Articles - Principal Component Methods in R: Practical Guide

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 is presented differently depending on the used package.

To help in the interpretation and in the visualization of multivariate analysis - such as cluster analysis and principal component methods - we developed an easy-to-use R package named factoextra (official online documentation: and Mundt 2017).

No matter which package you decide to use for computing principal component methods, the factoextra R package can help to extract easily, in a human readable data format, the analysis results from the different packages mentioned above. factoextra provides also convenient solutions to create ggplot2-based beautiful graphs.

In this book, we’ll use mainly:

  • the FactoMineR package (Husson et al. 2017) to compute principal component methods;
  • and the factoextra package (Kassambara and Mundt 2017) for extracting, visualizing and interpreting the results.

The other packages - ade4, ExPosition, etc - will be presented briefly.

The Figure 2.1 illustrates the key functionality of FactoMineR and factoextra.

Key features of FactoMineR and factoextra for multivariate analysis

Methods, which outputs can be visualized using the factoextra package are shown on the Figure 2.2:

Principal component methods and clustering methods supported by the factoextra R package


Installing FactoMineR

The FactoMineR package can be installed and loaded as follow:

# Install
# Load

Installing factoextra

  • factoextra can be installed from CRAN as follow:
  • Or, install the latest developmental version from Github
if(!require(devtools)) install.packages("devtools")
  • Load factoextra as follow :

Main R functions

Main functions in FactoMineR

Functions for computing principal component methods and clustering:

Functions Description
PCA Principal component analysis.
CA Correspondence analysis.
MCA Multiple correspondence analysis.
FAMD Factor analysis of mixed data.
MFA Multiple factor analysis.
HCPC Hierarchical clustering on principal components.
dimdesc Dimension description.

Main functions in factoextra

factoextra functions covered in this book are listed in the table below. See the online documentation ( for a complete list.

  • Visualizing principal component method outputs
Functions Description
fviz_eig (or fviz_eigenvalue) Visualize eigenvalues.
fviz_pca Graph of PCA results.
fviz_ca Graph of CA results.
fviz_mca Graph of MCA results.
fviz_mfa Graph of MFA results.
fviz_famd Graph of FAMD results.
fviz_hmfa Graph of HMFA results.
fviz_ellipses Plot ellipses around groups.
fviz_cos2 Visualize element cos2.1
fviz_contrib Visualize element contributions.2
  • Extracting data from principal component method outputs. The following functions extract all the results (coordinates, squared cosine, contributions) for the active individuals/variables from the analysis outputs.
Functions Description
get_eigenvalue Access to the dimension eigenvalues.
get_pca Access to PCA outputs.
get_ca Access to CA outputs.
get_mca Access to MCA outputs.
get_mfa Access to MFA outputs.
get_famd Access to MFA outputs.
get_hmfa Access to HMFA outputs.
facto_summarize Summarize the analysis.
  • Clustering analysis and visualization
Functions Description
fviz_dend Enhanced Visualization of Dendrogram.
fviz_cluster Visualize Clustering Results.


Husson, Francois, Julie Josse, Sebastien Le, and Jeremy Mazet. 2017. FactoMineR: Multivariate Exploratory Data Analysis and Data Mining.

Kassambara, Alboukadel, and Fabian Mundt. 2017. Factoextra: Extract and Visualize the Results of Multivariate Data Analyses.

  1. Cos2: quality of representation of the row/column variables on the principal component maps.

  2. This is the contribution of row/column elements to the definition of the principal components.