R for Data Science
by Garrett Grolemund, Hadley Wickham
Publisher: O'Reilly Media 2016
Number of pages: 522
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.
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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 Patrick Burns - Burns Statistics
If you don't know of 'The R Inferno', this revised edition is a book-length (intermediate level) explanation of a few trouble spots when using the R language. If you are using R and you think you're in hell, this is a map for you.
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 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.