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

The Book:

Practical Guide to Principal Component Methods in R

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:

BASICS

Introduction to R
R packages for principal component methods

CLASSICAL METHODS

PCA - Principal Component Analysis, for analyzing a data set containing continuous variables
CA - Correspondence Analysis, for analyzing the association between two categorical variables.
MCA - Multiple Correspondence Analysis, for analyzing a data set containing more than 2 categorical variables.

ADVANCED METHODS

FAMD - Factor Analysis of Mixed Data, for analyzing a data set containing both quantitative and qualitative variables.
MFA - Multiple Factor Analysis, for analyzing a data set containing variables structured into groups.

CLUSTERING

HCPC - Hierarchical Clustering on Principal Components

More examples

Course videos

Required R Packages for Principal Component Methods

By kassambara, The 16/10/2017 in Principal Component Methods in R: Practical Guide

FactoMineR & factoextra There are a number of R packages implementing principal component methods. These packages include: FactoMineR, ade4, stats, ca, MASS and ExPosition. However, the result... [Read more]

Multidimensional Scaling Essentials: Algorithms and R Code

By kassambara, The 16/10/2017 in Principal Component Methods in R: Practical Guide

Multidimensional scaling (MDS) is a multivariate data analysis approach that is used to visualize the similarity/dissimilarity between samples by plotting points in two dimensional... [Read more]

Correspondence Analysis in R: Million Ways

By kassambara, The 08/10/2017 in Principal Component Methods in R: Practical Guide

This article describes the multiple ways to compute correspondence analysis in R (CA). Recall that, correspondence analysis is used to study the association between two categorical variables by... [Read more]

Correspondence Analysis: Theory and Practice

By kassambara, The 08/10/2017 in Principal Component Methods in R: Practical Guide

This article presents the theory and the mathematical procedures behind correspondence Analysis. We write all the formula in a very simple format so that beginners can understand the... [Read more]

PCA in R Using Ade4: Quick Scripts

By kassambara, The 08/10/2017 in Principal Component Methods in R: Practical Guide

This article provides quick start R codes to compute principal component analysis (PCA) using the function dudi.pca() in the ade4 R package. We’ll use the factoextra R package to visualize the... [Read more]

Principal Component Analysis in R: prcomp vs princomp

By kassambara, The 08/10/2017 in Principal Component Methods in R: Practical Guide

This R tutorial describes how to perform a Principal Component Analysis (PCA) using the built-in R functions prcomp() and princomp(). You will learn how to predict new individuals and variables... [Read more]

HCPC - Hierarchical Clustering on Principal Components: Essentials

By kassambara, The 25/09/2017 in Principal Component Methods in R: Practical Guide

Clustering is one of the important data mining methods for discovering knowledge in multivariate data sets. The goal is to identify groups (i.e. clusters) of similar objects within a data... [Read more]

MFA - Multiple Factor Analysis in R: Essentials

By kassambara, The 25/09/2017 in Principal Component Methods in R: Practical Guide

Multiple factor analysis (MFA) (J. Pagès 2002) is a multivariate data analysis method for summarizing and visualizing a complex data table in which individuals are described by several sets... [Read more]

FAMD - Factor Analysis of Mixed Data in R: Essentials

By kassambara, The 24/09/2017 in Principal Component Methods in R: Practical Guide

Factor analysis of mixed data (FAMD) is a principal component method dedicated to analyze a data set containing both quantitative and qualitative variables (Pagès 2004). It makes it possible... [Read more]

MCA - Multiple Correspondence Analysis in R: Essentials

By kassambara, The 24/09/2017 in Principal Component Methods in R: Practical Guide

The Multiple correspondence analysis (MCA) is an extension of the simple correspondence analysis (chapter @ref(correspondence-analysis)) for summarizing and visualizing a data table containing... [Read more]

CA - Correspondence Analysis in R: Essentials

By kassambara, The 24/09/2017 in Principal Component Methods in R: Practical Guide

Correspondence analysis (CA) is an extension of principal component analysis (Chapter @ref(principal-component-analysis)) suited to explore relationships among qualitative variables (or... [Read more]

PCA - Principal Component Analysis Essentials

By kassambara, The 23/09/2017 in Principal Component Methods in R: Practical Guide

Principal component analysis (PCA) allows us to summarize and to visualize the information in a data set containing individuals/observations described by multiple inter-correlated quantitative... [Read more]