Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition 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 visualising 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 using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors.
"While the book has some of the clearest geometric explanations written on the topic, in terms of inertia possessed by clouds of individuals and variables, its primary function is to operate as a step-by-step walk through on how to visualize, analyze and portray the results of analyses in R. This is accomplished via thought-provoking examples, ranging from wine ratings, decathlons to high-dimensional text-mining and genomic breeding. Data and code are available online, enabling fast cut-and-paste implementation…the book makes an excellent self-tutorial or teaching aid for the whole gamut of students and researchers working in applied fields. The authors are to be congratulated for their contribution to making the implementation of complex analyses ideas simple and implementable in practice."
—Donna Ankherst, in Biometrics, September 2018
"In the days of "big data" every researcher should be able to summarize and explain multivariate data sets. The purpose of "Exploratory Multivariate Analysis by Example using R" is to provide the practitioner with a sound understanding of, and the tools to apply, an array of multivariate technique (including Principal Components, Correspondence Analysis, and Clustering). The focus is on descriptive techniques, whose purpose is to explore the data from different perspectives, trying to find patterns, but without going into the realm of inferential statistics, with its formal tests of hypotheses, confidence intervals and other more advanced topics. This seems to be the right choice for the audience of non-statisticians to whom the book is directed. The second edition of the book includes a more extensive treatment of missing data and a new chapter on multivariate data visualization - both of which I consider very welcome additions.
In summary, I consider "Exploratory Multivariate Analysis by Example using R" to be a good introduction, with an applied slant, to the fundamental multivariate techni