R FAQ

[Starscream]

R FAQ
Frequently Asked Questions on R
Version 2.12.2010-12-13
ISBN 3-900051-08-9
Kurt Hornik
http://cran.r-project.org/doc/FAQ/R-FAQ.pdf

Table of Contents
*****************

R FAQ
1 Introduction
1.1 Legalese
1.2 Obtaining this document
1.3 Citing this document
1.4 Notation
1.5 Feedback
2 R Basics
2.1 What is R?
2.2 What machines does R run on?
2.3 What is the current version of R?
2.4 How can R be obtained?
2.5 How can R be installed?
2.5.1 How can R be installed (Unix-like)
2.5.2 How can R be installed (Windows)
2.5.3 How can R be installed (Macintosh)
2.6 Are there Unix-like binaries for R?
2.7 What documentation exists for R?
2.8 Citing R
2.9 What mailing lists exist for R?
2.10 What is CRAN?
2.11 Can I use R for commercial purposes?
2.12 Why is R named R?
2.13 What is the R Foundation?
2.14 What is R-Forge?
3 R and S
3.1 What is S?
3.2 What is S-PLUS?
3.3 What are the differences between R and S?
3.3.1 Lexical scoping
3.3.2 Models
3.3.3 Others
3.4 Is there anything R can do that S-PLUS cannot?
3.5 What is R-plus?
4 R Web Interfaces
5 R Add-On Packages
5.1 Which add-on packages exist for R?
5.1.1 Add-on packages in R
5.1.2 Add-on packages from CRAN
5.1.3 Add-on packages from Omegahat
5.1.4 Add-on packages from Bioconductor
5.1.5 Other add-on packages
5.2 How can add-on packages be installed?
5.3 How can add-on packages be used?
5.4 How can add-on packages be removed?
5.5 How can I create an R package?
5.6 How can I contribute to R?
6 R and Emacs
6.1 Is there Emacs support for R?
6.2 Should I run R from within Emacs?
6.3 Debugging R from within Emacs
7 R Miscellanea
7.1 How can I set components of a list to NULL?
7.2 How can I save my workspace?
7.3 How can I clean up my workspace?
7.4 How can I get eval() and D() to work?
7.5 Why do my matrices lose dimensions?
7.6 How does autoloading work?
7.7 How should I set options?
7.8 How do file names work in Windows?
7.9 Why does plotting give a color allocation error?
7.10 How do I convert factors to numeric?
7.11 Are Trellis displays implemented in R?
7.12 What are the enclosing and parent environments?
7.13 How can I substitute into a plot label?
7.14 What are valid names?
7.15 Are GAMs implemented in R?
7.16 Why is the output not printed when I source() a file?
7.17 Why does outer() behave strangely with my function?
7.18 Why does the output from anova() depend on the order of factors
in the model?
7.19 How do I produce PNG graphics in batch mode?
7.20 How can I get command line editing to work?
7.21 How can I turn a string into a variable?
7.22 Why do lattice/trellis graphics not work?
7.23 How can I sort the rows of a data frame?
7.24 Why does the help.start() search engine not work?
7.25 Why did my .Rprofile stop working when I updated R?
7.26 Where have all the methods gone?
7.27 How can I create rotated axis labels?
7.28 Why is read.table() so inefficient?
7.29 What is the difference between package and library?
7.30 I installed a package but the functions are not there
7.31 Why doesn't R think these numbers are equal?
7.32 How can I capture or ignore errors in a long simulation?
7.33 Why are powers of negative numbers wrong?
7.34 How can I save the result of each iteration in a loop into a
separate file?
7.35 Why are p-values not displayed when using lmer()?
7.36 Why are there unwanted borders, lines or grid-like artifacts
when viewing a plot saved to a PS or PDF file?
7.37 Why does backslash behave strangely inside strings?
7.38 How can I put error bars or confidence bands on my plot?
7.39 How do I create a plot with two y-axes?
8 R Programming
8.1 How should I write summary methods?
8.2 How can I debug dynamically loaded code?
8.3 How can I inspect R objects when debugging?
8.4 How can I change compilation flags?
8.5 How can I debug S4 methods?
9 R Bugs
9.1 What is a bug?
9.2 How to report a bug
10 Acknowledgments


R FAQ
*****

1 Introduction
**************

This document contains answers to some of the most frequently asked
questions about R.

1.1 Legalese
============

This document is copyright (C) 1998-2010 by Kurt Hornik.

This document is free software; you can redistribute it and/or
modify it
under the terms of the GNU General Public License as published by the
Free
Software Foundation; either version 2, or (at your option) any later
version.

This document is distributed in the hope that it will be useful,
but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY
or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
License
for more details.

Copies of the GNU General Public License versions are available at

`http://www.R-project.org/Licenses/'

1.2 Obtaining this document
===========================

The latest version of this document is always available from

`http://CRAN.R-project.org/doc/FAQ/'

From there, you can obtain versions converted to plain ASCII text,
DVI,
GNU info, HTML, PDF, PostScript as well as the Texinfo source used for
creating all these formats using the GNU Texinfo system
(http://texinfo.org/).

You can also obtain the R FAQ from the `doc/FAQ' subdirectory of a
CRAN
site (*note What is CRAN?::).

1.3 Citing this document
========================

In publications, please refer to this FAQ as Hornik (2010), "The R
FAQ",
and give the above, _official_ URL and the ISBN 3-900051-08-9:

@Misc{,
author = {Kurt Hornik},
title = {The {R} {FAQ}},
year = {2010},
note = {{ISBN} 3-900051-08-9},
url = {http://CRAN.R-project.org/doc/FAQ/R-FAQ.html}
}

1.4 Notation
============

Everything should be pretty standard. `R>' is used for the R prompt,
and a
`$' for the shell prompt (where applicable).

1.5 Feedback
============

Feedback via email to <Kurt....@R-project.org> is of course most
welcome.

In particular, note that I do not have access to Windows or
Macintosh
systems. Features specific to the Windows and Mac OS X ports of R are
described in the "R for Windows FAQ"
(http://CRAN.R-project.org/bin/windows/base/rw-FAQ.html) and the "R
for Mac
OS X FAQ (http://CRAN.R-project.org/bin/macosx/RMacOSX-FAQ.html). If
you
have information on Macintosh or Windows systems that you think should
be
added to this document, please let me know.

2 R Basics
**********

2.1 What is R?
==============

R is a system for statistical computation and graphics. It consists
of a
language plus a run-time environment with graphics, a debugger, access
to
certain system functions, and the ability to run programs stored in
script
files.

The design of R has been heavily influenced by two existing
languages:
Becker, Chambers & Wilks' S (*note What is S?::) and Sussman's Scheme
(http://www.cs.indiana.edu/scheme-repository/home.html). Whereas the
resulting language is very similar in appearance to S, the underlying
implementation and semantics are derived from Scheme. *Note What are
the
differences between R and S?::, for further details.

The core of R is an interpreted computer language which allows
branching
and looping as well as modular programming using functions. Most of
the
user-visible functions in R are written in R. It is possible for the
user
to interface to procedures written in the C, C++, or FORTRAN languages
for
efficiency. The R distribution contains functionality for a large
number
of statistical procedures. Among these are: linear and generalized
linear
models, nonlinear regression models, time series analysis, classical
parametric and nonparametric tests, clustering and smoothing. There
is
also a large set of functions which provide a flexible graphical
environment for creating various kinds of data presentations.
Additional
modules ("add-on packages") are available for a variety of specific
purposes (*note R Add-On Packages::).

R was initially written by Ross Ihaka <Ross....@R-project.org>
and
Robert Gentleman <Robert.G...@R-project.org> at the Department of
Statistics of the University of Auckland in Auckland, New Zealand. In
addition, a large group of individuals has contributed to R by sending
code
and bug reports.

Since mid-1997 there has been a core group (the "R Core Team") who
can
modify the R source code archive. The group currently consists of
Doug
Bates, John Chambers, Peter Dalgaard, Robert Gentleman, Kurt Hornik,
Stefano Iacus, Ross Ihaka, Friedrich Leisch, Thomas Lumley, Martin
Maechler, Duncan Murdoch, Paul Murrell, Martyn Plummer, Brian Ripley,
Duncan Temple Lang, Luke Tierney, and Simon Urbanek.

R has a home page at `http://www.R-project.org/'. It is free
software
(http://www.gnu.org/philosophy/free-sw.html) distributed under a GNU-
style
copyleft (http://www.gnu.org/copyleft/copyleft.html), and an official
part
of the GNU (http://www.gnu.org/) project ("GNU S").

2.2 What machines does R run on?
================================

R is being developed for the Unix-like, Windows and Mac families of
operating systems. Support for Mac OS Classic ended with R 1.7.1.

The current version of R will configure and build under a number of
common Unix-like (e.g., `http://en.wikipedia.org/wiki/Unix-like')
platforms
including CPU-linux-gnu for the i386, amd64, alpha, arm/armel, hppa,
ia64,
m68k, mips/mipsel, powerpc, s390 and sparc CPUs (e.g.,
`http://buildd.debian.org/build.php?&pkg=r-base'), i386-hurd-gnu,
CPU-kfreebsd-gnu for i386 and amd64, powerpc-apple-darwin, mips-sgi-
irix,
i386-freebsd, rs6000-ibm-aix, and sparc-sun-solaris.

If you know about other platforms, please drop us a note.

2.3 What is the current version of R?
=====================================

The current released version is 2.12.1. Based on this
`major.minor.patchlevel' numbering scheme, there are two development
versions of R, a patched version of the current release (`r-patched')
and
one working towards the next minor or eventually major (`r-devel')
releases
of R, respectively. Version r-patched is for bug fixes mostly. New
features are typically introduced in r-devel.

2.4 How can R be obtained?
==========================

Sources, binaries and documentation for R can be obtained via CRAN,
the
"Comprehensive R Archive Network" (see *note What is CRAN?::).

Sources are also available via `https://svn.R-project.org/R/', the
R
Subversion repository, but currently not via anonymous rsync (nor
CVS).

Tarballs with daily snapshots of the r-devel and r-patched
development
versions of R can be found at `ftp://ftp.stat.math.ethz.ch/Software/
R'.

2.5 How can R be installed?
===========================

2.5.1 How can R be installed (Unix-like)
----------------------------------------

If R is already installed, it can be started by typing `R' at the
shell
prompt (of course, provided that the executable is in your path).

If binaries are available for your platform (see *note Are there
Unix-like binaries for R?::), you can use these, following the
instructions
that come with them.

Otherwise, you can compile and install R yourself, which can be
done
very easily under a number of common Unix-like platforms (see *note
What
machines does R run on?::). The file `INSTALL' that comes with the R
distribution contains a brief introduction, and the "R Installation
and
Administration" guide (*note What documentation exists for R?::) has
full
details.

Note that you need a FORTRAN compiler or perhaps `f2c' in addition
to a
C compiler to build R.

In the simplest case, untar the R source code, change to the
directory
thus created, and issue the following commands (at the shell prompt):

$ ./configure
$ make

If these commands execute successfully, the R binary and a shell
script
front-end called `R' are created and copied to the `bin' directory.
You
can copy the script to a place where users can invoke it, for example
to
`/usr/local/bin'. In addition, plain text help pages as well as HTML
and
LaTeX versions of the documentation are built.

Use `make dvi' to create DVI versions of the R manuals, such as
`refman.dvi' (an R object reference index) and `R-exts.dvi', the "R
Extension Writers Guide", in the `doc/manual' subdirectory. These
files
can be previewed and printed using standard programs such as `xdvi'
and
`dvips'. You can also use `make pdf' to build PDF (Portable Document
Format) version of the manuals, and view these using e.g. Acrobat.
Manuals
written in the GNU Texinfo system can also be converted to info files
suitable for reading online with Emacs or stand-alone GNU Info; use
`make
info' to create these versions (note that this requires Makeinfo
version
4.5).

Finally, use `make check' to find out whether your R system works
correctly.

You can also perform a "system-wide" installation using `make
install'.
By default, this will install to the following directories:

`${prefix}/bin'
the front-end shell script

`${prefix}/man/man1'
the man page

`${prefix}/lib/R'
all the rest (libraries, on-line help system, ...). This is the
"R
Home Directory" (`R_HOME') of the installed system.

In the above, `prefix' is determined during configuration (typically
`/usr/local') and can be set by running `configure' with the option

$ ./configure --prefix=/where/you/want/R/to/go

(E.g., the R executable will then be installed into
`/where/you/want/R/to/go/bin'.)

To install DVI, info and PDF versions of the manuals, use `make
install-dvi', `make install-info' and `make install-pdf',
respectively.

