A First Course in Statistical Programming with R, 3rd Edition
- Length: 280 pages
- Edition: 3
- Language: English
- Publisher: Cambridge University Press
- Publication Date: 2021-07-08
- ISBN-10: 1108995144
- ISBN-13: 9781108995146
- Sales Rank: #1245569 (See Top 100 Books)
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.
Cover Half-title Title page Copyright information Contents Expanded contents Preface to the third edition Preface to the second edition Preface to the first edition 1 Getting started 1.1 What is statistical programming? 1.2 Outline of this book 1.3 The R package 1.4 Why use a command line? 1.5 Font conventions 1.6 Installation of R and RStudio 1.7 Getting started in RStudio 1.8 Going further 2 Introduction to the R language 2.1 First steps 2.2 Basic features of R 2.3 Vectors in R 2.4 Data storage in R 2.5 Packages, libraries, and repositories 2.6 Getting help 2.7 Useful R features 2.8 Logical vectors and relational operators 2.9 Data frames, tibbles, and lists 2.10 Data input and output 3 Programming statistical graphics 3.1 Simple high level plots 3.2 Choosing a high level graphic 3.3 Low level graphics functions 3.4 Graphics as a language: ggplot2 3.5 Other graphics systems 4 Programming with R 4.1 Flow control 4.2 Managing complexity through functions 4.3 The replicate() function 4.4 Miscellaneous programming tips 4.5 Some general programming guidelines 4.6 Debugging and maintenance 4.7 Efficient programming 5 Complex programming in the tidyverse 5.1 The tidyverse principles 5.2 The tibble package: a data frame improvement 5.3 The readr package: reading data in the tidyverse 5.4 The stringr package for manipulating strings 5.5 The dplyr package for manipulating data sets 5.6 Other tidyverse packages 6 Simulation 6.1 Monte Carlo simulation 6.2 Generation of pseudorandom numbers 6.3 Simulation of other random variables 6.4 Multivariate random number generation 6.5 Markov chain simulation 6.6 Monte Carlo integration 6.7 Advanced simulation methods 7 Computational linear algebra 7.1 Vectors and matrices in R 7.2 Matrix multiplication and inversion 7.3 Eigenvalues and eigenvectors 7.4 Other matrix decompositions 7.5 Other matrix operations 8 Numerical optimization 8.1 The golden section search method 8.2 Newton–Raphson 8.3 The Nelder–Mead simplex method 8.4 Built-in functions 8.5 Linear programming Appendix A Review of random variables and distributions Appendix B Base graphics details B.1 The plotting region and margins B.2 Adjusting axis tick labels B.3 Setting graphical parameters Index
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