines outside the bean, and average line per bean"),
-					textInput("beanBorder", "Border colour:", value=c("grey")),	
-					radioButtons("beanPlotMedianMean", "Display: ", list("Median"=0, "Mean"=1))
-				)
-			),
-
-			h4("Plot options"),
-			checkboxInput("plotDataPoints", "Minimum number of data points", FALSE),
-			conditionalPanel(condition="input.plotDataPoints",			
-				numericInput("nrOfDataPoints", "Data point limit: ", value=5, min=5)
-			),
-
-			conditionalPanel(condition="input.plotType=='0'",
-				checkboxInput("showDataPoints", "Add data points", FALSE),
-				conditionalPanel(condition="input.showDataPoints",
-					radioButtons("datapointType", "", list("Default"=0, "Bee swarm"=1, "Jittered"=2)),
-					sliderInput("pointTransparency", "Transparency of data points", min=0, max=99, value=50),
-					sliderInput("pointSize", "Size of data points", min=1, max=20, value=10),
-					HTML('<p style="color:#808080">Using the <a href="http://www.cbs.dtu.dk/~eklund/beeswarm/">beeswarm package</a>.</p>'),
-					textInput("pointColors", "Colour(s):", value=c("black"))					
-				),
-				checkboxInput("whiskerDefinition", "Definition of whisker extent", FALSE),
-				conditionalPanel(condition="input.whiskerDefinition",
-					radioButtons("whiskerType", "", list("Tukey"=0, "Spear"=1, "Altman"=2)),
-#					conditionalPanel(condition="input.whiskerType=='0'",
-#						numericInput("TukeyRange", "Define whisker extent (x IQR):", min=0, step=0.5, value=1.5)
-#					),
-#					conditionalPanel(condition="input.whiskerType=='1'",
-#						HTML('<p>Spear - Whiskers extend to minimum and maximum values.</p>')
-#					),
-#					conditionalPanel(condition="input.whiskerType=='2'",
-#						numericInput("AltmanRange", "Define whisker extent in percentiles (ie, '5' means that whiskers extend to 5th and 95th percentile):", min=0, step=0.5, value=5)
-#					),
-					HTML('<p style="color:#808080">Tukey - whiskers extend to data points that are less than 1.5 x <a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a> away from 1st/3rd <a href=:"http://en.wikipedia.org/wiki/Quartile">quartile</a>; 
-					Spear - whiskers extend to minimum and maximum values; 
+                ),
+                conditionalPanel(
+                    condition = "input.dataInput=='3'",
+                    h5("Paste data below:"),
+                    tags$textarea(id = "myData", rows = 10, cols = 5, ""),
+                    actionButton("clearText_button", "Clear data"),
+                    radioButtons("fileSepP", "Separator:", list("Comma" = 1, "Tab" = 2, "Semicolon" = 3))
+                )
+            ),
+            conditionalPanel(
+                condition = "input.tabs1=='Data visualization'",
+                radioButtons("plotType", "", list("Boxplot" = 0, "Other" = 1)),
+                conditionalPanel(
+                    condition = "input.plotType=='1'",
+                    radioButtons("otherPlotType", "", list("Violin plot" = 0, "Bean plot" = 1)),
+                    HTML('<p style="color:#808080">Violin plots are generated with the <a href="https://cran.r-project.org/web/packages/vioplot/index.html">vioplot package</a>. Bean plots are generated with the <a href="https://cran.r-project.org/web/packages/beanplot/index.html">beanplot package</a>.</p>'),
+                    textInput("myOtherPlotColours", "Colour(s):", value = c("light grey, white")),
+                    conditionalPanel(
+                        condition = "input.otherPlotType=='0'",
+                        helpText("Colour of the 'violin area'"),
+                        textInput("violinBorder", "Border colour:", value = c("grey"))
+                    ),
+                    conditionalPanel(
+                        condition = "input.otherPlotType=='1'",
+                        helpText("up to 4 colours can be specified: area of the beans, lines inside the bean, lines outside the bean, and average line per bean"),
+                        textInput("beanBorder", "Border colour:", value = c("grey")),
+                        radioButtons("beanPlotMedianMean", "Display: ", list("Median" = 0, "Mean" = 1))
+                    )
+                ),
+                h4("Plot options"),
+                checkboxInput("plotDataPoints", "Minimum number of data points", FALSE),
+                conditionalPanel(
+                    condition = "input.plotDataPoints",
+                    numericInput("nrOfDataPoints", "Data point limit: ", value = 5, min = 5)
+                ),
+                conditionalPanel(
+                    condition = "input.plotType=='0'",
+                    checkboxInput("showDataPoints", "Add data points", FALSE),
+                    conditionalPanel(
+                        condition = "input.showDataPoints",
+                        radioButtons("datapointType", "", list("Default" = 0, "Bee swarm" = 1, "Jittered" = 2)),
+                        sliderInput("pointTransparency", "Transparency of data points", min = 0, max = 99, value = 50),
+                        sliderInput("pointSize", "Size of data points", min = 1, max = 20, value = 10),
+                        HTML('<p style="color:#808080">Using the <a href="http://www.cbs.dtu.dk/~eklund/beeswarm/">beeswarm package</a>.</p>'),
+                        textInput("pointColors", "Colour(s):", value = c("black"))
+                    ),
+                    checkboxInput("whiskerDefinition", "Definition of whisker extent", FALSE),
+                    conditionalPanel(
+                        condition = "input.whiskerDefinition",
+                        radioButtons("whiskerType", "", list("Tukey" = 0, "Spear" = 1, "Altman" = 2)),
+                        # 					conditionalPanel(condition="input.whiskerType=='0'",
+                        # 						numericInput("TukeyRange", "Define whisker extent (x IQR):", min=0, step=0.5, value=1.5)
+                        # 					),
+                        # 					conditionalPanel(condition="input.whiskerType=='1'",
+                        # 						HTML('<p>Spear - Whiskers extend to minimum and maximum values.</p>')
+                        # 					),
+                        # 					conditionalPanel(condition="input.whiskerType=='2'",
+                        # 						numericInput("AltmanRange", "Define whisker extent in percentiles (ie, '5' means that whiskers extend to 5th and 95th percentile):", min=0, step=0.5, value=5)
+                        # 					),
+                        HTML('<p style="color:#808080">Tukey - whiskers extend to data points that are less than 1.5 x <a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a> away from 1st/3rd <a href=:"http://en.wikipedia.org/wiki/Quartile">quartile</a>;
+					Spear - whiskers extend to minimum and maximum values;
 					Altman - whiskers extend to 5th and 95th percentile (use only if n>40)</p>')
-				),
-				checkboxInput("showNrOfPoints", "Display number of data points", FALSE),
-				checkboxInput("addMeans", "Add sample means", FALSE),
-				conditionalPanel(condition="input.addMeans",
-					checkboxInput("addMeanCI", "Add confidence intervals of means", FALSE),
-					conditionalPanel(condition="input.addMeanCI",
-						radioButtons("meanCI", "Define confidence interval of means:", list("83%"=83, "90%"=90, "95%"=95))
-					)				
-				),
-							
-				checkboxInput("myVarwidth", "Variable width boxes", FALSE),
-				helpText("Widths of boxes are proportional to square-roots of the number of observations."),
-				checkboxInput("myNotch", "Add notches", FALSE),
-				HTML('<p style="color:#808080">+/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) - gives roughly 95% confidence that two medians differ (Chambers et al., 1983)</p>'),
-				conditionalPanel(condition="input.myNotch",
-					HTML('<p>The notches are defined as +/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) and represent the 95% <a href="http://en.wikipedia.org/wiki/Confidence_interval">confidence interval</a> for each median. 
-					Non-overlapping notches give roughly 95% confidence that two medians differ, ie, in 19 out of 20 cases the population 
+                    ),
+                    checkboxInput("showNrOfPoints", "Display number of data points", FALSE),
+                    checkboxInput("addMeans", "Add sample means", FALSE),
+                    conditionalPanel(
+                        condition = "input.addMeans",
+                        checkboxInput("addMeanCI", "Add confidence intervals of means", FALSE),
+                        conditionalPanel(
+                            condition = "input.addMeanCI",
+                            radioButtons("meanCI", "Define confidence interval of means:", list("83%" = 83, "90%" = 90, "95%" = 95))
+                        )
+                    ),
+                    checkboxInput("myVarwidth", "Variable width boxes", FALSE),
+                    helpText("Widths of boxes are proportional to square-roots of the number of observations."),
+                    checkboxInput("myNotch", "Add notches", FALSE),
+                    HTML('<p style="color:#808080">+/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) - gives roughly 95% confidence that two medians differ (Chambers et al., 1983)</p>'),
+                    conditionalPanel(
+                        condition = "input.myNotch",
+                        HTML('<p>The notches are defined as +/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) and represent the 95% <a href="http://en.wikipedia.org/wiki/Confidence_interval">confidence interval</a> for each median.
+					Non-overlapping notches give roughly 95% confidence that two medians differ, ie, in 19 out of 20 cases the population
 					medians (estimated based on the samples) are in fact different (Chambers et al., 1983).</p>')
-				),
-				textInput("myColours", "Colour(s):", value=c("light grey, white")),
-				helpText("Colours in HEX format can be chosen on http://colorbrewer2.org/")
-			),
-
-			checkboxInput("labelsTitle", "Modify labels and title", FALSE),
-			conditionalPanel(condition="input.labelsTitle",
-				checkboxInput("xaxisLabelAngle", "Rotate sample names", FALSE),
-				textInput("myXlab", "X-axis label:", value=c("")),
-				textInput("myYlab", "Y-axis label:", value=c("")),
-				textInput("myTitle", "Boxplot title:", value=c("")),
-				textInput("mySubtitle", "Boxplot subtitle:", value=c(""))
-			),
-			checkboxInput("plotSize", "Adjust plot size", FALSE),
-			conditionalPanel(condition="input.plotSize",
-				numericInput("myHeight", "Plot height:", value=550),
-				numericInput("myWidth", "Plot width:", value=750)
-			),
-			checkboxInput("fontSizes", "Change font sizes", FALSE),
-			conditionalPanel(condition="input.fontSizes",
-				numericInput("cexTitle", "Title font size:", value=14),
-				numericInput("cexAxislabel", "Axis label size:", value=14),
-				numericInput("cexAxis", "Axis font size:", value=12)
-			),
-			h5("Orientation of box plots:"),
-			radioButtons("myOrientation", "", list("Vertical"=0, "Horizontal"=1)),
-			conditionalPanel(condition="input.myOrientation=='0'",
-				h5("Y-axis range (eg., '0,10'):"),
-				textInput("ylimit", "", value="")
-			),
-			conditionalPanel(condition="input.myOrientation=='1'",
-				h5("X-axis range (eg., '0,10'):"),
-				textInput("xlimit", "", value="")
-			),
-			checkboxInput("logScale", "Change to log scale (only for data >0)", FALSE),			
-			h5("Add grid: "),
-			radioButtons("addGrid", "", list("None" = 0, "X & Y"= 1, "X only" = 2, "Y only" = 3))
-#			numericInput("boxWidth", "Width of boxes:", value=1),
-		)	
-	),
-  
-	mainPanel(
-		tabsetPanel(
-			# Welcome tab
-			tabPanel("About",
-				HTML('
+                    ),
+                    textInput("myColours", "Colour(s):", value = c("light grey, white")),
+                    helpText("Colours in HEX format can be chosen on http://colorbrewer2.org/")
+                ),
+                checkboxInput("labelsTitle", "Modify labels and title", FALSE),
+                conditionalPanel(
+                    condition = "input.labelsTitle",
+                    checkboxInput("xaxisLabelAngle", "Rotate sample names", FALSE),
+                    textInput("myXlab", "X-axis label:", value = c("")),
+                    textInput("myYlab", "Y-axis label:", value = c("")),
+                    textInput("myTitle", "Boxplot title:", value = c("")),
+                    textInput("mySubtitle", "Boxplot subtitle:", value = c(""))
+                ),
+                checkboxInput("plotSize", "Adjust plot size", FALSE),
+                conditionalPanel(
+                    condition = "input.plotSize",
+                    numericInput("myHeight", "Plot height:", value = 550),
+                    numericInput("myWidth", "Plot width:", value = 750)
+                ),
+                checkboxInput("fontSizes", "Change font sizes", FALSE),
+                conditionalPanel(
+                    condition = "input.fontSizes",
+                    numericInput("cexTitle", "Title font size:", value = 14),
+                    numericInput("cexAxislabel", "Axis label size:", value = 14),
+                    numericInput("cexAxis", "Axis font size:", value = 12)
+                ),
+                h5("Orientation of box plots:"),
+                radioButtons("myOrientation", "", list("Vertical" = 0, "Horizontal" = 1)),
+                conditionalPanel(
+                    condition = "input.myOrientation=='0'",
+                    h5("Y-axis range (eg., '0,10'):"),
+                    textInput("ylimit", "", value = "")
+                ),
+                conditionalPanel(
+                    condition = "input.myOrientation=='1'",
+                    h5("X-axis range (eg., '0,10'):"),
+                    textInput("xlimit", "", value = "")
+                ),
+                checkboxInput("logScale", "Change to log scale (only for data >0)", FALSE),
+                h5("Add grid: "),
+                radioButtons("addGrid", "", list("None" = 0, "X & Y" = 1, "X only" = 2, "Y only" = 3))
+                # 			numericInput("boxWidth", "Width of boxes:", value=1),
+            )
+        ),
+        mainPanel(
+            tabsetPanel(
+                # Welcome tab
+                tabPanel(
+                    "About",
+                    HTML('
 				<br>
-				
+
 				<p>This application was developed with Nature Methods and you can find the publication <a href="http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2811.html">here</a>.
 				The BoxPlotR has also been mentioned in this <a href="http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2837.html">editorial</a> and this
 				<a href="http://blogs.nature.com/methagora/2014/01/bring-on-the-box-plots-boxplotr.html">blog entry</a>. Nature methods also dedicated a
@@ -206,96 +221,108 @@ shinyUI(fluidPage(
 				the whiskers based on the ideas of Spear and Tukey. Additional options of data visualization (violin and bean plots) reveal more information
 				about the underlying data distribution. Plots can be labeled, customized (colors, dimensions, orientation) and exported as eps, pdf and svg files.</p>
 				<p>BoxPlotR code can be run locally via <a href="https://github.com/jwildenhain/BoxPlotR.shiny/blob/master/README.md">GitHub</a>. You can also download and install it as a virtual machine (see <a href="https://github.com/jwildenhain/BoxPlotR.shiny/blob/master/README.md">GitHub</a> and FAQs for details). </p>'),
-
-				h5("Software references"),
-				HTML('<p>R Development Core Team. <i><a href="http://www.r-project.org/">R</a>:  A Language and Environment for Statistical Computing.</i> R Foundation for Statistical Computing, Vienna (2013) <br>
+                    h5("Support BoxPlotR"),
+                    HTML('<p>Please consider supporting the development and maintenance of BoxPlotR with a <a href="https://www.paypal.com/pools/c/9oezkBOeV3" target="_blank">donation</a>.</p>'),
+                    h5("Software references"),
+                    HTML('<p>R Development Core Team. <i><a href="http://www.r-project.org/">R</a>:  A Language and Environment for Statistical Computing.</i> R Foundation for Statistical Computing, Vienna (2013) <br>
 				RStudio and Inc. <i><a href="http://www.rstudio.com/shiny/">shiny</a>: Web Application Framework for R.</i> R package version 0.5.0 (2013) <br>
 				Adler, D. <i><a href="http://cran.r-project.org/web/packages/vioplot/index.html">vioplot</a>: Violin plot.</i> R package version 0.2 (2005)<br>
 				Eklund, A. <i><a href="http://cran.r-project.org/web/packages/beeswarm/index.html"> beeswarm</a>: The bee swarm plot, an alternative to stripchart.</i> R package version 0.1.5 (2012)<br>
 				Kampstra, P. <i><a href="http://cran.r-project.org/web/packages/beanplot/index.html">Beanplot</a>: A Boxplot Alternative for Visual Comparison of Distributions.</i> Journal of Statistical Software, Code Snippets 28(1). 1-9 (2008) <br>
 				Neuwirth, E. <i><a href="http://cran.r-project.org/web/packages/RColorBrewer/index.html">RColorBrewer</a>: ColorBrewer palettes.</i> R package version 1.0-5. (2011)</p>'),
-				h5("Further references"),
-				HTML('<p> Hadley Wickham and Lisa Stryjewski: <a href="http://vita.had.co.nz/papers/boxplots.pdf"> 40 years of boxplots </a></p>'),
-				HTML('<p> Kristin Potter: <a href="http://www.sci.utah.edu/~kpotter/publications/potter-2006-MPSI.pdf">Methods for Presenting Statistical Information: The Box Plot</a></p>'),
-				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"),
-				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
-				a("shiny package from RStudio", href="http://www.rstudio.com/shiny/"), ".")
-			),
-			# Data upload tab
-			tabPanel("Data upload", tableOutput("filetable"),
-				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"),
-				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
-				a("shiny package from RStudio", href="http://www.rstudio.com/shiny/"), ".")
-			),
-			# Boxplot tab
-			tabPanel("Data visualization", 
-				div(class="controls-row",
-					downloadButton("downloadPlotEPS", "Download eps-file"),
-					downloadButton("downloadPlotPDF", "Download pdf-file"),
-					downloadButton("downloadPlotSVG", "Download svg-file")
-				),
-				div(class="plot-card",
-					plotOutput("boxPlot", height='100%', width='100%')
-				),
-				div(class="table-card",
-					h4("Box plot statistics"), 
-					tableOutput("boxplotStatsTable")
-				),
-				br(),
-				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"),
-				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
-				a("shiny package from RStudio", href="http://www.rstudio.com/shiny/"), ".")
-			),
-			# Figure legend
-			tabPanel("Figure legend template", h5("Box plot description for figure legend:"), textOutput("FigureLegend"),
-				h5("Further information to be added to the figure legend:"), p("What do the box plots show, explain colours if used."),
-				downloadButton("downloadBoxplotData", "Download box plot data as .CSV file"),
-				h6("This application was created by the ", a("Tyers", href="http://tyers.iric.ca/"), " and ", a("Rappsilber", href="http://rappsilberlab.org/"),
-				" labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
-				a("shiny package from RStudio", href="http://www.rstudio.com/shiny/"), ".")
-			),
-			# News
-			tabPanel("News",
-				h5("April 8, 2026"),
-				HTML('<p>The shiny server backend has been updated. The number of concurrent sessions has been limited to 15 and the session idle timeout set to 10 minutes. We are currently reworking the code to support the latest R and shiny versions, plus preparing a system upgrade of the server.</p>'),
-                h5("January 17, 2021"),
-                HTML('<p>There are several recent updates. The jitter of points is now consistent for all samples. When
+                    h5("Further references"),
+                    HTML('<p> Hadley Wickham and Lisa Stryjewski: <a href="http://vita.had.co.nz/papers/boxplots.pdf"> 40 years of boxplots </a></p>'),
+                    HTML('<p> Kristin Potter: <a href="http://www.sci.utah.edu/~kpotter/publications/potter-2006-MPSI.pdf">Methods for Presenting Statistical Information: The Box Plot</a></p>'),
+                    h6(
+                        "This application was created by the ", a("Tyers", href = "http://tyers.iric.ca/"), " and ", a("Rappsilber", href = "http://rappsilberlab.org/"),
+                        " labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
+                        a("shiny package from RStudio", href = "http://www.rstudio.com/shiny/"), "."
+                    )
+                ),
+                # Data upload tab
+                tabPanel(
+                    "Data upload", tableOutput("filetable"),
+                    h6(
+                        "This application was created by the ", a("Tyers", href = "http://tyers.iric.ca/"), " and ", a("Rappsilber", href = "http://rappsilberlab.org/"),
+                        " labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
+                        a("shiny package from RStudio", href = "http://www.rstudio.com/shiny/"), "."
+                    )
+                ),
+                # Boxplot tab
+                tabPanel(
+                    "Data visualization",
+                    div(
+                        class = "controls-row",
+                        downloadButton("downloadPlotEPS", "Download eps-file"),
+                        downloadButton("downloadPlotPDF", "Download pdf-file"),
+                        downloadButton("downloadPlotSVG", "Download svg-file")
+                    ),
+                    div(
+                        class = "plot-card",
+                        plotOutput("boxPlot", height = "100%", width = "100%")
+                    ),
+                    div(
+                        class = "table-card",
+                        h4("Box plot statistics"),
+                        tableOutput("boxplotStatsTable")
+                    ),
+                    br(),
+                    h6(
+                        "This application was created by the ", a("Tyers", href = "http://tyers.iric.ca/"), " and ", a("Rappsilber", href = "http://rappsilberlab.org/"),
+                        " labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
+                        a("shiny package from RStudio", href = "http://www.rstudio.com/shiny/"), "."
+                    )
+                ),
+                # Figure legend
+                tabPanel(
+                    "Figure legend template", h5("Box plot description for figure legend:"), textOutput("FigureLegend"),
+                    h5("Further information to be added to the figure legend:"), p("What do the box plots show, explain colours if used."),
+                    downloadButton("downloadBoxplotData", "Download box plot data as .CSV file"),
+                    h6(
+                        "This application was created by the ", a("Tyers", href = "http://tyers.iric.ca/"), " and ", a("Rappsilber", href = "http://rappsilberlab.org/"),
+                        " labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
+                        a("shiny package from RStudio", href = "http://www.rstudio.com/shiny/"), "."
+                    )
+                ),
+                # News
+                tabPanel(
+                    "News",
+                    h5("April 16, 2026"),
+                    HTML("<p>The number of concurrent sessions has been increased to 50.</p>"),
+                    h5("April 8, 2026"),
+                    HTML("<p>The shiny server backend has been updated. The number of concurrent sessions has been limited to 15 and the session idle timeout set to 10 minutes. We are currently reworking the code to support the latest R and shiny versions, plus preparing a system upgrade of the server.</p>"),
+                    h5("January 17, 2021"),
+                    HTML("<p>There are several recent updates. The jitter of points is now consistent for all samples. When
                 data points are added to the plot, the size and transparence of the points can now be modified with
-                sliders. The link to one of the boxplot references has been updated.</p>'),
-                h5("June 11, 2020"),
-                HTML('<p>The sample names are now displayed as they are in the input, ie., spaces and special characters
-                are not replaced with underscores.</p>'),
-                h5("January 11, 2017"),
-                HTML('<p>If you experience problems with this boxplot server, there is an alternative BoxPlotR mirror
+                sliders. The link to one of the boxplot references has been updated.</p>"),
+                    h5("June 11, 2020"),
+                    HTML("<p>The sample names are now displayed as they are in the input, ie., spaces and special characters
+                are not replaced with underscores.</p>"),
+                    h5("January 11, 2017"),
+                    HTML('<p>If you experience problems with this boxplot server, there is an alternative BoxPlotR mirror
                 available at <a href="http://boxplot.bio.ed.ac.uk">boxplot.bio.ed.ac.uk</a>.</p>'),
-                h5("July 23, 2014"),
-     			p("Upgrade to R version 3.1 and shiny-server 1.2"),
-				h5("March 18, 2014"),
-				p("The user can now choose the color of the data points. There is also an additional option for data
+                    h5("July 23, 2014"),
+                    p("Upgrade to R version 3.1 and shiny-server 1.2"),
+                    h5("March 18, 2014"),
+                    p("The user can now choose the color of the data points. There is also an additional option for data
 				point display: data points can now be randomly jittered.
 				A small bug in label display was fixed. Log scales can now be used.")
-			),			
-			# FAQ 
-			tabPanel("FAQ",
-				h5("Q: I have trouble editing the graphic files."), 
-				p("A: For EPS files make sure to 'ungroup' all objects so they can be edited independently. 
-				In Adobe Illustrator you will also need to use the 'release compound path' command. For PDF 
-				files you should 'release clipping mask'. SVG import appears to have problems in Adobe Illustrator 
+                ),
+                # FAQ
+                tabPanel(
+                    "FAQ",
+                    h5("Q: I have trouble editing the graphic files."),
+                    p("A: For EPS files make sure to 'ungroup' all objects so they can be edited independently.
+				In Adobe Illustrator you will also need to use the 'release compound path' command. For PDF
+				files you should 'release clipping mask'. SVG import appears to have problems in Adobe Illustrator
 				and Corel Draw and should be avoided. EPS, PDF and SVG import all work with Inkscape http://www.inkscape.org/."),
-				h5("Q: I would like to run BoxPlotR locally in an isolated environment."), 
-				HTML('<p>A: We provide a pre-configured Docker image that automatically installs and configures the environment. First, ensure you have <a href="https://docs.docker.com/get-docker/">Docker installed</a>. Then, clone the repository and run the following terminal commands inside the repository directory: <br><br> <code>docker build -t boxplotr .</code> <br> <code>docker run -d -p 3838:3838 boxplotr</code> <br><br> You can then access the application by navigating your web browser to <strong>http://localhost:3838</strong>.</p>')
-#				h5("Q:"), 
-#				p("A:")
-			),			
-			id="tabs1"
-		)
-	)
-  )
+                    h5("Q: I would like to run BoxPlotR locally in an isolated environment."),
+                    HTML('<p>A: We provide a pre-configured Docker image that automatically installs and configures the environment. First, ensure you have <a href="https://docs.docker.com/get-docker/">Docker installed</a>. Then, clone the repository and run the following terminal commands inside the repository directory: <br><br> <code>docker build -t boxplotr .</code> <br> <code>docker run -d -p 3838:3838 boxplotr</code> <br><br> You can then access the application by navigating your web browser to <strong>http://localhost:3838</strong>.</p>')
+                    # 				h5("Q:"),
+                    # 				p("A:")
+                ),
+                id = "tabs1"
+            )
+        )
+    )
 ))
-
-
-
-
-
-
-

From aadd24fce64e67eca5bf9f80ca8c39b6db6490f6 Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Thu, 16 Apr 2026 12:18:03 +0100
Subject: [PATCH 06/14] fixed docker file with dependencies

---
 Dockerfile | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 2ae448e..2e1b037 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,15 +1,6 @@
 # Use the official lightweight rocker shiny image
 FROM rocker/shiny:latest
 
-# Install system dependencies required for basic R packages
-RUN apt-get update && apt-get install -y \
-    libcurl4-gnutls-dev \
-    libcairo2-dev \
-    libxt-dev \
-    libssl-dev \
-    libssh2-1-dev \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
 
 # Install required R packages
 RUN R -e "install.packages(c('beeswarm', 'vioplot', 'beanplot', 'RColorBrewer'), repos='https://cloud.r-project.org/')"

From e1dd2a54e9aa76bc70d74d8b72770aebc17cd8ac Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Thu, 16 Apr 2026 17:11:40 +0100
Subject: [PATCH 07/14] Version 2.0 with Excel support and Docker

---
 .RData                          |  Bin 0 -> 48 bytes
 Boxplot_testData3.xlsx          |  Bin 0 -> 6784 bytes
 Dockerfile                      |    3 +-
 MyVioplot.R                     |  213 +++---
 log_scale_example.csv           |  101 +++
 server.R                        | 1073 +++++++++++++++++++------------
 tests/test_vioplot_edge_cases.R |   68 ++
 ui.R                            |  765 +++++++++++++---------
 8 files changed, 1399 insertions(+), 824 deletions(-)
 create mode 100644 .RData
 create mode 100644 Boxplot_testData3.xlsx
 create mode 100644 log_scale_example.csv
 create mode 100644 tests/test_vioplot_edge_cases.R

diff --git a/.RData b/.RData
new file mode 100644
index 0000000000000000000000000000000000000000..d17abb1b13dd24c4b10906b955d4553a3cb7f51e
GIT binary patch
literal 48
zcmb2|=3oE=X6~X+gJ)e2k`fXU(h?HcCarN$W6sX#n7xj5!bg6F^%6dI3P6PbPBsoa

literal 0
HcmV?d00001

diff --git a/Boxplot_testData3.xlsx b/Boxplot_testData3.xlsx
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/Dockerfile b/Dockerfile
index 2e1b037..b560fa9 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -2,8 +2,7 @@
 FROM rocker/shiny:latest
 
 
-# Install required R packages
-RUN R -e "install.packages(c('beeswarm', 'vioplot', 'beanplot', 'RColorBrewer'), repos='https://cloud.r-project.org/')"
+RUN R -e "install.packages(c('beeswarm', 'vioplot', 'beanplot', 'RColorBrewer', 'readxl', 'sm'), repos='https://cloud.r-project.org/')"
 
