A First Course in Statistical Programming with R

by W. John Braun , Duncan J. Murdoch

Edition: 1st

ISBN13: 9780521694247

ISBN10: 0521694248

Format: Paperback

Pub. Date: 2008-01-28

Publisher(s): Cambridge University Press

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Summary

This is the only introduction you'll need to start programming in R, the open-source language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R Core Development Team, and by 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 ground-breaking R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects. Learning the language is made easier by the frequent exercises and end-of-chapter reviews that help you progress confidently through the book. Solutions, datasets and any errata will be available from the book's web site. The many examples, all from real applications, make it particularly useful for anyone working in practical data analysis.

Preface	p. ix
Getting started	p. 1
What is statistical programming?	p. 1
Outline of the book	p. 2
The R package	p. 3
Why use a command line?	p. 3
Font conventions	p. 4
Installation of R	p. 4
Introduction to the R language	p. 5
Starting and quitting R	p. 5
Recording your work	p. 6
Basic features of R	p. 7
Calculating with R	p. 7
Named storage	p. 7
Functions	p. 9
Exact or approximate?	p. 9
R is case-sensitive	p. 12
Listing the objects in the workspace	p. 12
Vectors	p. 12
Extracting elements from vectors	p. 13
Vector arithmetic	p. 14
Simple patterned vectors	p. 15
Missing values and other special values	p. 16
Character vectors	p. 16
Factors	p. 17
More on extracting elements from vectors	p. 18
Matrices and arrays	p. 18
Data frames	p. 19
Dates and times	p. 21
Built-in functions and online help	p. 21
Built-in examples	p. 22
Finding help when you don't know the function name	p. 23
Built-in graphics functions	p. 23
Additional elementary built-in functions	p. 25
Logical vectors and relational operators	p. 26
Boolean algebra	p. 26
Logical operations in R	p. 27
Relational operators	p. 28
Data input and output	p. 29
Changing directories	p. 29
dump () and source ()	p. 29
Redirecting R output	p. 30
Saving and retrieving image files	p. 31
Data frames and the read.table function	p. 31
Lists	p. 31
Chapter exercises	p. 32
Programming statistical graphics	p. 33
High-level plots	p. 33
Bar charts and dot charts	p. 34
Pie charts	p. 35
Histograms	p. 35
Box plots	p. 36
Scatterplots	p. 38
QQ plots	p. 39
Choosing a high-level graphic	p. 41
Low-level graphics functions	p. 42
The plotting region and margins	p. 42
Adding to plots	p. 43
Setting graphical parameters	p. 45
Chapter exercises	p. 46
Programming with R	p. 47
Flow control	p. 47
The for () loop	p. 47
The if () statement	p. 50
The while () loop	p. 54
Newton's method for root finding	p. 55
The repeat loop, and the break and next statements	p. 57
Managing complexity through functions	p. 59
What are functions?	p. 59
Scope of variables	p. 62
Miscellaneous programming tips	p. 63
Using fix ()	p. 63
Documentation using #	p. 64
Some general programming guidelines	p. 65
Top-down design	p. 67
Debugging and maintenance	p. 72
Recognizing that a bug exists	p. 72
Make the bug reproducible	p. 73
Identify the cause of the bug	p. 73
Fixing errors and testing	p. 75
Look for similar errors elsewhere	p. 75
The browser () and debug () functions	p. 75
Efficient programming	p. 77
Learn your tools	p. 77
Use efficient algorithms	p. 78
Measure the time your program takes	p. 79
Be willing to use different tools	p. 80
Optimize with care	p. 80
Chapter exercises	p. 80
Simulation	p. 82
Monte Carlo simulation	p. 82
Generation of pseudorandom numbers	p. 83
Simulation of other random variables	p. 88
Bernoulli random variables	p. 88
Binomial random variables	p. 89
Poisson random variables	p. 93
Exponential random numbers	p. 97
Normal random variables	p. 99
Monte Carlo integration	p. 101
Advanced simulation methods	p. 104
Rejection sampling	p. 104
Importance sampling	p. 107
Chapter exercises	p. 109
Computational linear algebra	p. 112
Vectors and matrices in R	p. 113
Constructing matrix objects	p. 113
Accessing matrix elements; row and column names	p. 115
Matrix properties	p. 117
Triangular matrices	p. 118
Matrix arithmetic	p. 118
Matrix multiplication and inversion	p. 119
Matrix inversion	p. 120
The LU decomposition	p. 121
Matrix inversion in R	p. 122
Solving linear systems	p. 123
Eigenvalues and eigenvectors	p. 124
Advanced topics	p. 125
The singular value decomposition of a matrix	p. 125
The Choleski decomposition of a positive definite matrix	p. 126
The QR decomposition of a matrix	p. 127
The condition number of a matrix	p. 128
Outer products	p. 129
Kronecker products	p. 129
apply ()	p. 129
Chapter exercises	p. 130
Numerical optimization	p. 132
The golden section search method	p. 132
Newton-Raphson	p. 135
The Nelder-Mead simplex method	p. 138
Built-in functions	p. 142
Linear programming	p. 142
Solving linear programming problems in R	p. 145
Maximization and other kinds of constraints	p. 145
Special situations	p. 146
Unrestricted variables	p. 149
Integer programming	p. 150
Alternatives to lp ()	p. 151
Quadratic programming	p. 151
Chapter exercises	p. 157
Review of random variables and distributions	p. 158
Index	p. 161
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A First Course in Statistical Programming with R

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