2.5.2 How can R be installed (Windows)
--------------------------------------

The `bin/windows' directory of a CRAN site contains binaries for a
base
distribution and a large number of add-on packages from CRAN to run on
Windows 2000 and later (including 64-bit versions of Windows) on ix86
and
x86_64 chips. The Windows version of R was created by Robert Gentleman
and
Guido Masarotto, and is now being developed and maintained by Duncan
Murdoch <mur...@stats.uwo.ca> and Brian D. Ripley
<Brian....@R-project.org>.

For most installations the Windows installer program will be the
easiest
tool to use.

See the "R for Windows FAQ"
(http://CRAN.R-project.org/bin/windows/base/rw-FAQ.html) for more
details.

2.5.3 How can R be installed (Macintosh)
----------------------------------------

The `bin/macosx' directory of a CRAN site contains a standard Apple
installer package inside a disk image named `R.dmg'. Once downloaded
and
executed, the installer will install the current non-developer release
of
R. RAqua is a native Mac OS X Darwin version of R with a R.app Mac OS
X
GUI. Inside `bin/macosx/powerpc/contrib/X.Y' there are prebuilt
binary
packages (for powerpc version of Mac OS X) to be used with RAqua
corresponding to the "X.Y" release of R. The installation of these
packages
is available through the "Package" menu of the R.app GUI. This port
of R
for Mac OS X is maintained by Stefano Iacus <Stefano.Iacus@R-
project.org>.
The "R for Mac OS X FAQ
(http://CRAN.R-project.org/bin/macosx/RMacOSX-FAQ.html) has more
details.

The `bin/macos' directory of a CRAN site contains bin-hexed (`hqx')
and
stuffit (`sit') archives for a base distribution and a large number of
add-on packages of R 1.7.1 to run under Mac OS 8.6 to Mac OS 9.2.2.
This
port of R for Macintosh is no longer supported.

2.6 Are there Unix-like binaries for R?
=======================================

The `bin/linux' directory of a CRAN site contains the following
packages.

CPU Versions Provider

-----------------------------------------------------------------------
Debian i386/amd64 etch-cran Johannes
Ranke
i386 lenny-cran Johannes
Ranke
Red Hat i386/x86_64 fedora8/fedora9/fedora10 Martyn
Plummer
i386/x86_64 el4/el5 Bob Kinney
SuSE i586/x86_64 10.3/11.0/11.1 Detlef
Steuer
Ubuntu i386 hardy/intrepid/jaunty/karmic Vincent
Goulet
amd64 hardy/intrepid/jaunty/karmic Michael
Rutter

Debian packages, maintained by Dirk Eddelbuettel, have long been
part of
the Debian distribution, and can be accessed through APT, the Debian
package maintenance tool. Use e.g. `apt-get install r-base r-
recommended'
to install the R environment and recommended packages. If you also
want to
build R packages from source, also run `apt-get install r-base-dev' to
obtain the additional tools required for this. So-called "backports"
of
the current R packages for at least the "stable" distribution of
Debian are
provided by Johannes Ranke, and available from CRAN. See
`http://CRAN.R-project.org/bin/linux/debian/README' for details on R
Debian
packages and installing the backports, which should also be suitable
for
other Debian derivatives. Native backports for Ubuntu are provided by
Vincent Goulet and Michael Rutter.

On SUSE, you can set up an installation source for R within Yast by
setting (e.g.)

Protocol: HTTP
Server name: software.openSUSE.org
Directory: /download/home:/dsteuer/openSUSE_11.1/

With this setting, online updates will check for new versions of R.

The `bin/solaris' directory of a CRAN site contains binary packages
for
Solaris on the SPARC and x64 platforms, provided by Mithun Sridharan.

No other binary distributions are currently publically available
via
CRAN.

2.7 What documentation exists for R?
====================================

Online documentation for most of the functions and variables in R
exists,
and can be printed on-screen by typing `help(NAME)' (or `?NAME') at
the R
prompt, where NAME is the name of the topic help is sought for. (In
the
case of unary and binary operators and control-flow special forms, the
name
may need to be be quoted.)

This documentation can also be made available as one reference
manual
for on-line reading in HTML and PDF formats, and as hardcopy via
LaTeX, see
*note How can R be installed?::. An up-to-date HTML version is always
available for web browsing at `http://stat.ethz.ch/R-manual/'.

Printed copies of the R reference manual for some version(s) are
available from Network Theory Ltd, at
`http://www.network-theory.co.uk/R/base/'. For each set of manuals
sold,
the publisher donates USD 10 to the R Foundation (*note What is the R
Foundation?::).

The R distribution also comes with the following manuals.

* "An Introduction to R" (`R-intro') includes information on data
types,
programming elements, statistical modeling and graphics. This
document is based on the "Notes on S-PLUS" by Bill Venables and
David
Smith.

* "Writing R Extensions" (`R-exts') currently describes the process
of
creating R add-on packages, writing R documentation, R's system
and
foreign language interfaces, and the R API.

* "R Data Import/Export" (`R-data') is a guide to importing and
exporting data to and from R.

* "The R Language Definition" (`R-lang'), a first version of the
"Kernighan & Ritchie of R", explains evaluation, parsing, object
oriented programming, computing on the language, and so forth.

* "R Installation and Administration" (`R-admin').

* "R Internals" (`R-ints') is a guide to R's internal structures.
(Added in R 2.4.0.)

An annotated bibliography (BibTeX format) of R-related publications
can
be found at

`http://www.R-project.org/doc/bib/R.bib'

Books on R by R Core Team members include

John M. Chambers (2008), "Software for Data Analysis: Programming
with
R". Springer, New York, ISBN 978-0-387-75935-7,
`http://stat.stanford.edu/~jmc4/Rbook/'.

Peter Dalgaard (2008), "Introductory Statistics with R", 2nd
edition.
Springer, ISBN 978-0-387-79053-4,
`http://www.biostat.ku.dk/~pd/ISwR.html'.

Robert Gentleman (2008), "R Programming for Bioinformatics".
Chapman
& Hall/CRC, Boca Raton, FL, ISBN 978-1-420-06367-7,
`http://www.bioconductor.org/pub/RBioinf/'.

Stefano M. Iacus (2008), "Simulation and Inference for Stochastic
Differential Equations: With R Examples". Springer, New York,
ISBN
978-0-387-75838-1.

Deepayan Sarkar (2007), "Lattice: Multivariate Data Visualization
with
R". Springer, New York, ISBN 978-0-387-75968-5.

W. John Braun and Duncan J. Murdoch (2007), "A First Course in
Statistical Programming with R". Cambridge University Press,
Cambridge, ISBN 978-0521872652.

P. Murrell (2005), "R Graphics", Chapman & Hall/CRC, ISBN:
1-584-88486-X,
`http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html'.

William N. Venables and Brian D. Ripley (2002), "Modern Applied
Statistics with S" (4th edition). Springer, ISBN 0-387-95457-0,
`http://www.stats.ox.ac.uk/pub/MASS4/'.

Jose C. Pinheiro and Douglas M. Bates (2000), "Mixed-Effects
Models in
S and S-Plus". Springer, ISBN 0-387-98957-0.

Last, but not least, Ross' and Robert's experience in designing and
implementing R is described in Ihaka & Gentleman (1996), "R: A
Language for
Data Analysis and Graphics", _Journal of Computational and Graphical
Statistics_, *5*, 299-314.

2.8 Citing R
============

To cite R in publications, use

@Manual{,
title = {R: A Language and Environment for Statistical
Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = 2010,
note = {{ISBN} 3-900051-07-0},
url = {http://www.R-project.org}
}

Citation strings (or BibTeX entries) for R and R packages can also
be
obtained by `citation()'.

2.9 What mailing lists exist for R?
===================================

Thanks to Martin Maechler <Martin....@R-project.org>, there are
four
mailing lists devoted to R.

`R-announce'
A moderated list for major announcements about the development of
R and
the availability of new code.

`R-packages'
A moderated list for announcements on the availability of new or
enhanced contributed packages.

`R-help'
The `main' R mailing list, for discussion about problems and
solutions
using R, announcements (not covered by `R-announce' and `R-
packages')
about the development of R and the availability of new code.

`R-devel'
This list is for questions and discussion about code development
in R.

Please read the posting guide (http://www.R-project.org/posting-
guide.html)
_before_ sending anything to any mailing list.

Note in particular that R-help is intended to be comprehensible to
people who want to use R to solve problems but who are not necessarily
interested in or knowledgeable about programming. Questions likely to
prompt discussion unintelligible to non-programmers (e.g., questions
involving C or C++) should go to R-devel.

Convenient access to information on these lists, subscription, and
archives is provided by the web interface at
`http://stat.ethz.ch/mailman/listinfo/'. One can also subscribe (or
unsubscribe) via email, e.g. to R-help by sending `subscribe' (or
`unsubscribe') in the _body_ of the message (not in the subject!) to
<R-help-...@lists.R-project.org>.

Send email to <R-h...@lists.R-project.org> to send a message to
everyone
on the R-help mailing list. Subscription and posting to the other
lists is
done analogously, with `R-help' replaced by `R-announce', `R-
packages', and
`R-devel', respectively. Note that the R-announce and R-packages
lists are
gatewayed into R-help. Hence, you should subscribe to either of them
only
in case you are not subscribed to R-help.

It is recommended that you send mail to R-help rather than only to
the R
Core developers (who are also subscribed to the list, of course).
This may
save them precious time they can use for constantly improving R, and
will
typically also result in much quicker feedback for yourself.

Of course, in the case of bug reports it would be very helpful to
have
code which reliably reproduces the problem. Also, make sure that you
include information on the system and version of R being used. See
*note R
Bugs:: for more details.

See `http://www.R-project.org/mail.html' for more information on
the R
mailing lists.

The R Core Team can be reached at <R-c...@lists.R-project.org> for
comments and reports.

Many of the R project's mailing lists are also available via Gmane
(http://gmane.org), from which they can be read with a web browser,
using
an NNTP news reader, or via RSS feeds. See
`http://dir.gmane.org/index.php?prefix=gmane.comp.lang.r.' for the
available mailing lists, and `http://www.gmane.org/rss.php' for
details on
RSS feeds.

2.10 What is CRAN?
==================

The "Comprehensive R Archive Network" (CRAN) is a collection of sites
which
carry identical material, consisting of the R distribution(s), the
contributed extensions, documentation for R, and binaries.

The CRAN master site at Wirtschaftsuniversität Wien, Austria, can
be
found at the URL

`http://CRAN.R-project.org/'

Daily mirrors are available at URLs including

`http://cran.at.R-project.org/' (WU Wien, Austria)
`http://cran.au.R-project.org/' (PlanetMirror, Australia)
`http://cran.br.R-project.org/' (Universidade Federal do
Paraná, Brazil)
`http://cran.ch.R-project.org/' (ETH Zürich, Switzerland)
`http://cran.dk.R-project.org/' (SunSITE, Denmark)
`http://cran.es.R-project.org/' (Spanish National Research
Network, Madrid, Spain)
`http://cran.fr.R-project.org/' (INRA, Toulouse, France)
`http://cran.pt.R-project.org/' (Universidade do Porto,
Portugal)
`http://cran.uk.R-project.org/' (U of Bristol, United
Kingdom)
`http://cran.za.R-project.org/' (Rhodes U, South Africa)

See `http://CRAN.R-project.org/mirrors.html' for a complete list of
mirrors. Please use the CRAN site closest to you to reduce network
load.

From CRAN, you can obtain the latest official release of R, daily
snapshots of R (copies of the current source trees), as gzipped and
bzipped
tar files, a wealth of additional contributed code, as well as
prebuilt
binaries for various operating systems (Linux, Mac OS Classic, Mac OS
X,
and MS Windows). CRAN also provides access to documentation on R,
existing
mailing lists and the R Bug Tracking system.

To "submit" to CRAN, simply upload to
`ftp://CRAN.R-project.org/incoming/' and send an email to
<CR...@R-project.org>. Note that CRAN generally does not accept
submissions
of precompiled binaries due to security reasons. In particular,
binary
packages for Windows and Mac OS X are provided by the respective
binary
package maintainers.

Note: It is very important that you indicate the copyright
(license)
information (GPL-2, GPL-3, BSD, Artistic, ...) in your
submission.

Please always use the URL of the master site when referring to
CRAN.

2.11 Can I use R for commercial purposes?
=========================================

R is released under the GNU General Public License (GPL) version 2.
If you
have any questions regarding the legality of using R in any particular
situation you should bring it up with your legal counsel. We are in
no
position to offer legal advice.

