library(FactoMineR)
library(factoextra)
library(ggplot2)

datos<-read.csv("C:/Users/RV/Desktop/analisis de correspondencia/Datos_discretizados.xls")
datos<-datos[,-1]
names(datos)<-c("Empresa", "CIIU", "HHIN", "ID" ,  "C4" ,  "HN")

categ <- apply(datos[,-1], 2, function(x) nlevels(as.factor(x)))

modelo<-MCA(datos[,-1])

modelo$eig
modelo$svd
modelo$ind
modelo$var

fviz_contrib(modelo, choice ="var", axes = 1)
fviz_contrib(modelo, choice ="var", axes = 2)

#Nube de variables, y variables e individuos ------
fviz_mca_var(modelo, repel = TRUE)
fviz_mca_biplot(modelo, repel = TRUE) + theme_minimal()


#Opcional
#Grafico incluyendo densidad(por numero de empresas)----
map_var<-data.frame(modelo$var$coord, Variable = rep(names(categ), categ))
map_indiv<-data.frame(modelo$ind$coord)

ggplot(data = map_indiv, aes(x = Dim.1, y = Dim.2)) +
  geom_hline(yintercept = 0, colour = "gray70") +
  geom_vline(xintercept = 0, colour = "gray70") +
  geom_point(colour = "gray50", alpha = 0.7) +
  geom_density2d(colour = "gray80") +
  geom_text(data = map_var, 
            aes(x = Dim.1, y = Dim.2, 
                label = rownames(map_var), colour = Variable)) +
  ggtitle("MCA plot of variables using R package FactoMineR") +
  scale_colour_discrete(name = "Variable")