| \input texinfo |
| |
| @c %**start of header |
| @setfilename R-FAQ.info |
| @settitle R FAQ |
| @setchapternewpage on |
| @set FAQ_YEAR 2018 |
| @set FAQ_DATE @value{FAQ_YEAR}-10-18 |
| @set FAQ_VERSION @value{FAQ_DATE} |
| @documentlanguage en |
| @documentencoding ISO-8859-1 |
| @c %**end of header |
| |
| @dircategory Programming |
| @direntry |
| * R FAQ: (R-FAQ). The R statistical system FAQ. |
| @end direntry |
| |
| @finalout |
| |
| @macro SPLUS{} |
| @sc{S-Plus} |
| @end macro |
| |
| @macro CRAN{} |
| @acronym{CRAN} |
| @end macro |
| |
| @macro HTML{} |
| @acronym{HTML} |
| @end macro |
| |
| @macro FORTRAN{} |
| FORTRAN |
| @end macro |
| |
| @macro XML{} |
| @acronym{XML} |
| @end macro |
| |
| @macro XSL{} |
| @acronym{XSL} |
| @end macro |
| |
| @macro pkg {p} |
| @strong{\p\} |
| @end macro |
| |
| @macro CRANpkg {p} |
| @url{https://CRAN.R-project.org/package=\p\, @strong{\p\}} |
| @end macro |
| |
| @titlepage |
| @title R @acronym{FAQ} |
| @subtitle Frequently Asked Questions on R |
| @subtitle Version @value{FAQ_VERSION} |
| @author Kurt Hornik |
| @end titlepage |
| |
| @ifinfo |
| @c We do not really see this in info, but in plain text output. |
| R FAQ @* |
| Frequently Asked Questions on R @* |
| Version @value{FAQ_VERSION} @* |
| Kurt Hornik @* |
| |
| @sp 2 |
| @end ifinfo |
| |
| @ifhtml |
| @html |
| <h2>Frequently Asked Questions on R</h2> |
| <h3 style="text-align: center;">Version @value{FAQ_VERSION}</h3> |
| <h3 style="text-align: center;">Kurt Hornik</h3> |
| <hr> |
| @end html |
| @end ifhtml |
| |
| @c @ifnothtml |
| @contents |
| @c @end ifnothtml |
| |
| @ifnottex |
| @node Top, Introduction, (dir), (dir) |
| @top R FAQ |
| @end ifnottex |
| |
| @menu |
| * Introduction:: |
| * R Basics:: |
| * R and S:: |
| * R Web Interfaces:: |
| * R Add-On Packages:: |
| * R and Emacs:: |
| * R Miscellanea:: |
| * R Programming:: |
| * R Bugs:: |
| * Acknowledgments:: |
| @end menu |
| |
| @node Introduction, R Basics, Top, Top |
| @chapter Introduction |
| |
| This document contains answers to some of the most frequently asked |
| questions about R. |
| |
| @menu |
| * Legalese:: |
| * Obtaining this document:: |
| * Citing this document:: |
| * Notation:: |
| * Feedback:: |
| @end menu |
| |
| @node Legalese, Obtaining this document, Introduction, Introduction |
| @section Legalese |
| |
| This document is copyright @copyright{} 1998--@value{FAQ_YEAR} by Kurt |
| Hornik. |
| |
| This document is free software; you can redistribute it and/or modify it |
| under the terms of the @acronym{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 |
| @acronym{GNU} General Public License for more details. |
| |
| Copies of the @acronym{GNU} General Public License versions are |
| available at |
| |
| @display |
| @url{https://www.R-project.org/Licenses/} |
| @end display |
| |
| |
| @node Obtaining this document, Citing this document, Legalese, Introduction |
| @section Obtaining this document |
| |
| The latest version of this document is always available from |
| |
| @display |
| @url{https://CRAN.R-project.org/doc/FAQ/} |
| @end display |
| |
| From there, you can obtain versions converted to |
| @url{https://CRAN.R-project.org/doc/FAQ/R-FAQ.txt,, plain |
| @acronym{ASCII} text}, |
| @c @url{https://CRAN.R-project.org/doc/FAQ/R-FAQ.dvi.gz,, DVI}, |
| @url{https://CRAN.R-project.org/doc/FAQ/R-FAQ.info.gz,, @acronym{GNU} |
| info}, @url{https://CRAN.R-project.org/doc/FAQ/R-FAQ.html,, @HTML{}}, |
| @url{https://CRAN.R-project.org/doc/FAQ/R-FAQ.pdf,, PDF}, |
| @c @url{https://CRAN.R-project.org/doc/FAQ/R-FAQ.ps.gz,, PostScript} |
| as well as the @url{https://CRAN.R-project.org/doc/FAQ/R-FAQ.texi,, |
| Texinfo source} used for creating all these formats using the |
| @acronym{GNU} Texinfo system. |
| |
| You can also obtain the R @acronym{FAQ} from the @file{doc/FAQ} |
| subdirectory of a @CRAN{} site (@pxref{What is CRAN?}). |
| |
| @node Citing this document, Notation, Obtaining this document, Introduction |
| @section Citing this document |
| |
| In publications, please refer to this @acronym{FAQ} as Hornik |
| (@value{FAQ_YEAR}), ``The R @acronym{FAQ}'', and give the above, |
