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 you’re a beginner, *R Cookbook* will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll 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

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.

*The Art of R Programming* takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.

Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to:

- Create artful graphs to visualize complex data sets and functions
- Write more efficient code using parallel R and vectorization
- Interface R with C/C++ and Python for increased speed or functionality
- Find new R packages for text analysis, image manipulation, and more
- Squash annoying bugs with advanced debugging techniques

Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, *The Art of R Programming* is your guide to harnessing the power of statistical computing.

If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports.

Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop.

- Get started quickly with an R tutorial and hundreds of examples
- Explore R syntax, objects, and other language details
- Find thousands of user-contributed R packages online, including Bioconductor
- Learn how to use R to prepare data for analysis
- Visualize your data with R’s graphics, lattice, and ggplot2 packages
- Use R to calculate statistical fests, fit models, and compute probability distributions
- Speed up intensive computations by writing parallel R programs for Hadoop
- Get a complete desktop reference to R

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users

The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research.

This edition:

- Features full colour text and extensive graphics throughout.
- Introduces a clear structure with numbered section headings to help readers locate information more efficiently.
- Looks at the evolution of R over the past five years.
- Features a new chapter on Bayesian Analysis and Meta-Analysis.
- Presents a fully revised and updated bibliography and reference section.
- Is supported by an accompanying website allowing examples from the text to be run by the user.

Praise for the first edition:

‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (*The American Statistician*, August 2008)

‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (*Professional Pensions,* July 2007)

This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started.

- Use R’s default graphics for quick exploration of data
- Create a variety of bar graphs, line graphs, and scatter plots
- Summarize data distributions with histograms, density curves, box plots, and other examples
- Provide annotations to help viewers interpret data
- Control the overall appearance of graphics
- Render data groups alongside each other for easy comparison
- Use colors in plots
- Create network graphs, heat maps, and 3D scatter plots
- Structure data for graphing

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