Data Analysis and Graphics Using R – Maindonald and Braun (2003)

Title: Data Analysis and Graphics Using R: An Example-Based Approach
Author(s): John Maindonald; John Braun
Publisher/Date: Cambridge University Press/2003
Statistics level: Intermediate to advanced
Programming level: Beginner to intermediate
Overall recommendation: Highly recommended

Data Analysis and Graphics Using R (DAAG) covers an exceptionally large range of topics. Because of the book’s breadth, new and experienced R users alike will find the text helpful as a learning tool and resource, but it will be of most service to those who already have a basic understanding of statistics and the R system.

Although the text includes both an Introduction to R section (chapter one) and a discussion of the basics of quantitative data analysis (chapters two through four), these chapters will be most useful as overviews (or reviews for more experienced readers), as they lack the detail required to take a reader from no knowledge of these subjects to a functional understanding. For example, chapter one discusses importing data in .txt and .csv format, but the foreign package is not discussed until chapter fourteen – the final chapter of the book. In practice, .txt data structures are not common enough to justify relegating a discussion of the foreign package to the supplemental materials and a researcher stuck with a .sav or .dbf file would not leave chapter one with enough knowledge to import their data into R.

Chapters five through thirteen deal primarily with different flavors of regression techniques. These chapters are the truly valuable pieces of this work as each chapter covers one or two approaches in detail. The major analyses covered in this section include bivariate and multivariate regression, GLM and survival models, time-series analyses, repeated measures, classification trees, and factor analysis. As regression techniques are a core component of quantitative methods these chapters will be useful to many researchers across many industries and disciplines. Much of the discussion of graphing comes via diagnostic and exploratory techniques that are related to the analyses in this section.

As the subtitle suggests, examples of code accompany most significant discussions of analyses. Additionally, several full color plates of graphs are included in the appendices, allowing the authors to provide examples of color options.

DAAG is highly recommended for readers who have at least a basic understanding of quantitative analysis and at least some limited experience with R, however, more advanced readers will also find this book useful as a review and reference.



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