| @emph{official} @acronym{URL}: |
| |
| @example |
| @group |
| @@Misc@{, |
| author = @{Kurt Hornik@}, |
| title = @{@{R@} @{FAQ@}@}, |
| year = @{@value{FAQ_YEAR}@}, |
| url = @{https://CRAN.R-project.org/doc/FAQ/R-FAQ.html@} |
| @} |
| @end group |
| @end example |
| |
| |
| @node Notation, Feedback, Citing this document, Introduction |
| @section Notation |
| |
| Everything should be pretty standard. @samp{R>} is used for the R |
| prompt, and a @samp{$} for the shell prompt (where applicable). |
| |
| @node Feedback, , Notation, Introduction |
| @section Feedback |
| |
| Feedback via email to @email{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 |
| @url{https://CRAN.R-project.org/bin/windows/base/rw-FAQ.html, ``R for |
| Windows @acronym{FAQ}''} and the |
| @url{https://CRAN.R-project.org/bin/macosx/RMacOSX-FAQ.html, ``R for Mac |
| OS X @acronym{FAQ}''}. If you have information on Mac or Windows |
| systems that you think should be added to this document, please let me |
| know. |
| |
| @c <FIXME> |
| @c Should we maybe have direct links inside the R tree to the various |
| @c rw-FAQ versions? |
| @c </FIXME> |
| |
| @node R Basics, R and S, Introduction, Top |
| @chapter R Basics |
| |
| @menu |
| * What is R?:: |
| * What machines does R run on?:: |
| * What is the current version of R?:: |
| * How can R be obtained?:: |
| * How can R be installed?:: |
| * Are there Unix-like binaries for R?:: |
| * What documentation exists for R?:: |
| * Citing R:: |
| * What mailing lists exist for R?:: |
| * What is CRAN?:: |
| * Can I use R for commercial purposes?:: |
| * Why is R named R?:: |
| * What is the R Foundation?:: |
| * What is R-Forge?:: |
| @end menu |
| |
| @node What is R?, What machines does R run on?, R Basics, R Basics |
| @section 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 (@pxref{What is S?}) and Sussman's |
| @url{https://www.cs.indiana.edu/scheme-repository/home.html, Scheme}. |
| Whereas the resulting language is very similar in appearance to S, the |
| underlying implementation and semantics are derived from Scheme. |
| @xref{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 (@pxref{R Add-On Packages}). |
| |
| R was initially written by @email{Ross.Ihaka@@R-project.org, Ross Ihaka} |
| and @email{Robert.Gentleman@@R-project.org, Robert Gentleman} 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 @url{https://www.R-project.org/}. It is |
| @url{https://www.gnu.org/philosophy/free-sw.html, free software} |
| distributed under a @acronym{GNU}-style |
| @url{https://www.gnu.org/copyleft/copyleft.html, copyleft}, and an |
| official part of the @url{https://www.gnu.org/, @acronym{GNU}} project |
| (``@acronym{GNU} S''). |
| |
| @node What machines does R run on?, What is the current version of R?, What is R?, R Basics |
| @section 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., @uref{https://en.wikipedia.org/wiki/Unix-like}) |
| platforms including @var{cpu}-linux-gnu for the i386, amd64/x86_64, |
| alpha, arm, arm64, hppa, mips/mipsel, powerpc, s390x and sparc |
| @acronym{CPU}s (e.g., |
| @url{https://buildd.debian.org/build.php?&pkg=r-base}), i386-hurd-gnu, |
| @var{cpu}-kfreebsd-gnu for i386 and amd64, |
| @c <FIXME> |
| @c Not sure anymore ... |
| @c alpha-dec-osf4, |
| @c <COMMENT> |
| @c i386-freebsd reported by B. Gian James" <gian.james@gmail.com> on |
| @c 2009-01-11 |
| @c (Architecture: i386, OS: FreeBSD 7.1-PRERELEASE, Vendor: PC-BSD) |
| @c i386-freebsd, |
| @c but all recent reports are on x86_64 |
| @c </COMMENT> |
| @c hppa-hp-hpux, |
| @c mips-sgi-irix, |
| @c </FIXME> |
| i386-pc-solaris, rs6000-ibm-aix, sparc-sun-solaris, x86_64-apple-darwin, |
| x86_64-unknown-freebsd and x86_64-unknown-openbsd. |
| |
| @c and according to @email{jlindsey@@luc.ac.be, Jim Lindsey} also on |
| @c Mac, Amiga and Atari under m68k-linux. |
| |
| If you know about other platforms, please drop us a note. |
| |
| @node What is the current version of R?, How can R be obtained?, What machines does R run on?, R Basics |
| @section 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 @url{https://CRAN.R-project.org/sources.html} for the current |
