The second part of this guide covers loadings plots, adding convex hulls to the biplot, and controlling which variable arrows and/or labels are plotted. Part 1 (this guide) covers using prcomp() to do the PCA, and creating beautiful looking biplots using R's base functionality. This guide assumes you already know what PCA is and why you might use it. For a good overview of PCA and why you might use it, check out the Wikipedia entry. Principal components analysis is a statistical technique designed to replace a large set of correlated variables with a reduced set of uncorrelated variables, and it is generally used for exploratory data analysis.