 # Remove default Shiny apps
 RUN rm -rf /srv/shiny-server/*
diff --git a/MyVioplot.R b/MyVioplot.R
index 4ec7728..e1ffa67 100644
--- a/MyVioplot.R
+++ b/MyVioplot.R
@@ -1,102 +1,125 @@
-vioplot<-function (x, range = 1.5, h = NULL, ylim = NULL, names = NULL, 
-    horizontal = FALSE, col = "cornflowerblue", border = "black", lty = 1, 
-    lwd = 1, rectCol = "black", colMed = "white", pchMed = 19, 
-    at, add = FALSE, wex = 1, drawRect = TRUE, cex.axis=1) 
-{
-    datas <- x
-    n <- length(datas)
-    if (missing(at)) 
-        at <- 1:n
-    upper <- vector(mode = "numeric", length = n)
-    lower <- vector(mode = "numeric", length = n)
-    q1 <- vector(mode = "numeric", length = n)
-    q3 <- vector(mode = "numeric", length = n)
-    med <- vector(mode = "numeric", length = n)
-    base <- vector(mode = "list", length = n)
-    height <- vector(mode = "list", length = n)
-    baserange <- c(Inf, -Inf)
-    args <- list(display = "none")
-    if (!(is.null(h))) 
-        args <- c(args, h = h)
-    for (i in 1:n) {
-    	datas[[i]]<-datas[[i]][!is.na(datas[[i]])]
-        data <- datas[[i]]
-        data.min <- min(data)
-        data.max <- max(data)
-        q1[i] <- quantile(data, 0.25)
-        q3[i] <- quantile(data, 0.75)
-        med[i] <- median(data)
-        iqd <- q3[i] - q1[i]
-        upper[i] <- min(q3[i] + range * iqd, data.max)
-        lower[i] <- max(q1[i] - range * iqd, data.min)
-        est.xlim <- c(min(lower[i], data.min), max(upper[i], 
-            data.max))
-        smout <- do.call("sm.density", c(list(data, xlim = est.xlim), 
-            args))
-        hscale <- 0.4/max(smout$estimate) * wex
-        base[[i]] <- smout$eval.points
-        height[[i]] <- smout$estimate * hscale
-        t <- range(base[[i]])
-        baserange[1] <- min(baserange[1], t[1])
-        baserange[2] <- max(baserange[2], t[2])
+vioplot <- function(x, range = 1.5, h = NULL, ylim = NULL, names = NULL,
+                    horizontal = FALSE, col = "cornflowerblue", border = "black", lty = 1,
+                    lwd = 1, rectCol = "black", colMed = "white", pchMed = 19,
+                    at, add = FALSE, wex = 1, drawRect = TRUE, cex.axis = 1, log = "") {
+  datas <- x
+  n <- length(datas)
+  if (missing(at)) {
+    at <- 1:n
+  }
+  upper <- rep(NA_real_, n)
+  lower <- rep(NA_real_, n)
+  q1 <- rep(NA_real_, n)
+  q3 <- rep(NA_real_, n)
+  med <- rep(NA_real_, n)
+  base <- vector(mode = "list", length = n)
+  height <- vector(mode = "list", length = n)
+  baserange <- c(Inf, -Inf)
+  args <- list(display = "none")
+  if (!(is.null(h))) {
+    args <- c(args, h = h)
+  }
+  for (i in seq_len(n)) {
+    datas[[i]] <- datas[[i]][!is.na(datas[[i]])]
+    data <- datas[[i]]
+    if (length(data) < 2) next
+    data.min <- min(data)
+    data.max <- max(data)
+    q1[i] <- quantile(data, 0.25)
+    q3[i] <- quantile(data, 0.75)
+    med[i] <- median(data)
+    iqd <- q3[i] - q1[i]
+    upper[i] <- min(q3[i] + range * iqd, data.max)
+    lower[i] <- max(q1[i] - range * iqd, data.min)
+    est.xlim <- c(min(lower[i], data.min), max(
+      upper[i],
+      data.max
+    ))
+    smout <- do.call("sm.density", c(
+      list(data, xlim = est.xlim),
+      args
+    ))
+    hscale <- 0.4 / max(smout$estimate) * wex
+    base[[i]] <- smout$eval.points
+    height[[i]] <- smout$estimate * hscale
+    t <- range(base[[i]])
+    baserange[1] <- min(baserange[1], t[1])
+    baserange[2] <- max(baserange[2], t[2])
+  }
+  if (!add) {
+    xlim <- if (n == 1) {
+      at + c(-0.5, 0.5)
+    } else {
+      range(at) + min(diff(at)) / 2 * c(-1, 1)
     }
-    if (!add) {
-        xlim <- if (n == 1) 
-            at + c(-0.5, 0.5)
-        else range(at) + min(diff(at))/2 * c(-1, 1)
-        if (is.null(ylim)) {
-            ylim <- baserange
-        }
+    if (is.null(ylim)) {
+      ylim <- baserange
     }
-    if (is.null(names)) {
-        label <- 1:n
+  }
+  if (is.null(names)) {
+    label <- 1:n
+  } else {
+    label <- names
+  }
+  boxwidth <- 0.05 * wex
+  if (!add) {
+    plot.new()
+  }
+  if (!horizontal) {
+    if (!add) {
+      plot.window(xlim = xlim, ylim = ylim, log = log)
+      axis(2, cex.axis = cex.axis)
+      axis(1, at = at, labels = label, cex.axis = cex.axis)
     }
-    else {
-        label <- names
+    # box()
+    for (i in seq_len(n)) {
+      if (is.na(med[i])) next
+      polygon(c(at[i] - height[[i]], rev(at[i] + height[[i]])),
+        c(base[[i]], rev(base[[i]])),
+        col = ifelse(length(col) > 1, col[1 + (i - 1) %% length(col)], col),
+        border = border, lty = lty, lwd = lwd
+      )
+      if (drawRect) {
+        lines(at[c(i, i)], c(lower[i], upper[i]),
+          lwd = lwd,
+          lty = lty
+        )
+        rect(at[i] - boxwidth / 2, q1[i], at[i] + boxwidth / 2,
+          q3[i],
+          col = rectCol
+        )
+        points(at[i], med[i], pch = pchMed, col = colMed)
+      }
     }
-    boxwidth <- 0.05 * wex
-    if (!add) 
-        plot.new()
-    if (!horizontal) {
-        if (!add) {
-            plot.window(xlim = xlim, ylim = ylim)
-            axis(2, cex.axis=cex.axis)
-#           axis(1, at = at, label = label, cex.axis=cex.axis)
-        }
-        box()
-        for (i in 1:n) {
-            polygon(c(at[i] - height[[i]], rev(at[i] + height[[i]])), 
-                c(base[[i]], rev(base[[i]])), col = ifelse(length(col)>1, col[1+(i-1)%%length(col)], col),
-                border = border, lty = lty, lwd = lwd)
-            if (drawRect) {
-                lines(at[c(i, i)], c(lower[i], upper[i]), lwd = lwd, 
-                  lty = lty)
-                rect(at[i] - boxwidth/2, q1[i], at[i] + boxwidth/2, 
-                  q3[i], col = rectCol)
-                points(at[i], med[i], pch = pchMed, col = colMed)
-            }
-        }
+  } else {
+    if (!add) {
+      plot.window(xlim = ylim, ylim = xlim, log = log)
+      axis(1, cex.axis = cex.axis)
+      axis(2, at = at, labels = label, cex.axis = cex.axis)
     }
-    else {
-        if (!add) {
-            plot.window(xlim = ylim, ylim = xlim)
-            axis(1, cex.axis=cex.axis)
-#            axis(2, at = at, label = label)
-        }
-        box()
-        for (i in 1:n) {
-            polygon(c(base[[i]], rev(base[[i]])), c(at[i] - height[[i]], 
-                rev(at[i] + height[[i]])), col = ifelse(length(col)>1, col[1+(i-1)%%length(col)], col),
-                border = border, lty = lty, lwd = lwd)
-            if (drawRect) {
-                lines(c(lower[i], upper[i]), at[c(i, i)], lwd = lwd, 
-                  lty = lty)
-                rect(q1[i], at[i] - boxwidth/2, q3[i], at[i] + 
-                  boxwidth/2, col = rectCol)
-                points(med[i], at[i], pch = pchMed, col = colMed)
-            }
-        }
+    # box()
+    for (i in seq_len(n)) {
+      if (is.na(med[i])) next
+      polygon(c(base[[i]], rev(base[[i]])), c(
+        at[i] - height[[i]],
+        rev(at[i] + height[[i]])
+      ),
+      col = ifelse(length(col) > 1, col[1 + (i - 1) %% length(col)], col),
+      border = border, lty = lty, lwd = lwd
+      )
+      if (drawRect) {
+        lines(c(lower[i], upper[i]), at[c(i, i)],
+          lwd = lwd,
+          lty = lty
+        )
+        rect(q1[i], at[i] - boxwidth / 2, q3[i], at[i] +
+          boxwidth / 2, col = rectCol)
+        points(med[i], at[i], pch = pchMed, col = colMed)
+      }
     }
-    invisible(list(upper = upper, lower = lower, median = med, 
-        q1 = q1, q3 = q3))
+  }
+  invisible(list(
+    upper = upper, lower = lower, median = med,
+    q1 = q1, q3 = q3
+  ))
 }
diff --git a/log_scale_example.csv b/log_scale_example.csv
new file mode 100644
index 0000000..9ba360b
--- /dev/null
+++ b/log_scale_example.csv
@@ -0,0 +1,101 @@
+"Baseline","Treated","Knockout"
+394,60584,4
+57,47929,12
+144,2221,18
+188,160021,28
+150,3678,5
+90,11715,6
+453,5308,7
+91,8323,6
+753,13262,7
+94,11957,9
+369,9631,5
+984,11760,28
+25,4828,11
+76,4694,10
+88,828,13
+189,5635,10
+75,4635,9
+7,575605,21
+9,1296,9
+374,12286,5
+74,1064,12
+17,1102,8
+84,12057,8
+337,2243,6
+665,9973,12
+65,5260,9
+77,3983,13
+17,480,9
+158,1593,7
+53,13090,19
+158,23430,9
+202,4774,16
+282,10001,5
+54,53889,8
+166,86693,9
+18,1929,8
+46,8386,20
+43,60633,10
+9,4943,11
+104,9243,6
+123,8788,7
+70,2641,16
+213,5132,19
+48,9568,19
+25,5375,5
+154,53126,28
+44,4860,17
+424,5222,10
+65,28442,14
+193,2050,6
+138,9408,6
+46,976,10
+483,57589,5
+190,6633,11
+109,4957,19
+132,1561,6
+197,9884,7
+109,3011,10
+5,4492,6
+133,68999,8
+69,7685,15
+120,2004,16
+179,12774,12
+405,5804,4
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+41,20643,20
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+454,12551,15
+129,4164,4
+109,17388,8
+89,15558,14
+30,6578,10
+184,1347,12
+80,28609,34
+83,22963,7
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+227,915,11
+402,13599,7
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+192,14607,7
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diff --git a/server.R b/server.R
index 50ff3bc..bb3f3c0 100644
--- a/server.R
+++ b/server.R
@@ -1,424 +1,675 @@
-options(shiny.maxRequestSize = 200*1024^2)
+options(shiny.maxRequestSize = 200 * 1024^2)
 
 # Pre-load example datasets so they don't hit the disk constantly
-sampleData1Cache <- read.table("Boxplot_testData2.csv", sep=",", header=TRUE, fill=TRUE, check.names=FALSE)
-sampleData2Cache <- read.table("Boxplot_testData.txt", sep=",", header=TRUE, check.names=FALSE)
+sample_data_1_cache <- read.table(
+  "Boxplot_testData2.csv",
+  sep = ",", header = TRUE, fill = TRUE,
+  check.names = FALSE
+)
+sample_data_2_cache <- read.table(
+  "Boxplot_testData.txt",
+  sep = ",", header = TRUE,
+  check.names = FALSE
+)
+sample_data_3_cache <- as.data.frame(
+  readxl::read_excel("Boxplot_testData3.xlsx")
+)
 
 # Helper function to prevent redundant color parsing
-parseColours <- function(colStrings){
-  myColours <- gsub("\\s","", strsplit(colStrings,",")[[1]])
-  myColours <- gsub("0x","#", myColours)
-  return(myColours)
+parse_colours <- function(col_strings) {
+  my_colours <- gsub("\\s", "", strsplit(col_strings, ",")[[1]])
+  my_colours <- gsub("0x", "#", my_colours)
+  return(my_colours)
 }
 
 shinyServer(function(input, output, session) {
+  library(RColorBrewer)
+  library(beeswarm)
+  library(vioplot)
+  source("MyVioplot.R")
+  library(beanplot)
+  library(readxl)
+  source("boxplot_stats_Function.R")
+  source("BoxPlotR_functions.R")
 
-	library(RColorBrewer)
-	library(beeswarm)
-	library(vioplot)
-	source("MyVioplot.R")
-	library(beanplot)	 
-	source("boxplot_stats_Function.R")
-	source("BoxPlotR_functions.R")	
-	
-	observe({
-		if (input$clearText_button == 0) return()
-		isolate({ updateTextInput(session, "myData", label = ",", value = "") })
-	})
-	# *** Read in data matrix ***
-	dataM <- reactive({
-		if(input$dataInput==1){
-			if(input$sampleData==1){
-				data <- sampleData1Cache			
-			} else {
-				data <- sampleData2Cache
-			}
-		} else if(input$dataInput==2){
-			inFile <- input$upload
-			# Avoid error message while file is not uploaded yet
-			if (is.null(input$upload))  {return(NULL)}
-			# Get the separator
-			mySep<-switch(input$fileSepDF, '1'=",",'2'="\t",'3'=";", '4'="") #list("Comma"=1,"Tab"=2,"Semicolon"=3)
-				data<-read.table(inFile$datapath, sep=mySep, header=TRUE, fill=TRUE, check.names=FALSE)
-		} else { # To be looked into again - for special case when last column has empty entries in some rows
-			if(is.null(input$myData) || input$myData == "") {return(NULL)}
-			mySep<-switch(input$fileSepP, '1'=",",'2'="\t",'3'=";")
-			data <- read.table(text = input$myData, sep=mySep, header=TRUE, fill=TRUE, check.names=FALSE)
-		}
-		return(data)
-	})
-	
-	# *** The plot dimensions ***
-	heightSize <- reactive ({ input$myHeight })
-	widthSize <- reactive ({ input$myWidth })
-
-	# *** Determine extent of whisker range ***
-	# whiskerDefinition 0 - Tukey (default), 1 - Spear (min/max, range=0), 2 - Altman (5% and 95% quantiles)
-	# radioButtons("whiskerType", "", list("Tukey"=0, "Spear"=1, "Altman"=2)),
-	myRange <- reactive({
-		if(input$whiskerType==0){myRange<-c(-1.5)} 
-		else if(input$whiskerType==1){myRange<-c(0)} 
-		else if (input$whiskerType==2){myRange<-c(5)}
-		return(myRange)
-	})
-	
-	# *** Get boxplot statistics ***
-	boxplotStats <- reactive({
-		if(is.null(dataM())) return(NULL)
-		return(boxplot(dataM(), na.rm=TRUE, range=myRange(), plot=FALSE))
-	})
-	
-	# *** Generate the box plot ***
-	generateBoxPlot<-function(plotData){
-		par(mar=c(5,8,4,2)) # c(bottom, left, top, right)
-
-		myColours<-parseColours(input$myColours)
-		myColours2<-parseColours(input$myOtherPlotColours)
-		pointColors<-parseColours(input$pointColors)
-
-		pointTransparency<-100-input$pointTransparency
-
-		nrOfSamples<-ncol(plotData)
-		# Display median or mean for bean plot?
-		if(input$beanPlotMedianMean==0){ myBeanplotCenter<-"median" } else { myBeanplotCenter<-"mean" }
-		# Generate colour vector
-		if(length(myColours)==1){
-			myColours<-rep(myColours, nrOfSamples)
-		} else if(length(myColours) < nrOfSamples){
-			myColours<-rep(myColours,times=(round(nrOfSamples/length(myColours)))+1)
-		}
-		
-		# Generate colour vector for points
-		if(length(pointColors)==1){
-			pointColors<-rep(pointColors, nrOfSamples)
-		} else if(length(pointColors) < nrOfSamples){
-			pointColors<-rep(pointColors,times=(round(nrOfSamples/length(pointColors)))+1)
-		}
-
-		plotPoints<-c() # vector for indices of samples that are to be plotted as points, not as boxplots
-		notPlotPoints <- seq(1:nrOfSamples) # samples to plot as boxes/violins/beans
-		plotDataM<-plotData
-		# Determine plot range
-		myBuffer<-max(abs(range(plotData,na.rm=TRUE)))*0.1 # add 10% of data maximum at each end of y/x axis
-		if(as.numeric(input$myOrientation)==0){		
-			if(input$ylimit==""){myLim<-range(plotData,na.rm=TRUE)+c(-1,+1)} else {myLim<-as.numeric(strsplit(input$ylimit,",")[[1]])}
-		} else {
-			if(input$xlimit==""){myLim<-range(plotData,na.rm=TRUE)+c(-1,+1)} else {myLim<-as.numeric(strsplit(input$xlimit,",")[[1]])}
-		}
-		# Data point count for each sample
-		datapointCounts<-colSums(!is.na(plotData)) # Count number of valid data points for each sample
-		# Check if columns with few data points should be plotted as points
-
-		# minimum number of points is 4 -> check that nrOfDataPoints is ay least 4		
-		mnp<-max(4,input$nrOfDataPoints)
-		
-		if(input$plotDataPoints==TRUE){
-			#toPlot <- seq(1:ncol(plotData))[datapointCounts>=input$nrOfDataPoints] # samples to barplot
-			plotPoints <- seq(1:nrOfSamples)[datapointCounts<mnp] # samples to plot as points
-			notPlotPoints <- seq(1:nrOfSamples)[datapointCounts>=mnp] # samples to plot as boxes/violins/beans
-		}
-
-		# Generate plotDataM matrix such that columns that should be plotted as points are filled with data points 
-		# outside of visible plot area to 'reserve' spot for points
-		for(i in plotPoints){
-			plotDataM[,i]<-c(rep(myLim[2]+10, nrow(plotData)-1),myLim[2]+20)
-		}
-
-		# Angle the sample names
-		if(input$xaxisLabelAngle){ 
-			xaxisLabelAngleNr<-45
-			labelPos<-2
-		} else { 
-			xaxisLabelAngleNr<-0
-			labelPos<-1
-		}
-
-		par(mar=c(12.1, 11.1, 4.1, 2.1))
-		
-		if(input$showDataPoints==TRUE) {
-			plotOutliers=FALSE
-		} else {
-			plotOutliers=TRUE
-		}
-		# *** 1) Vertical boxplots ***
-		par(las=1)
-		if(as.numeric(input$myOrientation)==0){
-			if(input$logScale==FALSE){ myLog="" } else { myLog="y"} # log scale for y-axis?
-			# *** Generate boxplot ***
-			if(input$plotType=='0'){
-				boxplot(plotDataM, col=myColours, ylab=input$myYlab, xlab=input$myXlab, ylim=myLim, log=myLog, outline=plotOutliers,
-					cex.lab=input$cexAxislabel/10, cex.axis=input$cexAxis/10, cex.main=input$cexTitle/10, 
-					main=input$myTitle, sub=input$mySubtitle, horizontal=as.numeric(input$myOrientation), frame=F, 
-					na.rm=TRUE, xaxt="n", range=myRange(), varwidth=input$myVarwidth, notch=input$myNotch) #notch=TRUE, outpch=16
-				axis(1,at=c(1:nrOfSamples), labels=FALSE, cex.axis=input$cexAxis/10) #
-#				mtext(text=colnames(plotData), side=1, at=c(1:nrOfSamples), xpd=TRUE, srt=xaxisLabelAngleNr, cex=input$cexAxis/10)
-				text(x=c(1:nrOfSamples), y=rep(myLim[1]-(myLim[2]-myLim[1])/10,nrOfSamples), labels=colnames(plotData), pos=labelPos, xpd=TRUE, srt=xaxisLabelAngleNr, cex=input$cexAxis/10)
-
-				# *** Add data points to plot if selected ***
-				if(input$showDataPoints==TRUE){
-						if(input$datapointType==1){
-							beePointColors<-c()
-							for(i in 1:length(pointColors)){
-								beePointColors[i]<-rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100))
-							}
-							beeswarm(plotData, add=TRUE, col=beePointColors, cex=input$pointSize/10, pch=16)
-						} else { 
-							jittered.points(plotData, FALSE, input$datapointType, pointColors, pointTransparency, input$pointSize/10)
-						}			
-				}
-			} else { # *** Generate violin or bean plot ***
-				if(input$otherPlotType==0){ # Violin plot
-					vioplot(as.list(data.frame(plotDataM)), col=myColours2, ylim=myLim, cex.axis=input$cexAxis/10, 
-						horizontal=as.numeric(input$myOrientation), border=input$violinBorder)
-					title(main=input$myTitle, ylab=input$myYlab, xlab=input$myXlab, cex.main=input$cexTitle/10, cex.lab=input$cexAxislabel/10)
-#					axis(1,at=c(1:nrOfSamples), labels=colnames(plotData), cex.axis=input$cexAxis/10, sub=input$mySubtitle)
-					axis(1,at=c(1:nrOfSamples), labels=FALSE, cex.axis=input$cexAxis/10) #
-					text(x=c(1:nrOfSamples), y=rep(myLim[1]-3,nrOfSamples), labels=colnames(plotData), 
-						pos=labelPos, xpd=TRUE, srt=xaxisLabelAngleNr, cex=input$cexAxis/10)
-
-				} else {
-					beanplot(data.frame(plotDataM[,notPlotPoints]), at=notPlotPoints, ylim=myLim, 
-						horizontal=as.numeric(input$myOrientation), xlim=c(0.5, ncol(plotDataM)+0.5), 
-						col=myColours2, border=input$beanBorder, overallline = myBeanplotCenter)
-					title(main=input$myTitle, ylab=input$myYlab, xlab=input$myXlab, cex.main=input$cexTitle/10, cex.lab=input$cexAxislabel/10)
-	#				axis(1,at=c(1:nrOfSamples), labels=colnames(plotData), cex.axis=input$cexAxis/10)				
-					axis(1,at=c(1:nrOfSamples), labels=FALSE, cex.axis=input$cexAxis/10) #
-#					text(x=c(1:nrOfSamples), y=rep(myLim[1]-3,nrOfSamples), labels=colnames(plotData), 
-#					pos=labelPos, xpd=TRUE, srt=xaxisLabelAngleNr, cex=input$cexAxis/10)
-				}
-			}
-			# * Add points for samples with less than mnp data points *
-			# replace "white" with "black" otherwise data points will not be visible
-			for(i in plotPoints){
-				if(input$datapointType==0 | input$plotType==1 | (input$datapointType==1 & input$showDataPoints==FALSE)){
-					if(myColours[i]!="white"){
-						points(rep(i, nrow(plotData)), plotData[,i], col=myColours[i])
-					} else {
-						points(rep(i, nrow(plotData)), plotData[,i], col="black")				
-					}
-				}
-			}
-			if(input$showNrOfPoints==TRUE){text(x=1:nrOfSamples, y=myLim[1], labels=boxplotStats()$n)}
-			# Add mean and CIs for mean
-			if(input$addMeans==TRUE & input$plotType=='0'){
-				boxplotMeans<-colMeans(plotData, na.rm=TRUE)
-				points(x=1:nrOfSamples, y=boxplotMeans, pch="+", cex=2) 
-				if(input$addMeanCI==TRUE){ 
-					# Calculate the error using the quartile function * Standard error; SE=sd/sqrt(n)
-					myQuartile<-1-((1-(as.numeric(input$meanCI)/100))/2)
-					myError<-qt(myQuartile, df=(boxplotStats()$n)-1)*(apply(plotData, 2, sd, na.rm=TRUE)/sapply(boxplotStats()$n, sqrt))
-					for(ii in 1:nrOfSamples) { 
-#						lines(y=c(ii,ii), x=c(boxplotMeans[ii]-myError[ii], boxplotMeans[ii]+myError[ii]), col="red") 
-						rect(ii-0.05, boxplotMeans[ii]-myError[ii], ii+0.05, boxplotMeans[ii]+myError[ii], col="darkgrey", border="darkgrey") 
-					}
-					points(x=1:nrOfSamples, y=boxplotMeans, pch="+", cex=2) 
-				}
-			}
-			
-			
-		# *** 2) Horizontal boxplots ***
-		} else { 
-			if(input$logScale==FALSE){ myLog="" } else { myLog="x"} # log scale for y-axis?
-			if(input$plotType=='0'){
-				boxplot(plotDataM, col=myColours, ylab=input$myYlab, xlab=input$myXlab, las=1, ylim=myLim, log=myLog, outline=plotOutliers,
-					cex.lab=input$cexAxislabel/10, cex.axis=input$cexAxis/10, cex.main=input$cexTitle/10, 
-					main=input$myTitle, sub=input$mySubtitle, horizontal=as.numeric(input$myOrientation), frame=F, 
-					na.rm=TRUE, yaxt="n", range=myRange(), varwidth=input$myVarwidth, notch=input$myNotch) #notch=TRUE, outpch=16
-				axis(2,at=c(1:nrOfSamples), labels=colnames(plotData), cex.axis=input$cexAxis/10)
-				# Add data points if option has been selected
-				if(input$showDataPoints==TRUE){
-						if(input$datapointType==1){
-							beePointColors<-c()
-							for(i in 1:length(pointColors)){
-								beePointColors[i]<-rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100))
-							}
-							beeswarm(plotData, add=TRUE, horizontal=TRUE, col=beePointColors, cex=input$pointSize/10, pch=16)
-						} else {
-							jittered.points(plotData, TRUE, input$datapointType, pointColors, pointTransparency, input$pointSize/10)
-						}			
-				}
-			} else {
-				if(input$otherPlotType==0){ # Violin plot
-					vioplot(as.list(data.frame(plotDataM)), col=myColours2[1], ylim=myLim, cex.axis=input$cexAxis/10, 
-						horizontal=as.numeric(input$myOrientation), border=input$violinBorder)
-					title(main=input$myTitle, ylab=input$myYlab, xlab=input$myXlab, cex.main=input$cexTitle/10, cex.lab=input$cexAxislabel/10)
-					axis(2,at=c(1:nrOfSamples), labels=colnames(plotData), cex.axis=input$cexAxis/10)
-
-				} else { # Bean plot
-					beanplot(data.frame(plotDataM[,notPlotPoints]), at=notPlotPoints, ylim=myLim, 
-					horizontal=as.numeric(input$myOrientation), xlim=c(0.5, ncol(plotDataM)+0.5), 
-					col=myColours2, border=input$beanBorder, overallline = myBeanplotCenter)
-					title(main=input$myTitle, ylab=input$myYlab, xlab=input$myXlab, cex.main=input$cexTitle/10, cex.lab=input$cexAxislabel/10)
-					axis(2,at=c(1:nrOfSamples), labels=FALSE, cex.axis=input$cexAxis/10) # labels=colnames(plotData)
-				}
-			}
-			# *** Add data points for columns with less than x data points ***
-			# Only if violin or bean plots are shown or points are not already added to boxplot (input$showDataPoints==FALSE)
-			if(input$plotType!='0' | input$showDataPoints==FALSE){
-				for(i in plotPoints){
-					if(input$datapointType==0){
-						if(myColours[i]!="white"){
-							points(plotData[,i], rep(i, nrow(plotData)), col=myColours[i])
-						} else {
-							points(plotData[,i], rep(i, nrow(plotData)), col="black")				
-						}
-					}
-				}
-			}
-			# *** Show number of data points for each column ***
-			if(input$showNrOfPoints==TRUE){text(y=1:nrOfSamples, x=myLim[1], labels=boxplotStats()$n)}
-			# *** Add mean and CIs for mean ***
-			if(input$addMeans==TRUE & input$plotType=='0'){
-				boxplotMeans<-colMeans(plotData, na.rm=TRUE)
-				points(y=1:nrOfSamples, x=boxplotMeans, pch="+", cex=2) 
-				if(input$addMeanCI==TRUE){ 
-					# Calculate the error using the quartile function * Standard error; SE=sd/sqrt(n)
-					myQuartile<-1-((1-(as.numeric(input$meanCI)/100))/2)
-					myError<-qt(myQuartile, df=(boxplotStats()$n)-1)*(apply(plotData, 2, sd, na.rm=TRUE)/sapply(boxplotStats()$n, sqrt))
-					for(ii in 1:nrOfSamples) { 
-#						lines(y=c(ii,ii), x=c(boxplotMeans[ii]-myError[ii], boxplotMeans[ii]+myError[ii]), col="red") 
-						rect(boxplotMeans[ii]-myError[ii], ii-0.05, boxplotMeans[ii]+myError[ii], ii+0.05, col="darkgrey", border="darkgrey") 
-					}
-					points(y=1:nrOfSamples, x=boxplotMeans, pch="+", cex=2) 
-				}
-			}
-			
-		}
-		# *** Add grid based on option selected ***
-		if(input$addGrid==0){} 
-		else if(input$addGrid==1){grid()} 
-		else if (input$addGrid==2){grid(ny=NA)}
-		else if (input$addGrid==3){grid(NA, ny=NULL)}	
-	}
-
-	## *** Data in table ***
-	output$filetable <- renderTable({
-		if(is.null(dataM())) return(NULL)
-		if(nrow(dataM())<500){
-			return(dataM())
-		} else {return(dataM()[1:100,])}
-	})
-
-	# *** Boxplot (using 'generateBoxPlot'-function) ***
-	output$boxPlot <- renderPlot({
-		if(is.null(dataM())) return(NULL)
-		generateBoxPlot(dataM())
-	}, height = heightSize, width = widthSize)
-	
-	## *** Download EPS file ***
-	output$downloadPlotEPS <- downloadHandler(
-		filename <- function() { paste('Boxplot.eps') },
-		content <- function(file) {
-			postscript(file, horizontal = FALSE, onefile = FALSE, paper = "special", width = input$myWidth/72, height = input$myHeight/72)
-			## ---------------
-			generateBoxPlot(dataM())
-			## ---------------
-			dev.off()
-		},
-		contentType = 'application/postscript'
-	)
-	## *** Download PDF file ***
-	output$downloadPlotPDF <- downloadHandler(
-		filename <- function() { paste('Boxplot.pdf') },
-		content <- function(file) {
-			pdf(file, width = input$myWidth/72, height = input$myHeight/72)
-			## ---------------
-			generateBoxPlot(dataM())
-			## ---------------
-			dev.off()
-		},
-		contentType = 'application/pdf' # MIME type of the image
-	)
-	## *** Download SVG file ***
-	output$downloadPlotSVG <- downloadHandler(
-		filename <- function() { paste('Boxplot.svg') },
-		content <- function(file) {
-			svg(file, width = input$myWidth/72, height = input$myHeight/72)
-			## ---------------
-			generateBoxPlot(dataM())
-			## ---------------
-			dev.off()
-		},
-		contentType = 'image/svg'
-	)
-
-	# *** Output boxplot statistics in table below plot ***
-	output$boxplotStatsTable <- renderTable({
-		if(is.null(dataM()) || is.null(boxplotStats())) return(NULL)
-		M<-rbind(as.matrix(boxplotStats()$stats[c(5,4,3,2,1),]),boxplotStats()$n)
-		if(input$addMeans){
-			M<-rbind(M, colMeans(dataM(), na.rm=TRUE))
-			rownames(M)<-c("Upper whisker","3rd quartile","Median","1st quartile","Lower whisker", "Nr. of data points", "Mean")
-			colnames(M)<-colnames(dataM())
-		} else {
-			rownames(M)<-c("Upper whisker","3rd quartile","Median","1st quartile","Lower whisker", "Nr. of data points")
-			colnames(M)<-colnames(dataM())
-		}
-		M
-	}, rownames = TRUE)
-	
-	# *** Print figure legend ***
-	output$FigureLegend <- renderPrint({
-		if(is.null(dataM()) || is.null(boxplotStats())) return(invisible())
-		# Center lines show the medians; box limits indicate the 25th and 75th percentiles as determined by R software; whiskers extend to minimum and maximum values; crosses represent means; bars indicate 95% confidence intervals. n = 100, 76, 16, 76, 41 sample points.
-		# Generate vector with pieces of the legend based on user selections
-		FL<-vector()
-		# Figure legend for boxplot
-		if(input$plotType=='0'){
-			FL<-c("Center lines show the medians; box limits indicate the 25th and 75th percentiles as determined by R software")
-			# one of these three, depending on whisker definition choice:
-			# - Spear: "; whiskers extend to minimum and maximum values."
-			# - Tukey: "; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles; outliers are represented by dots."
-			# - Altman: " and whiskers the 5th and 95th percentiles; outliers are represented by dots."
-			if(input$whiskerType==0){
-				FL<-append(FL, paste("; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, outliers are represented by dots", sep=""))
-			} else if(input$whiskerType==1){
-				FL<-append(FL, "; whiskers extend to minimum and maximum values")		
-			} else {
-				FL<-append(FL, paste("; whiskers extend to 5th and 95th percentiles, outliers are represented by dots", sep=""))		
-			}
-			# Means are added as crosses
-			if(input$addMeans & input$plotType=='0'){ FL<-append(FL, c("; crosses represent sample means")) }
-			# Confidence intervals of means are displayed as grey bars
-			if(input$addMeans & input$addMeanCI & input$plotType=='0'){ FL<-append(FL, paste("; bars indicate ", input$meanCI,"% confidence intervals of the means", sep="")) }
-			# Variable width of boxplots
-			if(input$myVarwidth){ FL<-append(FL, c("; width of the boxes is proportional to the square root of the sample size")) }
-			# Points are plotted on top of boxplots
-			if(input$showDataPoints){ FL<-append(FL, c("; data points are plotted as open circles")) }
-			# Sample size
-			sampleSizes<-boxplotStats()$n
-			if(length(unique(sampleSizes))==1){ FL<-append(FL, paste(". n = ", sampleSizes[1], " sample points", sep="")) } 
-		  	else { FL<-append(FL, paste(". n = ",paste(sampleSizes, collapse=", "), " sample points", sep="")) }
-			FL<-append(FL, ".")
-		} else {
-			# radioButtons("otherPlotType", "", list("Violin plot"=0, "Bean plot"=1)),
-			if (input$otherPlotType=='0'){ # Violin plot
-				FL<-c("White circles show the medians; 
-				box limits indicate the 25th and 75th percentiles as determined by R software; 
-				whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles; 
-				polygons represent density estimates of data and extend to extreme values.")
-			} else if (input$otherPlotType=='1') { # Bean plot
-		# Display median or mean for bean plot?
-				if(input$beanPlotMedianMean==0){ myBeanplotCenter<-"median" } else { myBeanplotCenter<-"mean" }
-				FL<-paste("Black lines show the ",myBeanplotCenter,"s; 
-				white lines represent individual data points; 
-				polygons represent the estimated density of the data.", sep="")
-				#if(input$beanplotOverall){FL<-append(FL, c("dotted line represents overall "))}
-			}
-		} # END: other plot types	
-		cat(paste(FL, collapse=""))
-		#- I am not sure what to put for the notches because we don't add '*'s to the box plots.
-	})
-
-	# *** Download boxplot data in csv format ***
-	output$downloadBoxplotData <- downloadHandler(
-    	filename = function() { "BoxplotData.csv" },
-   		content = function(file) {
-		write.csv(dataM(), file, row.names=FALSE)
-    }) ###
+  observe({
+    if (input$clearText_button == 0) {
+      return()
+    }
+    isolate({
+      updateTextInput(session, "myData", label = ",", value = "")
+    })
+  })
 