| versions of r-release, r-patched and r-devel. |
| |
| @node How can R be obtained?, How can R be installed?, What is the current version of R?, R Basics |
| @section How can R be obtained? |
| |
| Sources, binaries and documentation for R can be obtained via @CRAN{}, |
| the ``Comprehensive R Archive Network'' (see @ref{What is CRAN?}). |
| |
| Sources are also available via @url{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 |
| @url{https://stat.ethz.ch/R/daily}. |
| |
| @c Sources are also available via anonymous rsync. Use |
| |
| @c @example |
| @c rsync -rptC --delete rsync.R-project.org::@var{module} R |
| @c @end example |
| |
| @c @noindent |
| @c to create a copy of the source tree specified by @var{module} in the |
| @c subdirectory @file{R} of the current directory, where @var{module} |
| @c specifies one of the three existing flavors of the R sources, and can be |
| @c one of @samp{r-release} (current released version), @samp{r-patched} |
| @c (patched released version), and @samp{r-devel} (development version). |
| @c The rsync trees are created directly from the master CVS archive and are |
| @c updated hourly. The @option{-C} and in the @command{rsync} command |
| @c is to cause it to skip the CVS directories. Further information on |
| @c @command{rsync} is available at @url{http://rsync.samba.org/rsync/}. |
| |
| @c @c <NOTE> |
| @c @c Keep in sync with R-admin. |
| @c Note that the sources available via rsync do not include the recommended |
| @c packages, whereas these are included in the tarballs of released |
| @c versions. To install the appropriate sources for the recommended |
| @c packages, run @command{./tools/rsync-recommended} from the top-level of |
| @c the R sources that you pulled by rsync. |
| @c @c </NOTE> |
| |
| @c The sources of the development version are also available via anonymous |
| @c CVS. See @url{http://anoncvs.R-project.org} for more information. |
| |
| @node How can R be installed?, Are there Unix-like binaries for R?, How can R be obtained?, R Basics |
| @section How can R be installed? |
| |
| @menu |
| * How can R be installed (Unix-like):: |
| * How can R be installed (Windows):: |
| * How can R be installed (Mac):: |
| @end menu |
| |
| @node How can R be installed (Unix-like), How can R be installed (Windows), How can R be installed?, How can R be installed? |
| @subsection How can R be installed (Unix-like) |
| |
| If R is already installed, it can be started by typing @kbd{R} at the |
| shell prompt (of course, provided that the executable is in your path). |
| |
| If binaries are available for your platform (see @ref{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 @ref{What |
| machines does R run on?}). The file @file{INSTALL} that comes with the |
| R distribution contains a brief introduction, and the ``R Installation |
| and Administration'' guide (@pxref{What documentation exists for R?}) |
| has full details. |
| |
| Note that you need a @FORTRAN{} compiler or perhaps @command{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): |
| |
| @example |
| $ ./configure |
| $ make |
| @end example |
| |
| If these commands execute successfully, the R binary and a shell script |
| front-end called @file{R} are created and copied to the @file{bin} |
| directory. You can copy the script to a place where users can invoke |
| it, for example to @file{/usr/local/bin}. In addition, plain text help |
| pages as well as @HTML{} and @LaTeX{} versions of the documentation are |
| built. |
| |
| Use @kbd{make dvi} to create DVI versions of the R manuals, such as |
| @file{refman.dvi} (an R object reference index) and @file{R-exts.dvi}, |
| the ``R Extension Writers Guide'', in the @file{doc/manual} |
| subdirectory. These files can be previewed and printed using standard |
| programs such as @command{xdvi} and @command{dvips}. You can also use |
| @kbd{make pdf} to build PDF (Portable Document Format) version of the |
| manuals, and view these using e.g.@: Acrobat. Manuals written in the |
| @acronym{GNU} Texinfo system can also be converted to info files |
| suitable for reading online with Emacs or stand-alone @acronym{GNU} |
