Articles - Principal Component Methods in R: Practical Guide

Principal component methods are used to summarize and visualize the information contained in a large multivariate data sets. Here, we provide practical examples and course videos to compute and interpret principal component methods (PCA, CA, MCA, MFA, etc) using R software.

The following figure illustrates the type of analysis to be performed depending on the type of variables contained in the data set.

Principal component methods


Practical guide: R code and interpretation

We’ll mainly use two R packages:

  • FactoMineR: for computing principal component methods;
  • factoextra: for extracting, visualizing and interpreting the results.

This section is organized as follow:

  1. BASICS
  1. CLASSICAL METHODS
  1. ADVANCED METHODS
  1. CLUSTERING

HCPC - Hierarchical Clustering on Principal Components

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