importer les données depuis un fichier excel.csv
M<-read.csv2(file.choose(),row.names=1);M
## PAO PAA The JE POT LEC RAI PLP
## AGRI 167 1 163 23 41 8 6 6
## SAAG 162 2 141 12 40 12 4 15
## PRIN 119 6 69 56 39 5 13 41
## CSUP 87 11 63 111 27 3 18 39
## CMOY 103 5 68 77 32 4 11 30
## EMPL 111 4 72 66 34 6 10 28
## OUVR 130 3 76 52 43 7 7 16
## INAC 138 7 117 74 53 8 12 20
## PAO PAA The JE
## Min. : 87.0 Min. : 1.000 Min. : 63.00 Min. : 12.00
## 1st Qu.:109.0 1st Qu.: 2.750 1st Qu.: 68.75 1st Qu.: 44.75
## Median :124.5 Median : 4.500 Median : 74.00 Median : 61.00
## Mean :127.1 Mean : 4.875 Mean : 96.12 Mean : 58.88
## 3rd Qu.:144.0 3rd Qu.: 6.250 3rd Qu.:123.00 3rd Qu.: 74.75
## Max. :167.0 Max. :11.000 Max. :163.00 Max. :111.00
## POT LEC RAI PLP
## Min. :27.00 Min. : 3.000 Min. : 4.00 Min. : 6.00
## 1st Qu.:33.50 1st Qu.: 4.750 1st Qu.: 6.75 1st Qu.:15.75
## Median :39.50 Median : 6.500 Median :10.50 Median :24.00
## Mean :38.62 Mean : 6.625 Mean :10.12 Mean :24.38
## 3rd Qu.:41.50 3rd Qu.: 8.000 3rd Qu.:12.25 3rd Qu.:32.25
## Max. :53.00 Max. :12.000 Max. :18.00 Max. :41.00
la matrice de corrélation
## PAO PAA The JE POT LEC RAI PLP
## PAO 1.00 -0.77 0.93 -0.91 0.66 0.89 -0.83 -0.86
## PAA -0.77 1.00 -0.60 0.90 -0.33 -0.67 0.96 0.77
## The 0.93 -0.60 1.00 -0.75 0.52 0.79 -0.67 -0.83
## JE -0.91 0.90 -0.75 1.00 -0.42 -0.84 0.92 0.72
## POT 0.66 -0.33 0.52 -0.42 1.00 0.60 -0.41 -0.55
## LEC 0.89 -0.67 0.79 -0.84 0.60 1.00 -0.82 -0.75
## RAI -0.83 0.96 -0.67 0.92 -0.41 -0.82 1.00 0.83
## PLP -0.86 0.77 -0.83 0.72 -0.55 -0.75 0.83 1.00
charger les packages
## Le chargement a nécessité le package : FactoMineR
## Le chargement a nécessité le package : factoextra
## Le chargement a nécessité le package : ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
## Warning: le package 'GGally' a été compilé avec la version R 4.1.2
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
les valeurs propres
## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 6.207946839 77.59933549 77.59934
## Dim.2 0.879681393 10.99601741 88.59535
## Dim.3 0.415961123 5.19951404 93.79487
## Dim.4 0.306454670 3.83068337 97.62555
## Dim.5 0.168441497 2.10551872 99.73107
## Dim.6 0.018067709 0.22584636 99.95692
## Dim.7 0.003446769 0.04308461 100.00000
fviz_eig(res.pca,addlabels=TRUE, ylim = c(0, 80))
## Principal Component Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"
## Principal Component Analysis Results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the variables"
## 2 "$cor" "Correlations between variables and dimensions"
## 3 "$cos2" "Cos2 for the variables"
## 4 "$contrib" "contributions of the variables"
fviz_pca_var(res.pca,col.var="red")
## corrplot 0.90 loaded
corrplot(res.pca$var$cos2, is.corr=FALSE)
fviz_cos2(res.pca,choice="var",axes=1:2)
fviz_pca_var(res.pca,col.var="cos2",gradient.cols=c("#00AFBB","#E7B800","#FC4E07","#E7B800","#FC4E07","#E7B800","#E7B800"),repel=TRUE)
fviz_pca_var(res.pca, alpha.var="cos2")
corrplot(res.pca$var$contrib,is.corr=FALSE)
la contribution des variables
fviz_pca_var(res.pca,col.var="contrib",gradient.cols=c("#00AFBB","#E7B800","#FC4E07","#E7B800","#FC4E07","#E7B800","#E7B800"))
fviz_pca_var(res.pca, alpha.var="contrib")
set.seed(123)
my.cont.var <-rnorm(8)
fviz_pca_var(res.pca,col.var=my.cont.var,gradient.cols=c("blue","yellow","red",3:7),legend.title= "Cont.var")
Description de la dimension 1
res.desc <-dimdesc(res.pca,axes=c(1,2),proba=0.05)
res.desc$Dim.1
## $quanti
## correlation p.value
## JE 0.9309151 7.821882e-04
## RAI 0.9294859 8.308315e-04
## PLP 0.9011429 2.239726e-03
## PAA 0.8687483 5.110853e-03
## The -0.8700402 4.966446e-03
## LEC -0.9089814 1.758745e-03
## PAO -0.9749797 3.842664e-05
##
## attr(,"class")
## [1] "condes" "list"
PCA des individus
ind <- get_pca_ind(res.pca);ind
## Principal Component Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"
fviz_pca_ind(res.pca,col.ind="cos2",gradient.cols=c("#00AFBB", "#E7B800", "#FC4E07"),repel = TRUE)
fviz_pca_ind(res.pca,pointsize="cos2",pointshape=21,fill="#E7B800",repel=TRUE)
fviz_pca_ind(res.pca, col.ind = "cos2", pointsize = "cos2",gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),repel = TRUE)
fviz_cos2(res.pca,choice="ind")
afficher le point et le texte du variable
fviz_pca_var(res.pca,geom.var=c("point","text"))
afficher le texte de l’individu uniquement
fviz_pca_ind(res.pca,geom.ind="text")
