R FAQ
Frequently Asked Questions on R
Kurt Hornik and the R Core Team



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 (Mac)
  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 Bioconductor
    5.1.4 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?
  7.40 How do I access the source code for a function?
  7.41 Why does summary() report strange results for the R^2 estimate when I fit a linear model with no intercept?
  7.42 Why is R apparently not releasing memory?
  7.43 How can I enable secure https downloads in R?
  7.44 How can I get CRAN package binaries for outdated versions of R?
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
Acknowledgments
R FAQ
*****

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

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

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

Copyright © 1998–2020 Kurt Hornik
Copyright © 2021–2025 R Core Team

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

     <https://www.R-project.org/Licenses/>

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

The latest version of this document is always available from

     <https://CRAN.R-project.org/doc/manuals/>

From there, you can obtain versions converted to HTML and PDF.

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

In publications, please refer to this FAQ as Hornik and R Core Team (2024),
“The R FAQ”, and give the above, _official_ URL:

     @Misc{,
       author        = {Kurt Hornik and the R Core Team},
       title         = {{R} {FAQ}},
       year          = {2024},
       url           = {https://CRAN.R-project.org/doc/manuals/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 <R-devel@R-project.org> is most welcome.

Features specific to the Windows and macOS ports of R are described in the
“R for Windows FAQ”
(https://CRAN.R-project.org/bin/windows/base/rw-FAQ.html) and the “R for Mac
OS X FAQ” (https://CRAN.R-project.org/bin/macosx/RMacOSX-FAQ.html).  If you
have information on Mac or Windows systems that you think should be added to
this document, please let us 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://community.schemewiki.org/?scheme-faq).  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.Ihaka@R-project.org> and Robert
Gentleman <Robert.Gentleman@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, currently consisting of
     John Chambers,
     Peter Dalgaard,
     Robert Gentleman,
     Kurt Hornik,
     Ross Ihaka,
     Tomas Kalibera,
     Michael Lawrence,
     Uwe Ligges,
     Thomas Lumley,
     Martin Maechler,
     Sebastian Meyer,
     Paul Murrell,
     Martyn Plummer,
     Brian Ripley,
     Deepayan Sarkar,
     Duncan Temple Lang,
     Luke Tierney, and
     Simon Urbanek,
plus Heiner Schwarte up to October 1999, Guido Masarotto up to June 2003,
Stefano Iacus up to July 2014, Seth Falcon up to August 2015, Duncan Murdoch
up to September 2017, Martin Morgan up to June 2021, Douglas Bates up to
March 2024, and Friedrich Leisch up to April 2024.

R has a home page at <https://www.R-project.org/>.  It is free software
(https://www.gnu.org/philosophy/free-sw.html) distributed under a GNU-style
copyleft (https://www.gnu.org/copyleft/copyleft.html), and an official part
of the GNU (https://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., <https://en.wikipedia.org/wiki/Unix-like>) platforms
including CPU-linux-gnu for the i386, amd64/x86_64, alpha, arm, arm64, hppa,
mips/mipsel, powerpc, s390x and sparc CPUs (e.g.,
<https://buildd.debian.org/build.php?&pkg=r-base>), 386-hurd-gnu,
CPU-kfreebsd-gnu for i386 and amd64, i386-pc-solaris, rs6000-ibm-aix,
sparc-sun-solaris, x86_64-apple-darwin, aarch64-apple-darwin,
x86_64-unknown-freebsd and x86_64-unknown-openbsd.

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

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

R uses a ‘major.minor.patchlevel’ numbering scheme.  Based on this, there
are the current release version of R (‘r-release’) as well as 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.  New features are typically
introduced in r-devel, while r-patched is for bug fixes mostly.

See <https://CRAN.R-project.org/sources.html> for the current versions of
r-release, r-patched and 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
<https://cran.r-project.org/src/base-prerelease/>.  An alternative source is
<https://stat.ethz.ch/R/daily/>.

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 90 compiler as well as 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, HTML versions of the R manuals (e.g.,
‘R-exts.html’, the “Writing R Extensions” manual) are built in the
‘doc/manual’ subdirectory (if a suitable ‘texi2any’ program was found).