It is the opinion of the R Core Team that one can use R for
commercial
purposes (e.g., in business or in consulting). The GPL, like all Open
Source licenses, permits all and any use of the package. It only
restricts
distribution of R or of other programs containing code from R. This
is
made clear in clause 6 ("No Discrimination Against Fields of
Endeavor") of
the Open Source Definition (http://www.opensource.org/docs/
definition.html):

The license must not restrict anyone from making use of the
program in
a specific field of endeavor. For example, it may not restrict
the
program from being used in a business, or from being used for
genetic
research.

It is also explicitly stated in clause 0 of the GPL, which says in
part

Activities other than copying, distribution and modification are
not
covered by this License; they are outside its scope. The act of
running the Program is not restricted, and the output from the
Program
is covered only if its contents constitute a work based on the
Program.

Most add-on packages, including all recommended ones, also
explicitly
allow commercial use in this way. A few packages are restricted to
"non-commercial use"; you should contact the author to clarify whether
these may be used or seek the advice of your legal counsel.

None of the discussion in this section constitutes legal advice.
The R
Core Team does not provide legal advice under any circumstances.

2.12 Why is R named R?
======================

The name is partly based on the (first) names of the first two R
authors
(Robert Gentleman and Ross Ihaka), and partly a play on the name of
the
Bell Labs language `S' (*note What is S?::).

2.13 What is the R Foundation?
==============================

The R Foundation is a not for profit organization working in the
public
interest. It was founded by the members of the R Core Team in order
to
provide support for the R project and other innovations in statistical
computing, provide a reference point for individuals, institutions or
commercial enterprises that want to support or interact with the R
development community, and to hold and administer the copyright of R
software and documentation. See `http://www.R-project.org/
foundation/' for
more information.

2.14 What is R-Forge?
=====================

R-Forge (`http://R-Forge.R-project.org/') offers a central platform
for the
development of R packages, R-related software and further projects.
It is
based on GForge (http://www.gforge.org/) offering easy access to the
best
in SVN, daily built and checked packages, mailing lists, bug tracking,
message boards/forums, site hosting, permanent file archival, full
backups,
and total web-based administration. For more information, see the R-
Forge
web page and Stefan Theußl and Achim Zeileis (2009), "Collaborative
software
development using R-Forge", _The R Journal_, *1*(1):9-14.

3 R and S
*********

3.1 What is S?
==============

S is a very high level language and an environment for data analysis
and
graphics. In 1998, the Association for Computing Machinery (ACM)
presented
its Software System Award to John M. Chambers, the principal designer
of S,
for

the S system, which has forever altered the way people analyze,
visualize, and manipulate data ...

S is an elegant, widely accepted, and enduring software system,
with
conceptual integrity, thanks to the insight, taste, and effort of
John
Chambers.

The evolution of the S language is characterized by four books by
John
Chambers and coauthors, which are also the primary references for S.

* Richard A. Becker and John M. Chambers (1984), "S. An
Interactive
Environment for Data Analysis and Graphics," Monterey: Wadsworth
and
Brooks/Cole.

This is also referred to as the "_Brown Book_", and of historical
interest only.

* Richard A. Becker, John M. Chambers and Allan R. Wilks (1988),
"The New
S Language," London: Chapman & Hall.

This book is often called the "_Blue Book_", and introduced what
is
now known as S version 2.

* John M. Chambers and Trevor J. Hastie (1992), "Statistical Models
in
S," London: Chapman & Hall.

This is also called the "_White Book_", and introduced S version
3,
which added structures to facilitate statistical modeling in S.

* John M. Chambers (1998), "Programming with Data," New York:
Springer,
ISBN 0-387-98503-4
(`http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/').

This "_Green Book_" describes version 4 of S, a major revision of
S
designed by John Chambers to improve its usefulness at every
stage of
the programming process.

See `http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html'
for
further information on "Stages in the Evolution of S".

There is a huge amount of user-contributed code for S, available at
the
S Repository (http://lib.stat.cmu.edu/S/) at CMU.

3.2 What is S-PLUS?
===================

S-PLUS is a value-added version of S sold by Insightful Corporation
(http://www.insightful.com), which in 2008 was acquired by TIBCO
Software
Inc (http://www.tibco.com/). See the Insightful S-PLUS page
(http://www.insightful.com/products/splus/) and the TIBCO Spotfire S+
Products page for further information.

3.3 What are the differences between R and S?
=============================================

We can regard S as a language with three current implementations or
"engines", the "old S engine" (S version 3; S-PLUS 3.x and 4.x), the
"new S
engine" (S version 4; S-PLUS 5.x and above), and R. Given this
understanding, asking for "the differences between R and S" really
amounts
to asking for the specifics of the R implementation of the S language,
i.e., the difference between the R and S _engines_.

For the remainder of this section, "S" refers to the S engines and
not
the S language.

3.3.1 Lexical scoping
---------------------

Contrary to other implementations of the S language, R has adopted an
evaluation model in which nested function definitions are lexically
scoped.
This is analogous to the evaluation model in Scheme.

This difference becomes manifest when _free_ variables occur in a
function. Free variables are those which are neither formal
parameters
(occurring in the argument list of the function) nor local variables
(created by assigning to them in the body of the function). In S, the
values of free variables are determined by a set of global variables
(similar to C, there is only local and global scope). In R, they are
determined by the environment in which the function was created.

Consider the following function:

cube <- function(n) {
sq <- function() n * n
n * sq()
}

Under S, `sq()' does not "know" about the variable `n' unless it is
defined globally:

S> cube(2)
Error in sq(): Object "n" not found
Dumped
S> n <- 3
S> cube(2)
[1] 18

In R, the "environment" created when `cube()' was invoked is also
looked
in:

R> cube(2)
[1] 8

As a more "interesting" real-world problem, suppose you want to
write a
function which returns the density function of the r-th order
statistic
from a sample of size n from a (continuous) distribution. For
simplicity,
we shall use both the cdf and pdf of the distribution as explicit
arguments. (Example compiled from various postings by Luke Tierney.)

The S-PLUS documentation for `call()' basically suggests the
following:

dorder <- function(n, r, pfun, dfun) {
f <- function(x) NULL
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r +
1)))
PF <- call(substitute(pfun), as.name("x"))
DF <- call(substitute(dfun), as.name("x"))
f[[length(f)]] <-
call("*", con,
call("*", call("^", PF, r - 1),
call("*", call("^", call("-", 1, PF), n - r),
DF)))
f
}

Rather tricky, isn't it? The code uses the fact that in S, functions
are
just lists of special mode with the function body as the last
argument, and
hence does not work in R (one could make the idea work, though).

A version which makes heavy use of `substitute()' and seems to work
under both S and R is

dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r +
1)))
eval(substitute(function(x) K * PF(x)^a * (1 - PF(x))^b *
DF(x),
list(PF = substitute(pfun), DF =
substitute(dfun),
a = r - 1, b = n - r, K = con)))
}

(the `eval()' is not needed in S).

However, in R there is a much easier solution:

dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r +
1)))
function(x) {
con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
}
}

This seems to be the "natural" implementation, and it works because
the
free variables in the returned function can be looked up in the
defining
environment (this is lexical scope).

Note that what you really need is the function _closure_, i.e., the
body
along with all variable bindings needed for evaluating it. Since in
the
above version, the free variables in the value function are not
modified,
you can actually use it in S as well if you abstract out the closure
operation into a function `MC()' (for "make closure"):

dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r +
1)))
MC(function(x) {
con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
},
list(con = con, pfun = pfun, dfun = dfun, r = r, n = n))
}

Given the appropriate definitions of the closure operator, this
works in
both R and S, and is much "cleaner" than a substitute/eval solution
(or one
which overrules the default scoping rules by using explicit access to
evaluation frames, as is of course possible in both R and S).

For R, `MC()' simply is

MC <- function(f, env) f

(lexical scope!), a version for S is

MC <- function(f, env = NULL) {
env <- as.list(env)
if (mode(f) != "function")
stop(paste("not a function:", f))
if (length(env) > 0 && any(names(env) == ""))
stop(paste("not all arguments are named:", env))
fargs <- if(length(f) > 1) f[1:(length(f) - 1)] else NULL
fargs <- c(fargs, env)
if (any(duplicated(names(fargs))))
stop(paste("duplicated arguments:", paste(names(fargs)),
collapse = ", "))
fbody <- f[length(f)]
cf <- c(fargs, fbody)
mode(cf) <- "function"
return(cf)
}

Similarly, most optimization (or zero-finding) routines need some
arguments to be optimized over and have other parameters that depend
on the
data but are fixed with respect to optimization. With R scoping
rules,
this is a trivial problem; simply make up the function with the
required
definitions in the same environment and scoping takes care of it.
With S,
one solution is to add an extra parameter to the function and to the
optimizer to pass in these extras, which however can only work if the
optimizer supports this.

Nested lexically scoped functions allow using function closures and
maintaining local state. A simple example (taken from Abelson and
Sussman)
is obtained by typing `demo("scoping")' at the R prompt. Further
information is provided in the standard R reference "R: A Language for
Data
Analysis and Graphics" (*note What documentation exists for R?::) and
in
Robert Gentleman and Ross Ihaka (2000), "Lexical Scope and Statistical
Computing", _Journal of Computational and Graphical Statistics_, *9*,
491-508.

Nested lexically scoped functions also imply a further major
difference.
Whereas S stores all objects as separate files in a directory
somewhere
(usually `.Data' under the current directory), R does not. All
objects in
R are stored internally. When R is started up it grabs a piece of
memory
and uses it to store the objects. R performs its own memory
management of
this piece of memory, growing and shrinking its size as needed.
Having
everything in memory is necessary because it is not really possible to
externally maintain all relevant "environments" of symbol/value pairs.
This difference also seems to make R _faster_ than S.

The down side is that if R crashes you will lose all the work for
the
current session. Saving and restoring the memory "images" (the
functions
and data stored in R's internal memory at any time) can be a bit slow,
especially if they are big. In S this does not happen, because
everything
is saved in disk files and if you crash nothing is likely to happen to
them. (In fact, one might conjecture that the S developers felt that
the
price of changing their approach to persistent storage just to
accommodate
lexical scope was far too expensive.) Hence, when doing important
work,
you might consider saving often (see *note How can I save my
workspace?::)
to safeguard against possible crashes. Other possibilities are
logging
your sessions, or have your R commands stored in text files which can
be
read in using `source()'.

Note: If you run R from within Emacs (see *note R and Emacs::),
you
can save the contents of the interaction buffer to a file and
conveniently manipulate it using `ess-transcript-mode', as well
as
save source copies of all functions and data used.

3.3.2 Models
------------

There are some differences in the modeling code, such as

* Whereas in S, you would use `lm(y ~ x^3)' to regress `y' on
`x^3', in
R, you have to insulate powers of numeric vectors (using `I()'),
i.e.,
you have to use `lm(y ~ I(x^3))'.

* The glm family objects are implemented differently in R and S.
The
same functionality is available but the components have different
names.

* Option `na.action' is set to `"na.omit"' by default in R, but not
set
in S.

* Terms objects are stored differently. In S a terms object is an
expression with attributes, in R it is a formula with
attributes. The
attributes have the same names but are mostly stored differently.

* Finally, in R `y ~ x + 0' is an alternative to `y ~ x - 1' for
specifying a model with no intercept. Models with no parameters
at all
can be specified by `y ~ 0'.

3.3.3 Others
------------

Apart from lexical scoping and its implications, R follows the S
language
definition in the Blue and White Books as much as possible, and hence
really is an "implementation" of S. There are some intentional
differences
where the behavior of S is considered "not clean". In general, the
rationale is that R should help you detect programming errors, while
at the
same time being as compatible as possible with S.

Some known differences are the following.

* In R, if `x' is a list, then `x[i] <- NULL' and `x[[i]] <- NULL'
remove the specified elements from `x'. The first of these is
incompatible with S, where it is a no-op. (Note that you can set
elements to `NULL' using `x[i] <- list(NULL)'.)

* In S, the functions named `.First' and `.Last' in the `.Data'
directory can be used for customizing, as they are executed at
the
very beginning and end of a session, respectively.

In R, the startup mechanism is as follows. Unless `--no-environ'
was
given on the command line, R searches for site and user files to
process for setting environment variables. Then, R searches for
a
site-wide startup profile unless the command line option
`--no-site-file' was given. This code is loaded in package
*base*.
Then, unless `--no-init-file' was given, R searches for a user
profile
file, and sources it into the user workspace. It then loads a
saved
image of the user workspace from `.RData' in case there is one
(unless
`--no-restore-data' or `--no-restore' were specified). Next, a
function `.First()' is run if found on the search path. Finally,
function `.First.sys' in the *base* package is run. When
terminating
an R session, by default a function `.Last' is run if found on
the
search path, followed by `.Last.sys'. If needed, the functions
`.First()' and `.Last()' should be defined in the appropriate
startup
profiles. See the help pages for `.First' and `.Last' for more
details.