| Info; use @kbd{make info} to create these versions (note that this |
| requires Makeinfo version 4.5). |
| |
| Finally, use @kbd{make check} to find out whether your R system works |
| correctly. |
| |
| You can also perform a ``system-wide'' installation using @kbd{make |
| install}. By default, this will install to the following directories: |
| |
| @table @file |
| @item $@{prefix@}/bin |
| the front-end shell script |
| @item $@{prefix@}/man/man1 |
| the man page |
| @item $@{prefix@}/lib/R |
| all the rest (libraries, on-line help system, @dots{}). This is the ``R |
| Home Directory'' (@env{R_HOME}) of the installed system. |
| @end table |
| |
| @noindent |
| In the above, @code{prefix} is determined during configuration |
| (typically @file{/usr/local}) and can be set by running |
| @command{configure} with the option |
| |
| @example |
| $ ./configure --prefix=/where/you/want/R/to/go |
| @end example |
| |
| @noindent |
| (E.g., the R executable will then be installed into |
| @file{/where/you/want/R/to/go/bin}.) |
| |
| To install DVI, info and PDF versions of the manuals, use @kbd{make |
| install-dvi}, @kbd{make install-info} and @kbd{make install-pdf}, |
| respectively. |
| |
| @node How can R be installed (Windows), How can R be installed (Mac), How can R be installed (Unix-like), How can R be installed? |
| @subsection How can R be installed (Windows) |
| |
| The @file{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 @url{https://CRAN.R-project.org/bin/windows/base/@/rw-FAQ.html, |
| ``R for Windows @acronym{FAQ}''} for more details. |
| |
| @node How can R be installed (Mac), , How can R be installed (Windows), How can R be installed? |
| @subsection How can R be installed (Mac) |
| |
| The @file{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 @acronym{GUI}. This port of R for macOS |
| is maintained by @email{Simon.Urbanek@@R-project.org, Simon Urbanek} |
| (and previously by Stefano Iacus). The |
| @url{https://CRAN.R-project.org/bin/macosx/RMacOSX-FAQ.html, ``R for Mac |
| macOS @acronym{FAQ}} has more details. |
| |
| Snapshots of the r-patched and r-devel versions of R are available as |
| Apple installer packages at @uref{https://mac.R-project.org}. |
| |
| @node Are there Unix-like binaries for R?, What documentation exists for R?, How can R be installed?, R Basics |
| @section Are there Unix-like binaries for R? |
| |
| @c Linux binaries as of 2014-07-27: |
| @c Providers: |
| @c Debian: Johannes Ranke <jranke@uni-bremen.de> |
| @c Ubuntu: Michael Rutter <mar36@psu.edu> |
| @c |
| @c debian |
| @c current: |
| @c wheezy-cran3 i386/amd64/armel 3.1.1 |
| @c squeeze-cran3 i386/amd64 3.1.1 |
| @c archive: |
| @c squeeze-cran i386/amd64 2.15.3 |
| @c lenny-cran i386/amd64 2.14.1 |
| @c etch-cran i386/amd64 2.11.0 |
| @c |
| @c ubuntu |
| @c Trusty Tahr (14.04) |
| @c Saucy Salamander (13.10) |
| @c Quantal Quetzal (12.10) |
| @c Precise Pangolin (12.04; LTS) |
| @c Lucid Lynx (10.04; LTS) |
| |
| The @file{bin/linux} directory of a @CRAN{} site contains the following |
| packages. |
| |
| @need 1000 |
| @quotation |
| @multitable {Red Hat} {i386/x86_64} {lucid/precise/trusty} {Martyn Plummer} |
| @headitem @tab CPU @tab Versions @tab Provider |
| @item Debian @tab i386/amd64 @tab squeeze/wheezy @tab Johannes Ranke |
| @item @tab armel @tab wheezy @tab Johannes Ranke |
| @item Ubuntu @tab i386/amd64 @tab lucid/precise/trusty @tab Michael Rutter |
| @end multitable |
| @end quotation |
| |
| 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.@: @code{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 @code{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 |
| @dfn{stable} distribution of Debian are provided by Johannes Ranke, and |
| available from @CRAN{}. See |
| @url{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 |
| @command{yum}, the RPM installer/updater. Note that the ``Software'' |
| application (gnome-software), which is the default @acronym{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 @code{R-core} and |
| @code{R-devel}), as well as @code{R-java}, which ensures that R is |
| configured for use with Java. The R RPM also installs the standalone R |