Use ‘make pdf’ to build PDF (Portable Document Format) versions of the R
manuals, including ‘fullrefman.pdf’ (an R object reference index).  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 files.

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 info and PDF versions of the manuals, use ‘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 add-on packages from CRAN to run on 64-bit versions of
Windows 10 and later on x86_64 chips (R 4.1.3 was the last version of R to
support 32-bit Windows).  The Windows version of R was created by Robert
Gentleman and Guido Masarotto; Brian Ripley and Duncan Murdoch made
substantial contributions and it is now being maintained by other members of
the R Core team.

The same directory has links to snapshots of the r-patched and r-devel
versions of R.

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

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

The ‘bin/macosx’ directory of a CRAN site contains a standard Apple
installer package to run on macOS 10.13 (‘High Sierra’) or later, and
another which runs only on ‘Apple Silicon’ Macs under macOS 11 (‘Big Sur’)
or later.  Once downloaded and executed, the installer will install the
current release of R and R.app, the macOS GUI.  This port of R for macOS is
maintained by Simon Urbanek <Simon.Urbanek@R-project.org> (and previously by
Stefano Iacus).  The “R for macOS FAQ”
(https://CRAN.R-project.org/bin/macosx/RMacOSX-FAQ.html) has more details.

Snapshots of the r-patched and r-devel versions of R are available as Apple
installer packages at <https://mac.R-project.org>.

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

Binary distributions of R are available on many Unix-like OSes: only some
can be mentioned here so check your OS’s search facilities to see if one is
available for yours.

The ‘bin/linux’ directory of a CRAN site contains R packages for Debian and
Ubuntu.

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
<https://CRAN.R-project.org/bin/linux/debian/index.html> 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
Michael Rutter, see <https://CRAN.R-project.org/bin/linux/ubuntu/index.html>
for instructions.

R binaries for Fedora, maintained by Tom “Spot” Callaway and Iñaki Ucar, are
provided as part of the Fedora distribution and can be accessed through
‘dnf’, the RPM installer/updater.  The Fedora R RPM is a “meta-package”
which installs all the user and developer components of R (available
separately as ‘R-core’ and ‘R-core-devel’), as well as ‘R-java’ and
‘R-java-devel’, which ensures that R is configured for use with Java.  The R
RPM also installs the standalone R math library (‘libRmath’ and
‘libRmath-devel’), although this is not necessary to use R. When a new
version of R is released, there may be a delay of up to 2 weeks until the
Fedora RPM becomes publicly available, as it must pass through the Fedora
update process.  The Extra Packages for Enterprise Linux (EPEL) project
(<https://docs.fedoraproject.org/en-US/epel/>) provides ports of the Fedora
RPMs for RedHat Enterprise Linux and compatible distributions (e.g., CentOS
Stream, Scientific Linux, Oracle Linux, AlmaLinux, or Rocky Linux among
others).  RPMs for selection of R packages are also provided by Fedora.
Additional RPMs for R packages are maintained by Iñaki Ucar on Fedora Copr.
See <https://CRAN.R-project.org/bin/linux/fedora/> for further details and
installation instructions.

No other binary distributions are currently publicly 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 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 <https://stat.ethz.ch/R-manual/>.

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

     <https://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,
     <https://johnmchambers.su.domains/Rbook/>.

     Peter Dalgaard (2008), “Introductory Statistics with R”, 2nd edition.
     Springer, ISBN 978-0-387-79053-4,
     <http://publicifsv.sund.ku.dk/~pd/ISwR.html>.

     Robert Gentleman (2008), “R Programming for Bioinformatics”.  Chapman &
     Hall/CRC, Boca Raton, FL, ISBN 978-1-420-06367-7,
     <https://bioconductor.org/help/publications/books/r-programming-for-bioinformatics/>.

     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,
     <https://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,
     <https://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 (doi: 10.1080/10618600.1996.10474713
(https://doi.org/10.1080/10618600.1996.10474713)).