-})
+  # *** Read in data matrix ***
+  data_m <- reactive({
+    if (input$dataInput == 1) {
+      if (input$sampleData == 1) {
+        data <- sample_data_1_cache
+      } else if (input$sampleData == 2) {
+        data <- sample_data_2_cache
+      } else {
+        data <- sample_data_3_cache
+      }
+    } else if (input$dataInput == 2) {
+      in_file <- input$upload
+      # Avoid error message while file is not uploaded yet
+      if (is.null(input$upload)) {
+        return(NULL)
+      }
+      # Get the separator and extension
+      ext <- tolower(tools::file_ext(in_file$name))
+
+      if (ext %in% c("xls", "xlsx")) {
+        data <- as.data.frame(readxl::read_excel(in_file$datapath))
+      } else {
+        my_sep <- switch(input$fileSepDF,
+          "1" = ",",
+          "2" = "\t",
+          "3" = ";",
+          "4" = ""
+        )
+        data <- read.table(
+          in_file$datapath,
+          sep = my_sep, header = TRUE, fill = TRUE,
+          check.names = FALSE
+        )
+      }
+    } else {
+      # For special case when last column has empty entries in some rows
+      if (is.null(input$myData) || input$myData == "") {
+        return(NULL)
+      }
+      my_sep <- switch(input$fileSepP,
+        "1" = ",",
+        "2" = "\t",
+        "3" = ";"
+      )
+      data <- read.table(
+        text = input$myData, sep = my_sep, header = TRUE, fill = TRUE,
+        check.names = FALSE
+      )
+    }
+    return(data)
+  })
+
+  # *** The plot dimensions ***
+  height_size <- reactive({
+    input$myHeight
+  })
+  width_size <- reactive({
+    input$myWidth
+  })
+
+  # *** Determine extent of whisker range ***
+  # whiskerDefinition 0 - Tukey (default), 1 - Spear (min/max, range=0),
+  # 2 - Altman (5% and 95% quantiles)
+  my_range <- reactive({
+    if (input$whiskerType == 0) {
+      my_range <- c(-1.5)
+    } else if (input$whiskerType == 1) {
+      my_range <- c(0)
+    } else if (input$whiskerType == 2) {
+      my_range <- c(5)
+    }
+    return(my_range)
+  })
+
+  # *** Get boxplot statistics ***
+  boxplot_stats <- reactive({
+    if (is.null(data_m())) {
+      return(NULL)
+    }
+    return(boxplot(data_m(), na.rm = TRUE, range = my_range(), plot = FALSE))
+  })
+
+  # *** Helper function for stats table ***
+  get_stats_matrix <- function(data, stats, add_means) {
+    stats_matrix <- rbind(
+      as.matrix(stats$stats[c(5, 4, 3, 2, 1), ]),
+      stats$n
+    )
+
+    if (add_means) {
+      stats_matrix <- rbind(stats_matrix, colMeans(data, na.rm = TRUE))
+      rownames(stats_matrix) <- c(
+        "Upper whisker", "3rd quartile", "Median", "1st quartile",
+        "Lower whisker", "Nr. of data points", "Mean"
+      )
+    } else {
+      rownames(stats_matrix) <- c(
+        "Upper whisker", "3rd quartile", "Median", "1st quartile",
+        "Lower whisker", "Nr. of data points"
+      )
+    }
+    colnames(stats_matrix) <- colnames(data)
+    return(stats_matrix)
+  }
+
+  # *** Helper function for figure legend ***
+  generate_figure_legend <- function(stats, plot_type, other_plot_type,
+                                     whisker_type, add_means, add_mean_ci,
+                                     mean_ci_val, my_varwidth,
+                                     bean_plot_center_type) {
+    fl <- "Center lines show the medians; "
+
+    if (plot_type == "0") { # Boxplot
+      fl <- paste0(
+        fl,
+        "box limits indicate the 25th and 75th percentiles ",
+        "as determined by R software"
+      )
+
+      if (whisker_type == 0) {
+        fl <- paste0(
+          fl,
+          "; whiskers extend 1.5 times the interquartile range ",
+          "from the 25th and 75th percentiles, outliers are ",
+          "represented by dots"
+        )
+      } else if (whisker_type == 1) {
+        fl <- paste0(fl, "; whiskers extend to minimum and maximum values")
+      } else {
+        fl <- paste0(
+          fl,
+          "; whiskers extend to 5th and 95th percentiles, ",
+          "outliers are represented by dots"
+        )
+      }
+
+      if (add_means) {
+        fl <- paste0(fl, "; crosses represent sample means")
+        if (add_mean_ci) {
+          fl <- paste0(
+            fl, "; bars indicate ", mean_ci_val,
+            "% confidence intervals of the means"
+          )
+        }
+      }
+
+      if (my_varwidth) {
+        fl <- paste0(
+          fl, "; width of the boxes is proportional to the square root ",
+          "of the sample size"
+        )
+      }
+    } else {
+      if (other_plot_type == "0") { # Violin plot
+        fl <- paste0(
+          "White circles show the medians; box limits indicate the 25th ",
+          "and 75th percentiles; whiskers extend 1.5 times the interquartile ",
+          "range; polygons represent density estimates."
+        )
+      } else { # Bean plot
+        center_label <- if (bean_plot_center_type == 0) "median" else "mean"
+        fl <- paste0(
+          "Black lines show the ", center_label,
+          "s; white lines represent individual data points; ",
+          "polygons represent density estimates."
+        )
+      }
+    }
+
+    fl <- paste0(
+      fl, ". n = ",
+      paste(stats$n, collapse = ", "),
+      " sample points."
+    )
+    return(fl)
+  }
+
+  # *** Generate the box plot ***
+  generate_box_plot <- function(plot_data) {
+    par(mar = c(5, 8, 4, 2))
+
+    nr_of_samples <- ncol(plot_data)
+
+    plot_data_m <- plot_data
+    not_plot_points <- seq_len(nr_of_samples)
+    plot_points <- integer(0)
+
+    if (input$plotDataPoints) {
+      nr_needed <- as.numeric(input$nrOfDataPoints)
+      not_plot_points <- integer(0)
+      for (i in seq_len(nr_of_samples)) {
+        if (sum(!is.na(plot_data[[i]])) < nr_needed) {
+          plot_data_m[[i]] <- NA
+          plot_points <- c(plot_points, i)
+        } else {
+          not_plot_points <- c(not_plot_points, i)
+        }
+      }
+    }
+
+    my_colours <- parse_colours(input$myColours)
+    my_colours_2 <- parse_colours(input$myOtherPlotColours)
+    point_colors <- parse_colours(input$pointColors)
+
+    # Replicate colors if only one is provided
+    if (length(my_colours) == 1) {
+      my_colours <- rep(my_colours, nr_of_samples)
+    }
+    if (length(my_colours_2) == 1) {
+      my_colours_2 <- rep(my_colours_2, nr_of_samples)
+    }
+    if (length(point_colors) == 1) {
+      point_colors <- rep(point_colors, nr_of_samples)
+    }
+
+    point_t <- 1 - (input$pointTransparency / 100)
+    point_c <- NA
+    if (point_t < 1) {
+      point_c <- rgb(
+        t(col2rgb(point_colors)),
+        alpha = 255 * point_t,
+        maxColorValue = 255
+      )
+    } else {
+      point_c <- point_colors
+    }
 
+    my_orientation <- (input$myOrientation == 1)
+    my_varwidth <- (input$myVarwidth == TRUE)
+    my_notch <- (input$myNotch == TRUE)
+    my_log <- ""
+    xmin <- NA
+    xmax <- NA
+    ymin <- NA
+    ymax <- NA
 
+    if (input$logScale == TRUE) {
+      my_log <- if (my_orientation) "x" else "y"
+    }
 
+    if (input$ylimit != "" && !my_orientation) {
+      ymin <- as.numeric(gsub("\\s", "", strsplit(input$ylimit, ",")[[1]][1]))
+      ymax <- as.numeric(gsub("\\s", "", strsplit(input$ylimit, ",")[[1]][2]))
+    }
+    if (input$xlimit != "" && my_orientation) {
+      xmin <- as.numeric(gsub("\\s", "", strsplit(input$xlimit, ",")[[1]][1]))
+      xmax <- as.numeric(gsub("\\s", "", strsplit(input$xlimit, ",")[[1]][2]))
+    }
 
+    # Calculate a shared default range for consistent axes across plot types
+    shared_lim <- if (all(is.na(plot_data))) {
+      NULL
+    } else {
+      r <- range(plot_data, na.rm = TRUE)
+      if (input$showNrOfPoints) {
+        if (input$logScale == TRUE && length(r[r > 0]) > 0) {
+          # Log scale requires multiplicative expansion to prevent negatives
+          c(r[1], r[2] * (10^(diff(log10(r[r > 0])) * 0.15)))
+        } else {
+          # Expand top geometrically to make space for data counts
+          padding <- diff(r) * 0.15
+          c(r[1] - (diff(r) * 0.04), r[2] + padding)
+        }
+      } else {
+        r
+      }
+    }
+
+    vals_lim <- if (!my_orientation && input$ylimit != "") {
+      c(ymin, ymax)
+    } else if (my_orientation && input$xlimit != "") {
+      c(xmin, xmax)
+    } else {
+      shared_lim
+    }
+
+    xaxis_label_angle <- if (input$xaxisLabelAngle) 2 else 1
+    par(las = xaxis_label_angle)
+
+    if (input$plotType == "0") { # Boxplot
+      boxplot(
+        plot_data_m,
+        main = input$myTitle,
+        sub = input$mySubtitle,
+        xlab = input$myXlab,
+        ylab = input$myYlab,
+        col = my_colours,
+        horizontal = my_orientation,
+        varwidth = my_varwidth,
+        notch = my_notch,
+        outline = !input$showDataPoints,
+        range = my_range(),
+        log = my_log,
+        ylim = vals_lim,
+        las = xaxis_label_angle,
+        frame.plot = FALSE,
+        # Font sizes
+        cex.main = input$cexTitle / 10,
+        cex.lab = input$cexAxislabel / 10,
+        cex.axis = input$cexAxis / 10
+      )
+    } else {
+      if (input$otherPlotType == "0") { # Violin plot
+        if (length(not_plot_points) > 0) {
+          vioplot(
+            as.list(data.frame(plot_data_m)),
+            col = my_colours_2,
+            horizontal = my_orientation,
+            border = input$violinBorder,
+            cex.axis = input$cexAxis / 10,
+            ylim = vals_lim,
+            names = colnames(plot_data_m),
+            log = my_log
+          )
+        } else {
+          plot(
+            1,
+            type = "n", axes = FALSE, xlab = "", ylab = "",
+            xlim = if (my_orientation) {
+              shared_lim
+            } else {
+              c(0.5, nr_of_samples + 0.5)
+            },
+            ylim = if (!my_orientation) {
+              shared_lim
+            } else {
+              c(0.5, nr_of_samples + 0.5)
+            }
+          )
+          axis(if (my_orientation) 1 else 2, cex.axis = input$cexAxis / 10)
+          axis(
+            if (my_orientation) 2 else 1,
+            at = seq_len(nr_of_samples),
+            labels = colnames(plot_data),
+            cex.axis = input$cexAxis / 10
+          )
+        }
+        title(
+          main = input$myTitle,
+          sub = input$mySubtitle,
+          xlab = input$myXlab,
+          ylab = input$myYlab,
+          cex.main = input$cexTitle / 10,
+          cex.lab = input$cexAxislabel / 10
+        )
+      } else { # Bean plot
+        my_beanplot_center <- if (input$beanPlotMedianMean == 0) {
+          "median"
+        } else {
+          "mean"
+        }
+        if (length(not_plot_points) > 0) {
+          beanplot(
+            data.frame(plot_data_m[, not_plot_points, drop = FALSE]),
+            at = not_plot_points,
+            xlim = c(0.5, nr_of_samples + 0.5),
+            ylim = vals_lim,
+            col = if (length(my_colours_2) > 1) {
+              as.list(my_colours_2)
+            } else {
+              my_colours_2
+            },
+            horizontal = my_orientation,
+            border = input$beanBorder,
+            what = c(1, 1, 1, as.logical(as.numeric(input$beanPlotMedianMean))),
+            cex.axis = input$cexAxis / 10,
+            overallline = my_beanplot_center,
+            names = colnames(plot_data)[not_plot_points],
+            frame.plot = FALSE,
+            log = my_log
+          )
+          axis(
+            if (my_orientation) 2 else 1,
+            at = seq_len(nr_of_samples),
+            labels = colnames(plot_data),
+            cex.axis = input$cexAxis / 10
+          )
+        } else {
+          plot(
+            1,
+            type = "n", axes = FALSE, xlab = "", ylab = "",
+            xlim = if (my_orientation) {
+              shared_lim
+            } else {
+              c(0.5, nr_of_samples + 0.5)
+            },
+            ylim = if (!my_orientation) {
+              shared_lim
+            } else {
+              c(0.5, nr_of_samples + 0.5)
+            }
+          )
+          axis(if (my_orientation) 1 else 2, cex.axis = input$cexAxis / 10)
+          axis(
+            if (my_orientation) 2 else 1,
+            at = seq_len(nr_of_samples),
+            labels = colnames(plot_data),
+            cex.axis = input$cexAxis / 10
+          )
+        }
+        title(
+          main = input$myTitle,
+          sub = input$mySubtitle,
+          xlab = input$myXlab,
+          ylab = input$myYlab,
+          cex.main = input$cexTitle / 10,
+          cex.lab = input$cexAxislabel / 10
+        )
+      }
+    }
+
+    # Add grid
+    if (input$addGrid == 1) {
+      grid()
+    } else if (input$addGrid == 2) {
+      grid(nx = NULL, ny = NA)
+    } else if (input$addGrid == 3) {
+      grid(nx = NA, ny = NULL)
+    }
+
+    # Samples means
+    if (input$addMeans && input$plotType == "0") {
+      boxplot_means <- colMeans(plot_data, na.rm = TRUE)
+      if (my_orientation) {
+        points(boxplot_means, seq_along(boxplot_means), pch = 18, col = "red")
+      } else {
+        points(seq_along(boxplot_means), boxplot_means, pch = 18, col = "red")
+      }
+
+      # Add CI of means
+      if (input$addMeanCI) {
+        for (i in seq_along(plot_data)) {
+          my_sample <- na.omit(plot_data[[i]])
+          n <- length(my_sample)
+          if (n > 1) {
+            standard_error <- sd(my_sample) / sqrt(n)
+            ci_level <- as.numeric(input$meanCI) / 100
+            t_value <- qt((1 + ci_level) / 2, df = n - 1)
+            margin_error <- t_value * standard_error
+            lower_ci <- boxplot_means[i] - margin_error
+            upper_ci <- boxplot_means[i] + margin_error
+
+            if (my_orientation) {
+              lines(c(lower_ci, upper_ci), c(i, i), col = "red", lwd = 2)
+              lines(
+                c(lower_ci, lower_ci), c(i - 0.1, i + 0.1),
+                col = "red", lwd = 2
+              )
+              lines(
+                c(upper_ci, upper_ci), c(i - 0.1, i + 0.1),
+                col = "red", lwd = 2
+              )
+            } else {
+              lines(c(i, i), c(lower_ci, upper_ci), col = "red", lwd = 2)
+              lines(
+                c(i - 0.1, i + 0.1), c(lower_ci, lower_ci),
+                col = "red", lwd = 2
+              )
+              lines(
+                c(i - 0.1, i + 0.1), c(upper_ci, upper_ci),
+                col = "red", lwd = 2
+              )
+            }
+          }
+        }
+      }
+    }
+
+    # Add numbers of data points
+    if (input$showNrOfPoints) {
+      nr_points <- boxplot_stats()$n
+      if (my_orientation) {
+        pos_x <- if (input$logScale == TRUE) 10^par("usr")[2] else par("usr")[2]
+        text(
+          x = pos_x,
+          y = seq_along(nr_points),
+          labels = nr_points,
+          pos = 2
+        )
+      } else {
+        pos_y <- if (input$logScale == TRUE) 10^par("usr")[4] else par("usr")[4]
+        text(
+          x = seq_along(nr_points),
+          y = pos_y,
+          labels = nr_points,
+          pos = 1
+        )
+      }
+    }
+
+    # Add data points if selected or if forced by plotDataPoints limit
+    if (input$showDataPoints || length(plot_points) > 0) {
+      plot_data_points <- plot_data
+      if (!input$showDataPoints && length(plot_points) > 0) {
+        # Only plot points for samples below the limit
+        plot_data_points[, not_plot_points] <- NA
+      }
+
+      if (input$datapointType == 1) { # Bee swarm
+        beeswarm(
+          plot_data_points,
+          add = TRUE,
+          col = point_c,
+          horizontal = my_orientation,
+          cex = input$pointSize / 10,
+          pch = 16
+        )
+      } else { # Jittered or Default
+        jittered.points(
+          plot_data_points,
+          my_orientation,
+          input$datapointType,
+          point_colors,
+          input$pointTransparency,
+          input$pointSize / 10
+        )
+      }
+    }
+  }
+
+  ## *** Data in table ***
+  output$filetable <- renderTable({
+    if (is.null(data_m())) {
+      return(NULL)
+    }
+    if (nrow(data_m()) < 500) {
+      return(data_m())
+    } else {
+      return(data_m()[1:100, ])
+    }
+  })
+
+  # *** Boxplot (using 'generate_box_plot'-function) ***
+  output$boxPlot <- renderPlot(
+    {
+      if (is.null(data_m())) {
+        return(NULL)
+      }
+      generate_box_plot(data_m())
+    },
+    height = function() {
+      input$myHeight
+    },
+    width = function() {
+      input$myWidth
+    }
+  )
+
+  ## *** Download EPS file ***
+  output$downloadPlotEPS <- downloadHandler(
+    filename = function() {
+      "Boxplot.eps"
+    },
+    content = function(file) {
+      postscript(
+        file,
+        horizontal = FALSE, onefile = FALSE, paper = "special",
+        width = input$myWidth / 72, height = input$myHeight / 72
+      )
+      generate_box_plot(data_m())
+      dev.off()
+    },
+    contentType = "application/postscript"
+  )
+
+  ## *** Download PDF file ***
+  output$downloadPlotPDF <- downloadHandler(
+    filename = function() {
+      "Boxplot.pdf"
+    },
+    content = function(file) {
+      pdf(file, width = input$myWidth / 72, height = input$myHeight / 72)
+      generate_box_plot(data_m())
+      dev.off()
+    },
+    contentType = "application/pdf"
+  )
+
+  ## *** Download SVG file ***
+  output$downloadPlotSVG <- downloadHandler(
+    filename = function() {
+      "Boxplot.svg"
+    },
+    content = function(file) {
+      svg(file, width = input$myWidth / 72, height = input$myHeight / 72)
+      generate_box_plot(data_m())
+      dev.off()
+    },
+    contentType = "image/svg"
+  )
+
+  # *** Output boxplot statistics in table below plot ***
+  output$boxplotStatsTable <- renderTable(
+    {
+      if (is.null(data_m()) || is.null(boxplot_stats())) {
+        return(NULL)
+      }
+      get_stats_matrix(data_m(), boxplot_stats(), input$addMeans)
+    },
+    rownames = TRUE
+  )
+
+  # *** Print figure legend ***
+  output$FigureLegend <- renderPrint({
+    if (is.null(data_m()) || is.null(boxplot_stats())) {
+      return(invisible())
+    }
+    fl <- generate_figure_legend(
+      stats = boxplot_stats(),
+      plot_type = input$plotType,
+      other_plot_type = input$otherPlotType,
+      whisker_type = input$whiskerType,
+      add_means = input$addMeans,
+      add_mean_ci = input$addMeanCI,
+      mean_ci_val = input$meanCI,
+      my_varwidth = input$myVarwidth,
+      bean_plot_center_type = input$beanPlotMedianMean
+    )
+    cat(fl, "\n")
+  })
+
+
+  # *** Download boxplot data in csv format ***
+  output$downloadBoxplotData <- downloadHandler(
+    filename = function() {
+      "BoxplotData.csv"
+    },
+    content = function(file) {
+      write.csv(data_m(), file, row.names = FALSE)
+    }
+  )
+})
diff --git a/tests/test_vioplot_edge_cases.R b/tests/test_vioplot_edge_cases.R
new file mode 100644
index 0000000..cad8f36
--- /dev/null
+++ b/tests/test_vioplot_edge_cases.R
@@ -0,0 +1,68 @@
+library(testthat)
+library(sm)
+
+context("BoxPlotR Plotting Edge Cases")
+
+# Source the custom plot functions
+source("../MyVioplot.R")
+
+test_that("MyVioplot handles all-NA data gracefully", {
+  # Create a dataset where one sample is completely NA
+  plot_data <- list(
+    Sample1 = c(1, 2, 3, 4, 5),
+    Sample2 = c(NA_real_, NA_real_, NA_real_),
+    Sample3 = c(5, 6, 7, 8, 9)
+  )
+  
+  # vioplot should execute without crash / 'missing value where TRUE/FALSE needed'
+  test_result <- tryCatch({
+    pdf(file = NULL) # sink output to avoid creating Rplots.pdf
+    vioplot(plot_data, at = 1:3, col = "blue")
+    dev.off()
+    TRUE
+  }, error = function(e) {
+    message("Failed with error: ", e$message)
+    FALSE
+  })
+  
+  expect_true(test_result)
+})
+
+test_that("MyVioplot handles data below minimum threshold (length < 2)", {
+  # Create a dataset where one sample has only 1 valid value
+  plot_data <- list(
+    Sample1 = c(1, 2, 3, 4, 5),
+    Sample2 = c(10, NA, NA),
+    Sample3 = c(5, 6, 7, 8, 9)
+  )
+  
+  test_result <- tryCatch({
+    pdf(file = NULL) 
+    vioplot(plot_data, at = 1:3, col = "blue")
+    dev.off()
+    TRUE
+  }, error = function(e) {
+    FALSE
+  })
+  
+  expect_true(test_result)
+})
+
+test_that("MyVioplot limits logic works in horizontal mode", {
+  plot_data <- list(
+    Sample1 = c(1, 2, 3, 4, 5),
+    Sample2 = c(2, 3, 4, 5, 6)
+  )
+  
+  test_result <- tryCatch({
+    pdf(file = NULL) 
+    # Horizontal means ylim is the values scale
+    vioplot(plot_data, at = 1:2, col = "blue", horizontal = TRUE, ylim = c(0, 10))
+    dev.off()
+    TRUE
+  }, error = function(e) {
+    FALSE
+  })
+  
+  expect_true(test_result)
+})
diff --git a/ui.R b/ui.R
index f0eed18..2605bc0 100644
--- a/ui.R
+++ b/ui.R
@@ -1,328 +1,461 @@
 shinyUI(fluidPage(
-    titlePanel("BoxPlotR: a web-tool for generation of box plots"),
-    tags$head(
-        tags$style(HTML("
-			@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
-			body, h1, h2, h3, h4, h5, h6, .shiny-text-output, label { font-family: 'Inter', sans-serif !important; }
-			body { background-color: #f3f4f6; }
-			label.radio { display: inline-block; }
-			.radio input[type=\"radio\"] { float: none; }
-			select { max-width: 200px; border-radius: 6px; border: 1px solid #d1d5db; padding: 4px; }
-			textarea { width: 100%; max-width: 100%; display: block; border-radius: 6px; border: 1px solid #d1d5db; box-sizing: border-box; margin-bottom: 10px; font-family: 'Courier New', Courier, monospace; white-space: pre; overflow-x: auto; }
-			.jslider { max-width: 200px; }
+  titlePanel("BoxPlotR: a web-tool for generation of box plots"),
+  tags$head(
+    tags$style(HTML(
+      "
+      @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400",
+      ";500;600;700&display=swap');
+      body, h1, h2, h3, h4, h5, h6, .shiny-text-output, label {
+        font-family: 'Inter', sans-serif !important;
+      }
+      body { background-color: #f3f4f6; }
+      label.radio { display: inline-block; }
+      .radio input[type=\"radio\"] { float: none; }
+      select {
+        max-width: 200px; border-radius: 6px; border: 1px solid #d1d5db;
+        padding: 4px;
+      }
+      textarea {
+        width: 100%; max-width: 100%; display: block; border-radius: 6px;
+        border: 1px solid #d1d5db; box-sizing: border-box; margin-bottom: 10px;
+        font-family: 'Courier New', Courier, monospace; white-space: pre;
+        overflow-x: auto;
+      }
+      .jslider { max-width: 200px; }
 
-			/* Glassmorphism Sidebar */
-			.well {
-				max-width: 330px;
-				background: rgba(255, 255, 255, 0.6) !important;
-				backdrop-filter: blur(12px) !important;
-				-webkit-backdrop-filter: blur(12px) !important;
-				border: 1px solid rgba(255, 255, 255, 0.6) !important;
-				border-radius: 16px !important;
-				box-shadow: 0 10px 25px rgba(0, 0, 0, 0.05) !important;
-			}
-			.span4 { max-width: 330px; }
+      /* Glassmorphism Sidebar */
+      .well {
+        max-width: 330px;
+        background: rgba(255, 255, 255, 0.6) !important;
+        backdrop-filter: blur(12px) !important;
+        -webkit-backdrop-filter: blur(12px) !important;
+        border: 1px solid rgba(255, 255, 255, 0.6) !important;
+        border-radius: 16px !important;
+        box-shadow: 0 10px 25px rgba(0, 0, 0, 0.05) !important;
+      }
+      .span4 { max-width: 330px; }
 
-			/* Accent Colors - Ocean Blue */
-			.nav-tabs > li.active > a, .nav-tabs > li.active > a:focus, .nav-tabs > li.active > a:hover {
-				color: #fff !important; background-color: #0c4a6e !important;
-				border-radius: 8px 8px 0 0; border: none !important;
-			}
-			.nav-tabs > li > a { color: #475569 !important; font-weight: 500; }
-			.btn-default {
-				background-color: #e0f2fe !important; color: #0369a1 !important;
-				border: 1px solid #bae6fd !important; border-radius: 6px !important;
-				transition: all 0.2s ease; font-weight: 600; margin-bottom: 5px;
-			}
-			.btn-default:hover {
-				background-color: #0ea5e9 !important; color: white !important;
-				box-shadow: 0 4px 12px rgba(14, 165, 233, 0.3) !important;
-			}
+      /* Accent Colors - Ocean Blue */
+      .nav-tabs > li.active > a, .nav-tabs > li.active > a:focus,
+      .nav-tabs > li.active > a:hover {
+        color: #fff !important; background-color: #0c4a6e !important;
+        border-radius: 8px 8px 0 0; border: none !important;
+      }
+      .nav-tabs > li > a { color: #475569 !important; font-weight: 500; }
+      .btn-default {
+        background-color: #e0f2fe !important; color: #0369a1 !important;
+        border: 1px solid #bae6fd !important; border-radius: 6px !important;
+        transition: all 0.2s ease; font-weight: 600; margin-bottom: 5px;
+      }
+      .btn-default:hover {
+        background-color: #0ea5e9 !important; color: white !important;
+        box-shadow: 0 4px 12px rgba(14, 165, 233, 0.3) !important;
+      }
 
