9 of the Best Free R Books

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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

Website www.burns-stat.com/documents/books/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

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, 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

Website sites.google.com/site/undergraduateguidetor/ 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

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9 of the Best Free R Books - Part 2

4. Using R for Introductory Statistics

Website cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf Author John Verzani Format PDF Pages 114 The cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption. Using R for Introductory Statistics fills this gap in the literature, making the software accessible to the introductory student. The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models. This text lays the foundation for further study and development in statistics using R. Appendices cover installation, graphical user interfaces, and teaching with R, as well as information on writing functions and producing graphics. Chapters include: Univariate Data Bivariate Data Multivariate Data Random Data Simulations Exploratory Data Analysis Confidence Interval Estimation Hypothesis Testing Two-sample tests Chi Square Tests Regression Analysis Multiple Linear Regression Analysis of Variance This is an ideal text for integrating the study of statistics with a powerful computational tool.

5. An Introduction to R

Website cran.r-project.org/doc/manuals/R-intro.pdf Author William N Venables, David M Smith, and the R Core Team Format PDF Pages 109 This tutorial 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. One of R's strengths is the ease with which publication-quality plots can be produced in a wide variety of formats. Chapters explore: Simple manipulations; numbers and vectors Objects, their modes and attributes Ordered and unordered functions Arrays and matrices Lists and data frames Reading data from files Probability distributions Grouping, loops and conditional execution Writing your own functions Statistical models in R Graphical procedures Packages

6. Practical Regression and Anova in R

Website www.maths.bath.ac.uk/~jjf23/book/ Author Julian J. Faraway Format PDF Pages 213 Practical Regression and Anova in R is an intermediate text on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied. The book is not an introduction to R. Chapters cover: Estimation Inference Errors in Predictors Generalized Least Squares Testing for Lack of Fit Diagnostics Transformation Scale Changes, Principal Components and Collinearity Variable Selection Statistical Strategy and Model Uncertainty Chicago Insurance Redlining - a complete example Robust and Registant Regression Missing Data Analysis of Covariance ANOVA

9 of the Best Free R Books - Part 3

7. Introduction to Statistical Thinking (With R, Without Calculus)

Website pluto.huji.ac.il/~msby/StatThink/index.html Author Benjamin Yakir Format PDF Pages 324 Introduction to Statistical Thinking is targeted at college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more. This book uses the basic structure of generic introduction to statistics course. Chapters cover: Short introduction to statistics and probability Data structures and variation Provides numerical and graphical tools for presenting and summarizing the distribution of data Fundamentals of probability: Concept of a random variable, Examples of special types of random variables, Normal random variable, Sampling distribution and presents the Central Limit Theorem and the Law of Large Numbers Discussion of statistical inference. It provides an overview of the topics that are presented in the subsequent chapter. Basic tools of statistical inference, namely point estimation, estimation with a confidence interval, and the testing of statistical hypothesis Discusses inference that involve the comparison of two measurements Analysis of two case studies Large portions of this book are based on material from the online book "Collaborative Statistics" by Barbara Illowsky and Susan Dean. The content of this book is licensed under the conditions of the Creative Commons Attribution License (CC-BY 3.0).

8. Multivariate Statistics with R

Website knowledgeforge.net/opentextbook/svn/multivariatestatistics Author Paul J. Hewson Format PDF Pages 189 The objective of Multivariate Statistics with R is to cover a basic core of multivariate material in such a way that the core mathematical principles are covered, and to provide access to current applications and developments. The author notes that numerous multivariate statistics books, but this book emphasises the applications (and introduces contemporary applications) with a little more mathematical detail than happens in many such "application/software" based books. Chapters cover: Multivariate data including graphical and dynamic graphical methods (Chernoff's Faces, scatterplots, 3d scatterplots, and other methods), animated exploration Matrix manipulation: Vectors, Matrices, Crossproduct matrix, Matrix inversion, Eigen values and eigen vectors, Singular Value Decomposition, Extended Cauchy-Schwarz Inequality, and Partitioning Measures of distance: Mahalanobis Distance, Definitions, Distance between points, Quantitative variables - Interval scaled, Distance between variables, Quantitative variables: Ratio Scaled, Dichotomous data, Qualitative variables, Different variables, Properties of proximity matrices Cluster analysis: Introduction to agglomerative hierarchical cluster analysis, Cophenetic Correlation, Divisive hierarchical clustering, K-means clustering, K-centroids Multidimensional scaling: Metric Scaling, Visualising multivariate distance, Assessing the quality of fit Multivariate normality: Exceptations and moments of continuous random functions, Multivariate normality (including R estimation), Transformations Inference for the mean: Two sample Hotellin's T2 test, Constant Density Ellipses, Multivariate Analysis of Variance Discriminant analysis: Fisher discrimination, Accuracy of discrimination, Importance of variables in discrimination, Canonical discriminant functions, Linear discrimation Principal component analysis: Derivation of Principal Components, Some properties of principal components, Ilustration of Principal Components, Principal Components Regression, "Model" criticism for principal components analysis, Sphericity, How many components to retain, Intrepreting the principal components Canonical Correlation: Canonical variates, Interpretation, Computer example Factor analysis: Role of factor analysis, The factor analysis model, Principal component extraction, Maximum likelihood solutions, Rotation, Factor scoring The content in this book is licensed under a Gnu Free Documentation Licence.

9. A Little Book of R for Biomedical Statistics

Website a-little-book-of-r-for-biomedical-statistics.readthedocs.org Author Avril Coghlan Format PDF, HTML Pages 35 Little Book of R for Biomedical Statistics is a simple introduction to biomedical statistics using the R statistics software. This booklet tells you how to use the R software to carry out some simple analyses that are common in biomedical statistics. In particular, the focus is on cohort and case-control studies that aim to test whether particular factors are associated with disease, randomised trials, and meta-analysis. This booklet assumes that the reader has some basic knowledge of biomedical statistics, and the principal focus of the booklet is not to explain biomedical statistics analyses, but rather to explain how to carry out these analyses using R. The booklet examines: Calculating Relative Risks for a Cohort Study Calculating Odds Ratios for a Cohort or Case-Control Study Testing for an Association Between Disease and Exposure, in a Cohort or Case-Control Study Calculating the (Mantel-Haenszel) Odds Ratio when there is a Stratifying Variable Testing for an Association Between Exposure and Disease in a Matched Case-Control Study Dose-response analysis Calculating the Sample Size Required for a Randomised Control Trial Calculating the Power of a Randomised Control Trial Making a Forest Plot for a Meta-analysis of Several Different Randomised Control Trials The content in this book is licensed under a Creative Commons Attribution 3.0 License. The author has written two other open source booklets about using R for time series analysis and for multivariate analysis. They can be viewed at alittle-book-of-r-for-time-series.readthedocs.org/ and littlebook-of-r-for-multivariate-analysis.readthedocs.org/ respectively.

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oliyiyi 发表于 2015-7-24 18:39
9 of the Best Free R Books - Part 2The cost of statistical computing software has precluded many uni ...

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