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

To cite R in publications, use

     @Manual{,
       title        = {R: A Language and Environment for Statistical
                       Computing},
       author       = {{R Core Team}},
       organization = {R Foundation for Statistical Computing},
       address      = {Vienna, Austria},
       year         = YEAR,
       url          = {https://www.R-project.org}
     }
where YEAR is the release year of the version of R used and can determined
as ‘R.version$year’.

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.Maechler@R-project.org>, there are several
mailing lists devoted to R, including the following:

‘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
     encountered using R, including using R packages in the standard R
     distribution and on CRAN; announcements (not covered by ‘R-announce’ or
     ‘R-packages’); the availability of new functionality for R and
     documentation of R; and for posting nice examples and benchmarks.
‘R-devel’
     This list is for questions and discussion about code development in R.
‘R-package-devel’
     A list which provides a forum for those developing R packages.

Please read the posting guide (https://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
<https://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-request@lists.R-project.org>.

Send email to <R-help@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 <https://www.R-project.org/mail.html> for more information on the R
mailing lists.

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 main site at WU (Wirtschaftsuniversität Wien) in Austria can be
found at the URL

     <https://CRAN.R-project.org/>

and is mirrored daily to many sites around the world.  See
<https://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, macOS, and MS Windows).
CRAN also provides access to documentation on R, existing mailing lists and
the R Bug Tracking system.

Since March 2016, “old” material is made available from a central CRAN
archive server (<https://CRAN-archive.R-project.org/>).

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 or
version 3.  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 (https://opensource.org/osd):

     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 <https://www.R-project.org/foundation/> for
more information.

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

R-Forge (<https://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 (https://en.wikipedia.org/wiki/GForge) 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 (<https://johnmchambers.su.domains/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 <https://johnmchambers.su.domains/papers/96.7.ps> for further
information on the “Evolution of the S Language”.

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

S-PLUS is a value-added version of S sold by TIBCO Software Inc
(https://www.tibco.com/) as ‘TIBCO Spotfire S+’.  See
<https://en.wikipedia.org/wiki/S-PLUS> for more 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 distribution and density functions 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 (doi: 10.1080/10618600.2000.10474895
(https://doi.org/10.1080/10618600.2000.10474895)).

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 (doi: 10.1080/10618600.2000.10474900
(https://doi.org/10.1080/10618600.2000.10474900)).

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

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

Revolution Analytics has released REvolution R, now available as Microsoft R
(see <https://blog.revolutionanalytics.com/2016/01/microsoft-r-open.html>
for more information).

See also
<https://en.wikipedia.org/wiki/R_programming_language#Commercialized_versions_of_R>
for pointers to commercialized versions of R.

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

Please refer to the CRAN task view on “Web Technologies and Services”
(<https://CRAN.R-project.org/view=WebTechnologies>), specifically section
“Web and Server Frameworks”, for up-to-date information on R web interface
packages.

Early references on R web interfaces include Jeff Banfield (1999), “Rweb:
Web-based Statistical Analysis” (doi: 10.18637/jss.v004.i01
(https://doi.org/10.18637/jss.v004.i01)), David Firth (2003), “CGIwithR:
Facilities for processing web forms using R” (doi: 10.18637/jss.v008.i10
(https://doi.org/10.18637/jss.v008.i10)), and Angelo Mineo and Alfredo
Pontillo (2006), “Using R via PHP for Teaching Purposes: R-php”
(doi: 10.18637/jss.v017.i04 (https://doi.org/10.18637/jss.v017.i04)).

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).
*compiler*
     R byte code compiler (added in R 2.13.0).
*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.
*parallel*
     Support for parallel computation, including by forking and by sockets,
     and random-number generation (added in R 2.14.0).
*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.

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”.
*Matrix*
     Support for spares and dense matrices
*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).
*cluster*
     Functions for cluster analysis.
*codetools*
     Code analysis tools.
*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.
*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.
*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
(https://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.

5.1.3 Add-on packages from Bioconductor
---------------------------------------

Bioconductor (https://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
(https://bioconductor.org/packages/release/BiocViews.html#___Software)
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 <https://bioconductor.org/install/> for
available packages and a complete taxonomy via BioC Views.