* In R, `T' and `F' are just variables being set to `TRUE' and
`FALSE',
respectively, but are not reserved words as in S and hence can be
overwritten by the user. (This helps e.g. when you have factors
with
levels `"T"' or `"F"'.) Hence, when writing code you should
always
use `TRUE' and `FALSE'.

* In R, `dyn.load()' can only load _shared objects_, as created for
example by `R CMD SHLIB'.

* In R, `attach()' currently only works for lists and data frames,
but
not for directories. (In fact, `attach()' also works for R data
files
created with `save()', which is analogous to attaching
directories in
S.) Also, you cannot attach at position 1.

* Categories do not exist in R, and never will as they are
deprecated now
in S. Use factors instead.

* In R, `For()' loops are not necessary and hence not supported.

* In R, `assign()' uses the argument `envir=' rather than `where='
as in
S.

* The random number generators are different, and the seeds have
different length.

* R passes integer objects to C as `int *' rather than `long *' as
in S.

* R has no single precision storage mode. However, as of version
0.65.1,
there is a single precision interface to C/FORTRAN subroutines.

* By default, `ls()' returns the names of the objects in the
current
(under R) and global (under S) environment, respectively. For
example,
given

x <- 1; fun <- function() {y <- 1; ls()}

then `fun()' returns `"y"' in R and `"x"' (together with the rest
of
the global environment) in S.

* R allows for zero-extent matrices (and arrays, i.e., some
elements of
the `dim' attribute vector can be 0). This has been determined a
useful feature as it helps reducing the need for special-case
tests for
empty subsets. For example, if `x' is a matrix, `x[, FALSE]' is
not
`NULL' but a "matrix" with 0 columns. Hence, such objects need
to be
tested for by checking whether their `length()' is zero (which
works
in both R and S), and not using `is.null()'.

* Named vectors are considered vectors in R but not in S (e.g.,
`is.vector(c(a = 1:3))' returns `FALSE' in S and `TRUE' in R).

* Data frames are not considered as matrices in R (i.e., if `DF' is
a
data frame, then `is.matrix(DF)' returns `FALSE' in R and `TRUE'
in S).

* R by default uses treatment contrasts in the unordered case,
whereas S
uses the Helmert ones. This is a deliberate difference
reflecting the
opinion that treatment contrasts are more natural.

* In R, the argument of a replacement function which corresponds to
the
right hand side must be named `value'. E.g., `f(a) <- b' is
evaluated
as `a <- "f<-"(a, value = b)'. S always takes the last argument,
irrespective of its name.

* In S, `substitute()' searches for names for substitution in the
given
expression in three places: the actual and the default arguments
of
the matching call, and the local frame (in that order). R looks
in
the local frame only, with the special rule to use a "promise" if
a
variable is not evaluated. Since the local frame is initialized
with
the actual arguments or the default expressions, this is usually
equivalent to S, until assignment takes place.

* In S, the index variable in a `for()' loop is local to the inside
of
the loop. In R it is local to the environment where the `for()'
statement is executed.

* In S, `tapply(simplify=TRUE)' returns a vector where R returns a
one-dimensional array (which can have named dimnames).

* In S(-PLUS) the C locale is used, whereas in R the current
operating
system locale is used for determining which characters are
alphanumeric and how they are sorted. This affects the set of
valid
names for R objects (for example accented chars may be allowed in
R)
and ordering in sorts and comparisons (such as whether `"aA" <
"Bb"' is
true or false). From version 1.2.0 the locale can be (re-)set in
R by
the `Sys.setlocale()' function.

* In S, `missing(ARG)' remains `TRUE' if ARG is subsequently
modified;
in R it doesn't.

* From R version 1.3.0, `data.frame' strips `I()' when creating
(column)
names.

* In R, the string `"NA"' is not treated as a missing value in a
character variable. Use `as.character(NA)' to create a missing
character value.

* R disallows repeated formal arguments in function calls.

* In S, `dump()', `dput()' and `deparse()' are essentially
different
interfaces to the same code. In R from version 2.0.0, this is
only
true if the same `control' argument is used, but by default it is
not.
By default `dump()' tries to write code that will evaluate to
reproduce the object, whereas `dput()' and `deparse()' default to
options for producing deparsed code that is readable.

* In R, indexing a vector, matrix, array or data frame with `['
using a
character vector index looks only for exact matches (whereas `[['
and
`$' allow partial matches). In S, `[' allows partial matches.

* S has a two-argument version of `atan' and no `atan2'. A call in
S
such as `atan(x1, x2)' is equivalent to R's `atan2(x1, x2)'.
However,
beware of named arguments since S's `atan(x = a, y = b)' is
equivalent
to R's `atan2(y = a, x = b)' with the meanings of `x' and `y'
interchanged. (R used to have undocumented support for a two-
argument
`atan' with positional arguments, but this has been withdrawn to
avoid
further confusion.)

* Numeric constants with no fractional and exponent (i.e., only
integer)
part are taken as integer in S-PLUS 6.x or later, but as double
in R.


There are also differences which are not intentional, and result
from
missing or incorrect code in R. The developers would appreciate
hearing
about any deficiencies you may find (in a written report fully
documenting
the difference as you see it). Of course, it would be useful if you
were
to implement the change yourself and make sure it works.

3.4 Is there anything R can do that S-PLUS cannot?
==================================================

Since almost anything you can do in R has source code that you could
port
to S-PLUS with little effort there will never be much you can do in R
that
you couldn't do in S-PLUS if you wanted to. (Note that using lexical
scoping may simplify matters considerably, though.)

R offers several graphics features that S-PLUS does not, such as
finer
handling of line types, more convenient color handling (via palettes),
gamma correction for color, and, most importantly, mathematical
annotation
in plot texts, via input expressions reminiscent of TeX constructs.
See
the help page for `plotmath', which features an impressive on-line
example.
More details can be found in Paul Murrell and Ross Ihaka (2000), "An
Approach to Providing Mathematical Annotation in Plots", _Journal of
Computational and Graphical Statistics_, *9*, 582-599.

3.5 What is R-plus?
===================

For a very long time, there was no such thing.

XLSolutions Corporation (http://www.xlsolutions-corp.com/) is
currently
beta testing a commercially supported version of R named R+ (read R
plus).

REvolution Computing (http://www.revolution-computing.com/) has
released
REvolution R
(http://www.revolution-computing.com/products/revolution-r.php), an
enterprise-class statistical analysis system based on R, suitable for
deployment in professional, commercial and regulated environments.

Random Technologies (http://www.random-technologies-llc.com/)
offers
RStat (http://random-technologies-llc.com/products/RStat/rstat), an
enterprise-strength statistical computing environment which combines R
with
enterprise-level validation, documentation, software support, and
consulting services, as well as related R-based products.

See also
`http://en.wikipedia.org/wiki/
R_programming_language#Commercialized_versions_of_R'
for pointers to commercialized versions of R.

4 R Web Interfaces
******************

*Rweb* is developed and maintained by Jeff Banfield
<je...@math.montana.edu>. The Rweb Home Page
(http://www.math.montana.edu/Rweb/) provides access to all three
versions
of Rweb--a simple text entry form that returns output and graphs, a
more
sophisticated JavaScript version that provides a multiple window
environment, and a set of point and click modules that are useful for
introductory statistics courses and require no knowledge of the R
language.
All of the Rweb versions can analyze Web accessible datasets if a URL
is
provided.

The paper "Rweb: Web-based Statistical Analysis", providing a
detailed
explanation of the different versions of Rweb and an overview of how
Rweb
works, was published in the Journal of Statistical Software
(`http://www.jstatsoft.org/v04/i01/').

Ulf Bartel <ul...@cs.tu-berlin.de> has developed *R-Online*, a
simple
on-line programming environment for R which intends to make the first
steps
in statistical programming with R (especially with time series) as
easy as
possible. There is no need for a local installation since the only
requirement for the user is a JavaScript capable browser. See
`http://osvisions.com/r-online/' for more information.

*Rcgi* is a CGI WWW interface to R by MJ Ray <m...@dsl.pipex.com>.
It
had the ability to use "embedded code": you could mix user input and
code,
allowing the HTML author to do anything from load in data sets to
enter
most of the commands for users without writing CGI scripts. Graphical
output was possible in PostScript or GIF formats and the executed code
was
presented to the user for revision. However, it is not clear if the
project is still active. Currently, a modified version of *Rcgi* by
Mai
Zhou <m...@ms.uky.edu> (actually, two versions: one with (bitmap)
graphics
and one without) as well as the original code are available from
`http://www.ms.uky.edu/~statweb/'.

CGI-based web access to R is also provided at
`http://hermes.sdu.dk/cgi-bin/go/'. There are many additional
examples of
web interfaces to R which basically allow to submit R code to a remote
server, see for example the collection of links available from
`http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/StatCompCourse'.

David Firth (http://www.warwick.ac.uk/go/dfirth) has written
*CGIwithR*
(http://CRAN.R-project.org/package=CGIwithR), an R add-on package
available
from CRAN. It provides some simple extensions to R to facilitate
running R
scripts through the CGI interface to a web server, and allows
submission of
data using both GET and POST methods. It is easily installed using
Apache
under Linux and in principle should run on any platform that supports
R and
a web server provided that the installer has the necessary security
permissions. David's paper "CGIwithR: Facilities for Processing Web
Forms
Using R" was published in the Journal of Statistical Software
(`http://www.jstatsoft.org/v08/i10/'). The package is now maintained
by
Duncan Temple Lang <dun...@wald.ucdavis.edu> and has a web page at
`http://www.omegahat.org/CGIwithR/'.

Rpad (http://www.rpad.org/Rpad), developed and actively maintained
by
Tom Short, provides a sophisticated environment which combines some of
the
features of the previous approaches with quite a bit of JavaScript,
allowing for a GUI-like behavior (with sortable tables, clickable
graphics,
editable output), etc.

Jeff Horner is working on the R/Apache Integration Project which
embeds
the R interpreter inside Apache 2 (and beyond). A tutorial and
presentation are available from the project web page at
`http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RApacheProject'.

Rserve (http://stats.math.uni-augsburg.de/Rserve/) is a project
actively
developed by Simon Urbanek. It implements a TCP/IP server which
allows
other programs to use facilities of R. Clients are available from the
web
site for Java and C++ (and could be written for other languages that
support TCP/IP sockets).

OpenStatServer (http://openstatserver.org/index.html) is being
developed
by a team lead by Greg Warnes; it aims "to provide clean access to
computational modules defined in a variety of computational
environments
(R, SAS, Matlab, etc) via a single well-defined client interface" and
to
turn computational services into web services.

Two projects use PHP to provide a web interface to R. R_PHP_Online
(http://steve-chen.net/R_PHP/) by Steve Chen (though it is unclear if
this
project is still active) is somewhat similar to the above Rcgi and
Rweb.
R-php (http://dssm.unipa.it/R-php/?cmd=home) is actively developed by
Alfredo Pontillo and Angelo Mineo and provides both a web interface to
R
and a set of pre-specified analyses that need no R code input.

webbioc (http://www.bioconductor.org/) is "an integrated web
interface
for doing microarray analysis using several of the Bioconductor
packages"
and is designed to be installed at local sites as a shared computing
resource.

Rwui (http://sysbio.mrc-bsu.cam.ac.uk/Rwui) is a web application to
create user-friendly web interfaces for R scripts. All code for the
web
interface is created automatically. There is no need for the user to
do
any extra scripting or learn any new scripting techniques. Rwui can
also
be found at `http://rwui.cryst.bbk.ac.uk'.

Finally, the *R.rsp* (http://CRAN.R-project.org/package=R.rsp)
package
by Henrik Bengtsson introduces "R Server Pages". Analogous to Java
Server
Pages, an R server page is typically HTML with embedded R code that
gets
evaluated when the page is requested. The package includes an
internal
cross-platform HTTP server implemented in Tcl, so provides a good
framework
for including web-based user interfaces in packages. The approach is
similar to the use of the *brew* (http://CRAN.R-project.org/
package=brew)
package with Rapache (http://rapache.net/) with the advantage of
cross-platform support and easy installation.

5 R Add-On Packages
*******************

5.1 Which add-on packages exist for R?
======================================

5.1.1 Add-on packages in R
--------------------------

The R distribution comes with the following packages:

*base*
Base R functions (and datasets before R 2.0.0).

*datasets*
Base R datasets (added in R 2.0.0).

*grDevices*
Graphics devices for base and grid graphics (added in R 2.0.0).

