A First Course in Statistical Programming with R, 3rd Edition

DescriptionContentsResourcesCoursesAbout the AuthorsThis 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 Cover 1 Half-title 3 Title page 5 Copyright information 6 Contents 7 Expanded contents 11 Preface to the third edition 17 Preface to the second edition 19 Preface to the first edition 21 1 Getting started 23 1.1 What is statistical programming? 23 1.2 Outline of this book 24 1.3 The R package 25 1.4 Why use a command line? 25 1.5 Font conventions 26 1.6 Installation of R and RStudio 26 1.7 Getting started in RStudio 27 1.8 Going further 28 2 Introduction to the R language 29 2.1 First steps 29 2.2 Basic features of R 34 2.3 Vectors in R 35 2.4 Data storage in R 44 2.5 Packages, libraries, and repositories 49 2.6 Getting help 51 2.7 Useful R features 54 2.8 Logical vectors and relational operators 59 2.9 Data frames, tibbles, and lists 62 2.10 Data input and output 68 3 Programming statistical graphics 75 3.1 Simple high level plots 76 3.2 Choosing a high level graphic 89 3.3 Low level graphics functions 90 3.4 Graphics as a language: ggplot2 92 3.5 Other graphics systems 109 4 Programming with R 115 4.1 Flow control 115 4.2 Managing complexity through functions 130 4.3 The replicate() function 136 4.4 Miscellaneous programming tips 137 4.5 Some general programming guidelines 140 4.6 Debugging and maintenance 147 4.7 Efficient programming 154 5 Complex programming in the tidyverse 161 5.1 The tidyverse principles 162 5.2 The tibble package: a data frame improvement 163 5.3 The readr package: reading data in the tidyverse 165 5.4 The stringr package for manipulating strings 166 5.5 The dplyr package for manipulating data sets 168 5.6 Other tidyverse packages 171 6 Simulation 172 6.1 Monte Carlo simulation 172 6.2 Generation of pseudorandom numbers 173 6.3 Simulation of other random variables 178 6.4 Multivariate random number generation 195 6.5 Markov chain simulation 197 6.6 Monte Carlo integration 199 6.7 Advanced simulation methods 201 7 Computational linear algebra 219 7.1 Vectors and matrices in R 220 7.2 Matrix multiplication and inversion 227 7.3 Eigenvalues and eigenvectors 232 7.4 Other matrix decompositions 233 7.5 Other matrix operations 240 8 Numerical optimization 244 8.1 The golden section search method 244 8.2 Newton–Raphson 247 8.3 The Nelder–Mead simplex method 249 8.4 Built-in functions 253 8.5 Linear programming 253 Appendix A Review of random variables and distributions 270 Appendix B Base graphics details 273 B.1 The plotting region and margins 273 B.2 Adjusting axis tick labels 274 B.3 Setting graphical parameters 277 Index 279 This book is for students who are learning computing to use in data science and scientific applications. It takes the reader from basic principles of computing to numerical methods, using the R programming language. Worked examples, hundreds of exercises, and downloadable code, datasets, and solutions make a complete package.