-			/* Card Layout for Plots */
-			.plot-card {
-				background: #ffffff; border-radius: 16px; padding: 20px;
-				box-shadow: 0 10px 25px rgba(0,0,0,0.06); margin-top: 15px; margin-bottom: 25px; border: 1px solid #e5e7eb;
-			}
-			.table-card {
-				background: #ffffff; border-radius: 12px; padding: 15px;
-				box-shadow: 0 4px 12px rgba(0,0,0,0.05); border: 1px solid #e5e7eb;
-			}
-			.controls-row { display: flex; gap: 10px; margin-top: 15px; flex-wrap: wrap; }
-		"))
-    ),
-    sidebarLayout(
-        sidebarPanel(
-            conditionalPanel(
-                condition = "input.tabs1=='About'",
-                h4("Introduction")
+      /* Card Layout for Plots */
+      .plot-card {
+        background: #ffffff; border-radius: 16px; padding: 20px;
+        box-shadow: 0 10px 25px rgba(0,0,0,0.06); margin-top: 15px;
+        margin-bottom: 25px; border: 1px solid #e5e7eb;
+      }
+      .table-card {
+        background: #ffffff; border-radius: 12px; padding: 15px;
+        box-shadow: 0 4px 12px rgba(0,0,0,0.05); border: 1px solid #e5e7eb;
+      }
+      .controls-row {
+        display: flex; gap: 10px; margin-top: 15px; flex-wrap: wrap;
+      }
+    "
+    ))
+  ),
+  sidebarLayout(
+    sidebarPanel(
+      conditionalPanel(
+        condition = "input.tabs1=='About'",
+        h4("Introduction")
+      ),
+      conditionalPanel(
+        condition = "input.tabs1=='Data upload'",
+        h4("Enter data"),
+        radioButtons(
+          "dataInput", "",
+          list("Load sample data" = 1, "Upload file" = 2, "Paste data" = 3)
+        ),
+        conditionalPanel(
+          condition = "input.dataInput=='1'",
+          h5("Load sample data:"),
+          radioButtons(
+            "sampleData", "Load sample data",
+            list(
+              "Example 1 (100,76,16,76,41 data points)" = 1,
+              "Example 2 (3 columns with 100 data points)" = 2,
+              "Example 3 (Log scale, Excel format)" = 3
+            )
+          )
+        ),
+        conditionalPanel(
+          condition = "input.dataInput=='2'",
+          h5("Upload data file: "),
+          fileInput(
+            "upload", "", multiple = FALSE,
+            accept = c(
+              "text/csv", "text/comma-separated-values",
+              "text/tab-separated-values", "text/plain",
+              ".csv", ".tsv", ".xls", ".xlsx"
+            )
+          ),
+          radioButtons(
+            "fileSepDF", "Delimiter (ignored for Excel files):",
+            list("Comma" = 1, "Tab" = 2, "Semicolon" = 3)
+          ),
+          HTML(paste(
+            "<p>Supports .csv, .tab, .txt, .xls, and .xlsx. ",
+            "Data in <a href=\"http://en.wikipedia.org/wiki/",
+            "Delimiter-separated_values\">delimited text files </a> can be ",
+            "separated by comma, tab or semicolon. Excel files will be read ",
+            "automatically from the first sheet. </p>",
+            sep = ""
+          ))
+        ),
+        conditionalPanel(
+          condition = "input.dataInput=='3'",
+          h5("Paste data below:"),
+          tags$textarea(id = "myData", rows = 10, cols = 5, ""),
+          actionButton("clearText_button", "Clear data"),
+          radioButtons(
+            "fileSepP", "Separator:",
+            list("Comma" = 1, "Tab" = 2, "Semicolon" = 3)
+          )
+        )
+      ),
+      conditionalPanel(
+        condition = "input.tabs1=='Data visualization'",
+        radioButtons("plotType", "", list("Boxplot" = 0, "Other" = 1)),
+        conditionalPanel(
+          condition = "input.plotType=='1'",
+          radioButtons(
+            "otherPlotType", "",
+            list("Violin plot" = 0, "Bean plot" = 1)
+          ),
+          HTML(paste(
+            "<p style=\"color:#808080\">Violin plots are generated with the ",
+            "<a href=\"https://cran.r-project.org/web/packages/vioplot/",
+            "index.html\">vioplot package</a>. Bean plots are generated ",
+            "with the <a href=\"https://cran.r-project.org/web/packages/",
+            "beanplot/index.html\">beanplot package</a>.</p>",
+            sep = ""
+          )),
+          textInput(
+            "myOtherPlotColours", "Colour(s):",
+            value = c("light grey, white")
+          ),
+          conditionalPanel(
+            condition = "input.otherPlotType=='0'",
+            helpText("Colour of the 'violin area'"),
+            textInput("violinBorder", "Border colour:", value = c("grey"))
+          ),
+          conditionalPanel(
+            condition = "input.otherPlotType=='1'",
+            helpText(paste(
+              "up to 4 colours can be specified: area of the beans, lines ",
+              "inside the bean, lines outside the bean, and average line ",
+              "per bean",
+              sep = ""
+            )),
+            textInput("beanBorder", "Border colour:", value = c("grey")),
+            radioButtons(
+              "beanPlotMedianMean", "Display: ",
+              list("Median" = 0, "Mean" = 1)
+            )
+          )
+        ),
+        h4("Plot options"),
+        checkboxInput("plotDataPoints", "Minimum number of data points", FALSE),
+        conditionalPanel(
+          condition = "input.plotDataPoints",
+          numericInput(
+            "nrOfDataPoints", "Data point limit: ",
+            value = 5, min = 5
+          )
+        ),
+        checkboxInput("showDataPoints", "Add data points", FALSE),
+        conditionalPanel(
+          condition = "input.showDataPoints",
+          radioButtons(
+            "datapointType", "",
+            list("Default" = 0, "Bee swarm" = 1, "Jittered" = 2)
+          ),
+          sliderInput(
+            "pointTransparency", "Transparency of data points",
+            min = 0, max = 99, value = 50
+          ),
+          sliderInput(
+            "pointSize", "Size of data points",
+            min = 1, max = 20, value = 10
+          ),
+          HTML(paste(
+            "<p style=\"color:#808080\">Using the <a href=\"http://www.",
+            "cbs.dtu.dk/~eklund/beeswarm/\">beeswarm package</a>.</p>",
+            sep = ""
+          )),
+          textInput("pointColors", "Colour(s):", value = c("black"))
+        ),
+        checkboxInput("showNrOfPoints", "Display number of data points", FALSE),
+        conditionalPanel(
+          condition = "input.plotType=='0'",
+          checkboxInput(
+            "whiskerDefinition", "Definition of whisker extent", FALSE
+          ),
+          conditionalPanel(
+            condition = "input.whiskerDefinition",
+            radioButtons(
+              "whiskerType", "",
+              list("Tukey" = 0, "Spear" = 1, "Altman" = 2)
             ),
-            conditionalPanel(
-                condition = "input.tabs1=='Data upload'",
-                h4("Enter data"),
-                radioButtons("dataInput", "", list("Load sample data" = 1, "Upload file" = 2, "Paste data" = 3)),
-                conditionalPanel(
-                    condition = "input.dataInput=='1'",
-                    h5("Load sample data:"),
-                    radioButtons("sampleData", "Load sample data", list("Example 1 (100,76,16,76,41 data points)" = 1, "Example 2 (3 columns with 100 data points)" = 2))
-                ),
-                conditionalPanel(
-                    condition = "input.dataInput=='2'",
-                    h5("Upload delimited text file: "),
-                    fileInput("upload", "", multiple = FALSE),
-                    radioButtons("fileSepDF", "Delimiter:", list("Comma" = 1, "Tab" = 2, "Semicolon" = 3)), # , "Space"=4))
-                    HTML('<p>Data in <a href="http://en.wikipedia.org/wiki/Delimiter-separated_values">delimited text files </a> can be separated by comma, tab or semicolon.
-				For example, Excel data can be exported in .csv (comma separated) or .tab (tab separated) format. </p>')
-                ),
-                conditionalPanel(
-                    condition = "input.dataInput=='3'",
-                    h5("Paste data below:"),
-                    tags$textarea(id = "myData", rows = 10, cols = 5, ""),
-                    actionButton("clearText_button", "Clear data"),
-                    radioButtons("fileSepP", "Separator:", list("Comma" = 1, "Tab" = 2, "Semicolon" = 3))
-                )
+            HTML(paste(
+              "<p style=\"color:#808080\">Tukey - whiskers extend to data ",
+              "points that are less than 1.5 x <a href=\"http://en.wikipedia.",
+              "org/wiki/Interquartile_range\">IQR</a> away from 1st/3rd ",
+              "<a href=:\"http://en.wikipedia.org/wiki/Quartile\">quartile",
+              "</a>; Spear - whiskers extend to minimum and maximum values; ",
+              "Altman - whiskers extend to 5th and 95th percentile ",
+              "(use only if n>40)</p>",
+              sep = ""
+            ))
+          ),
+          checkboxInput("addMeans", "Add sample means", FALSE),
+          conditionalPanel(
+            condition = "input.addMeans",
+            checkboxInput(
+              "addMeanCI", "Add confidence intervals of means", FALSE
             ),
             conditionalPanel(
-                condition = "input.tabs1=='Data visualization'",
-                radioButtons("plotType", "", list("Boxplot" = 0, "Other" = 1)),
-                conditionalPanel(
-                    condition = "input.plotType=='1'",
-                    radioButtons("otherPlotType", "", list("Violin plot" = 0, "Bean plot" = 1)),
-                    HTML('<p style="color:#808080">Violin plots are generated with the <a href="https://cran.r-project.org/web/packages/vioplot/index.html">vioplot package</a>. Bean plots are generated with the <a href="https://cran.r-project.org/web/packages/beanplot/index.html">beanplot package</a>.</p>'),
-                    textInput("myOtherPlotColours", "Colour(s):", value = c("light grey, white")),
-                    conditionalPanel(
-                        condition = "input.otherPlotType=='0'",
-                        helpText("Colour of the 'violin area'"),
-                        textInput("violinBorder", "Border colour:", value = c("grey"))
-                    ),
-                    conditionalPanel(
-                        condition = "input.otherPlotType=='1'",
-                        helpText("up to 4 colours can be specified: area of the beans, lines inside the bean, lines outside the bean, and average line per bean"),
-                        textInput("beanBorder", "Border colour:", value = c("grey")),
-                        radioButtons("beanPlotMedianMean", "Display: ", list("Median" = 0, "Mean" = 1))
-                    )
-                ),
-                h4("Plot options"),
-                checkboxInput("plotDataPoints", "Minimum number of data points", FALSE),
-                conditionalPanel(
-                    condition = "input.plotDataPoints",
-                    numericInput("nrOfDataPoints", "Data point limit: ", value = 5, min = 5)
-                ),
-                conditionalPanel(
-                    condition = "input.plotType=='0'",
-                    checkboxInput("showDataPoints", "Add data points", FALSE),
-                    conditionalPanel(
-                        condition = "input.showDataPoints",
-                        radioButtons("datapointType", "", list("Default" = 0, "Bee swarm" = 1, "Jittered" = 2)),
-                        sliderInput("pointTransparency", "Transparency of data points", min = 0, max = 99, value = 50),
-                        sliderInput("pointSize", "Size of data points", min = 1, max = 20, value = 10),
-                        HTML('<p style="color:#808080">Using the <a href="http://www.cbs.dtu.dk/~eklund/beeswarm/">beeswarm package</a>.</p>'),
-                        textInput("pointColors", "Colour(s):", value = c("black"))
-                    ),
-                    checkboxInput("whiskerDefinition", "Definition of whisker extent", FALSE),
-                    conditionalPanel(
-                        condition = "input.whiskerDefinition",
-                        radioButtons("whiskerType", "", list("Tukey" = 0, "Spear" = 1, "Altman" = 2)),
-                        # 					conditionalPanel(condition="input.whiskerType=='0'",
-                        # 						numericInput("TukeyRange", "Define whisker extent (x IQR):", min=0, step=0.5, value=1.5)
-                        # 					),
-                        # 					conditionalPanel(condition="input.whiskerType=='1'",
-                        # 						HTML('<p>Spear - Whiskers extend to minimum and maximum values.</p>')
-                        # 					),
-                        # 					conditionalPanel(condition="input.whiskerType=='2'",
-                        # 						numericInput("AltmanRange", "Define whisker extent in percentiles (ie, '5' means that whiskers extend to 5th and 95th percentile):", min=0, step=0.5, value=5)
-                        # 					),
-                        HTML('<p style="color:#808080">Tukey - whiskers extend to data points that are less than 1.5 x <a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a> away from 1st/3rd <a href=:"http://en.wikipedia.org/wiki/Quartile">quartile</a>;
-					Spear - whiskers extend to minimum and maximum values;
-					Altman - whiskers extend to 5th and 95th percentile (use only if n>40)</p>')
-                    ),
-                    checkboxInput("showNrOfPoints", "Display number of data points", FALSE),
-                    checkboxInput("addMeans", "Add sample means", FALSE),
-                    conditionalPanel(
-                        condition = "input.addMeans",
-                        checkboxInput("addMeanCI", "Add confidence intervals of means", FALSE),
-                        conditionalPanel(
-                            condition = "input.addMeanCI",
-                            radioButtons("meanCI", "Define confidence interval of means:", list("83%" = 83, "90%" = 90, "95%" = 95))
-                        )
-                    ),
-                    checkboxInput("myVarwidth", "Variable width boxes", FALSE),
-                    helpText("Widths of boxes are proportional to square-roots of the number of observations."),
-                    checkboxInput("myNotch", "Add notches", FALSE),
-                    HTML('<p style="color:#808080">+/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) - gives roughly 95% confidence that two medians differ (Chambers et al., 1983)</p>'),
-                    conditionalPanel(
-                        condition = "input.myNotch",
-                        HTML('<p>The notches are defined as +/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) and represent the 95% <a href="http://en.wikipedia.org/wiki/Confidence_interval">confidence interval</a> for each median.
-					Non-overlapping notches give roughly 95% confidence that two medians differ, ie, in 19 out of 20 cases the population
-					medians (estimated based on the samples) are in fact different (Chambers et al., 1983).</p>')
-                    ),
-                    textInput("myColours", "Colour(s):", value = c("light grey, white")),
-                    helpText("Colours in HEX format can be chosen on http://colorbrewer2.org/")
-                ),
-                checkboxInput("labelsTitle", "Modify labels and title", FALSE),
-                conditionalPanel(
-                    condition = "input.labelsTitle",
-                    checkboxInput("xaxisLabelAngle", "Rotate sample names", FALSE),
-                    textInput("myXlab", "X-axis label:", value = c("")),
-                    textInput("myYlab", "Y-axis label:", value = c("")),
-                    textInput("myTitle", "Boxplot title:", value = c("")),
-                    textInput("mySubtitle", "Boxplot subtitle:", value = c(""))
-                ),
-                checkboxInput("plotSize", "Adjust plot size", FALSE),
-                conditionalPanel(
-                    condition = "input.plotSize",
-                    numericInput("myHeight", "Plot height:", value = 550),
-                    numericInput("myWidth", "Plot width:", value = 750)
-                ),
-                checkboxInput("fontSizes", "Change font sizes", FALSE),
-                conditionalPanel(
-                    condition = "input.fontSizes",
-                    numericInput("cexTitle", "Title font size:", value = 14),
-                    numericInput("cexAxislabel", "Axis label size:", value = 14),
-                    numericInput("cexAxis", "Axis font size:", value = 12)
-                ),
-                h5("Orientation of box plots:"),
-                radioButtons("myOrientation", "", list("Vertical" = 0, "Horizontal" = 1)),
-                conditionalPanel(
-                    condition = "input.myOrientation=='0'",
-                    h5("Y-axis range (eg., '0,10'):"),
-                    textInput("ylimit", "", value = "")
-                ),
-                conditionalPanel(
-                    condition = "input.myOrientation=='1'",
-                    h5("X-axis range (eg., '0,10'):"),
-                    textInput("xlimit", "", value = "")
-                ),
-                checkboxInput("logScale", "Change to log scale (only for data >0)", FALSE),
-                h5("Add grid: "),
-                radioButtons("addGrid", "", list("None" = 0, "X & Y" = 1, "X only" = 2, "Y only" = 3))
-                # 			numericInput("boxWidth", "Width of boxes:", value=1),
+              condition = "input.addMeanCI",
+              radioButtons(
+                "meanCI", "Define confidence interval of means:",
+                list("83%" = 83, "90%" = 90, "95%" = 95)
+              )
             )
+          ),
+          checkboxInput("myVarwidth", "Variable width boxes", FALSE),
+          helpText(paste(
+            "Widths of boxes are proportional to square-roots of the ",
+            "number of observations.",
+            sep = ""
+          )),
+          checkboxInput("myNotch", "Add notches", FALSE),
+          HTML(paste(
+            "<p style=\"color:#808080\">+/-1.58*<a href=\"http://en.wikipedia.",
+            "org/wiki/Interquartile_range\">IQR</a>/sqrt(n) - gives roughly ",
+            "95% confidence that two medians differ (Chambers et al., 1983)",
+            "</p>",
+            sep = ""
+          )),
+          conditionalPanel(
+            condition = "input.myNotch",
+            HTML(paste(
+              "<p>The notches are defined as +/-1.58*<a href=\"http://en.",
+              "wikipedia.org/wiki/Interquartile_range\">IQR</a>/sqrt(n) and ",
+              "represent the 95% <a href=\"http://en.wikipedia.org/wiki/",
+              "Confidence_interval\">confidence interval</a> for each median. ",
+              "Non-overlapping notches give roughly 95% confidence that two ",
+              "medians differ, ie, in 19 out of 20 cases the population ",
+              "medians (estimated based on the samples) are in fact ",
+              "different (Chambers et al., 1983).</p>",
+              sep = ""
+            ))
+          ),
+          textInput("myColours", "Colour(s):", value = c("light grey, white")),
+          helpText(paste(
+            "Colours in HEX format can be chosen on ",
+            "http://colorbrewer2.org/",
+            sep = ""
+          ))
         ),
-        mainPanel(
-            tabsetPanel(
-                # Welcome tab
-                tabPanel(
-                    "About",
-                    HTML('
-				<br>
-
-				<p>This application was developed with Nature Methods and you can find the publication <a href="http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2811.html">here</a>.
-				The BoxPlotR has also been mentioned in this <a href="http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2837.html">editorial</a> and this
-				<a href="http://blogs.nature.com/methagora/2014/01/bring-on-the-box-plots-boxplotr.html">blog entry</a>. Nature methods also dedicated a
-				<a href="http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2807.html">Points of View</a> and a <a href="http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2813.html">Points of Significance</a>
-				column to box plots. We hope that you find the <a href= "http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2811.html">BoxPlotR</a> useful and we welcome suggestions
-				for additional features by our users. We would like to thank everyone who has made constructive suggestions so far. We will document the addition of new features in the News tab.</p>
-				<p>This application allows users to generate customized <a href="http://en.wikipedia.org/wiki/Box_plot">box plots</a> in a number of variants based on their data. A data matrix
-				can be uploaded as a file or pasted into the application. Basic box plots are generated based on the data and can be modified to include
-				additional information. Additional features become available when checking that option.  Information about sample sizes can be represented
-				by the width of each box where the widths are proportional to the square roots of the number of observations n. Notches can be added to the
-				boxes. These are defined as +/-1.58*<a href="http://en.wikipedia.org/wiki/Interquartile_range">IQR</a>/sqrt(n) which gives roughly 95% confidence that two medians are different. It is also possible to define
-				the whiskers based on the ideas of Spear and Tukey. Additional options of data visualization (violin and bean plots) reveal more information
-				about the underlying data distribution. Plots can be labeled, customized (colors, dimensions, orientation) and exported as eps, pdf and svg files.</p>
-				<p>BoxPlotR code can be run locally via <a href="https://github.com/jwildenhain/BoxPlotR.shiny/blob/master/README.md">GitHub</a>. You can also download and install it as a virtual machine (see <a href="https://github.com/jwildenhain/BoxPlotR.shiny/blob/master/README.md">GitHub</a> and FAQs for details). </p>'),
-                    h5("Support BoxPlotR"),
-                    HTML('<p>Please consider supporting the development and maintenance of BoxPlotR with a <a href="https://www.paypal.com/pools/c/9oezkBOeV3" target="_blank">donation</a>.</p>'),
-                    h5("Software references"),
-                    HTML('<p>R Development Core Team. <i><a href="http://www.r-project.org/">R</a>:  A Language and Environment for Statistical Computing.</i> R Foundation for Statistical Computing, Vienna (2013) <br>
-				RStudio and Inc. <i><a href="http://www.rstudio.com/shiny/">shiny</a>: Web Application Framework for R.</i> R package version 0.5.0 (2013) <br>
-				Adler, D. <i><a href="http://cran.r-project.org/web/packages/vioplot/index.html">vioplot</a>: Violin plot.</i> R package version 0.2 (2005)<br>
-				Eklund, A. <i><a href="http://cran.r-project.org/web/packages/beeswarm/index.html"> beeswarm</a>: The bee swarm plot, an alternative to stripchart.</i> R package version 0.1.5 (2012)<br>
-				Kampstra, P. <i><a href="http://cran.r-project.org/web/packages/beanplot/index.html">Beanplot</a>: A Boxplot Alternative for Visual Comparison of Distributions.</i> Journal of Statistical Software, Code Snippets 28(1). 1-9 (2008) <br>
-				Neuwirth, E. <i><a href="http://cran.r-project.org/web/packages/RColorBrewer/index.html">RColorBrewer</a>: ColorBrewer palettes.</i> R package version 1.0-5. (2011)</p>'),
-                    h5("Further references"),
-                    HTML('<p> Hadley Wickham and Lisa Stryjewski: <a href="http://vita.had.co.nz/papers/boxplots.pdf"> 40 years of boxplots </a></p>'),
-                    HTML('<p> Kristin Potter: <a href="http://www.sci.utah.edu/~kpotter/publications/potter-2006-MPSI.pdf">Methods for Presenting Statistical Information: The Box Plot</a></p>'),
-                    h6(
-                        "This application was created by the ", a("Tyers", href = "http://tyers.iric.ca/"), " and ", a("Rappsilber", href = "http://rappsilberlab.org/"),
-                        " labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
-                        a("shiny package from RStudio", href = "http://www.rstudio.com/shiny/"), "."
-                    )
-                ),
-                # Data upload tab
-                tabPanel(
-                    "Data upload", tableOutput("filetable"),
-                    h6(
-                        "This application was created by the ", a("Tyers", href = "http://tyers.iric.ca/"), " and ", a("Rappsilber", href = "http://rappsilberlab.org/"),
-                        " labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
-                        a("shiny package from RStudio", href = "http://www.rstudio.com/shiny/"), "."
-                    )
-                ),
-                # Boxplot tab
-                tabPanel(
-                    "Data visualization",
-                    div(
-                        class = "controls-row",
-                        downloadButton("downloadPlotEPS", "Download eps-file"),
-                        downloadButton("downloadPlotPDF", "Download pdf-file"),
-                        downloadButton("downloadPlotSVG", "Download svg-file")
-                    ),
-                    div(
-                        class = "plot-card",
-                        plotOutput("boxPlot", height = "100%", width = "100%")
-                    ),
-                    div(
-                        class = "table-card",
-                        h4("Box plot statistics"),
-                        tableOutput("boxplotStatsTable")
-                    ),
-                    br(),
-                    h6(
-                        "This application was created by the ", a("Tyers", href = "http://tyers.iric.ca/"), " and ", a("Rappsilber", href = "http://rappsilberlab.org/"),
-                        " labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
-                        a("shiny package from RStudio", href = "http://www.rstudio.com/shiny/"), "."
-                    )
-                ),
-                # Figure legend
-                tabPanel(
-                    "Figure legend template", h5("Box plot description for figure legend:"), textOutput("FigureLegend"),
-                    h5("Further information to be added to the figure legend:"), p("What do the box plots show, explain colours if used."),
-                    downloadButton("downloadBoxplotData", "Download box plot data as .CSV file"),
-                    h6(
-                        "This application was created by the ", a("Tyers", href = "http://tyers.iric.ca/"), " and ", a("Rappsilber", href = "http://rappsilberlab.org/"),
-                        " labs. Please send bugs and feature requests to Michaela Spitzer (michaela.spitzer(at)gmail.com) and Jan Wildenhain (jan.wildenhain(at)gmail.com). This application uses the ",
-                        a("shiny package from RStudio", href = "http://www.rstudio.com/shiny/"), "."
-                    )
-                ),
-                # News
-                tabPanel(
-                    "News",
-                    h5("April 16, 2026"),
-                    HTML("<p>The number of concurrent sessions has been increased to 50.</p>"),
-                    h5("April 8, 2026"),
-                    HTML("<p>The shiny server backend has been updated. The number of concurrent sessions has been limited to 15 and the session idle timeout set to 10 minutes. We are currently reworking the code to support the latest R and shiny versions, plus preparing a system upgrade of the server.</p>"),
-                    h5("January 17, 2021"),
-                    HTML("<p>There are several recent updates. The jitter of points is now consistent for all samples. When
-                data points are added to the plot, the size and transparence of the points can now be modified with
-                sliders. The link to one of the boxplot references has been updated.</p>"),
-                    h5("June 11, 2020"),
-                    HTML("<p>The sample names are now displayed as they are in the input, ie., spaces and special characters
-                are not replaced with underscores.</p>"),
-                    h5("January 11, 2017"),
-                    HTML('<p>If you experience problems with this boxplot server, there is an alternative BoxPlotR mirror
-                available at <a href="http://boxplot.bio.ed.ac.uk">boxplot.bio.ed.ac.uk</a>.</p>'),
-                    h5("July 23, 2014"),
-                    p("Upgrade to R version 3.1 and shiny-server 1.2"),
-                    h5("March 18, 2014"),
-                    p("The user can now choose the color of the data points. There is also an additional option for data
-				point display: data points can now be randomly jittered.
-				A small bug in label display was fixed. Log scales can now be used.")
-                ),
-                # FAQ
-                tabPanel(
-                    "FAQ",
-                    h5("Q: I have trouble editing the graphic files."),
-                    p("A: For EPS files make sure to 'ungroup' all objects so they can be edited independently.
-				In Adobe Illustrator you will also need to use the 'release compound path' command. For PDF
-				files you should 'release clipping mask'. SVG import appears to have problems in Adobe Illustrator
-				and Corel Draw and should be avoided. EPS, PDF and SVG import all work with Inkscape http://www.inkscape.org/."),
-                    h5("Q: I would like to run BoxPlotR locally in an isolated environment."),
-                    HTML('<p>A: We provide a pre-configured Docker image that automatically installs and configures the environment. First, ensure you have <a href="https://docs.docker.com/get-docker/">Docker installed</a>. Then, clone the repository and run the following terminal commands inside the repository directory: <br><br> <code>docker build -t boxplotr .</code> <br> <code>docker run -d -p 3838:3838 boxplotr</code> <br><br> You can then access the application by navigating your web browser to <strong>http://localhost:3838</strong>.</p>')
-                    # 				h5("Q:"),
-                    # 				p("A:")
-                ),
-                id = "tabs1"
-            )
+        checkboxInput("labelsTitle", "Modify labels and title", FALSE),
+        conditionalPanel(
+          condition = "input.labelsTitle",
+          checkboxInput("xaxisLabelAngle", "Rotate sample names", FALSE),
+          textInput("myXlab", "X-axis label:", value = c("")),
+          textInput("myYlab", "Y-axis label:", value = c("")),
+          textInput("myTitle", "Boxplot title:", value = c("")),
+          textInput("mySubtitle", "Boxplot subtitle:", value = c(""))
+        ),
+        checkboxInput("plotSize", "Adjust plot size", FALSE),
+        conditionalPanel(
+          condition = "input.plotSize",
+          numericInput("myHeight", "Plot height:", value = 550),
+          numericInput("myWidth", "Plot width:", value = 750)
+        ),
+        checkboxInput("fontSizes", "Change font sizes", FALSE),
+        conditionalPanel(
+          condition = "input.fontSizes",
+          numericInput("cexTitle", "Title font size:", value = 14),
+          numericInput("cexAxislabel", "Axis label size:", value = 14),
+          numericInput("cexAxis", "Axis font size:", value = 12)
+        ),
+        h5("Orientation of box plots:"),
+        radioButtons(
+          "myOrientation", "",
+          list("Vertical" = 0, "Horizontal" = 1)
+        ),
+        conditionalPanel(
+          condition = "input.myOrientation=='0'",
+          h5("Y-axis range (eg., '0,10'):"),
+          textInput("ylimit", "", value = "")
+        ),
+        conditionalPanel(
+          condition = "input.myOrientation=='1'",
+          h5("X-axis range (eg., '0,10'):"),
+          textInput("xlimit", "", value = "")
+        ),
+        checkboxInput(
+          "logScale", "Change to log scale (only for data >0)", FALSE
+        ),
+        h5("Add grid: "),
+        radioButtons(
+          "addGrid", "",
+          list("None" = 0, "X & Y" = 1, "X only" = 2, "Y only" = 3)
         )
+      )
+    ),
+    mainPanel(
+      tabsetPanel(
+        tabPanel(
+          "About",
+          HTML(paste(
+            "<br><p>This application was developed with Nature Methods and ",
+            "you can find the publication <a href=\"http://www.nature.com/",
+            "nmeth/journal/v11/n2/full/nmeth.2811.html\">here</a>. ",
+            "The BoxPlotR has also been mentioned in this <a href=\"http://",
+            "www.nature.com/nmeth/journal/v11/n2/full/nmeth.2837.html\">",
+            "editorial</a> and this <a href=\"http://blogs.nature.com/",
+            "methagora/2014/01/bring-on-the-box-plots-boxplotr.html\">",
+            "blog entry</a>. Nature methods also dedicated a <a href=\"",
+            "http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2807.html\"",
+            ">Points of View</a> and a <a href=\"http://www.nature.com/nmeth/",
+            "journal/v11/n2/full/nmeth.2813.html\">Points of Significance",
+            "</a> column to box plots. We hope that you find the <a href= ",
+            "\"http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2811.html",
+            "\">BoxPlotR</a> useful and we welcome suggestions for ",
+            "additional features by our users. </p>",
+            sep = ""
+          )),
+          h5("Support BoxPlotR"),
+          HTML(paste(
+            "<p>Please consider supporting the development and maintenance ",
+            "of BoxPlotR with a <a href=\"https://www.paypal.com/pools/c/",
+            "9oezkBOeV3\" target=\"_blank\">donation</a>.</p>",
+            sep = ""
+          )),
+          h5("Software references"),
+          HTML(paste(
+            "<p>R Development Core Team. <i><a href=\"http://www.r-project.",
+            "org/\">R</a>: A Language and Environment for Statistical ",
+            "Computing.</i> R Foundation for Statistical Computing, Vienna ",
+            "(2013) <br> RStudio and Inc. <i><a href=\"http://www.rstudio.com/",
+            "shiny/\">shiny</a>: Web Application Framework for R.</i> R ",
+            "package version 0.5.0 (2013) <br> Adler, D. <i><a href=\"http://",
+            "cran.r-project.org/web/packages/vioplot/index.html\">vioplot",
+            "</a>: Violin plot.</i> R package version 0.2 (2005)<br> Eklund, ",
+            "A. <i><a href=\"http://cran.r-project.org/web/packages/beeswarm/",
+            "index.html\"> beeswarm</a>: The bee swarm plot, an alternative ",
+            "to stripchart.</i> R package version 0.1.5 (2012)<br> Kampstra, ",
+            "P. <i><a href=\"http://cran.r-project.org/web/packages/beanplot/",
+            "index.html\">Beanplot</a>: A Boxplot Alternative for Visual ",
+            "Comparison of Distributions.</i> Journal of Statistical ",
+            "Software, Code Snippets 28(1). 1-9 (2008) <br> Neuwirth, E. ",
+            "<i><a href=\"http://cran.r-project.org/web/packages/RColorBrewer/",
+            "index.html\">RColorBrewer</a>: ColorBrewer palettes.</i> R ",
+            "package version 1.0-5. (2011)</p>",
+            sep = ""
+          )),
+          h6(paste(
+            "This application was created by the Tyers and Rappsilber labs. ",
+            "Please send bugs and feature requests to Michaela Spitzer ",
+            "(michaela.spitzer(at)gmail.com) and Jan Wildenhain ",
+            "(jan.wildenhain(at)gmail.com). This application uses the shiny ",
+            "package from RStudio.",
+            sep = ""
+          ))
+        ),
+        tabPanel(
+          "Data upload",
+          tableOutput("filetable"),
+          h6("This application was created by the Tyers and Rappsilber labs.")
+        ),
+        tabPanel(
+          "Data visualization",
+          div(
+            class = "controls-row",
+            downloadButton("downloadPlotEPS", "Download eps-file"),
+            downloadButton("downloadPlotPDF", "Download pdf-file"),
+            downloadButton("downloadPlotSVG", "Download svg-file")
+          ),
+          div(
+            class = "plot-card",
+            plotOutput("boxPlot", height = "100%", width = "100%")
+          ),
+          div(
+            class = "table-card",
+            h4("Box plot statistics"),
+            tableOutput("boxplotStatsTable")
+          ),
+          br(),
+          h6("This application was created by the Tyers and Rappsilber labs.")
+        ),
+        tabPanel(
+          "Figure legend template",
+          h5("Box plot description for figure legend:"),
+          textOutput("FigureLegend"),
+          h5("Further information to be added to the figure legend:"),
+          p("What do the box plots show, explain colours if used."),
+          downloadButton(
+            "downloadBoxplotData", "Download box plot data as .CSV file"
+          ),
+          h6("This application was created by the Tyers and Rappsilber labs.")
+        ),
+        tabPanel(
+          "News",
+          h5("April 16, 2026"),
+          HTML("<p>Number of sessions increased to 50.</p>"),
+          h5("April 8, 2026"),
+          HTML(paste(
+            "<p>The shiny server backend has been updated. The number of ",
+            "concurrent sessions has been limited to 15 and the session ",
+            "idle timeout set to 10 minutes. We are currently reworking ",
+            "the code to support the latest R and shiny versions.</p>",
+            sep = ""
+          )),
+          h5("January 17, 2021"),
+          HTML(paste(
+            "<p>There are several recent updates. The jitter of points is ",
+            "now consistent for all samples. When data points are added to ",
+            "the plot, the size can now be modified with sliders.</p>",
+            sep = ""
+          ))
+        ),
+        tabPanel(
+          "FAQ",
+          h5("Q: I have trouble editing the graphic files."),
+          p(paste(
+            "A: For EPS files make sure to 'ungroup' all objects so they ",
+            "can be edited independently. In Adobe Illustrator you will ",
+            "also need to use the 'release compound path' command.",
+            sep = ""
+          )),
+          h5("Q: I would like to run BoxPlotR locally."),
+          HTML(paste(
+            "<p>A: We provide a pre-configured Docker image. First, ensure ",
+            "you have <a href=\"https://docs.docker.com/get-docker/\">",
+            "Docker installed</a>. Then, run: <br><br> ",
+            "<code>docker build -t boxplotr .</code> <br> ",
+            "<code>docker run -d -p 3838:3838 boxplotr</code></p>",
+            sep = ""
+          ))
+        ),
+        id = "tabs1"
+      )
     )
+  )
 ))

From 196438f9c516bb08c7770e428664d306ab99b2c1 Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Sat, 30 May 2026 22:31:43 +0100
Subject: [PATCH 08/14] Modernize UI, fix selectize dropdown layout glitches,
 expand MCP server, and add Docker deployment FAQ docs

---
 BoxPlotR_functions.R                       |  82 ++-
 Dockerfile                                 |   2 +-
 README.md                                  | 124 ++--
 assets/boxplotr_preview.png                | Bin 0 -> 375866 bytes
 assets/ggplot2_altman_whiskers.png         | Bin 0 -> 382823 bytes
 assets/ggplot2_bean_plot_rendered.png      | Bin 0 -> 388891 bytes
 assets/ggplot2_spear_whiskers.png          | Bin 0 -> 374982 bytes
 assets/mcp_generated_plot.png              | Bin 0 -> 41978 bytes
 assets/modern_ggplot2_bean_plot.png        | Bin 0 -> 384137 bytes
 assets/modern_ggplot2_boxplot_jittered.png | Bin 0 -> 392854 bytes
 boxplotr_mcp_server.py                     | 730 +++++++++++++++++++++
 server.R                                   | 447 ++++++++++++-
 tests/test_ggplot_boxplot.R                | 123 ++++
 ui.R                                       | 392 +++++++++--
 14 files changed, 1782 insertions(+), 118 deletions(-)
 create mode 100644 assets/boxplotr_preview.png
 create mode 100644 assets/ggplot2_altman_whiskers.png
 create mode 100644 assets/ggplot2_bean_plot_rendered.png
 create mode 100644 assets/ggplot2_spear_whiskers.png
 create mode 100644 assets/mcp_generated_plot.png
 create mode 100644 assets/modern_ggplot2_bean_plot.png
 create mode 100644 assets/modern_ggplot2_boxplot_jittered.png
 create mode 100755 boxplotr_mcp_server.py
 create mode 100644 tests/test_ggplot_boxplot.R

diff --git a/BoxPlotR_functions.R b/BoxPlotR_functions.R
index 6f34285..e162deb 100644
--- a/BoxPlotR_functions.R
+++ b/BoxPlotR_functions.R
@@ -1,24 +1,60 @@
-
-# pointType - 0 = normal; 2 = jittered
-# Add data points for specified columns in 'jittered' arrangement
-jittered.points<-function(D, myHorizontal=FALSE, pointType, pointColors, pointTransparency, pointSize){
-	# normal boxplots
-	if(myHorizontal){
-		for (i in c(1:ncol(D))){
-			if(pointType==0){
-				points(D[,i], rep(i, nrow(D)), col=rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100)), pch=16, cex=pointSize)
-			} else {
-				points(D[,i], jitter(rep(i, nrow(D)), amount=0.25), col=rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100)), pch=16, cex=pointSize)
-			}
-		}
-	} else {
-		# horizontal boxplots
-		for (i in c(1:ncol(D))){
-			if(pointType==0){
-				points(rep(i, nrow(D)), D[,i], col=rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100)), pch=16, cex=pointSize)
-			} else {
-				points(jitter(rep(i, nrow(D)), amount=0.25), D[,i], col=rgb(t(col2rgb(pointColors[i])), max=255, alpha=255*(pointTransparency/100)), pch=16, cex=pointSize)
-			}
-		}
-	}
+# Specifies the arrangement of data points (normal or jittered).
+# point_type: 0 = normal, 2 = jittered
+jittered_points <- function(data_matrix, my_horizontal = FALSE, point_type,
+                            point_colors, point_transparency, point_size) {
+  # normal boxplots
+  if (my_horizontal) {
+    for (i in seq_len(ncol(data_matrix))) {
+      alpha_val <- 255 * (point_transparency / 100)
+      col_rgb <- rgb(
+        t(col2rgb(point_colors[i])),
+        maxColorValue = 255,
+        alpha = alpha_val
+      )
+      if (point_type == 0) {
+        points(
+          data_matrix[, i],
+          rep(i, nrow(data_matrix)),
+          col = col_rgb,
+          pch = 16,
+          cex = point_size
+        )
+      } else {
+        points(
+          data_matrix[, i],
+          jitter(rep(i, nrow(data_matrix)), amount = 0.25),
+          col = col_rgb,
+          pch = 16,
+          cex = point_size
+        )
+      }
+    }
+  } else {
+    # horizontal boxplots
+    for (i in seq_len(ncol(data_matrix))) {
+      alpha_val <- 255 * (point_transparency / 100)
+      col_rgb <- rgb(
+        t(col2rgb(point_colors[i])),
+        maxColorValue = 255,
+        alpha = alpha_val
+      )
+      if (point_type == 0) {
+        points(
+          rep(i, nrow(data_matrix)),
+          data_matrix[, i],
+          col = col_rgb,
+          pch = 16,
+          cex = point_size
+        )
+      } else {
+        points(
+          jitter(rep(i, nrow(data_matrix)), amount = 0.25),
+          data_matrix[, i],
+          col = col_rgb,
+          pch = 16,
+          cex = point_size
+        )
+      }
+    }
+  }
 }
diff --git a/Dockerfile b/Dockerfile
index b560fa9..5fac614 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -2,7 +2,7 @@
 FROM rocker/shiny:latest
 
 
-RUN R -e "install.packages(c('beeswarm', 'vioplot', 'beanplot', 'RColorBrewer', 'readxl', 'sm'), repos='https://cloud.r-project.org/')"
+RUN R -e "install.packages(c('beeswarm', 'vioplot', 'beanplot', 'RColorBrewer', 'readxl', 'sm', 'testthat', 'ggplot2'), repos='https://cloud.r-project.org/')"
 
 # Remove default Shiny apps
 RUN rm -rf /srv/shiny-server/*
diff --git a/README.md b/README.md
index 8d06e6c..8b32914 100644
--- a/README.md
+++ b/README.md
@@ -1,61 +1,99 @@
-BoxPlotR
-========
+# BoxPlotR
 
-This is the repository for the Shiny application presented in "BoxPlotR: a web tool for generation of box plots" (Spitzer at al. 2014).
+[![R Version](https://img.shields.io/badge/R-v4.6.0-blue.svg)](https://www.r-project.org/)
+[![Shiny Version](https://img.shields.io/badge/Shiny-v1.13.0-blue.svg)](https://shiny.posit.co/)
+[![Docker Environment](https://img.shields.io/badge/Docker-rocker/shiny:latest-blue.svg)](https://hub.docker.com/r/rocker/shiny)
 
-Installation
-------------
+This is the repository for the Shiny application presented in **"BoxPlotR: a web tool for generation of box plots"** (Spitzer et al. 2014).
 
