This new color edition of Braun and Murdoch's bestselling textbook integrates use of the RStudio platform and adds discussion of newer graphics systems, extensive exploration of Markov chain Monte Carlo, expert advice on common error messages, motivating applications of matrix decompositions, and numerous new examples and exercises. This is the only introduction needed to start programming in R, the computing standard for analyzing data. Co-written by an R core team member and 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 R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from the book's website. The many examples, all from real applications, make it particularly useful for anyone working in practical data analysis.
|Publisher:||Cambridge University Press|
|Edition description:||New Edition|
|Product dimensions:||7.48(w) x 9.72(h) x 0.39(d)|
About the Author
W. John Braun is Deputy Director of the Canadian Statistical Sciences Institute. He is also Professor and Head of the Departments of Computer Science, Physics, Mathematics and Statistics at the University of British Columbia, Okanagan. His research interests are in the modeling of environmental phenomena, such as wildfire, as well as statistical education, particularly as it relates to the R programming language.
Duncan J. Murdoch is a member of the R core team of developers and is co-president of the R Foundation. He is one of the developers of the rgl package for 3D visualization in R and has also developed numerous other R packages. Murdoch is also a professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario.
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.