This new color edition of Braun and Murdoch’s bestselling textbook integrates use of the RStudio platform and adds discussion of newer graphics systems, extensive exploration of Markov chain Monte Carlo, expert advice on common error messages, motivating applications of matrix decompositions, and numerous new examples and exercises. This is the only introduction needed to start programming in R, the computing standard for analyzing data. Co-written by an R core team member and an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from the book’s website. The many examples, all from real applications, make it particularly useful for anyone working in practical data analysis.
Cover Half title Title Copyright Table of Contents Expanded contents 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 Logical vectors and relational operators 2.8 Data frames and lists 2.9 Data input and output 3 Programming statistical graphics 3.1 High level plots 3.2 Choosing a high level graphic 3.3 Low level graphics functions 3.4 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 Simulation 5.1 Monte Carlo simulation 5.2 Generation of pseudorandom numbers 5.3 Simulation of other random variables 5.4 Multivariate random number generation 5.5 Markov chain simulation 5.6 Monte Carlo integration 5.7 Advanced simulation methods 6 Computational linear algebra 6.1 Vectors and matrices in R 6.2 Matrix multiplication and inversion 6.3 Eigenvalues and eigenvectors 6.4 Other matrix decompositions 6.5 Other matrix operations 7 Numerical optimization 7.1 The golden section search method 7.2 Newton–Raphson 7.3 The Nelder–Mead simplex method 7.4 Built-in functions 7.5 Linear programming Appendix Review of random variables and distributions Index
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