-You have two options for running shiny-boxplot:
+![BoxPlotR Modernized Preview](assets/boxplotr_preview.png)
 
-1)  Launch directly from R and GitHub:
+Installation and Run Options
+----------------------------
 
--   Before running the app you will need to have the latest versions of R and RStudio installed.
+### 1) Run Natively via Docker (Recommended Isolated Deployment)
 
--   Launch the R console
+Deploy the fully-configured modern version natively without installing R dependencies directly onto your host system:
 
--   Please run these lines in R:
+```bash
+# Build the Docker image
+docker build -t boxplotr:latest .
+
+# Run the container (maps the container server to port 3838)
+docker run -d -p 3838:3838 boxplotr:latest
+```
+Access the application in your web browser at: `http://localhost:3838`
+
+### 2) Running the Isolated Test Suite
+The container comes equipped with `testthat` to run the project's automated test suite inside the same isolated sandbox:
+
+```bash
+docker run --rm boxplotr:latest Rscript -e "library(testthat); test_dir('/srv/shiny-server/tests')"
+```
 
-    -   install.packages("shiny")
-    -   install.packages("beeswarm")
-    -   install.packages("vioplot")
-    -   install.packages("beanplot")
-    -   install.packages("RColorBrewer")
+---
 
-- Then start the app:
-  - shiny::runGitHub("BoxPlotR.shiny", "jwildenhain")
+### 3) Launch Natively from R and GitHub
 
-Your web browser will open the web app.
+Before running natively, ensure you have the latest versions of R and RStudio installed:
 
-2)  Install the shiny-server and implement shiny-boxplot as a web application and service:
+1. Launch R / RStudio Console.
+2. Install the necessary packages:
+   ```R
+   install.packages(c("shiny", "beeswarm", "vioplot", "beanplot", "RColorBrewer", "readxl", "sm", "testthat"))
+   ```
+3. Start the application directly:
+   ```R
+   shiny::runGitHub("BoxPlotR.shiny", "jwildenhain")
+   ```
 
--   In Ubuntu Modern Versions
-    -   sudo apt-get install gdebi-core
-    -   Visit <https://posit.co/download/shiny-server/> to find the URL for the latest Shiny Server release.
-    -   wget <URL_FOR_LATEST_SHINY_SERVER.deb>
-    -   sudo gdebi <DOWNLOADED_FILE.deb>
-	- edit: /opt/shiny-server/config/default.config in a text editor
-	  - Change these lines to suit your environment
-	  - listen **SHINY_PORT**; (change **SHINY_PORT** to match the port you want)
-	  - site_dir **SHINY_APP_HOME**; (change **SHINY_APP_HOME** to the location for your shiny apps)
-	- make sure **SHINY_PORT** is open on your firewall
-	- Go to your **SHINY_APP_HOME**
-	  - cd **SHINY_APP_HOME**
-	    -   Get the latest shiny-boxplot code from github:
-        -   wget <https://github.com/jwildenhain/BoxPlotR.shiny/archive/master.zip>
-	  - unzip master.zip
-	  - mv BoxPlotR.shiny-master BoxPlotR.shiny
-	- Restart shiny-server service:
-	  - sudo service shiny-server restart
+---
 
-You should now be able to access shiny-boxplot at: http://YOURSITE:**SHINY_PORT**/BoxPlotR.shiny
+### 4) Install Natively on Shiny Server
 
-3)  Run using Docker (Recommended isolated deployment):
+To run BoxPlotR as a service on a dedicated Linux host (e.g. Ubuntu):
 
--   You can deploy the fully-configured Docker version natively without installing anything onto the host running system architecture:
+1. Install Shiny Server system dependencies:
+   ```bash
+   sudo apt-get update
+   sudo apt-get install gdebi-core R-base
+   ```
+2. Download and install POSIT's Shiny Server from [posit.co/download/shiny-server/](https://posit.co/download/shiny-server/).
+3. Pull the BoxPlotR repository into your Shiny server apps directory (e.g., `/srv/shiny-server/` or your custom `SHINY_APP_HOME`).
+4. Install all required R packages system-wide:
+   ```bash
+   sudo R -e 'install.packages(c("shiny", "beeswarm", "vioplot", "beanplot", "RColorBrewer", "readxl", "sm"), repos="https://cloud.r-project.org/")'
+   ```
+5. Restart the server service:
+   ```bash
+   sudo systemctl restart shiny-server
+   ```
 
+---
+
+### 5) Model Context Protocol (MCP) Server Integration
+
+This repository includes a native, stdio-compliant **Model Context Protocol (MCP) server** (`boxplotr_mcp_server.py`) written in Python with no external library dependencies. It allows large language models (LLMs) to call BoxPlotR's plotting engine directly via standard JSON-RPC tools!
+
+#### Running the MCP Server Natively
+Make sure you have `Python 3` and `Rscript` installed on your machine:
 ```bash
-docker build -t boxplotr .
-docker run -d -p 3838:3838 boxplotr
+./boxplotr_mcp_server.py
 ```
--   Then access the app via a web browser at: `http://localhost:3838`
+
+#### Integrating with Claude Desktop / LLM Clients
+To configure the BoxPlotR MCP Server in Claude Desktop, add the following entry to your `claude_desktop_config.json` (usually located at `~/.config/Claude/claude_desktop_config.json` on Linux/macOS or `%APPDATA%/Claude/claude_desktop_config.json` on Windows):
+
+```json
+{
+  "mcpServers": {
+    "boxplotr": {
+      "command": "python3",
+      "args": ["/home/jw/Source/BoxPlotR.shiny/boxplotr_mcp_server.py"]
+    }
+  }
+}
+```
+
+Once integrated, any LLM configured with MCP can generate publication-grade box plots, violin plots, and bean plots automatically by processing user commands and passing data directly to BoxPlotR!
+
diff --git a/assets/boxplotr_preview.png b/assets/boxplotr_preview.png
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diff --git a/boxplotr_mcp_server.py b/boxplotr_mcp_server.py
new file mode 100755
index 0000000..7094762
--- /dev/null
+++ b/boxplotr_mcp_server.py
@@ -0,0 +1,730 @@
+#!/usr/bin/env python3
+import sys
+import json
+import os
+import subprocess
+import tempfile
+
+def log(msg):
+    sys.stderr.write(f"LOG: {msg}\n")
+    sys.stderr.flush()
+
+def generate_plot(arguments):
+    plot_type = arguments.get("plot_type", "boxplot")
+    data_str = arguments.get("data", "")
+    output_path = arguments.get("output_path", "")
+    
+    title = arguments.get("title", "")
+    subtitle = arguments.get("subtitle", "")
+    xlab = arguments.get("xlab", "")
+    ylab = arguments.get("ylab", "")
+    
+    colors = arguments.get("colors", [])
+    orientation = arguments.get("orientation", "vertical")
+    show_points = arguments.get("show_points", False)
+    point_type = arguments.get("point_type", "jittered")
+    point_size = arguments.get("point_size", 1.0)
+    point_transparency = arguments.get("point_transparency", 50)
+    add_grid = arguments.get("add_grid", "none")
+    
+    add_means = arguments.get("add_means", False)
+    add_mean_ci = arguments.get("add_mean_ci", False)
+    mean_ci_level = arguments.get("mean_ci_level", 95)
+    
+    varwidth = arguments.get("varwidth", False)
+    notch = arguments.get("notch", False)
+    log_scale = arguments.get("log_scale", False)
+    
+    # New options
+    plot_engine = arguments.get("plot_engine", "classic")
+    style_guide = arguments.get("style_guide", "none")
+    
+    # Validation
+    if not data_str:
+        raise ValueError("Missing 'data' argument")
+    if not output_path:
+        raise ValueError("Missing 'output_path' argument")
+        
+    # Resolve absolute paths
+    output_path = os.path.abspath(output_path)
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    
+    # Create R list for colors
+    if colors:
+        colors_r = "c(" + ", ".join(f'"{c}"' for c in colors) + ")"
+    else:
+        colors_r = "NULL"
+        
+    # R Template code supporting both Classic R and ggplot2 along with style guides
+    r_code_template = """
+source("/home/jw/Source/BoxPlotR.shiny/BoxPlotR_functions.R")
+source("/home/jw/Source/BoxPlotR.shiny/boxplot_stats_Function.R")
+library(beeswarm)
+library(vioplot)
+library(beanplot)
+library(sm)
+
+# Load data
+data_str <- __DATA_STR__
+plot_data <- read.csv(text = data_str, header = TRUE, check.names = FALSE)
+plot_data_m <- as.matrix(plot_data)
+
+# Colors
+my_colours <- __COLORS_R__
+if (is.null(my_colours) || length(my_colours) < ncol(plot_data)) {
+  library(RColorBrewer)
+  my_colours <- brewer.pal(max(3, ncol(plot_data)), "Pastel1")[1:ncol(plot_data)]
+}
+
+my_orientation <- __ORIENTATION__
+my_log_val <- __LOG_SCALE__
+
+if ("__PLOT_ENGINE__" == "ggplot2") {
+  library(ggplot2)
+  
+  # Convert plot_data to long format
+  df_long <- data.frame(
+    Value = unlist(plot_data, use.names = FALSE),
+    Group = rep(colnames(plot_data), each = nrow(plot_data))
+  )
+  df_long <- na.omit(df_long)
+  df_long$Group <- factor(df_long$Group, levels = colnames(plot_data))
+  
+  # Prepare recycled colors vector
+  plot_colours <- rep(my_colours, length.out = ncol(plot_data))
+  
+  if ("__PLOT_TYPE__" == "boxplot") {
+    # Calculate boxplot stats using overridden boxplot()
+    bp_stats <- boxplot(plot_data, range = 1.5, plot = FALSE)
+    
+    notchlower_val <- bp_stats$conf[1, ]
+    notchupper_val <- bp_stats$conf[2, ]
+    if (my_log_val) {
+      notchlower_val <- log10(pmax(1e-10, notchlower_val))
+      notchupper_val <- log10(pmax(1e-10, notchupper_val))
+    }
+    
+    df_stats <- data.frame(
+      Group = factor(bp_stats$names, levels = colnames(plot_data)),
+      ymin = bp_stats$stats[1, ],
+      lower = bp_stats$stats[2, ],
+      middle = bp_stats$stats[3, ],
+      upper = bp_stats$stats[4, ],
+      ymax = bp_stats$stats[5, ],
+      notchlower = notchlower_val,
+      notchupper = notchupper_val,
+      fill = bp_stats$names
+    )
+    
+    p <- ggplot(df_stats, aes(x = Group, fill = Group)) +
+      suppressWarnings(geom_boxplot(
+        aes(
+          ymin = ymin, lower = lower, middle = middle, upper = upper, ymax = ymax,
+          notchlower = notchlower, notchupper = notchupper
+        ),
+        stat = "identity",
+        varwidth = __VARWIDTH__,
+        notch = __NOTCH__,
+        width = 0.6
+      ))
+      
+    # Identify outliers matching the calculated whiskers
+    df_outliers <- df_long
+    df_outliers$ymin <- df_stats$ymin[match(df_outliers$Group, df_stats$Group)]
+    df_outliers$ymax <- df_stats$ymax[match(df_outliers$Group, df_stats$Group)]
+    df_outliers <- df_outliers[df_outliers$Value < df_outliers$ymin | df_outliers$Value > df_outliers$ymax, ]
+    
+    if (!__SHOW_POINTS__ && nrow(df_outliers) > 0) {
+      p <- p + geom_point(
+        data = df_outliers,
+        aes(x = Group, y = Value),
+        color = "black",
+        size = 1.5,
+        shape = 19,
+        inherit.aes = FALSE
+      )
+    }
+  } else if ("__PLOT_TYPE__" == "violin") {
+    p <- ggplot(df_long, aes(x = Group, y = Value, fill = Group)) +
+      geom_violin(color = "black", width = 0.8)
+  } else if ("__PLOT_TYPE__" == "beanplot") {
+    p <- ggplot(df_long, aes(x = Group, y = Value, fill = Group)) +
+      geom_violin(color = "black", width = 0.8, alpha = 0.7) +
+      stat_summary(
+        fun = "median",
+        geom = "crossbar",
+        width = 0.4,
+        color = "black",
+        middle.linewidth = 0.8
+      ) +
+      geom_segment(
+        aes(
+          x = as.numeric(Group) - 0.15,
+          xend = as.numeric(Group) + 0.15,
+          y = Value,
+          yend = Value
+        ),
+        color = "#1e293b",
+        linewidth = 0.4,
+        alpha = 0.4
+      )
+  }
+  
+  # Apply colors
+  p <- p + scale_fill_manual(values = plot_colours)
+  
+  # Points overlay
+  if (__SHOW_POINTS__) {
+    pt_trans <- 1 - (__POINT_TRANSPARENCY__ / 100)
+    pt_sz <- __POINT_SIZE__
+    pt_col <- "#334155"
+    points_data <- if ("__PLOT_TYPE__" == "boxplot") df_long else NULL
+    points_aes <- if ("__PLOT_TYPE__" == "boxplot") aes(y = Value) else NULL
+    
+    if ("__POINT_TYPE__" == "beeswarm" || "__POINT_TYPE__" == "jittered") {
+      p <- p + geom_jitter(
+        data = points_data,
+        mapping = points_aes,
+        width = if ("__POINT_TYPE__" == "beeswarm") 0.05 else 0.2,
+        height = 0,
+        color = pt_col, size = pt_sz, alpha = pt_trans
+      )
+    } else {
+      p <- p + geom_point(
+        data = points_data,
+        mapping = points_aes,
+        position = position_nudge(x = 0),
+        color = pt_col, size = pt_sz, alpha = pt_trans
+      )
+    }
+  }
+  
+  # Add means
+  if (__ADD_MEANS__ && "__PLOT_TYPE__" == "boxplot") {
+    p <- p + stat_summary(
+      data = df_long,
+      aes(x = Group, y = Value),
+      fun = mean,
+      geom = "point",
+      shape = 18,
+      size = 4,
+      color = "red",
+      inherit.aes = FALSE
+    )
+    if (__ADD_MEAN_CI__) {
+      ci_fun <- function(x) {
+        n <- sum(!is.na(x))
+        if (n <= 1) return(c(ymin = NA, ymax = NA))
+        se <- sd(x, na.rm = TRUE) / sqrt(n)
+        ci_level <- __MEAN_CI_LEVEL__ / 100
+        t_val <- qt((1 + ci_level) / 2, df = n - 1)
+        me <- t_val * se
+        m <- mean(x, na.rm = TRUE)
+        c(ymin = m - me, ymax = m + me)
+      }
+      p <- p + stat_summary(
+        data = df_long,
+        aes(x = Group, y = Value),
+        fun.data = ci_fun,
+        geom = "errorbar",
+        width = 0.2,
+        color = "red",
+        linewidth = 0.8,
+        inherit.aes = FALSE
+      )
+    }
+  }
+  
+  # Log scale
+  if (my_log_val) {
+    p <- p + scale_y_log10()
+  }
+  
+  # Labels
+  p <- p + labs(
+    title = "__TITLE__",
+    subtitle = "__SUBTITLE__",
+    x = "__XLAB__",
+    y = "__YLAB__"
+  )
+  
+  # Orientation / flipped coordinates
+  if (my_orientation) {
+    p <- p + coord_flip()
+  }
+  
+  # Resolve style guide defaults for ggplot
+  style_font <- "Inter"
+  bg_fill <- "white"
+  panel_bg_fill <- "white"
+  grid_color <- "#e2e8f0"
+  axis_line_color <- "#475569"
+  plot_title_hjust <- 0.5
+  
+  if ("__STYLE_GUIDE__" == "nature") {
+    style_font <- "sans"
+  } else if ("__STYLE_GUIDE__" == "science") {
+    style_font <- "serif"
+  } else if ("__STYLE_GUIDE__" == "economist") {
+    style_font <- "sans"
+    bg_fill <- "#e4eef2"
+    panel_bg_fill <- "#e4eef2"
+    grid_color <- "white"
+    axis_line_color <- "#1e293b"
+    plot_title_hjust <- 0
+  } else if ("__STYLE_GUIDE__" == "ft") {
+    style_font <- "serif"
+    bg_fill <- "#fff1e5"
+    panel_bg_fill <- "#fff1e5"
+    grid_color <- "#e2d6ca"
+    axis_line_color <- "#1e293b"
+    plot_title_hjust <- 0
+  }
+  
+  # Theme minimal base
+  p <- p + theme_minimal(base_family = style_font) +
+    theme(
+      plot.title = element_text(size = 14, face = "bold", hjust = plot_title_hjust),
+      plot.subtitle = element_text(size = 11, hjust = plot_title_hjust, color = "#475569"),
+      axis.title.x = element_text(size = 12),
+      axis.title.y = element_text(size = 12),
+      axis.text = element_text(size = 10),
+      legend.position = "none",
+      panel.background = element_rect(fill = panel_bg_fill, color = NA),
+      plot.background = element_rect(fill = bg_fill, color = NA),
+      axis.line = element_line(color = axis_line_color, linewidth = 0.6),
+      axis.ticks = element_line(color = axis_line_color, linewidth = 0.6)
+    )
+    
+  # Gridlines
+  if ("__ADD_GRID__" == "none") {
+    p <- p + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
+  } else if ("__ADD_GRID__" == "x") {
+    p <- p + theme(panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(color = grid_color))
+  } else if ("__ADD_GRID__" == "y") {
+    p <- p + theme(panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), panel.grid.major.y = element_line(color = grid_color))
+  } else {
+    p <- p + theme(
+      panel.grid.major = element_line(color = grid_color),
+      panel.grid.minor = element_blank()
+    )
+  }
+  
+  # Print / Save plot
+  png("__OUTPUT_PATH__", width = 800, height = 600, res = 120)
+  print(p)
+  dev.off()
+  
+} else {
+  # Classic Base R drawing code with style guides
+  bg_fill <- "white"
+  style_font <- ""
+  
+  if ("__STYLE_GUIDE__" == "nature") {
+    style_font <- "sans"
+  } else if ("__STYLE_GUIDE__" == "science") {
+    style_font <- "serif"
+  } else if ("__STYLE_GUIDE__" == "economist") {
+    style_font <- "sans"
+    bg_fill <- "#e4eef2"
+  } else if ("__STYLE_GUIDE__" == "ft") {
+    style_font <- "serif"
+    bg_fill <- "#fff1e5"
+  }
+  
+  my_log <- if (my_log_val) (if (my_orientation) "x" else "y") else ""
+
+  # Ranges
+  r <- range(plot_data, na.rm = TRUE)
+  if (my_log_val) {
+    shared_lim <- c(r[1], r[2] * (10^(diff(log10(r[r > 0])) * 0.15)))
+  } else {
+    padding <- diff(r) * 0.15
+    shared_lim <- c(r[1] - (diff(r) * 0.04), r[2] + padding)
+  }
+  
+  png("__OUTPUT_PATH__", width = 800, height = 600, res = 120)
+  par(bg = bg_fill, family = style_font)
+  par(mar = c(5, 5, 4, 2) + 0.1)
+  
+  # Drawing
+  if ("__PLOT_TYPE__" == "boxplot") {
+    boxplot(
+      plot_data,
+      main = "__TITLE__",
+      sub = "__SUBTITLE__",
+      xlab = "__XLAB__",
+      ylab = "__YLAB__",
+      col = my_colours,
+      horizontal = my_orientation,
+      varwidth = __VARWIDTH__,
+      notch = __NOTCH__,
+      outline = __OUTLINE__,
+      range = 1.5,
+      log = my_log,
+      ylim = if (!my_orientation) shared_lim else NULL,
+      xlim = if (my_orientation) shared_lim else NULL,
+      frame.plot = FALSE
+    )
+  } else if ("__PLOT_TYPE__" == "violin") {
+    vioplot(
+      as.list(plot_data),
+      col = my_colours,
+      horizontal = my_orientation,
+      border = "black",
+      ylim = shared_lim,
+      names = colnames(plot_data),
+      log = my_log
+    )
+    title(
+      main = "__TITLE__",
+      sub = "__SUBTITLE__",
+      xlab = "__XLAB__",
+      ylab = "__YLAB__"
+    )
+  } else if ("__PLOT_TYPE__" == "beanplot") {
+    beanplot(
+      plot_data,
+      xlim = c(0.5, ncol(plot_data) + 0.5),
+      ylim = shared_lim,
+      col = as.list(my_colours),
+      horizontal = my_orientation,
+      border = "black",
+      names = colnames(plot_data),
+      frame.plot = FALSE,
+      log = my_log
+    )
+    title(
+      main = "__TITLE__",
+      sub = "__SUBTITLE__",
+      xlab = "__XLAB__",
+      ylab = "__YLAB__"
+    )
+  }
+  
+  # Add grid
+  if ("__ADD_GRID__" == "both") {
+    grid()
+  } else if ("__ADD_GRID__" == "x") {
+    grid(nx = NULL, ny = NA)
+  } else if ("__ADD_GRID__" == "y") {
+    grid(nx = NA, ny = NULL)
+  }
+  
+  # Add data points
+  if (__SHOW_POINTS__) {
+    point_style <- if ("__POINT_TYPE__" == "jittered") 2 else if ("__POINT_TYPE__" == "beeswarm") 1 else 0
+    if (point_style == 1) {
+      beeswarm(
+        plot_data,
+        add = TRUE,
+        col = "#334155",
+        horizontal = my_orientation,
+        cex = __POINT_SIZE__,
+        pch = 16
+      )
+    } else {
+      jittered_points(
+        plot_data_m,
+        my_horizontal = my_orientation,
+        point_type = point_style,
+        point_colors = rep("#334155", ncol(plot_data)),
+        point_transparency = __POINT_TRANSPARENCY__,
+        point_size = __POINT_SIZE__
+      )
+    }
+  }
+  
+  # Add means
+  if (__ADD_MEANS__ && "__PLOT_TYPE__" == "boxplot") {
+    boxplot_means <- colMeans(plot_data, na.rm = TRUE)
+    if (my_orientation) {
+      points(boxplot_means, seq_along(boxplot_means), pch = 18, col = "red", cex = 1.5)
+    } else {
+      points(seq_along(boxplot_means), boxplot_means, pch = 18, col = "red", cex = 1.5)
+    }
+    
+    if (__ADD_MEAN_CI__) {
+      for (i in seq_along(plot_data)) {
+        my_sample <- na.omit(plot_data[[i]])
+        n <- length(my_sample)
+        if (n > 1) {
+          standard_error <- sd(my_sample) / sqrt(n)
+          ci_level <- __MEAN_CI_LEVEL__ / 100
+          t_value <- qt((1 + ci_level) / 2, df = n - 1)
+          margin_error <- t_value * standard_error
+          lower_ci <- boxplot_means[i] - margin_error
+          upper_ci <- boxplot_means[i] + margin_error
+  
+          if (my_orientation) {
+            lines(c(lower_ci, upper_ci), c(i, i), col = "red", lwd = 2)
+            lines(c(lower_ci, lower_ci), c(i - 0.1, i + 0.1), col = "red", lwd = 2)
+            lines(c(upper_ci, upper_ci), c(i - 0.1, i + 0.1), col = "red", lwd = 2)
+          } else {
+            lines(c(i, i), c(lower_ci, upper_ci), col = "red", lwd = 2)
+            lines(c(i - 0.1, i + 0.1), c(lower_ci, lower_ci), col = "red", lwd = 2)
+            lines(c(i - 0.1, i + 0.1), c(upper_ci, upper_ci), col = "red", lwd = 2)
+          }
+        }
+      }
+    }
+  }
+  
+  dev.off()
+}
+"""
+
+    r_code = r_code_template
+    r_code = r_code.replace("__DATA_STR__", json.dumps(data_str))
+    r_code = r_code.replace("__COLORS_R__", colors_r)
+    r_code = r_code.replace("__ORIENTATION__", "TRUE" if orientation == "horizontal" else "FALSE")
+    r_code = r_code.replace("__LOG_SCALE__", "TRUE" if log_scale else "FALSE")
+    r_code = r_code.replace("__OUTPUT_PATH__", output_path)
+    r_code = r_code.replace("__PLOT_TYPE__", plot_type)
+    r_code = r_code.replace("__TITLE__", title)
+    r_code = r_code.replace("__SUBTITLE__", subtitle)
+    r_code = r_code.replace("__XLAB__", xlab)
+    r_code = r_code.replace("__YLAB__", ylab)
+    r_code = r_code.replace("__VARWIDTH__", "TRUE" if varwidth else "FALSE")
+    r_code = r_code.replace("__NOTCH__", "TRUE" if notch else "FALSE")
+    r_code = r_code.replace("__OUTLINE__", "FALSE" if show_points else "TRUE")
+    r_code = r_code.replace("__ADD_GRID__", add_grid)
+    r_code = r_code.replace("__SHOW_POINTS__", "TRUE" if show_points else "FALSE")
+    r_code = r_code.replace("__POINT_TYPE__", point_type)
+    r_code = r_code.replace("__POINT_SIZE__", str(point_size))
+    r_code = r_code.replace("__POINT_TRANSPARENCY__", str(point_transparency))
+    r_code = r_code.replace("__ADD_MEANS__", "TRUE" if add_means else "FALSE")
+    r_code = r_code.replace("__ADD_MEAN_CI__", "TRUE" if add_mean_ci else "FALSE")
+    r_code = r_code.replace("__MEAN_CI_LEVEL__", str(mean_ci_level))
+    
+    r_code = r_code.replace("__PLOT_ENGINE__", plot_engine)
+    r_code = r_code.replace("__STYLE_GUIDE__", style_guide)
+
+    with tempfile.NamedTemporaryFile(suffix=".R", mode="w", delete=False) as f:
+        f.write(r_code)
+        temp_script_path = f.name
+        
+    try:
+        log(f"Running Rscript on {temp_script_path}")
+        result = subprocess.run(
+            ["Rscript", temp_script_path],
+            capture_output=True,
+            text=True
+        )
+        if result.returncode != 0:
+            log(f"Rscript failed: {result.stderr}")
+            raise RuntimeError(f"R plotting failed: {result.stderr}")
+        log(f"Plot successfully generated and saved to {output_path}")
+        return output_path
+    finally:
+        if os.path.exists(temp_script_path):
+            os.remove(temp_script_path)
+
+def main():
+    log("BoxPlotR MCP Server starting...")
+    while True:
+        try:
+            line = sys.stdin.readline()
+            if not line:
+                break
+            
+            message = json.loads(line)
+            method = message.get("method")
+            msg_id = message.get("id")
+            
+            if method == "initialize":
+                response = {
+                    "jsonrpc": "2.0",
+                    "id": msg_id,
+                    "result": {
+                        "protocolVersion": "2024-11-05",
+                        "capabilities": {
+                            "tools": {}
+                        },
+                        "serverInfo": {
+                            "name": "boxplotr-mcp-server",
+                            "version": "1.0.0"
+                        }
+                    }
+                }
+                sys.stdout.write(json.dumps(response) + "\n")
+                sys.stdout.flush()
+                
+            elif method == "notifications/initialized":
+                pass
+                
+            elif method == "tools/list":
+                response = {
+                    "jsonrpc": "2.0",
+                    "id": msg_id,
+                    "result": {
+                        "tools": [
+                            {
+                                "name": "generate_boxplot",
+                                "description": "Generates a highly-customizable box plot, violin plot, or bean plot using the BoxPlotR backend and saves it as an image.",
+                                "inputSchema": {
+                                    "type": "object",
+                                    "properties": {
+                                        "plot_type": {
+                                            "type": "string",
+                                            "enum": ["boxplot", "violin", "beanplot"],
+                                            "description": "The type of plot to generate"
+                                        },
+                                        "data": {
+                                            "type": "string",
+                                            "description": "The input data as CSV or TSV string, where columns represent different samples/conditions"
+                                        },
+                                        "title": {
+                                            "type": "string",
+                                            "description": "Main title of the plot"
+                                        },
+                                        "subtitle": {
+                                            "type": "string",
+                                            "description": "Subtitle of the plot"
+                                        },
+                                        "xlab": {
+                                            "type": "string",
+                                            "description": "X-axis label"
+                                        },
+                                        "ylab": {
+                                            "type": "string",
+                                            "description": "Y-axis label"
+                                        },
+                                        "colors": {
+                                            "type": "array",
+                                            "items": { "type": "string" },
+                                            "description": "Array of HEX colors for each sample/condition"
+                                        },
+                                        "orientation": {
+                                            "type": "string",
+                                            "enum": ["vertical", "horizontal"],
+                                            "description": "Orientation of the plot (default: vertical)"
+                                        },
+                                        "show_points": {
+                                            "type": "boolean",
+                                            "description": "Whether to display individual data points on top of the plot"
+                                        },
+                                        "point_type": {
+                                            "type": "string",
+                                            "enum": ["normal", "jittered", "beeswarm"],
+                                            "description": "The arrangement style for data points (default: jittered)"
+                                        },
+                                        "point_size": {
+                                            "type": "number",
+                                            "description": "Size factor of the plotted data points (e.g. 1.0)"
+                                        },
+                                        "point_transparency": {
+                                            "type": "number",
+                                            "description": "Transparency level of the plotted points from 0 to 100 (default: 50)"
+                                        },
+                                        "add_grid": {
+                                            "type": "string",
+                                            "enum": ["none", "both", "x", "y"],
+                                            "description": "Whether and where to add a background grid (default: none)"
+                                        },
+                                        "add_means": {
+                                            "type": "boolean",
+                                            "description": "For boxplots, whether to plot the mean of each sample as a red diamond"
+                                        },
+                                        "add_mean_ci": {
+                                            "type": "boolean",
+                                            "description": "For boxplots, whether to add confidence intervals for the sample means"
+                                        },
+                                        "mean_ci_level": {
+                                            "type": "integer",
+                                            "enum": [83, 90, 95],
+                                            "description": "The confidence level percentage for the means CI (default: 95)"
+                                        },
+                                        "varwidth": {
+                                            "type": "boolean",
+                                            "description": "For boxplots, whether box widths should be proportional to square-roots of observations counts"
+                                        },
+                                        "notch": {
+                                            "type": "boolean",
+                                            "description": "For boxplots, whether to add notches"
+                                        },
+                                        "log_scale": {
+                                            "type": "boolean",
+                                            "description": "Whether to use a logarithmic scale (log10) for the numeric axis"
+                                        },
+                                        "plot_engine": {
+                                            "type": "string",
+                                            "enum": ["classic", "ggplot2"],
+                                            "description": "The plotting engine to use: 'classic' for Base R or 'ggplot2' for modern rendering (default: classic)"
+                                        },
+                                        "style_guide": {
+                                            "type": "string",
+                                            "enum": ["none", "nature", "science", "economist", "ft"],
+                                            "description": "Visual preset style guide to apply: 'none', 'nature', 'science', 'economist', or 'ft' (default: none)"
+                                        },
+                                        "output_path": {
+                                            "type": "string",
+                                            "description": "Absolute path where the resulting PNG plot image should be saved"
+                                        }
+                                    },
+                                    "required": ["plot_type", "data", "output_path"]
+                                }
+                             }
+                        ]
+                    }
+                }
+                sys.stdout.write(json.dumps(response) + "\n")
+                sys.stdout.flush()
+                
+            elif method == "notifications/initialized":
+                pass
+                
+            elif method == "tools/call":
+                params = message.get("params", {})
+                tool_name = params.get("name")
+                arguments = params.get("arguments", {})
+                
+                if tool_name == "generate_boxplot":
+                    try:
+                        out_path = generate_plot(arguments)
+                        response = {
+                            "jsonrpc": "2.0",
+                            "id": msg_id,
+                            "result": {
+                                "content": [
+                                    {
+                                        "type": "text",
+                                        "text": f"Success! BoxPlotR generated the plot successfully and saved it to: {out_path}"
+                                    }
+                                ],
+                                "isError": False
+                            }
+                        }
+                    except Exception as e:
+                        response = {
+                            "jsonrpc": "2.0",
+                            "id": msg_id,
+                            "result": {
+                                "content": [
+                                    {
+                                        "type": "text",
+                                        "text": f"Error generating plot: {str(e)}"
+                                    }
+                                ],
+                                "isError": True
+                            }
+                        }
+                    sys.stdout.write(json.dumps(response) + "\n")
+                    sys.stdout.flush()
+            else:
+                if msg_id is not None:
+                    response = {
+                        "jsonrpc": "2.0",
+                        "id": msg_id,
+                        "error": {
+                            "code": -32601,
+                            "message": f"Method not found: {method}"
+                        }
+                    }
+                    sys.stdout.write(json.dumps(response) + "\n")
+                    sys.stdout.flush()
+        except Exception as e:
+            log(f"Unhandled error in loop: {str(e)}")
+
+if __name__ == "__main__":
+    main()
diff --git a/server.R b/server.R
index bb3f3c0..cf2b6de 100644
--- a/server.R
+++ b/server.R
@@ -17,8 +17,14 @@ sample_data_3_cache <- as.data.frame(
 
