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An Introduction to R


Notes on R: A Programming Environment for Data Analysis and Graphics
Version 2.10.1 (2009-12-14)
W. N. Venables, D. M. Smith
and the R Development Core Team
Copyright
c 1990 W. N. Venables
Copyright
c 1992 W. N. Venables & D. M. Smith
Copyright
c 1997 R. Gentleman & R. Ihaka
Copyright
c 1997, 1998 M. Maechler
Copyright
c 1997– R Core Development Team
Copyright
c 1999–2009 R Development Core Team
Permission is granted to make and distribute verbatim copies of this manual provided the
copyright notice and this permission notice are preserved on all copies.
Permission is granted to copy and distribute modified versions of this manual under the conditions
for verbatim copying, provided that the entire resulting derived work is distributed
under the terms of a permission notice identical to this one.
Permission is granted to copy and distribute translations of this manual into another language,
under the above conditions for modified versions, except that this permission notice
may be stated in a translation approved by the R Development Core Team.
ISBN 3-900051-12-7
i
Table of Contents
Preface : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1
1 Introduction and preliminaries : : : : : : : : : : : : : : : : 2
1.1 The R environment : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2
1.2 Related software and documentation: : : : : : : : : : : : : : : : : : : : : : : : : : : : 2
1.3 R and statistics : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2
1.4 R and the window system : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3
1.5 Using R interactively : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3
1.6 An introductory session : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4
1.7 Getting help with functions and features : : : : : : : : : : : : : : : : : : : : : : : : 4
1.8 R commands, case sensitivity, etc. : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5
1.9 Recall and correction of previous commands : : : : : : : : : : : : : : : : : : : : 5
1.10 Executing commands from or diverting output to a file : : : : : : : : 6
1.11 Data permanency and removing objects : : : : : : : : : : : : : : : : : : : : : : : 6
2 Simple manipulations; numbers and vectors : : 7
2.1 Vectors and assignment : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7
2.2 Vector arithmetic : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7
2.3 Generating regular sequences : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
2.4 Logical vectors : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
2.5 Missing values: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
2.6 Character vectors : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 10
2.7 Index vectors; selecting and modifying subsets of a data set : : : : 11
2.8 Other types of objects: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 12
3 Objects, their modes and attributes : : : : : : : : : 13
3.1 Intrinsic attributes: mode and length : : : : : : : : : : : : : : : : : : : : : : : : : : 13
3.2 Changing the length of an object : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 14
3.3 Getting and setting attributes : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 14
3.4 The class of an object : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 15
4 Ordered and unordered factors: : : : : : : : : : : : : : : 16
4.1 A specific example : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 16
4.2 The function tapply() and ragged arrays : : : : : : : : : : : : : : : : : : : : : 16
4.3 Ordered factors : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 17
ii
5 Arrays and matrices: : : : : : : : : : : : : : : : : : : : : : : : : : : 19
5.1 Arrays : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 19
5.2 Array indexing. Subsections of an array : : : : : : : : : : : : : : : : : : : : : : : 19
5.3 Index matrices : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 20
5.4 The array() function : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 21
5.4.1 Mixed vector and array arithmetic. The recycling rule : : : : 21
5.5 The outer product of two arrays : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 22
5.6 Generalized transpose of an array : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 23
5.7 Matrix facilities : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 23
5.7.1 Matrix multiplication: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 23
5.7.2 Linear equations and inversion: : : : : : : : : : : : : : : : : : : : : : : : : : : : 24
5.7.3 Eigenvalues and eigenvectors : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 24
5.7.4 Singular value decomposition and determinants : : : : : : : : : : : 24
5.7.5 Least squares fitting and the QR decomposition : : : : : : : : : : 25
5.8 Forming partitioned matrices, cbind() and rbind(): : : : : : : : : : : 25
5.9 The concatenation function, c(), with arrays : : : : : : : : : : : : : : : : : : 26
5.10 Frequency tables from factors : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 26
6 Lists and data frames : : : : : : : : : : : : : : : : : : : : : : : : : 28
6.1 Lists : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 28
6.2 Constructing and modifying lists : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 29
6.2.1 Concatenating lists : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 29
6.3 Data frames : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 29
6.3.1 Making data frames : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 29
6.3.2 attach() and detach() : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 30
6.3.3 Working with data frames : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 30
6.3.4 Attaching arbitrary lists : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 31
6.3.5 Managing the search path : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 31
7 Reading data from files : : : : : : : : : : : : : : : : : : : : : : : 32
7.1 The read.table() function : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 32
7.2 The scan() function : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 33
7.3 Accessing builtin datasets : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 33
7.3.1 Loading data from other R packages : : : : : : : : : : : : : : : : : : : : : : 34
7.4 Editing data : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 34
8 Probability distributions : : : : : : : : : : : : : : : : : : : : : : 35
8.1 R as a set of statistical tables : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 35
8.2 Examining the distribution of a set of data : : : : : : : : : : : : : : : : : : : : 36
8.3 One- and two-sample tests : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 39
9 Grouping, loops and conditional execution : : 42
9.1 Grouped expressions : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 42
9.2 Control statements : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 42
9.2.1 Conditional execution: if statements : : : : : : : : : : : : : : : : : : : : : 42
9.2.2 Repetitive execution: for loops, repeat and while : : : : : : : 42
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10 Writing your own functions : : : : : : : : : : : : : : : : : 44
10.1 Simple examples : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 44
10.2 Defining new binary operators: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 45
10.3 Named arguments and defaults : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 45
10.4 The ‘…’ argument : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 46
10.5 Assignments within functions : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 46
10.6 More advanced examples : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 46
10.6.1 Efficiency factors in block designs : : : : : : : : : : : : : : : : : : : : : : : 46
10.6.2 Dropping all names in a printed array : : : : : : : : : : : : : : : : : : : 47
10.6.3 Recursive numerical integration : : : : : : : : : : : : : : : : : : : : : : : : : 48
10.7 Scope : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 48
10.8 Customizing the environment : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 50
10.9 Classes, generic functions and object orientation : : : : : : : : : : : : : : 51
11 Statistical models in R: : : : : : : : : : : : : : : : : : : : : : : 54
11.1 Defining statistical models; formulae : : : : : : : : : : : : : : : : : : : : : : : : : 54
11.1.1 Contrasts : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 56
11.2 Linear models: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 57
11.3 Generic functions for extracting model information : : : : : : : : : : : 57
11.4 Analysis of variance and model comparison : : : : : : : : : : : : : : : : : : : 58
11.4.1 ANOVA tables : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 59
11.5 Updating fitted models: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 59
11.6 Generalized linear models : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 60
11.6.1 Families: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 60
11.6.2 The glm() function : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 61
11.7 Nonlinear least squares and maximum likelihood models : : : : : : 63
11.7.1 Least squares : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 64
11.7.2 Maximum likelihood : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 65
11.8 Some non-standard models : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 65
12 Graphical procedures : : : : : : : : : : : : : : : : : : : : : : : : 67
12.1 High-level plotting commands : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 67
12.1.1 The plot() function : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 67
12.1.2 Displaying multivariate data : : : : : : : : : : : : : : : : : : : : : : : : : : : : 68
12.1.3 Display graphics : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 68
12.1.4 Arguments to high-level plotting functions : : : : : : : : : : : : : : : 69
12.2 Low-level plotting commands: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 70
12.2.1 Mathematical annotation: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 71
12.2.2 Hershey vector fonts : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 72
12.3 Interacting with graphics : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 72
12.4 Using graphics parameters : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 73
12.4.1 Permanent changes: The par() function: : : : : : : : : : : : : : : : : 73
12.4.2 Temporary changes: Arguments to graphics functions : : : : 74
12.5 Graphics parameters list : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 74
12.5.1 Graphical elements : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 75
12.5.2 Axes and tick marks : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 76
12.5.3 Figure margins : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 76
iv
12.5.4 Multiple figure environment : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 78
12.6 Device drivers : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 79
12.6.1 PostScript diagrams for typeset documents : : : : : : : : : : : : : : 80
12.6.2 Multiple graphics devices : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 80
12.7 Dynamic graphics : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 81
13 Packages : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 82
13.1 Standard packages : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 82
13.2 Contributed packages and CRAN : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 82
13.3 Namespaces: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 83
Appendix A A sample session: : : : : : : : : : : : : : : : : : 84
Appendix B Invoking R : : : : : : : : : : : : : : : : : : : : : : : : 88
B.1 Invoking R from the command line: : : : : : : : : : : : : : : : : : : : : : : : : : : : 88
B.2 Invoking R under Windows : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 92
B.3 Invoking R under Mac OS X: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 93
B.4 Scripting with R: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 93
Appendix C The command-line editor : : : : : : : : 95
C.1 Preliminaries : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 95
C.2 Editing actions : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 95
C.3 Command-line editor summary : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 95
Appendix D Function and variable index : : : : : 97
Appendix E Concept index : : : : : : : : : : : : : : : : : : : 100
Appendix F References : : : : : : : : : : : : : : : : : : : : : : : 102
Preface 1
Preface
This introduction to R is derived from an original set of notes describing the S and SPlus
environments written in 1990–2 by Bill Venables and David M. Smith when at the
University of Adelaide. We have made a number of small changes to reflect differences
between the R and S programs, and expanded some of the material.
We would like to extend warm thanks to Bill Venables (and David Smith) for granting
permission to distribute this modified version of the notes in this way, and for being a
supporter of R from way back.
Comments and corrections are always welcome. Please address email correspondence to
R-core@R-project.org.
Suggestions to the reader
Most R novices will start with the introductory session in Appendix A. This should give
some familiarity with the style of R sessions and more importantly some instant feedback
on what actually happens.
Many users will come to R mainly for its graphical facilities. In this case, Chapter 12
[Graphics], page 67 on the graphics facilities can be read at almost any time and need not
wait until all the preceding sections have been digested.
Chapter 1: Introduction and preliminaries 2
1 Introduction and preliminaries
1.1 The R environment
R is an integrated suite of software facilities for data manipulation, calculation and graphical
display. Among other things it has
 an effective data handling and storage facility,
 a suite of operators for calculations on arrays, in particular matrices,
 a large, coherent, integrated collection of intermediate tools for data analysis,
 graphical facilities for data analysis and display either directly at the computer or on
hardcopy, and
 a well developed, simple and effective programming language (called ‘S’) which includes
conditionals, loops, user defined recursive functions and input and output facilities.
(Indeed most of the system supplied functions are themselves written in the S language.)
The term “environment” is intended to characterize it as a fully planned and coherent
system, rather than an incremental accretion of very specific and inflexible tools, as is
frequently the case with other data analysis software.
R is very much a vehicle for newly developing methods of interactive data analysis. It
has developed rapidly, and has been extended by a large collection of packages. However,
most programs written in R are essentially ephemeral, written for a single piece of data
analysis.
1.2 Related software and documentation
R can be regarded as an implementation of the S language which was developed at Bell
Laboratories by Rick Becker, John Chambers and Allan Wilks, and also forms the basis of
the S-Plus systems.
The evolution of the S language is characterized by four books by John Chambers and
coauthors. For R, the basic reference is The New S Language: A Programming Environment
for Data Analysis and Graphics by Richard A. Becker, John M. Chambers and Allan R.
Wilks. The new features of the 1991 release of S are covered in Statistical Models in S
edited by John M. Chambers and Trevor J. Hastie. The formal methods and classes of
the methods package are based on those described in Programming with Data by John M.
Chambers. See Appendix F [References], page 102, for precise references.
There are now a number of books which describe how to use R for data analysis and
statistics, and documentation for S/S-Plus can typically be used with R, keeping the
differences between the S implementations in mind. See Section “What documentation
exists for R?” in The R statistical system FAQ.
1.3 R and statistics
Our introduction to the R environment did not mention statistics, yet many people use
R as a statistics system. We prefer to think of it of an environment within which many
classical and modern statistical techniques have been implemented. A few of these are
built into the base R environment, but many are supplied as packages. There are about 25
Chapter 1: Introduction and preliminaries 3
packages supplied with R (called “standard” and “recommended” packages) and many more
are available through the CRAN family of Internet sites (via http://CRAN.R-project.org)
and elsewhere. More details on packages are given later (see Chapter 13 [Packages], page 82).
Most classical statistics and much of the latest methodology is available for use with R,
but users may need to be prepared to do a little work to find it.
There is an important difference in philosophy between S (and hence R) and the other
main statistical systems. In S a statistical analysis is normally done as a series of steps,
with intermediate results being stored in objects. Thus whereas SAS and SPSS will give
copious output from a regression or discriminant analysis, R will give minimal output and
store the results in a fit object for subsequent interrogation by further R functions.
1.4 R and the window system
The most convenient way to use R is at a graphics workstation running a windowing system.
This guide is aimed at users who have this facility. In particular we will occasionally refer to
the use of R on an X window system although the vast bulk of what is said applies generally
to any implementation of the R environment.
Most users will find it necessary to interact directly with the operating system on their
computer from time to time. In this guide, we mainly discuss interaction with the operating
system on UNIX machines. If you are running R under Windows or Mac OS you will need
to make some small adjustments.
Setting up a workstation to take full advantage of the customizable features of R is a
straightforward if somewhat tedious procedure, and will not be considered further here.
Users in difficulty should seek local expert help.
1.5 Using R interactively
When you use the R program it issues a prompt when it expects input commands. The
default prompt is ‘>’, which on UNIX might be the same as the shell prompt, and so it
may appear that nothing is happening. However, as we shall see, it is easy to change to a
different R prompt if you wish. We will assume that the UNIX shell prompt is ‘$’.
In using R under UNIX the suggested procedure for the first occasion is as follows:
1. Create a separate sub-directory, say ‘work’, to hold data files on which you will use
R for this problem. This will be the working directory whenever you use R for this
particular problem.
$ mkdir work
$ cd work
2. Start the R program with the command
$ R
3. At this point R commands may be issued (see later).
4. To quit the R program the command is
> q()
At this point you will be asked whether you want to save the data from your R session.
On some systems this will bring up a dialog box, and on others you will receive a text
prompt to which you can respond yes, no or cancel (a single letter abbreviation will
Chapter 1: Introduction and preliminaries 4
do) to save the data before quitting, quit without saving, or return to the R session.
Data which is saved will be available in future R sessions.
Further R sessions are simple.
1. Make ‘work’ the working directory and start the program as before:
$ cd work
$ R
2. Use the R program, terminating with the q() command at the end of the session.
To use R under Windows the procedure to follow is basically the same. Create a folder
as the working directory, and set that in the ‘Start In’ field in your R shortcut. Then
launch R by double clicking on the icon.
1.6 An introductory session
Readers wishing to get a feel for R at a computer before proceeding are strongly advised to
work through the introductory session given in Appendix A [A sample session], page 84.
1.7 Getting help with functions and features
R has an inbuilt help facility similar to the man facility of UNIX. To get more information
on any specific named function, for example solve, the command is
> help(solve)
An alternative is
> ?solve
For a feature specified by special characters, the argument must be enclosed in double
or single quotes, making it a “character string”: This is also necessary for a few words with
syntactic meaning including if, for and function.
> help(“[[“)
Either form of quote mark may be used to escape the other, as in the string “It’s
important”. Our convention is to use double quote marks for preference.
On most R installations help is available in HTML format by running
> help.start()
which will launch a Web browser that allows the help pages to be browsed with hyperlinks.
On UNIX, subsequent help requests are sent to the HTML-based help system. The ‘Search
Engine and Keywords’ link in the page loaded by help.start() is particularly useful as
it is contains a high-level concept list which searches though available functions. It can be
a great way to get your bearings quickly and to understand the breadth of what R has to
offer.
The help.search command (alternatively ??) allows searching for help in various ways.
For example,
> ??solve
Try ?help.search for details and more examples.
The examples on a help topic can normally be run by
Chapter 1: Introduction and preliminaries 5
> example(topic)
Windows versions of R have other optional help systems: use
> ?help
for further details.
1.8 R commands, case sensitivity, etc.
Technically R is an expression language with a very simple syntax. It is case sensitive
as are most UNIX based packages, so A and a are different symbols and would refer to
different variables. The set of symbols which can be used in R names depends on the
operating system and country within which R is being run (technically on the locale in
use). Normally all alphanumeric symbols are allowed1 (and in some countries this includes
accented letters) plus ‘.’ and ‘_’, with the restriction that a name must start with ‘.’ or a
letter, and if it starts with ‘.’ the second character must not be a digit.
Elementary commands consist of either expressions or assignments. If an expression is
given as a command, it is evaluated, printed (unless specifically made invisible), and the
value is lost. An assignment also evaluates an expression and passes the value to a variable
but the result is not automatically printed.
Commands are separated either by a semi-colon (‘;’), or by a newline. Elementary
commands can be grouped together into one compound expression by braces (‘{’ and ‘}’).
Comments can be put almost2 anywhere, starting with a hashmark (‘#’), everything to the
end of the line is a comment.
If a command is not complete at the end of a line, R will give a different prompt, by
default
+
on second and subsequent lines and continue to read input until the command is syntactically
complete. This prompt may be changed by the user. We will generally omit the continuation
prompt and indicate continuation by simple indenting.
Command lines entered at the console are limited3 to about 4095 bytes (not characters).
1.9 Recall and correction of previous commands
Under many versions of UNIX and on Windows, R provides a mechanism for recalling and
re-executing previous commands. The vertical arrow keys on the keyboard can be used to
scroll forward and backward through a command history. Once a command is located in
this way, the cursor can be moved within the command using the horizontal arrow keys, and
characters can be removed with the DEL key or added with the other keys. More details
are provided later: see Appendix C [The command-line editor], page 95.
The recall and editing capabilities under UNIX are highly customizable. You can find
out how to do this by reading the manual entry for the readline library.
1 For portable R code (including that to be used in R packages) only A–Za–z0–9 should be used.
2 not inside strings, nor within the argument list of a function definition
3 some of the consoles will not allow you to enter more, and amongst those which do some will silently
discard the excess and some will use it as the start of the next line.
Chapter 1: Introduction and preliminaries 6
Alternatively, the Emacs text editor provides more general support mechanisms (via
ESS, Emacs Speaks Statistics) for working interactively with R. See Section “R and Emacs”
in The R statistical system FAQ.
1.10 Executing commands from or diverting output to a file
If commands4 are stored in an external file, say ‘commands.R’ in the working directory
‘work’, they may be executed at any time in an R session with the command
> source(“commands.R”)
For Windows Source is also available on the File menu. The function sink,
> sink(“record.lis”)
will divert all subsequent output from the console to an external file, ‘record.lis’. The
command
> sink()
restores it to the console once again.
1.11 Data permanency and removing objects
The entities that R creates and manipulates are known as objects. These may be variables,
arrays of numbers, character strings, functions, or more general structures built from such
components.
During an R session, objects are created and stored by name (we discuss this process in
the next session). The R command
> objects()
(alternatively, ls()) can be used to display the names of (most of) the objects which are
currently stored within R. The collection of objects currently stored is called the workspace.
To remove objects the function rm is available:
> rm(x, y, z, ink, junk, temp, foo, bar)
All objects created during an R sessions can be stored permanently in a file for use in
future R sessions. At the end of each R session you are given the opportunity to save all
the currently available objects. If you indicate that you want to do this, the objects are
written to a file called ‘.RData’5 in the current directory, and the command lines used in
the session are saved to a file called ‘.Rhistory’.
When R is started at later time from the same directory it reloads the workspace from
this file. At the same time the associated commands history is reloaded.
It is recommended that you should use separate working directories for analyses conducted
with R. It is quite common for objects with names x and y to be created during an
analysis. Names like this are often meaningful in the context of a single analysis, but it can
be quite hard to decide what they might be when the several analyses have been conducted
in the same directory.
4 of unlimited length.
5 The leading “dot” in this file name makes it invisible in normal file listings in UNIX.
Chapter 2: Simple manipulations; numbers and vectors 7
2 Simple manipulations; numbers and vectors
2.1 Vectors and assignment
R operates on named data structures. The simplest such structure is the numeric vector,
which is a single entity consisting of an ordered collection of numbers. To set up a vector
named x, say, consisting of five numbers, namely 10.4, 5.6, 3.1, 6.4 and 21.7, use the R
command
> x <- c(10.4, 5.6, 3.1, 6.4, 21.7)
This is an assignment statement using the function c() which in this context can take
an arbitrary number of vector arguments and whose value is a vector got by concatenating
its arguments end to end.1
A number occurring by itself in an expression is taken as a vector of length one.
Notice that the assignment operator (‘<-’), which consists of the two characters ‘<’ (“less
than”) and ‘-’ (“minus”) occurring strictly side-by-side and it ‘points’ to the object receiving
the value of the expression. In most contexts the ‘=’ operator can be used as a alternative.
Assignment can also be made using the function assign(). An equivalent way of making
the same assignment as above is with:
> assign(“x”, c(10.4, 5.6, 3.1, 6.4, 21.7))
The usual operator, <-, can be thought of as a syntactic short-cut to this.
Assignments can also be made in the other direction, using the obvious change in the
assignment operator. So the same assignment could be made using
> c(10.4, 5.6, 3.1, 6.4, 21.7) -> x
If an expression is used as a complete command, the value is printed and lost2. So now
if we were to use the command
> 1/x
the reciprocals of the five values would be printed at the terminal (and the value of x, of
course, unchanged).
The further assignment
> y <- c(x, 0, x)
would create a vector y with 11 entries consisting of two copies of x with a zero in the
middle place.
2.2 Vector arithmetic
Vectors can be used in arithmetic expressions, in which case the operations are performed
element by element. Vectors occurring in the same expression need not all be of the same
length. If they are not, the value of the expression is a vector with the same length as the
longest vector which occurs in the expression. Shorter vectors in the expression are recycled
as often as need be (perhaps fractionally) until they match the length of the longest vector.
In particular a constant is simply repeated. So with the above assignments the command
1 With other than vector types of argument, such as list mode arguments, the action of c() is rather
different. See Section 6.2.1 [Concatenating lists], page 29.
2 Actually, it is still available as .Last.value before any other statements are executed.
Chapter 2: Simple manipulations; numbers and vectors 8
> v <- 2*x + y + 1
generates a new vector v of length 11 constructed by adding together, element by element,
2*x repeated 2.2 times, y repeated just once, and 1 repeated 11 times.
The elementary arithmetic operators are the usual +, -, *, / and ^ for raising to a power.
In addition all of the common arithmetic functions are available. log, exp, sin, cos, tan,
sqrt, and so on, all have their usual meaning. max and min select the largest and smallest
elements of a vector respectively. range is a function whose value is a vector of length two,
namely c(min(x), max(x)). length(x) is the number of elements in x, sum(x) gives the
total of the elements in x, and prod(x) their product.
Two statistical functions are mean(x) which calculates the sample mean, which is the
same as sum(x)/length(x), and var(x) which gives
sum((x-mean(x))^2)/(length(x)-1)
or sample variance. If the argument to var() is an n-by-p matrix the value is a p-by-p sample
covariance matrix got by regarding the rows as independent p-variate sample vectors.
sort(x) returns a vector of the same size as x with the elements arranged in increasing
order; however there are other more flexible sorting facilities available (see order() or
sort.list() which produce a permutation to do the sorting).
Note that max and min select the largest and smallest values in their arguments, even
if they are given several vectors. The parallel maximum and minimum functions pmax
and pmin return a vector (of length equal to their longest argument) that contains in each
element the largest (smallest) element in that position in any of the input vectors.
For most purposes the user will not be concerned if the “numbers” in a numeric vector
are integers, reals or even complex. Internally calculations are done as double precision real
numbers, or double precision complex numbers if the input data are complex.
To work with complex numbers, supply an explicit complex part. Thus
sqrt(-17)
will give NaN and a warning, but
sqrt(-17+0i)
will do the computations as complex numbers.
