# 13 e-books in R Programming Language category

## e-books in R Programming Language category

**R for Data Science**

by

**Garrett Grolemund, Hadley Wickham**–

**O’Reilly Media**,

**2016**

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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**1**views)

**R Packages: Organize, Test, Document and Share Your Code**

by

**Hadley Wickham**–

**O’Reilly Media**,

**2016**

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.

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**1**views)

**Exploratory Data Analysis with R**

by

**Roger D. Peng**–

**Leanpub**,

**2016**

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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**670**views)

**Just Enough R: Learn Data Analysis with R in a Day**

by

**Sivakumaran Raman**–

**Smashwords**,

**2017**

Learn R programming for data analysis in a single day. The book aims to teach data analysis using R within a day to anyone who already knows some programming in any other language. The book has sample code which can be downloaded as a zip file.

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**909**views)

**Efficient R Programming**

by

**Colin Gillespie, Robin Lovelace**–

**O’Reilly**,

**2016**

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.

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**1**views)

**Statistics with R**

by

**Vincent Zoonekynd**,

**2007**

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.

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**1**views)

**Advanced R programming**

by

**Hadley Wickham**,

**2013**

The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as it explains some of R’s quirks…

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**1**views)

**The Art of R Programming**

by

**Norman Matloff**–

**UC Davis**,

**2009**

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.

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**1**views)

**Practical Regression and Anova using R**

by

**Julian J. Faraway**,

**2002**

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.

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**4568**views)

**The R Inferno**

by

**Patrick Burns**–

**Burns Statistics**,

**2011**

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.

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**1**views)

**Using R for Data Analysis and Graphics**

by

**J H Maindonald**–

**Australian National University**,

**2008**

These notes are designed to allow individuals who have a basic grounding in statistical methodology to work through examples that demonstrate the use of R for a range of types of data manipulation, graphical presentation and statistical analysis.

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**6600**views)

**Using R for Introductory Statistics**

by

**John Verzani**–

**Chapman & Hall/CRC**,

**2004**

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.

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**19262**views)

**An Introduction to R**

by

**W. N. Venables, D. M. Smith**–

**Network Theory**,

**2008**

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.

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**13382**views)