*graphics*
R functions for base graphics.

*grid*
A rewrite of the graphics layout capabilities, plus some support
for
interaction.

*methods*
Formally defined methods and classes for R objects, plus other
programming tools, as described in the Green Book.

*splines*
Regression spline functions and classes.

*stats*
R statistical functions.

*stats4*
Statistical functions using S4 classes.

*tcltk*
Interface and language bindings to Tcl/Tk GUI elements.

*tools*
Tools for package development and administration.

*utils*
R utility functions.
These "base packages" were substantially reorganized in R 1.9.0.
The
former *base* was split into the four packages *base*, *graphics*,
*stats*,
and *utils*. Packages *ctest*, *eda*, *modreg*, *mva*, *nls*,
*stepfun* and
*ts* were merged into *stats*, package *lqs* returned to the
recommended
package *MASS*, and package *mle* moved to *stats4*.

5.1.2 Add-on packages from CRAN
-------------------------------

The CRAN `src/contrib' area contains a wealth of add-on packages,
including
the following _recommended_ packages which are to be included in all
binary
distributions of R.

*KernSmooth*
Functions for kernel smoothing (and density estimation)
corresponding
to the book "Kernel Smoothing" by M. P. Wand and M. C. Jones,
1995.

*MASS*
Functions and datasets from the main package of Venables and
Ripley,
"Modern Applied Statistics with S". (Contained in the `VR'
bundle for
R versions prior to 2.10.0.)

*Matrix*
A Matrix package. (Recommended for R 2.9.0 or later.)

*boot*
Functions and datasets for bootstrapping from the book "Bootstrap
Methods and Their Applications" by A. C. Davison and D. V.
Hinkley,
1997, Cambridge University Press.

*class*
Functions for classification (k-nearest neighbor and LVQ).
(Contained
in the `VR' bundle for R versions prior to 2.10.0.)

*cluster*
Functions for cluster analysis.

*codetools*
Code analysis tools. (Recommended for R 2.5.0 or later.)

*foreign*
Functions for reading and writing data stored by statistical
software
like Minitab, S, SAS, SPSS, Stata, Systat, etc.

*lattice*
Lattice graphics, an implementation of Trellis Graphics
functions.

*mgcv*
Routines for GAMs and other generalized ridge regression problems
with
multiple smoothing parameter selection by GCV or UBRE.

*nlme*
Fit and compare Gaussian linear and nonlinear mixed-effects
models.

*nnet*
Software for single hidden layer perceptrons ("feed-forward
neural
networks"), and for multinomial log-linear models. (Contained in
the
`VR' bundle for R versions prior to 2.10.0.)

*rpart*
Recursive PARTitioning and regression trees.

*spatial*
Functions for kriging and point pattern analysis from "Modern
Applied
Statistics with S" by W. Venables and B. Ripley. (Contained in
the
`VR' bundle for R versions prior to 2.10.0.)

*survival*
Functions for survival analysis, including penalized likelihood.
See the CRAN contributed packages page for more information.

Many of these packages are categorized into CRAN Task Views
(http://CRAN.R-project.org/web/views/), allowing to browse packages by
topic and providing tools to automatically install all packages for
special
areas of interest.

Some CRAN packages that do not build out of the box on Windows,
require
additional software, or are shipping third party libraries for Windows
cannot be made available on CRAN in form of a Windows binary packages.
Nevertheless, some of these packages are available at the "CRAN
extras"
repository at `http://www.stats.ox.ac.uk/pub/RWin/' kindly provided by
Brian
D. Ripley. Note that this repository is a default repository for
recent
versions of R for Windows.

5.1.3 Add-on packages from Omegahat
-----------------------------------

The Omega Project for Statistical Computing (http://www.omegahat.org/)
provides a variety of open-source software for statistical
applications,
with special emphasis on web-based software, Java, the Java virtual
machine, and distributed computing. A CRAN style R package repository
is
available via `http://www.omegahat.org/R/'. See `http://
www.omegahat.org/'
for information on most R packages available from the Omega project.

5.1.4 Add-on packages from Bioconductor
---------------------------------------

Bioconductor (http://www.bioconductor.org/) is an open source and open
development software project for the analysis and comprehension of
genomic
data. Most Bioconductor components are distributed as R add-on
packages.
Initially most of the Bioconductor software packages
(http://www.bioconductor.org/packages/bioc/) focused primarily on DNA
microarray data analysis. As the project has matured, the functional
scope
of the software packages broadened to include the analysis of all
types of
genomic data, such as SAGE, sequence, or SNP data. In addition, there
are
metadata (annotation, CDF and probe) and experiment data packages.
See
`http://www.bioconductor.org/download/' for available packages and a
complete taxonomy via BioC Views.

5.1.5 Other add-on packages
---------------------------

Many more packages are available from places other than the three
default
repositories discussed above (CRAN, Bioconductor and Omegahat). In
particular, R-Forge provides a CRAN style repository at
`http://R-Forge.R-project.org/'.

More code has been posted to the R-help mailing list, and can be
obtained from the mailing list archive.

5.2 How can add-on packages be installed?
=========================================

(Unix-like only.) The add-on packages on CRAN come as gzipped tar
files
named `PKG_VERSION.tar.gz', which may in fact be "bundles" containing
more
than one package. Let PATH be the path to such a package file.
Provided
that `tar' and `gzip' are available on your system, type

$ R CMD INSTALL PATH/PKG_VERSION.tar.gz

at the shell prompt to install to the library tree rooted at the first
directory in your library search path (see the help page for
`.libPaths()'
for details on how the search path is determined).

To install to another tree (e.g., your private one), use

$ R CMD INSTALL -l LIB PATH/PKG_VERSION.tar.gz

where LIB gives the path to the library tree to install to.

Even more conveniently, you can install and automatically update
packages from within R if you have access to repositories such as
CRAN.
See the help page for `available.packages()' for more information.

5.3 How can add-on packages be used?
====================================

To find out which additional packages are available on your system,
type

library()

at the R prompt.

This produces something like

Packages in `/home/me/lib/R':

mystuff My own R functions, nicely packaged but not
documented

Packages in `/usr/local/lib/R/library':

KernSmooth Functions for kernel smoothing for Wand &
Jones (1995)
MASS Main Package of Venables and Ripley's MASS
Matrix Sparse and Dense Matrix Classes and Methods
base The R Base package
boot Bootstrap R (S-Plus) Functions (Canty)
class Functions for Classification
cluster Functions for clustering (by Rousseeuw et al.)
codetools Code Analysis Tools for R
datasets The R Datasets Package
foreign Read Data Stored by Minitab, S, SAS, SPSS,
Stata, Systat,
dBase, ...
grDevices The R Graphics Devices and Support for Colours
and Fonts
graphics The R Graphics Package
grid The Grid Graphics Package
lattice Lattice Graphics
methods Formal Methods and Classes
mgcv GAMs with GCV/AIC/REML smoothness estimation
and GAMMs
by PQL
nlme Linear and Nonlinear Mixed Effects Models
nnet Feed-forward Neural Networks and Multinomial
Log-Linear
Models
rpart Recursive Partitioning
spatial Functions for Kriging and Point Pattern
Analysis
splines Regression Spline Functions and Classes
stats The R Stats Package
stats4 Statistical functions using S4 Classes
survival Survival analysis, including penalised
likelihood
tcltk Tcl/Tk Interface
tools Tools for Package Development
utils The R Utils Package

You can "load" the installed package PKG by

library(PKG)

You can then find out which functions it provides by typing one of

library(help = PKG)
help(package = PKG)

You can unload the loaded package PKG by

detach("package:PKG", unload = TRUE)

(where `unload = TRUE' is needed only for packages with a namespace,
see
`?unload').

5.4 How can add-on packages be removed?
=======================================

Use

$ R CMD REMOVE PKG_1 ... PKG_N

to remove the packages PKG_1, ..., PKG_N from the library tree rooted
at
the first directory given in `R_LIBS' if this is set and non-null, and
from
the default library otherwise. (Versions of R prior to 1.3.0 removed
from
the default library by default.)

To remove from library LIB, do

$ R CMD REMOVE -l LIB PKG_1 ... PKG_N

5.5 How can I create an R package?
==================================

A package consists of a subdirectory containing a file `DESCRIPTION'
and
the subdirectories `R', `data', `demo', `exec', `inst', `man', `po',
`src',
and `tests' (some of which can be missing). The package subdirectory
may
also contain files `INDEX', `NAMESPACE', `configure', `cleanup',
`LICENSE',
`LICENCE', `COPYING' and `NEWS'.

See section "Creating R packages" in `Writing R Extensions', for
details. This manual is included in the R distribution, *note What
documentation exists for R?::, and gives information on package
structure,
the configure and cleanup mechanisms, and on automated package
checking and
building.

R version 1.3.0 has added the function `package.skeleton()' which
will
set up directories, save data and code, and create skeleton help files
for
a set of R functions and datasets.

*Note What is CRAN?::, for information on uploading a package to
CRAN.

5.6 How can I contribute to R?
==============================

R is in active development and there is always a risk of bugs creeping
in.
Also, the developers do not have access to all possible machines
capable of
running R. So, simply using it and communicating problems is
certainly of
great value.

The R Developer Page (http://developer.R-project.org/) acts as an
intermediate repository for more or less finalized ideas and plans for
the
R statistical system. It contains (pointers to) TODO lists, RFCs,
various
other writeups, ideas lists, and SVN miscellanea.

6 R and Emacs
*************

6.1 Is there Emacs support for R?
=================================

There is an Emacs package called ESS ("Emacs Speaks Statistics") which
provides a standard interface between statistical programs and
statistical
processes. It is intended to provide assistance for interactive
statistical programming and data analysis. Languages supported
include: S
dialects (R, S 3/4, and S-PLUS 3.x/4.x/5.x/6.x/7.x), LispStat dialects
(XLispStat, ViSta), SAS, Stata, and BUGS.

ESS grew out of the need for bug fixes and extensions to S-mode 4.8
(which was a GNU Emacs interface to S/S-PLUS version 3 only). The
current
set of developers desired support for XEmacs, R, S4, and MS Windows.
In
addition, with new modes being developed for R, Stata, and SAS, it was
felt
that a unifying interface and framework for the user interface would
benefit both the user and the developer, by helping both groups
conform to
standard Emacs usage. The end result is an increase in efficiency for
statistical programming and data analysis, over the usual tools.

R support contains code for editing R source code (syntactic
indentation
and highlighting of source code, partial evaluations of code, loading
and
error-checking of code, and source code revision maintenance) and
documentation (syntactic indentation and highlighting of source code,
sending examples to running ESS process, and previewing), interacting
with
an inferior R process from within Emacs (command-line editing,
searchable
command history, command-line completion of R object and file names,
quick
access to object and search lists, transcript recording, and an
interface
to the help system), and transcript manipulation (recording and saving
transcript files, manipulating and editing saved transcripts, and
re-evaluating commands from transcript files).

The latest stable version of ESS are available via CRAN or the ESS
web
page (http://ESS.R-project.org/). The HTML version of the
documentation
can be found at `http://stat.ethz.ch/ESS/'.

ESS comes with detailed installation instructions.

For help with ESS, send email to <ESS-...@stat.math.ethz.ch>.

Please send bug reports and suggestions on ESS to
<ESS-...@stat.math.ethz.ch>. The easiest way to do this from is
within
Emacs by typing `M-x ess-submit-bug-report' or using the [ESS] or
[iESS]
pulldown menus.

6.2 Should I run R from within Emacs?
=====================================

Yes, _definitely_. Inferior R mode provides a readline/history
mechanism,
object name completion, and syntax-based highlighting of the
interaction
buffer using Font Lock mode, as well as a very convenient interface to
the
R help system.

Of course, it also integrates nicely with the mechanisms for
editing R
source using Emacs. One can write code in one Emacs buffer and send
whole
or parts of it for execution to R; this is helpful for both data
analysis
and programming. One can also seamlessly integrate with a revision
control
system, in order to maintain a log of changes in your programs and
data, as
well as to allow for the retrieval of past versions of the code.

In addition, it allows you to keep a record of your session, which
can
also be used for error recovery through the use of the transcript
mode.

To specify command line arguments for the inferior R process, use
`C-u
M-x R' for starting R.

6.3 Debugging R from within Emacs
=================================

To debug R "from within Emacs", there are several possibilities. To
use
the Emacs GUD (Grand Unified Debugger) library with the recommended
debugger GDB, type `M-x gdb' and give the path to the R _binary_ as
argument. At the `gdb' prompt, set `R_HOME' and other environment
variables as needed (using e.g. `set env R_HOME /path/to/R/', but see
also
below), and start the binary with the desired arguments (e.g., `run
--quiet').

