# 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.

## 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. CLUSTERING

HCPC - Hierarchical Clustering on Principal Components

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## Required R Packages for Principal Component Methods

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

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

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]

## PCA in R Using Ade4: Quick Scripts

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

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

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

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

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

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

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

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]