Exploratory Data Analysis with R
by Roger D. Peng
Publisher: Leanpub 2016
Number of pages: 208
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.
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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 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 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 W. N. Venables, D. M. Smith - Network Theory
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, or using modules written in C, C++ or Fortran.