R Cookbook

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

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If youre a beginner, R Cookbook will help get you started. If youre an experienced data programmer, it will jog your memory and expand your horizons. Youll get the job done faster and learn more about R in the process.

• Create vectors, handle variables, and perform other basic functions
• Input and output data
• Tackle data structures such as matrices, lists, factors, and data frames
• Work with probability, probability distributions, and random variables
• Calculate statistics and confidence intervals, and perform statistical tests
• Create a variety of graphic displays
• Build statistical models with linear regressions and analysis of variance (ANOVA)
• Explore advanced statistical techniques, such as finding clusters in your data

"Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time." —Jeffrey Ryan, software consultant and R package author

Perform data analysis with R quickly and efficiently with the task-oriented recipes in this cookbook. Although the R language and environment include everything you need to perform statistical work right out of the box, its structure can often be difficult to master. R Cookbook will help both beginners and experienced data programmers unlock and use the power of R.

This practical book provides a collection of concise recipes that will help you be productive with R immediately. Youll get the job done faster and learn more about R in the process.

Key topics include:

• Getting started with R
• Input/output
• Data structures
• Basic numerical calculations
• Basic probability calculations
• Basic statistical calculations and tests
• Graphics
• Regression and ANOVA
• Advanced statistical techniques
• Handy tips, techniques, and hacks that everyone can use

Paul Teetor is a quantitative developer with Masters degrees in statistics and computer science. He specializes in analytics and software engineering for investment management, securities trading, and risk management. He works with hedge funds, market makers, and portfolio managers in the greater Chicago area.