Clusters of (French) Regions

For the data scienec course of tomorrow, I just wanted to post some functions to illustrate cluster analysis. Consider the dataset of the French 2012 elections

> elections2012=read.table("http://freakonometrics.free.fr/elections_2012_T1.csv",sep=";",dec=",",header=TRUE)> voix=which(substr(names(+ elections2012),1,11)=="X..Voix.Exp")> elections2012=elections2012[1:96,]> X=as.matrix(elections2012[,voix])> colnames(X)=c("JOLY","LE PEN","SARKOZY","MÉLENCHON","POUTOU","ARTHAUD","CHEMINADE","BAYROU","DUPONT-AIGNAN","HOLLANDE")> rownames(X)=elections2012[,1]

The hierarchical cluster analysis is obtained using

> cah=hclust(dist(X))> plot(cah,cex=.6)

To get five groups, we have to prune the tree

> rect.hclust(cah,k=5)> groups.5 <- cutree(cah,5)

We have to zoom-in to visualize the French regions,

It is also possible to use

> library(dendroextras)> plot(colour_clusters(cah,k=5))

And again, if we zoom-in, we get

The interpretation of the clusters can be obtained using

> aggregate(X,list(groups.5),mean)  Group.1     JOLY   LE PEN  SARKOZY1       1 2.185000 18.00042 28.740422       2 1.943824 23.22324 25.780293       3 2.240667 15.34267 23.459334       4 2.620000 21.90600 34.322005       5 3.140000  9.05000 33.80000

It is also possible to visualize those clusters on a map, using

> library(RColorBrewer)> CL=brewer.pal(8,"Set3")> carte_classe <- function(groupes){+ library(stringr)+ elections2012$dep <- elections2012[,2]+ elections2012$dep <- tolower(elections2012$dep)+ elections2012$dep <- str_replace_all(elections2012$dep, pattern = " |-|'|/", replacement = "")+ library(maps)+ france<-map(database="france")+ france$dep <- france$names+ france$dep <- tolower(france$dep)+ france$dep <- str_replace_all(france$dep, pattern = " |-|'|/", replacement = "")+ corresp_noms <- elections2012[, c(1,2, ncol(elections2012))]+ corresp_noms$dep[which(corresp_noms$dep %in% "corsesud")] <- "corsedusud"+ col2001<-groupes+1+ names(col2001) <- corresp_noms$dep[match(names(col2001), corresp_noms[,1])]+ color <- col2001[match(france$dep, names(col2001))]+ map(database="france", fill=TRUE, col=CL[color])+ }> carte_classe(cutree(cah,5))

or, if we simply want 4 clusters

> carte_classe(cutree(cah,4))