This book is an English translation of the original French version (Le logiciel R), based on notes of lectures given at the Institute Universitaire de Technologie Grenoble 2, Department of Statistics and Business Intelligence.

R is open-source programming language and software environment. It provides a computing framework for data analysis and graphics whose approach is both broad and flexible. As open-source code, the R software is free and constantly being improved, and packages for specific analysis techniques are frequently added. R is widely used in statistical analysis of data analytics applications, as well as in education and research. R is suited for data analysis in general and not just for statistical tasks.

This book is for those truly committed to R, rather than those merely curious. If you are looking for an introduction to R; only have a casual interest in R; or are not planning to use the language regularly, then this would not be the book to pick. On the other hand, this unique book enables the truly committed to become proficient in R after practicing with its numerous hands-on sample programs.

The book has a dedicated website for the reader to download the datasets used in the numerical examples throughout the book. The numerical examples provide hands-on experience and reinforce the learning of theoretical notions introduced in the book.

The book is divided into three parts. Part 1, “Preliminaries,” has two chapters. Chapter 1 introduces the RCommander graphical user interface (GUI) used throughout the book. Chapter 2 describes the contents of the datasets and their context, which are used in the book examples to demonstrate R functionalities.

Part 2, “The Bases of R,” is the core of the book and consists of chapters 3 through 9. The chapters discuss, for example, how to perform R commands, how to import and export data, how to manipulate data, how to get help on the R software, how to draw basic plots, how to program in R, and how to use the R application programming interface (API) to integrate with code written in C/C++ and Fortran. Chapter 9, the last chapter of Part 2, reviews R methods for managing R sessions and introduces package creation.

Part 3, “Elementary Mathematics and Statistics,” is made up of chapters 10 through 15. Chapter 10 describes basic mathematical functions. Chapter 11 describes how to draw standard summary plots and calculate simple numerical statistical functions. Chapter 12 shows how to use R for empirical simulation, statistical inference, and sampling. Chapter 13 covers topics on confidence measures and hypothesis testing. Chapter 14 addresses simple and multiple linear regressions. Chapter 15 demonstrates the various R commands for analysis of variance (ANOVA). The notions on statistical analysis in Part 3 are all illustrated using the datasets presented in chapter 2.

The book has an appealing format, with inserts and icons to alert the reader to key information at strategic points. Each chapter follows a consistent template that begins with prerequisites and goals, and ends with memorandum, exercises, and worksheets. The book provides solutions to the exercises and memorandums of the chapters to help readers check their work.

This is a great addition to the chorus of books on R. It is a clear an excellent resource for teaching courses on data analysis and statistical computing using R at the graduate and advanced undergraduate levels. The book can be an asset for data scientists, and even more broadly for a wide variety of users including students, teachers, researchers, software engineers, and others whose work involves statistics, mathematics, and computer science.