If you have ESS, you can do `C-u M-x R <RET> - d <SPC> g d b <RET>'
to
start an inferior R process with arguments `-d gdb'.

A third option is to start an inferior R process via ESS (`M-x R')
and
then start GUD (`M-x gdb') giving the R binary (using its full path
name)
as the program to debug. Use the program `ps' to find the process
number
of the currently running R process then use the `attach' command in
gdb to
attach it to that process. One advantage of this method is that you
have
separate `*R*' and `*gud-gdb*' windows. Within the `*R*' window you
have
all the ESS facilities, such as object-name completion, that we know
and
love.

When using GUD mode for debugging from within Emacs, you may find
it
most convenient to use the directory with your code in it as the
current
working directory and then make a symbolic link from that directory to
the
R binary. That way `.gdbinit' can stay in the directory with the code
and
be used to set up the environment and the search paths for the source,
e.g.
as follows:

set env R_HOME /opt/R
set env R_PAPERSIZE letter
set env R_PRINTCMD lpr
dir /opt/R/src/appl
dir /opt/R/src/main
dir /opt/R/src/nmath
dir /opt/R/src/unix

7 R Miscellanea
***************

7.1 How can I set components of a list to NULL?
===============================================

You can use

x[i] <- list(NULL)

to set component `i' of the list `x' to `NULL', similarly for named
components. Do not set `x[i]' or `x[[i]]' to `NULL', because this
will
remove the corresponding component from the list.

For dropping the row names of a matrix `x', it may be easier to use
`rownames(x) <- NULL', similarly for column names.

7.2 How can I save my workspace?
================================

`save.image()' saves the objects in the user's `.GlobalEnv' to the
file
`.RData' in the R startup directory. (This is also what happens after
`q("yes")'.) Using `save.image(FILE)' one can save the image under a
different name.

7.3 How can I clean up my workspace?
====================================

To remove all objects in the currently active environment (typically
`.GlobalEnv'), you can do

rm(list = ls(all = TRUE))

(Without `all = TRUE', only the objects with names not starting with a
`.'
are removed.)

7.4 How can I get eval() and D() to work?
=========================================

Strange things will happen if you use `eval(print(x), envir = e)' or
`D(x^2, "x")'. The first one will either tell you that "`x'" is not
found,
or print the value of the wrong `x'. The other one will likely return
zero
if `x' exists, and an error otherwise.

This is because in both cases, the first argument is evaluated in
the
calling environment first. The result (which should be an object of
mode
`"expression"' or `"call"') is then evaluated or differentiated. What
you
(most likely) really want is obtained by "quoting" the first argument
upon
surrounding it with `expression()'. For example,

R> D(expression(x^2), "x")
2 * x

Although this behavior may initially seem to be rather strange, is
perfectly logical. The "intuitive" behavior could easily be
implemented,
but problems would arise whenever the expression is contained in a
variable, passed as a parameter, or is the result of a function call.
Consider for instance the semantics in cases like

D2 <- function(e, n) D(D(e, n), n)

or

g <- function(y) eval(substitute(y), sys.frame(sys.parent(n =
2)))
g(a * b)

See the help page for `deriv()' for more examples.

7.5 Why do my matrices lose dimensions?
=======================================

When a matrix with a single row or column is created by a subscripting
operation, e.g., `row <- mat[2, ]', it is by default turned into a
vector.
In a similar way if an array with dimension, say, 2 x 3 x 1 x 4 is
created
by subscripting it will be coerced into a 2 x 3 x 4 array, losing the
unnecessary dimension. After much discussion this has been determined
to
be a _feature_.

To prevent this happening, add the option `drop = FALSE' to the
subscripting. For example,

rowmatrix <- mat[2, , drop = FALSE] # creates a row matrix
colmatrix <- mat[, 2, drop = FALSE] # creates a column matrix
a <- b[1, 1, 1, drop = FALSE] # creates a 1 x 1 x 1 array

The `drop = FALSE' option should be used defensively when
programming.
For example, the statement

somerows <- mat[index, ]

will return a vector rather than a matrix if `index' happens to have
length
1, causing errors later in the code. It should probably be rewritten
as

somerows <- mat[index, , drop = FALSE]

7.6 How does autoloading work?
==============================

R has a special environment called `.AutoloadEnv'. Using
`autoload(NAME,
PKG)', where NAME and PKG are strings giving the names of an object
and the
package containing it, stores some information in this environment.
When R
tries to evaluate NAME, it loads the corresponding package PKG and
reevaluates NAME in the new package's environment.

Using this mechanism makes R behave as if the package was loaded,
but
does not occupy memory (yet).

See the help page for `autoload()' for a very nice example.

7.7 How should I set options?
=============================

The function `options()' allows setting and examining a variety of
global
"options" which affect the way in which R computes and displays its
results. The variable `.Options' holds the current values of these
options, but should never directly be assigned to unless you want to
drive
yourself crazy--simply pretend that it is a "read-only" variable.

For example, given

test1 <- function(x = pi, dig = 3) {
oo <- options(digits = dig); on.exit(options(oo));
cat(.Options$digits, x, "\n")
}
test2 <- function(x = pi, dig = 3) {
.Options$digits <- dig
cat(.Options$digits, x, "\n")
}

we obtain:

R> test1()
3 3.14
R> test2()
3 3.141593

What is really used is the _global_ value of `.Options', and using
`options(OPT = VAL)' correctly updates it. Local copies of
`.Options',
either in `.GlobalEnv' or in a function environment (frame), are just
silently disregarded.

7.8 How do file names work in Windows?
======================================

As R uses C-style string handling, `\' is treated as an escape
character,
so that for example one can enter a newline as `\n'. When you really
need
a `\', you have to escape it with another `\'.

Thus, in filenames use something like `"c:\\data\\money.dat"'. You
can
also replace `\' by `/' (`"c:/data/money.dat"').

7.9 Why does plotting give a color allocation error?
====================================================

On an X11 device, plotting sometimes, e.g., when running
`demo("image")',
results in "Error: color allocation error". This is an X problem, and
only
indirectly related to R. It occurs when applications started prior to
R
have used all the available colors. (How many colors are available
depends
on the X configuration; sometimes only 256 colors can be used.)

One application which is notorious for "eating" colors is
Netscape. If
the problem occurs when Netscape is running, try (re)starting it with
either the `-no-install' (to use the default colormap) or the `-
install'
(to install a private colormap) option.

You could also set the `colortype' of `X11()' to `"pseudo.cube"'
rather
than the default `"pseudo"'. See the help page for `X11()' for more
information.

7.10 How do I convert factors to numeric?
=========================================

It may happen that when reading numeric data into R (usually, when
reading
in a file), they come in as factors. If `f' is such a factor object,
you
can use

as.numeric(as.character(f))

to get the numbers back. More efficient, but harder to remember, is

as.numeric(levels(f))[as.integer(f)]

In any case, do not call `as.numeric()' or their likes directly for
the
task at hand (as `as.numeric()' or `unclass()' give the internal
codes).

7.11 Are Trellis displays implemented in R?
===========================================

The recommended package *lattice* (which is based on another
recommended
package, *grid*) provides graphical functionality that is compatible
with
most Trellis commands.

You could also look at `coplot()' and `dotchart()' which might do
at
least some of what you want. Note also that the R version of
`pairs()' is
fairly general and provides most of the functionality of `splom()',
and
that R's default plot method has an argument `asp' allowing to specify
(and
fix against device resizing) the aspect ratio of the plot.

(Because the word "Trellis" has been claimed as a trademark we do
not
use it in R. The name "lattice" has been chosen for the R
equivalent.)

7.12 What are the enclosing and parent environments?
====================================================

Inside a function you may want to access variables in two additional
environments: the one that the function was defined in ("enclosing"),
and
the one it was invoked in ("parent").

If you create a function at the command line or load it in a
package its
enclosing environment is the global workspace. If you define a
function
`f()' inside another function `g()' its enclosing environment is the
environment inside `g()'. The enclosing environment for a function is
fixed when the function is created. You can find out the enclosing
environment for a function `f()' using `environment(f)'.

The "parent" environment, on the other hand, is defined when you
invoke
a function. If you invoke `lm()' at the command line its parent
environment is the global workspace, if you invoke it inside a
function
`f()' then its parent environment is the environment inside `f()'.
You can
find out the parent environment for an invocation of a function by
using
`parent.frame()' or `sys.frame(sys.parent())'.

So for most user-visible functions the enclosing environment will
be the
global workspace, since that is where most functions are defined. The
parent environment will be wherever the function happens to be called
from.
If a function `f()' is defined inside another function `g()' it will
probably be used inside `g()' as well, so its parent environment and
enclosing environment will probably be the same.

Parent environments are important because things like model
formulas
need to be evaluated in the environment the function was called from,
since
that's where all the variables will be available. This relies on the
parent environment being potentially different with each invocation.

Enclosing environments are important because a function can use
variables in the enclosing environment to share information with other
functions or with other invocations of itself (see the section on
lexical
scoping). This relies on the enclosing environment being the same
each
time the function is invoked. (In C this would be done with static
variables.)

Scoping _is_ hard. Looking at examples helps. It is particularly
instructive to look at examples that work differently in R and S and
try to
see why they differ. One way to describe the scoping differences
between R
and S is to say that in S the enclosing environment is _always_ the
global
workspace, but in R the enclosing environment is wherever the function
was
created.

7.13 How can I substitute into a plot label?
============================================

Often, it is desired to use the value of an R object in a plot label,
e.g.,
a title. This is easily accomplished using `paste()' if the label is
a
simple character string, but not always obvious in case the label is
an
expression (for refined mathematical annotation). In such a case,
either
use `parse()' on your pasted character string or use `substitute()' on
an
expression. For example, if `ahat' is an estimator of your parameter
a of
interest, use

title(substitute(hat(a) == ahat, list(ahat = ahat)))

(note that it is `==' and not `='). Sometimes `bquote()' gives a more
compact form, e.g.,

title(bquote(hat(a) = .(ahat)))

where subexpressions enclosed in `.()' are replaced by their values.

There are more worked examples in the mailing list archives.

7.14 What are valid names?
==========================

When creating data frames using `data.frame()' or `read.table()', R by
default ensures that the variable names are syntactically valid. (The
argument `check.names' to these functions controls whether variable
names
are checked and adjusted by `make.names()' if needed.)

To understand what names are "valid", one needs to take into
account
that the term "name" is used in several different (but related) ways
in the
language:

1. A _syntactic name_ is a string the parser interprets as this type
of
expression. It consists of letters, numbers, and the dot and
(for
version of R at least 1.9.0) underscore characters, and starts
with
either a letter or a dot not followed by a number. Reserved
words are
not syntactic names.

2. An _object name_ is a string associated with an object that is
assigned in an expression either by having the object name on the
left
of an assignment operation or as an argument to the `assign()'
function. It is usually a syntactic name as well, but can be any
non-empty string if it is quoted (and it is always quoted in the
call
to `assign()').

3. An _argument name_ is what appears to the left of the equals sign
when
supplying an argument in a function call (for example, `f(trim=.
5)').
Argument names are also usually syntactic names, but again can be
anything if they are quoted.

4. An _element name_ is a string that identifies a piece of an
object (a
component of a list, for example.) When it is used on the right
of
the `$' operator, it must be a syntactic name, or quoted.
Otherwise,
element names can be any strings. (When an object is used as a
database, as in a call to `eval()' or `attach()', the element
names
become object names.)

5. Finally, a _file name_ is a string identifying a file in the
operating
system for reading, writing, etc. It really has nothing much to
do
with names in the language, but it is traditional to call these
strings file "names".

7.15 Are GAMs implemented in R?
===============================

Package *gam* (http://CRAN.R-project.org/package=gam) from CRAN
implements
all the Generalized Additive Models (GAM) functionality as described
in the
GAM chapter of the White Book. In particular, it implements
backfitting
with both local regression and smoothing splines, and is extendable.
There
is a `gam()' function for GAMs in package *mgcv*, but it is not an
exact
clone of what is described in the White Book (no `lo()' for example).
Package *gss* (http://CRAN.R-project.org/package=gss) can fit spline-
based
GAMs too. And if you can accept regression splines you can use
`glm()'.
For Gaussian GAMs you can use `bruto()' from package *mda*
(http://CRAN.R-project.org/package=mda).

7.16 Why is the output not printed when I source() a file?
==========================================================

Most R commands do not generate any output. The command

1+1

computes the value 2 and returns it; the command

summary(glm(y~x+z, family=binomial))

fits a logistic regression model, computes some summary information
and
returns an object of class `"summary.glm"' (*note How should I write
summary methods?::).

