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).
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1. The R Inferno
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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.
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2. Introduction to Probability and Statistics Using R
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| Website |
ipsur.org |
| Author |
G. Jay Kerns
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| 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, 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.
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3. The Undergraduate Guide to R
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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
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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 Saturday, March 16 2013 @ 07:27 PM EST
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