Abstract

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.

Keywords

Computer scienceCode (set theory)Programming languageSoftware engineering

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Publication Info

Year
2021
Type
book-chapter
Pages
222-247
Citations
14106
Access
Closed

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W. John Braun, Duncan J. Murdoch (2021). Numerical optimization. Cambridge University Press eBooks , 222-247. https://doi.org/10.1017/9781108993456.011

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DOI
10.1017/9781108993456.011

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