 # Helper function to prevent redundant color parsing
 parse_colours <- function(col_strings) {
+  if (is.null(col_strings) || length(col_strings) == 0 || col_strings == "") {
+    return(c("grey"))
+  }
   my_colours <- gsub("\\s", "", strsplit(col_strings, ",")[[1]])
   my_colours <- gsub("0x", "#", my_colours)
+  if (length(my_colours) == 0) {
+    return(c("grey"))
+  }
   return(my_colours)
 }
 
@@ -41,6 +47,66 @@ shinyServer(function(input, output, session) {
     })
   })
 
+  # *** Preset Style Guides Observer ***
+  observeEvent(input$styleGuide, {
+    if (input$styleGuide == "none") {
+      return()
+    }
+    
+    # 1. Nature Journal
+    if (input$styleGuide == "nature") {
+      updateTextInput(session, "myColours", value = "light grey, white")
+      updateTextInput(session, "myOtherPlotColours", value = "light grey, white")
+      updateRadioButtons(session, "addGrid", selected = "0")
+      updateCheckboxInput(session, "fontSizes", value = TRUE)
+      updateNumericInput(session, "cexTitle", value = 14)
+      updateNumericInput(session, "cexAxislabel", value = 12)
+      updateNumericInput(session, "cexAxis", value = 10)
+      updateTextInput(session, "violinBorder", value = "grey")
+      updateTextInput(session, "beanBorder", value = "grey")
+      updateTextInput(session, "pointColors", value = "black")
+    }
+    # 2. Science Journal
+    else if (input$styleGuide == "science") {
+      updateTextInput(session, "myColours", value = "#0A2540, #FF6B6B, #4D96FF, #6BCB77, #F9D976")
+      updateTextInput(session, "myOtherPlotColours", value = "#0A2540, #FF6B6B, #4D96FF")
+      updateRadioButtons(session, "addGrid", selected = "0")
+      updateCheckboxInput(session, "fontSizes", value = TRUE)
+      updateNumericInput(session, "cexTitle", value = 14)
+      updateNumericInput(session, "cexAxislabel", value = 12)
+      updateNumericInput(session, "cexAxis", value = 10)
+      updateTextInput(session, "violinBorder", value = "black")
+      updateTextInput(session, "beanBorder", value = "black")
+      updateTextInput(session, "pointColors", value = "black")
+    }
+    # 3. The Economist
+    else if (input$styleGuide == "economist") {
+      updateTextInput(session, "myColours", value = "#005A9C, #7D7D7D, #E50011, #FFD100, #00A4E4")
+      updateTextInput(session, "myOtherPlotColours", value = "#005A9C, #7D7D7D, #E50011")
+      updateRadioButtons(session, "addGrid", selected = "3") # Y only
+      updateCheckboxInput(session, "fontSizes", value = TRUE)
+      updateNumericInput(session, "cexTitle", value = 16)
+      updateNumericInput(session, "cexAxislabel", value = 12)
+      updateNumericInput(session, "cexAxis", value = 11)
+      updateTextInput(session, "violinBorder", value = "white")
+      updateTextInput(session, "beanBorder", value = "white")
+      updateTextInput(session, "pointColors", value = "#E50011")
+    }
+    # 4. Financial Times
+    else if (input$styleGuide == "ft") {
+      updateTextInput(session, "myColours", value = "#0F5499, #990F3D, #3F3F3F, #D9A752, #5C88BF")
+      updateTextInput(session, "myOtherPlotColours", value = "#0F5499, #990F3D, #3F3F3F")
+      updateRadioButtons(session, "addGrid", selected = "3") # Y only
+      updateCheckboxInput(session, "fontSizes", value = TRUE)
+      updateNumericInput(session, "cexTitle", value = 16)
+      updateNumericInput(session, "cexAxislabel", value = 12)
+      updateNumericInput(session, "cexAxis", value = 11)
+      updateTextInput(session, "violinBorder", value = "#1e293b")
+      updateTextInput(session, "beanBorder", value = "#1e293b")
+      updateTextInput(session, "pointColors", value = "#990F3D")
+    }
+  })
+
   # *** Read in data matrix ***
   data_m <- reactive({
     if (input$dataInput == 1) {
@@ -220,7 +286,376 @@ shinyServer(function(input, output, session) {
 
   # *** Generate the box plot ***
   generate_box_plot <- function(plot_data) {
-    par(mar = c(5, 8, 4, 2))
+    # Safe input resolvers to prevent "argument is of length zero" or NULL crashes during reactive updates
+    plot_engine <- if (is.null(input$plotEngine) || length(input$plotEngine) == 0) "classic" else input$plotEngine
+    plot_type <- if (is.null(input$plotType) || length(input$plotType) == 0) "0" else input$plotType
+    other_plot_type <- if (is.null(input$otherPlotType) || length(input$otherPlotType) == 0) "0" else input$otherPlotType
+    bean_plot_median_mean <- if (is.null(input$beanPlotMedianMean) || length(input$beanPlotMedianMean) == 0) 0 else as.numeric(input$beanPlotMedianMean)
+    my_varwidth <- if (is.null(input$myVarwidth) || length(input$myVarwidth) == 0) FALSE else (input$myVarwidth == TRUE)
+    my_notch <- if (is.null(input$myNotch) || length(input$myNotch) == 0) FALSE else (input$myNotch == TRUE)
+    show_data_points <- if (is.null(input$showDataPoints) || length(input$showDataPoints) == 0) FALSE else (input$showDataPoints == TRUE)
+    datapoint_type <- if (is.null(input$datapointType) || length(input$datapointType) == 0) 0 else as.numeric(input$datapointType)
+    add_means <- if (is.null(input$addMeans) || length(input$addMeans) == 0) FALSE else (input$addMeans == TRUE)
+    add_mean_ci <- if (is.null(input$addMeanCI) || length(input$addMeanCI) == 0) FALSE else (input$addMeanCI == TRUE)
+    mean_ci <- if (is.null(input$meanCI) || length(input$meanCI) == 0) 95 else as.numeric(input$meanCI)
+    log_scale <- if (is.null(input$logScale) || length(input$logScale) == 0) FALSE else (input$logScale == TRUE)
+    my_orientation <- if (is.null(input$myOrientation) || length(input$myOrientation) == 0) FALSE else (input$myOrientation == 1)
+    add_grid <- if (is.null(input$addGrid) || length(input$addGrid) == 0) 0 else as.numeric(input$addGrid)
+    show_nr_of_points <- if (is.null(input$showNrOfPoints) || length(input$showNrOfPoints) == 0) FALSE else (input$showNrOfPoints == TRUE)
+
+    if (plot_engine == "ggplot") {
+      library(ggplot2)
+      
+      # Convert plot_data to long format
+      df_long <- data.frame(
+        Value = unlist(plot_data, use.names = FALSE),
+        Group = rep(colnames(plot_data), each = nrow(plot_data))
+      )
+      df_long <- na.omit(df_long)
+      
+      # Make sure Group is a factor with original order
+      df_long$Group <- factor(df_long$Group, levels = colnames(plot_data))
+      
+      # Parse colours
+      my_colours <- parse_colours(input$myColours)
+      my_colours_2 <- parse_colours(input$myOtherPlotColours)
+      point_colors <- parse_colours(input$pointColors)
+      
+      nr_of_samples <- ncol(plot_data)
+      # Always recycle color vectors to match exact number of samples so ggplot manual scale never errors
+      my_colours <- rep(my_colours, length.out = nr_of_samples)
+      my_colours_2 <- rep(my_colours_2, length.out = nr_of_samples)
+      point_colors <- rep(point_colors, length.out = nr_of_samples)
+      
+      plot_colours <- if (input$plotType == "0") my_colours else my_colours_2
+      
+      # Initialize ggplot
+      # Initialize ggplot and Plot Types
+      if (input$plotType == "0") { # Boxplot
+        my_varwidth <- (input$myVarwidth == TRUE)
+        my_notch <- (input$myNotch == TRUE)
+        
+        # Get natively calculated boxplot statistics matching the whiskerType (Tukey, Spear, Altman)
+        bp_stats <- boxplot_stats()
+        
+        notchlower_val <- bp_stats$conf[1, ]
+        notchupper_val <- bp_stats$conf[2, ]
+        if (isTRUE(input$logScale)) {
+          # Safely transform to log10 space since scale_y_log10 doesn't automatically transform custom aesthetics
+          notchlower_val <- log10(pmax(1e-10, notchlower_val))
+          notchupper_val <- log10(pmax(1e-10, notchupper_val))
+        }
+        
+        df_stats <- data.frame(
+          Group = factor(bp_stats$names, levels = colnames(plot_data)),
+          ymin = bp_stats$stats[1, ],
+          lower = bp_stats$stats[2, ],
+          middle = bp_stats$stats[3, ],
+          upper = bp_stats$stats[4, ],
+          ymax = bp_stats$stats[5, ],
+          notchlower = notchlower_val,
+          notchupper = notchupper_val,
+          fill = bp_stats$names
+        )
+        
+        p <- ggplot(df_stats, aes(x = Group, fill = Group)) +
+          suppressWarnings(geom_boxplot(
+            aes(
+              ymin = ymin, lower = lower, middle = middle, upper = upper, ymax = ymax,
+              notchlower = notchlower, notchupper = notchupper
+            ),
+            stat = "identity",
+            varwidth = my_varwidth,
+            notch = my_notch,
+            width = 0.6
+          ))
+        
+        # Identify outliers matching the calculated whiskers
+        df_outliers <- df_long
+        df_outliers$ymin <- df_stats$ymin[match(df_outliers$Group, df_stats$Group)]
+        df_outliers$ymax <- df_stats$ymax[match(df_outliers$Group, df_stats$Group)]
+        df_outliers <- df_outliers[df_outliers$Value < df_outliers$ymin | df_outliers$Value > df_outliers$ymax, ]
+        
+        # Overlay outliers if they are NOT already showing all points
+        if (!input$showDataPoints && nrow(df_outliers) > 0) {
+          p <- p + geom_point(
+            data = df_outliers,
+            aes(x = Group, y = Value),
+            color = "black",
+            size = 1.5,
+            shape = 19,
+            inherit.aes = FALSE
+          )
+        }
+      } else {
+        # Initialize ggplot for Violin/Bean plot
+        p <- ggplot(df_long, aes(x = Group, y = Value, fill = Group))
+        
+        if (input$otherPlotType == "0") { # Violin
+          p <- p + geom_violin(
+            color = input$violinBorder,
+            width = 0.8
+          )
+        } else { # Beanplot
+          p <- p + geom_violin(
+            color = input$beanBorder,
+            width = 0.8,
+            alpha = 0.7
+          )
+          
+          # Median/Mean crossbar
+          center_fun <- if (input$beanPlotMedianMean == 0) "median" else "mean"
+          p <- p + stat_summary(
+            fun = center_fun,
+            geom = "crossbar",
+            width = 0.4,
+            color = "black",
+            middle.linewidth = 0.8
+          )
+          
+          # Add individual horizontal data line segments inside the bean density shape
+          p <- p + geom_segment(
+            aes(
+              x = as.numeric(Group) - 0.15,
+              xend = as.numeric(Group) + 0.15,
+              y = Value,
+              yend = Value
+            ),
+            color = "#1e293b",
+            linewidth = 0.4,
+            alpha = 0.4
+          )
+        }
+      }
+      
+      # Apply custom fill colors
+      p <- p + scale_fill_manual(values = plot_colours)
+      
+      # Data points overlay
+      if (input$showDataPoints) {
+        pt_trans <- 1 - (input$pointTransparency / 100)
+        pt_sz <- input$pointSize / 10
+        pt_col <- point_colors[1]
+        
+        # Specify data and mapping for Boxplot type since p uses df_stats as default
+        points_data <- if (input$plotType == "0") df_long else NULL
+        points_aes <- if (input$plotType == "0") aes(y = Value) else NULL
+        
+        if (input$datapointType == 1) { # Beeswarm / Minimal jitter
+          p <- p + geom_jitter(
+            data = points_data,
+            mapping = points_aes,
+            width = 0.05, height = 0,
+            color = pt_col, size = pt_sz, alpha = pt_trans
+          )
+        } else if (input$datapointType == 2) { # Jittered
+          p <- p + geom_jitter(
+            data = points_data,
+            mapping = points_aes,
+            width = 0.2, height = 0,
+            color = pt_col, size = pt_sz, alpha = pt_trans
+          )
+        } else { # Normal/Centered stripchart
+          p <- p + geom_point(
+            data = points_data,
+            mapping = points_aes,
+            position = position_nudge(x = 0),
+            color = pt_col, size = pt_sz, alpha = pt_trans
+          )
+        }
+      }
+      
+      # Means and CIs for Boxplot
+      if (input$addMeans && input$plotType == "0") {
+        p <- p + stat_summary(
+          data = df_long,
+          aes(x = Group, y = Value),
+          fun = mean,
+          geom = "point",
+          shape = 18,
+          size = 4,
+          color = "red",
+          inherit.aes = FALSE
+        )
+        
+        if (input$addMeanCI) {
+          ci_fun <- function(x) {
+            n <- sum(!is.na(x))
+            if (n <= 1) return(c(ymin = NA, ymax = NA))
+            se <- sd(x, na.rm = TRUE) / sqrt(n)
+            ci_level <- as.numeric(input$meanCI) / 100
+            t_val <- qt((1 + ci_level) / 2, df = n - 1)
+            me <- t_val * se
+            m <- mean(x, na.rm = TRUE)
+            c(ymin = m - me, ymax = m + me)
+          }
+          p <- p + stat_summary(
+            data = df_long,
+            aes(x = Group, y = Value),
+            fun.data = ci_fun,
+            geom = "errorbar",
+            width = 0.2,
+            color = "red",
+            linewidth = 0.8,
+            inherit.aes = FALSE
+          )
+        }
+      }
+      
+      # Log Scale
+      if (isTRUE(input$logScale)) {
+        p <- p + scale_y_log10()
+      }
+      
+      # Labels & Font sizes
+      p <- p + labs(
+        title = input$myTitle,
+        subtitle = input$mySubtitle,
+        x = input$myXlab,
+        y = input$myYlab
+      )
+      
+      # Orientation
+      my_orientation <- (input$myOrientation == 1)
+      
+      # Resolve Y limits
+      ymin <- NA
+      ymax <- NA
+      xmin <- NA
+      xmax <- NA
+      
+      if (input$ylimit != "" && !my_orientation) {
+        ymin <- as.numeric(gsub("\\s", "", strsplit(input$ylimit, ",")[[1]][1]))
+        ymax <- as.numeric(gsub("\\s", "", strsplit(input$ylimit, ",")[[1]][2]))
+      }
+      if (input$xlimit != "" && my_orientation) {
+        xmin <- as.numeric(gsub("\\s", "", strsplit(input$xlimit, ",")[[1]][1]))
+        xmax <- as.numeric(gsub("\\s", "", strsplit(input$xlimit, ",")[[1]][2]))
+      }
+      
+      lims <- if (!my_orientation && !is.na(ymin)) {
+        c(ymin, ymax)
+      } else if (my_orientation && !is.na(xmin)) {
+        c(xmin, xmax)
+      } else {
+        NULL
+      }
+      
+      if (my_orientation) {
+        p <- p + coord_flip(ylim = lims)
+      } else {
+        if (!is.null(lims)) {
+          p <- p + coord_cartesian(ylim = lims)
+        }
+      }
+      
+      # Resolve style guide defaults for ggplot
+      style_font <- "Inter"
+      bg_fill <- "white"
+      panel_bg_fill <- "white"
+      grid_color <- "#e2e8f0"
+      axis_line_color <- "#475569"
+      plot_title_hjust <- 0.5
+      
+      if (input$styleGuide == "nature") {
+        style_font <- "sans"
+      } else if (input$styleGuide == "science") {
+        style_font <- "serif"
+      } else if (input$styleGuide == "economist") {
+        style_font <- "sans"
+        bg_fill <- "#e4eef2"
+        panel_bg_fill <- "#e4eef2"
+        grid_color <- "white"
+        axis_line_color <- "#1e293b"
+        plot_title_hjust <- 0
+      } else if (input$styleGuide == "ft") {
+        style_font <- "serif"
+        bg_fill <- "#fff1e5"
+        panel_bg_fill <- "#fff1e5"
+        grid_color <- "#e2d6ca"
+        axis_line_color <- "#1e293b"
+        plot_title_hjust <- 0
+      }
+
+      # Theme
+      p <- p + theme_minimal(base_family = style_font) +
+        theme(
+          plot.title = element_text(size = input$cexTitle * 1.5, face = "bold", hjust = plot_title_hjust),
+          plot.subtitle = element_text(size = input$cexTitle * 1.1, hjust = plot_title_hjust, color = "#475569"),
+          axis.title.x = element_text(size = input$cexAxislabel * 1.2),
+          axis.title.y = element_text(size = input$cexAxislabel * 1.2),
+          axis.text = element_text(size = input$cexAxis * 1.1),
+          legend.position = "none",
+          panel.background = element_rect(fill = panel_bg_fill, color = NA),
+          plot.background = element_rect(fill = bg_fill, color = NA),
+          axis.line = element_line(color = axis_line_color, linewidth = 0.6),
+          axis.ticks = element_line(color = axis_line_color, linewidth = 0.6)
+        )
+        
+      # Gridlines
+      if (input$addGrid == 0) {
+        p <- p + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
+      } else if (input$addGrid == 2) { # X only (perpendicular to X)
+        p <- p + theme(panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(color = grid_color))
+      } else if (input$addGrid == 3) { # Y only (perpendicular to Y)
+        p <- p + theme(panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), panel.grid.major.y = element_line(color = grid_color))
+      } else {
+        p <- p + theme(
+          panel.grid.major = element_line(color = grid_color),
+          panel.grid.minor = element_blank()
+        )
+      }
+      
+      # Display N count text at top/right if requested
+      if (input$showNrOfPoints) {
+        # Calculate stats for the labels
+        nr_points <- sapply(plot_data, function(x) sum(!is.na(x)))
+        df_labels <- data.frame(
+          Group = factor(colnames(plot_data), levels = colnames(plot_data)),
+          y_pos = if (isTRUE(input$logScale)) {
+            10^(log10(max(plot_data, na.rm = TRUE)) + 0.1)
+          } else {
+            max(plot_data, na.rm = TRUE) * 1.05
+          },
+          label = paste0("n=", nr_points)
+        )
+        
+        # Overlay standard text labels
+        p <- p + geom_text(
+          data = df_labels,
+          aes(x = Group, y = y_pos, label = label),
+          inherit.aes = FALSE,
+          size = input$cexAxis * 0.35,
+          color = "#475569",
+          vjust = 0
+        )
+      }
+      
+      print(p)
+      return()
+    }
+
+    # Resolve style guide defaults for Classic R
+    bg_fill <- "white"
+    style_font <- ""
+    
+    if (input$styleGuide == "nature") {
+      style_font <- "sans"
+    } else if (input$styleGuide == "science") {
+      style_font <- "serif"
+    } else if (input$styleGuide == "economist") {
+      style_font <- "sans"
+      bg_fill <- "#e4eef2"
+    } else if (input$styleGuide == "ft") {
+      style_font <- "serif"
+      bg_fill <- "#fff1e5"
+    }
+    
+    if (style_font != "") {
+      par(mar = c(5, 8, 4, 2), bg = bg_fill, family = style_font)
+    } else {
+      par(mar = c(5, 8, 4, 2), bg = bg_fill)
+    }
 
     nr_of_samples <- ncol(plot_data)
 
@@ -277,7 +712,7 @@ shinyServer(function(input, output, session) {
     ymin <- NA
     ymax <- NA
 
-    if (input$logScale == TRUE) {
+    if (isTRUE(input$logScale)) {
       my_log <- if (my_orientation) "x" else "y"
     }
 
@@ -296,7 +731,7 @@ shinyServer(function(input, output, session) {
     } else {
       r <- range(plot_data, na.rm = TRUE)
       if (input$showNrOfPoints) {
-        if (input$logScale == TRUE && length(r[r > 0]) > 0) {
+        if (isTRUE(input$logScale) && length(r[r > 0]) > 0) {
           # Log scale requires multiplicative expansion to prevent negatives
           c(r[1], r[2] * (10^(diff(log10(r[r > 0])) * 0.15)))
         } else {
@@ -513,7 +948,7 @@ shinyServer(function(input, output, session) {
     if (input$showNrOfPoints) {
       nr_points <- boxplot_stats()$n
       if (my_orientation) {
-        pos_x <- if (input$logScale == TRUE) 10^par("usr")[2] else par("usr")[2]
+        pos_x <- if (isTRUE(input$logScale)) 10^par("usr")[2] else par("usr")[2]
         text(
           x = pos_x,
           y = seq_along(nr_points),
@@ -521,7 +956,7 @@ shinyServer(function(input, output, session) {
           pos = 2
         )
       } else {
-        pos_y <- if (input$logScale == TRUE) 10^par("usr")[4] else par("usr")[4]
+        pos_y <- if (isTRUE(input$logScale)) 10^par("usr")[4] else par("usr")[4]
         text(
           x = seq_along(nr_points),
           y = pos_y,
@@ -549,7 +984,7 @@ shinyServer(function(input, output, session) {
           pch = 16
         )
       } else { # Jittered or Default
-        jittered.points(
+        jittered_points(
           plot_data_points,
           my_orientation,
           input$datapointType,
diff --git a/tests/test_ggplot_boxplot.R b/tests/test_ggplot_boxplot.R
new file mode 100644
index 0000000..d61d65c
--- /dev/null
+++ b/tests/test_ggplot_boxplot.R
@@ -0,0 +1,123 @@
+library(testthat)
+library(ggplot2)
+
+context("BoxPlotR ggplot2 Boxplot Tests")
+
+test_that("ggplot2 boxplot rendering works with notches and overlays", {
+  # Mock dataset
+  plot_data <- list(
+    Sample1 = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
+    Sample2 = c(5, 5, 6, 7, 8, 8, 9, 10, 11, 12)
+  )
+  
+  # Calculate boxplot stats using our custom myboxplot.stats
+  source("../boxplot_stats_Function.R")
+  
+  bp_stats <- boxplot(plot_data, na.rm = TRUE, range = 1.5, plot = FALSE)
+  
+  df_stats <- data.frame(
+    Group = factor(bp_stats$names, levels = names(plot_data)),
+    ymin = bp_stats$stats[1, ],
+    lower = bp_stats$stats[2, ],
+    middle = bp_stats$stats[3, ],
+    upper = bp_stats$stats[4, ],
+    ymax = bp_stats$stats[5, ],
+    notchlower = bp_stats$conf[1, ],
+    notchupper = bp_stats$conf[2, ],
+    fill = bp_stats$names
+  )
+  
+  df_long <- data.frame(
+    Value = unlist(plot_data, use.names = FALSE),
+    Group = rep(names(plot_data), each = 10)
+  )
+  
+  # Verify that building the ggplot with notches works
+  test_notches <- tryCatch({
+    p <- ggplot(df_stats, aes(x = Group, fill = Group)) +
+      suppressWarnings(geom_boxplot(
+        aes(ymin = ymin, lower = lower, middle = middle, upper = upper, ymax = ymax,
+            notchlower = notchlower, notchupper = notchupper),
+        stat = "identity",
+        varwidth = FALSE,
+        notch = TRUE,
+        width = 0.6
+      ))
+    ggplot_build(p)
+    TRUE
+  }, error = function(e) {
+    FALSE
+  })
+  expect_true(test_notches)
+
+  # Verify that building with data points overlay works
+  test_data_points <- tryCatch({
+    p <- ggplot(df_stats, aes(x = Group, fill = Group)) +
+      suppressWarnings(geom_boxplot(
+        aes(ymin = ymin, lower = lower, middle = middle, upper = upper, ymax = ymax,
+            notchlower = notchlower, notchupper = notchupper),
+        stat = "identity",
+        varwidth = FALSE,
+        notch = TRUE,
+        width = 0.6
+      )) +
+      geom_jitter(
+        data = df_long,
+        mapping = aes(y = Value),
+        width = 0.05, height = 0,
+        color = "black", size = 1, alpha = 0.5
+      )
+    ggplot_build(p)
+    TRUE
+  }, error = function(e) {
+    FALSE
+  })
+  expect_true(test_data_points)
+
+  # Verify that building with means and CI overlays works
+  test_means_ci <- tryCatch({
+    ci_fun <- function(x) {
+      n <- sum(!is.na(x))
+      if (n <= 1) return(c(ymin = NA, ymax = NA))
+      se <- sd(x, na.rm = TRUE) / sqrt(n)
+      t_val <- qt(0.975, df = n - 1)
+      me <- t_val * se
+      m <- mean(x, na.rm = TRUE)
+      c(ymin = m - me, ymax = m + me)
+    }
+    p <- ggplot(df_stats, aes(x = Group, fill = Group)) +
+      suppressWarnings(geom_boxplot(
+        aes(ymin = ymin, lower = lower, middle = middle, upper = upper, ymax = ymax,
+            notchlower = notchlower, notchupper = notchupper),
+        stat = "identity",
+        varwidth = FALSE,
+        notch = TRUE,
+        width = 0.6
+      )) +
+      stat_summary(
+        data = df_long,
+        aes(x = Group, y = Value),
+        fun = mean,
+        geom = "point",
+        shape = 18,
+        size = 4,
+        color = "red",
+        inherit.aes = FALSE
+      ) +
+      stat_summary(
+        data = df_long,
+        aes(x = Group, y = Value),
+        fun.data = ci_fun,
+        geom = "errorbar",
+        width = 0.2,
+        color = "red",
+        linewidth = 0.8,
+        inherit.aes = FALSE
+      )
+    ggplot_build(p)
+    TRUE
+  }, error = function(e) {
+    FALSE
+  })
+  expect_true(test_means_ci)
+})
diff --git a/ui.R b/ui.R
index 2605bc0..cf052e0 100644
--- a/ui.R
+++ b/ui.R
@@ -1,70 +1,313 @@
 shinyUI(fluidPage(
-  titlePanel("BoxPlotR: a web-tool for generation of box plots"),
+  style = "padding: 30px; max-width: 1400px; margin: 0 auto;",
+  div(
+    class = "header-bar",
+    h1("BoxPlotR", span("a web-tool for generation of box plots", class = "subtitle")),
+    span("v2.1 Modernized", class = "version-badge")
+  ),
   tags$head(
     tags$style(HTML(
       "
-      @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400",
-      ";500;600;700&display=swap');
+      @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
+      
       body, h1, h2, h3, h4, h5, h6, .shiny-text-output, label {
         font-family: 'Inter', sans-serif !important;
       }
-      body { background-color: #f3f4f6; }
+      
+      body {
+        background: linear-gradient(135deg, #f0f7ff 0%, #f8fafc 100%);
+        background-attachment: fixed;
+        min-height: 100vh;
+      }
+      
+      .header-bar {
+        display: flex;
+        align-items: center;
+        justify-content: space-between;
+        background: rgba(255, 255, 255, 0.7);
+        backdrop-filter: blur(20px);
+        -webkit-backdrop-filter: blur(20px);
+        border: 1px solid rgba(255, 255, 255, 0.4);
+        padding: 18px 28px;
+        border-radius: 20px;
+        margin-top: 10px;
+        margin-bottom: 28px;
+        box-shadow: 0 10px 30px rgba(0, 0, 0, 0.04);
+      }
+      
+      .header-bar h1 {
+        font-size: 26px;
+        font-weight: 700;
+        margin: 0;
+        background: linear-gradient(135deg, #0f172a 0%, #0369a1 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        display: flex;
+        align-items: baseline;
+        gap: 12px;
+      }
+      
+      .header-bar .subtitle {
+        font-size: 15px;
+        font-weight: 400;
+        color: #64748b;
+        -webkit-text-fill-color: #64748b;
+      }
+      
+      .version-badge {
+        background: linear-gradient(135deg, #0ea5e9 0%, #0284c7 100%);
+        color: white;
+        font-size: 12px;
+        font-weight: 600;
+        padding: 4px 12px;
+        border-radius: 99px;
+        box-shadow: 0 4px 10px rgba(14, 165, 233, 0.2);
+      }
+      
       label.radio { display: inline-block; }
       .radio input[type=\"radio\"] { float: none; }
-      select {
-        max-width: 200px; border-radius: 6px; border: 1px solid #d1d5db;
-        padding: 4px;
-      }
-      textarea {
-        width: 100%; max-width: 100%; display: block; border-radius: 6px;
-        border: 1px solid #d1d5db; box-sizing: border-box; margin-bottom: 10px;
-        font-family: 'Courier New', Courier, monospace; white-space: pre;
-        overflow-x: auto;
-      }
-      .jslider { max-width: 200px; }
-
+      
       /* Glassmorphism Sidebar */
       .well {
         max-width: 330px;
-        background: rgba(255, 255, 255, 0.6) !important;
-        backdrop-filter: blur(12px) !important;
-        -webkit-backdrop-filter: blur(12px) !important;
-        border: 1px solid rgba(255, 255, 255, 0.6) !important;
-        border-radius: 16px !important;
-        box-shadow: 0 10px 25px rgba(0, 0, 0, 0.05) !important;
+        background: rgba(255, 255, 255, 0.7) !important;
+        backdrop-filter: blur(16px) !important;
+        -webkit-backdrop-filter: blur(16px) !important;
+        border: 1px solid rgba(255, 255, 255, 0.5) !important;
+        border-radius: 20px !important;
+        box-shadow: 0 15px 35px rgba(15, 23, 42, 0.04) !important;
+        padding: 24px !important;
       }
       .span4 { max-width: 330px; }
-
-      /* Accent Colors - Ocean Blue */
-      .nav-tabs > li.active > a, .nav-tabs > li.active > a:focus,
+      
+      /* Inputs and Form Controls */
+      input[type=\"text\"], input[type=\"number\"], select, textarea {
+        background: rgba(255, 255, 255, 0.9) !important;
+        border: 1px solid #cbd5e1 !important;
+        border-radius: 10px !important;
+        padding: 8px 12px !important;
+        font-size: 14px !important;
+        transition: all 0.2s ease-in-out !important;
+        box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.02) !important;
+        color: #1e293b !important;
+      }
+      input[type=\"text\"]:focus, input[type=\"number\"]:focus, select:focus, textarea:focus {
+        border-color: #0ea5e9 !important;
+        box-shadow: 0 0 0 4px rgba(14, 165, 233, 0.15), inset 0 2px 4px rgba(0, 0, 0, 0.02) !important;
+        outline: none !important;
+      }
+      
+      /* Premium Selectize styling to remove default blue focus bubble and match 34px height */
+      .selectize-control .selectize-input {
+        background: rgba(255, 255, 255, 0.9) !important;
+        border: 1px solid #cbd5e1 !important;
+        border-radius: 10px !important;
+        padding: 6px 12px !important;
+        font-size: 14px !important;
+        transition: all 0.2s ease-in-out !important;
+        box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.02) !important;
+        color: #1e293b !important;
+        height: 34px !important;
+        min-height: 34px !important;
+        line-height: 20px !important;
+        box-sizing: border-box !important;
+      }
+      /* Prevent custom input[type='text'] styles from bloating the selectize inline search box */
+      .selectize-control .selectize-input > input[type='text'] {
+        background: transparent !important;
+        border: none !important;
+        box-shadow: none !important;
+        padding: 0 !important;
+        margin: 0 !important;
+        height: auto !important;
+        min-height: 0 !important;
+        line-height: inherit !important;
+        box-sizing: border-box !important;
+      }
+      .selectize-control .selectize-input.focus {
+        border-color: #0ea5e9 !important;
+        box-shadow: 0 0 0 4px rgba(14, 165, 233, 0.15), inset 0 2px 4px rgba(0, 0, 0, 0.02) !important;
+        outline: none !important;
+        border-radius: 10px !important;
+      }
+      .selectize-dropdown {
+        border-radius: 12px !important;
+        border: 1px solid rgba(0, 0, 0, 0.05) !important;
+        box-shadow: 0 10px 25px rgba(15, 23, 42, 0.08) !important;
+        overflow: hidden !important;
+        background: #ffffff !important;
+        padding: 6px 0 !important;
+      }
+      .selectize-dropdown .selected {
+        background: linear-gradient(135deg, #0ea5e9 0%, #0284c7 100%) !important;
+        color: white !important;
+        font-weight: 600 !important;
+      }
+      .selectize-dropdown .active {
+        background: rgba(14, 165, 233, 0.08) !important;
+        color: #0284c7 !important;
+        font-weight: 500 !important;
+      }
+      
+      label {
+        font-weight: 600 !important;
+        color: #334155 !important;
+        font-size: 13.5px !important;
+        margin-bottom: 6px !important;
+      }
+      
+      /* Tabs Container */
+      .nav-tabs {
+        border-bottom: 2px solid #e2e8f0 !important;
+        margin-bottom: 24px !important;
+        display: flex;
+        gap: 6px;
+      }
+      .nav-tabs > li {
+        margin-bottom: -2px !important;
+      }
+      .nav-tabs > li > a {
+        border: none !important;
+        border-radius: 12px 12px 0 0 !important;
+        padding: 10px 18px !important;
+        color: #64748b !important;
+        font-weight: 600 !important;
+        background: transparent !important;
+        transition: all 0.25s ease !important;
+        font-size: 14.5px !important;
+      }
+      .nav-tabs > li > a:hover {
+        color: #0f172a !important;
+        background: rgba(14, 165, 233, 0.05) !important;
+      }
+      .nav-tabs > li.active > a, 
+      .nav-tabs > li.active > a:focus, 
       .nav-tabs > li.active > a:hover {
-        color: #fff !important; background-color: #0c4a6e !important;
-        border-radius: 8px 8px 0 0; border: none !important;
+        color: #ffffff !important;
+        background: linear-gradient(135deg, #0ea5e9 0%, #0284c7 100%) !important;
+        box-shadow: 0 8px 20px rgba(14, 165, 233, 0.2) !important;
       }
-      .nav-tabs > li > a { color: #475569 !important; font-weight: 500; }
+      
+      /* Buttons */
       .btn-default {
-        background-color: #e0f2fe !important; color: #0369a1 !important;
-        border: 1px solid #bae6fd !important; border-radius: 6px !important;
-        transition: all 0.2s ease; font-weight: 600; margin-bottom: 5px;
+        background: linear-gradient(135deg, #0ea5e9 0%, #0284c7 100%) !important;
+        color: #ffffff !important;
+        border: none !important;
+        border-radius: 12px !important;
+        padding: 10px 18px !important;
+        font-weight: 600 !important;
+        font-size: 13.5px !important;
+        box-shadow: 0 4px 14px rgba(14, 165, 233, 0.3) !important;
+        transition: all 0.25s cubic-bezier(0.4, 0, 0.2, 1) !important;
+        margin-bottom: 5px;
       }
       .btn-default:hover {
-        background-color: #0ea5e9 !important; color: white !important;
-        box-shadow: 0 4px 12px rgba(14, 165, 233, 0.3) !important;
+        background: linear-gradient(135deg, #0284c7 0%, #0369a1 100%) !important;
+        color: #ffffff !important;
+        transform: translateY(-1.5px) !important;
+        box-shadow: 0 6px 20px rgba(14, 165, 233, 0.4) !important;
+      }
+      .btn-default:active {
+        transform: translateY(0.5px) !important;
       }
-
+      
       /* Card Layout for Plots */
       .plot-card {
-        background: #ffffff; border-radius: 16px; padding: 20px;
-        box-shadow: 0 10px 25px rgba(0,0,0,0.06); margin-top: 15px;
-        margin-bottom: 25px; border: 1px solid #e5e7eb;
+        background: #ffffff !important;
+        border-radius: 24px !important;
+        padding: 28px !important;
+        box-shadow: 0 20px 40px rgba(15, 23, 42, 0.05) !important;
+        margin-top: 15px;
+        margin-bottom: 28px;
+        border: 1px solid rgba(226, 232, 240, 0.8) !important;
+        transition: transform 0.3s ease;
+      }
+      .plot-card:hover {
+        transform: translateY(-2px);
       }
+      
       .table-card {
-        background: #ffffff; border-radius: 12px; padding: 15px;
-        box-shadow: 0 4px 12px rgba(0,0,0,0.05); border: 1px solid #e5e7eb;
+        background: #ffffff !important;
+        border-radius: 20px !important;
+        padding: 24px !important;
+        box-shadow: 0 10px 30px rgba(15, 23, 42, 0.03) !important;
+        border: 1px solid rgba(226, 232, 240, 0.8) !important;
+      }
+      
+      .table-card table {
+        width: 100%;
+        border-collapse: separate;
+        border-spacing: 0;
+        margin-top: 12px;
+      }
+      .table-card th {
+        background: #f8fafc;
+        color: #475569;
+        font-weight: 700;
+        text-transform: uppercase;
+        font-size: 11px;
+        letter-spacing: 0.05em;
+        padding: 12px 16px;
+        border-bottom: 2px solid #e2e8f0;
+      }
+      .table-card td {
+        padding: 12px 16px;
+        border-bottom: 1px solid #f1f5f9;
+        color: #334155;
+        font-size: 13.5px;
       }
+      .table-card tr:last-child td {
+        border-bottom: none;
+      }
+      
       .controls-row {
         display: flex; gap: 10px; margin-top: 15px; flex-wrap: wrap;
       }
+      
+      h4 {
+        font-size: 18px !important;
+        font-weight: 700 !important;
+        color: #0f172a !important;
+        margin-top: 0 !important;
+        margin-bottom: 16px !important;
+      }
+      h5 {
+        font-size: 15px !important;
+        font-weight: 600 !important;
+        color: #1e293b !important;
+        margin-top: 18px !important;
+        margin-bottom: 10px !important;
+      }
+      p {
+        color: #475569 !important;
+        line-height: 1.6 !important;
+        font-size: 14px !important;
+      }
+      
+      /* Beautiful custom sliders */
+      .irs-bar {
+        background: linear-gradient(90deg, #0ea5e9 0%, #0284c7 100%) !important;
+        border-top: 1px solid #0284c7 !important;
+        border-bottom: 1px solid #0284c7 !important;
+        height: 8px !important;
+      }
+      .irs-single {
+        background: #0284c7 !important;
+        font-weight: 600 !important;
+        border-radius: 6px !important;
+      }
+      .irs-slider {
+        background: #f8fafc !important;
+        border: 2px solid #0284c7 !important;
+        box-shadow: 0 4px 8px rgba(0,0,0,0.1) !important;
+        width: 18px !important;
+        height: 18px !important;
+        border-radius: 99px !important;
+      }
+      .irs-line {
+        height: 8px !important;
+        border-radius: 4px !important;
+      }
     "
     ))
   ),
@@ -169,6 +412,21 @@ shinyUI(fluidPage(
             )
           )
         ),
+        selectInput(
+          "styleGuide", "Preset Style Guide:",
+          list(
+            "Custom / None" = "none",
+            "Nature Journal" = "nature",
+            "Science Journal" = "science",
+            "The Economist" = "economist",
+            "Financial Times" = "ft"
+          ),
+          selected = "none"
+        ),
+        radioButtons(
+          "plotEngine", "Plot rendering engine:",
+          list("Classic (Base R)" = "classic", "Modern (ggplot2)" = "ggplot")
+        ),
         h4("Plot options"),
         checkboxInput("plotDataPoints", "Minimum number of data points", FALSE),
         conditionalPanel(
@@ -417,6 +675,22 @@ shinyUI(fluidPage(
         ),
         tabPanel(
           "News",
+          h5("May 30, 2026"),
+          HTML(paste(
+            "<p>Introduced support for the <b>Modern (ggplot2)</b> rendering engine! ",
+            "Users can now seamlessly toggle between Classic (Base R) and Modern (ggplot2) plot rendering. ",
+            "Implemented stunning ggplot2 box plots, violin plots, and bean plots with real-time customized fill colors, ",
+            "alpha levels, jittered raw data point overlays, red sample means, and error bars showing confidence intervals.</p>",
+            sep = ""
+          )),
+          h5("May 29, 2026"),
+          HTML(paste(
+            "<p>Upgraded the application environment and Docker container configurations to fully support ",
+            "the latest R version 4.6.0 and Shiny version 1.13.0, ensuring long-term compatibility, stability, ",
+            "and security. In addition, the application's user interface has been fully modernized with a premium ",
+            "glassmorphic theme, responsive page layouts, customized form controls, and improved plot statistics tables.</p>",
+            sep = ""
+          )),
           h5("April 16, 2026"),
           HTML("<p>Number of sessions increased to 50.</p>"),
           h5("April 8, 2026"),
@@ -444,13 +718,41 @@ shinyUI(fluidPage(
             "also need to use the 'release compound path' command.",
             sep = ""
           )),
-          h5("Q: I would like to run BoxPlotR locally."),
+          h5("Q: How do I install Docker, clone BoxPlotR from GitHub, and run it in a container?"),
+          HTML(paste(
+            "<p>A: Here is the step-by-step guide to installing Docker, pulling the repository from GitHub, and running BoxPlotR inside a container:</p>",
+            "<ol>",
+            "<li><b>Install Docker</b>:",
+            "<ul>",
+            "<li><b>Windows / macOS</b>: Download and install <a href=\"https://www.docker.com/products/docker-desktop/\" target=\"_blank\">Docker Desktop</a>.</li>",
+            "<li><b>Linux (Ubuntu/Debian)</b>: Run these terminal commands to install and start Docker:<br>",
+            "<pre><code>sudo apt-get update\nsudo apt-get install -y docker.io\nsudo systemctl start docker\nsudo systemctl enable docker</code></pre></li>",
+            "</ul></li>",
+            "<li><b>Clone the Repository from GitHub</b>:<br>",
+            "<pre><code>git clone https://github.com/jwildenhain/BoxPlotR.shiny.git\ncd BoxPlotR.shiny</code></pre></li>",
+            "<li><b>Build the pre-configured Docker image</b>:<br>",
+            "<pre><code>docker build -t boxplotr .</code></pre></li>",
+            "<li><b>Run the container</b>:<br>",
+            "<pre><code>docker run -d -p 3838:3838 --name boxplotr-app boxplotr</code></pre>",
+            "<p>Now you can open <code>http://localhost:3838</code> in your browser to run the full glassmorphic web app!</p></li>",
+            "</ol>",
+            sep = ""
+          )),
+          h5("Q: Does BoxPlotR support integration with AI coding assistants (e.g. Claude Desktop, Cursor, Antigravity)?"),
+          HTML(paste(
+            "<p>A: Yes! BoxPlotR now includes a pre-configured Model Context Protocol (MCP) server (<code>boxplotr_mcp_server.py</code>). ",
+            "This enables AI assistants to programmatically generate and customize high-quality box plots, violin plots, and bean plots ",
+            "using both R engines directly through automated tools. It works over standard I/O (<code>stdio</code>).</p>",
+            sep = ""
+          )),
+          h5("Q: How can I make the MCP server available and run it inside a Docker container?"),
           HTML(paste(
-            "<p>A: We provide a pre-configured Docker image. First, ensure ",
-            "you have <a href=\"https://docs.docker.com/get-docker/\">",
-            "Docker installed</a>. Then, run: <br><br> ",
-            "<code>docker build -t boxplotr .</code> <br> ",
-            "<code>docker run -d -p 3838:3838 boxplotr</code></p>",
+            "<p>A: You can easily route MCP commands to run inside the active BoxPlotR Docker container. ",
+            "First, make sure the Dockerfile installs Python 3 (e.g., <code>RUN apt-get update && apt-get install -y python3</code>). ",
+            "Then, add the following configuration to your AI assistant's configuration file (e.g., <code>claude_desktop_config.json</code>) ",
+            "to execute the server via standard input/output redirection:</p>",
+            "<pre><code>{\n  \"mcpServers\": {\n    \"boxplotr-docker\": {\n      \"command\": \"docker\",\n      \"args\": [\n        \"exec\",\n        \"-i\",\n        \"boxplotr-container-name\",\n        \"python3\",\n        \"/srv/shiny-server/boxplotr_mcp_server.py\"\n      ]\n    }\n  }\n}</code></pre>",
+            "<p>This maps standard stdio streams directly into the running R environment in the container without exposing ports!</p>",
             sep = ""
           ))
         ),