2.3 Generating regular sequences
R has a number of facilities for generating commonly used sequences of numbers. For
example 1:30 is the vector c(1, 2, …, 29, 30). The colon operator has high priority
within an expression, so, for example 2*1:15 is the vector c(2, 4, …, 28, 30). Put n
<- 10 and compare the sequences 1:n-1 and 1:(n-1).
The construction 30:1 may be used to generate a sequence backwards.
The function seq() is a more general facility for generating sequences. It has five
arguments, only some of which may be specified in any one call. The first two arguments, if
given, specify the beginning and end of the sequence, and if these are the only two arguments
given the result is the same as the colon operator. That is seq(2,10) is the same vector as
2:10.
Parameters to seq(), and to many other R functions, can also be given in named form,
in which case the order in which they appear is irrelevant. The first two parameters may be
Chapter 2: Simple manipulations; numbers and vectors 9
named from=value and to=value; thus seq(1,30), seq(from=1, to=30) and seq(to=30,
from=1) are all the same as 1:30. The next two parameters to seq() may be named
by=value and length=value, which specify a step size and a length for the sequence
respectively. If neither of these is given, the default by=1 is assumed.
For example
> seq(-5, 5, by=.2) -> s3
generates in s3 the vector c(-5.0, -4.8, -4.6, …, 4.6, 4.8, 5.0). Similarly
> s4 <- seq(length=51, from=-5, by=.2)
generates the same vector in s4.
The fifth parameter may be named along=vector, which if used must be the only
parameter, and creates a sequence 1, 2, …, length(vector), or the empty sequence if
the vector is empty (as it can be).
A related function is rep() which can be used for replicating an object in various complicated
ways. The simplest form is
> s5 <- rep(x, times=5)
which will put five copies of x end-to-end in s5. Another useful version is
> s6 <- rep(x, each=5)
which repeats each element of x five times before moving on to the next.
2.4 Logical vectors
As well as numerical vectors, R allows manipulation of logical quantities. The elements of
a logical vector can have the values TRUE, FALSE, and NA (for “not available”, see below).
The first two are often abbreviated as T and F, respectively. Note however that T and F are
just variables which are set to TRUE and FALSE by default, but are not reserved words and
hence can be overwritten by the user. Hence, you should always use TRUE and FALSE.
Logical vectors are generated by conditions. For example
> temp <- x > 13
sets temp as a vector of the same length as x with values FALSE corresponding to elements
of x where the condition is not met and TRUE where it is.
The logical operators are <, <=, >, >=, == for exact equality and != for inequality. In
addition if c1 and c2 are logical expressions, then c1 & c2 is their intersection (“and”),
c1 | c2 is their union (“or”), and !c1 is the negation of c1.
Logical vectors may be used in ordinary arithmetic, in which case they are coerced into
numeric vectors, FALSE becoming 0 and TRUE becoming 1. However there are situations
where logical vectors and their coerced numeric counterparts are not equivalent, for example
see the next subsection.
2.5 Missing values
In some cases the components of a vector may not be completely known. When an element
or value is “not available” or a “missing value” in the statistical sense, a place within a
vector may be reserved for it by assigning it the special value NA. In general any operation
Chapter 2: Simple manipulations; numbers and vectors 10
on an NA becomes an NA. The motivation for this rule is simply that if the specification of
an operation is incomplete, the result cannot be known and hence is not available.
The function is.na(x) gives a logical vector of the same size as x with value TRUE if
and only if the corresponding element in x is NA.
> z <- c(1:3,NA); ind <- is.na(z)
Notice that the logical expression x == NA is quite different from is.na(x) since NA is not
really a value but a marker for a quantity that is not available. Thus x == NA is a vector of
the same length as x all of whose values are NA as the logical expression itself is incomplete
and hence undecidable.
Note that there is a second kind of “missing” values which are produced by numerical
computation, the so-called Not a Number, NaN, values. Examples are
> 0/0
or
> Inf – Inf
which both give NaN since the result cannot be defined sensibly.
In summary, is.na(xx) is TRUE both for NA and NaN values. To differentiate these,
is.nan(xx) is only TRUE for NaNs.
Missing values are sometimes printed as <NA> when character vectors are printed without
quotes.
2.6 Character vectors
Character quantities and character vectors are used frequently in R, for example as plot
labels. Where needed they are denoted by a sequence of characters delimited by the double
quote character, e.g., “x-values”, “New iteration results”.
Character strings are entered using either matching double (“) or single (’) quotes, but
are printed using double quotes (or sometimes without quotes). They use C-style escape
sequences, using \ as the escape character, so \\ is entered and printed as \\, and inside
double quotes ” is entered as \”. Other useful escape sequences are \n, newline, \t, tab
and \b, backspace—see ?Quotes for a full list.
Character vectors may be concatenated into a vector by the c() function; examples of
their use will emerge frequently.
The paste() function takes an arbitrary number of arguments and concatenates them
one by one into character strings. Any numbers given among the arguments are coerced
into character strings in the evident way, that is, in the same way they would be if they were
printed. The arguments are by default separated in the result by a single blank character,
but this can be changed by the named parameter, sep=string, which changes it to string,
possibly empty.
For example
> labs <- paste(c(“X”,”Y”), 1:10, sep=””)
makes labs into the character vector
c(“X1”, “Y2”, “X3”, “Y4”, “X5”, “Y6”, “X7”, “Y8”, “X9”, “Y10”)
Chapter 2: Simple manipulations; numbers and vectors 11
Note particularly that recycling of short lists takes place here too; thus c(“X”, “Y”) is
repeated 5 times to match the sequence 1:10.3
2.7 Index vectors; selecting and modifying subsets of a data
set
Subsets of the elements of a vector may be selected by appending to the name of the vector
an index vector in square brackets. More generally any expression that evaluates to a vector
may have subsets of its elements similarly selected by appending an index vector in square
brackets immediately after the expression.
Such index vectors can be any of four distinct types.
1. A logical vector. In this case the index vector must be of the same length as the vector
from which elements are to be selected. Values corresponding to TRUE in the index
vector are selected and those corresponding to FALSE are omitted. For example
> y <- x[!is.na(x)]
creates (or re-creates) an object y which will contain the non-missing values of x, in
the same order. Note that if x has missing values, y will be shorter than x. Also
> (x+1)[(!is.na(x)) & x>0] -> z
creates an object z and places in it the values of the vector x+1 for which the corresponding
value in x was both non-missing and positive.
2. A vector of positive integral quantities. In this case the values in the index vector must
lie in the set {1, 2, . . . , length(x)}. The corresponding elements of the vector are
selected and concatenated, in that order, in the result. The index vector can be of any
length and the result is of the same length as the index vector. For example x[6] is
the sixth component of x and
> x[1:10]
selects the first 10 elements of x (assuming length(x) is not less than 10). Also
> c(“x”,”y”)[rep(c(1,2,2,1), times=4)]
(an admittedly unlikely thing to do) produces a character vector of length 16 consisting
of “x”, “y”, “y”, “x” repeated four times.
3. A vector of negative integral quantities. Such an index vector specifies the values to
be excluded rather than included. Thus
> y <- x[-(1:5)]
gives y all but the first five elements of x.
4. A vector of character strings. This possibility only applies where an object has a names
attribute to identify its components. In this case a sub-vector of the names vector may
be used in the same way as the positive integral labels in item 2 further above.
> fruit <- c(5, 10, 1, 20)
> names(fruit) <- c(“orange”, “banana”, “apple”, “peach”)
> lunch <- fruit[c(“apple”,”orange”)]
3 paste(…, collapse=ss) joins the arguments into a single character string putting ss in between.
There are more tools for character manipulation, see the help for sub and substring.
Chapter 2: Simple manipulations; numbers and vectors 12
The advantage is that alphanumeric names are often easier to remember than numeric
indices. This option is particularly useful in connection with data frames, as we shall
see later.
An indexed expression can also appear on the receiving end of an assignment, in which
case the assignment operation is performed only on those elements of the vector. The
expression must be of the form vector[index_vector] as having an arbitrary expression
in place of the vector name does not make much sense here.
The vector assigned must match the length of the index vector, and in the case of a
logical index vector it must again be the same length as the vector it is indexing.
For example
> x[is.na(x)] <- 0
replaces any missing values in x by zeros and
> y[y < 0] <- -y[y < 0]
has the same effect as
> y <- abs(y)
2.8 Other types of objects
Vectors are the most important type of object in R, but there are several others which we
will meet more formally in later sections.
 matrices or more generally arrays are multi-dimensional generalizations of vectors. In
fact, they are vectors that can be indexed by two or more indices and will be printed
in special ways. See Chapter 5 [Arrays and matrices], page 19.
 factors provide compact ways to handle categorical data. See Chapter 4 [Factors],
page 16.
 lists are a general form of vector in which the various elements need not be of the same
type, and are often themselves vectors or lists. Lists provide a convenient way to return
the results of a statistical computation. See Section 6.1 [Lists], page 28.
 data frames are matrix-like structures, in which the columns can be of different types.
Think of data frames as ‘data matrices’ with one row per observational unit but with
(possibly) both numerical and categorical variables. Many experiments are best described
by data frames: the treatments are categorical but the response is numeric.
See Section 6.3 [Data frames], page 29.
 functions are themselves objects in R which can be stored in the project’s workspace.
This provides a simple and convenient way to extend R. See Chapter 10 [Writing your
own functions], page 44.
Chapter 3: Objects, their modes and attributes 13
3 Objects, their modes and attributes
3.1 Intrinsic attributes: mode and length
The entities R operates on are technically known as objects. Examples are vectors of numeric
(real) or complex values, vectors of logical values and vectors of character strings. These
are known as “atomic” structures since their components are all of the same type, or mode,
namely numeric1, complex, logical, character and raw.
Vectors must have their values all of the same mode. Thus any given vector must be
unambiguously either logical, numeric, complex, character or raw. (The only apparent
exception to this rule is the special “value” listed as NA for quantities not available, but in
fact there are several types of NA). Note that a vector can be empty and still have a mode.
For example the empty character string vector is listed as character(0) and the empty
numeric vector as numeric(0).
R also operates on objects called lists, which are of mode list. These are ordered sequences
of objects which individually can be of any mode. lists are known as “recursive” rather
than atomic structures since their components can themselves be lists in their own right.
The other recursive structures are those of mode function and expression. Functions are
the objects that form part of the R system along with similar user written functions, which
we discuss in some detail later. Expressions as objects form an advanced part of R which
will not be discussed in this guide, except indirectly when we discuss formulae used with
modeling in R.
By the mode of an object we mean the basic type of its fundamental constituents. This
is a special case of a “property” of an object. Another property of every object is its length.
The functions mode(object) and length(object) can be used to find out the mode and
length of any defined structure2.
Further properties of an object are usually provided by attributes(object), see
Section 3.3 [Getting and setting attributes], page 14. Because of this, mode and length are
also called “intrinsic attributes” of an object.
For example, if z is a complex vector of length 100, then in an expression mode(z) is the
character string “complex” and length(z) is 100.
R caters for changes of mode almost anywhere it could be considered sensible to do so,
(and a few where it might not be). For example with
> z <- 0:9
we could put
> digits <- as.character(z)
after which digits is the character vector c(“0”, “1”, “2”, …, “9”). A further coercion,
or change of mode, reconstructs the numerical vector again:
1 numeric mode is actually an amalgam of two distinct modes, namely integer and double precision, as
explained in the manual.
2 Note however that length(object) does not always contain intrinsic useful information, e.g., when
object is a function.
Chapter 3: Objects, their modes and attributes 14
> d <- as.integer(digits)
Now d and z are the same.3 There is a large collection of functions of the form
as.something() for either coercion from one mode to another, or for investing an object
with some other attribute it may not already possess. The reader should consult the
different help files to become familiar with them.
3.2 Changing the length of an object
An “empty” object may still have a mode. For example
> e <- numeric()
makes e an empty vector structure of mode numeric. Similarly character() is a empty
character vector, and so on. Once an object of any size has been created, new components
may be added to it simply by giving it an index value outside its previous range. Thus
> e[3] <- 17
now makes e a vector of length 3, (the first two components of which are at this point both
NA). This applies to any structure at all, provided the mode of the additional component(s)
agrees with the mode of the object in the first place.
This automatic adjustment of lengths of an object is used often, for example in the
scan() function for input. (see Section 7.2 [The scan() function], page 33.)
Conversely to truncate the size of an object requires only an assignment to do so. Hence
if alpha is an object of length 10, then
> alpha <- alpha[2 * 1:5]
makes it an object of length 5 consisting of just the former components with even index.
(The old indices are not retained, of course.) We can then retain just the first three values
by
> length(alpha) <- 3
and vectors can be extended (by missing values) in the same way.
3.3 Getting and setting attributes
The function attributes(object) returns a list of all the non-intrinsic attributes currently
defined for that object. The function attr(object, name) can be used to select a specific
attribute. These functions are rarely used, except in rather special circumstances when
some new attribute is being created for some particular purpose, for example to associate
a creation date or an operator with an R object. The concept, however, is very important.
Some care should be exercised when assigning or deleting attributes since they are an
integral part of the object system used in R.
When it is used on the left hand side of an assignment it can be used either to associate
a new attribute with object or to change an existing one. For example
> attr(z, “dim”) <- c(10,10)
allows R to treat z as if it were a 10-by-10 matrix.
3 In general, coercion from numeric to character and back again will not be exactly reversible, because of
roundoff errors in the character representation.
Chapter 3: Objects, their modes and attributes 15
3.4 The class of an object
All objects in R have a class, reported by the function class. For simple vectors this is
just the mode, for example “numeric”, “logical”, “character” or “list”, but “matrix”,
“array”, “factor” and “data.frame” are other possible values.
A special attribute known as the class of the object is used to allow for an object-oriented
style4 of programming in R. For example if an object has class “data.frame”, it will be
printed in a certain way, the plot() function will display it graphically in a certain way,
and other so-called generic functions such as summary() will react to it as an argument in
a way sensitive to its class.
To remove temporarily the effects of class, use the function unclass(). For example if
winter has the class “data.frame” then
> winter
will print it in data frame form, which is rather like a matrix, whereas
> unclass(winter)
will print it as an ordinary list. Only in rather special situations do you need to use this
facility, but one is when you are learning to come to terms with the idea of class and generic
functions.
Generic functions and classes will be discussed further in Section 10.9 [Object orientation],
page 51, but only briefly.
4 A different style using ‘formal’ or ‘S4’ classes is provided in package methods.
Chapter 4: Ordered and unordered factors 16
4 Ordered and unordered factors
A factor is a vector object used to specify a discrete classification (grouping) of the components
of other vectors of the same length. R provides both ordered and unordered factors.
While the “real” application of factors is with model formulae (see Section 11.1.1 [Contrasts],
page 56), we here look at a specific example.
4.1 A specific example
Suppose, for example, we have a sample of 30 tax accountants from all the states and
territories of Australia1 and their individual state of origin is specified by a character vector
of state mnemonics as
> state <- c(“tas”, “sa”, “qld”, “nsw”, “nsw”, “nt”, “wa”, “wa”,
“qld”, “vic”, “nsw”, “vic”, “qld”, “qld”, “sa”, “tas”,
“sa”, “nt”, “wa”, “vic”, “qld”, “nsw”, “nsw”, “wa”,
“sa”, “act”, “nsw”, “vic”, “vic”, “act”)
Notice that in the case of a character vector, “sorted” means sorted in alphabetical order.
A factor is similarly created using the factor() function:
> statef <- factor(state)
The print() function handles factors slightly differently from other objects:
> statef
[1] tas sa qld nsw nsw nt wa wa qld vic nsw vic qld qld sa
[16] tas sa nt wa vic qld nsw nsw wa sa act nsw vic vic act
Levels: act nsw nt qld sa tas vic wa
To find out the levels of a factor the function levels() can be used.
> levels(statef)
[1] “act” “nsw” “nt” “qld” “sa” “tas” “vic” “wa”
4.2 The function tapply() and ragged arrays
To continue the previous example, suppose we have the incomes of the same tax accountants
in another vector (in suitably large units of money)
> incomes <- c(60, 49, 40, 61, 64, 60, 59, 54, 62, 69, 70, 42, 56,
61, 61, 61, 58, 51, 48, 65, 49, 49, 41, 48, 52, 46,
59, 46, 58, 43)
To calculate the sample mean income for each state we can now use the special function
tapply():
> incmeans <- tapply(incomes, statef, mean)
giving a means vector with the components labelled by the levels
act nsw nt qld sa tas vic wa
44.500 57.333 55.500 53.600 55.000 60.500 56.000 52.250
1 Readers should note that there are eight states and territories in Australia, namely the Australian Capital
Territory, New South Wales, the Northern Territory, Queensland, South Australia, Tasmania, Victoria
and Western Australia.
Chapter 4: Ordered and unordered factors 17
The function tapply() is used to apply a function, here mean(), to each group of components
of the first argument, here incomes, defined by the levels of the second component,
here statef2, as if they were separate vector structures. The result is a structure of the
same length as the levels attribute of the factor containing the results. The reader should
consult the help document for more details.
Suppose further we needed to calculate the standard errors of the state income means.
To do this we need to write an R function to calculate the standard error for any given
vector. Since there is an builtin function var() to calculate the sample variance, such a
function is a very simple one liner, specified by the assignment:
> stderr <- function(x) sqrt(var(x)/length(x))
(Writing functions will be considered later in Chapter 10 [Writing your own functions],
page 44, and in this case was unnecessary as R also has a builtin function sd().) After this
assignment, the standard errors are calculated by
> incster <- tapply(incomes, statef, stderr)
and the values calculated are then
> incster
act nsw nt qld sa tas vic wa
1.5 4.3102 4.5 4.1061 2.7386 0.5 5.244 2.6575
As an exercise you may care to find the usual 95% confidence limits for the state mean
incomes. To do this you could use tapply() once more with the length() function to find
the sample sizes, and the qt() function to find the percentage points of the appropriate
t-distributions. (You could also investigate R’s facilities for t-tests.)
The function tapply() can also be used to handle more complicated indexing of a vector
by multiple categories. For example, we might wish to split the tax accountants by both
state and sex. However in this simple instance (just one factor) what happens can be
thought of as follows. The values in the vector are collected into groups corresponding
to the distinct entries in the factor. The function is then applied to each of these groups
individually. The value is a vector of function results, labelled by the levels attribute of
the factor.
The combination of a vector and a labelling factor is an example of what is sometimes
called a ragged array, since the subclass sizes are possibly irregular. When the subclass sizes
are all the same the indexing may be done implicitly and much more efficiently, as we see
in the next section.
4.3 Ordered factors
The levels of factors are stored in alphabetical order, or in the order they were specified to
factor if they were specified explicitly.
Sometimes the levels will have a natural ordering that we want to record and want our
statistical analysis to make use of. The ordered() function creates such ordered factors
but is otherwise identical to factor. For most purposes the only difference between ordered
2 Note that tapply() also works in this case when its second argument is not a factor, e.g.,
‘tapply(incomes, state)’, and this is true for quite a few other functions, since arguments are coerced
to factors when necessary (using as.factor()).
Chapter 4: Ordered and unordered factors 18
and unordered factors is that the former are printed showing the ordering of the levels, but
the contrasts generated for them in fitting linear models are different.
Chapter 5: Arrays and matrices 19
5 Arrays and matrices
5.1 Arrays
An array can be considered as a multiply subscripted collection of data entries, for example
numeric. R allows simple facilities for creating and handling arrays, and in particular the
special case of matrices.
A dimension vector is a vector of non-negative integers. If its length is k then the array
is k-dimensional, e.g. a matrix is a 2-dimensional array. The dimensions are indexed from
one up to the values given in the dimension vector.
A vector can be used by R as an array only if it has a dimension vector as its dim
attribute. Suppose, for example, z is a vector of 1500 elements. The assignment
> dim(z) <- c(3,5,100)
gives it the dim attribute that allows it to be treated as a 3 by 5 by 100 array.
Other functions such as matrix() and array() are available for simpler and more natural
looking assignments, as we shall see in Section 5.4 [The array() function], page 21.
The values in the data vector give the values in the array in the same order as they
would occur in FORTRAN, that is “column major order,” with the first subscript moving
fastest and the last subscript slowest.
For example if the dimension vector for an array, say a, is c(3,4,2) then there are
342 = 24 entries in a and the data vector holds them in the order a[1,1,1], a[2,1,1],
…, a[2,4,2], a[3,4,2].
Arrays can be one-dimensional: such arrays are usually treated in the same way as
vectors (including when printing), but the exceptions can cause confusion.
5.2 Array indexing. Subsections of an array
Individual elements of an array may be referenced by giving the name of the array followed
by the subscripts in square brackets, separated by commas.
More generally, subsections of an array may be specified by giving a sequence of index
vectors in place of subscripts; however if any index position is given an empty index vector,
then the full range of that subscript is taken.
Continuing the previous example, a[2,,] is a 42 array with dimension vector c(4,2)
and data vector containing the values
c(a[2,1,1], a[2,2,1], a[2,3,1], a[2,4,1],
a[2,1,2], a[2,2,2], a[2,3,2], a[2,4,2])
in that order. a[,,] stands for the entire array, which is the same as omitting the subscripts
entirely and using a alone.
For any array, say Z, the dimension vector may be referenced explicitly as dim(Z) (on
either side of an assignment).
Also, if an array name is given with just one subscript or index vector, then the corresponding
values of the data vector only are used; in this case the dimension vector is
ignored. This is not the case, however, if the single index is not a vector but itself an array,
as we next discuss.
Chapter 5: Arrays and matrices 20
5.3 Index matrices
As well as an index vector in any subscript position, a matrix may be used with a single
index matrix in order either to assign a vector of quantities to an irregular collection of
elements in the array, or to extract an irregular collection as a vector.
A matrix example makes the process clear. In the case of a doubly indexed array, an
index matrix may be given consisting of two columns and as many rows as desired. The
entries in the index matrix are the row and column indices for the doubly indexed array.
Suppose for example we have a 4 by 5 array X and we wish to do the following:
 Extract elements X[1,3], X[2,2] and X[3,1] as a vector structure, and
 Replace these entries in the array X by zeroes.
In this case we need a 3 by 2 subscript array, as in the following example.
> x <- array(1:20, dim=c(4,5)) # Generate a 4 by 5 array.
> x
[,1] [,2] [,3] [,4] [,5]
[1,] 1 5 9 13 17
[2,] 2 6 10 14 18
[3,] 3 7 11 15 19
[4,] 4 8 12 16 20
> i <- array(c(1:3,3:1), dim=c(3,2))
> i # i is a 3 by 2 index array.
[,1] [,2]
[1,] 1 3
[2,] 2 2
[3,] 3 1
> x[i] # Extract those elements
[1] 9 6 3
> x[i] <- 0 # Replace those elements by zeros.
> x
[,1] [,2] [,3] [,4] [,5]
[1,] 1 5 0 13 17
[2,] 2 0 10 14 18
[3,] 0 7 11 15 19
[4,] 4 8 12 16 20
>
Negative indices are not allowed in index matrices. NA and zero values are allowed: rows in
the index matrix containing a zero are ignored, and rows containing an NA produce an NA
in the result.
As a less trivial example, suppose we wish to generate an (unreduced) design matrix
for a block design defined by factors blocks (b levels) and varieties (v levels). Further
suppose there are n plots in the experiment. We could proceed as follows:
> Xb <- matrix(0, n, b)
> Xv <- matrix(0, n, v)
> ib <- cbind(1:n, blocks)
> iv <- cbind(1:n, varieties)
Chapter 5: Arrays and matrices 21
> Xb[ib] <- 1
> Xv[iv] <- 1
> X <- cbind(Xb, Xv)
To construct the incidence matrix, N say, we could use
> N <- crossprod(Xb, Xv)
However a simpler direct way of producing this matrix is to use table():
> N <- table(blocks, varieties)
Index matrices must be numerical: any other form of matrix (e.g. a logical or character
matrix) supplied as a matrix is treated as an indexing vector.
