A First Course in Statistical Programming with R

by W. John Braun, Duncan J. Murdoch

Overview

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.

Product Details

ISBN-13:	9781108995146
Publisher:	Cambridge University Press
Publication date:	05/20/2021
Edition description:	3rd Revised ed.
Pages:	280
Product dimensions:	7.40(w) x 9.65(h) x 0.59(d)

About the Author

W. John Braun is Professor of Statistics at UBC's Okanagan campus. 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 Professor Emeritus and was a member of the R Core Team of developers and 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.

Table of Contents

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.

Editorial Reviews

"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

"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

From the Publisher