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pca = PCA(data, graph = FALSE)

library("factoextra")  # Get Eigen Values
eig.val <- get_eigenvalue(res.pca)


fviz_eig(res.pca, addlabels = TRUE, ylim = c(0, 50))  # Plot Eigen Values

fviz_pca_var(res.pca, col.var = "black")  # Cercle de Correlation (Explains very similar factors, oposite factors, different factors)

#Get PCA Variables (Correlation, cos2, etc)

var <- get_pca_var(res.pca)
# Coordonnées
head(var$coord)
# Cos2: qualité de répresentation
head(var$cos2)
# Contributions aux composantes principales
head(var$contrib)

#Correlation Plot
library("corrplot")
corrplot(var$cos2, is.corr=FALSE)

# Plot Individuals
fviz_pca_ind (pca, col.ind = "cos2",
              gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
              repel = TRUE # Évite le chevauchement de texte
)

#Individuals Contribution
fviz_contrib(pca, choice = "ind", axes = 1:2)