5.4 The array() function
As well as giving a vector structure a dim attribute, arrays can be constructed from vectors
by the array function, which has the form
> Z <- array(data_vector, dim_vector)
For example, if the vector h contains 24 or fewer, numbers then the command
> Z <- array(h, dim=c(3,4,2))
would use h to set up 3 by 4 by 2 array in Z. If the size of h is exactly 24 the result is the
same as
> Z <- h ; dim(Z) <- c(3,4,2)
However if h is shorter than 24, its values are recycled from the beginning again to make
it up to size 24 (see Section 5.4.1 [The recycling rule], page 21) but dim(h) <- c(3,4,2)
would signal an error about mismatching length. As an extreme but common example
> Z <- array(0, c(3,4,2))
makes Z an array of all zeros.
At this point dim(Z) stands for the dimension vector c(3,4,2), and Z[1:24] stands
for the data vector as it was in h, and Z[] with an empty subscript or Z with no subscript
stands for the entire array as an array.
Arrays may be used in arithmetic expressions and the result is an array formed by
element-by-element operations on the data vector. The dim attributes of operands generally
need to be the same, and this becomes the dimension vector of the result. So if A, B and C
are all similar arrays, then
> D <- 2*A*B + C + 1
makes D a similar array with its data vector being the result of the given element-by-element
operations. However the precise rule concerning mixed array and vector calculations has to
be considered a little more carefully.
5.4.1 Mixed vector and array arithmetic. The recycling rule
The precise rule affecting element by element mixed calculations with vectors and arrays is
somewhat quirky and hard to find in the references. From experience we have found the
following to be a reliable guide.
 The expression is scanned from left to right.
Chapter 5: Arrays and matrices 22
 Any short vector operands are extended by recycling their values until they match the
size of any other operands.
 As long as short vectors and arrays only are encountered, the arrays must all have the
same dim attribute or an error results.
 Any vector operand longer than a matrix or array operand generates an error.
 If array structures are present and no error or coercion to vector has been precipitated,
the result is an array structure with the common dim attribute of its array operands.
5.5 The outer product of two arrays
An important operation on arrays is the outer product. If a and b are two numeric arrays,
their outer product is an array whose dimension vector is obtained by concatenating their
two dimension vectors (order is important), and whose data vector is got by forming all
possible products of elements of the data vector of a with those of b. The outer product is
formed by the special operator %o%:
> ab <- a %o% b
An alternative is
> ab <- outer(a, b, “*”)
The multiplication function can be replaced by an arbitrary function of two variables.
For example if we wished to evaluate the function f(x; y) = cos(y)=(1 + x2) over a regular
grid of values with x- and y-coordinates defined by the R vectors x and y respectively, we
could proceed as follows:
> f <- function(x, y) cos(y)/(1 + x^2)
> z <- outer(x, y, f)
In particular the outer product of two ordinary vectors is a doubly subscripted array
(that is a matrix, of rank at most 1). Notice that the outer product operator is of course
non-commutative. Defining your own R functions will be considered further in Chapter 10
[Writing your own functions], page 44.
An example: Determinants of 2 by 2 single-digit matrices
As an artificial but cute example, consider the determinants of 2 by 2 matrices [a; b; c; d]
where each entry is a non-negative integer in the range 0; 1; : : : ; 9, that is a digit.
The problem is to find the determinants, ad 􀀀 bc, of all possible matrices of this form
and represent the frequency with which each value occurs as a high density plot. This
amounts to finding the probability distribution of the determinant if each digit is chosen
independently and uniformly at random.
A neat way of doing this uses the outer() function twice:
> d <- outer(0:9, 0:9)
> fr <- table(outer(d, d, “-“))
> plot(as.numeric(names(fr)), fr, type=”h”,
xlab=”Determinant”, ylab=”Frequency”)
Notice the coercion of the names attribute of the frequency table to numeric in order to
recover the range of the determinant values. The “obvious” way of doing this problem with
for loops, to be discussed in Chapter 9 [Loops and conditional execution], page 42, is so
inefficient as to be impractical.
Chapter 5: Arrays and matrices 23
It is also perhaps surprising that about 1 in 20 such matrices is singular.
5.6 Generalized transpose of an array
The function aperm(a, perm) may be used to permute an array, a. The argument perm
must be a permutation of the integers f1; : : : ; kg, where k is the number of subscripts in a.
The result of the function is an array of the same size as a but with old dimension given by
perm[j] becoming the new j-th dimension. The easiest way to think of this operation is
as a generalization of transposition for matrices. Indeed if A is a matrix, (that is, a doubly
subscripted array) then B given by
> B <- aperm(A, c(2,1))
is just the transpose of A. For this special case a simpler function t() is available, so we
could have used B <- t(A).
5.7 Matrix facilities
As noted above, a matrix is just an array with two subscripts. However it is such an
important special case it needs a separate discussion. R contains many operators and
functions that are available only for matrices. For example t(X) is the matrix transpose
function, as noted above. The functions nrow(A) and ncol(A) give the number of rows and
columns in the matrix A respectively.
5.7.1 Matrix multiplication
The operator %*% is used for matrix multiplication. An n by 1 or 1 by n matrix may of
course be used as an n-vector if in the context such is appropriate. Conversely, vectors
which occur in matrix multiplication expressions are automatically promoted either to row
or column vectors, whichever is multiplicatively coherent, if possible, (although this is not
always unambiguously possible, as we see later).
If, for example, A and B are square matrices of the same size, then
> A * B
is the matrix of element by element products and
> A %*% B
is the matrix product. If x is a vector, then
> x %*% A %*% x
is a quadratic form.1
The function crossprod() forms “crossproducts”, meaning that crossprod(X, y) is
the same as t(X) %*% y but the operation is more efficient. If the second argument to
crossprod() is omitted it is taken to be the same as the first.
The meaning of diag() depends on its argument. diag(v), where v is a vector, gives
a diagonal matrix with elements of the vector as the diagonal entries. On the other hand
1 Note that x %*% x is ambiguous, as it could mean either xT x or xxT , where x is the column form. In such
cases the smaller matrix seems implicitly to be the interpretation adopted, so the scalar xT x is in this
case the result. The matrix xxT may be calculated either by cbind(x) %*% x or x %*% rbind(x) since
the result of rbind() or cbind() is always a matrix. However, the best way to compute xT x or xxT is
crossprod(x) or x %o% x respectively.
Chapter 5: Arrays and matrices 24
diag(M), where M is a matrix, gives the vector of main diagonal entries of M. This is the
same convention as that used for diag() in Matlab. Also, somewhat confusingly, if k is a
single numeric value then diag(k) is the k by k identity matrix!
5.7.2 Linear equations and inversion
Solving linear equations is the inverse of matrix multiplication. When after
> b <- A %*% x
only A and b are given, the vector x is the solution of that linear equation system. In R,
> solve(A,b)
solves the system, returning x (up to some accuracy loss). Note that in linear algebra,
formally x = A􀀀1b where A􀀀1 denotes the inverse of A, which can be computed by
solve(A)
but rarely is needed. Numerically, it is both inefficient and potentially unstable to compute
x <- solve(A) %*% b instead of solve(A,b).
The quadratic form xTA􀀀1x which is used in multivariate computations, should be computed
by something like2 x %*% solve(A,x), rather than computing the inverse of A.
5.7.3 Eigenvalues and eigenvectors
The function eigen(Sm) calculates the eigenvalues and eigenvectors of a symmetric matrix
Sm. The result of this function is a list of two components named values and vectors.
The assignment
> ev <- eigen(Sm)
will assign this list to ev. Then ev$val is the vector of eigenvalues of Sm and ev$vec is the
matrix of corresponding eigenvectors. Had we only needed the eigenvalues we could have
used the assignment:
> evals <- eigen(Sm)$values
evals now holds the vector of eigenvalues and the second component is discarded. If the
expression
> eigen(Sm)
is used by itself as a command the two components are printed, with their names. For large
matrices it is better to avoid computing the eigenvectors if they are not needed by using
the expression
> evals <- eigen(Sm, only.values = TRUE)$values
5.7.4 Singular value decomposition and determinants
The function svd(M) takes an arbitrary matrix argument, M, and calculates the singular
value decomposition of M. This consists of a matrix of orthonormal columns U with the
same column space as M, a second matrix of orthonormal columns V whose column space
is the row space of M and a diagonal matrix of positive entries D such that M = U %*% D %*%
t(V). D is actually returned as a vector of the diagonal elements. The result of svd(M) is
actually a list of three components named d, u and v, with evident meanings.
2 Even better would be to form a matrix square root B with A = BBT and find the squared length of the
solution of By = x, perhaps using the Cholesky or eigendecomposition of A.
Chapter 5: Arrays and matrices 25
If M is in fact square, then, it is not hard to see that
> absdetM <- prod(svd(M)$d)
calculates the absolute value of the determinant of M. If this calculation were needed often
with a variety of matrices it could be defined as an R function
> absdet <- function(M) prod(svd(M)$d)
after which we could use absdet() as just another R function. As a further trivial but
potentially useful example, you might like to consider writing a function, say tr(), to
calculate the trace of a square matrix. [Hint: You will not need to use an explicit loop.
Look again at the diag() function.]
R has a builtin function det to calculate a determinant, including the sign, and another,
determinant, to give the sign and modulus (optionally on log scale),
5.7.5 Least squares fitting and the QR decomposition
The function lsfit() returns a list giving results of a least squares fitting procedure. An
assignment such as
> ans <- lsfit(X, y)
gives the results of a least squares fit where y is the vector of observations and X is the
design matrix. See the help facility for more details, and also for the follow-up function
ls.diag() for, among other things, regression diagnostics. Note that a grand mean term is
automatically included and need not be included explicitly as a column of X. Further note
that you almost always will prefer using lm(.) (see Section 11.2 [Linear models], page 57)
to lsfit() for regression modelling.
Another closely related function is qr() and its allies. Consider the following assignments
> Xplus <- qr(X)
> b <- qr.coef(Xplus, y)
> fit <- qr.fitted(Xplus, y)
> res <- qr.resid(Xplus, y)
These compute the orthogonal projection of y onto the range of X in fit, the projection
onto the orthogonal complement in res and the coefficient vector for the projection in b,
that is, b is essentially the result of the Matlab ‘backslash’ operator.
It is not assumed that X has full column rank. Redundancies will be discovered and
removed as they are found.
This alternative is the older, low-level way to perform least squares calculations. Although
still useful in some contexts, it would now generally be replaced by the statistical
models features, as will be discussed in Chapter 11 [Statistical models in R], page 54.
5.8 Forming partitioned matrices, cbind() and rbind()
As we have already seen informally, matrices can be built up from other vectors and matrices
by the functions cbind() and rbind(). Roughly cbind() forms matrices by binding
together matrices horizontally, or column-wise, and rbind() vertically, or row-wise.
In the assignment
> X <- cbind(arg_1, arg_2, arg_3, …)
Chapter 5: Arrays and matrices 26
the arguments to cbind() must be either vectors of any length, or matrices with the same
column size, that is the same number of rows. The result is a matrix with the concatenated
arguments arg 1, arg 2, . . . forming the columns.
If some of the arguments to cbind() are vectors they may be shorter than the column
size of any matrices present, in which case they are cyclically extended to match the matrix
column size (or the length of the longest vector if no matrices are given).
The function rbind() does the corresponding operation for rows. In this case any vector
argument, possibly cyclically extended, are of course taken as row vectors.
Suppose X1 and X2 have the same number of rows. To combine these by columns into a
matrix X, together with an initial column of 1s we can use
> X <- cbind(1, X1, X2)
The result of rbind() or cbind() always has matrix status. Hence cbind(x) and
rbind(x) are possibly the simplest ways explicitly to allow the vector x to be treated as a
column or row matrix respectively.
5.9 The concatenation function, c(), with arrays
It should be noted that whereas cbind() and rbind() are concatenation functions that
respect dim attributes, the basic c() function does not, but rather clears numeric objects
of all dim and dimnames attributes. This is occasionally useful in its own right.
The official way to coerce an array back to a simple vector object is to use as.vector()
> vec <- as.vector(X)
However a similar result can be achieved by using c() with just one argument, simply
for this side-effect:
> vec <- c(X)
There are slight differences between the two, but ultimately the choice between them is
largely a matter of style (with the former being preferable).
5.10 Frequency tables from factors
Recall that a factor defines a partition into groups. Similarly a pair of factors defines a
two way cross classification, and so on. The function table() allows frequency tables to be
calculated from equal length factors. If there are k factor arguments, the result is a k-way
array of frequencies.
Suppose, for example, that statef is a factor giving the state code for each entry in a
data vector. The assignment
> statefr <- table(statef)
gives in statefr a table of frequencies of each state in the sample. The frequencies are
ordered and labelled by the levels attribute of the factor. This simple case is equivalent
to, but more convenient than,
> statefr <- tapply(statef, statef, length)
Further suppose that incomef is a factor giving a suitably defined “income class” for
each entry in the data vector, for example with the cut() function:
Chapter 5: Arrays and matrices 27
> factor(cut(incomes, breaks = 35+10*(0:7))) -> incomef
Then to calculate a two-way table of frequencies:
> table(incomef,statef)
statef
incomef act nsw nt qld sa tas vic wa
(35,45] 1 1 0 1 0 0 1 0
(45,55] 1 1 1 1 2 0 1 3
(55,65] 0 3 1 3 2 2 2 1
(65,75] 0 1 0 0 0 0 1 0
Extension to higher-way frequency tables is immediate.
Chapter 6: Lists and data frames 28
6 Lists and data frames
6.1 Lists
An R list is an object consisting of an ordered collection of objects known as its components.
There is no particular need for the components to be of the same mode or type, and,
for example, a list could consist of a numeric vector, a logical value, a matrix, a complex
vector, a character array, a function, and so on. Here is a simple example of how to make
a list:
> Lst <- list(name=”Fred”, wife=”Mary”, no.children=3,
child.ages=c(4,7,9))
Components are always numbered and may always be referred to as such. Thus if Lst is
the name of a list with four components, these may be individually referred to as Lst[[1]],
Lst[[2]], Lst[[3]] and Lst[[4]]. If, further, Lst[[4]] is a vector subscripted array then
Lst[[4]][1] is its first entry.
If Lst is a list, then the function length(Lst) gives the number of (top level) components
it has.
Components of lists may also be named, and in this case the component may be referred
to either by giving the component name as a character string in place of the number in
double square brackets, or, more conveniently, by giving an expression of the form
> name$component_name
for the same thing.
This is a very useful convention as it makes it easier to get the right component if you
forget the number.
So in the simple example given above:
Lst$name is the same as Lst[[1]] and is the string “Fred”,
Lst$wife is the same as Lst[[2]] and is the string “Mary”,
Lst$child.ages[1] is the same as Lst[[4]][1] and is the number 4.
Additionally, one can also use the names of the list components in double square brackets,
i.e., Lst[[“name”]] is the same as Lst$name. This is especially useful, when the name of
the component to be extracted is stored in another variable as in
> x <- “name”; Lst[[x]]
It is very important to distinguish Lst[[1]] from Lst[1]. ‘[[…]]’ is the operator
used to select a single element, whereas ‘[…]’ is a general subscripting operator. Thus the
former is the first object in the list Lst, and if it is a named list the name is not included.
The latter is a sublist of the list Lst consisting of the first entry only. If it is a named list,
the names are transferred to the sublist.
The names of components may be abbreviated down to the minimum number of letters
needed to identify them uniquely. Thus Lst$coefficients may be minimally specified as
Lst$coe and Lst$covariance as Lst$cov.
The vector of names is in fact simply an attribute of the list like any other and may be
handled as such. Other structures besides lists may, of course, similarly be given a names
attribute also.
Chapter 6: Lists and data frames 29
6.2 Constructing and modifying lists
New lists may be formed from existing objects by the function list(). An assignment of
the form
> Lst <- list(name_1=object_1, …, name_m=object_m)
sets up a list Lst of m components using object 1, . . . , object m for the components and
giving them names as specified by the argument names, (which can be freely chosen). If
these names are omitted, the components are numbered only. The components used to form
the list are copied when forming the new list and the originals are not affected.
Lists, like any subscripted object, can be extended by specifying additional components.
For example
> Lst[5] <- list(matrix=Mat)
6.2.1 Concatenating lists
When the concatenation function c() is given list arguments, the result is an object of mode
list also, whose components are those of the argument lists joined together in sequence.
> list.ABC <- c(list.A, list.B, list.C)
Recall that with vector objects as arguments the concatenation function similarly joined
together all arguments into a single vector structure. In this case all other attributes, such
as dim attributes, are discarded.
6.3 Data frames
A data frame is a list with class “data.frame”. There are restrictions on lists that may be
made into data frames, namely
 The components must be vectors (numeric, character, or logical), factors, numeric
matrices, lists, or other data frames.
 Matrices, lists, and data frames provide as many variables to the new data frame as
they have columns, elements, or variables, respectively.
 Numeric vectors, logicals and factors are included as is, and character vectors are
coerced to be factors, whose levels are the unique values appearing in the vector.
 Vector structures appearing as variables of the data frame must all have the same
length, and matrix structures must all have the same row size.
A data frame may for many purposes be regarded as a matrix with columns possibly
of differing modes and attributes. It may be displayed in matrix form, and its rows and
columns extracted using matrix indexing conventions.
6.3.1 Making data frames
Objects satisfying the restrictions placed on the columns (components) of a data frame may
be used to form one using the function data.frame:
> accountants <- data.frame(home=statef, loot=incomes, shot=incomef)
A list whose components conform to the restrictions of a data frame may be coerced into
a data frame using the function as.data.frame()
Chapter 6: Lists and data frames 30
The simplest way to construct a data frame from scratch is to use the read.table()
function to read an entire data frame from an external file. This is discussed further in
Chapter 7 [Reading data from files], page 32.
6.3.2 attach() and detach()
The $ notation, such as accountants$statef, for list components is not always very convenient.
A useful facility would be somehow to make the components of a list or data frame
temporarily visible as variables under their component name, without the need to quote
the list name explicitly each time.
The attach() function takes a ‘database’ such as a list or data frame as its argument.
Thus suppose lentils is a data frame with three variables lentils$u, lentils$v,
lentils$w. The attach
> attach(lentils)
places the data frame in the search path at position 2, and provided there are no variables
u, v or w in position 1, u, v and w are available as variables from the data frame in their
own right. At this point an assignment such as
> u <- v+w
does not replace the component u of the data frame, but rather masks it with another
variable u in the working directory at position 1 on the search path. To make a permanent
change to the data frame itself, the simplest way is to resort once again to the $ notation:
> lentils$u <- v+w
However the new value of component u is not visible until the data frame is detached
and attached again.
To detach a data frame, use the function
> detach()
More precisely, this statement detaches from the search path the entity currently at
position 2. Thus in the present context the variables u, v and w would be no longer visible,
except under the list notation as lentils$u and so on. Entities at positions greater than 2
on the search path can be detached by giving their number to detach, but it is much safer
to always use a name, for example by detach(lentils) or detach(“lentils”)
Note: In R lists and data frames can only be attached at position 2 or above,
and what is attached is a copy of the original object. You can alter the attached
values via assign, but the original list or data frame is unchanged.
6.3.3 Working with data frames
A useful convention that allows you to work with many different problems comfortably
together in the same working directory is
 gather together all variables for any well defined and separate problem in a data frame
under a suitably informative name;
 when working with a problem attach the appropriate data frame at position 2, and use
the working directory at level 1 for operational quantities and temporary variables;
 before leaving a problem, add any variables you wish to keep for future reference to
the data frame using the $ form of assignment, and then detach();
Chapter 6: Lists and data frames 31
 finally remove all unwanted variables from the working directory and keep it as clean
of left-over temporary variables as possible.
In this way it is quite simple to work with many problems in the same directory, all of
which have variables named x, y and z, for example.
6.3.4 Attaching arbitrary lists
attach() is a generic function that allows not only directories and data frames to be
attached to the search path, but other classes of object as well. In particular any object of
mode “list” may be attached in the same way:
> attach(any.old.list)
Anything that has been attached can be detached by detach, by position number or,
preferably, by name.
6.3.5 Managing the search path
The function search shows the current search path and so is a very useful way to keep track
of which data frames and lists (and packages) have been attached and detached. Initially
it gives
> search()
[1] “.GlobalEnv” “Autoloads” “package:base”
where .GlobalEnv is the workspace.1
After lentils is attached we have
> search()
[1] “.GlobalEnv” “lentils” “Autoloads” “package:base”
> ls(2)
[1] “u” “v” “w”
and as we see ls (or objects) can be used to examine the contents of any position on the
search path.
Finally, we detach the data frame and confirm it has been removed from the search path.
> detach(“lentils”)
> search()
[1] “.GlobalEnv” “Autoloads” “package:base”
1 See the on-line help for autoload for the meaning of the second term.
Chapter 7: Reading data from files 32
7 Reading data from files
Large data objects will usually be read as values from external files rather than entered
during an R session at the keyboard. R input facilities are simple and their requirements
are fairly strict and even rather inflexible. There is a clear presumption by the designers of
R that you will be able to modify your input files using other tools, such as file editors or
Perl1 to fit in with the requirements of R. Generally this is very simple.
If variables are to be held mainly in data frames, as we strongly suggest they should be,
an entire data frame can be read directly with the read.table() function. There is also a
more primitive input function, scan(), that can be called directly.
For more details on importing data into R and also exporting data, see the R Data
Import/Export manual.
7.1 The read.table() function
To read an entire data frame directly, the external file will normally have a special form.
 The first line of the file should have a name for each variable in the data frame.
 Each additional line of the file has as its first item a row label and the values for each
variable.
If the file has one fewer item in its first line than in its second, this arrangement is
presumed to be in force. So the first few lines of a file to be read as a data frame might
look as follows.

Input file form with names and row labels:
Price Floor Area Rooms Age Cent.heat
01 52.00 111.0 830 5 6.2 no
02 54.75 128.0 710 5 7.5 no
03 57.50 101.0 1000 5 4.2 no
04 57.50 131.0 690 6 8.8 no
05 59.75 93.0 900 5 1.9 yes

By default numeric items (except row labels) are read as numeric variables and nonnumeric
variables, such as Cent.heat in the example, as factors. This can be changed if
necessary.
The function read.table() can then be used to read the data frame directly
> HousePrice <- read.table(“houses.data”)
Often you will want to omit including the row labels directly and use the default labels.
In this case the file may omit the row label column as in the following.
1 Under UNIX, the utilities Sed or Awk can be used.
Chapter 7: Reading data from files 33

Input file form without row labels:
Price Floor Area Rooms Age Cent.heat
52.00 111.0 830 5 6.2 no
54.75 128.0 710 5 7.5 no
57.50 101.0 1000 5 4.2 no
57.50 131.0 690 6 8.8 no
59.75 93.0 900 5 1.9 yes

The data frame may then be read as
> HousePrice <- read.table(“houses.data”, header=TRUE)
where the header=TRUE option specifies that the first line is a line of headings, and hence,
by implication from the form of the file, that no explicit row labels are given.
