R for Statistics

Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples.

Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R.

Focusing on the R software, the first section covers:

• Basic elements of the R software and data processing
• Clear, concise visualization of results, using simple and complex graphs
• Programming basics: pre-defined and user-created functions

The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including:

• Regression methods
• Analyses of variance and covariance
• Classification methods
• Exploratory multivariate analysis
• Clustering methods
• Hypothesis tests

After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist.

Datasets and all the results described in this book are available on the book’s webpage at http://www.agrocampus-ouest.fr/math/RforStat

Pierre-Andre Cornillon, Arnaud Guyader, Francois Husson, Nicolas Jegou, Julie Josse, Maela Kloareg, ric Matzner-Lober, Laurent Rouvière