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Archives: Book 1 Book 2 More. Top Content Archives. Books about the R programming language fall in different categories: Learning R Reference books for the professional R programmer Books about data science or visualization, using R to illustrate the concepts Books are a great way to learn a new programming language. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports. This comprehensive video course shows you how to explore and understand data, as well as how to build linear and non-linear models in the R language and environment.

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 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.

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Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Each chapter includes a brief account of the relevant statistical background, along with appropriate references. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.

Data Analysis - Learn statistics and more

You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. R was first written as a research project by Ross Ihaka and Robert Gentleman, and is now under active development by a group of statisticians called 'the R core team', with a home page at www. You can view R and S-Plus as alternative implementations of the same underlying S language.

W & R Base: why reference?

The modern R implementation, however, is by far the most popular. The source code is also available for download and can be compiled for other platforms.

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These notes are organized in several sections, corresponding to the tabs at the top. I have tried to introduce key features of R as they are needed by students in my statistics classes. As a result, I often postpone or altogether omit discussion of some of the more powerful features of R as a programming language. Notes of local interest, such as where to find R at Princeton University, appear in framed boxes and are labeled as such.

This theme is concerned with aspects of dealing with data. In particular:

Permission is hereby given to reproduce these pages freely and host them in your own server if you wish. You may add, edit or delete material in the local notes as long as the rest of the text is left unchanged and due credit is given.

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Obviously I welcome corrections and suggestions for enhancement. S was first introduced by Becker and Chambers in what's known as the 'brown' book. The new S language was described by Becker, Chambers and Wilks in the 'blue' book. Chambers and Hastie edited a book discussing statistical modeling in S, called the 'white' book.

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  4. The latest version of the S language is described by Chambers in the 'green' book, but R is largely an implementation of the versions documented in the blue and white books.