Principal Component Analysis | PCA in R (What & How)

Principal Component Analysis | PCA in R (What & How)

Key Points Principal component analysis (PCA) is a method for dimensionality reduction and data visualization that transforms a set of correlated variables into a new set of uncorrelated variables called principal components. The principal components are linear combinations of the original variables that explain the maximum amount of variance in the data. The first principal component (PC1) explains the most variance, the second principal component (PC2) explains the next most variance, and so on. To perform PCA in R, we can use the prcomp function, which takes a data frame or a matrix as input and returns an object of class prcomp that contains the results of the PCA. To visualize and interpret the results of PCA in R, we can use the plot and biplot functions to plot the principal component scores and loadings or other functions such as fviz_pca from the factoextra package to produce more advanced plots with colors, labels, ellipses, etc. PCA can help to simplify the data, remove noise, a…