Book description
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 usercontributed 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 objectoriented 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 highlevel data visualization
 Learn about bioinformatics packages provided by Bioconductor
"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."
Publisher resources
Table of contents
 R in a Nutshell
 A Note Regarding Supplemental Files
 Preface
 I. R Basics

II. The R Language
 5. An Overview of the R Language
 6. R Syntax
 7. R Objects
 8. Symbols and Environments
 9. Functions
 10. ObjectOriented Programming
 11. HighPerformance R

III. Working with Data
 12. Saving, Loading, and Editing Data
 13. Preparing Data
 14. Graphics
 15. Lattice Graphics

IV. Statistics with R
 16. Analyzing Data
 17. Probability Distributions

18. Statistical Tests

Continuous Data

Normal DistributionBased Tests
 Comparing means
 Comparing paired data
 Comparing variances of two populations
 Comparing means across more than two groups
 Pairwise ttests between multiple groups
 Testing for normality
 Testing if a data vector came from an arbitrary distribution
 Testing if two data vectors came from the same distribution
 Correlation tests
 NonParametric Tests

Normal DistributionBased Tests
 Discrete Data

Continuous Data
 19. Power Tests
 20. Regression Models
 21. Classification Models
 22. Machine Learning
 23. Time Series Analysis
 24. Bioconductor
 A. R Reference
 Bibliography
 Index
 About the Author
 Colophon
 Copyright
Product information
 Title: R in a Nutshell
 Author(s):
 Release date: January 2010
 Publisher(s): O'Reilly Media, Inc.
 ISBN: 9780596801700
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