FAQ

R FAQ
Frequently Asked Questions on R
Version 2020-08-03
Kurt Hornik


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
10 Acknowledgments
R FAQ
*****

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

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

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

This document is copyright © 1998-2020 by Kurt Hornik.

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

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

   Copies of the GNU General Public License versions are available at

     <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/FAQ/>

   From there, you can obtain versions converted to plain ASCII text,
GNU info, HTML, PDF, as well as the Texinfo source used for creating all
these formats using the GNU Texinfo system.

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

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

In publications, please refer to this FAQ as Hornik (2020), "The R FAQ",
and give the above, _official_ URL:

     @Misc{,
       author        = {Kurt Hornik},
       title         = {{R} {FAQ}},
       year          = {2020},
       url           = {https://CRAN.R-project.org/doc/FAQ/R-FAQ.html}
     }

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

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

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

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

   In particular, note that I do not have access to Windows or Mac
systems.  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 me know.

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

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

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

   The design of R has been heavily influenced by two existing
languages: Becker, Chambers & Wilks' S (*note What is S?::) and
Sussman's Scheme (http://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.  The group currently consists of
Doug Bates, John Chambers, Peter Dalgaard, Robert Gentleman, Kurt
Hornik, Ross Ihaka, Tomas Kalibera, Michael Lawrence, Friedrich Leisch,
Uwe Ligges, Thomas Lumley, Martin Maechler, Martin Morgan, Paul Murrell,
Martyn Plummer, Brian Ripley, Deepayan Sarkar, Duncan Temple Lang, Luke
Tierney, and Simon Urbanek.

   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>), i386-hurd-gnu,
CPU-kfreebsd-gnu for i386 and amd64, i386-pc-solaris, rs6000-ibm-aix,
sparc-sun-solaris, x86_64-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://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 compiler or perhaps 'f2c' in addition to
a C compiler to build R.

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

     $ ./configure
     $ make

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

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

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

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

'${prefix}/bin'
     the front-end shell script
'${prefix}/man/man1'
     the man page
'${prefix}/lib/R'
     all the rest (libraries, on-line help system, ...).  This is the "R
     Home Directory" ('R_HOME') of the installed system.

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

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

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

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

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

The 'bin/windows' directory of a CRAN site contains binaries for a base
distribution and add-on packages from CRAN to run on Windows 7 and later
(including 64-bit versions of Windows) on ix86 and x86_64 chips.  The
Windows version of R was created by Robert Gentleman and Guido
Masarotto, Brian D. 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 10.9 ('Mavericks') and 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 Mac 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?
=======================================

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

               CPU           Versions               Provider
     ----------------------------------------------------------------
     Debian    i386/amd64    squeeze/wheezy         Johannes Ranke
               armel         wheezy                 Johannes Ranke
     Ubuntu    i386/amd64    lucid/precise/trusty   Michael Rutter

   Debian packages, maintained by Dirk Eddelbuettel, have long been part
of the Debian distribution, and can be accessed through APT, the Debian
package maintenance tool.  Use e.g. 'apt-get install r-base
r-recommended' to install the R environment and recommended packages.
If you also want to build R packages from source, also run 'apt-get
install r-base-dev' to obtain the additional tools required for this.
So-called "backports" of the current R packages for at least the
"stable" distribution of Debian are provided by Johannes Ranke, and
available from CRAN.  See
<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.

   R binaries for Fedora, maintained by Tom "Spot" Callaway, are
provided as part of the Fedora distribution and can be accessed through
'yum', the RPM installer/updater.  Note that the "Software" application
(gnome-software), which is the default GUI for software installation in
Fedora 20, cannot be used to install R. It is therefore recommended to
use the yum command line tool.  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-devel'), as well as 'R-java', 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 statutory Fedora
review process.  RPMs for a selection of R packages are also provided by
Fedora.  The Extra Packages for Enterprise Linux (EPEL) project
(<https://fedoraproject.org/wiki/EPEL>) provides ports of the Fedora
RPMs for RedHat Enterprise Linux and compatible distributions (e.g.,
Centos, Scientific Linux, Oracle Linux).

   See <https://CRAN.R-project.org/bin/linux/suse/README.html> for
information about RPMs for openSUSE.

   No other binary distributions are currently publically available via
CRAN.