If you type `1+1' or `summary(glm(y~x+z, family=binomial))' at the
command line the returned value is automatically printed (unless it is
`invisible()'), but in other circumstances, such as in a `source()'d
file
or inside a function it isn't printed unless you specifically print
it.

To print the value use

print(1+1)

or

print(summary(glm(y~x+z, family=binomial)))

instead, or use `source(FILE, echo=TRUE)'.

7.17 Why does outer() behave strangely with my function?
========================================================

As the help for `outer()' indicates, it does not work on arbitrary
functions the way the `apply()' family does. It requires functions
that
are vectorized to work elementwise on arrays. As you can see by
looking at
the code, `outer(x, y, FUN)' creates two large vectors containing
every
possible combination of elements of `x' and `y' and then passes this
to
`FUN' all at once. Your function probably cannot handle two large
vectors
as parameters.

If you have a function that cannot handle two vectors but can
handle two
scalars, then you can still use `outer()' but you will need to wrap
your
function up first, to simulate vectorized behavior. Suppose your
function
is

foo <- function(x, y, happy) {
stopifnot(length(x) == 1, length(y) == 1) # scalars only!
(x + y) * happy
}

If you define the general function

wrapper <- function(x, y, my.fun, ...) {
sapply(seq_along(x), FUN = function(i) my.fun(x[i], y[i], ...))
}

then you can use `outer()' by writing, e.g.,

outer(1:4, 1:2, FUN = wrapper, my.fun = foo, happy = 10)

7.18 Why does the output from anova() depend on the order of factors
in the model?
==================================================================================

In a model such as `~A+B+A:B', R will report the difference in sums of
squares between the models `~1', `~A', `~A+B' and `~A+B+A:B'. If the
model
were `~B+A+A:B', R would report differences between `~1', `~B', `~A
+B', and
`~A+B+A:B' . In the first case the sum of squares for `A' is comparing
`~1'
and `~A', in the second case it is comparing `~B' and `~B+A'. In a
non-orthogonal design (i.e., most unbalanced designs) these
comparisons are
(conceptually and numerically) different.

Some packages report instead the sums of squares based on comparing
the
full model to the models with each factor removed one at a time (the
famous
`Type III sums of squares' from SAS, for example). These do not
depend on
the order of factors in the model. The question of which set of sums
of
squares is the Right Thing provokes low-level holy wars on R-help from
time
to time.

There is no need to be agitated about the particular sums of
squares
that R reports. You can compute your favorite sums of squares quite
easily. Any two models can be compared with `anova(MODEL1, MODEL2)',
and
`drop1(MODEL1)' will show the sums of squares resulting from dropping
single terms.

7.19 How do I produce PNG graphics in batch mode?
=================================================

Under a Unix-like, if your installation supports the `type="cairo"'
option
to the `png()' device there should be no problems, and the default
settings
should just work. This option is not available for versions of R
prior to
2.7.0, or without support for cairo. From R 2.7.0 `png()' by default
uses
the Quartz device on Mac OS X, and that too works in batch mode.

Earlier versions of the `png()' device uses the X11 driver, which
is a
problem in batch mode or for remote operation. If you have
Ghostscript you
can use `bitmap()', which produces a PostScript or PDF file then
converts
it to any bitmap format supported by Ghostscript. On some
installations
this produces ugly output, on others it is perfectly satisfactory.
Many
systems now come with Xvfb from X.Org (http://www.x.org/
Downloads.html)
(possibly as an optional install), which is an X11 server that does
not
require a screen; and there is the *GDD*
(http://CRAN.R-project.org/package=GDD) package from CRAN, which
produces
PNG, JPEG and GIF bitmaps without X11.

7.20 How can I get command line editing to work?
================================================

The Unix-like command-line interface to R can only provide the inbuilt
command line editor which allows recall, editing and re-submission of
prior
commands provided that the GNU readline library is available at the
time R
is configured for compilation. Note that the `development' version of
readline including the appropriate headers is needed: users of Linux
binary
distributions will need to install packages such as `libreadline-dev'
(Debian) or `readline-devel' (Red Hat).

7.21 How can I turn a string into a variable?
=============================================

If you have

varname <- c("a", "b", "d")

you can do

get(varname[1]) + 2

for

a + 2

or

assign(varname[1], 2 + 2)

for

a <- 2 + 2

or

eval(substitute(lm(y ~ x + variable),
list(variable = as.name(varname[1]))))

for

lm(y ~ x + a)

At least in the first two cases it is often easier to just use a
list,
and then you can easily index it by name

vars <- list(a = 1:10, b = rnorm(100), d = LETTERS)
vars[["a"]]

without any of this messing about.

7.22 Why do lattice/trellis graphics not work?
==============================================

The most likely reason is that you forgot to tell R to display the
graph.
Lattice functions such as `xyplot()' create a graph object, but do not
display it (the same is true of *ggplot2*
(http://CRAN.R-project.org/package=ggplot2) graphics, and Trellis
graphics
in S-PLUS). The `print()' method for the graph object produces the
actual
display. When you use these functions interactively at the command
line,
the result is automatically printed, but in `source()' or inside your
own
functions you will need an explicit `print()' statement.

7.23 How can I sort the rows of a data frame?
=============================================

To sort the rows within a data frame, with respect to the values in
one or
more of the columns, simply use `order()' (e.g., `DF[order(DF$a,
DF[["b"]]), ]' to sort the data frame `DF' on columns named `a' and
`b').

7.24 Why does the help.start() search engine not work?
======================================================

The browser-based search engine in `help.start()' utilizes a Java
applet.
In order for this to function properly, a compatible version of Java
must
installed on your system and linked to your browser, and both Java
_and_
JavaScript need to be enabled in your browser.

There have been a number of compatibility issues with versions of
Java
and of browsers. For further details please consult section "Enabling
search in HTML help" in `R Installation and Administration'. This
manual is
included in the R distribution, *note What documentation exists for
R?::,
and its HTML version is linked from the HTML search page.

7.25 Why did my .Rprofile stop working when I updated R?
========================================================

Did you read the `NEWS' file? For functions that are not in the
*base*
package you need to specify the correct package namespace, since the
code
will be run _before_ the packages are loaded. E.g.,

ps.options(horizontal = FALSE)
help.start()

needs to be

grDevices::ps.options(horizontal = FALSE)
utils::help.start()

(`graphics::ps.options(horizontal = FALSE)' in R 1.9.x).

7.26 Where have all the methods gone?
=====================================

Many functions, particularly S3 methods, are now hidden in namespaces.
This has the advantage that they cannot be called inadvertently with
arguments of the wrong class, but it makes them harder to view.

To see the code for an S3 method (e.g., `[.terms') use

getS3method("[", "terms")

To see the code for an unexported function `foo()' in the namespace of
package `"bar"' use `bar:::foo'. Don't use these constructions to
call
unexported functions in your own code--they are probably unexported
for a
reason and may change without warning.

7.27 How can I create rotated axis labels?
==========================================

To rotate axis labels (using base graphics), you need to use `text()',
rather than `mtext()', as the latter does not support `par("srt")'.

## Increase bottom margin to make room for rotated labels
par(mar = c(7, 4, 4, 2) + 0.1)
## Create plot with no x axis and no x axis label
plot(1 : 8, xaxt = "n", xlab = "")
## Set up x axis with tick marks alone
axis(1, labels = FALSE)
## Create some text labels
labels <- paste("Label", 1:8, sep = " ")
## Plot x axis labels at default tick marks
text(1:8, par("usr")[3] - 0.25, srt = 45, adj = 1,
labels = labels, xpd = TRUE)
## Plot x axis label at line 6 (of 7)
mtext(1, text = "X Axis Label", line = 6)

When plotting the x axis labels, we use `srt = 45' for text rotation
angle,
`adj = 1' to place the right end of text at the tick marks, and `xpd =
TRUE' to allow for text outside the plot region. You can adjust the
value
of the `0.25' offset as required to move the axis labels up or down
relative to the x axis. See `?par' for more information.

Also see Figure 1 and associated code in Paul Murrell (2003),
"Integrating grid Graphics Output with Base Graphics Output", _R
News_,
*3/2*, 7-12.

7.28 Why is read.table() so inefficient?
========================================

By default, `read.table()' needs to read in everything as character
data,
and then try to figure out which variables to convert to numerics or
factors. For a large data set, this takes considerable amounts of
time and
memory. Performance can substantially be improved by using the
`colClasses' argument to specify the classes to be assumed for the
columns
of the table.

7.29 What is the difference between package and library?
========================================================

A "package" is a standardized collection of material extending R, e.g.
providing code, data, or documentation. A "library" is a place
(directory)
where R knows to find packages it can use (i.e., which were
"installed").
R is told to use a package (to "load" it and add it to the search
path) via
calls to the function `library'. I.e., `library()' is employed to
load a
package from libraries containing packages.

*Note R Add-On Packages::, for more details. See also Uwe Ligges
(2003),
"R Help Desk: Package Management", _R News_, *3/3*, 37-39.

7.30 I installed a package but the functions are not there
==========================================================

To actually _use_ the package, it needs to be _loaded_ using
`library()'.

See *note R Add-On Packages:: and *note What is the difference
between
package and library?:: for more information.

7.31 Why doesn't R think these numbers are equal?
=================================================

The only numbers that can be represented exactly in R's numeric type
are
integers and fractions whose denominator is a power of 2. Other
numbers
have to be rounded to (typically) 53 binary digits accuracy. As a
result,
two floating point numbers will not reliably be equal unless they have
been
computed by the same algorithm, and not always even then. For example

R> a <- sqrt(2)
R> a * a == 2
[1] FALSE
R> a * a - 2
[1] 4.440892e-16

The function `all.equal()' compares two objects using a numeric
tolerance of `.Machine$double.eps ^ 0.5'. If you want much greater
accuracy than this you will need to consider error propagation
carefully.

For more information, see e.g. David Goldberg (1991), "What Every
Computer Scientist Should Know About Floating-Point Arithmetic", _ACM
Computing Surveys_, *23/1*, 5-48, also available via
`http://docs.sun.com/source/806-3568/ncg_goldberg.html'.

To quote from "The Elements of Programming Style" by Kernighan and
Plauger:

_10.0 times 0.1 is hardly ever 1.0_.

7.32 How can I capture or ignore errors in a long simulation?
=============================================================

Use `try()', which returns an object of class `"try-error"' instead of
an
error, or preferably `tryCatch()', where the return value can be
configured
more flexibly. For example

beta[i,] <- tryCatch(coef(lm(formula, data)),
error = function(e) rep(NaN, 4))

would return the coefficients if the `lm()' call succeeded and would
return
`c(NaN, NaN, NaN, NaN)' if it failed (presumably there are supposed to
be 4
coefficients in this example).

7.33 Why are powers of negative numbers wrong?
==============================================

You are probably seeing something like

R> -2^2
[1] -4

and misunderstanding the precedence rules for expressions in R. Write

R> (-2)^2
[1] 4

to get the square of -2.

The precedence rules are documented in `?Syntax', and to see how R
interprets an expression you can look at the parse tree

R> as.list(quote(-2^2))
[[1]]
`-`

[[2]]
2^2

7.34 How can I save the result of each iteration in a loop into a
separate file?
================================================================================

One way is to use `paste()' (or `sprintf()') to concatenate a stem
filename
and the iteration number while `file.path()' constructs the path. For
example, to save results into files `result1.rda', ...,
`result100.rda' in
the subdirectory `Results' of the current working directory, one can
use

for(i in 1:100) {
## Calculations constructing "some_object" ...
fp <- file.path("Results", paste("result", i, ".rda", sep =
""))
save(list = "some_object", file = fp)
}

7.35 Why are p-values not displayed when using lmer()?
======================================================

Doug Bates has kindly provided an extensive response in a post to the
r-help list, which can be reviewed at
`https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html'.

7.36 Why are there unwanted borders, lines or grid-like artifacts when
viewing a plot saved to a PS or PDF file?
================================================================================================================

This can occur when using functions such as `polygon()',
`filled.contour()', `image()' or other functions which may call these
internally. In the case of `polygon()', you may observe unwanted
borders
between the polygons even when setting the `border' argument to `NA'
or
`"transparent"'.

The source of the problem is the PS/PDF viewer when the plot is
anti-aliased. The details for the solution will be different
depending
upon the viewer used, the operating system and may change over time.
For
some common viewers, consider the following:

Acrobat Reader (cross platform)
There are options in Preferences to enable/disable text
smoothing,
image smoothing and line art smoothing. Disable line art
smoothing.

Preview (Mac OS X)
There is an option in Preferences to enable/disable anti-aliasing
of
text and line art. Disable this option.

