With Stéphane Tufféry we’ve been working on credit scoring¹ and we’ve been using the popular german credit dataset,

> myVariableNames <- 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")

> credit = read.table(
+ "http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data",
+ header=FALSE,col.names=myVariableNames)
> credit$class <- credit$class-1

We 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

> library(RColorBrewer)
> CL=brewer.pal(6, "RdBu")
> 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 <- base[base[,y]==0,]
+	base1 <- base[base[,y]==1,]
+	xlim1 <- range(c(base0[,x],base1[,x]))
+	ylim1 <- 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 <- 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)])
+}
> 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}