5.1.4 Other add-on packages
---------------------------

Many more packages are available from places other than the default
repositories discussed above (CRAN and Bioconductor).  In particular,
R-Forge provides a CRAN style repository at
<https://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’.  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 library '/home/me/lib/R':

          mystuff       My own R functions, nicely packaged but not documented

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

          KernSmooth    Functions for Kernel Smoothing Supporting Wand & Jones (1995)
          MASS          Support Functions and Datasets for Venables and Ripley's MASS
          Matrix        Sparse and Dense Matrix Classes and Methods
          base          The R Base Package
          boot          Bootstrap Functions (Originally by Angelo Canty for S)
          class         Functions for Classification
          cluster       "Finding Groups in Data": Cluster Analysis Extended
                        Rousseeuw et al.
          codetools     Code Analysis Tools for R
          compiler      The R Compiler Package
          datasets      The R Datasets Package
          foreign       Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata',
                        'Systat', 'Weka', 'dBase', ...
          grDevices     The R Graphics Devices and Support for Colours and Fonts
          graphics      The R Graphics Package
          grid          The Grid Graphics Package
          lattice       Trellis Graphics for R
          methods       Formal Methods and Classes
          mgcv          Mixed GAM Computation Vehicle with Automatic Smoothness
                        Estimation
          nlme          Linear and Nonlinear Mixed Effects Models
          nnet          Feed-Forward Neural Networks and Multinomial Log-Linear
                        Models
          parallel      Support for Parallel Computation in R
          rpart         Recursive Partitioning and Regression Trees
          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
          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 remove the loaded package PKG from the ‘search()’ path by

     detach("package:PKG")

(which does not by default unload the namespace, see ‘?detach’).

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.

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’.

*Note (R-exts)Creating R packages:: 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 (https://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 is available via CRAN or the ESS web page
(https://ESS.R-project.org/).

ESS comes with detailed installation instructions.

For help with ESS, send email to <ESS-help@r-project.org>.

Please send bug reports and suggestions on ESS to <ESS-bugs@r-project.org>.
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, instead of just running it in a console, _definitely_.  As an
alternative to other IDEs such as RStudio, _possibly_, notably if you are
interested to use Emacs for other computer interaction.  You’d be using ESS,
Emacs Speaks Statistics, see previous FAQ.

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.names = 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, it 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?
==============================

Autoloading is rarely used since packages became lazy-loaded.

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?
====================================================

This is about a problem rarely seen with modern X11 installations.

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.)

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*
(https://CRAN.R-project.org/package=lattice) (which is based on base 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 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
     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* (https://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*
(https://CRAN.R-project.org/package=mgcv), but it is not an exact clone of
what is described in the White Book (no ‘lo()’ for example).  Package *gss*
(https://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*
(https://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)

Scalar functions can also be vectorized using ‘Vectorize()’.

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 macOS, and that too works in batch mode.

Earlier versions of the ‘png()’ device used 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 (https://www.x.org/) (possibly as an optional
install), which is an X11 server that does not require a screen.

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.  This becomes especially true if you are
finding yourself creating and trying to programmatically access groups of
related variables such as ‘result1’, ‘result2’, ‘result3’, and so on:
instead of fighting against the language to use

     assign(paste0("result", i), process(get(paste0("dataset", i))))

it is much easier to put the related variables in lists and use

     result[[i]] <- process(dataset[[i]])

and, eventually,

     result <- lapply(dataset, process)

which is easy to replace with ‘parLapply’ for parallel processing.

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*
(https://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’).

From R 4.4.0, ‘sort_by()’ provides a less verbose alternative with a formula
interface (e.g., ‘sort_by(DF, ~a + b)’).

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

Since R 2.10.0, the browser-based search engine in ‘help.start()’ is an HTML
interface to ‘help.search()’, and should always work.  Before that, the
engine utilized a Java applet.  In order for this to function properly, one
needed a compatible version of Java installed on the system and linked to
the browser, and both Java _and_ JavaScript enabled in the browser.

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()

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.  All other numbers
are internally 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
     R> print(a * a, digits = 18)
     [1] 2.00000000000000044

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.