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

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

   This documentation can also be made available as one reference manual
for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX,
see *note How can R be installed?::.  An up-to-date HTML version is
always available for web browsing at <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://statweb.stanford.edu/~jmc4/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://master.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 using R, announcements (not covered by 'R-announce' and
     'R-packages') about the development of R and the availability of
     new code.
'R-devel'
     This list is for questions and discussion about code development in
     R.
'R-package-devel'
     A list which provides a forum for learning about the R package
     development process.

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 master 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/docs/definition.html):

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

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

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

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

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

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

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

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

The R Foundation is a not for profit organization working in the public
interest.  It was founded by the members of the R Core Team in order to
provide support for the R project and other innovations in statistical
computing, provide a reference point for individuals, institutions or
commercial enterprises that want to support or interact with the R
development community, and to hold and administer the copyright of R
software and documentation.  See <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://statweb.stanford.edu/~jmc4/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://statweb.stanford.edu/~jmc4/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 currently 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 cdf and pdf of the distribution as
explicit arguments.  (Example compiled from various postings by Luke
Tierney.)

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

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

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

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

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

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

   However, in R there is a much easier solution:

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

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

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

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

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

   For R, 'MC()' simply is

     MC <- function(f, env) f

(lexical scope!), a version for S is

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

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

   Nested lexically scoped functions allow using function closures and
maintaining local state.  A simple example (taken from Abelson and
Sussman) is obtained by typing 'demo("scoping")' at the R prompt.
Further information is provided in the standard R reference "R: A
Language for Data Analysis and Graphics" (*note What documentation
exists for R?::) and in Robert Gentleman and Ross Ihaka (2000), "Lexical
Scope and Statistical Computing", _Journal of Computational and
Graphical Statistics_, *9*, 491-508 (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.
   These "base packages" were substantially reorganized in R 1.9.0.  The
former *base* was split into the four packages *base*, *graphics*,
*stats*, and *utils*.  Packages *ctest*, *eda*, *modreg*, *mva*, *nls*,
*stepfun* and *ts* were merged into *stats*, package *lqs* returned to
the recommended package *MASS*
(https://CRAN.R-project.org/package=MASS), and package *mle* moved to
*stats4*.

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

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

*KernSmooth*
     Functions for kernel smoothing (and density estimation)
     corresponding to the book "Kernel Smoothing" by M. P. Wand and M.
     C. Jones, 1995.
*MASS*
     Functions and datasets from the main package of Venables and
     Ripley, "Modern Applied Statistics with S". (Contained in the 'VR'
     bundle for R versions prior to 2.10.0.)
*Matrix*
     A Matrix package.  (Recommended for R 2.9.0 or later.)
*boot*
     Functions and datasets for bootstrapping from the book "Bootstrap
     Methods and Their Applications" by A. C. Davison and D. V. Hinkley,
     1997, Cambridge University Press.
*class*
     Functions for classification (k-nearest neighbor and LVQ).
     (Contained in the 'VR' bundle for R versions prior to 2.10.0.)
*cluster*
     Functions for cluster analysis.
*codetools*
     Code analysis tools.  (Recommended for R 2.5.0 or later.)
*foreign*
     Functions for reading and writing data stored by statistical
     software like Minitab, S, SAS, SPSS, Stata, Systat, etc.
*lattice*
     Lattice graphics, an implementation of Trellis Graphics functions.
*mgcv*
     Routines for GAMs and other generalized ridge regression problems
     with multiple smoothing parameter selection by GCV or UBRE.
*nlme*
     Fit and compare Gaussian linear and nonlinear mixed-effects models.
*nnet*
     Software for single hidden layer perceptrons ("feed-forward neural
     networks"), and for multinomial log-linear models.  (Contained in
     the 'VR' bundle for R versions prior to 2.10.0.)
*rpart*
     Recursive PARTitioning and regression trees.
*spatial*
     Functions for kriging and point pattern analysis from "Modern
     Applied Statistics with S" by W. Venables and B. Ripley.
     (Contained in the 'VR' bundle for R versions prior to 2.10.0.)
*survival*
     Functions for survival analysis, including penalized likelihood.
   See the CRAN contributed packages page for more information.

