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