7.2 The scan() function
Suppose the data vectors are of equal length and are to be read in parallel. Further suppose
that there are three vectors, the first of mode character and the remaining two of mode
numeric, and the file is ‘input.dat’. The first step is to use scan() to read in the three
vectors as a list, as follows
> inp <- scan(“input.dat”, list(“”,0,0))
The second argument is a dummy list structure that establishes the mode of the three
vectors to be read. The result, held in inp, is a list whose components are the three vectors
read in. To separate the data items into three separate vectors, use assignments like
> label <- inp[[1]]; x <- inp[[2]]; y <- inp[[3]]
More conveniently, the dummy list can have named components, in which case the names
can be used to access the vectors read in. For example
> inp <- scan(“input.dat”, list(id=””, x=0, y=0))
If you wish to access the variables separately they may either be re-assigned to variables
in the working frame:
> label <- inp$id; x <- inp$x; y <- inp$y
or the list may be attached at position 2 of the search path (see Section 6.3.4 [Attaching
arbitrary lists], page 31).
If the second argument is a single value and not a list, a single vector is read in, all
components of which must be of the same mode as the dummy value.
> X <- matrix(scan(“light.dat”, 0), ncol=5, byrow=TRUE)
There are more elaborate input facilities available and these are detailed in the manuals.
7.3 Accessing builtin datasets
Around 100 datasets are supplied with R (in package datasets), and others are available in
packages (including the recommended packages supplied with R). To see the list of datasets
currently available use
Chapter 7: Reading data from files 34
data()
As from R version 2.0.0 all the datasets supplied with R are available directly by name.
However, many packages still use the earlier convention in which data was also used to load
datasets into R, for example
data(infert)
and this can still be used with the standard packages (as in this example). In most cases
this will load an R object of the same name. However, in a few cases it loads several objects,
so see the on-line help for the object to see what to expect.
7.3.1 Loading data from other R packages
To access data from a particular package, use the package argument, for example
data(package=”rpart”)
data(Puromycin, package=”datasets”)
If a package has been attached by library, its datasets are automatically included in
the search.
User-contributed packages can be a rich source of datasets.
7.4 Editing data
When invoked on a data frame or matrix, edit brings up a separate spreadsheet-like environment
for editing. This is useful for making small changes once a data set has been read.
The command
> xnew <- edit(xold)
will allow you to edit your data set xold, and on completion the changed object is assigned
to xnew. If you want to alter the original dataset xold, the simplest way is to use fix(xold),
which is equivalent to xold <- edit(xold).
Use
> xnew <- edit(data.frame())
to enter new data via the spreadsheet interface.
Chapter 8: Probability distributions 35
8 Probability distributions
8.1 R as a set of statistical tables
One convenient use of R is to provide a comprehensive set of statistical tables. Functions
are provided to evaluate the cumulative distribution function P(X  x), the probability
density function and the quantile function (given q, the smallest x such that P(X  x) > q),
and to simulate from the distribution.
Distribution R name additional arguments
beta beta shape1, shape2, ncp
binomial binom size, prob
Cauchy cauchy location, scale
chi-squared chisq df, ncp
exponential exp rate
F f df1, df2, ncp
gamma gamma shape, scale
geometric geom prob
hypergeometric hyper m, n, k
log-normal lnorm meanlog, sdlog
logistic logis location, scale
negative binomial nbinom size, prob
normal norm mean, sd
Poisson pois lambda
Student’s t t df, ncp
uniform unif min, max
Weibull weibull shape, scale
Wilcoxon wilcox m, n
Prefix the name given here by ‘d’ for the density, ‘p’ for the CDF, ‘q’ for the quantile
function and ‘r’ for simulation (random deviates). The first argument is x for dxxx, q for
pxxx, p for qxxx and n for rxxx (except for rhyper and rwilcox, for which it is nn). In not
quite all cases is the non-centrality parameter ncp are currently available: see the on-line
help for details.
The pxxx and qxxx functions all have logical arguments lower.tail and log.p and
the dxxx ones have log. This allows, e.g., getting the cumulative (or “integrated”) hazard
function, H(t) = 􀀀log(1 􀀀 F(t)), by
– pxxx(t, …, lower.tail = FALSE, log.p = TRUE)
or more accurate log-likelihoods (by dxxx(…, log = TRUE)), directly.
In addition there are functions ptukey and qtukey for the distribution of the studentized
range of samples from a normal distribution.
Here are some examples
> ## 2-tailed p-value for t distribution
> 2*pt(-2.43, df = 13)
> ## upper 1% point for an F(2, 7) distribution
> qf(0.01, 2, 7, lower.tail = FALSE)
Chapter 8: Probability distributions 36
8.2 Examining the distribution of a set of data
Given a (univariate) set of data we can examine its distribution in a large number of ways.
The simplest is to examine the numbers. Two slightly different summaries are given by
summary and fivenum and a display of the numbers by stem (a “stem and leaf” plot).
> attach(faithful)
> summary(eruptions)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.600 2.163 4.000 3.488 4.454 5.100
> fivenum(eruptions)
[1] 1.6000 2.1585 4.0000 4.4585 5.1000
> stem(eruptions)
The decimal point is 1 digit(s) to the left of the |
16 | 070355555588
18 | 000022233333335577777777888822335777888
20 | 00002223378800035778
22 | 0002335578023578
24 | 00228
26 | 23
28 | 080
30 | 7
32 | 2337
34 | 250077
36 | 0000823577
38 | 2333335582225577
40 | 0000003357788888002233555577778
42 | 03335555778800233333555577778
44 | 02222335557780000000023333357778888
46 | 0000233357700000023578
48 | 00000022335800333
50 | 0370
A stem-and-leaf plot is like a histogram, and R has a function hist to plot histograms.
> hist(eruptions)
## make the bins smaller, make a plot of density
> hist(eruptions, seq(1.6, 5.2, 0.2), prob=TRUE)
> lines(density(eruptions, bw=0.1))
> rug(eruptions) # show the actual data points
More elegant density plots can be made by density, and we added a line produced by
density in this example. The bandwidth bw was chosen by trial-and-error as the default
gives too much smoothing (it usually does for “interesting” densities). (Better automated
Chapter 8: Probability distributions 37
methods of bandwidth choice are available, and in this example bw = “SJ” gives a good
result.)
Histogram of eruptions
eruptions
Relative Frequency
1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
We can plot the empirical cumulative distribution function by using the function ecdf.
> plot(ecdf(eruptions), do.points=FALSE, verticals=TRUE)
This distribution is obviously far from any standard distribution. How about the righthand
mode, say eruptions of longer than 3 minutes? Let us fit a normal distribution and
overlay the fitted CDF.
> long <- eruptions[eruptions > 3]
> plot(ecdf(long), do.points=FALSE, verticals=TRUE)
> x <- seq(3, 5.4, 0.01)
> lines(x, pnorm(x, mean=mean(long), sd=sqrt(var(long))), lty=3)
3.0 3.5 4.0 4.5 5.0
0.0 0.2 0.4 0.6 0.8 1.0
ecdf(long)
x
Fn(x)
Quantile-quantile (Q-Q) plots can help us examine this more carefully.
Chapter 8: Probability distributions 38
par(pty=”s”) # arrange for a square figure region
qqnorm(long); qqline(long)
which shows a reasonable fit but a shorter right tail than one would expect from a normal
distribution. Let us compare this with some simulated data from a t distribution
−2 −1 0 1 2
3.0 3.5 4.0 4.5 5.0
Normal Q−Q Plot
Theoretical Quantiles
Sample Quantiles
x <- rt(250, df = 5)
qqnorm(x); qqline(x)
which will usually (if it is a random sample) show longer tails than expected for a normal.
We can make a Q-Q plot against the generating distribution by
qqplot(qt(ppoints(250), df = 5), x, xlab = “Q-Q plot for t dsn”)
qqline(x)
Finally, we might want a more formal test of agreement with normality (or not). R
provides the Shapiro-Wilk test
> shapiro.test(long)
Shapiro-Wilk normality test
data: long
W = 0.9793, p-value = 0.01052
and the Kolmogorov-Smirnov test
> ks.test(long, “pnorm”, mean = mean(long), sd = sqrt(var(long)))
One-sample Kolmogorov-Smirnov test
data: long
D = 0.0661, p-value = 0.4284
alternative hypothesis: two.sided
(Note that the distribution theory is not valid here as we have estimated the parameters of
the normal distribution from the same sample.)
Chapter 8: Probability distributions 39
8.3 One- and two-sample tests
So far we have compared a single sample to a normal distribution. A much more common
operation is to compare aspects of two samples. Note that in R, all “classical” tests including
the ones used below are in package stats which is normally loaded.
Consider the following sets of data on the latent heat of the fusion of ice (cal/gm) from
Rice (1995, p.490)
Method A: 79.98 80.04 80.02 80.04 80.03 80.03 80.04 79.97
80.05 80.03 80.02 80.00 80.02
Method B: 80.02 79.94 79.98 79.97 79.97 80.03 79.95 79.97
Boxplots provide a simple graphical comparison of the two samples.
A <- scan()
79.98 80.04 80.02 80.04 80.03 80.03 80.04 79.97
80.05 80.03 80.02 80.00 80.02
B <- scan()
80.02 79.94 79.98 79.97 79.97 80.03 79.95 79.97
boxplot(A, B)
which indicates that the first group tends to give higher results than the second.
1 2
79.94 79.96 79.98 80.00 80.02 80.04
To test for the equality of the means of the two examples, we can use an unpaired t-test
by
> t.test(A, B)
Welch Two Sample t-test
data: A and B
t = 3.2499, df = 12.027, p-value = 0.00694
alternative hypothesis: true difference in means is not equal to 0
Chapter 8: Probability distributions 40
95 percent confidence interval:
0.01385526 0.07018320
sample estimates:
mean of x mean of y
80.02077 79.97875
which does indicate a significant difference, assuming normality. By default the R function
does not assume equality of variances in the two samples (in contrast to the similar S-Plus
t.test function). We can use the F test to test for equality in the variances, provided that
the two samples are from normal populations.
> var.test(A, B)
F test to compare two variances
data: A and B
F = 0.5837, num df = 12, denom df = 7, p-value = 0.3938
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.1251097 2.1052687
sample estimates:
ratio of variances
0.5837405
which shows no evidence of a significant difference, and so we can use the classical t-test
that assumes equality of the variances.
> t.test(A, B, var.equal=TRUE)
Two Sample t-test
data: A and B
t = 3.4722, df = 19, p-value = 0.002551
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.01669058 0.06734788
sample estimates:
mean of x mean of y
80.02077 79.97875
All these tests assume normality of the two samples. The two-sample Wilcoxon (or
Mann-Whitney) test only assumes a common continuous distribution under the null hypothesis.
> wilcox.test(A, B)
Wilcoxon rank sum test with continuity correction
data: A and B
W = 89, p-value = 0.007497
alternative hypothesis: true location shift is not equal to 0
Chapter 8: Probability distributions 41
Warning message:
Cannot compute exact p-value with ties in: wilcox.test(A, B)
Note the warning: there are several ties in each sample, which suggests strongly that these
data are from a discrete distribution (probably due to rounding).
There are several ways to compare graphically the two samples. We have already seen
a pair of boxplots. The following
> plot(ecdf(A), do.points=FALSE, verticals=TRUE, xlim=range(A, B))
> plot(ecdf(B), do.points=FALSE, verticals=TRUE, add=TRUE)
will show the two empirical CDFs, and qqplot will perform a Q-Q plot of the two samples.
The Kolmogorov-Smirnov test is of the maximal vertical distance between the two ecdf’s,
assuming a common continuous distribution:
> ks.test(A, B)
Two-sample Kolmogorov-Smirnov test
data: A and B
D = 0.5962, p-value = 0.05919
alternative hypothesis: two-sided
Warning message:
cannot compute correct p-values with ties in: ks.test(A, B)
Chapter 9: Grouping, loops and conditional execution 42
9 Grouping, loops and conditional execution
9.1 Grouped expressions
R is an expression language in the sense that its only command type is a function or
expression which returns a result. Even an assignment is an expression whose result is the
value assigned, and it may be used wherever any expression may be used; in particular
multiple assignments are possible.
Commands may be grouped together in braces, {expr_1; …; expr_m}, in which case
the value of the group is the result of the last expression in the group evaluated. Since such
a group is also an expression it may, for example, be itself included in parentheses and used
a part of an even larger expression, and so on.
9.2 Control statements
9.2.1 Conditional execution: if statements
The language has available a conditional construction of the form
> if (expr_1) expr_2 else expr_3
where expr 1 must evaluate to a single logical value and the result of the entire expression
is then evident.
The “short-circuit” operators && and || are often used as part of the condition in an if
statement. Whereas & and | apply element-wise to vectors, && and || apply to vectors of
length one, and only evaluate their second argument if necessary.
There is a vectorized version of the if/else construct, the ifelse function. This has the
form ifelse(condition, a, b) and returns a vector of the length of its longest argument,
with elements a[i] if condition[i] is true, otherwise b[i].
9.2.2 Repetitive execution: for loops, repeat and while
There is also a for loop construction which has the form
> for (name in expr_1) expr_2
where name is the loop variable. expr 1 is a vector expression, (often a sequence like 1:20),
and expr 2 is often a grouped expression with its sub-expressions written in terms of the
dummy name. expr 2 is repeatedly evaluated as name ranges through the values in the
vector result of expr 1.
As an example, suppose ind is a vector of class indicators and we wish to produce
separate plots of y versus x within classes. One possibility here is to use coplot(),1 which
will produce an array of plots corresponding to each level of the factor. Another way to do
this, now putting all plots on the one display, is as follows:
> xc <- split(x, ind)
> yc <- split(y, ind)
> for (i in 1:length(yc)) {
plot(xc[[i]], yc[[i]])
1 to be discussed later, or use xyplot from package lattice.
Chapter 9: Grouping, loops and conditional execution 43
abline(lsfit(xc[[i]], yc[[i]]))
}
(Note the function split() which produces a list of vectors obtained by splitting a larger
vector according to the classes specified by a factor. This is a useful function, mostly used
in connection with boxplots. See the help facility for further details.)
Warning: for() loops are used in R code much less often than in compiled
languages. Code that takes a ‘whole object’ view is likely to be both clearer
and faster in R.
Other looping facilities include the
> repeat expr
statement and the
> while (condition) expr
statement.
The break statement can be used to terminate any loop, possibly abnormally. This is
the only way to terminate repeat loops.
The next statement can be used to discontinue one particular cycle and skip to the
“next”.
Control statements are most often used in connection with functions which are discussed
in Chapter 10 [Writing your own functions], page 44, and where more examples will emerge.
Chapter 10: Writing your own functions 44
10 Writing your own functions
As we have seen informally along the way, the R language allows the user to create objects
of mode function. These are true R functions that are stored in a special internal form and
may be used in further expressions and so on. In the process, the language gains enormously
in power, convenience and elegance, and learning to write useful functions is one of the main
ways to make your use of R comfortable and productive.
It should be emphasized that most of the functions supplied as part of the R system,
such as mean(), var(), postscript() and so on, are themselves written in R and thus do
not differ materially from user written functions.
A function is defined by an assignment of the form
> name <- function(arg_1, arg_2, …) expression
The expression is an R expression, (usually a grouped expression), that uses the arguments,
arg i, to calculate a value. The value of the expression is the value returned for the function.
A call to the function then usually takes the form name(expr_1, expr_2, …) and may
occur anywhere a function call is legitimate.
10.1 Simple examples
As a first example, consider a function to calculate the two sample t-statistic, showing “all
the steps”. This is an artificial example, of course, since there are other, simpler ways of
achieving the same end.
The function is defined as follows:
> twosam <- function(y1, y2) {
n1 <- length(y1); n2 <- length(y2)
yb1 <- mean(y1); yb2 <- mean(y2)
s1 <- var(y1); s2 <- var(y2)
s <- ((n1-1)*s1 + (n2-1)*s2)/(n1+n2-2)
tst <- (yb1 – yb2)/sqrt(s*(1/n1 + 1/n2))
tst
}
With this function defined, you could perform two sample t-tests using a call such as
> tstat <- twosam(data$male, data$female); tstat
As a second example, consider a function to emulate directly the Matlab backslash
command, which returns the coefficients of the orthogonal projection of the vector y onto
the column space of the matrix, X. (This is ordinarily called the least squares estimate of
the regression coefficients.) This would ordinarily be done with the qr() function; however
this is sometimes a bit tricky to use directly and it pays to have a simple function such as
the following to use it safely.
Thus given a n by 1 vector y and an n by p matrix X then X y is defined as (XTX)􀀀XT y,
where (XTX)􀀀 is a generalized inverse of X0X.
> bslash <- function(X, y) {
X <- qr(X)
qr.coef(X, y)
Chapter 10: Writing your own functions 45
}
After this object is created it may be used in statements such as
> regcoeff <- bslash(Xmat, yvar)
and so on.
The classical R function lsfit() does this job quite well, and more1. It in turn uses the
functions qr() and qr.coef() in the slightly counterintuitive way above to do this part of
the calculation. Hence there is probably some value in having just this part isolated in a
simple to use function if it is going to be in frequent use. If so, we may wish to make it a
matrix binary operator for even more convenient use.
10.2 Defining new binary operators
Had we given the bslash() function a different name, namely one of the form
%anything%
it could have been used as a binary operator in expressions rather than in function form.
Suppose, for example, we choose ! for the internal character. The function definition would
then start as
> “%!%” <- function(X, y) { … }
(Note the use of quote marks.) The function could then be used as X %!% y. (The backslash
symbol itself is not a convenient choice as it presents special problems in this context.)
The matrix multiplication operator, %*%, and the outer product matrix operator %o% are
other examples of binary operators defined in this way.
10.3 Named arguments and defaults
As first noted in Section 2.3 [Generating regular sequences], page 8, if arguments to called
functions are given in the “name=object” form, they may be given in any order. Furthermore
the argument sequence may begin in the unnamed, positional form, and specify
named arguments after the positional arguments.
Thus if there is a function fun1 defined by
> fun1 <- function(data, data.frame, graph, limit) {
[function body omitted]
}
then the function may be invoked in several ways, for example
> ans <- fun1(d, df, TRUE, 20)
> ans <- fun1(d, df, graph=TRUE, limit=20)
> ans <- fun1(data=d, limit=20, graph=TRUE, data.frame=df)
are all equivalent.
In many cases arguments can be given commonly appropriate default values, in which
case they may be omitted altogether from the call when the defaults are appropriate. For
example, if fun1 were defined as
> fun1 <- function(data, data.frame, graph=TRUE, limit=20) { … }
it could be called as
1 See also the methods described in Chapter 11 [Statistical models in R], page 54
Chapter 10: Writing your own functions 46
> ans <- fun1(d, df)
which is now equivalent to the three cases above, or as
> ans <- fun1(d, df, limit=10)
which changes one of the defaults.
It is important to note that defaults may be arbitrary expressions, even involving other
arguments to the same function; they are not restricted to be constants as in our simple
example here.
10.4 The ‘…’ argument
Another frequent requirement is to allow one function to pass on argument settings to
another. For example many graphics functions use the function par() and functions like
plot() allow the user to pass on graphical parameters to par() to control the graphical
output. (See Section 12.4.1 [The par() function], page 73, for more details on the par()
function.) This can be done by including an extra argument, literally ‘…’, of the function,
which may then be passed on. An outline example is given below.
fun1 <- function(data, data.frame, graph=TRUE, limit=20, …) {
[omitted statements]
if (graph)
par(pch=”*”, …)
[more omissions]
}
10.5 Assignments within functions
Note that any ordinary assignments done within the function are local and temporary and
are lost after exit from the function. Thus the assignment X <- qr(X) does not affect the
value of the argument in the calling program.
To understand completely the rules governing the scope of R assignments the reader
needs to be familiar with the notion of an evaluation frame. This is a somewhat advanced,
though hardly difficult, topic and is not covered further here.
If global and permanent assignments are intended within a function, then either the
“superassignment” operator, <<- or the function assign() can be used. See the help
document for details. S-Plus users should be aware that <<- has different semantics in R.
These are discussed further in Section 10.7 [Scope], page 48.
10.6 More advanced examples
10.6.1 Efficiency factors in block designs
As a more complete, if a little pedestrian, example of a function, consider finding the
efficiency factors for a block design. (Some aspects of this problem have already been
discussed in Section 5.3 [Index matrices], page 20.)
A block design is defined by two factors, say blocks (b levels) and varieties (v levels).
If R and K are the v by v and b by b replications and block size matrices, respectively, and
Chapter 10: Writing your own functions 47
N is the b by v incidence matrix, then the efficiency factors are defined as the eigenvalues
of the matrix
E = Iv 􀀀 R􀀀1=2NTK􀀀1NR􀀀1=2 = Iv 􀀀 AT A;
where A = K􀀀1=2NR􀀀1=2. One way to write the function is given below.
> bdeff <- function(blocks, varieties) {
blocks <- as.factor(blocks) # minor safety move
b <- length(levels(blocks))
varieties <- as.factor(varieties) # minor safety move
v <- length(levels(varieties))
K <- as.vector(table(blocks)) # remove dim attr
R <- as.vector(table(varieties)) # remove dim attr
N <- table(blocks, varieties)
A <- 1/sqrt(K) * N * rep(1/sqrt(R), rep(b, v))
sv <- svd(A)
list(eff=1 – sv$d^2, blockcv=sv$u, varietycv=sv$v)
}
It is numerically slightly better to work with the singular value decomposition on this
occasion rather than the eigenvalue routines.
The result of the function is a list giving not only the efficiency factors as the first
component, but also the block and variety canonical contrasts, since sometimes these give
additional useful qualitative information.
10.6.2 Dropping all names in a printed array
For printing purposes with large matrices or arrays, it is often useful to print them in close
block form without the array names or numbers. Removing the dimnames attribute will
not achieve this effect, but rather the array must be given a dimnames attribute consisting
of empty strings. For example to print a matrix, X
> temp <- X
> dimnames(temp) <- list(rep(“”, nrow(X)), rep(“”, ncol(X)))
> temp; rm(temp)
This can be much more conveniently done using a function, no.dimnames(), shown
below, as a “wrap around” to achieve the same result. It also illustrates how some effective
and useful user functions can be quite short.
no.dimnames <- function(a) {
## Remove all dimension names from an array for compact printing.
d <- list()
l <- 0
for(i in dim(a)) {
d[[l <- l + 1]] <- rep(“”, i)
}
dimnames(a) <- d
a
}
With this function defined, an array may be printed in close format using
Chapter 10: Writing your own functions 48
> no.dimnames(X)
This is particularly useful for large integer arrays, where patterns are the real interest
rather than the values.
10.6.3 Recursive numerical integration
Functions may be recursive, and may themselves define functions within themselves. Note,
however, that such functions, or indeed variables, are not inherited by called functions in
higher evaluation frames as they would be if they were on the search path.
The example below shows a naive way of performing one-dimensional numerical integration.
The integrand is evaluated at the end points of the range and in the middle. If
the one-panel trapezium rule answer is close enough to the two panel, then the latter is
returned as the value. Otherwise the same process is recursively applied to each panel. The
result is an adaptive integration process that concentrates function evaluations in regions
where the integrand is farthest from linear. There is, however, a heavy overhead, and the
function is only competitive with other algorithms when the integrand is both smooth and
very difficult to evaluate.
The example is also given partly as a little puzzle in R programming.
area <- function(f, a, b, eps = 1.0e-06, lim = 10) {
fun1 <- function(f, a, b, fa, fb, a0, eps, lim, fun) {
## function ‘fun1’ is only visible inside ‘area’
d <- (a + b)/2
h <- (b – a)/4
fd <- f(d)
a1 <- h * (fa + fd)
a2 <- h * (fd + fb)
if(abs(a0 – a1 – a2) < eps || lim == 0)
return(a1 + a2)
else {
return(fun(f, a, d, fa, fd, a1, eps, lim – 1, fun) +
fun(f, d, b, fd, fb, a2, eps, lim – 1, fun))
}
}
fa <- f(a)
fb <- f(b)
a0 <- ((fa + fb) * (b – a))/2
fun1(f, a, b, fa, fb, a0, eps, lim, fun1)
}
10.7 Scope
The discussion in this section is somewhat more technical than in other parts of this document.