| math library (@code{libRmath} and @code{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 |
| (@url{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 @url{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{}. |
| |
| @node What documentation exists for R?, Citing R, Are there Unix-like binaries for R?, R Basics |
| @section 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 @kbd{help(@var{name})} |
| (or @kbd{?@var{name}}) at the R prompt, where @var{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 @ref{How can R be installed?}. An up-to-date @HTML{} |
| version is always available for web browsing at |
| @url{https://stat.ethz.ch/R-manual/}. |
| |
| Printed copies of the R reference manual for some version(s) are |
| available from Network Theory Ltd, at |
| @c https: is untrusted |
| @url{http://www.network-theory.co.uk/R/base/}. For each set of manuals |
| sold, the publisher donates USD 10 to the R Foundation (@pxref{What is |
| the R Foundation?}). |
| |
| The R distribution also comes with the following manuals. |
| |
| @itemize @bullet |
| @item ``An Introduction to R'' (@file{R-intro}) |
| includes information on data types, programming elements, statistical |
| modeling and graphics. This document is based on the ``Notes on |
| @SPLUS{}'' by Bill Venables and David Smith. |
| @item ``Writing R Extensions'' (@file{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 |
| @acronym{API}. |
| @item ``R Data Import/Export'' (@file{R-data}) |
| is a guide to importing and exporting data to and from R. |
| @item ``The R Language Definition'' (@file{R-lang}), |
| a first version of the ``Kernighan & Ritchie of R'', explains |
| evaluation, parsing, object oriented programming, computing on the |
| language, and so forth. |
| @item ``R Installation and Administration'' (@file{R-admin}). |
| @item ``R Internals'' (@file{R-ints}) |
| is a guide to R's internal structures. |
| (Added in R 2.4.0.) |
| @end itemize |
| |
| An annotated bibliography (Bib@TeX{} format) of R-related publications |
| can be found at |
| |
| @display |
| @url{https://www.R-project.org/doc/bib/R.bib} |
| @end display |
| |
| Books on R by R Core Team members include |
| |
| @quotation |
| John M. Chambers (2008), ``Software for Data Analysis: Programming with |
| R''. Springer, New York, ISBN 978-0-387-75935-7, |
| @url{http://statweb.stanford.edu/~jmc4/Rbook/}. |
| |
| Peter Dalgaard (2008), ``Introductory Statistics with R'', 2nd edition. |
| Springer, ISBN 978-0-387-79053-4, |
| @url{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, |
| @url{https://www.bioconductor.org/pub/RBioinf/}. |
| |
| Stefano M. Iacus (2008), ``Simulation and Inference for Stochastic |
| Differential Equations: With R Examples''. Springer, New York, ISBN |
| 978-0-387-75838-1. |
| |
| Deepayan Sarkar (2007), ``Lattice: Multivariate Data Visualization with |
| R''. Springer, New York, ISBN 978-0-387-75968-5. |
| |
| W. John Braun and Duncan J. Murdoch (2007), ``A First Course in |
| Statistical Programming with R''. Cambridge University Press, |
| Cambridge, ISBN 978-0521872652. |
| |
| P. Murrell (2005), ``R Graphics'', Chapman & Hall/CRC, ISBN: |
| 1-584-88486-X, |
| @url{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, |
| @url{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. |
| @end quotation |
| |
| 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'', |
| @url{http://www.amstat.org/publications/jcgs/, , @emph{Journal of |
| Computational and Graphical Statistics}}, @strong{5}, 299--314. |
| |
| @node Citing R, What mailing lists exist for R?, What documentation exists for R?, R Basics |
| @section Citing R |
| |
| To cite R in publications, use |
| |
| @example |
| @group |
| @@Manual@{, |
| title = @{R: A Language and Environment for Statistical |
| Computing@}, |
| author = @{@{R Core Team@}@}, |
| organization = @{R Foundation for Statistical Computing@}, |
| address = @{Vienna, Austria@}, |
| year = @var{YEAR}, |
| url = @{https://www.R-project.org@} |
| @} |
| @end group |
| @end example |
| @noindent |
| where @var{YEAR} is the release year of the version of R used and can |
