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, objectoriented programming with generic functions. It
includes conditionals, loops, userdefined 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 SPLUS featuring objectoriented
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, timeseries analysis, classification, clustering,
and others.
R is highly extensible through the use of usersubmitted
packages for specific functions or specific areas of study. Packages
are collections of R functions, data, and
compiled code in a welldefined 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 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 opensource 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
 OverVectorizing
 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


Website 
ipsur.org 
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 Bayes’ 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, TwoSample 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 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 (forloop, ifstatement,
ifelsestatement)
 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 builtin editor, this
chapter gives links to other popular editors for R, including WinEDT,
TinnR, 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
Last Updated Sunday, May 25 2014 @ 06:19 AM EDT 