A discussion with many easily followed examples is in Appendix G
“Computational Precision and Floating Point Arithmetic”, pages 753–771 of
_Statistical Analysis and Data Display: An Intermediate Course with Examples
in R_, Richard M. Heiberger and Burt Holland (Springer 2015, second
edition).  This appendix is a free download from
<https://link.springer.com/content/pdf/bbm:978-1-4939-2122-5/1.pdf>.

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
<https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html>.

Here is another example, this time using addition:

     R> .3 + .6 == .9
     [1] FALSE
     R> .3 + .6 - .9
     [1] -1.110223e-16
     R> print(matrix(c(.3,  .6,  .9, .3 + .6)), digits = 18)
                          [,1]
     [1,] 0.299999999999999989
     [2,] 0.599999999999999978
     [3,] 0.900000000000000022
     [4,] 0.899999999999999911

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", paste0("result", i, ".rda"))
       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 (macOS)
     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 GraphicsAntialias 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
contain 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*
(https://CRAN.R-project.org/package=agricolae) package, the ‘plotCI()’
function in the *gplots* (https://CRAN.R-project.org/package=gplots)
package, the ‘plotCI()’ and ‘brkdn.plot()’ functions in the *plotrix*
(https://CRAN.R-project.org/package=plotrix) package and the ‘error.bars()’,
‘error.crosses()’ and ‘error.bars.by()’ functions in the *psych*
(https://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*
(https://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* (https://CRAN.R-project.org/package=ggplot2) package.  In the
lattice system, error bars can be displayed with ‘Dotplot()’ or ‘xYplot()’
in the *Hmisc* (https://CRAN.R-project.org/package=Hmisc) package and
‘segplot()’ in the *latticeExtra*
(https://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*
(https://CRAN.R-project.org/package=plotrix) package) and ‘doubleYScale()’
(in the *latticeExtra* (https://CRAN.R-project.org/package=latticeExtra)
package) automate the process somewhat.

7.40 How do I access the source code for a function?
====================================================

In most cases, typing the name of the function will print its source code.
However, code is sometimes hidden in a namespace, or compiled.  For a
complete overview on how to access source code, see Uwe Ligges (2006), “Help
Desk: Accessing the sources”, _R News_, *6/4*, 43–45
(<https://CRAN.R-project.org/doc/Rnews/Rnews_2006-4.pdf>).

7.41 Why does summary() report strange results for the R^2 estimate when I fit a linear model with no intercept?
================================================================================================================

As described in ‘?summary.lm’, when the intercept is zero (e.g., from ‘y ~ x
- 1’ or ‘y ~ x + 0’), ‘summary.lm()’ uses the formula R^2 = 1 - Sum(R[i]^2)
/ Sum((y[i])^2), which is different from the usual R^2 = 1 - Sum(R[i]^2) /
Sum((y[i] - mean(y))^2).  There are several reasons for this:
   • Otherwise the R^2 could be negative (because the model with zero
     intercept can fit _worse_ than the constant-mean model it is implicitly
     compared to).
   • If you set the slope to zero in the model with a line through the
     origin you get fitted values y*=0
   • The model with constant, non-zero mean is not nested in the model with
     a line through the origin.

All these come down to saying that if you know _a priori_ that E[Y]=0 when
x=0 then the ‘null’ model that you should compare to the fitted line, the
model where x doesn’t explain any of the variance, is the model where E[Y]=0
everywhere.  (If you don’t know a priori that E[Y]=0 when x=0, then you
probably shouldn’t be fitting a line through the origin.)

7.42 Why is R apparently not releasing memory?
==============================================

This question is often asked in different flavors along the lines of “I have
removed objects in R and run ‘gc()’ and yet ‘ps’/‘top’ still shows the R
process using a lot of memory”, often on Linux machines.

This is an artifact of the way the operating system (OS) allocates memory.
In general it is common that the OS is not capable of releasing all unused
memory.  In extreme cases it is possible that even if R frees almost all its
memory, the OS can not release any of it due to its design and thus tools
such as ‘ps’ or ‘top’ will report substantial amount of resident RAM used by
the R process even though R has released all that memory.  In general such
tools do _not_ report the actual memory usage of the process but rather what
the OS is reserving for that process.

