R for Statistics

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:


Basic elements of the R software and data processing
Clear, concise visualization of results, using simple and complex graphs
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:



Regression methods
Analyses of variance and covariance
Classification methods
Exploratory multivariate analysis
Clustering methods
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.

Datasets and all the results described in this book are available on the book’s webpage at http://www.agrocampus-ouest.fr/math/RforStat

An Overview of RMain ConceptsInstalling RWork SessionHelpR ObjectsFunctionsPackagesExercisesPreparing DataReading Data from FileExporting ResultsManipulating VariablesManipulating IndividualsConcatenating Data TablesCross-TabulationExercisesR GraphicsConventional Graphical FunctionsGraphical Functions with latticeExercisesMaking Programs with RControl FlowsPredefined FunctionsCreating a FunctionExercisesStatistical MethodsIntroduction to the Statistical MethodsA Quick Start with RInstalling ROpening and Closing RThe Command PromptAttribution, Objects, and FunctionSelectionOther Rcmdr PackageImporting (or Inputting) DataGraphsStatistical AnalysisHypothesis TestConfidence Intervals for a MeanChi-Square Test of IndependenceComparison of Two MeansTesting Conformity of a ProportionComparing Several ProportionsThe Power of a TestRegressionSimple Linear RegressionMultiple Linear RegressionPartial Least Squares (PLS) RegressionAnalysis of Variance and CovarianceOne-Way Analysis of VarianceMulti-Way Analysis of Variance with InteractionAnalysis of CovarianceClassificationLinear Discriminant AnalysisLogistic RegressionDecision TreeExploratory Multivariate AnalysisPrincipal Component AnalysisCorrespondence AnalysisMultiple Correspondence AnalysisClusteringAscending Hierarchical ClusteringThe k-Means MethodAppendixThe Most Useful FunctionsWriting a Formula for the ModelsThe Rcmdr PackageThe FactoMineR PackageAnswers to the Exercises