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With Stéphane Tufféry we've been working on credit scoring1 and we've been using the popular german credit dataset,&#62; myVariableNames &#60;- c("checking_status","duration","credit_history",+ "purpose","credit_amount","savings","employment","installment_rate",+ "personal_status","other_parties","residence_since","property_magnitude",+ "age","other_payment_plans","housing","existing_credits","job",+ "num_dependents","telephone","foreign_worker","class")&#62; credit = read.table(+ "http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data",+ header=FALSE,col.names=myVariableNames)&#62; credit$class &#60;- credit$class-1We wanted to get a nice code to produce a graph like the one below,Yesterday, Stéphane came up with the following code, that can easily be adapted&#62; library(RColorBrewer)&#62; CL=brewer.pal(6, "RdBu")&#62; varQuanti = function(base,y,x)+ {+ layout(matrix(c(1, 2), 2, 1, byrow = TRUE),heights=c(3, 1))+par(mar = c(2, 4, 2, 1))+base0 &#60;- base[base[,y]==0,]+base1 &#60;- base[base[,y]==1,]+xlim1 &#60;- range(c(base0[,x],base1[,x]))+ylim1 &#60;- c(0,max(max(density(base0[,x])$y),max(density(base1[,x])$y)))+plot(density(base0[,x]),main=" ",col=CL[1],ylab=paste("Density of ",x),+ xlim = xlim1, ylim = ylim1 ,lwd=2)+par(new = TRUE)+plot(density(base1[,x]),col=CL[6],lty=1,lwd=2,+ xlim = xlim1, ylim = ylim1,xlab = '', ylab = '',main=' ')+legend("topright",c(paste(y," = 0"),paste(y," = 1")),+   lty=1,col=CL[c(1,6)],lwd=2)+texte &#60;- c("Kruskal-Wallis'Chi² = \n\n",+       round(kruskal.test(base[,x]~base[,y])$statistic*1000)/1000)+text(xlim1[2]*0.8, ylim1[2]*0.5, texte,cex=0.75)+boxplot(base[,x]~base[,y],horizontal = TRUE,xlab= y,col=CL[c(2,5)])+}&#62; varQuanti(credit,"class","duration")The code is not complex, but since I usually waste a lot of time on my graphs, I will try to upload more frequently short posts, dedicated to graphs, in R (without ggplot).1.for a chapter on statistical learning in the forthcoming Computational Actuarial Science with R