A First Course in Statistical Programming with R
2nd Edition
Published on 5. July 2016
978-1-316-71580-2 (ISBN)
Description
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.
Reviews / Votes
'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, Canberra '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 CambridgeMore details
Edition
2nd Revised edition
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
College/higher education
Professional and scholarly
Edition type
Revised edition
Illustrations
Worked examples or Exercises; 7 Halftones, black and white; 5 Line drawings, color; 33 Line drawings, black and white
File size
3683,63 MB
ISBN-13
978-1-316-71580-2 (9781316715802)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
W. John Braun | Duncan J. Murdoch
A First Course in Statistical Programming with R
Book
07/2016
2nd Edition
Cambridge University Press
€43.35
Article exhausted; check for reprint
Persons
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.
Content
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.
