R for Statistics

R for Statistics

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Features

  • Offers a basic introduction to the use of R for statistics
  • Includes detailed information on installing and getting started with R
  • Presents material on data visualization, graphics, and programming
  • Uses an example-based approach to data analysis
  • Provides code and data sets from CRAN

Summary

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

Table of Contents

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

Author Bio(s)

Editorial Reviews

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

 
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