However, it details one of the major differences between S-Plus and R.
The symbols which occur in the body of a function can be divided into three classes;
formal parameters, local variables and free variables. The formal parameters of a function
are those occurring in the argument list of the function. Their values are determined by
the process of binding the actual function arguments to the formal parameters. Local
Chapter 10: Writing your own functions 49
variables are those whose values are determined by the evaluation of expressions in the
body of the functions. Variables which are not formal parameters or local variables are
called free variables. Free variables become local variables if they are assigned to. Consider
the following function definition.
f <- function(x) {
y <- 2*x
print(x)
print(y)
print(z)
}
In this function, x is a formal parameter, y is a local variable and z is a free variable.
In R the free variable bindings are resolved by first looking in the environment in which
the function was created. This is called lexical scope. First we define a function called cube.
cube <- function(n) {
sq <- function() n*n
n*sq()
}
The variable n in the function sq is not an argument to that function. Therefore it
is a free variable and the scoping rules must be used to ascertain the value that is to be
associated with it. Under static scope (S-Plus) the value is that associated with a global
variable named n. Under lexical scope (R) it is the parameter to the function cube since
that is the active binding for the variable n at the time the function sq was defined. The
difference between evaluation in R and evaluation in S-Plus is that S-Plus looks for a
global variable called n while R first looks for a variable called n in the environment created
when cube was invoked.
## first evaluation in S
S> cube(2)
Error in sq(): Object “n” not found
Dumped
S> n <- 3
S> cube(2)
[1] 18
## then the same function evaluated in R
R> cube(2)
[1] 8
Lexical scope can also be used to give functions mutable state. In the following example
we show how R can be used to mimic a bank account. A functioning bank account needs to
have a balance or total, a function for making withdrawals, a function for making deposits
and a function for stating the current balance. We achieve this by creating the three functions
within account and then returning a list containing them. When account is invoked
it takes a numerical argument total and returns a list containing the three functions. Because
these functions are defined in an environment which contains total, they will have
access to its value.
The special assignment operator, <<-, is used to change the value associated with total.
This operator looks back in enclosing environments for an environment that contains the
Chapter 10: Writing your own functions 50
symbol total and when it finds such an environment it replaces the value, in that environment,
with the value of right hand side. If the global or top-level environment is reached
without finding the symbol total then that variable is created and assigned to there. For
most users <<- creates a global variable and assigns the value of the right hand side to
it2. Only when <<- has been used in a function that was returned as the value of another
function will the special behavior described here occur.
open.account <- function(total) {
list(
deposit = function(amount) {
if(amount <= 0)
stop(“Deposits must be positive!\n”)
total <<- total + amount
cat(amount, “deposited. Your balance is”, total, “\n\n”)
},
withdraw = function(amount) {
if(amount > total)
stop(“You don’t have that much money!\n”)
total <<- total – amount
cat(amount, “withdrawn. Your balance is”, total, “\n\n”)
},
balance = function() {
cat(“Your balance is”, total, “\n\n”)
}
)
}
ross <- open.account(100)
robert <- open.account(200)
ross$withdraw(30)
ross$balance()
robert$balance()
ross$deposit(50)
ross$balance()
ross$withdraw(500)
10.8 Customizing the environment
Users can customize their environment in several different ways. There is a site initialization
file and every directory can have its own special initialization file. Finally, the special
functions .First and .Last can be used.
The location of the site initialization file is taken from the value of the R_PROFILE
environment variable. If that variable is unset, the file ‘Rprofile.site’ in the R home
2 In some sense this mimics the behavior in S-Plus since in S-Plus this operator always creates or assigns
to a global variable.
Chapter 10: Writing your own functions 51
subdirectory ‘etc’ is used. This file should contain the commands that you want to execute
every time R is started under your system. A second, personal, profile file named
‘.Rprofile’3 can be placed in any directory. If R is invoked in that directory then that file
will be sourced. This file gives individual users control over their workspace and allows for
different startup procedures in different working directories. If no ‘.Rprofile’ file is found
in the startup directory, then R looks for a ‘.Rprofile’ file in the user’s home directory
and uses that (if it exists). If the environment variable R_PROFILE_USER is set, the file it
points to is used instead of the ‘.Rprofile’ files.
Any function named .First() in either of the two profile files or in the ‘.RData’ image
has a special status. It is automatically performed at the beginning of an R session and
may be used to initialize the environment. For example, the definition in the example below
alters the prompt to $ and sets up various other useful things that can then be taken for
granted in the rest of the session.
Thus, the sequence in which files are executed is, ‘Rprofile.site’, the user profile,
‘.RData’ and then .First(). A definition in later files will mask definitions in earlier files.
> .First <- function() {
options(prompt=”$ “, continue=”+\t”) # $ is the prompt
options(digits=5, length=999) # custom numbers and printout
x11() # for graphics
par(pch = “+”) # plotting character
source(file.path(Sys.getenv(“HOME”), “R”, “mystuff.R”))
# my personal functions
library(MASS) # attach a package
}
Similarly a function .Last(), if defined, is (normally) executed at the very end of the
session. An example is given below.
> .Last <- function() {
graphics.off() # a small safety measure.
cat(paste(date(),”\nAdios\n”)) # Is it time for lunch?
}
10.9 Classes, generic functions and object orientation
The class of an object determines how it will be treated by what are known as generic
functions. Put the other way round, a generic function performs a task or action on its
arguments specific to the class of the argument itself. If the argument lacks any class
attribute, or has a class not catered for specifically by the generic function in question,
there is always a default action provided.
An example makes things clearer. The class mechanism offers the user the facility
of designing and writing generic functions for special purposes. Among the other generic
functions are plot() for displaying objects graphically, summary() for summarizing analyses
of various types, and anova() for comparing statistical models.
3 So it is hidden under UNIX.
Chapter 10: Writing your own functions 52
The number of generic functions that can treat a class in a specific way can be quite
large. For example, the functions that can accommodate in some fashion objects of class
“data.frame” include
[ [[<- any as.matrix
[<- mean plot summary
A currently complete list can be got by using the methods() function:
> methods(class=”data.frame”)
Conversely the number of classes a generic function can handle can also be quite large.
For example the plot() function has a default method and variants for objects of classes
“data.frame”, “density”, “factor”, and more. A complete list can be got again by using
the methods() function:
> methods(plot)
For many generic functions the function body is quite short, for example
> coef
function (object, …)
UseMethod(“coef”)
The presence of UseMethod indicates this is a generic function. To see what methods are
available we can use methods()
> methods(coef)
[1] coef.aov* coef.Arima* coef.default* coef.listof*
[5] coef.nls* coef.summary.nls*
Non-visible functions are asterisked
In this example there are six methods, none of which can be seen by typing its name. We
can read these by either of
> getAnywhere(“coef.aov”)
A single object matching ’coef.aov’ was found
It was found in the following places
registered S3 method for coef from namespace stats
namespace:stats
with value
function (object, …)
{
z <- object$coef
z[!is.na(z)]
}
> getS3method(“coef”, “aov”)
function (object, …)
{
z <- object$coef
z[!is.na(z)]
}
Chapter 10: Writing your own functions 53
The reader is referred to the R Language Definition for a more complete discussion of
this mechanism.
Chapter 11: Statistical models in R 54
11 Statistical models in R
This section presumes the reader has some familiarity with statistical methodology, in
particular with regression analysis and the analysis of variance. Later we make some rather
more ambitious presumptions, namely that something is known about generalized linear
models and nonlinear regression.
The requirements for fitting statistical models are sufficiently well defined to make it
possible to construct general tools that apply in a broad spectrum of problems.
R provides an interlocking suite of facilities that make fitting statistical models very
simple. As we mention in the introduction, the basic output is minimal, and one needs to
ask for the details by calling extractor functions.
11.1 Defining statistical models; formulae
The template for a statistical model is a linear regression model with independent, homoscedastic
errors
yi =
Xp
j=0
jxij + ei; ei  NID(0; 2); i = 1; : : : ; n
In matrix terms this would be written
y = X + e
where the y is the response vector, X is the model matrix or design matrix and has columns
x0; x1; : : : ; xp, the determining variables. Very often x0 will be a column of ones defining an
intercept term.
Examples
Before giving a formal specification, a few examples may usefully set the picture.
Suppose y, x, x0, x1, x2, . . . are numeric variables, X is a matrix and A, B, C, . . . are
factors. The following formulae on the left side below specify statistical models as described
on the right.
y ~ x
y ~ 1 + x Both imply the same simple linear regression model of y on x. The first has an
implicit intercept term, and the second an explicit one.
y ~ 0 + x
y ~ -1 + x
y ~ x – 1 Simple linear regression of y on x through the origin (that is, without an intercept
term).
log(y) ~ x1 + x2
Multiple regression of the transformed variable, log(y), on x1 and x2 (with an
implicit intercept term).
Chapter 11: Statistical models in R 55
y ~ poly(x,2)
y ~ 1 + x + I(x^2)
Polynomial regression of y on x of degree 2. The first form uses orthogonal
polynomials, and the second uses explicit powers, as basis.
y ~ X + poly(x,2)
Multiple regression y with model matrix consisting of the matrix X as well as
polynomial terms in x to degree 2.
y ~ A Single classification analysis of variance model of y, with classes determined by
A.
y ~ A + x Single classification analysis of covariance model of y, with classes determined
by A, and with covariate x.
y ~ A*B
y ~ A + B + A:B
y ~ B %in% A
y ~ A/B Two factor non-additive model of y on A and B. The first two specify the same
crossed classification and the second two specify the same nested classification.
In abstract terms all four specify the same model subspace.
y ~ (A + B + C)^2
y ~ A*B*C – A:B:C
Three factor experiment but with a model containing main effects and two
factor interactions only. Both formulae specify the same model.
y ~ A * x
y ~ A/x
y ~ A/(1 + x) – 1
Separate simple linear regression models of y on x within the levels of A, with
different codings. The last form produces explicit estimates of as many different
intercepts and slopes as there are levels in A.
y ~ A*B + Error(C)
An experiment with two treatment factors, A and B, and error strata determined
by factor C. For example a split plot experiment, with whole plots (and
hence also subplots), determined by factor C.
The operator ~ is used to define a model formula in R. The form, for an ordinary linear
model, is
response ~ op_1 term_1 op_2 term_2 op_3 term_3 …
where
response is a vector or matrix, (or expression evaluating to a vector or matrix) defining
the response variable(s).
op i is an operator, either + or -, implying the inclusion or exclusion of a term in
the model, (the first is optional).
term i is either
 a vector or matrix expression, or 1,
Chapter 11: Statistical models in R 56
 a factor, or
 a formula expression consisting of factors, vectors or matrices connected
by formula operators.
In all cases each term defines a collection of columns either to be added to or
removed from the model matrix. A 1 stands for an intercept column and is by
default included in the model matrix unless explicitly removed.
The formula operators are similar in effect to the Wilkinson and Rogers notation used
by such programs as Glim and Genstat. One inevitable change is that the operator ‘.’
becomes ‘:’ since the period is a valid name character in R.
The notation is summarized below (based on Chambers & Hastie, 1992, p.29):
Y ~ M Y is modeled as M.
M_1 + M_2 Include M 1 and M 2.
M_1 – M_2 Include M 1 leaving out terms of M 2.
M_1 : M_2 The tensor product of M 1 and M 2. If both terms are factors, then the “subclasses”
factor.
M_1 %in% M_2
Similar to M_1:M_2, but with a different coding.
M_1 * M_2 M_1 + M_2 + M_1:M_2.
M_1 / M_2 M_1 + M_2 %in% M_1.
M^n All terms in M together with “interactions” up to order n
I(M) Insulate M. Inside M all operators have their normal arithmetic meaning, and
that term appears in the model matrix.
Note that inside the parentheses that usually enclose function arguments all operators
have their normal arithmetic meaning. The function I() is an identity function used to
allow terms in model formulae to be defined using arithmetic operators.
Note particularly that the model formulae specify the columns of the model matrix, the
specification of the parameters being implicit. This is not the case in other contexts, for
example in specifying nonlinear models.
11.1.1 Contrasts
We need at least some idea how the model formulae specify the columns of the model
matrix. This is easy if we have continuous variables, as each provides one column of the
model matrix (and the intercept will provide a column of ones if included in the model).
What about a k-level factor A? The answer differs for unordered and ordered factors.
For unordered factors k 􀀀 1 columns are generated for the indicators of the second, . . . ,
kth levels of the factor. (Thus the implicit parameterization is to contrast the response at
each level with that at the first.) For ordered factors the k 􀀀 1 columns are the orthogonal
polynomials on 1; : : : ; k, omitting the constant term.
Although the answer is already complicated, it is not the whole story. First, if the
intercept is omitted in a model that contains a factor term, the first such term is encoded
into k columns giving the indicators for all the levels. Second, the whole behavior can be
changed by the options setting for contrasts. The default setting in R is
Chapter 11: Statistical models in R 57
options(contrasts = c(“contr.treatment”, “contr.poly”))
The main reason for mentioning this is that R and S have different defaults for unordered
factors, S using Helmert contrasts. So if you need to compare your results to those of a
textbook or paper which used S-Plus, you will need to set
options(contrasts = c(“contr.helmert”, “contr.poly”))
This is a deliberate difference, as treatment contrasts (R’s default) are thought easier for
newcomers to interpret.
We have still not finished, as the contrast scheme to be used can be set for each term in
the model using the functions contrasts and C.
We have not yet considered interaction terms: these generate the products of the columns
introduced for their component terms.
Although the details are complicated, model formulae in R will normally generate the
models that an expert statistician would expect, provided that marginality is preserved.
Fitting, for example, a model with an interaction but not the corresponding main effects
will in general lead to surprising results, and is for experts only.
11.2 Linear models
The basic function for fitting ordinary multiple models is lm(), and a streamlined version
of the call is as follows:
> fitted.model <- lm(formula, data = data.frame)
For example
> fm2 <- lm(y ~ x1 + x2, data = production)
would fit a multiple regression model of y on x1 and x2 (with implicit intercept term).
The important (but technically optional) parameter data = production specifies that
any variables needed to construct the model should come first from the production data
frame. This is the case regardless of whether data frame production has been attached on
the search path or not.
11.3 Generic functions for extracting model information
The value of lm() is a fitted model object; technically a list of results of class “lm”. Information
about the fitted model can then be displayed, extracted, plotted and so on by using
generic functions that orient themselves to objects of class “lm”. These include
add1 deviance formula predict step
alias drop1 kappa print summary
anova effects labels proj vcov
coef family plot residuals
A brief description of the most commonly used ones is given below.
anova(object_1, object_2)
Compare a submodel with an outer model and produce an analysis of variance
table.
coef(object)
Extract the regression coefficient (matrix).
Long form: coefficients(object).
Chapter 11: Statistical models in R 58
deviance(object)
Residual sum of squares, weighted if appropriate.
formula(object)
Extract the model formula.
plot(object)
Produce four plots, showing residuals, fitted values and some diagnostics.
predict(object, newdata=data.frame)
The data frame supplied must have variables specified with the same labels as
the original. The value is a vector or matrix of predicted values corresponding
to the determining variable values in data.frame.
print(object)
Print a concise version of the object. Most often used implicitly.
residuals(object)
Extract the (matrix of) residuals, weighted as appropriate.
Short form: resid(object).
step(object)
Select a suitable model by adding or dropping terms and preserving hierarchies.
The model with the smallest value of AIC (Akaike’s An Information Criterion)
discovered in the stepwise search is returned.
summary(object)
Print a comprehensive summary of the results of the regression analysis.
vcov(object)
Returns the variance-covariance matrix of the main parameters of a fitted model
object.
11.4 Analysis of variance and model comparison
The model fitting function aov(formula, data=data.frame) operates at the simplest level
in a very similar way to the function lm(), and most of the generic functions listed in the
table in Section 11.3 [Generic functions for extracting model information], page 57 apply.
It should be noted that in addition aov() allows an analysis of models with multiple error
strata such as split plot experiments, or balanced incomplete block designs with recovery
of inter-block information. The model formula
response ~ mean.formula + Error(strata.formula)
specifies a multi-stratum experiment with error strata defined by the strata.formula. In the
simplest case, strata.formula is simply a factor, when it defines a two strata experiment,
namely between and within the levels of the factor.
For example, with all determining variables factors, a model formula such as that in:
> fm <- aov(yield ~ v + n*p*k + Error(farms/blocks), data=farm.data)
would typically be used to describe an experiment with mean model v + n*p*k and three
error strata, namely “between farms”, “within farms, between blocks” and “within blocks”.
Chapter 11: Statistical models in R 59
11.4.1 ANOVA tables
Note also that the analysis of variance table (or tables) are for a sequence of fitted models.
The sums of squares shown are the decrease in the residual sums of squares resulting from an
inclusion of that term in the model at that place in the sequence. Hence only for orthogonal
experiments will the order of inclusion be inconsequential.
For multistratum experiments the procedure is first to project the response onto the
error strata, again in sequence, and to fit the mean model to each projection. For further
details, see Chambers & Hastie (1992).
A more flexible alternative to the default full ANOVA table is to compare two or more
models directly using the anova() function.
> anova(fitted.model.1, fitted.model.2, …)
The display is then an ANOVA table showing the differences between the fitted models
when fitted in sequence. The fitted models being compared would usually be an hierarchical
sequence, of course. This does not give different information to the default, but rather makes
it easier to comprehend and control.
11.5 Updating fitted models
The update() function is largely a convenience function that allows a model to be fitted
that differs from one previously fitted usually by just a few additional or removed terms.
Its form is
> new.model <- update(old.model, new.formula)
In the new.formula the special name consisting of a period, ‘.’, only, can be used to
stand for “the corresponding part of the old model formula”. For example,
> fm05 <- lm(y ~ x1 + x2 + x3 + x4 + x5, data = production)
> fm6 <- update(fm05, . ~ . + x6)
> smf6 <- update(fm6, sqrt(.) ~ .)
would fit a five variate multiple regression with variables (presumably) from the data frame
production, fit an additional model including a sixth regressor variable, and fit a variant
on the model where the response had a square root transform applied.
Note especially that if the data= argument is specified on the original call to the model
fitting function, this information is passed on through the fitted model object to update()
and its allies.
The name ‘.’ can also be used in other contexts, but with slightly different meaning.
For example
> fmfull <- lm(y ~ . , data = production)
would fit a model with response y and regressor variables all other variables in the data
frame production.
Other functions for exploring incremental sequences of models are add1(), drop1() and
step(). The names of these give a good clue to their purpose, but for full details see the
on-line help.
Chapter 11: Statistical models in R 60
11.6 Generalized linear models
Generalized linear modeling is a development of linear models to accommodate both nonnormal
response distributions and transformations to linearity in a clean and straightforward
way. A generalized linear model may be described in terms of the following sequence of
assumptions:
 There is a response, y, of interest and stimulus variables x1, x2, : : : , whose values
influence the distribution of the response.
 The stimulus variables influence the distribution of y through a single linear function,
only. This linear function is called the linear predictor, and is usually written
 = 1×1 + 2×2 +    + pxp;
hence xi has no influence on the distribution of y if and only if i = 0.
 The distribution of y is of the form
fY (y; ; ‘) = exp

A

fy() 􀀀
(())g +  (y; ‘)

where ‘ is a scale parameter (possibly known), and is constant for all observations, A
represents a prior weight, assumed known but possibly varying with the observations,
and  is the mean of y. So it is assumed that the distribution of y is determined by its
mean and possibly a scale parameter as well.
 The mean, , is a smooth invertible function of the linear predictor:
 = m();  = m􀀀1() = `()
and this inverse function, `(), is called the link function.
These assumptions are loose enough to encompass a wide class of models useful in
statistical practice, but tight enough to allow the development of a unified methodology
of estimation and inference, at least approximately. The reader is referred to any of the
current reference works on the subject for full details, such as McCullagh & Nelder (1989)
or Dobson (1990).
11.6.1 Families
The class of generalized linear models handled by facilities supplied in R includes gaussian,
binomial, poisson, inverse gaussian and gamma response distributions and also quasilikelihood
models where the response distribution is not explicitly specified. In the latter
case the variance function must be specified as a function of the mean, but in other cases
this function is implied by the response distribution.
Each response distribution admits a variety of link functions to connect the mean with
the linear predictor. Those automatically available are shown in the following table:
Family name Link functions
binomial logit, probit, log, cloglog
gaussian identity, log, inverse
Gamma identity, inverse, log
Chapter 11: Statistical models in R 61
inverse.gaussian 1/mu^2, identity, inverse, log
poisson identity, log, sqrt
quasi logit, probit, cloglog, identity, inverse,
log, 1/mu^2, sqrt
The combination of a response distribution, a link function and various other pieces of
information that are needed to carry out the modeling exercise is called the family of the
generalized linear model.
11.6.2 The glm() function
Since the distribution of the response depends on the stimulus variables through a single
linear function only, the same mechanism as was used for linear models can still be used to
specify the linear part of a generalized model. The family has to be specified in a different
way.
The R function to fit a generalized linear model is glm() which uses the form
> fitted.model <- glm(formula, family=family.generator, data=data.frame)
The only new feature is the family.generator, which is the instrument by which the family
is described. It is the name of a function that generates a list of functions and expressions
that together define and control the model and estimation process. Although this may seem
a little complicated at first sight, its use is quite simple.
The names of the standard, supplied family generators are given under “Family Name”
in the table in Section 11.6.1 [Families], page 60. Where there is a choice of links, the name
of the link may also be supplied with the family name, in parentheses as a parameter. In
the case of the quasi family, the variance function may also be specified in this way.
Some examples make the process clear.
The gaussian family
A call such as
> fm <- glm(y ~ x1 + x2, family = gaussian, data = sales)
achieves the same result as
> fm <- lm(y ~ x1+x2, data=sales)
but much less efficiently. Note how the gaussian family is not automatically provided with
a choice of links, so no parameter is allowed. If a problem requires a gaussian family with
a nonstandard link, this can usually be achieved through the quasi family, as we shall see
later.
The binomial family
Consider a small, artificial example, from Silvey (1970).
On the Aegean island of Kalythos the male inhabitants suffer from a congenital eye
disease, the effects of which become more marked with increasing age. Samples of islander
males of various ages were tested for blindness and the results recorded. The data is shown
below:
Age: 20 35 45 55 70
No. tested: 50 50 50 50 50
No. blind: 6 17 26 37 44
Chapter 11: Statistical models in R 62
The problem we consider is to fit both logistic and probit models to this data, and to
estimate for each model the LD50, that is the age at which the chance of blindness for a
male inhabitant is 50%.
If y is the number of blind at age x and n the number tested, both models have the form
y  B(n; F( 0 + 1x))
where for the probit case, F(z) = Φ(z) is the standard normal distribution function, and in
the logit case (the default), F(z) = ez=(1 + ez). In both cases the LD50 is
LD50 = 􀀀 0= 1
that is, the point at which the argument of the distribution function is zero.
The first step is to set the data up as a data frame
> kalythos <- data.frame(x = c(20,35,45,55,70), n = rep(50,5),
y = c(6,17,26,37,44))
To fit a binomial model using glm() there are three possibilities for the response:
 If the response is a vector it is assumed to hold binary data, and so must be a 0=1
vector.
 If the response is a two-column matrix it is assumed that the first column holds the
number of successes for the trial and the second holds the number of failures.
 If the response is a factor, its first level is taken as failure (0) and all other levels as
‘success’ (1).
