**An Introduction to R**

by W. N. Venables, D. M. Smith

**Publisher**: Network Theory 2008**ISBN/ASIN**: 0954161742**ISBN-13**: 9780954161743**Number of pages**: 100

**Description**:

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.

Download or read it online for free here:

**Download link**

(630KB, PDF)

## Similar books

**Practical Regression and Anova using R**

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.

(

**6025**views)

**Efficient R Programming**

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.

(

**2711**views)

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

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.

(

**1904**views)

**R for Data Science**

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.

(

**2458**views)