provinces_chine_donnees_chinois <- read.table("provinces_chine_donnees_chinois.csv",
                                              sep = ";", header=TRUE,row.names=1,stringsAsFactors = TRUE)
library(FactoMineR)
res.PCAchinois<-PCA(provinces_chine_donnees_chinois[,-c(2)],quali.sup=c(1),graph=FALSE)
plot.PCA(res.PCAchinois,invisible="quali",title="")
plot.PCA(res.PCAchinois,choix='var',title="")
plot.PCA(res.PCAchinois,invisible=c('ind.sup'),habillage=1,title="",label =c('ind','quali'),theme=theme_bw())


provinces_chine_donnees_Fr <- read.table("provinces_chine_economie_transition_ecologique.csv",
                                              sep = ";", header=TRUE,row.names=1,stringsAsFactors = TRUE)
res.PCAFr<-PCA(provinces_chine_donnees_Fr[,-c(2)],quali.sup=c(1),graph=FALSE)
plot.PCA(res.PCAFr,title="",invisible="quali")
plot.PCA(res.PCAFr,choix='var',title="")
plot.PCA(res.PCAFr,invisible=c('ind.sup'),habillage=1,title="",label =c('ind','quali'),theme=theme_bw())


## Exemple : à vous de jouer
pays_donnees_CN <- read.table("pays_economie_transition_ecologique_chinois.csv",
      sep = ";", header=TRUE,row.names=1,stringsAsFactors = TRUE)
res.paysCN<-PCA(pays_donnees_CN[,-c(2)],quali.sup=c(1),graph=FALSE)
plot.PCA(res.paysCN,habillage = 1,title="",theme=theme_bw())
plot.PCA(res.paysCN,choix='var',title="")

pays_donnees_FR <- read.table("pays_economie_transition_ecologique.csv",
                                   sep = ";", header=TRUE,row.names=1,stringsAsFactors = TRUE,quote="£")
res.paysFR<-PCA(pays_donnees_FR[,-c(2)],quali.sup=c(1),graph=FALSE)
plot.PCA(res.paysFR,habillage = 1,title="",theme=theme_bw())
plot.PCA(res.paysFR,choix='var',title="")

### AFM
res.paysFR<-MFA(pays_donnees_FR_mfa [,-(1:2)],group=c(9,9),type=c("s","s"),name.group=c("2025","2000"),graph=FALSE)
plot.MFA(res.paysFR,title="",theme=theme_bw())
plot.MFA(res.paysFR,choix='var',title="")
plot.MFA(res.paysFR,title="",theme=theme_bw(),partial="all")


res<-PCA(scale(provinces_chine_donnees_chinois[,-(1:2)]),graph=FALSE)
zz=HCPC(res,nb.clust = 4)
don=scale(provinces_chine_donnees_chinois[,-(1:2)])
i=1
kk=30
nbvar=9
ll=nbvar+1
nbclust=nlevels(zz$data.clust$clust)
plot(rep(ll-i,kk)~zz$data.clust[,i],xlim=c(min(don[,1:nbvar]),max(don[,1:nbvar])),ylim=c(1,ll),yaxt="n",xlab="",ylab="",type="n")
axis(side=2,at=nbvar:1,labels=colnames(don)[1:nbvar],las=1)
for (i in 1:nbvar) abline(h=i,lty=2,col="grey70")
for (i in 1:nbvar) points(rep(ll-i,kk)~zz$data.clust[,i],col=nbclust+1-as.integer(zz$data.clust$clust),pch=20,cex=1.4)
legend("topright",bg="white",rownames(zz$data.clust)[rev(order(zz$data.clust$clust))],cex=0.49,text.col=nbclust+1-as.integer(zz$data.clust$clust[rev(order(zz$data.clust$clust))]),text.font=2)


set.seed(123);kmeans.ani(res.pca$ind$coord,hints=c(" "," "),centers=res.pca$ind$coord[c(12:15),1:5],xl=paste("Dim 1 (",round(res.pca$eig[1,2],2),"%)"),yl=paste("Dim 2 (",round(res.pca$eig[2,2],2),"%)"),printpng=TRUE)


####  AFC
Nobel_Fr <- read.table("prix_nobel_par_pays.csv", sep = ",", header=TRUE,row.names=1,stringsAsFactors = TRUE)
caFR=CA(Nobel_Fr[,-7])

Nobel <- read.table("prix_nobel_pays_chinois.csv", sep = ",", header=TRUE,row.names=1,stringsAsFactors = TRUE)
caCN=CA(Nobel[,-7])

### ACM
loisirs_CN<- read.table("hobbies_cn.csv", sep = ";", header=TRUE,stringsAsFactors = TRUE)
for (i in 1:22) loisirs_CN[,i]= as.factor(loisirs_CN[,i])
res.mca <- MCA(loisirs_CN,quali.sup=19:22,quanti.sup=23,graph=FALSE)
plot(res.mca,theme=theme_bw(),invisible=c("quali.sup","var"),title="",label="none",cex=.3,col.ind="grey30",col.var=rep(c("black","red"),18))
plot(res.mca,theme=theme_bw(),invisible=c("ind","quali.sup"),title="",col.var=rep(c("black","red"),18))
plot(res.mca,theme=theme_bw(),invisible=c("ind","var"),title="",col.var=rep(c("black","red"),18))
dimdesc(res.mca)

## classif
HCPC(res.PCAchinois,consol=F,nb.clust=4)