A First Course in Statistical Programming with R
- W. John Braun, University of British Columbia, Okanagan
- Duncan J. Murdoch, University of Western Ontario
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.Read more
- Self-contained and accessible to anyone familiar with university-level calculus
- The basics of R syntax and statistical graphics are explained with real R code
- Programming ideas, including good, efficient style and debugging, are introduced
- Applications in simulation and optimization as well as numerical linear algebra are discussed
Reviews & endorsements
'For what has come to be called data analytics, R is a remarkable tour de force. Strong skills with R programming are needed to allow really effective use. Mastering the content of this carefully staged text is an excellent starting point for gaining those skills.' John Maindonald, Australian National University, CanberraSee more reviews
'This book should be especially useful for those without any prior programming background. The core programming material, such as loops and functions, is postponed to Chapter 4, allowing the student to first become comfortable with R in a broader manner. The placement of Chapter 3, on graphical methods, is particularly helpful in this regard, and is very motivating. The book is written by two recognized experts in the R language, so the reader attains the benefit of being taught by the 'insiders'.' Norm Matloff, University of California, Davis
'This book is an excellent introduction to programming in R. It gently takes the reader from first principles in programming through to more advanced topics such as simulation and plotting. We recommend this book to our graduate students in computational biology as a concise guide to learning R.' Stephen Eglen, University of Cambridge
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- Edition: 2nd Edition
- Date Published: July 2016
- format: Adobe eBook Reader
- isbn: 9781316715802
- contains: 40 b/w illus. 5 colour illus. 200 exercises
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
1. Getting started
2. Introduction to the R language
3. Programming statistical graphics
4. Programming with R
6. Computational linear algebra
7. Numerical optimization
Appendix. Review of random variables and distributions
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