320 Pages 89 B/W Illustrations
    by Chapman & Hall

    320 Pages
    by Chapman & Hall

    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 R
    Main Concepts
    Installing R
    Work Session
    R Objects

    Preparing Data
    Reading Data from File
    Exporting Results
    Manipulating Variables
    Manipulating Individuals
    Concatenating Data Tables

    R Graphics
    Conventional Graphical Functions
    Graphical Functions with lattice

    Making Programs with R
    Control Flows
    Predefined Functions
    Creating a Function

    Statistical Methods
    Introduction to the Statistical Methods

    A Quick Start with R
    Installing R
    Opening and Closing R
    The Command Prompt
    Attribution, Objects, and Function
    Rcmdr Package
    Importing (or Inputting) Data
    Statistical Analysis

    Hypothesis Test
    Confidence Intervals for a Mean
    Chi-Square Test of Independence
    Comparison of Two Means
    Testing Conformity of a Proportion
    Comparing Several Proportions
    The Power of a Test

    Simple Linear Regression
    Multiple Linear Regression
    Partial Least Squares (PLS) Regression

    Analysis of Variance and Covariance
    One-Way Analysis of Variance
    Multi-Way Analysis of Variance with Interaction
    Analysis of Covariance

    Linear Discriminant Analysis
    Logistic Regression
    Decision Tree

    Exploratory Multivariate Analysis
    Principal Component Analysis
    Correspondence Analysis
    Multiple Correspondence Analysis

    Ascending Hierarchical Clustering
    The k-Means Method

    The Most Useful Functions
    Writing a Formula for the Models
    The Rcmdr Package
    The FactoMineR Package
    Answers to the Exercises


    Pierre-Andre Cornillon, Arnaud Guyader, Francois Husson, Nicolas Jegou, Julie Josse, Maela Kloareg, ric Matzner-Lober, Laurent Rouvière

    Section 4.2 on the apply family of functions and related functions for matrices, arrays, and data frames is by far the most friendly and helpful introduction to the subject that I have seen. … All datasets, along with the R-code in the book, are available on the website for the text. … If you are not a trained programmer but you aspire to write code that is efficient and perhaps, from time to time, clever, then this book is a fine place for you to start learning R.
    —Homer S. White, MAA Reviews, January 2013

    [T]he book is accessible for statisticians of all levels and areas of expertise as well as for novice and advanced R users. … I recommend it for anyone who wants to learn about the why and how of the most commonly employed statistical methods and their extensions.
    —Irina Kukuyeva, Journal of Statistical Software, Vol. 51, November 2012