Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples.
Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R.
Focusing on the R software, the first section covers:
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Basic elements of the R software and data processing
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Clear, concise visualization of results, using simple and complex graphs
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Programming basics: pre-defined and user-created functions
The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including:
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Regression methods
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Analyses of variance and covariance
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Classification methods
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Exploratory multivariate analysis
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Clustering methods
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Hypothesis tests
After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist.
Preface
Part I : An Overview of R
Chapter 1 : Main Concepts
Chapter 2 : Preparing Data
Chapter 3 : R Graphics
Chapter 4 : Making Programs with R
Part II : Statistical Methods
Chapter 5 : Introduction to the Statistical Methods
Chapter 6 : A Quick Start with R
Chapter 7 : Hypothesis Test
Chapter 8 : Regression
Chapter 9 : Analysis of Variance and Covariance
Chapter 10 : Classification
Chapter 11 : Exploratory Multivariate Analysis
Chapter 12 : Clustering
Appendix A : The Most Useful Functions
Appendix B : Writing a Formula for the Models
Appendix C : The Rcmdr Package
Appendix D : The FactoMineR Package
Appendix E : Answers to the Exercises
Index
