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 Garrett Grolemund, Hadley Wickham - O'Reilly Media
This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science.
by Norman Matloff - UC Davis
This book is for those who wish to write code in R, as opposed to those who use R mainly for a sequence of separate, discrete statistical operations. The reader's level of programming background may range from professional to novice.
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 John Verzani - Chapman & Hall/CRC
A self-contained treatment of statistical topics and the intricacies of the R software. The book focuses on exploratory data analysis, includes chapters on simulation and linear models. It lays the foundation for further study and development using R.