GSview (cross platform)
There are settings for Text Alpha and Graphics Alpha. Change
Graphics
Alpha from 4 bits to 1 bit to disable graphic anti-aliasing.

gv (Unix-like X)
There is an option to enable/disable anti-aliasing. Disable this
option.

Evince (Linux/GNOME)
There is not an option to disable anti-aliasing in this viewer.

Okular (Linux/KDE)
There is not an option in the GUI to enable/disable anti-
aliasing.
From a console command line, use:
$ kwriteconfig --file okularpartrc --group 'Dlg Performance'
\
--key TextAntialias Disabled
Then restart Okular. Change the final word to `Enabled' to
restore
the original setting.

7.37 Why does backslash behave strangely inside strings?
========================================================

This question most often comes up in relation to file names (see *note
How
do file names work in Windows?::) but it also happens that people
complain
that they cannot seem to put a single `\' character into a text string
unless it happens to be followed by certain other characters.

To understand this, you have to distinguish between character
strings
and _representations_ of character strings. Mostly, the
representation in
R is just the string with a single or double quote at either end, but
there
are strings that cannot be represented that way, e.g., strings that
themselves contains the quote character. So

> str <- "This \"text\" is quoted"
> str
[1] "This \"text\" is quoted"
> cat(str, "\n")
This "text" is quoted

The _escape sequences_ `\"' and `\n' represent a double quote and the
newline character respectively. Printing text strings, using `print()'
or
by typing the name at the prompt will use the escape sequences too,
but the
`cat()' function will display the string as-is. Notice that `"\n"' is
a
one-character string, not two; the backslash is not actually in the
string,
it is just generated in the printed representation.

> nchar("\n")
[1] 1
> substring("\n", 1, 1)
[1] "\n"

So how do you put a backslash in a string? For this, you have to
escape
the escape character. I.e., you have to double the backslash. as in

> cat("\\n", "\n")
\n

Some functions, particularly those involving regular expression
matching, themselves use metacharacters, which may need to be escaped
by
the backslash mechanism. In those cases you may need a _quadruple_
backslash to represent a single literal one.

In versions of R up to 2.4.1 an unknown escape sequence like `\p'
was
quietly interpreted as just `p'. Current versions of R emit a
warning.

7.38 How can I put error bars or confidence bands on my plot?
=============================================================

Some functions will display a particular kind of plot with error bars,
such
as the `bar.err()' function in the *agricolae*
(http://CRAN.R-project.org/package=agricolae) package, the `plotCI()'
function in the *gplots* (http://CRAN.R-project.org/package=gplots)
package, the `plotCI()' and `brkdn.plot()' functions in the *plotrix*
(http://CRAN.R-project.org/package=plotrix) package and the
`error.bars()',
`error.crosses()' and `error.bars.by()' functions in the *psych*
(http://CRAN.R-project.org/package=psych) package. Within these types
of
functions, some will accept the measures of dispersion (e.g.,
`plotCI'),
some will calculate the dispersion measures from the raw values
(`bar.err',
`brkdn.plot'), and some will do both (`error.bars'). Still other
functions
will just display error bars, like the dispersion function in the
*plotrix*
(http://CRAN.R-project.org/package=plotrix) package. Most of the
above
functions use the `arrows()' function in the base *graphics* package
to
draw the error bars.

The above functions all use the base graphics system. The grid and
lattice graphics systems also have specific functions for displaying
error
bars, e.g., the `grid.arrow()' function in the *grid* package, and the
`geom_errorbar()', `geom_errorbarh()', `geom_pointrange()',
`geom_linerange()', `geom_crossbar()' and `geom_ribbon()' functions in
the
*ggplot2* (http://CRAN.R-project.org/package=ggplot2) package. In the
lattice system, error bars can be displayed with `Dotplot()' or
`xYplot()'
in the *Hmisc* (http://CRAN.R-project.org/package=Hmisc) package and
`segplot()' in the *latticeExtra*
(http://CRAN.R-project.org/package=latticeExtra) package.

7.39 How do I create a plot with two y-axes?
============================================

Creating a graph with two y-axes, i.e., with two sorts of data that
are
scaled to the same vertical size and showing separate vertical axes on
the
left and right sides of the plot that reflect the original scales of
the
data, is possible in R but is not recommended. The basic approach for
constructing such graphs is to use `par(new=TRUE)' (see `?par');
functions
`twoord.plot()' (in the *plotrix*
(http://CRAN.R-project.org/package=plotrix) package) and
`doubleYScale()'
(in the *latticeExtra* (http://CRAN.R-project.org/
package=latticeExtra)
package) automate the process somewhat. See
`http://rwiki.sciviews.org/doku.php?id=tips:graphics-base:2yaxes' for
more
information, including strong arguments against this sort of graph.

8 R Programming
***************

8.1 How should I write summary methods?
=======================================

Suppose you want to provide a summary method for class `"foo"'. Then
`summary.foo()' should not print anything, but return an object of
class
`"summary.foo"', _and_ you should write a method `print.summary.foo()'
which nicely prints the summary information and invisibly returns its
object. This approach is preferred over having `summary.foo()' print
summary information and return something useful, as sometimes you need
to
grab something computed by `summary()' inside a function or similar.
In
such cases you don't want anything printed.

8.2 How can I debug dynamically loaded code?
============================================

Roughly speaking, you need to start R inside the debugger, load the
code,
send an interrupt, and then set the required breakpoints.

See section "Finding entry points in dynamically loaded code" in
`Writing R Extensions'. This manual is included in the R
distribution,
*note What documentation exists for R?::.

8.3 How can I inspect R objects when debugging?
===============================================

The most convenient way is to call `R_PV' from the symbolic debugger.

See section "Inspecting R objects when debugging" in `Writing R
Extensions'.

8.4 How can I change compilation flags?
=======================================

Suppose you have C code file for dynloading into R, but you want to
use `R
CMD SHLIB' with compilation flags other than the default ones (which
were
determined when R was built).

Starting with R 2.1.0, users can provide personal Makevars
configuration
files in `$`HOME'/.R' to override the default flags. See section "Add-
on
packages" in `R Installation and Administration'.

For earlier versions of R, you could change the file
`R_HOME/etc/Makeconf' to reflect your preferences, or (at least for
systems
using GNU Make) override them by the environment variable
`MAKEFLAGS'. See
section "Creating shared objects" in `Writing R Extensions'.

8.5 How can I debug S4 methods?
===============================

Use the `trace()' function with argument `signature=' to add calls to
the
browser or any other code to the method that will be dispatched for
the
corresponding signature. See `?trace' for details.

9 R Bugs
********

9.1 What is a bug?
==================

If R executes an illegal instruction, or dies with an operating system
error message that indicates a problem in the program (as opposed to
something like "disk full"), then it is certainly a bug. If you call
`.C()', `.Fortran()', `.External()' or `.Call()' (or `.Internal()')
yourself (or in a function you wrote), you can always crash R by using
wrong argument types (modes). This is not a bug.

Taking forever to complete a command can be a bug, but you must
make
certain that it was really R's fault. Some commands simply take a
long
time. If the input was such that you _know_ it should have been
processed
quickly, report a bug. If you don't know whether the command should
take a
long time, find out by looking in the manual or by asking for
assistance.

If a command you are familiar with causes an R error message in a
case
where its usual definition ought to be reasonable, it is probably a
bug.
If a command does the wrong thing, that is a bug. But be sure you
know for
certain what it ought to have done. If you aren't familiar with the
command, or don't know for certain how the command is supposed to
work,
then it might actually be working right. For example, people
sometimes
think there is a bug in R's mathematics because they don't understand
how
finite-precision arithmetic works. Rather than jumping to
conclusions,
show the problem to someone who knows for certain. Unexpected results
of
comparison of decimal numbers, for example `0.28 * 100 != 28' or `0.1
+ 0.2
!= 0.3', are not a bug. *Note Why doesn't R think these numbers are
equal?::, for more details.

Finally, a command's intended definition may not be best for
statistical
analysis. This is a very important sort of problem, but it is also a
matter of judgment. Also, it is easy to come to such a conclusion out
of
ignorance of some of the existing features. It is probably best not
to
complain about such a problem until you have checked the documentation
in
the usual ways, feel confident that you understand it, and know for
certain
that what you want is not available. If you are not sure what the
command
is supposed to do after a careful reading of the manual this indicates
a
bug in the manual. The manual's job is to make everything clear. It
is
just as important to report documentation bugs as program bugs.
However,
we know that the introductory documentation is seriously inadequate,
so you
don't need to report this.

If the online argument list of a function disagrees with the
manual, one
of them must be wrong, so report the bug.

9.2 How to report a bug
=======================

When you decide that there is a bug, it is important to report it and
to
report it in a way which is useful. What is most useful is an exact
description of what commands you type, starting with the shell command
to
run R, until the problem happens. Always include the version of R,
machine, and operating system that you are using; type `version' in R
to
print this.

The most important principle in reporting a bug is to report
_facts_,
not hypotheses or categorizations. It is always easier to report the
facts, but people seem to prefer to strain to posit explanations and
report
them instead. If the explanations are based on guesses about how R is
implemented, they will be useless; others will have to try to figure
out
what the facts must have been to lead to such speculations. Sometimes
this
is impossible. But in any case, it is unnecessary work for the ones
trying
to fix the problem.

For example, suppose that on a data set which you know to be quite
large
the command

R> data.frame(x, y, z, monday, tuesday)

never returns. Do not report that `data.frame()' fails for large data
sets. Perhaps it fails when a variable name is a day of the week. If
this
is so then when others got your report they would try out the
`data.frame()' command on a large data set, probably with no day of
the
week variable name, and not see any problem. There is no way in the
world
that others could guess that they should try a day of the week
variable
name.

Or perhaps the command fails because the last command you used was
a
method for `"["()' that had a bug causing R's internal data structures
to
be corrupted and making the `data.frame()' command fail from then on.
This
is why others need to know what other commands you have typed (or read
from
your startup file).

It is very useful to try and find simple examples that produce
apparently the same bug, and somewhat useful to find simple examples
that
might be expected to produce the bug but actually do not. If you want
to
debug the problem and find exactly what caused it, that is wonderful.
You
should still report the facts as well as any explanations or
solutions.
Please include an example that reproduces (e.g.,
`http://en.wikipedia.org/wiki/Reproducibility') the problem,
preferably the
simplest one you have found.

Invoking R with the `--vanilla' option may help in isolating a bug.
This ensures that the site profile and saved data files are not read.

Before you actually submit a bug report, you should check whether
the
bug has already been reported and/or fixed. First, try the "Search
Existing Reports" facility in the Bug Tracking page at
`http://bugs.R-project.org/'. Second, consult
`https://svn.R-project.org/R/trunk/NEWS', which records changes that
will
appear in the _next_ release of R, including some bug fixes that do
not
appear in Bug Tracking. (Windows users should additionally consult
`https://svn.R-project.org/R/trunk/src/gnuwin32/CHANGES'.) Third, if
possible try the current r-patched or r-devel version of R. If a bug
has
already been reported or fixed, please do not submit further bug
reports on
it. Finally, check carefully whether the bug is with R, or a
contributed
package. Bug reports on contributed packages should be sent first to
the
package maintainer, and only submitted to the R-bugs repository by
package
maintainers, mentioning the package in the subject line.

On Unix-like systems a bug report can be generated using the
function
`bug.report()'. This automatically includes the version information
and
sends the bug to the correct address. Alternatively the bug report
can be
emailed to <R-b...@R-project.org> or submitted to the Web page at
`http://bugs.R-project.org/'. Please try including results of
`sessionInfo()' in your bug report.

There is a section of the bug repository for suggestions for
enhancements for R labelled `wishlist'. Suggestions can be submitted
in
the same ways as bugs, but please ensure that the subject line makes
clear
that this is for the wishlist and not a bug report, for example by
starting
with `Wishlist:'.

Comments on and suggestions for the Windows port of R should be
sent to
<R-wi...@R-project.org>.

Corrections to and comments on message translation should be sent
to the
last translator (listed at the top of the appropriate `.po' file) or
to the
translation team as listed at
`http://developer.R-project.org/TranslationTeams.html'.

10 Acknowledgments
******************

Of course, many many thanks to Robert and Ross for the R system, and
to the
package writers and porters for adding to it.

Special thanks go to Doug Bates, Peter Dalgaard, Paul Gilbert,
Stefano
Iacus, Fritz Leisch, Jim Lindsey, Thomas Lumley, Martin Maechler,
Brian D.
Ripley, Anthony Rossini, and Andreas Weingessel for their comments
which
helped me improve this FAQ.

More to come soon ...