An Introduction to R
by W. N. Venables, D. M. Smith
Publisher: Network Theory 2008
Number of pages: 100
This manual provides a comprehensive introduction to R, a software package for statistical computing and graphics. R supports a wide range of statistical techniques, and is easily extensible via user-defined functions written in its own language, or using dynamically loaded modules written in C, C++ or Fortran. One of R's strengths is the ease with which well-designed publication-quality plots can be produced.
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by Colin Gillespie, Robin Lovelace - O'Reilly
The book is about increasing the amount of work you can do with R in a given amount of time. It's about both computational and programmer efficiency. It's for anyone who uses R and who wants to make their use of R more reproducible and faster.
by Julian J. Faraway
The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and when they should be applied. Many examples are presented to clarify the use of the techniques.
by Roger D. Peng - Leanpub
This book teaches you to use R to effectively visualize and explore complex datasets. Exploratory data analysis is a key part of the data science process because it allows you to sharpen your question and refine your modeling strategies.
by Hadley Wickham - O'Reilly Media
This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham's package development philosophy. You'll work with devtools, roxygen, and testthat, a set of R packages.