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 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.
by Vincent Zoonekynd
Contents: Introduction to R; Programming in R; From Data to Graphics; Customizing graphics; Factorial methods; Clustering; Probability Distributions; Estimators and Statistical Tests; Regression; Other regressions; Regression Problems; etc.
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.
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.