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9 of the Best Free R Books - Part 1

9 of the Best Free R Books

R is an open source programming language and software environment for statistical computing and visualization. The R language is frequently used by statisticians and data miners for developing statistical software and data analysis. The language is mature, simple, and effective. R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It offers a large collection of intermediate tools for data analysis. R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions. It includes conditionals, loops, user-defined recursive functions and input and output facilities.

R is an offshoot of the S programming language combined with lexical scoping semantics inspired by Scheme. The other modern implementation of S is S-PLUS featuring object-oriented programming capabilities and advanced analytical algorithms. R provides an open source way to participate in statistical methodology research.

R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others.

R is highly extensible through the use of user-submitted packages for specific functions or specific areas of study. Packages are collections of R functions, data, and compiled code in a well-defined format. The directory where packages are stored is called the library. R comes with a standard set of packages. Add additional functionality by defining new functions.

R is not the easiest language to learn. The focus of this article is to select some informative R books that aid statisticians and data miners to master this refined language, and exploit its full power. All of the books are available to download for free, with many of them released under a freely distributable license.

To cater for all tastes, we have chosen a good range of books, with introductory, intermediate and specialized texts included. All of the texts here come with our strongest recommendation. So get reading (and downloading).

1. The R Inferno

The R Inferno
Author Patrick Burns
Format PDF
Pages 126

The R Inferno is an essential must read guide to the trouble spots and oddities of R. The book shares with the reader a lot of useful information and maintains the reader's interest. The book provides many useful techniques and tips for reducing memory usage, improving performance, and avoiding errors in computational analysis.

R is regarded as an excellent computing environment for most data analysis tasks. R is free, released under an open-source license, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music.

Chapters are headed:

  • Falling into the Floating Trap
  • Growing Objects
  • Failing to Vectorize - includes coverage on subscripting (a key part of effective vectorization), vecorized if, and looks at when vectorization is not possible
  • Over-Vectorizing
  • Not Writing Functions - the power of language is abstraction. To make abstractions in R the programmer writes functions. This chapter also highlights the importance of making functions as simple as possible
  • Doing Global Assignment - which can be useful in memoization
  • Tripping on Object Orientation - S3 methods (including generic functions, the methods function, and inheritance) S4 methods (multiple dispatch, S4 structure), and Namespaces
  • Believing It Does as Intended - looks at ghosts, chimeras, and devils - exorcised using the browser function
  • Seeking Help

The book is illuminated with famous Botticelli artworks: The Giants, The Sowers of Discord, and The Thieves.

2. Introduction to Probability and Statistics Using R

Introduction to Proabability and Statistics Using R
Author G. Jay Kerns
Format PDF, HTML, LaTeX sources
Pages 412

Introduction to Proabability and Statistics Using R is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.

Chapters cover:

  • An Introduction to Probability and Statistics
  • An Introduction to R: Installation, Basic R Operations and Concepts, Assignment, Object names, and Data types, Vectors
  • Data Description: Introduces the different types of data that a statistician is likely to encounter
  • Probability: Defines the basic terminology associated with probability and derive some of its properties, discusses three interpretations of probability, conditional probability and independent events, along with Bayesí Theorem. The chapter concludes with an introduction to random variables
  • Discrete Distributions: Introduces discrete random variables, discusses probability mass functions and some special expectations, namely, the mean, variance and standard deviation. Important discrete distributions are examined in detail, and attention is given to the concept of expection and the empirical distribution
  • Continuous Distributions: Continuous random variables and the associated PDFs and CDFs. The continuous uniform distribution is highlighted, along with the Gaussian, or normal, distribution. Some mathematical details pave the way for a catalogue of models
  • Multivariate Distributions: Studies the notion of dependence between random variables in some detail
  • Sampling Distributions: The bridge from probability and descriptive statistics
  • Estimation: Discusses two branches of estimation procedures: point estimation and interval estimation
  • Hypothesis Testing: Tests for Proportions, One Sample Tests for Means and Variances, Two-Sample Tests for Means and Variances, Other Hypothesis Tests, Analysis of Variance, Sample Size and Power
  • Simple Linear Regression: Estimation, Model Utility and Inference, Residual Analysis, and Other Diagnostic Tools
  • Multiple Linear Regression: The Multiple Linear Regression Model, Estimation and Prediction, Model Utility and Inference, Polynomial Regression, Interaction, Qualitative Explanatory Variables, Partial F Statistic, Residual Analysis and Diagnostic Tools
  • Resampling Methods: Bootstrap Standard Errors, Bootstrap Confidence Intervals, Resampling in Hypothesis Tests
  • Categorical Data Analysis: this chapter is under revision
  • Nonparametric Statistics: this chapter is under revision
  • Time Series: this chapter is under revision

Introduction to Proabability and Statistics Using R is licensed under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation.

3. The Undergraduate Guide to R

The Undergraduate Guide to R
Author Trevor Martin
Format PDF
Pages 68

The Undergraduate Guide to R is an introduction to the R programming language for beginners.

After reading this book, you will be able to perform most common data manipulating, analyzing, comparing and viewing tasks with R. The book also provides the necessary foundation blocks to enable the reader to progress to more advanced R techniques, and offers general tips and suggestions about how to code in R.

The Undergraduate Guide to R is written so that the reader needs no prior knowledge of programming (although basic knowledge of general computer skills and statistics is essential).

Sections cover:

  • What is R?
  • How to Install R
  • The Basics: Algebra, Vectors, Matrices, Manipulation to arrange your data, and Loops/Statements (for-loop, if-statement, ifelse-statement)
  • Data Types: Types, Converting/Using
  • Reading in Data: Types of Data, How to Read In Data
  • Plotting Data: Dot Plots, Histograms, Box Plots, and Additions
  • Exporting Data: Types of Output, How to Export Data
  • Functions: Built In, Custom
  • Tips for Writing Good R Code: General, Matrix Multiplication, Plan, Debug, Help, Packages
  • R Editors: Besides the RGui built-in editor, this chapter gives links to other popular editors for R, including WinEDT, Tinn-R, and explains that other popular editors such as Eclipse and Emacs can be configured to use R syntax highlighting

Next Section: 9 of the Best Free R Books - Part 2

This article is divided into three parts:

Part 1, Part 2, Part 3

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Last Updated Sunday, June 07 2015 @ 02:09 PM EDT

We have written a range of guides highlighting excellent free books for popular programming languages. Check out the following guides: C, C++, C#, Java, JavaScript, CoffeeScript, HTML, Python, Ruby, Perl, Haskell, PHP, Lisp, R, Prolog, Scala, Scheme, Forth, SQL, Node.js (new), Fortran (new), Erlang (new), Pascal (new), and Ada (new).

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