| determined as @code{R.version$year}. |
| |
| Citation strings (or Bib@TeX{} entries) for R and R packages can also be |
| obtained by @code{citation()}. |
| |
| @node What mailing lists exist for R?, What is CRAN?, Citing R, R Basics |
| @section What mailing lists exist for R? |
| |
| Thanks to @email{Martin.Maechler@@R-project.org, Martin Maechler}, there |
| are several mailing lists devoted to R, including the following: |
| |
| @table @code |
| @item R-announce |
| A moderated list for major announcements about the development of R and |
| the availability of new code. |
| @item R-packages |
| A moderated list for announcements on the availability of new or |
| enhanced contributed packages. |
| @item 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. |
| @c enhancements and patches to the source code and documentation of R, |
| @c comparison and compatibility with S and @SPLUS{}, and for the posting of |
| @c nice examples and benchmarks. |
| @item R-devel |
| This list is for questions and discussion about code development in R. |
| @c discussions about the future of R, proposals of new functionality, and |
| @c pre-testing of new versions. It is meant for those who maintain an |
| @c active position in the development of R. |
| @item R-package-devel |
| A list which provides a forum for learning about the R package |
| development process. |
| @end table |
| |
| @noindent |
| Please read the @url{https://www.R-project.org/posting-guide.html, |
| posting guide} @emph{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 |
| @url{https://stat.ethz.ch/mailman/listinfo/}. One can also subscribe |
| (or unsubscribe) via email, e.g.@: to R-help by sending @samp{subscribe} |
| (or @samp{unsubscribe}) in the @emph{body} of the message (not in the |
| subject!) to @email{R-help-request@@lists.R-project.org}. |
| |
| Send email to @email{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 @samp{R-help} replaced by |
| @samp{R-announce}, @samp{R-packages}, and @samp{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 |
| @ref{R Bugs} for more details. |
| |
| See @url{https://www.R-project.org/mail.html} for more information on |
| the R mailing lists. |
| |
| The R Core Team can be reached at @email{R-core@@lists.R-project.org} |
| for comments and reports. |
| |
| @c <FIXME> |
| @c As of 2017-10, accessing GMANE's gmane.comp.lang.r still says |
| @c Not all of Gmane is back yet ... |
| @c Hence comment out for the time being. |
| @c Many of the R project's mailing lists are also available via |
| @c @url{http://gmane.org, Gmane}, from which they can be read with a web |
| @c browser, using an NNTP news reader, or via RSS feeds. See |
| @c @uref{http://dir.gmane.org/@/index.php?prefix=gmane.comp.lang.r.}@: for |
| @c the available mailing lists, and @uref{http://www.gmane.org/rss.php} for |
| @c details on RSS feeds. |
| @c </FIXME> |
| |
| @node What is CRAN?, Can I use R for commercial purposes?, What mailing lists exist for R?, R Basics |
| @section What is @acronym{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 (Wirtschaftsuniversit@"at Wien) in Austria |
| can be found at the @acronym{URL} |
| |
| @quotation |
| @c @multitable @columnfractions .45 .30 |
| @c @item |
| @url{https://CRAN.R-project.org/} |
| @c @tab (Austria) |
| @c @end multitable |
| @end quotation |
| |
| @noindent |
| and is mirrored daily to many sites around the world. |
| See @url{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 (@url{https://CRAN-archive.R-project.org/}). |
| |
| Please always use the @acronym{URL} of the master site when referring to |
| @CRAN{}. |
| |
| @node Can I use R for commercial purposes?, Why is R named R?, What is CRAN?, R Basics |
| @section Can I use R for commercial purposes? |
| |
| R is released under the |
| @url{https://www.gnu.org/licenses/old-licenses/gpl-2.0.html,, |
| @acronym{GNU} General Public License (@acronym{GPL}), version 2}. If |
| you have any questions regarding the legality of using R in any |
| particular situation you should bring it up with your legal counsel. We |
| are in no position to offer legal advice. |
| |
