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.

Organised into 2 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.