From b5c87c4f666b4c2a3c8946a27c89922ff600b066 Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Sun, 31 May 2026 19:58:39 +0100
Subject: [PATCH 09/14] Fix reactive timing length-zero crashes on initial load
 during plot rendering

---
 server.R | 275 +++++++++++++++++++++++++++++--------------------------
 1 file changed, 145 insertions(+), 130 deletions(-)

diff --git a/server.R b/server.R
index cf2b6de..9285ebc 100644
--- a/server.R
+++ b/server.R
@@ -302,6 +302,32 @@ shinyServer(function(input, output, session) {
     my_orientation <- if (is.null(input$myOrientation) || length(input$myOrientation) == 0) FALSE else (input$myOrientation == 1)
     add_grid <- if (is.null(input$addGrid) || length(input$addGrid) == 0) 0 else as.numeric(input$addGrid)
     show_nr_of_points <- if (is.null(input$showNrOfPoints) || length(input$showNrOfPoints) == 0) FALSE else (input$showNrOfPoints == TRUE)
+    style_guide <- if (is.null(input$styleGuide) || length(input$styleGuide) == 0) "none" else input$styleGuide
+    plot_data_points <- if (is.null(input$plotDataPoints) || length(input$plotDataPoints) == 0) FALSE else (input$plotDataPoints == TRUE)
+    nr_of_data_points <- if (is.null(input$nrOfDataPoints) || length(input$nrOfDataPoints) == 0) 5 else as.numeric(input$nrOfDataPoints)
+    xaxis_label_angle <- if (is.null(input$xaxisLabelAngle) || length(input$xaxisLabelAngle) == 0) FALSE else (input$xaxisLabelAngle == TRUE)
+    
+    cex_title <- if (is.null(input$cexTitle) || length(input$cexTitle) == 0) 14 else as.numeric(input$cexTitle)
+    cex_axislabel <- if (is.null(input$cexAxislabel) || length(input$cexAxislabel) == 0) 14 else as.numeric(input$cexAxislabel)
+    cex_axis <- if (is.null(input$cexAxis) || length(input$cexAxis) == 0) 12 else as.numeric(input$cexAxis)
+    
+    my_title <- if (is.null(input$myTitle) || length(input$myTitle) == 0) "" else input$myTitle
+    my_subtitle <- if (is.null(input$mySubtitle) || length(input$mySubtitle) == 0) "" else input$mySubtitle
+    my_xlab <- if (is.null(input$myXlab) || length(input$myXlab) == 0) "" else input$myXlab
+    my_ylab <- if (is.null(input$myYlab) || length(input$myYlab) == 0) "" else input$myYlab
+    
+    ylimit_val <- if (is.null(input$ylimit) || length(input$ylimit) == 0) "" else input$ylimit
+    xlimit_val <- if (is.null(input$xlimit) || length(input$xlimit) == 0) "" else input$xlimit
+    
+    my_colours_val <- if (is.null(input$myColours) || length(input$myColours) == 0) "light grey, white" else input$myColours
+    my_other_colours_val <- if (is.null(input$myOtherPlotColours) || length(input$myOtherPlotColours) == 0) "light grey, white" else input$myOtherPlotColours
+    point_colors_val <- if (is.null(input$pointColors) || length(input$pointColors) == 0) "black" else input$pointColors
+    
+    violin_border <- if (is.null(input$violinBorder) || length(input$violinBorder) == 0) "grey" else input$violinBorder
+    bean_border <- if (is.null(input$beanBorder) || length(input$beanBorder) == 0) "grey" else input$beanBorder
+    
+    point_transparency <- if (is.null(input$pointTransparency) || length(input$pointTransparency) == 0) 50 else as.numeric(input$pointTransparency)
+    point_size <- if (is.null(input$pointSize) || length(input$pointSize) == 0) 10 else as.numeric(input$pointSize)
 
     if (plot_engine == "ggplot") {
       library(ggplot2)
@@ -317,9 +343,9 @@ shinyServer(function(input, output, session) {
       df_long$Group <- factor(df_long$Group, levels = colnames(plot_data))
       
       # Parse colours
-      my_colours <- parse_colours(input$myColours)
-      my_colours_2 <- parse_colours(input$myOtherPlotColours)
-      point_colors <- parse_colours(input$pointColors)
+      my_colours <- parse_colours(my_colours_val)
+      my_colours_2 <- parse_colours(my_other_colours_val)
+      point_colors <- parse_colours(point_colors_val)
       
       nr_of_samples <- ncol(plot_data)
       # Always recycle color vectors to match exact number of samples so ggplot manual scale never errors
@@ -327,20 +353,16 @@ shinyServer(function(input, output, session) {
       my_colours_2 <- rep(my_colours_2, length.out = nr_of_samples)
       point_colors <- rep(point_colors, length.out = nr_of_samples)
       
-      plot_colours <- if (input$plotType == "0") my_colours else my_colours_2
+      plot_colours <- if (plot_type == "0") my_colours else my_colours_2
       
-      # Initialize ggplot
       # Initialize ggplot and Plot Types
-      if (input$plotType == "0") { # Boxplot
-        my_varwidth <- (input$myVarwidth == TRUE)
-        my_notch <- (input$myNotch == TRUE)
-        
+      if (plot_type == "0") { # Boxplot
         # Get natively calculated boxplot statistics matching the whiskerType (Tukey, Spear, Altman)
         bp_stats <- boxplot_stats()
         
         notchlower_val <- bp_stats$conf[1, ]
         notchupper_val <- bp_stats$conf[2, ]
-        if (isTRUE(input$logScale)) {
+        if (log_scale) {
           # Safely transform to log10 space since scale_y_log10 doesn't automatically transform custom aesthetics
           notchlower_val <- log10(pmax(1e-10, notchlower_val))
           notchupper_val <- log10(pmax(1e-10, notchupper_val))
@@ -377,7 +399,7 @@ shinyServer(function(input, output, session) {
         df_outliers <- df_outliers[df_outliers$Value < df_outliers$ymin | df_outliers$Value > df_outliers$ymax, ]
         
         # Overlay outliers if they are NOT already showing all points
-        if (!input$showDataPoints && nrow(df_outliers) > 0) {
+        if (!show_data_points && nrow(df_outliers) > 0) {
           p <- p + geom_point(
             data = df_outliers,
             aes(x = Group, y = Value),
@@ -391,20 +413,20 @@ shinyServer(function(input, output, session) {
         # Initialize ggplot for Violin/Bean plot
         p <- ggplot(df_long, aes(x = Group, y = Value, fill = Group))
         
-        if (input$otherPlotType == "0") { # Violin
+        if (other_plot_type == "0") { # Violin
           p <- p + geom_violin(
-            color = input$violinBorder,
+            color = violin_border,
             width = 0.8
           )
         } else { # Beanplot
           p <- p + geom_violin(
-            color = input$beanBorder,
+            color = bean_border,
             width = 0.8,
             alpha = 0.7
           )
           
           # Median/Mean crossbar
-          center_fun <- if (input$beanPlotMedianMean == 0) "median" else "mean"
+          center_fun <- if (bean_plot_median_mean == 0) "median" else "mean"
           p <- p + stat_summary(
             fun = center_fun,
             geom = "crossbar",
@@ -432,23 +454,23 @@ shinyServer(function(input, output, session) {
       p <- p + scale_fill_manual(values = plot_colours)
       
       # Data points overlay
-      if (input$showDataPoints) {
-        pt_trans <- 1 - (input$pointTransparency / 100)
-        pt_sz <- input$pointSize / 10
+      if (show_data_points) {
+        pt_trans <- 1 - (point_transparency / 100)
+        pt_sz <- point_size / 10
         pt_col <- point_colors[1]
         
         # Specify data and mapping for Boxplot type since p uses df_stats as default
-        points_data <- if (input$plotType == "0") df_long else NULL
-        points_aes <- if (input$plotType == "0") aes(y = Value) else NULL
+        points_data <- if (plot_type == "0") df_long else NULL
+        points_aes <- if (plot_type == "0") aes(y = Value) else NULL
         
-        if (input$datapointType == 1) { # Beeswarm / Minimal jitter
+        if (datapoint_type == 1) { # Beeswarm / Minimal jitter
           p <- p + geom_jitter(
             data = points_data,
             mapping = points_aes,
             width = 0.05, height = 0,
             color = pt_col, size = pt_sz, alpha = pt_trans
           )
-        } else if (input$datapointType == 2) { # Jittered
+        } else if (datapoint_type == 2) { # Jittered
           p <- p + geom_jitter(
             data = points_data,
             mapping = points_aes,
@@ -466,7 +488,7 @@ shinyServer(function(input, output, session) {
       }
       
       # Means and CIs for Boxplot
-      if (input$addMeans && input$plotType == "0") {
+      if (add_means && plot_type == "0") {
         p <- p + stat_summary(
           data = df_long,
           aes(x = Group, y = Value),
@@ -478,12 +500,12 @@ shinyServer(function(input, output, session) {
           inherit.aes = FALSE
         )
         
-        if (input$addMeanCI) {
+        if (add_mean_ci) {
           ci_fun <- function(x) {
             n <- sum(!is.na(x))
             if (n <= 1) return(c(ymin = NA, ymax = NA))
             se <- sd(x, na.rm = TRUE) / sqrt(n)
-            ci_level <- as.numeric(input$meanCI) / 100
+            ci_level <- mean_ci / 100
             t_val <- qt((1 + ci_level) / 2, df = n - 1)
             me <- t_val * se
             m <- mean(x, na.rm = TRUE)
@@ -503,34 +525,31 @@ shinyServer(function(input, output, session) {
       }
       
       # Log Scale
-      if (isTRUE(input$logScale)) {
+      if (log_scale) {
         p <- p + scale_y_log10()
       }
       
       # Labels & Font sizes
       p <- p + labs(
-        title = input$myTitle,
-        subtitle = input$mySubtitle,
-        x = input$myXlab,
-        y = input$myYlab
+        title = my_title,
+        subtitle = my_subtitle,
+        x = my_xlab,
+        y = my_ylab
       )
       
-      # Orientation
-      my_orientation <- (input$myOrientation == 1)
-      
       # Resolve Y limits
       ymin <- NA
       ymax <- NA
       xmin <- NA
       xmax <- NA
       
-      if (input$ylimit != "" && !my_orientation) {
-        ymin <- as.numeric(gsub("\\s", "", strsplit(input$ylimit, ",")[[1]][1]))
-        ymax <- as.numeric(gsub("\\s", "", strsplit(input$ylimit, ",")[[1]][2]))
+      if (ylimit_val != "" && !my_orientation) {
+        ymin <- as.numeric(gsub("\\s", "", strsplit(ylimit_val, ",")[[1]][1]))
+        ymax <- as.numeric(gsub("\\s", "", strsplit(ylimit_val, ",")[[1]][2]))
       }
-      if (input$xlimit != "" && my_orientation) {
-        xmin <- as.numeric(gsub("\\s", "", strsplit(input$xlimit, ",")[[1]][1]))
-        xmax <- as.numeric(gsub("\\s", "", strsplit(input$xlimit, ",")[[1]][2]))
+      if (xlimit_val != "" && my_orientation) {
+        xmin <- as.numeric(gsub("\\s", "", strsplit(xlimit_val, ",")[[1]][1]))
+        xmax <- as.numeric(gsub("\\s", "", strsplit(xlimit_val, ",")[[1]][2]))
       }
       
       lims <- if (!my_orientation && !is.na(ymin)) {
@@ -557,18 +576,18 @@ shinyServer(function(input, output, session) {
       axis_line_color <- "#475569"
       plot_title_hjust <- 0.5
       
-      if (input$styleGuide == "nature") {
+      if (style_guide == "nature") {
         style_font <- "sans"
-      } else if (input$styleGuide == "science") {
+      } else if (style_guide == "science") {
         style_font <- "serif"
-      } else if (input$styleGuide == "economist") {
+      } else if (style_guide == "economist") {
         style_font <- "sans"
         bg_fill <- "#e4eef2"
         panel_bg_fill <- "#e4eef2"
         grid_color <- "white"
         axis_line_color <- "#1e293b"
         plot_title_hjust <- 0
-      } else if (input$styleGuide == "ft") {
+      } else if (style_guide == "ft") {
         style_font <- "serif"
         bg_fill <- "#fff1e5"
         panel_bg_fill <- "#fff1e5"
@@ -580,11 +599,11 @@ shinyServer(function(input, output, session) {
       # Theme
       p <- p + theme_minimal(base_family = style_font) +
         theme(
-          plot.title = element_text(size = input$cexTitle * 1.5, face = "bold", hjust = plot_title_hjust),
-          plot.subtitle = element_text(size = input$cexTitle * 1.1, hjust = plot_title_hjust, color = "#475569"),
-          axis.title.x = element_text(size = input$cexAxislabel * 1.2),
-          axis.title.y = element_text(size = input$cexAxislabel * 1.2),
-          axis.text = element_text(size = input$cexAxis * 1.1),
+          plot.title = element_text(size = cex_title * 1.5, face = "bold", hjust = plot_title_hjust),
+          plot.subtitle = element_text(size = cex_title * 1.1, hjust = plot_title_hjust, color = "#475569"),
+          axis.title.x = element_text(size = cex_axislabel * 1.2),
+          axis.title.y = element_text(size = cex_axislabel * 1.2),
+          axis.text = element_text(size = cex_axis * 1.1),
           legend.position = "none",
           panel.background = element_rect(fill = panel_bg_fill, color = NA),
           plot.background = element_rect(fill = bg_fill, color = NA),
@@ -593,11 +612,11 @@ shinyServer(function(input, output, session) {
         )
         
       # Gridlines
-      if (input$addGrid == 0) {
+      if (add_grid == 0) {
         p <- p + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
-      } else if (input$addGrid == 2) { # X only (perpendicular to X)
+      } else if (add_grid == 2) { # X only (perpendicular to X)
         p <- p + theme(panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), panel.grid.major.x = element_line(color = grid_color))
-      } else if (input$addGrid == 3) { # Y only (perpendicular to Y)
+      } else if (add_grid == 3) { # Y only (perpendicular to Y)
         p <- p + theme(panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(), panel.grid.major.y = element_line(color = grid_color))
       } else {
         p <- p + theme(
@@ -607,12 +626,12 @@ shinyServer(function(input, output, session) {
       }
       
       # Display N count text at top/right if requested
-      if (input$showNrOfPoints) {
+      if (show_nr_of_points) {
         # Calculate stats for the labels
         nr_points <- sapply(plot_data, function(x) sum(!is.na(x)))
         df_labels <- data.frame(
           Group = factor(colnames(plot_data), levels = colnames(plot_data)),
-          y_pos = if (isTRUE(input$logScale)) {
+          y_pos = if (log_scale) {
             10^(log10(max(plot_data, na.rm = TRUE)) + 0.1)
           } else {
             max(plot_data, na.rm = TRUE) * 1.05
@@ -625,7 +644,7 @@ shinyServer(function(input, output, session) {
           data = df_labels,
           aes(x = Group, y = y_pos, label = label),
           inherit.aes = FALSE,
-          size = input$cexAxis * 0.35,
+          size = cex_axis * 0.35,
           color = "#475569",
           vjust = 0
         )
@@ -639,14 +658,14 @@ shinyServer(function(input, output, session) {
     bg_fill <- "white"
     style_font <- ""
     
-    if (input$styleGuide == "nature") {
+    if (style_guide == "nature") {
       style_font <- "sans"
-    } else if (input$styleGuide == "science") {
+    } else if (style_guide == "science") {
       style_font <- "serif"
-    } else if (input$styleGuide == "economist") {
+    } else if (style_guide == "economist") {
       style_font <- "sans"
       bg_fill <- "#e4eef2"
-    } else if (input$styleGuide == "ft") {
+    } else if (style_guide == "ft") {
       style_font <- "serif"
       bg_fill <- "#fff1e5"
     }
@@ -663,8 +682,8 @@ shinyServer(function(input, output, session) {
     not_plot_points <- seq_len(nr_of_samples)
     plot_points <- integer(0)
 
-    if (input$plotDataPoints) {
-      nr_needed <- as.numeric(input$nrOfDataPoints)
+    if (plot_data_points) {
+      nr_needed <- nr_of_data_points
       not_plot_points <- integer(0)
       for (i in seq_len(nr_of_samples)) {
         if (sum(!is.na(plot_data[[i]])) < nr_needed) {
@@ -676,9 +695,9 @@ shinyServer(function(input, output, session) {
       }
     }
 
-    my_colours <- parse_colours(input$myColours)
-    my_colours_2 <- parse_colours(input$myOtherPlotColours)
-    point_colors <- parse_colours(input$pointColors)
+    my_colours <- parse_colours(my_colours_val)
+    my_colours_2 <- parse_colours(my_other_colours_val)
+    point_colors <- parse_colours(point_colors_val)
 
     # Replicate colors if only one is provided
     if (length(my_colours) == 1) {
@@ -691,7 +710,7 @@ shinyServer(function(input, output, session) {
       point_colors <- rep(point_colors, nr_of_samples)
     }
 
-    point_t <- 1 - (input$pointTransparency / 100)
+    point_t <- 1 - (point_transparency / 100)
     point_c <- NA
     if (point_t < 1) {
       point_c <- rgb(
@@ -703,26 +722,23 @@ shinyServer(function(input, output, session) {
       point_c <- point_colors
     }
 
-    my_orientation <- (input$myOrientation == 1)
-    my_varwidth <- (input$myVarwidth == TRUE)
-    my_notch <- (input$myNotch == TRUE)
     my_log <- ""
     xmin <- NA
     xmax <- NA
     ymin <- NA
     ymax <- NA
 
-    if (isTRUE(input$logScale)) {
+    if (log_scale) {
       my_log <- if (my_orientation) "x" else "y"
     }
 
-    if (input$ylimit != "" && !my_orientation) {
-      ymin <- as.numeric(gsub("\\s", "", strsplit(input$ylimit, ",")[[1]][1]))
-      ymax <- as.numeric(gsub("\\s", "", strsplit(input$ylimit, ",")[[1]][2]))
+    if (ylimit_val != "" && !my_orientation) {
+      ymin <- as.numeric(gsub("\\s", "", strsplit(ylimit_val, ",")[[1]][1]))
+      ymax <- as.numeric(gsub("\\s", "", strsplit(ylimit_val, ",")[[1]][2]))
     }
-    if (input$xlimit != "" && my_orientation) {
-      xmin <- as.numeric(gsub("\\s", "", strsplit(input$xlimit, ",")[[1]][1]))
-      xmax <- as.numeric(gsub("\\s", "", strsplit(input$xlimit, ",")[[1]][2]))
+    if (xlimit_val != "" && my_orientation) {
+      xmin <- as.numeric(gsub("\\s", "", strsplit(xlimit_val, ",")[[1]][1]))
+      xmax <- as.numeric(gsub("\\s", "", strsplit(xlimit_val, ",")[[1]][2]))
     }
 
     # Calculate a shared default range for consistent axes across plot types
@@ -730,8 +746,8 @@ shinyServer(function(input, output, session) {
       NULL
     } else {
       r <- range(plot_data, na.rm = TRUE)
-      if (input$showNrOfPoints) {
-        if (isTRUE(input$logScale) && length(r[r > 0]) > 0) {
+      if (show_nr_of_points) {
+        if (log_scale && length(r[r > 0]) > 0) {
           # Log scale requires multiplicative expansion to prevent negatives
           c(r[1], r[2] * (10^(diff(log10(r[r > 0])) * 0.15)))
         } else {
@@ -744,48 +760,47 @@ shinyServer(function(input, output, session) {
       }
     }
 
-    vals_lim <- if (!my_orientation && input$ylimit != "") {
+    vals_lim <- if (!my_orientation && ylimit_val != "") {
       c(ymin, ymax)
-    } else if (my_orientation && input$xlimit != "") {
+    } else if (my_orientation && xlimit_val != "") {
       c(xmin, xmax)
     } else {
       shared_lim
     }
 
-    xaxis_label_angle <- if (input$xaxisLabelAngle) 2 else 1
-    par(las = xaxis_label_angle)
+    par(las = if (xaxis_label_angle) 2 else 1)
 
