Load the necessary libraries

  Load the data set

  Perform a principal component analysis on the data

  Take the summary

  Visualize the result

require(MASS)
require(ggplot2)
require(scales)
require(gridExtra)
data=read.csv(‘/…./iris.csv’)
pca summary(pca)
#To visualize the data separation
prop.pca = pca$sdev^2/sum(pca$sdev^2)
p2 labs(x = paste(“PC1 (“, percent(prop.pca[1]), “)”, sep=””),
y = paste(“PC2 (“, percent(prop.pca[2]), “)”, sep=””))
grid.arrange(p2)
#To see how our features are transformed
biplot (pca)
#To determine what should be an ‘ideal’ set of features we should take after using PCA
screeplot(pca, type=”lines”)