Here we need the second of these conventions, so we add a matrix to our data frame:
> kalythos$Ymat <- cbind(kalythos$y, kalythos$n – kalythos$y)
To fit the models we use
> fmp <- glm(Ymat ~ x, family = binomial(link=probit), data = kalythos)
> fml <- glm(Ymat ~ x, family = binomial, data = kalythos)
Since the logit link is the default the parameter may be omitted on the second call. To
see the results of each fit we could use
> summary(fmp)
> summary(fml)
Both models fit (all too) well. To find the LD50 estimate we can use a simple function:
> ld50 <- function(b) -b[1]/b[2]
> ldp <- ld50(coef(fmp)); ldl <- ld50(coef(fml)); c(ldp, ldl)
The actual estimates from this data are 43.663 years and 43.601 years respectively.
Poisson models
With the Poisson family the default link is the log, and in practice the major use of
this family is to fit surrogate Poisson log-linear models to frequency data, whose actual
distribution is often multinomial. This is a large and important subject we will not discuss
further here. It even forms a major part of the use of non-gaussian generalized models
overall.
Chapter 11: Statistical models in R 63
Occasionally genuinely Poisson data arises in practice and in the past it was often analyzed
as gaussian data after either a log or a square-root transformation. As a graceful
alternative to the latter, a Poisson generalized linear model may be fitted as in the following
example:
> fmod <- glm(y ~ A + B + x, family = poisson(link=sqrt),
data = worm.counts)
Quasi-likelihood models
For all families the variance of the response will depend on the mean and will have the
scale parameter as a multiplier. The form of dependence of the variance on the mean
is a characteristic of the response distribution; for example for the poisson distribution
Var[y] = .
For quasi-likelihood estimation and inference the precise response distribution is not
specified, but rather only a link function and the form of the variance function as it depends
on the mean. Since quasi-likelihood estimation uses formally identical techniques to those
for the gaussian distribution, this family provides a way of fitting gaussian models with
non-standard link functions or variance functions, incidentally.
For example, consider fitting the non-linear regression
y = 1z1
z2 􀀀 2
+ e
which may be written alternatively as
y =
1
1×1 + 2×2
+ e
where x1 = z2=z1, x2 = 􀀀1=z1, 1 = 1=1 and 2 = 2=1. Supposing a suitable data frame
to be set up we could fit this non-linear regression as
> nlfit <- glm(y ~ x1 + x2 – 1,
family = quasi(link=inverse, variance=constant),
data = biochem)
The reader is referred to the manual and the help document for further information, as
needed.
11.7 Nonlinear least squares and maximum likelihood
models
Certain forms of nonlinear model can be fitted by Generalized Linear Models (glm()). But
in the majority of cases we have to approach the nonlinear curve fitting problem as one
of nonlinear optimization. R’s nonlinear optimization routines are optim(), nlm() and
(from R 2.2.0) nlminb(), which provide the functionality (and more) of S-Plus’s ms()
and nlminb(). We seek the parameter values that minimize some index of lack-of-fit, and
they do this by trying out various parameter values iteratively. Unlike linear regression for
example, there is no guarantee that the procedure will converge on satisfactory estimates.
All the methods require initial guesses about what parameter values to try, and convergence
may depend critically upon the quality of the starting values.
Chapter 11: Statistical models in R 64
11.7.1 Least squares
One way to fit a nonlinear model is by minimizing the sum of the squared errors (SSE) or
residuals. This method makes sense if the observed errors could have plausibly arisen from
a normal distribution.
Here is an example from Bates & Watts (1988), page 51. The data are:
> x <- c(0.02, 0.02, 0.06, 0.06, 0.11, 0.11, 0.22, 0.22, 0.56, 0.56,
1.10, 1.10)
> y <- c(76, 47, 97, 107, 123, 139, 159, 152, 191, 201, 207, 200)
The fit criterion to be minimized is:
> fn <- function(p) sum((y – (p[1] * x)/(p[2] + x))^2)
In order to do the fit we need initial estimates of the parameters. One way to find
sensible starting values is to plot the data, guess some parameter values, and superimpose
the model curve using those values.
> plot(x, y)
> xfit <- seq(.02, 1.1, .05)
> yfit <- 200 * xfit/(0.1 + xfit)
> lines(spline(xfit, yfit))
We could do better, but these starting values of 200 and 0.1 seem adequate. Now do the
fit:
> out <- nlm(fn, p = c(200, 0.1), hessian = TRUE)
After the fitting, out$minimum is the SSE, and out$estimate are the least squares estimates
of the parameters. To obtain the approximate standard errors (SE) of the estimates
we do:
> sqrt(diag(2*out$minimum/(length(y) – 2) * solve(out$hessian)))
The 2 in the line above represents the number of parameters. A 95% confidence interval
would be the parameter estimate  1.96 SE. We can superimpose the least squares fit on a
new plot:
> plot(x, y)
> xfit <- seq(.02, 1.1, .05)
> yfit <- 212.68384222 * xfit/(0.06412146 + xfit)
> lines(spline(xfit, yfit))
The standard package stats provides much more extensive facilities for fitting non-linear
models by least squares. The model we have just fitted is the Michaelis-Menten model, so
we can use
> df <- data.frame(x=x, y=y)
> fit <- nls(y ~ SSmicmen(x, Vm, K), df)
> fit
Nonlinear regression model
model: y ~ SSmicmen(x, Vm, K)
data: df
Vm K
212.68370711 0.06412123
residual sum-of-squares: 1195.449
Chapter 11: Statistical models in R 65
> summary(fit)
Formula: y ~ SSmicmen(x, Vm, K)
Parameters:
Estimate Std. Error t value Pr(>|t|)
Vm 2.127e+02 6.947e+00 30.615 3.24e-11
K 6.412e-02 8.281e-03 7.743 1.57e-05
Residual standard error: 10.93 on 10 degrees of freedom
Correlation of Parameter Estimates:
Vm
K 0.7651
11.7.2 Maximum likelihood
Maximum likelihood is a method of nonlinear model fitting that applies even if the errors
are not normal. The method finds the parameter values which maximize the log likelihood,
or equivalently which minimize the negative log-likelihood. Here is an example from Dobson
(1990), pp. 108–111. This example fits a logistic model to dose-response data, which clearly
could also be fit by glm(). The data are:
> x <- c(1.6907, 1.7242, 1.7552, 1.7842, 1.8113,
1.8369, 1.8610, 1.8839)
> y <- c( 6, 13, 18, 28, 52, 53, 61, 60)
> n <- c(59, 60, 62, 56, 63, 59, 62, 60)
The negative log-likelihood to minimize is:
> fn <- function(p)
sum( – (y*(p[1]+p[2]*x) – n*log(1+exp(p[1]+p[2]*x))
+ log(choose(n, y)) ))
We pick sensible starting values and do the fit:
> out <- nlm(fn, p = c(-50,20), hessian = TRUE)
After the fitting, out$minimum is the negative log-likelihood, and out$estimate are the
maximum likelihood estimates of the parameters. To obtain the approximate SEs of the
estimates we do:
> sqrt(diag(solve(out$hessian)))
A 95% confidence interval would be the parameter estimate  1.96 SE.
11.8 Some non-standard models
We conclude this chapter with just a brief mention of some of the other facilities available
in R for special regression and data analysis problems.
 Mixed models. The recommended nlme package provides functions lme() and nlme()
for linear and non-linear mixed-effects models, that is linear and non-linear regressions
in which some of the coefficients correspond to random effects. These functions make
heavy use of formulae to specify the models.
Chapter 11: Statistical models in R 66
 Local approximating regressions. The loess() function fits a nonparametric regression
by using a locally weighted regression. Such regressions are useful for highlighting a
trend in messy data or for data reduction to give some insight into a large data set.
Function loess is in the standard package stats, together with code for projection
pursuit regression.
 Robust regression. There are several functions available for fitting regression models
in a way resistant to the influence of extreme outliers in the data. Function lqs in
the recommended package MASS provides state-of-art algorithms for highly-resistant
fits. Less resistant but statistically more efficient methods are available in packages,
for example function rlm in package MASS.
 Additive models. This technique aims to construct a regression function from smooth
additive functions of the determining variables, usually one for each determining variable.
Functions avas and ace in package acepack and functions bruto and mars in
package mda provide some examples of these techniques in user-contributed packages
to R. An extension is Generalized Additive Models, implemented in user-contributed
packages gam and mgcv.
 Tree-based models. Rather than seek an explicit global linear model for prediction
or interpretation, tree-based models seek to bifurcate the data, recursively, at critical
points of the determining variables in order to partition the data ultimately into groups
that are as homogeneous as possible within, and as heterogeneous as possible between.
The results often lead to insights that other data analysis methods tend not to yield.
Models are again specified in the ordinary linear model form. The model fitting function
is tree(), but many other generic functions such as plot() and text() are well
adapted to displaying the results of a tree-based model fit in a graphical way.
Tree models are available in R via the user-contributed packages rpart and tree.
Chapter 12: Graphical procedures 67
12 Graphical procedures
Graphical facilities are an important and extremely versatile component of the R environment.
It is possible to use the facilities to display a wide variety of statistical graphs and
also to build entirely new types of graph.
The graphics facilities can be used in both interactive and batch modes, but in most
cases, interactive use is more productive. Interactive use is also easy because at startup
time R initiates a graphics device driver which opens a special graphics window for the
display of interactive graphics. Although this is done automatically, it is useful to know
that the command used is X11() under UNIX, windows() under Windows and quartz()
under Mac OS X.
Once the device driver is running, R plotting commands can be used to produce a variety
of graphical displays and to create entirely new kinds of display.
Plotting commands are divided into three basic groups:
 High-level plotting functions create a new plot on the graphics device, possibly with
axes, labels, titles and so on.
 Low-level plotting functions add more information to an existing plot, such as extra
points, lines and labels.
 Interactive graphics functions allow you interactively add information to, or extract
information from, an existing plot, using a pointing device such as a mouse.
In addition, R maintains a list of graphical parameters which can be manipulated to
customize your plots.
This manual only describes what are known as ‘base’ graphics. A separate graphics
sub-system in package grid coexists with base – it is more powerful but harder to use.
There is a recommended package lattice which builds on grid and provides ways to produce
multi-panel plots akin to those in the Trellis system in S.
12.1 High-level plotting commands
High-level plotting functions are designed to generate a complete plot of the data passed
as arguments to the function. Where appropriate, axes, labels and titles are automatically
generated (unless you request otherwise.) High-level plotting commands always start a new
plot, erasing the current plot if necessary.
12.1.1 The plot() function
One of the most frequently used plotting functions in R is the plot() function. This is a
generic function: the type of plot produced is dependent on the type or class of the first
argument.
plot(x, y)
plot(xy) If x and y are vectors, plot(x, y) produces a scatterplot of y against x. The
same effect can be produced by supplying one argument (second form) as either
a list containing two elements x and y or a two-column matrix.
plot(x) If x is a time series, this produces a time-series plot. If x is a numeric vector, it
produces a plot of the values in the vector against their index in the vector. If
Chapter 12: Graphical procedures 68
x is a complex vector, it produces a plot of imaginary versus real parts of the
vector elements.
plot(f)
plot(f, y)
f is a factor object, y is a numeric vector. The first form generates a bar plot
of f ; the second form produces boxplots of y for each level of f.
plot(df)
plot(~ expr)
plot(y ~ expr)
df is a data frame, y is any object, expr is a list of object names separated
by ‘+’ (e.g., a + b + c). The first two forms produce distributional plots of the
variables in a data frame (first form) or of a number of named objects (second
form). The third form plots y against every object named in expr.
12.1.2 Displaying multivariate data
R provides two very useful functions for representing multivariate data. If X is a numeric
matrix or data frame, the command
> pairs(X)
produces a pairwise scatterplot matrix of the variables defined by the columns of X, that
is, every column of X is plotted against every other column of X and the resulting n(n 􀀀 1)
plots are arranged in a matrix with plot scales constant over the rows and columns of the
matrix.
When three or four variables are involved a coplot may be more enlightening. If a and
b are numeric vectors and c is a numeric vector or factor object (all of the same length),
then the command
> coplot(a ~ b | c)
produces a number of scatterplots of a against b for given values of c. If c is a factor,
this simply means that a is plotted against b for every level of c. When c is numeric, it is
divided into a number of conditioning intervals and for each interval a is plotted against b
for values of c within the interval. The number and position of intervals can be controlled
with given.values= argument to coplot()—the function co.intervals() is useful for
selecting intervals. You can also use two given variables with a command like
> coplot(a ~ b | c + d)
which produces scatterplots of a against b for every joint conditioning interval of c and d.
The coplot() and pairs() function both take an argument panel= which can be used to
customize the type of plot which appears in each panel. The default is points() to produce
a scatterplot but by supplying some other low-level graphics function of two vectors x and
y as the value of panel= you can produce any type of plot you wish. An example panel
function useful for coplots is panel.smooth().
12.1.3 Display graphics
Other high-level graphics functions produce different types of plots. Some examples are:
Chapter 12: Graphical procedures 69
qqnorm(x)
qqline(x)
qqplot(x, y)
Distribution-comparison plots. The first form plots the numeric vector x against
the expected Normal order scores (a normal scores plot) and the second adds a
straight line to such a plot by drawing a line through the distribution and data
quartiles. The third form plots the quantiles of x against those of y to compare
their respective distributions.
hist(x)
hist(x, nclass=n)
hist(x, breaks=b, …)
Produces a histogram of the numeric vector x. A sensible number of classes is
usually chosen, but a recommendation can be given with the nclass= argument.
Alternatively, the breakpoints can be specified exactly with the breaks= argument.
If the probability=TRUE argument is given, the bars represent relative
frequencies divided by bin width instead of counts.
dotchart(x, …)
Constructs a dotchart of the data in x. In a dotchart the y-axis gives a labelling
of the data in x and the x-axis gives its value. For example it allows easy visual
selection of all data entries with values lying in specified ranges.
image(x, y, z, …)
contour(x, y, z, …)
persp(x, y, z, …)
Plots of three variables. The image plot draws a grid of rectangles using different
colours to represent the value of z, the contour plot draws contour lines to
represent the value of z, and the persp plot draws a 3D surface.
12.1.4 Arguments to high-level plotting functions
There are a number of arguments which may be passed to high-level graphics functions, as
follows:
add=TRUE Forces the function to act as a low-level graphics function, superimposing the
plot on the current plot (some functions only).
axes=FALSE
Suppresses generation of axes—useful for adding your own custom axes with
the axis() function. The default, axes=TRUE, means include axes.
log=”x”
log=”y”
log=”xy” Causes the x, y or both axes to be logarithmic. This will work for many, but
not all, types of plot.
type= The type= argument controls the type of plot produced, as follows:
type=”p” Plot individual points (the default)
type=”l” Plot lines
type=”b” Plot points connected by lines (both)
Chapter 12: Graphical procedures 70
type=”o” Plot points overlaid by lines
type=”h” Plot vertical lines from points to the zero axis (high-density)
type=”s”
type=”S” Step-function plots. In the first form, the top of the vertical defines
the point; in the second, the bottom.
type=”n” No plotting at all. However axes are still drawn (by default) and
the coordinate system is set up according to the data. Ideal for
creating plots with subsequent low-level graphics functions.
xlab=string
ylab=string
Axis labels for the x and y axes. Use these arguments to change the default
labels, usually the names of the objects used in the call to the high-level plotting
function.
main=string
Figure title, placed at the top of the plot in a large font.
sub=string
Sub-title, placed just below the x-axis in a smaller font.
12.2 Low-level plotting commands
Sometimes the high-level plotting functions don’t produce exactly the kind of plot you
desire. In this case, low-level plotting commands can be used to add extra information
(such as points, lines or text) to the current plot.
Some of the more useful low-level plotting functions are:
points(x, y)
lines(x, y)
Adds points or connected lines to the current plot. plot()’s type= argument
can also be passed to these functions (and defaults to “p” for points() and
“l” for lines().)
text(x, y, labels, …)
Add text to a plot at points given by x, y. Normally labels is an integer or
character vector in which case labels[i] is plotted at point (x[i], y[i]).
The default is 1:length(x).
Note: This function is often used in the sequence
> plot(x, y, type=”n”); text(x, y, names)
The graphics parameter type=”n” suppresses the points but sets up the axes,
and the text() function supplies special characters, as specified by the character
vector names for the points.
abline(a, b)
abline(h=y)
abline(v=x)
abline(lm.obj)
Adds a line of slope b and intercept a to the current plot. h=y may be used to
specify y-coordinates for the heights of horizontal lines to go across a plot, and
Chapter 12: Graphical procedures 71
v=x similarly for the x-coordinates for vertical lines. Also lm.obj may be list
with a coefficients component of length 2 (such as the result of model-fitting
functions,) which are taken as an intercept and slope, in that order.
polygon(x, y, …)
Draws a polygon defined by the ordered vertices in (x, y) and (optionally) shade
it in with hatch lines, or fill it if the graphics device allows the filling of figures.
legend(x, y, legend, …)
Adds a legend to the current plot at the specified position. Plotting characters,
line styles, colors etc., are identified with the labels in the character vector
legend. At least one other argument v (a vector the same length as legend)
with the corresponding values of the plotting unit must also be given, as follows:
legend( , fill=v)
Colors for filled boxes
legend( , col=v)
Colors in which points or lines will be drawn
legend( , lty=v)
Line styles
legend( , lwd=v)
Line widths
legend( , pch=v)
Plotting characters (character vector)
title(main, sub)
Adds a title main to the top of the current plot in a large font and (optionally)
a sub-title sub at the bottom in a smaller font.
axis(side, …)
Adds an axis to the current plot on the side given by the first argument (1 to 4,
counting clockwise from the bottom.) Other arguments control the positioning
of the axis within or beside the plot, and tick positions and labels. Useful for
adding custom axes after calling plot() with the axes=FALSE argument.
Low-level plotting functions usually require some positioning information (e.g., x and y
coordinates) to determine where to place the new plot elements. Coordinates are given in
terms of user coordinates which are defined by the previous high-level graphics command
and are chosen based on the supplied data.
Where x and y arguments are required, it is also sufficient to supply a single argument
being a list with elements named x and y. Similarly a matrix with two columns is also
valid input. In this way functions such as locator() (see below) may be used to specify
positions on a plot interactively.
12.2.1 Mathematical annotation
In some cases, it is useful to add mathematical symbols and formulae to a plot. This can
be achieved in R by specifying an expression rather than a character string in any one of
text, mtext, axis, or title. For example, the following code draws the formula for the
Binomial probability function:
Chapter 12: Graphical procedures 72
> text(x, y, expression(paste(bgroup(“(“, atop(n, x), “)”), p^x, q^{n-x})))
More information, including a full listing of the features available can obtained from
within R using the commands:
> help(plotmath)
> example(plotmath)
> demo(plotmath)
12.2.2 Hershey vector fonts
It is possible to specify Hershey vector fonts for rendering text when using the text and
contour functions. There are three reasons for using the Hershey fonts:
 Hershey fonts can produce better output, especially on a computer screen, for rotated
and/or small text.
 Hershey fonts provide certain symbols that may not be available in the standard fonts.
In particular, there are zodiac signs, cartographic symbols and astronomical symbols.
 Hershey fonts provide cyrillic and japanese (Kana and Kanji) characters.
More information, including tables of Hershey characters can be obtained from within
R using the commands:
> help(Hershey)
> demo(Hershey)
> help(Japanese)
> demo(Japanese)
12.3 Interacting with graphics
R also provides functions which allow users to extract or add information to a plot using a
mouse. The simplest of these is the locator() function:
locator(n, type)
Waits for the user to select locations on the current plot using the left mouse
button. This continues until n (default 512) points have been selected, or
another mouse button is pressed. The type argument allows for plotting at the
selected points and has the same effect as for high-level graphics commands; the
default is no plotting. locator() returns the locations of the points selected
as a list with two components x and y.
locator() is usually called with no arguments. It is particularly useful for interactively
selecting positions for graphic elements such as legends or labels when it is difficult to
calculate in advance where the graphic should be placed. For example, to place some
informative text near an outlying point, the command
> text(locator(1), “Outlier”, adj=0)
may be useful. (locator() will be ignored if the current device, such as postscript does
not support interactive pointing.)
identify(x, y, labels)
Allow the user to highlight any of the points defined by x and y (using the left
mouse button) by plotting the corresponding component of labels nearby (or
Chapter 12: Graphical procedures 73
the index number of the point if labels is absent). Returns the indices of the
selected points when another button is pressed.
Sometimes we want to identify particular points on a plot, rather than their positions.
For example, we may wish the user to select some observation of interest from a graphical
display and then manipulate that observation in some way. Given a number of (x; y)
coordinates in two numeric vectors x and y, we could use the identify() function as
follows:
> plot(x, y)
> identify(x, y)
The identify() functions performs no plotting itself, but simply allows the user to
move the mouse pointer and click the left mouse button near a point. If there is a point
near the mouse pointer it will be marked with its index number (that is, its position in the
x/y vectors) plotted nearby. Alternatively, you could use some informative string (such as a
case name) as a highlight by using the labels argument to identify(), or disable marking
altogether with the plot = FALSE argument. When the process is terminated (see above),
identify() returns the indices of the selected points; you can use these indices to extract
the selected points from the original vectors x and y.
12.4 Using graphics parameters
When creating graphics, particularly for presentation or publication purposes, R’s defaults
do not always produce exactly that which is required. You can, however, customize almost
every aspect of the display using graphics parameters. R maintains a list of a large number
of graphics parameters which control things such as line style, colors, figure arrangement
and text justification among many others. Every graphics parameter has a name (such as
‘col’, which controls colors,) and a value (a color number, for example.)
A separate list of graphics parameters is maintained for each active device, and each
device has a default set of parameters when initialized. Graphics parameters can be set
in two ways: either permanently, affecting all graphics functions which access the current
device; or temporarily, affecting only a single graphics function call.
12.4.1 Permanent changes: The par() function
The par() function is used to access and modify the list of graphics parameters for the
current graphics device.
par() Without arguments, returns a list of all graphics parameters and their values
for the current device.
par(c(“col”, “lty”))
With a character vector argument, returns only the named graphics parameters
(again, as a list.)
par(col=4, lty=2)
With named arguments (or a single list argument), sets the values of the named
graphics parameters, and returns the original values of the parameters as a list.
Setting graphics parameters with the par() function changes the value of the parameters
permanently, in the sense that all future calls to graphics functions (on the current device)
Chapter 12: Graphical procedures 74
will be affected by the new value. You can think of setting graphics parameters in this way
as setting “default” values for the parameters, which will be used by all graphics functions
unless an alternative value is given.
Note that calls to par() always affect the global values of graphics parameters, even
when par() is called from within a function. This is often undesirable behavior—usually
we want to set some graphics parameters, do some plotting, and then restore the original
values so as not to affect the user’s R session. You can restore the initial values by saving
the result of par() when making changes, and restoring the initial values when plotting is
complete.
> oldpar <- par(col=4, lty=2)
. . . plotting commands . . .
> par(oldpar)
To save and restore all settable1 graphical parameters use
> oldpar <- par(no.readonly=TRUE)
. . . plotting commands . . .
> par(oldpar)
12.4.2 Temporary changes: Arguments to graphics functions
Graphics parameters may also be passed to (almost) any graphics function as named arguments.
This has the same effect as passing the arguments to the par() function, except
that the changes only last for the duration of the function call. For example:
> plot(x, y, pch=”+”)
produces a scatterplot using a plus sign as the plotting character, without changing the
default plotting character for future plots.
Unfortunately, this is not implemented entirely consistently and it is sometimes necessary
to set and reset graphics parameters using par().
12.5 Graphics parameters list
The following sections detail many of the commonly-used graphical parameters. The R help
documentation for the par() function provides a more concise summary; this is provided
as a somewhat more detailed alternative.