The short answer is that this is a limitation of the memory allocator in the
operating system and there is nothing R can do about it.  That space is
simply kept by the OS in the hope that R will ask for it later.  The
following paragraph gives more in-depth answer with technical details on how
this happens.

Most systems use two separate ways to allocate memory.  For allocation of
large chunks they will use ‘mmap’ to map memory into the process address
space.  Such chunks can be released immediately when they are completely
free, because they can reside anywhere in the virtual memory.  However, this
is a relatively expensive operation and many OSes have a limit on the number
of such allocated chunks, so this is only used for allocating large memory
regions.  For smaller allocations the system can expand the data segment of
the process (historically using the ‘brk’ system call), but this whole area
is always contiguous.  The OS can only move the end of this space, it cannot
create any “holes”.  Since this operation is fairly cheap, it is used for
allocations of small pieces of memory.  However, the side-effect is that
even if there is just one byte that is in use at the end of the data
segment, the OS cannot release any memory at all, because it cannot change
the address of that byte.  This is actually more common than it may seem,
because allocating a lot of intermediate objects, then allocating a result
object and removing all intermediate objects is a very common practice.
Since the result is allocated at the end it will prevent the OS from
releasing any memory used by the intermediate objects.  In practice, this is
not necessarily a problem, because modern operating systems can page out
unused portions of the virtual memory so it does not necessarily reduce the
amount of real memory available for other applications.  Typically, small
objects such as strings or pairlists will be affected by this behavior,
whereas large objects such as long vectors will be allocated using ‘mmap’
and thus not affected.  On Linux (and possibly other Unix-like systems) it
is possible to use the ‘mallinfo’ system call (also see the mallinfo
(https://rforge.net/mallinfo) package) to query the allocator about the
layout of the allocations, including the actually used memory as well as
unused memory that cannot be released.

7.43 How can I enable secure https downloads in R?
==================================================

From R 4.2.0, ‘"libcurl"’ download method is always available and used for
HTTPS by default on all platforms.  It has been used since R 3.3.0
everywhere but Windows where the default method ‘"wininet"’ also supported
HTTPS.

So nothing needs to be done to access ‘https://’ websites in recent versions
of R.

7.44 How can I get CRAN package binaries for outdated versions of R?
====================================================================

Since March 2016, Windows and macOS binaries of CRAN packages for old
versions of R (released more than 5 years ago) are made available from a
central CRAN archive server instead of the CRAN mirrors.  To get these, one
should set the CRAN “mirror” element of the ‘repos’ option accordingly, by
something like
     local({r <- getOption("repos")
            r["CRAN"] <- "http://CRAN-archive.R-project.org"
            options(repos = r)
           })
(see ‘?options’ for more information).

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.

*Note Finding entry points in dynamically loaded code: (R-exts)Finding entry
points.  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.

*Note Inspecting R objects when debugging: (R-exts)Inspecting R objects.

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.  *Note (R-admin)Add-on
packages::.

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.

See also “Making sure it’s a bug” in Bug Reporting in R
(https://www.r-project.org/bugs.html) for more information.

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.,
<https://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 “Show open bugs
new-to-old” or the search facility on <https://bugs.R-project.org/>.
Second, consult <https://svn.R-project.org/R/trunk/doc/NEWS.Rd>, which
records changes that will appear in the _next_ release of R, including bug
fixes that do not appear on the Bug Tracker.  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.

A bug report can be generated using the function ‘bug.report()’.  For
reports on R this will open the R Bugzilla page at
<https://bugs.R-project.org/>: for a contributed package it will open the
package’s bug tracker Web page or help you compose an email to the
maintainer.  Since 2016, only “members” (including all who have previously
submitted bugs) can submit new bugs on the R Bugzilla.  See “Where to submit
bug reports and patches” on Bug Reporting in R
(https://www.r-project.org/bugs.html) for more information.

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-windows@R-project.org>.

Corrections to and comments on message translations should be sent to the
last translator (listed at the top of the appropriate ‘.po’ file) or to the
translation team as listed at
<https://developer.R-project.org/TranslationTeams.html>.

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 ...

