Synopses & Reviews
Learn programming in the language you want: R! Until now a gap has separated generalised introductory computer programming and the application of these programming skills specifically to statistics and data analysis. This textbook bridges that gap with a self-contained first course in statistical computing. The book uses the open-source R statistical package to introduce students to basic programming notions common to most computing languages. The basics of R syntax and statistical graphics are explained, and elementary programming is discussed, including how to develop complex projects reliably. Programming applications in simulation and optimization as well as numerical linear algebra are introduced. Real code in R is shown and all examples are drawn from statistical applications. Unlike other introductory books on the ground-breaking R system, this book emphasizes programming. Particularly useful for those studying statistics, biostatistics and econometrics, it is accessible to any student familiar with the basics of probability.
This is the only introduction you'll need to start programming in R, the open-source language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R Core Development Team, and by an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the ground-breaking R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects. Learning the language is made easier by the frequent exercises and end-of-chapter reviews that help you progress confidently through the book. Solutions, datasets and any errata will be available from the book's web site. The many examples, all from real applications, make it particularly useful for anyone working in practical data analysis.
The only introduction you'll need to start programming in R.
About the Author
W. John Braun is an Associate Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. He is also a co-author, with John Maindonald, of Data Analysis and Graphics Using R, 2nd edition (Cambridge University Press, 2007).Duncan J. Murdoch is an Associate Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. He is a member of the R Development Core Team and was columnist and column editor of the statistical computing column of Chance 1999-2000.
Table of Contents
1. Getting started; 2. Introduction to the R language; 3. Programming statistical graphics; 4. Programming with R; 5. Simulation; 6. Computational linear algebra; 7. Numerical optimization; Appendix. Review of random variables and distributions; Index.