Graphics parameters will be presented in the following form:
name=value
A description of the parameter’s effect. name is the name of the parameter,
that is, the argument name to use in calls to par() or a graphics function.
value is a typical value you might use when setting the parameter.
Note that axes is not a graphics parameter but an argument to a few plot methods:
see xaxt and yaxt.
1 Some graphics parameters such as the size of the current device are for information only.
Chapter 12: Graphical procedures 75
12.5.1 Graphical elements
R plots are made up of points, lines, text and polygons (filled regions.) Graphical parameters
exist which control how these graphical elements are drawn, as follows:
pch=”+” Character to be used for plotting points. The default varies with graphics
drivers, but it is usually ‘’. Plotted points tend to appear slightly above or
below the appropriate position unless you use “.” as the plotting character,
which produces centered points.
pch=4 When pch is given as an integer between 0 and 25 inclusive, a specialized
plotting symbol is produced. To see what the symbols are, use the command
> legend(locator(1), as.character(0:25), pch = 0:25)
Those from 21 to 25 may appear to duplicate earlier symbols, but can be
coloured in different ways: see the help on points and its examples.
In addition, pch can be a character or a number in the range 32:255 representing
a character in the current font.
lty=2 Line types. Alternative line styles are not supported on all graphics devices
(and vary on those that do) but line type 1 is always a solid line, line type 0
is always invisible, and line types 2 and onwards are dotted or dashed lines, or
some combination of both.
lwd=2 Line widths. Desired width of lines, in multiples of the “standard” line width.
Affects axis lines as well as lines drawn with lines(), etc. Not all devices
support this, and some have restrictions on the widths that can be used.
col=2 Colors to be used for points, lines, text, filled regions and images. A number
from the current palette (see ?palette) or a named colour.
col.axis
col.lab
col.main
col.sub The color to be used for axis annotation, x and y labels, main and sub-titles,
respectively.
font=2 An integer which specifies which font to use for text. If possible, device drivers
arrange so that 1 corresponds to plain text, 2 to bold face, 3 to italic, 4 to bold
italic and 5 to a symbol font (which include Greek letters).
font.axis
font.lab
font.main
font.sub The font to be used for axis annotation, x and y labels, main and sub-titles,
respectively.
adj=-0.1 Justification of text relative to the plotting position. 0 means left justify, 1
means right justify and 0.5 means to center horizontally about the plotting
position. The actual value is the proportion of text that appears to the left of
the plotting position, so a value of -0.1 leaves a gap of 10% of the text width
between the text and the plotting position.
Chapter 12: Graphical procedures 76
cex=1.5 Character expansion. The value is the desired size of text characters (including
plotting characters) relative to the default text size.
cex.axis
cex.lab
cex.main
cex.sub The character expansion to be used for axis annotation, x and y labels, main
and sub-titles, respectively.
12.5.2 Axes and tick marks
Many of R’s high-level plots have axes, and you can construct axes yourself with the lowlevel
axis() graphics function. Axes have three main components: the axis line (line style
controlled by the lty graphics parameter), the tick marks (which mark off unit divisions
along the axis line) and the tick labels (which mark the units.) These components can be
customized with the following graphics parameters.
lab=c(5, 7, 12)
The first two numbers are the desired number of tick intervals on the x and
y axes respectively. The third number is the desired length of axis labels, in
characters (including the decimal point.) Choosing a too-small value for this
parameter may result in all tick labels being rounded to the same number!
las=1 Orientation of axis labels. 0 means always parallel to axis, 1 means always
horizontal, and 2 means always perpendicular to the axis.
mgp=c(3, 1, 0)
Positions of axis components. The first component is the distance from the axis
label to the axis position, in text lines. The second component is the distance to
the tick labels, and the final component is the distance from the axis position to
the axis line (usually zero). Positive numbers measure outside the plot region,
negative numbers inside.
tck=0.01 Length of tick marks, as a fraction of the size of the plotting region. When
tck is small (less than 0.5) the tick marks on the x and y axes are forced to
be the same size. A value of 1 gives grid lines. Negative values give tick marks
outside the plotting region. Use tck=0.01 and mgp=c(1,-1.5,0) for internal
tick marks.
xaxs=”r”
yaxs=”i” Axis styles for the x and y axes, respectively. With styles “i” (internal) and
“r” (the default) tick marks always fall within the range of the data, however
style “r” leaves a small amount of space at the edges. (S has other styles not
implemented in R.)
12.5.3 Figure margins
A single plot in R is known as a figure and comprises a plot region surrounded by margins
(possibly containing axis labels, titles, etc.) and (usually) bounded by the axes themselves.
Chapter 12: Graphical procedures 77
A typical figure is
x
y
−3.0 −1.5 0.0 1.5 3.0
−3.0 −1.5 0.0 1.5 3.0
Plot region
mai[1]
mai[2]
Margin
−−−−−−−−−−−−−−−−−−
−−−−−−−−−−−−−−−−−−
−−−−−−−−−−−−−−−−−−
−−−−−−−−−−−−−−−−−−
−−−−−−−−−−−−−−−−−−
−−−−−−−−−−−−−−−−−−
mar[3]
Graphics parameters controlling figure layout include:
mai=c(1, 0.5, 0.5, 0)
Widths of the bottom, left, top and right margins, respectively, measured in
inches.
mar=c(4, 2, 2, 1)
Similar to mai, except the measurement unit is text lines.
mar and mai are equivalent in the sense that setting one changes the value of the other.
The default values chosen for this parameter are often too large; the right-hand margin is
rarely needed, and neither is the top margin if no title is being used. The bottom and left
margins must be large enough to accommodate the axis and tick labels. Furthermore, the
default is chosen without regard to the size of the device surface: for example, using the
postscript() driver with the height=4 argument will result in a plot which is about 50%
margin unless mar or mai are set explicitly. When multiple figures are in use (see below) the
margins are reduced, however this may not be enough when many figures share the same
page.
Chapter 12: Graphical procedures 78
12.5.4 Multiple figure environment
R allows you to create an n by m array of figures on a single page. Each figure has its own
margins, and the array of figures is optionally surrounded by an outer margin, as shown in
the following figure.
mfg=c(3,2,3,2)
omi[1]
omi[4]
mfrow=c(3,2)
−−−−−−−−−−−−−−−
−−−−−−−−−−−−−−−
−−−−−−−−−−−−−−−
−−−−−−−−−−−−−−−
−−−−−−−−−−−−−−−
oma[3]
The graphical parameters relating to multiple figures are as follows:
mfcol=c(3, 2)
mfrow=c(2, 4)
Set the size of a multiple figure array. The first value is the number of rows;
the second is the number of columns. The only difference between these two
parameters is that setting mfcol causes figures to be filled by column; mfrow
fills by rows.
The layout in the Figure could have been created by setting mfrow=c(3,2); the
figure shows the page after four plots have been drawn.
Setting either of these can reduce the base size of symbols and text (controlled
by par(“cex”) and the pointsize of the device). In a layout with exactly two
rows and columns the base size is reduced by a factor of 0.83: if there are three
or more of either rows or columns, the reduction factor is 0.66.
mfg=c(2, 2, 3, 2)
Position of the current figure in a multiple figure environment. The first two
numbers are the row and column of the current figure; the last two are the
number of rows and columns in the multiple figure array. Set this parameter
to jump between figures in the array. You can even use different values for the
last two numbers than the true values for unequally-sized figures on the same
page.
fig=c(4, 9, 1, 4)/10
Position of the current figure on the page. Values are the positions of the left,
right, bottom and top edges respectively, as a percentage of the page measured
Chapter 12: Graphical procedures 79
from the bottom left corner. The example value would be for a figure in the
bottom right of the page. Set this parameter for arbitrary positioning of figures
within a page. If you want to add a figure to a current page, use new=TRUE as
well (unlike S).
oma=c(2, 0, 3, 0)
omi=c(0, 0, 0.8, 0)
Size of outer margins. Like mar and mai, the first measures in text lines and
the second in inches, starting with the bottom margin and working clockwise.
Outer margins are particularly useful for page-wise titles, etc. Text can be added to the
outer margins with the mtext() function with argument outer=TRUE. There are no outer
margins by default, however, so you must create them explicitly using oma or omi.
More complicated arrangements of multiple figures can be produced by the
split.screen() and layout() functions, as well as by the grid and lattice packages.
12.6 Device drivers
R can generate graphics (of varying levels of quality) on almost any type of display or
printing device. Before this can begin, however, R needs to be informed what type of
device it is dealing with. This is done by starting a device driver. The purpose of a device
driver is to convert graphical instructions from R (“draw a line,” for example) into a form
that the particular device can understand.
Device drivers are started by calling a device driver function. There is one such function
for every device driver: type help(Devices) for a list of them all. For example, issuing the
command
> postscript()
causes all future graphics output to be sent to the printer in PostScript format. Some
commonly-used device drivers are:
X11() For use with the X11 window system on Unix-alikes
windows()
For use on Windows
quartz() For use on Mac OS X
postscript()
For printing on PostScript printers, or creating PostScript graphics files.
pdf() Produces a PDF file, which can also be included into PDF files.
png() Produces a bitmap PNG file. (Not always available: see its help page.)
jpeg() Produces a bitmap JPEG file, best used for image plots. (Not always available:
see its help page.)
When you have finished with a device, be sure to terminate the device driver by issuing
the command
> dev.off()
This ensures that the device finishes cleanly; for example in the case of hardcopy devices
this ensures that every page is completed and has been sent to the printer. (This will
happen automatically at the normal end of a session.)
Chapter 12: Graphical procedures 80
12.6.1 PostScript diagrams for typeset documents
By passing the file argument to the postscript() device driver function, you may store
the graphics in PostScript format in a file of your choice. The plot will be in landscape
orientation unless the horizontal=FALSE argument is given, and you can control the size
of the graphic with the width and height arguments (the plot will be scaled as appropriate
to fit these dimensions.) For example, the command
> postscript(“file.ps”, horizontal=FALSE, height=5, pointsize=10)
will produce a file containing PostScript code for a figure five inches high, perhaps for
inclusion in a document. It is important to note that if the file named in the command
already exists, it will be overwritten. This is the case even if the file was only created earlier
in the same R session.
Many usages of PostScript output will be to incorporate the figure in another document.
This works best when encapsulated PostScript is produced: R always produces conformant
output, but only marks the output as such when the onefile=FALSE argument is supplied.
This unusual notation stems from S-compatibility: it really means that the output will be a
single page (which is part of the EPSF specification). Thus to produce a plot for inclusion
use something like
> postscript(“plot1.eps”, horizontal=FALSE, onefile=FALSE,
height=8, width=6, pointsize=10)
12.6.2 Multiple graphics devices
In advanced use of R it is often useful to have several graphics devices in use at the same
time. Of course only one graphics device can accept graphics commands at any one time,
and this is known as the current device. When multiple devices are open, they form a
numbered sequence with names giving the kind of device at any position.
The main commands used for operating with multiple devices, and their meanings are
as follows:
X11() [UNIX]
windows()
win.printer()
win.metafile()
[Windows]
quartz() [Mac OS X]
postscript()
pdf()
png()
jpeg()
tiff()
bitmap()
… Each new call to a device driver function opens a new graphics device, thus
extending by one the device list. This device becomes the current device, to
which graphics output will be sent.
Chapter 12: Graphical procedures 81
dev.list()
Returns the number and name of all active devices. The device at position 1
on the list is always the null device which does not accept graphics commands
at all.
dev.next()
dev.prev()
Returns the number and name of the graphics device next to, or previous to
the current device, respectively.
dev.set(which=k)
Can be used to change the current graphics device to the one at position k of
the device list. Returns the number and label of the device.
dev.off(k)
Terminate the graphics device at point k of the device list. For some devices,
such as postscript devices, this will either print the file immediately or correctly
complete the file for later printing, depending on how the device was
initiated.
dev.copy(device, …, which=k)
dev.print(device, …, which=k)
Make a copy of the device k. Here device is a device function, such as
postscript, with extra arguments, if needed, specified by ‘…’. dev.print is
similar, but the copied device is immediately closed, so that end actions, such
as printing hardcopies, are immediately performed.
graphics.off()
Terminate all graphics devices on the list, except the null device.
12.7 Dynamic graphics
R does not have builtin capabilities for dynamic or interactive graphics, e.g. rotating point
clouds or to “brushing” (interactively highlighting) points. However, extensive dynamic
graphics facilities are available in the system GGobi by Swayne, Cook and Buja available
from
http://www.ggobi.org/
and these can be accessed from R via the package rggobi, described at
http://www.ggobi.org/rggobi.
Also, package rgl provides ways to interact with 3D plots, for example of surfaces.
Chapter 13: Packages 82
13 Packages
All R functions and datasets are stored in packages. Only when a package is loaded are its
contents available. This is done both for efficiency (the full list would take more memory and
would take longer to search than a subset), and to aid package developers, who are protected
from name clashes with other code. The process of developing packages is described in
Section “Creating R packages” in Writing R Extensions. Here, we will describe them from
a user’s point of view.
To see which packages are installed at your site, issue the command
> library()
with no arguments. To load a particular package (e.g., the boot package containing functions
from Davison & Hinkley (1997)), use a command like
> library(boot)
Users connected to the Internet can use the install.packages() and
update.packages() functions (available through the Packages menu in the Windows and
RAqua GUIs, see Section “Installing packages” in R Installation and Administration) to
install and update packages.
To see which packages are currently loaded, use
> search()
to display the search list. Some packages may be loaded but not available on the search list
(see Section 13.3 [Namespaces], page 83): these will be included in the list given by
> loadedNamespaces()
To see a list of all available help topics in an installed package, use
> help.start()
to start the HTML help system, and then navigate to the package listing in the Reference
section.
13.1 Standard packages
The standard (or base) packages are considered part of the R source code. They contain the
basic functions that allow R to work, and the datasets and standard statistical and graphical
functions that are described in this manual. They should be automatically available in any
R installation. See Section “R packages” in R FAQ, for a complete list.
13.2 Contributed packages and CRAN
There are hundreds of contributed packages for R, written by many different authors. Some
of these packages implement specialized statistical methods, others give access to data or
hardware, and others are designed to complement textbooks. Some (the recommended packages)
are distributed with every binary distribution of R. Most are available for download
from CRAN (http://CRAN.R-project.org/ and its mirrors), and other repositories such
as Bioconductor (http://www.bioconductor.org/). The R FAQ contains a list that was
current at the time of release, but the collection of available packages changes frequently.
Chapter 13: Packages 83
13.3 Namespaces
Packages can have namespaces, and currently all of the base and recommended packages do
except the datasets package. Namespaces do three things: they allow the package writer
to hide functions and data that are meant only for internal use, they prevent functions from
breaking when a user (or other package writer) picks a name that clashes with one in the
package, and they provide a way to refer to an object within a particular package.
For example, t() is the transpose function in R, but users might define their own function
named t. Namespaces prevent the user’s definition from taking precedence, and breaking
every function that tries to transpose a matrix.
There are two operators that work with namespaces. The double-colon operator ::
selects definitions from a particular namespace. In the example above, the transpose function
will always be available as base::t, because it is defined in the base package. Only
functions that are exported from the package can be retrieved in this way.
The triple-colon operator ::: may be seen in a few places in R code: it acts like the
double-colon operator but also allows access to hidden objects. Users are more likely to use
the getAnywhere() function, which searches multiple packages.
Packages are often inter-dependent, and loading one may cause others to be automatically
loaded. The colon operators described above will also cause automatic loading of the
associated package. When packages with namespaces are loaded automatically they are not
added to the search list.
Appendix A: A sample session 84
Appendix A A sample session
The following session is intended to introduce to you some features of the R environment
by using them. Many features of the system will be unfamiliar and puzzling at first, but
this puzzlement will soon disappear.
Login, start your windowing system.
$ R Start R as appropriate for your platform.
The R program begins, with a banner.
(Within R, the prompt on the left hand side will not be shown to avoid confusion.)
help.start()
Start the HTML interface to on-line help (using a web browser available at
your machine). You should briefly explore the features of this facility with the
mouse.
Iconify the help window and move on to the next part.
x <- rnorm(50)
y <- rnorm(x)
Generate two pseudo-random normal vectors of x- and y-coordinates.
plot(x, y)
Plot the points in the plane. A graphics window will appear automatically.
ls() See which R objects are now in the R workspace.
rm(x, y) Remove objects no longer needed. (Clean up).
x <- 1:20 Make x = (1; 2; : : : ; 20).
w <- 1 + sqrt(x)/2
A ‘weight’ vector of standard deviations.
dummy <- data.frame(x=x, y= x + rnorm(x)*w)
dummy Make a data frame of two columns, x and y, and look at it.
fm <- lm(y ~ x, data=dummy)
summary(fm)
Fit a simple linear regression and look at the analysis. With y to the left of the
tilde, we are modelling y dependent on x.
fm1 <- lm(y ~ x, data=dummy, weight=1/w^2)
summary(fm1)
Since we know the standard deviations, we can do a weighted regression.
attach(dummy)
Make the columns in the data frame visible as variables.
lrf <- lowess(x, y)
Make a nonparametric local regression function.
plot(x, y)
Standard point plot.
Appendix A: A sample session 85
lines(x, lrf$y)
Add in the local regression.
abline(0, 1, lty=3)
The true regression line: (intercept 0, slope 1).
abline(coef(fm))
Unweighted regression line.
abline(coef(fm1), col = “red”)
Weighted regression line.
detach() Remove data frame from the search path.
plot(fitted(fm), resid(fm),
xlab=”Fitted values”,
ylab=”Residuals”,
main=”Residuals vs Fitted”)
A standard regression diagnostic plot to check for heteroscedasticity. Can you
see it?
qqnorm(resid(fm), main=”Residuals Rankit Plot”)
A normal scores plot to check for skewness, kurtosis and outliers. (Not very
useful here.)
rm(fm, fm1, lrf, x, dummy)
Clean up again.
The next section will look at data from the classical experiment of Michaelson and Morley
to measure the speed of light. This dataset is available in the morley object, but we will
read it to illustrate the read.table function.
filepath <- system.file(“data”, “morley.tab” , package=”datasets”)
filepath Get the path to the data file.
file.show(filepath)
Optional. Look at the file.
mm <- read.table(filepath)
mm Read in the Michaelson and Morley data as a data frame, and look at it. There
are five experiments (column Expt) and each has 20 runs (column Run) and sl
is the recorded speed of light, suitably coded.
mm$Expt <- factor(mm$Expt)
mm$Run <- factor(mm$Run)
Change Expt and Run into factors.
attach(mm)
Make the data frame visible at position 3 (the default).
plot(Expt, Speed, main=”Speed of Light Data”, xlab=”Experiment No.”)
Compare the five experiments with simple boxplots.
fm <- aov(Speed ~ Run + Expt, data=mm)
summary(fm)
Analyze as a randomized block, with ‘runs’ and ‘experiments’ as factors.
Appendix A: A sample session 86
fm0 <- update(fm, . ~ . – Run)
anova(fm0, fm)
Fit the sub-model omitting ‘runs’, and compare using a formal analysis of variance.
detach()
rm(fm, fm0)
Clean up before moving on.
We now look at some more graphical features: contour and image plots.
x <- seq(-pi, pi, len=50)
y <- x x is a vector of 50 equally spaced values in 􀀀  x  . y is the same.
f <- outer(x, y, function(x, y) cos(y)/(1 + x^2))
f is a square matrix, with rows and columns indexed by x and y respectively,
of values of the function cos(y)=(1 + x2).
oldpar <- par(no.readonly = TRUE)
par(pty=”s”)
Save the plotting parameters and set the plotting region to “square”.
contour(x, y, f)
contour(x, y, f, nlevels=15, add=TRUE)
Make a contour map of f; add in more lines for more detail.
fa <- (f-t(f))/2
fa is the “asymmetric part” of f. (t() is transpose).
contour(x, y, fa, nlevels=15)
Make a contour plot, . . .
par(oldpar)
. . . and restore the old graphics parameters.
image(x, y, f)
image(x, y, fa)
Make some high density image plots, (of which you can get hardcopies if you
wish), . . .
objects(); rm(x, y, f, fa)
. . . and clean up before moving on.
R can do complex arithmetic, also.
th <- seq(-pi, pi, len=100)
z <- exp(1i*th)
1i is used for the complex number i.
par(pty=”s”)
plot(z, type=”l”)
Plotting complex arguments means plot imaginary versus real parts. This
should be a circle.
Appendix A: A sample session 87
w <- rnorm(100) + rnorm(100)*1i
Suppose we want to sample points within the unit circle. One method would be
to take complex numbers with standard normal real and imaginary parts . . .
w <- ifelse(Mod(w) > 1, 1/w, w)
. . . and to map any outside the circle onto their reciprocal.
plot(w, xlim=c(-1,1), ylim=c(-1,1), pch=”+”,xlab=”x”, ylab=”y”)
lines(z)
All points are inside the unit circle, but the distribution is not uniform.
w <- sqrt(runif(100))*exp(2*pi*runif(100)*1i)
plot(w, xlim=c(-1,1), ylim=c(-1,1), pch=”+”, xlab=”x”, ylab=”y”)
lines(z)
The second method uses the uniform distribution. The points should now look
more evenly spaced over the disc.
rm(th, w, z)
Clean up again.
q() Quit the R program. You will be asked if you want to save the R workspace,
and for an exploratory session like this, you probably do not want to save it.
Appendix B: Invoking R 88
Appendix B Invoking R
B.1 Invoking R from the command line
When working in UNIX or at a command line in Windows, the command ‘R’ can be used
both for starting the main R program in the form
R [options] [<infile] [>outfile],
or, via the R CMD interface, as a wrapper to various R tools (e.g., for processing files in
R documentation format or manipulating add-on packages) which are not intended to be
called “directly”.
You need to ensure that either the environment variable TMPDIR is unset or it points to
a valid place to create temporary files and directories.
Most options control what happens at the beginning and at the end of an R session.
The startup mechanism is as follows (see also the on-line help for topic ‘Startup’ for more
information, and the section below for some Windows-specific details).
 Unless ‘–no-environ’ was given, R searches for user and site files to process for
setting environment variables. The name of the site file is the one pointed to by
the environment variable R_ENVIRON; if this is unset, ‘R_HOME/etc/Renviron.site’ is
used (if it exists). The user file is the one pointed to by the environment variable R_
ENVIRON_USER if this is set; otherwise, files ‘.Renviron’ in the current or in the user’s
home directory (in that order) are searched for. These files should contain lines of
the form ‘name=value’. (See help(“Startup”) for a precise description.) Variables
you might want to set include R_PAPERSIZE (the default paper size), R_PRINTCMD (the
default print command) and R_LIBS (specifies the list of R library trees searched for
add-on packages).
 Then R searches for the site-wide startup profile unless the command line option
‘–no-site-file’ was given. The name of this file is taken from the value
of the R_PROFILE environment variable. If that variable is unset, the default
‘R_HOME/etc/Rprofile.site’ is used if this exists.
 Then, unless ‘–no-init-file’ was given, R searches for a user profile and sources it.
The name of this file is taken from the environment variable R_PROFILE_USER; if unset,
a file called ‘.Rprofile’ in the current directory or in the user’s home directory (in
that order) is searched for.
 It also loads a saved image from ‘.RData’ if there is one (unless ‘–no-restore’ or
‘–no-restore-data’ was specified).
 Finally, if a function .First exists, it is executed. This function (as well as .Last
which is executed at the end of the R session) can be defined in the appropriate startup
profiles, or reside in ‘.RData’.
In addition, there are options for controlling the memory available to the R process (see
the on-line help for topic ‘Memory’ for more information). Users will not normally need to
use these unless they are trying to limit the amount of memory used by R.
R accepts the following command-line options.
