- Part I.R basics
- Getting and installing R
- The R user interface
- A short R tutorial
- R packages
- Part II. The R language
- An overview of the R language
- R syntax
- R objects
- Symbols and environments
- Functions
- Object-oriented programming
- High-performance R
- Part III. Working with data
- Saving, loading, and editing data
- Preparing data
- Graphics
- Lattice graphics
- Part IV. Statistics with R
- Analyzing data
- Probability distributions
- Statistical tests
- Power tests
- Regression models
- Classification models
- Machine learning
- Time series analysis
- Bioconductor.

What people are saying about R in a Nutshell "I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians." --Martin Schultz, Arthur K. Watson Professor of Computer Science, Yale University "R in a Nutshell is an ideal book for getting started with R. Newcomers will find the fundamentals for performing statistical analysis and graphics, all illustrated with practical examples. This book is an invaluable reference for anyone who wants to learn what R is and what is can do, even for longtime R users looking for new tips and tricks." --David M. Smith, Editor of the "Revolutions" blog at REvolution Computing Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics. The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems. * Understand the basics of the language, including the nature of R objects * Learn how to write R functions and build your own packages * Work with data through visualization, statistical analysis, and other methods * Explore the wealth of packages contributed by the R community * Become familiar with the lattice graphics package for high-level data visualization * Learn about bioinformatics packages provided by Bioconductor.

(source: Nielsen Book Data)