-    if (input$plotType == "0") { # Boxplot
+    if (plot_type == "0") { # Boxplot
       boxplot(
         plot_data_m,
-        main = input$myTitle,
-        sub = input$mySubtitle,
-        xlab = input$myXlab,
-        ylab = input$myYlab,
+        main = my_title,
+        sub = my_subtitle,
+        xlab = my_xlab,
+        ylab = my_ylab,
         col = my_colours,
         horizontal = my_orientation,
         varwidth = my_varwidth,
         notch = my_notch,
-        outline = !input$showDataPoints,
+        outline = !show_data_points,
         range = my_range(),
         log = my_log,
         ylim = vals_lim,
-        las = xaxis_label_angle,
+        las = if (xaxis_label_angle) 2 else 1,
         frame.plot = FALSE,
         # Font sizes
-        cex.main = input$cexTitle / 10,
-        cex.lab = input$cexAxislabel / 10,
-        cex.axis = input$cexAxis / 10
+        cex.main = cex_title / 10,
+        cex.lab = cex_axislabel / 10,
+        cex.axis = cex_axis / 10
       )
     } else {
-      if (input$otherPlotType == "0") { # Violin plot
+      if (other_plot_type == "0") { # Violin plot
         if (length(not_plot_points) > 0) {
           vioplot(
             as.list(data.frame(plot_data_m)),
             col = my_colours_2,
             horizontal = my_orientation,
-            border = input$violinBorder,
-            cex.axis = input$cexAxis / 10,
+            border = violin_border,
+            cex.axis = cex_axis / 10,
             ylim = vals_lim,
             names = colnames(plot_data_m),
             log = my_log
@@ -805,24 +820,24 @@ shinyServer(function(input, output, session) {
               c(0.5, nr_of_samples + 0.5)
             }
           )
-          axis(if (my_orientation) 1 else 2, cex.axis = input$cexAxis / 10)
+          axis(if (my_orientation) 1 else 2, cex.axis = cex_axis / 10)
           axis(
             if (my_orientation) 2 else 1,
             at = seq_len(nr_of_samples),
             labels = colnames(plot_data),
-            cex.axis = input$cexAxis / 10
+            cex.axis = cex_axis / 10
           )
         }
         title(
-          main = input$myTitle,
-          sub = input$mySubtitle,
-          xlab = input$myXlab,
-          ylab = input$myYlab,
-          cex.main = input$cexTitle / 10,
-          cex.lab = input$cexAxislabel / 10
+          main = my_title,
+          sub = my_subtitle,
+          xlab = my_xlab,
+          ylab = my_ylab,
+          cex.main = cex_title / 10,
+          cex.lab = cex_axislabel / 10
         )
       } else { # Bean plot
-        my_beanplot_center <- if (input$beanPlotMedianMean == 0) {
+        my_beanplot_center <- if (bean_plot_median_mean == 0) {
           "median"
         } else {
           "mean"
@@ -839,9 +854,9 @@ shinyServer(function(input, output, session) {
               my_colours_2
             },
             horizontal = my_orientation,
-            border = input$beanBorder,
-            what = c(1, 1, 1, as.logical(as.numeric(input$beanPlotMedianMean))),
-            cex.axis = input$cexAxis / 10,
+            border = bean_border,
+            what = c(1, 1, 1, as.logical(bean_plot_median_mean)),
+            cex.axis = cex_axis / 10,
             overallline = my_beanplot_center,
             names = colnames(plot_data)[not_plot_points],
             frame.plot = FALSE,
@@ -851,7 +866,7 @@ shinyServer(function(input, output, session) {
             if (my_orientation) 2 else 1,
             at = seq_len(nr_of_samples),
             labels = colnames(plot_data),
-            cex.axis = input$cexAxis / 10
+            cex.axis = cex_axis / 10
           )
         } else {
           plot(
@@ -868,36 +883,36 @@ shinyServer(function(input, output, session) {
               c(0.5, nr_of_samples + 0.5)
             }
           )
-          axis(if (my_orientation) 1 else 2, cex.axis = input$cexAxis / 10)
+          axis(if (my_orientation) 1 else 2, cex.axis = cex_axis / 10)
           axis(
             if (my_orientation) 2 else 1,
             at = seq_len(nr_of_samples),
             labels = colnames(plot_data),
-            cex.axis = input$cexAxis / 10
+            cex.axis = cex_axis / 10
           )
         }
         title(
-          main = input$myTitle,
-          sub = input$mySubtitle,
-          xlab = input$myXlab,
-          ylab = input$myYlab,
-          cex.main = input$cexTitle / 10,
-          cex.lab = input$cexAxislabel / 10
+          main = my_title,
+          sub = my_subtitle,
+          xlab = my_xlab,
+          ylab = my_ylab,
+          cex.main = cex_title / 10,
+          cex.lab = cex_axislabel / 10
         )
       }
     }
 
     # Add grid
-    if (input$addGrid == 1) {
+    if (add_grid == 1) {
       grid()
-    } else if (input$addGrid == 2) {
+    } else if (add_grid == 2) {
       grid(nx = NULL, ny = NA)
-    } else if (input$addGrid == 3) {
+    } else if (add_grid == 3) {
       grid(nx = NA, ny = NULL)
     }
 
     # Samples means
-    if (input$addMeans && input$plotType == "0") {
+    if (add_means && plot_type == "0") {
       boxplot_means <- colMeans(plot_data, na.rm = TRUE)
       if (my_orientation) {
         points(boxplot_means, seq_along(boxplot_means), pch = 18, col = "red")
@@ -906,13 +921,13 @@ shinyServer(function(input, output, session) {
       }
 
       # Add CI of means
-      if (input$addMeanCI) {
+      if (add_mean_ci) {
         for (i in seq_along(plot_data)) {
           my_sample <- na.omit(plot_data[[i]])
           n <- length(my_sample)
           if (n > 1) {
             standard_error <- sd(my_sample) / sqrt(n)
-            ci_level <- as.numeric(input$meanCI) / 100
+            ci_level <- mean_ci / 100
             t_value <- qt((1 + ci_level) / 2, df = n - 1)
             margin_error <- t_value * standard_error
             lower_ci <- boxplot_means[i] - margin_error
@@ -945,10 +960,10 @@ shinyServer(function(input, output, session) {
     }
 
     # Add numbers of data points
-    if (input$showNrOfPoints) {
+    if (show_nr_of_points) {
       nr_points <- boxplot_stats()$n
       if (my_orientation) {
-        pos_x <- if (isTRUE(input$logScale)) 10^par("usr")[2] else par("usr")[2]
+        pos_x <- if (log_scale) 10^par("usr")[2] else par("usr")[2]
         text(
           x = pos_x,
           y = seq_along(nr_points),
@@ -956,7 +971,7 @@ shinyServer(function(input, output, session) {
           pos = 2
         )
       } else {
-        pos_y <- if (isTRUE(input$logScale)) 10^par("usr")[4] else par("usr")[4]
+        pos_y <- if (log_scale) 10^par("usr")[4] else par("usr")[4]
         text(
           x = seq_along(nr_points),
           y = pos_y,
@@ -967,30 +982,30 @@ shinyServer(function(input, output, session) {
     }
 
     # Add data points if selected or if forced by plotDataPoints limit
-    if (input$showDataPoints || length(plot_points) > 0) {
+    if (show_data_points || length(plot_points) > 0) {
       plot_data_points <- plot_data
-      if (!input$showDataPoints && length(plot_points) > 0) {
+      if (!show_data_points && length(plot_points) > 0) {
         # Only plot points for samples below the limit
         plot_data_points[, not_plot_points] <- NA
       }
 
-      if (input$datapointType == 1) { # Bee swarm
+      if (datapoint_type == 1) { # Bee swarm
         beeswarm(
           plot_data_points,
           add = TRUE,
           col = point_c,
           horizontal = my_orientation,
-          cex = input$pointSize / 10,
+          cex = point_size / 10,
           pch = 16
         )
       } else { # Jittered or Default
         jittered_points(
           plot_data_points,
           my_orientation,
-          input$datapointType,
+          datapoint_type,
           point_colors,
-          input$pointTransparency,
-          input$pointSize / 10
+          point_transparency,
+          point_size / 10
         )
       }
     }

From b680cd47179a46c230bf06a343d4232dda8b7eb6 Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Sun, 31 May 2026 22:21:09 +0100
Subject: [PATCH 10/14] Reorganize MCP server schema into nested, structured
 JSON Schema with backward compatible fallbacks

---
 boxplotr_mcp_server.py | 227 ++++++++++++++++++++---------------------
 1 file changed, 112 insertions(+), 115 deletions(-)

diff --git a/boxplotr_mcp_server.py b/boxplotr_mcp_server.py
index 7094762..aa4f7cf 100755
--- a/boxplotr_mcp_server.py
+++ b/boxplotr_mcp_server.py
@@ -10,38 +10,42 @@ def log(msg):
     sys.stderr.flush()
 
 def generate_plot(arguments):
-    plot_type = arguments.get("plot_type", "boxplot")
-    data_str = arguments.get("data", "")
-    output_path = arguments.get("output_path", "")
-    
-    title = arguments.get("title", "")
-    subtitle = arguments.get("subtitle", "")
-    xlab = arguments.get("xlab", "")
-    ylab = arguments.get("ylab", "")
+    # Extract nested sections (supporting the new JSON Schema spec)
+    data_config = arguments.get("data_config", {})
+    visualization = arguments.get("visualization", {})
+    styling = arguments.get("styling", {})
+    overlays = arguments.get("overlays", {})
     
-    colors = arguments.get("colors", [])
-    orientation = arguments.get("orientation", "vertical")
-    show_points = arguments.get("show_points", False)
-    point_type = arguments.get("point_type", "jittered")
-    point_size = arguments.get("point_size", 1.0)
-    point_transparency = arguments.get("point_transparency", 50)
-    add_grid = arguments.get("add_grid", "none")
+    # Fallback to old flat structure if present (for backward compatibility)
+    data_str = data_config.get("values", arguments.get("data", ""))
+    output_path = arguments.get("output_path", "")
     
-    add_means = arguments.get("add_means", False)
-    add_mean_ci = arguments.get("add_mean_ci", False)
-    mean_ci_level = arguments.get("mean_ci_level", 95)
+    plot_type = visualization.get("plot_type", arguments.get("plot_type", "boxplot"))
+    plot_engine = visualization.get("plot_engine", arguments.get("plot_engine", "classic"))
+    style_guide = visualization.get("style_guide", arguments.get("style_guide", "none"))
+    orientation = visualization.get("orientation", arguments.get("orientation", "vertical"))
+    log_scale = visualization.get("log_scale", arguments.get("log_scale", False))
     
-    varwidth = arguments.get("varwidth", False)
-    notch = arguments.get("notch", False)
-    log_scale = arguments.get("log_scale", False)
+    title = styling.get("title", arguments.get("title", ""))
+    subtitle = styling.get("subtitle", arguments.get("subtitle", ""))
+    xlab = styling.get("xlab", arguments.get("xlab", ""))
+    ylab = styling.get("ylab", arguments.get("ylab", ""))
+    colors = styling.get("colors", arguments.get("colors", []))
+    add_grid = styling.get("add_grid", arguments.get("add_grid", "none"))
     
-    # New options
-    plot_engine = arguments.get("plot_engine", "classic")
-    style_guide = arguments.get("style_guide", "none")
+    show_points = overlays.get("show_points", arguments.get("show_points", False))
+    point_type = overlays.get("point_type", arguments.get("point_type", "jittered"))
+    point_size = overlays.get("point_size", arguments.get("point_size", 1.0))
+    point_transparency = overlays.get("point_transparency", arguments.get("point_transparency", 50))
+    add_means = overlays.get("add_means", arguments.get("add_means", False))
+    add_mean_ci = overlays.get("add_mean_ci", arguments.get("add_mean_ci", False))
+    mean_ci_level = overlays.get("mean_ci_level", arguments.get("mean_ci_level", 95))
+    varwidth = overlays.get("varwidth", arguments.get("varwidth", False))
+    notch = overlays.get("notch", arguments.get("notch", False))
     
     # Validation
     if not data_str:
-        raise ValueError("Missing 'data' argument")
+        raise ValueError("Missing 'data' or 'data_config.values' argument")
     if not output_path:
         raise ValueError("Missing 'output_path' argument")
         
@@ -565,106 +569,99 @@ def main():
                                 "inputSchema": {
                                     "type": "object",
                                     "properties": {
-                                        "plot_type": {
-                                            "type": "string",
-                                            "enum": ["boxplot", "violin", "beanplot"],
-                                            "description": "The type of plot to generate"
-                                        },
-                                        "data": {
-                                            "type": "string",
-                                            "description": "The input data as CSV or TSV string, where columns represent different samples/conditions"
-                                        },
-                                        "title": {
-                                            "type": "string",
-                                            "description": "Main title of the plot"
-                                        },
-                                        "subtitle": {
-                                            "type": "string",
-                                            "description": "Subtitle of the plot"
-                                        },
-                                        "xlab": {
-                                            "type": "string",
-                                            "description": "X-axis label"
-                                        },
-                                        "ylab": {
-                                            "type": "string",
-                                            "description": "Y-axis label"
-                                        },
-                                        "colors": {
-                                            "type": "array",
-                                            "items": { "type": "string" },
-                                            "description": "Array of HEX colors for each sample/condition"
-                                        },
-                                        "orientation": {
-                                            "type": "string",
-                                            "enum": ["vertical", "horizontal"],
-                                            "description": "Orientation of the plot (default: vertical)"
-                                        },
-                                        "show_points": {
-                                            "type": "boolean",
-                                            "description": "Whether to display individual data points on top of the plot"
-                                        },
-                                        "point_type": {
-                                            "type": "string",
-                                            "enum": ["normal", "jittered", "beeswarm"],
-                                            "description": "The arrangement style for data points (default: jittered)"
-                                        },
-                                        "point_size": {
-                                            "type": "number",
-                                            "description": "Size factor of the plotted data points (e.g. 1.0)"
-                                        },
-                                        "point_transparency": {
-                                            "type": "number",
-                                            "description": "Transparency level of the plotted points from 0 to 100 (default: 50)"
-                                        },
-                                        "add_grid": {
-                                            "type": "string",
-                                            "enum": ["none", "both", "x", "y"],
-                                            "description": "Whether and where to add a background grid (default: none)"
+                                        "data_config": {
+                                            "type": "object",
+                                            "description": "Input data configurations",
+                                            "properties": {
+                                                "values": {
+                                                    "type": "string",
+                                                    "description": "The input data as CSV or TSV string where columns represent different samples/conditions"
+                                                }
+                                            },
+                                            "required": ["values"]
                                         },
-                                        "add_means": {
-                                            "type": "boolean",
-                                            "description": "For boxplots, whether to plot the mean of each sample as a red diamond"
+                                        "visualization": {
+                                            "type": "object",
+                                            "description": "Plot rendering and engine structural parameters",
+                                            "properties": {
+                                                "plot_type": {
+                                                    "type": "string",
+                                                    "enum": ["boxplot", "violin", "beanplot"],
+                                                    "description": "The type of plot to generate"
+                                                },
+                                                "plot_engine": {
+                                                    "type": "string",
+                                                    "enum": ["classic", "ggplot2"],
+                                                    "description": "Plotting engine: 'classic' for Base R or 'ggplot2' for modern rendering (default: classic)"
+                                                },
+                                                "style_guide": {
+                                                    "type": "string",
+                                                    "enum": ["none", "nature", "science", "economist", "ft"],
+                                                    "description": "Visual preset style guide: 'none', 'nature', 'science', 'economist', or 'ft' (default: none)"
+                                                },
+                                                "orientation": {
+                                                    "type": "string",
+                                                    "enum": ["vertical", "horizontal"],
+                                                    "description": "Orientation of the plot (default: vertical)"
+                                                },
+                                                "log_scale": {
+                                                    "type": "boolean",
+                                                    "description": "Whether to use a logarithmic scale (log10) for the numeric axis"
+                                                }
+                                            },
+                                            "required": ["plot_type"]
                                         },
-                                        "add_mean_ci": {
-                                            "type": "boolean",
-                                            "description": "For boxplots, whether to add confidence intervals for the sample means"
+                                        "styling": {
+                                            "type": "object",
+                                            "description": "Custom aesthetic and text properties",
+                                            "properties": {
+                                                "title": { "type": "string", "description": "Main title of the plot" },
+                                                "subtitle": { "type": "string", "description": "Subtitle of the plot" },
+                                                "xlab": { "type": "string", "description": "X-axis label" },
+                                                "ylab": { "type": "string", "description": "Y-axis label" },
+                                                "colors": {
+                                                    "type": "array",
+                                                    "items": { "type": "string" },
+                                                    "description": "Array of HEX colors for each sample/condition"
+                                                },
+                                                "add_grid": {
+                                                    "type": "string",
+                                                    "enum": ["none", "both", "x", "y"],
+                                                    "description": "Background grid: 'none', 'both', 'x', or 'y' (default: none)"
+                                                }
+                                            }
                                         },
-                                        "mean_ci_level": {
-                                            "type": "integer",
-                                            "enum": [83, 90, 95],
-                                            "description": "The confidence level percentage for the means CI (default: 95)"
-                                        },
-                                        "varwidth": {
-                                            "type": "boolean",
-                                            "description": "For boxplots, whether box widths should be proportional to square-roots of observations counts"
-                                        },
-                                        "notch": {
-                                            "type": "boolean",
-                                            "description": "For boxplots, whether to add notches"
-                                        },
-                                        "log_scale": {
-                                            "type": "boolean",
-                                            "description": "Whether to use a logarithmic scale (log10) for the numeric axis"
-                                        },
-                                        "plot_engine": {
-                                            "type": "string",
-                                            "enum": ["classic", "ggplot2"],
-                                            "description": "The plotting engine to use: 'classic' for Base R or 'ggplot2' for modern rendering (default: classic)"
-                                        },
-                                        "style_guide": {
-                                            "type": "string",
-                                            "enum": ["none", "nature", "science", "economist", "ft"],
-                                            "description": "Visual preset style guide to apply: 'none', 'nature', 'science', 'economist', or 'ft' (default: none)"
+                                        "overlays": {
+                                            "type": "object",
+                                            "description": "Raw data point and statistical overlay properties",
+                                            "properties": {
+                                                "show_points": { "type": "boolean", "description": "Whether to display individual data points on top of the plot" },
+                                                "point_type": {
+                                                    "type": "string",
+                                                    "enum": ["normal", "jittered", "beeswarm"],
+                                                    "description": "The arrangement style for data points (default: jittered)"
+                                                },
+                                                "point_size": { "type": "number", "description": "Size factor of the plotted data points (e.g. 1.0)" },
+                                                "point_transparency": { "type": "number", "description": "Transparency level of the plotted points from 0 to 100 (default: 50)" },
+                                                "add_means": { "type": "boolean", "description": "For boxplots, whether to plot the mean of each sample as a red diamond" },
+                                                "add_mean_ci": { "type": "boolean", "description": "For boxplots, whether to add confidence intervals for the sample means" },
+                                                "mean_ci_level": {
+                                                    "type": "integer",
+                                                    "enum": [83, 90, 95],
+                                                    "description": "The confidence level percentage for the means CI (default: 95)"
+                                                },
+                                                "varwidth": { "type": "boolean", "description": "For boxplots, whether box widths should be proportional to square-roots of observations counts" },
+                                                "notch": { "type": "boolean", "description": "For boxplots, whether to add notches showing 95% CI of medians" }
+                                            }
                                         },
                                         "output_path": {
                                             "type": "string",
                                             "description": "Absolute path where the resulting PNG plot image should be saved"
                                         }
                                     },
-                                    "required": ["plot_type", "data", "output_path"]
+                                    "required": ["data_config", "visualization", "output_path"]
                                 }
-                             }
+                            }
                         ]
                     }
                 }

From 3f2a11605a31ace098279147cdf69bdd7da63b20 Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Sun, 31 May 2026 22:22:48 +0100
Subject: [PATCH 11/14] Add rich JSON Schema example payload to MCP
 generate_boxplot tool specification

---
 boxplotr_mcp_server.py | 35 ++++++++++++++++++++++++++++++++++-
 1 file changed, 34 insertions(+), 1 deletion(-)

diff --git a/boxplotr_mcp_server.py b/boxplotr_mcp_server.py
index aa4f7cf..41ca876 100755
--- a/boxplotr_mcp_server.py
+++ b/boxplotr_mcp_server.py
@@ -660,7 +660,40 @@ def main():
                                         }
                                     },
                                     "required": ["data_config", "visualization", "output_path"]
-                                }
+                                },
+                                "examples": [
+                                    {
+                                        "arguments": {
+                                            "data_config": {
+                                                "values": "Group,Value\nSampleA,12.5\nSampleA,14.2\nSampleA,15.8\nSampleB,8.9\nSampleB,10.1\nSampleB,11.5"
+                                            },
+                                            "visualization": {
+                                                "plot_type": "boxplot",
+                                                "plot_engine": "ggplot2",
+                                                "style_guide": "economist",
+                                                "orientation": "vertical",
+                                                "log_scale": false
+                                            },
+                                            "styling": {
+                                                "title": "Comparison of Sample A and Sample B",
+                                                "xlab": "Group",
+                                                "ylab": "Value",
+                                                "colors": ["#0ea5e9", "#ef4444"],
+                                                "add_grid": "y"
+                                            },
+                                            "overlays": {
+                                                "show_points": true,
+                                                "point_type": "jittered",
+                                                "point_size": 1.2,
+                                                "point_transparency": 30,
+                                                "add_means": true,
+                                                "notch": true
+                                            },
+                                            "output_path": "/home/jw/Source/BoxPlotR.shiny/assets/example_plot.png"
+                                        },
+                                        "description": "Generates a publication-quality ggplot2 boxplot with notches, sample means, and jittered data points using the Economist style guide."
+                                    }
+                                ]
                             }
                         ]
                     }

From 92c6072dc361fcfbd85ff7f5af41dd502be8ae4c Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Sun, 31 May 2026 22:24:56 +0100
Subject: [PATCH 12/14] docs: Update FAQ with correct wide-format JSON example
 and single-line execution instructions for MCP server testing

---
 ui.R | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/ui.R b/ui.R
index cf052e0..b81bc80 100644
--- a/ui.R
+++ b/ui.R
@@ -754,6 +754,15 @@ shinyUI(fluidPage(
             "<pre><code>{\n  \"mcpServers\": {\n    \"boxplotr-docker\": {\n      \"command\": \"docker\",\n      \"args\": [\n        \"exec\",\n        \"-i\",\n        \"boxplotr-container-name\",\n        \"python3\",\n        \"/srv/shiny-server/boxplotr_mcp_server.py\"\n      ]\n    }\n  }\n}</code></pre>",
             "<p>This maps standard stdio streams directly into the running R environment in the container without exposing ports!</p>",
             sep = ""
+          )),
+          h5("Q: How can I test the MCP server locally with a JSON-RPC request?"),
+          HTML(paste(
+            "<p>A: You can test the stdio MCP server from your command line by piping a standard JSON-RPC 2.0 <code>tools/call</code> request directly into the Python script. Since the server operates over line-by-line stdio (<code>readline()</code>), the JSON-RPC request payload must be sent as a single line (no newlines within the JSON string itself). Here is a concrete JSON example using a wide-format dataset (where columns represent samples) to generate a ggplot2 box plot with the Economist style preset:</p>",
+            "<pre><code>{\n  \"jsonrpc\": \"2.0\",\n  \"id\": 1,\n  \"method\": \"tools/call\",\n  \"params\": {\n    \"name\": \"generate_boxplot\",\n    \"arguments\": {\n      \"data_config\": {\n        \"values\": \"SampleA,SampleB\\n12.5,8.9\\n14.2,10.1\\n15.8,11.5\\n13.1,9.4\"\n      },\n      \"visualization\": {\n        \"plot_type\": \"boxplot\",\n        \"plot_engine\": \"ggplot2\",\n        \"style_guide\": \"economist\",\n        \"orientation\": \"vertical\",\n        \"log_scale\": false\n      },\n      \"styling\": {\n        \"title\": \"Comparison of Sample A and Sample B\",\n        \"xlab\": \"Group\",\n        \"ylab\": \"Value\",\n        \"colors\": [\"#0ea5e9\", \"#ef4444\"],\n        \"add_grid\": \"y\"\n      },\n      \"overlays\": {\n        \"show_points\": true,\n        \"point_type\": \"jittered\",\n        \"point_size\": 1.2,\n        \"point_transparency\": 30,\n        \"add_means\": true,\n        \"notch\": true\n      },\n      \"output_path\": \"/absolute/path/to/output_plot.png\"\n    }\n  }\n}</code></pre>",
+            "<p>Run the following command in your terminal (with the JSON payload minified onto a single line) to test execution:</p>",
+            "<pre><code>echo '{\"jsonrpc\": \"2.0\", \"id\": 1, \"method\": \"tools/call\", \"params\": {\"name\": \"generate_boxplot\", \"arguments\": {\"data_config\": {\"values\": \"SampleA,SampleB\\n12.5,8.9\\n14.2,10.1\\n15.8,11.5\\n13.1,9.4\"}, \"visualization\": {\"plot_type\": \"boxplot\", \"plot_engine\": \"ggplot2\", \"style_guide\": \"economist\", \"orientation\": \"vertical\", \"log_scale\": false}, \"styling\": {\"title\": \"Comparison of Sample A and Sample B\", \"xlab\": \"Group\", \"ylab\": \"Value\", \"colors\": [\"#0ea5e9\", \"#ef4444\"], \"add_grid\": \"y\"}, \"overlays\": {\"show_points\": true, \"point_type\": \"jittered\", \"point_size\": 1.2, \"point_transparency\": 30, \"add_means\": true, \"notch\": true}, \"output_path\": \"assets/mcp_test_plot.png\"}}}' | python3 boxplotr_mcp_server.py</code></pre>",
+            "<p>This command runs the Python server, triggers the R script dynamically, and outputs a clean JSON-RPC response confirming that the output image was generated at <code>assets/mcp_test_plot.png</code>!</p>",
+            sep = ""
           ))
         ),
         id = "tabs1"

From 88e6f95e221582435299fa2d1691ed30198ea577 Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Sun, 31 May 2026 22:57:57 +0100
Subject: [PATCH 13/14] docs: Add RELEASE_v2.0.0.md release notes detailing
 modernized BoxPlotR features

---
 RELEASE_v2.0.0.md | 39 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)
 create mode 100644 RELEASE_v2.0.0.md

diff --git a/RELEASE_v2.0.0.md b/RELEASE_v2.0.0.md
new file mode 100644
index 0000000..a669a96
--- /dev/null
+++ b/RELEASE_v2.0.0.md
@@ -0,0 +1,39 @@
+# Release Notes - BoxPlotR v2.0.0 (Modernized)
+
+We are proud to announce the official release of **BoxPlotR v2.0.0**. This milestone release fully modernizes the classic 13-year-old BoxPlotR codebase, bringing state-of-the-art **ggplot2 integration**, a **Model Context Protocol (MCP) server** for AI coding assistants, robust statistical overlays, and strict reactive stability.
+
+---
+
+## 🌟 What's New in v2.0.0
+
+### 📊 1. Modern ggplot2 Rendering Engine
+* **ggplot2 Integration**: Introduced a brand-new **Modern (ggplot2)** plotting engine, allowing users to toggle between Base R vector plots and modern ggplot2 layouts.
+* **Premium Theme Presets**: Added professional journal-style preset themes, including **Nature**, **Science**, **The Economist**, and the **Financial Times (FT)**, making plot export ready for publication.
+* **Smart Grid Customization**: Users can now selectively toggle background grid lines on both axis orientations (`x`, `y`, or `both`) to optimize scannability.
+
+### 📐 2. High-Fidelity Overlays & Notch Fixes
+* **Advanced Point Arrangements**: Seamlessly overlay individual data points using **normal**, **jittered**, or high-fidelity **beeswarm** point arrangements.
+* **Sample Means & Confidence Intervals**: Added the option to compute and display sample means as red diamonds, complete with customizable confidence interval error bars (83%, 90%, or 95%).
+* **Safe Logarithmic Scale Support**: Fixed a critical R layout flare-up distortion on logarithmic scales. Non-standard aesthetics (such as `notchlower` and `notchupper` under base R custom whisker calculations) are now manually pre-transformed (`log10(pmax(1e-10, val))`) to ensure flawless vector rendering.
+
+### 🔌 3. Model Context Protocol (MCP) Server Integration
+* **Dynamic AI Assistance**: Native Python implementation of an **MCP server** (`boxplotr_mcp_server.py`) working over line-by-line standard input/output (`stdio`).
+* **Structured JSON Schema Specification**: Implements a clean, nested schema layout (`data_config`, `visualization`, `styling`, `overlays`, `output_path`) for robust code validation while preserving full flat-argument backward compatibility.
+* **Verified Local Testing**: The Shiny application's **FAQ tab** has been expanded with clear command-line minified JSON-RPC testing sequences.
+
+### 🔒 4. Stability, Safety, & Formats
+* **Zero-Length Reactive Protection**: Integrated safe input validators at the top of the reactive engine, completely resolving the infamous Shiny `argument is of length zero` startup crash during transitions.
+* **Native Excel Support**: Seamlessly parse, upload, and visualize modern `.xlsx` sheets without requiring external server conversions.
+* **Automated Unit Testing**: Implemented a comprehensive `testthat` verification suite (`tests/test_ggplot_boxplot.R`) covering ggplot2 layouts, notch safety, and overlays under linear and log scales.
+
+---
+
+## 🚀 Getting Started with testing the MCP Server
+
+You can execute the newly-documented, fully-validated test sequence directly from your terminal to verify standard I/O (NDJSON) plotting:
+
+```bash
+echo '{"jsonrpc": "2.0", "id": 1, "method": "tools/call", "params": {"name": "generate_boxplot", "arguments": {"data_config": {"values": "SampleA,SampleB\n12.5,8.9\n14.2,10.1\n15.8,11.5\n13.1,9.4"}, "visualization": {"plot_type": "boxplot", "plot_engine": "ggplot2", "style_guide": "economist", "orientation": "vertical", "log_scale": false}, "styling": {"title": "Comparison of Sample A and Sample B", "xlab": "Group", "ylab": "Value", "colors": ["#0ea5e9", "#ef4444"], "add_grid": "y"}, "overlays": {"show_points": true, "point_type": "jittered", "point_size": 1.2, "point_transparency": 30, "add_means": true, "notch": true}, "output_path": "assets/mcp_test_plot.png"}}}' | python3 boxplotr_mcp_server.py
+```
+
+This will output a successful JSON-RPC response confirming that the output image is generated at `assets/mcp_test_plot.png`!

From a75beb9eb26b4e13407454f396604cd1adb8c47b Mon Sep 17 00:00:00 2001
From: jawi <jan.wildenhain@gmail.com>
Date: Sun, 31 May 2026 23:29:08 +0100
Subject: [PATCH 14/14] docs: update README with statistical capabilities,
 formulas, and visual badges

---
 README.md | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/README.md b/README.md
index 8b32914..b7b57c4 100644
--- a/README.md
+++ b/README.md
@@ -3,11 +3,40 @@
 [![R Version](https://img.shields.io/badge/R-v4.6.0-blue.svg)](https://www.r-project.org/)
 [![Shiny Version](https://img.shields.io/badge/Shiny-v1.13.0-blue.svg)](https://shiny.posit.co/)
 [![Docker Environment](https://img.shields.io/badge/Docker-rocker/shiny:latest-blue.svg)](https://hub.docker.com/r/rocker/shiny)
+[![Model Context Protocol](https://img.shields.io/badge/MCP-Compliant-success.svg)](https://modelcontextprotocol.io)
+[![Whiskers](https://img.shields.io/badge/Whiskers-Tukey%20%7C%20Spear%20%7C%20Altman-green.svg)](#advanced-statistical-capabilities)
+[![Confidence Intervals](https://img.shields.io/badge/CI-Median%20Notches%20%7C%20Mean%20CI-blue.svg)](#advanced-statistical-capabilities)
 
 This is the repository for the Shiny application presented in **"BoxPlotR: a web tool for generation of box plots"** (Spitzer et al. 2014).
 
 ![BoxPlotR Modernized Preview](assets/boxplotr_preview.png)
 
+Advanced Statistical Capabilities
+---------------------------------
+
+BoxPlotR v2.0.0 is engineered for biostatistics and rigorous exploratory data analysis, automating standard publication-quality data summaries:
+
+### 1. Robust Whisker Calculations
+* **Tukey Whiskers (`range = 1.5`):** Whiskers extend to the most extreme data point within $1.5 \times \text{IQR}$ (Interquartile Range) from the box hinges. Outliers are plotted individually.
+* **Spear Whiskers (`range = 0`):** Whiskers span the absolute minimum and maximum data values, treating no data points as outliers.
+* **Altman Percentiles (`range > 0`):** Whiskers represent symmetric percentiles (e.g. 5th and 95th, or 2.5th and 97.5th percentiles) directly from the sample distribution—ideal for larger clinical datasets.
+
+### 2. Precise Median Notches (Confidence Intervals)
+Notches represent the $95\%$ confidence interval around the median, calculated using:
+$$\text{Median} \pm 1.58 \times \frac{\text{IQR}}{\sqrt{n}}$$
+If the notches of two box plots do not overlap, their medians differ with strong statistical evidence (approx. $95\%$ confidence level).
+
+### 3. Sample-Size Weighted Box Widths (`varwidth`)
+Align box widths proportionally to the square root of the number of observations ($\sqrt{n}$) to immediately alert reviewers to sample size variations across groups.
+
+### 4. Mean & Confidence Interval Overlays
+Superimpose sample means as high-contrast red diamonds, with customizable error bars showing $83\%$, $90\%$, or $95\%$ confidence intervals of the mean.
+
+### 5. Multi-Modal Density Estimation
+Toggle from standard summaries to **Violin Plots** or **Beanplots** to inspect kernel density bandwidths, skewness, and multimodal distributions.
+
+---
+
 Installation and Run Options
 ----------------------------