‘–help’
‘-h’ Print short help message to standard output and exit successfully.
Appendix B: Invoking R 89
‘–version’
Print version information to standard output and exit successfully.
‘–encoding=enc’
Specify the encoding to be assumed for input from the console or stdin. This
needs to be an encoding known to iconv: see its help page.
‘RHOME’ Print the path to the R “home directory” to standard output and exit successfully.
Apart from the front-end shell script and the man page, R installation
puts everything (executables, packages, etc.) into this directory.
‘–save’
‘–no-save’
Control whether data sets should be saved or not at the end of the R session.
If neither is given in an interactive session, the user is asked for the desired
behavior when ending the session with q(); in non-interactive use one of these
must be specified or implied by some other option (see below).
‘–no-environ’
Do not read any user file to set environment variables.
‘–no-site-file’
Do not read the site-wide profile at startup.
‘–no-init-file’
Do not read the user’s profile at startup.
‘–restore’
‘–no-restore’
‘–no-restore-data’
Control whether saved images (file ‘.RData’ in the directory where R was
started) should be restored at startup or not. The default is to restore.
(‘–no-restore’ implies all the specific ‘–no-restore-*’ options.)
‘–no-restore-history’
Control whether the history file (normally file ‘.Rhistory’ in the directory
where R was started, but can be set by the environment variable R_HISTFILE)
should be restored at startup or not. The default is to restore.
‘–no-Rconsole’
(Windows only) Prevent loading the ‘Rconsole’ file at startup.
‘–vanilla’
Combine ‘–no-save’, ‘–no-environ’, ‘–no-site-file’, ‘–no-init-file’
and ‘–no-restore’. Under Windows, this also includes ‘–no-Rconsole’.
‘-f file’
‘–file=file’
Take input from file: ‘-’ means stdin. Implies ‘–no-save’ unless ‘–save’ has
been set.
‘-e expression’
Use expression as an input line. One or more ‘-e’ options can be used, but not
together with ‘-f’ or ‘–file’. Implies ‘–no-save’ unless ‘–save’ has been
Appendix B: Invoking R 90
set. (There is a limit of 10,000 bytes on the total length of expressions used in
this way.)
‘–no-readline’
(UNIX only) Turn off command-line editing via readline. This is useful when
running R from within Emacs using the ESS (“Emacs Speaks Statistics”)
package. See Appendix C [The command-line editor], page 95, for more
information. Command-line editing is enabled by default interactive use (see
‘–interactive’). This option also affects tilde-epansion: see the help for
path.expand.
‘–min-vsize=N’
‘–max-vsize=N’
Specify the minimum or maximum amount of memory used for variable size
objects by setting the “vector heap” size to N bytes. Here, N must either be
an integer or an integer ending with ‘G’, ‘M’, ‘K’, or ‘k’, meaning ‘Giga’ (2^30),
‘Mega’ (2^20), (computer) ‘Kilo’ (2^10), or regular ‘kilo’ (1000).
‘–min-nsize=N’
‘–max-nsize=N’
Specify the amount of memory used for fixed size objects by setting the number
of “cons cells” to N. See the previous option for details on N. A cons cell takes
28 bytes on a 32-bit machine, and usually 56 bytes on a 64-bit machine.
‘–max-ppsize=N’
Specify the maximum size of the pointer protection stack as N locations. This
defaults to 10000, but can be increased to allow large and complicated calculations
to be done. Currently the maximum value accepted is 100000.
‘–max-mem-size=N’
(Windows only) Specify a limit for the amount of memory to be used both for
R objects and working areas. This is set by default to the smaller of 1.5Gb1
and the amount of physical RAM in the machine, and must be between 32Mb
and the maximum allowed on that version of Windows.
‘–quiet’
‘–silent’
‘-q’ Do not print out the initial copyright and welcome messages.
‘–slave’ Make R run as quietly as possible. This option is intended to support programs
which use R to compute results for them. It implies ‘–quiet’ and ‘–no-save’.
‘–interactive’
(UNIX only) Assert that R really is being run interactively even if input has
been redirected: use if input is from a FIFO or pipe and fed from an interactive
program. (The default is to deduce that R is being run interactively if and
only if ‘stdin’ is connected to a terminal or pty.) Using ‘-e’, ‘-f’ or ‘–file’
asserts non-interactive use even if ‘–interactive’ is given.
1 2.5Gb on versions of Windows that support 3Gb per process and have the support enabled: see the
‘rw-FAQ’ Q2.9; 3.5Gb on some 64-bit versions of Windows.
Appendix B: Invoking R 91
‘–ess’ (Windows only) Set Rterm up for use by R-inferior-mode in ESS, including
asserting interactive use without the command-line editor.
‘–verbose’
Print more information about progress, and in particular set R’s option verbose
to TRUE. R code uses this option to control the printing of diagnostic messages.
‘–debugger=name’
‘-d name’ (UNIX only) Run R through debugger name. For most debuggers (the exceptions
are valgrind and recent versions of gdb), further command line options
are disregarded, and should instead be given when starting the R executable
from inside the debugger.
‘–gui=type’
‘-g type’ (UNIX only) Use type as graphical user interface (note that this also includes
interactive graphics). Currently, possible values for type are ‘X11’ (the default)
and, provided that ‘Tcl/Tk’ support is available, ‘Tk’. (For back-compatibility,
‘x11’ and ‘tk’ are accepted.)
‘–args’ This flag does nothing except cause the rest of the command line to be skipped:
this can be useful to retrieve values from it with commandArgs(TRUE).
Note that input and output can be redirected in the usual way (using ‘<’ and ‘>’), but
the line length limit of 4095 bytes still applies. Warning and error messages are sent to the
error channel (stderr).
The command R CMD allows the invocation of various tools which are useful in conjunction
with R, but not intended to be called “directly”. The general form is
R CMD command args
where command is the name of the tool and args the arguments passed on to it.
Currently, the following tools are available.
BATCH Run R in batch mode.
COMPILE (UNIX only) Compile files for use with R.
SHLIB Build shared library for dynamic loading.
INSTALL Install add-on packages.
REMOVE Remove add-on packages.
build Build (that is, package) add-on packages.
check Check add-on packages.
LINK (UNIX only) Front-end for creating executable programs.
Rprof Post-process R profiling files.
Rdconv
Rd2txt Convert Rd format to various other formats, including HTML, LATEX, plain text,
and extracting the examples. Rd2txt can be used as shorthand for Rd2txt -t
txt.
Appendix B: Invoking R 92
Rd2dvi
Rd2pdf Convert Rd format to DVI/PDF. Rd2pdf can be used as shorthand for Rd2dvi
–pdf.
Sd2Rd Convert S documentation to Rd format.
Stangle Extract S/R code from Sweave documentation
Sweave Process Sweave documentation
Rdiff Diff R output ignoring headers etc
config Obtain configuration information.
javareconf
(Unix only) Update the Java configuration variables
Use
R CMD command –help
to obtain usage information for each of the tools accessible via the R CMD interface.
In addition, you can use
R CMD cmd args
for any other executable cmd on the path: this is useful to have the same environment
as R or the specific commands run under, for example to run ldd or pdflatex. Under
a Unix-alike, if cmd is ‘perl’ or ‘awk’ it is replaced by the full path to the Perl or AWK
command found when R was configured.
B.2 Invoking R under Windows
There are two ways to run R under Windows. Within a terminal window (e.g. cmd.exe or
command.com or a more capable shell), the methods described in the previous section may
be used, invoking by R.exe or more directly by Rterm.exe. (These are principally intended
for batch use.) For interactive use, there is a console-based GUI (Rgui.exe).
The startup procedure under Windows is very similar to that under UNIX, but references
to the ‘home directory’ need to be clarified, as this is not always defined on
Windows. If the environment variable R_USER is defined, that gives the home directory.
Next, if the environment variable HOME is defined, that gives the home directory.
After those two user-controllable settings, R tries to find system defined home directories.
It first tries to use the Windows “personal” directory (typically C:\Documents and
Settings\username\My Documents in Windows XP). If that fails, and environment variables
HOMEDRIVE and HOMEPATH are defined (and they normally are) these define the home
directory. Failing all those, the home directory is taken to be the starting directory.
You need to ensure that either the environment variables TMPDIR, TMP and TEMP are
either unset or one of them points to a valid place to create temporary files and directories.
Environment variables can be supplied as ‘name=value’ pairs on the command line.
If there is an argument ending ‘.RData’ (in any case) it is interpreted as the path to
the workspace to be restored: it implies ‘–restore’ and sets the working directory to the
parent of the named file. (This mechanism is used for drag-and-drop and file association
with RGui.exe, but also works for Rterm.exe. If the named file does not exist it sets the
working directory if the parent directory exists.)
Appendix B: Invoking R 93
The following additional command-line options are available when invoking RGui.exe.
‘–mdi’
‘–sdi’
‘–no-mdi’
Control whether Rgui will operate as an MDI program (with multiple child
windows within one main window) or an SDI application (with multiple toplevel
windows for the console, graphics and pager). The command-line setting
overrides the setting in the user’s ‘Rconsole’ file.
‘–debug’ Enable the “Break to debugger” menu item in Rgui, and trigger a break to the
debugger during command line processing.
In Windows with R CMD you may also specify your own ‘.bat’, ‘.exe’, ‘.sh’ or ‘.pl’
file. It will be run under the appropriate interpreter (Perl for ‘.pl’) with several environment
variables set appropriately, including R_HOME, R_VERSION, R_CMD, R_OSTYPE, PATH,
PERL5LIB, and TEXINPUTS. For example, if you already have ‘latex.exe’ on your path,
then
R CMD latex.exe mydoc
will run LATEX on ‘mydoc.tex’, with the path to R’s ‘share/texmf’ macros appended to
TEXINPUTS. (Unfortunately, this does not help with the MiKTeX build of LATEX.)
B.3 Invoking R under Mac OS X
There are two ways to run R under Mac OS X. Within a Terminal.app window by invoking
R, the methods described in the first subsection apply. There is also console-based GUI
(R.app) that by default is installed in the Applications folder on your system. It is a
standard double-clickable Mac OS X application.
The startup procedure under Mac OS X is very similar to that under UNIX. The ‘home
directory’ is the one inside the R.framework, but the startup and current working directory
are set as the user’s home directory unless a different startup directory is given in the
Preferences window accessible from within the GUI.
B.4 Scripting with R
If you just want to run a file ‘foo.R’ of R commands, the recommended way is to use R CMD
BATCH foo.R. If you want to run this in the background or as a batch job use OS-specific
facilities to do so: for example in most shells on Unix-alike OSes R CMD BATCH foo.R & runs
a background job.
You can pass parameters to scripts via additional arguments on the command line: for
example
R CMD BATCH –args arg1 arg2 foo.R &
will pass arguments to a script which can be retrieved as a character vector by
args <- commandArgs(TRUE)
This is made simpler by the alternative front-end Rscript, which can be invoked by
Rscript foo.R arg1 arg2
and this can also be used to write executable script files like (at least on Unix-alikes, and
in some Windows shells)
Appendix B: Invoking R 94
#! /path/to/Rscript
args <- commandArgs(TRUE)

q(status=<exit status code>)
If this is entered into a text file ‘runfoo’ and this is made executable (by chmod 755 runfoo),
it can be invoked for different arguments by
runfoo arg1 arg2
For further options see help(“Rscript”). This writes R output to ‘stdout’ and ‘stderr’,
and this can be redirected in the usual way for the shell running the command.
If you do not wish to hardcode the path to Rscript but have it in your path (which is
normally the case for an installed R except on Windows), use
#! /usr/bin/env Rscript

At least in Bourne and bash shells, the #! mechanism does not allow extra arguments like
#! /usr/bin/env Rscript –vanilla.
One thing to consider is what stdin() refers to. It is commonplace to write R scripts
with segments like
chem <- scan(n=24)
2.90 3.10 3.40 3.40 3.70 3.70 2.80 2.50 2.40 2.40 2.70 2.20
5.28 3.37 3.03 3.03 28.95 3.77 3.40 2.20 3.50 3.60 3.70 3.70
and stdin() refers to the script file to allow such traditional usage. If you want to refer to
the process’s ‘stdin’, use “stdin” as a file connection, e.g. scan(“stdin”, …).
Another way to write executable script files (suggested by Fran¸cois Pinard) is to use a
here document like
#!/bin/sh
[environment variables can be set here]
R –slave [other options] <<EOF
R program goes here…
EOF
but here stdin() refers to the program source and “stdin” will not be usable.
Very short scripts can be passed to Rscript on the command-line via the ‘-e’ flag.
Appendix C: The command-line editor 95
Appendix C The command-line editor
C.1 Preliminaries
When the GNU readline library is available at the time R is configured for compilation
under UNIX, an inbuilt command line editor allowing recall, editing and re-submission of
prior commands is used. Note that other versions of readline exist and may be used by the
inbuilt command line editor: this may happen on Mac OS X.
It can be disabled (useful for usage with ESS1) using the startup option ‘–no-readline’.
Windows versions of R have somewhat simpler command-line editing: see ‘Console’
under the ‘Help’ menu of the GUI, and the file ‘README.Rterm’ for command-line editing
under Rterm.exe.
When using R with readline capabilities, the functions described below are available.
Many of these use either Control or Meta characters. Control characters, such as
Control-m, are obtained by holding the CTRL down while you press the M key, and are
written as C-m below. Meta characters, such as Meta-b, are typed by holding down META2
and pressing B, and written as M-b in the following. If your terminal does not have a
META key, you can still type Meta characters using two-character sequences starting with
ESC. Thus, to enter M-b, you could type ESCB. The ESC character sequences are also
allowed on terminals with real Meta keys. Note that case is significant for Meta characters.
C.2 Editing actions
The R program keeps a history of the command lines you type, including the erroneous lines,
and commands in your history may be recalled, changed if necessary, and re-submitted as
new commands. In Emacs-style command-line editing any straight typing you do while in
this editing phase causes the characters to be inserted in the command you are editing,
displacing any characters to the right of the cursor. In vi mode character insertion mode
is started by M-i or M-a, characters are typed and insertion mode is finished by typing a
further ESC.
Pressing the RET command at any time causes the command to be re-submitted.
Other editing actions are summarized in the following table.
C.3 Command-line editor summary
Command recall and vertical motion
C-p Go to the previous command (backwards in the history).
C-n Go to the next command (forwards in the history).
C-r text Find the last command with the text string in it.
On most terminals, you can also use the up and down arrow keys instead of C-p and
C-n, respectively.
1 The ‘Emacs Speaks Statistics’ package; see the URL http://ESS.R-project.org
2 On a PC keyboard this is usually the Alt key, occasionally the ‘Windows’ key.
Appendix C: The command-line editor 96
Horizontal motion of the cursor
C-a Go to the beginning of the command.
C-e Go to the end of the line.
M-b Go back one word.
M-f Go forward one word.
C-b Go back one character.
C-f Go forward one character.
On most terminals, you can also use the left and right arrow keys instead of C-b and
C-f, respectively.
Editing and re-submission
text Insert text at the cursor.
C-f text Append text after the cursor.
DEL Delete the previous character (left of the cursor).
C-d Delete the character under the cursor.
M-d Delete the rest of the word under the cursor, and “save” it.
C-k Delete from cursor to end of command, and “save” it.
C-y Insert (yank) the last “saved” text here.
C-t Transpose the character under the cursor with the next.
M-l Change the rest of the word to lower case.
M-c Change the rest of the word to upper case.
RET Re-submit the command to R.
The final RET terminates the command line editing sequence.
Appendix D: Function and variable index 97
Appendix D Function and variable index
!
! : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
!= : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
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&
& : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
&& : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 42
*
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+
+ : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8

– : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
.
. : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 59
.First : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 51
.Last : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 51
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:: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 83
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^ : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
|
| : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
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A
abline : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 70
ace: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
add1 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 59
anova: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 57, 59
aov: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 58
aperm : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 23
array : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 21
as.data.frame : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 29
as.vector : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 26
attach : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 30
attr : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 14
attributes: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 14
avas : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
axis : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 71
B
boxplot : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 39
break : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 43
bruto : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
C
c : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7, 10, 26, 29
C : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 57
cbind : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 25
coef : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 57
coefficients : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 57
contour : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 69
contrasts : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 57
Appendix D: Function and variable index 98
coplot : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 68
cos : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
crossprod : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 21, 23
cut: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 27
D
data : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 33
data.frame: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 29
density : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 36
det: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 25
detach : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 30
determinant : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 25
dev.list : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 81
dev.next : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 81
dev.off : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 81
dev.prev : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 81
dev.set : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 81
deviance : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 57
diag : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 23
dim: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 19
dotchart : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 69
drop1 : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 59
E
ecdf : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 37
edit : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 34
eigen : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 24
else : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 42
Error : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 58
example : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5
exp : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
F
F : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
factor : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 16
FALSE : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
fivenum : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 36
for: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 42
formula : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 58
function : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 44
G
getAnywhere : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 52
getS3method : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 52
glm: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 61
H
help: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4
help.search: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4
help.start: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4
hist : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 36, 69
I
identify : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 72
if : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 42
ifelse : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 42
image : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 69
is.na : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 10
is.nan : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 10
J
jpeg : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 79
K
ks.test : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 38
L
legend : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 71
length: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8, 13
levels : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 16
lines : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 70
list : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 28
lm : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 57
lme: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 65
locator : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 72
loess : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
log : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
lqs: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
lsfit : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 25
M
mars : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
max : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
mean: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
methods : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 52
min : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
mode : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 13
N
NA : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
NaN: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 10
ncol : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 23
next : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 43
nlm : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 63, 64, 65
nlme : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 65
nlminb : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 63
nrow : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 23
O
optim : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 63
order : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
ordered : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 17
outer : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 22
Appendix D: Function and variable index 99
P
pairs : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 68
par: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 73
paste : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 10
pdf: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 79
persp : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 69
plot : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 58, 67
pmax: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
pmin: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
png: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 79
points : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 70
polygon : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 71
postscript: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 79
predict : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 58
print : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 58
prod: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
Q
qqline: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 37, 69
qqnorm: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 37, 69
qqplot : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 69
qr : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 25
quartz : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 79
R
range : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
rbind : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 25
read.table: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 32
rep : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
repeat : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 43
resid : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 58
residuals : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 58
rlm: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
rm : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6
S
scan : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 33
sd : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 17
search : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 31
seq : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
shapiro.test : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 38
sin : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
sink: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6
solve : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 24
sort: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
source : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6
split : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 43
sqrt: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
stem : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 36
step : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 58, 59
sum : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
summary : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 36, 58
svd: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 24
T
t : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 23
T : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
t.test : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 39
table: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 21, 26
tan : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
tapply : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 16
text : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 70
title : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 71
tree : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
TRUE: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
U
unclass : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 15
update : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 59
V
var : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8, 17
var.test : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 40
vcov : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 58
vector : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7
W
while : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 43
wilcox.test : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 40
windows : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 79
X
X11: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 79
Appendix E: Concept index 100
Appendix E Concept index
A
Accessing builtin datasets: : : : : : : : : : : : : : : : : : : : : : 33
Additive models : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
Analysis of variance : : : : : : : : : : : : : : : : : : : : : : : : : : : 58
Arithmetic functions and operators : : : : : : : : : : : : : 8
Arrays : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 19
Assignment : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7
Attributes : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 13
B
Binary operators : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 45
Box plots : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 39
C
Character vectors : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 10
Classes : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 15, 51
Concatenating lists : : : : : : : : : : : : : : : : : : : : : : : : : : : : 29
Contrasts: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 56
Control statements : : : : : : : : : : : : : : : : : : : : : : : : : : : : 42
CRAN : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 82
Customizing the environment : : : : : : : : : : : : : : : : : : 50
D
Data frames : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 29
Default values : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 45
Density estimation: : : : : : : : : : : : : : : : : : : : : : : : : : : : : 36
Determinants : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 25
Diverting input and output : : : : : : : : : : : : : : : : : : : : : 6
Dynamic graphics : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 81
E
Eigenvalues and eigenvectors : : : : : : : : : : : : : : : : : : : 24
Empirical CDFs : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 37
F
Factors : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 16, 56
Families : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 60
Formulae : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 54
G
Generalized linear models : : : : : : : : : : : : : : : : : : : : : : 60
Generalized transpose of an array : : : : : : : : : : : : : : 23
Generic functions : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 51
Graphics device drivers : : : : : : : : : : : : : : : : : : : : : : : : 79
Graphics parameters: : : : : : : : : : : : : : : : : : : : : : : : : : : 73
Grouped expressions : : : : : : : : : : : : : : : : : : : : : : : : : : : 42
I
Indexing of and by arrays : : : : : : : : : : : : : : : : : : : : : : 19
Indexing vectors : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 11
K
Kolmogorov-Smirnov test : : : : : : : : : : : : : : : : : : : : : : 38
L
Least squares fitting : : : : : : : : : : : : : : : : : : : : : : : : : : : 25
Linear equations : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 24
Linear models : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 57
Lists : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 28
Local approximating regressions : : : : : : : : : : : : : : : 66
Loops and conditional execution : : : : : : : : : : : : : : : 42
M
Matrices: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 19
Matrix multiplication : : : : : : : : : : : : : : : : : : : : : : : : : : 23
Maximum likelihood : : : : : : : : : : : : : : : : : : : : : : : : : : : 65
Missing values : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9
Mixed models : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 65
N
Named arguments : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 45
Namespace : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 83
Nonlinear least squares : : : : : : : : : : : : : : : : : : : : : : : : 63
O
Object orientation : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 51
Objects : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 13
One- and two-sample tests : : : : : : : : : : : : : : : : : : : : : 39
Ordered factors : : : : : : : : : : : : : : : : : : : : : : : : : : : : 16, 56
Outer products of arrays: : : : : : : : : : : : : : : : : : : : : : : 22
P
Packages : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2, 82
Probability distributions : : : : : : : : : : : : : : : : : : : : : : : 35
Q
QR decomposition : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 25
Quantile-quantile plots : : : : : : : : : : : : : : : : : : : : : : : : 37
R
Reading data from files : : : : : : : : : : : : : : : : : : : : : : : : 32
Recycling rule: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7, 21
Appendix E: Concept index 101
Regular sequences : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8
Removing objects : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6
Robust regression: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
S
Scope : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 48
Search path : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 31
Shapiro-Wilk test : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 38
Singular value decomposition : : : : : : : : : : : : : : : : : : 24
Statistical models: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 54
Student’s t test : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 39
T
Tabulation : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 26
Tree-based models : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66
U
Updating fitted models : : : : : : : : : : : : : : : : : : : : : : : : 59
V
Vectors : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7
W
Wilcoxon test : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 40
Workspace: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6
Writing functions : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 44
Appendix F: References 102
Appendix F References
D. M. Bates and D. G. Watts (1988), Nonlinear Regression Analysis and Its Applications.
John Wiley & Sons, New York.
Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), The New S Language.
Chapman & Hall, New York. This book is often called the “Blue Book”.
John M. Chambers and Trevor J. Hastie eds. (1992), Statistical Models in S. Chapman &
Hall, New York. This is also called the “White Book”.
John M. Chambers (1998) Programming with Data. Springer, New York. This is also called
the “Green Book”.
A. C. Davison and D. V. Hinkley (1997), Bootstrap Methods and Their Applications, Cambridge
University Press.
Annette J. Dobson (1990), An Introduction to Generalized Linear Models, Chapman and
Hall, London.
Peter McCullagh and John A. Nelder (1989), Generalized Linear Models. Second edition,
Chapman and Hall, London.
John A. Rice (1995), Mathematical Statistics and Data Analysis. Second edition. Duxbury
Press, Belmont, CA.
S. D. Silvey (1970), Statistical Inference. Penguin, London.

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