feu

library(data.table)
library(FactoMineR)
library(factoextra)
library(car)
library(explor)
library(rgl)
library(dplyr)
library(ggplot2)
library(tidyr)

hapiness <- fread("C:/Users/PThav/Documents/M2_TIDE_PRE_RENTREE/PRE_RENTREE_R/data_td/Defi/hapiness.csv")

names(hapiness)[c(6,8,10)] <- c("Economy","Health","Trust")

hapiness_quanti <- hapiness %>% select_if(is.numeric)
hapiness_quanti <- hapiness_quanti[,-c(1,2,3)]

ql_sup <- which(names(hapiness) == "Region")
hapiness_pca <- PCA(hapiness_quanti, scale.unit = T, quali.sup = ql_sup, graph = F)

# Visualisation des valeurs propres
fviz_screeplot(hapiness_pca, alpha = 0.5, addlabels = T, geom = "line") +
        geom_bar(stat = "identity", alpha = 0.2) +
        geom_point(color = "Orange") +
        geom_line(color = "Orange") +
        ggtitle("Part d'inertie conservée après projection sur le nouvel espace")

# Description des axes
hapiness_pca$var

#valeurs propres
hapiness_pca$eig

# Cercles des corrélations
fviz_pca_var(hapiness_pca)

# Représentation des individus sur le premier plan factoriel
fviz_pca_ind(hapiness_pca, label = "none", habillage = ql_sup, pointshape = 19,axes = c(1, 2), geom = c("point", "text"), alpha.ind = "cos2", invisible = "quali") +
        ggtitle("Representation des pays sur le premier plan factoriel")+
        theme(legend.position = "none")

dim_1 = hapiness_pca$var$coord[,1]
dim_2 = hapiness_pca$var$coord[,2]
dim_3 = hapiness_pca$var$coord[,3]

#plot en 3D
dim3 <- scatter3d(x = dim_1, y = dim_2,0 z = dim_3, surface = FALSE,labels = rownames(hapiness_pca$var$coord),id.n=nrow(hapiness_pca$var$coord),groups = hapiness$)
explor(scatter3d(x = dim_1, y = dim_2, z = dim_3, surface = FALSE,labels = rownames(hapiness_pca$var$coord),id.n=nrow(hapiness_pca$var$coord)))
class(dim3)