   Many of these packages are categorized into CRAN Task Views
(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://master.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 three
default repositories discussed above (CRAN, Bioconductor and Omegahat).
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', which may in fact be "bundles" containing
more than one package.  Let PATH be the path to such a package file.
Provided that 'tar' and 'gzip' are available on your system, type

     $ R CMD INSTALL PATH/PKG_VERSION.tar.gz

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

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

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

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

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

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

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

     library()

at the R prompt.

   This produces something like

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

          mystuff       My own R functions, nicely packaged but not documented

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

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

   You can "load" the installed package PKG by

     library(PKG)

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

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

   You can unload the loaded package PKG by

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

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

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

Use

     $ R CMD REMOVE PKG_1 ... PKG_N

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

   To remove from library LIB, do

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

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

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

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

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

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

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

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

   The R Developer Page (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 IDE's 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 = 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?
==============================

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

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

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

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

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

   For example, given

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

we obtain:

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

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

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

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

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

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

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

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

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

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

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

     as.numeric(as.character(f))

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

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

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

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

The recommended package *lattice*
(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
     (for versions of R at least 1.9.0) underscore characters, and
     starts with either a letter or a dot not followed by a number.
     Reserved words are not syntactic names.
  2. An _object name_ is a string associated with an object that is
     assigned in an expression either by having the object name on the
     left of an assignment operation or as an argument to the 'assign()'
     function.  It is usually a syntactic name as well, but can be any
     non-empty string if it is quoted (and it is always quoted in the
     call to 'assign()').

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

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

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

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

Package *gam* (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; and there is the *GDD*
(https://CRAN.R-project.org/package=GDD) package from CRAN, which
produces PNG, JPEG and GIF bitmaps without X11.

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

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

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

If you have

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

you can do

     get(varname[1]) + 2

for

     a + 2

or

     assign(varname[1], 2 + 2)

for

     a <- 2 + 2

or

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

for

     lm(y ~ x + a)

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

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

without any of this messing about.

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

The most likely reason is that you forgot to tell R to display the
graph.  Lattice functions such as 'xyplot()' create a graph object, but
do not display it (the same is true of *ggplot2*
(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').

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

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

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

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

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

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

needs to be

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

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

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

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

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

     getS3method("[", "terms")

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The only numbers that can be represented exactly in R's numeric type are
integers and fractions whose denominator is a power of 2.  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%3A978-1-4939-2122-5%2F1.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", paste("result", i, ".rda", sep = ""))
       save(list = "some_object", file = fp)
     }

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

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

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

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

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

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

When R transfers files over HTTP (e.g., using the 'install.packages()'
or 'download.file()' function), a download method is chosen based on the
'download.file.method' option.  There are several methods available and
the default behavior if no option is explicitly specified is to use R's
internal HTTP implementation.  In most circumstances this internal
method will not support HTTPS URLs so you will need to override the
default: this is done automatically for such URLs as from R 3.2.2.

   R versions 3.2.0 and greater include two download methods
('"libcurl"' and '"wininet"') that both support HTTPS connections: we
recommend that you use these methods.  The requisite code to add to
'.Rprofile' or 'Rprofile.site' is:

     options(download.file.method = "wininet", url.method = "wininet")
     (Windows)
     options(download.file.method = "libcurl", url.method = "libcurl")
     (Linux and macOS)

(Method '"wininet"' is the default on Windows as from R 3.2.2.)

   Note that the '"libcurl"' method may or may not have been compiled
in.  In the case that it was not, i.e. 'capabilities("libcurl") ==
FALSE', we recommend method '"wget"' on Linux and '"curl"' on macOS. It
is possible that system versions of '"libcurl"', 'wget' or 'curl' may
have been compiled without HTTPS support, but this is unlikely.  As from
R 3.3.0 '"libcurl"' with HTTPS support is required except on Windows.

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.

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

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

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

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

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

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

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

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

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

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

9 R Bugs
********

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

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

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

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

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

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

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

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

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

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

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

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

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

   It is very useful to try and find simple examples that produce
apparently the same bug, and somewhat useful to find simple examples
that might be expected to produce the bug but actually do not.  If you
want to debug the problem and find exactly what caused it, that is
wonderful.  You should still report the facts as well as any
explanations or solutions.  Please include an example that reproduces
(e.g., <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 Web 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.

   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>.

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

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

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

   More to come soon ...