A First Course in Statistical Programming with R
3rd Edition
Published on 9. June 2021
978-1-108-99941-0 (ISBN)
Description
This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box-Muller and Metropolis-Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data science.
More details
Edition
3rd Revised edition
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Professional and scholarly
Edition type
Revised edition
Illustrations
Worked examples or Exercises
File size
6,48 MB
ISBN-13
978-1-108-99941-0 (9781108999410)
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
05/2021
3rd Edition
Cambridge University Press
€72.70
Shipment within 15-20 days
Persons
Content
1. Getting Started; 2. Introduction to the R Language; 3. Programming Statistical Graphics; 4. Programming with R; 5. Complex Programming in the Tidyverse; 6. Simulation; 7. Computational Linear Algebra; 8. Numerical Optimization; A. Review of Random Variables and Distributions; B. Base Graphics Details; Index.
