1st Edition
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 R
Main Concepts
Installing R
Work Session
Help
R Objects
Functions
Packages
Exercises
Preparing Data
Reading Data from File
Exporting Results
Manipulating Variables
Manipulating Individuals
Concatenating Data Tables
Cross-Tabulation
Exercises
R Graphics
Conventional Graphical Functions
Graphical Functions with lattice
Exercises
Making Programs with R
Control Flows
Predefined Functions
Creating a Function
Exercises
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
Selection
Other
Rcmdr Package
Importing (or Inputting) Data
Graphs
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
Regression
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
Classification
Linear Discriminant Analysis
Logistic Regression
Decision Tree
Exploratory Multivariate Analysis
Principal Component Analysis
Correspondence Analysis
Multiple Correspondence Analysis
Clustering
Ascending Hierarchical Clustering
The k-Means Method
Appendix
The Most Useful Functions
Writing a Formula for the Models
The Rcmdr Package
The FactoMineR Package
Answers to the Exercises
Biography
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
