Exploratory Multivariate Analysis by Example Using R (Hardback) book cover

Exploratory Multivariate Analysis by Example Using R

By Francois Husson, Sebastien Le, Jérôme Pagès

© 2010 – CRC Press

240 pages | 87 B/W Illus.

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About the Book

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.

The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualizing objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods and the ways they can be exploited using examples from various fields.

Throughout the text, each result correlates with an R command accessible in the FactoMineR package developed by the authors. All of the data sets and code are available at http://factominer.free.fr/book

By using the theory, examples, and software presented in this book, readers will be fully equipped to tackle real-life multivariate data.


Exploratory Multivariate Analysis by Example Using R provides a very good overview of the application of three multivariate analysis techniques … There is a clear exposition of the use of [R] code throughout … this book does not express the mathematical concepts in matrix form. This is clearly advantageous for those who are considering the book from an applied perspective. This, I think, is refreshing and is done well. … I therefore recommend the book to those who are interested in an introduction to these multivariate techniques. … the book does provide a solid starting point for those who are just starting out. … definitely a book to have in one’s … library.

—Eric J. Beh, Journal of Applied Statistics, June 2012

Its strength is its detailed advice on interpretation, in the context of varied examples. It is written in a pleasant and engaging style … This text is a great source of worked examples and accompanying commentary.

—John H. Maindonald, International Statistical Review (2011), 79

It is an excellent book which I would strongly recommend as a secondary text, supporting or accompanying the main text for any advanced undergraduate or graduate course in multivariate analysis. … this is a compact book with a plethora of visualizations teaching all subtleties of major data exploratory methods. It would supplement well any primary textbook in an advanced undergraduate or graduate course in multivariate analysis.

MAA Reviews, July 2011

… a truly excellent [chapter] on clustering … is an example of what upper-division undergraduate writing should aspire to. … this enjoyable book and the FactoMineR package are highly recommended for an upper-division undergraduate or beginning graduate-level course in MVA. The acid test for such a work must be whether it is likely to spark an interest in students and prepare them adequately for more detailed, serious study of the subject and this book easily passes that test.

Journal of Statistical Software, April 2011, Vol. 40

Table of Contents

Principal Component Analysis (PCA)

Data — Notation — Examples


Studying Individuals

Studying Variables

Relationships between the Two Representations NI and NK

Interpreting the Data

Implementation with FactoMineR

Additional Results

Example: The Decathlon Dataset

Example: The Temperature Dataset

Example of Genomic Data: The Chicken Dataset

Correspondence Analysis (CA)

Data — Notation — Examples

Objectives and the Independence Model

Fitting the Clouds

Interpreting the Data

Supplementary Elements (= Illustrative)

Implementation with FactoMineR

CA and Textual Data Processing

Example: The Olympic Games Dataset

Example: The White Wines Dataset

Example: The Causes of Mortality Dataset

Multiple Correspondence Analysis (MCA)

Data — Notation — Examples


Defining Distances between Individuals and Distances between Categories

CA on the Indicator Matrix

Interpreting the Data

Implementation with FactoMineR


Example: The Survey on the Perception of Genetically Modified Organisms

Example: The Sorting Task Dataset


Data — Issues

Formalising the Notion of Similarity

Constructing an Indexed Hierarchy

Ward’s Method

Direct Search for Partitions: K-means Algorithm

Partitioning and Hierarchical Clustering

Clustering and Principal Component Methods

Example: The Temperature Dataset

Example: The Tea Dataset

Dividing Quantitative Variables into Classes


Percentage of Inertia Explained by the First Component or by the First Plane

R Software

Bibliography of Software Packages



About the Authors

François Husson is an assistant professor of statistics at Agrocampus Ouest in France. Sébastien Lê is an assistant professor of statistics at Agrocampus Ouest in France. Jérôme Pagès is a professor of statistics and head of the applied mathematics department at Agrocampus Ouest in France.

They are all developers of the FactoMineR package dedicated to multivariate exploratory data analysis.

About the Series

Chapman & Hall/CRC Computer Science & Data Analysis

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General