PCAmix

install.packages('PCAmixdata')

## Import library
library(PCAmixdata)

## Import data
df <- read.csv('https://github.com/nchelaru/data-prep/raw/master/telco_cleaned_renamed.csv')

## Split mixed dataset into quantitative and qualitative variables
## For now excluding the target variable "Churn", which will be added later as a supplementary variable
split <- splitmix(df[1:18])  

## PCA
res.pcamix <- PCAmix(X.quanti=split$X.quanti,  
                     X.quali=split$X.quali, 
                     rename.level=TRUE, 
                     graph=FALSE, 
                     ndim=3)

## Inspect principal components
Proportion = res.pcamix$eig[1:5,2]

barplot(Proportion, main="Scree Plot",
        xlab="Dimensions", ylab="Percentage of explained variance",ylim = c(0, 10))

a<- rbind(res.pcamix$quanti$contrib.pct, res.pcamix$quali$contrib.pct)

b <- sort(a[1:18,1])

contributions <- b[14:18]

cc <- data.frame(contributions)

cc

cc$variables <- row.names(cc)

#install ggplot2 and all dependencies
install.packages("ggplot2", dependencies=TRUE)

library(ggplot2)