| It is the opinion of the R Core Team that one can use R for commercial |
| purposes (e.g., in business or in consulting). The @acronym{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 |
| @url{https://opensource.org/docs/definition.html, Open Source |
| Definition}: |
| |
| @quotation |
| 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. |
| @end quotation |
| |
| @noindent |
| It is also explicitly stated in clause 0 of the GPL, which says in part |
| |
| @quotation |
| 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. |
| @end quotation |
| |
| 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. |
| |
| @node Why is R named R?, What is the R Foundation?, Can I use R for commercial purposes?, R Basics |
| @section 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' (@pxref{What is S?}). |
| |
| @c At the time the name was coined no one expected that the software would |
| @c get used outside of Auckland, so it seemed ok to make a joke of it. |
| |
| @node What is the R Foundation?, What is R-Forge?, Why is R named R?, R Basics |
| @section 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 |
| @url{https://www.R-project.org/foundation/} for more information. |
| |
| @node What is R-Forge?, , What is the R Foundation?, R Basics |
| @section What is R-Forge? |
| |
| R-Forge (@url{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 @url{https://en.wikipedia.org/wiki/GForge, 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 Theu@ss{}l and Achim Zeileis (2009), ``Collaborative software |
| development using R-Forge'', @url{https://journal.R-project.org/, , |
| @emph{The R Journal}}, @strong{1}(1):9--14. |
| |
| |
| @node R and S, R Web Interfaces, R Basics, Top |
| @chapter R and S |
| |
| @menu |
| * What is S?:: |
| * What is S-PLUS?:: |
| * What are the differences between R and S?:: |
| * Is there anything R can do that S-PLUS cannot?:: |
| * What is R-plus?:: |
| @end menu |
| |
| @node What is S?, What is S-PLUS?, R and S, R and S |
| @section 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 |
| (@acronym{ACM}) presented its Software System Award to John M. Chambers, |
| the principal designer of S, for |
| |
| @quotation |
| the S system, which has forever altered the way people analyze, |
| visualize, and manipulate data @dots{} |
| |
| S is an elegant, widely accepted, and enduring software system, with |
| conceptual integrity, thanks to the insight, taste, and effort of John |
| Chambers. |
| @end quotation |
| |
| The evolution of the S language is characterized by four books by John |
| Chambers and coauthors, which are also the primary references for S. |
| |
| @itemize @bullet |
| @item |
| 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 ``@emph{Brown Book}'', and of historical |
| interest only. |
| |
| @item |
| Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), ``The New |
| S Language,'' London: Chapman & Hall. |
| |
| This book is often called the ``@emph{Blue Book}'', and introduced what |
| is now known as S version 2. |
| |
| @item |
| John M. Chambers and Trevor J. Hastie (1992), ``Statistical Models in |
| S,'' London: Chapman & Hall. |
| |
| This is also called the ``@emph{White Book}'', and introduced S version |
| 3, which added structures to facilitate statistical modeling in S. |
| |
| @item |
| John M. Chambers (1998), ``Programming with Data,'' New York: Springer, |
| ISBN 0-387-98503-4 |
| (@url{http://statweb.stanford.edu/~jmc4/Sbook/}). |
| |
| This ``@emph{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. |
| @end itemize |
| |
| See @url{http://statweb.stanford.edu/~jmc4/papers/96.7.ps} |
| for further information on the ``Evolution of the S Language''. |
| |
| @c There is a huge amount of user-contributed code for S, available at the |
| @c @url{http://lib.stat.cmu.edu/S/, S Repository} at @acronym{CMU}. |
| |
| @c The @url{http://lib.stat.cmu.edu/S/faq, ``Frequently Asked Questions |
| @c about S''} contains further information about S, but is not |
| @c up-to-date. |
| |
| @node What is S-PLUS?, What are the differences between R and S?, What is S?, R and S |
| @section What is @sc{S-Plus}? |
| |
| @SPLUS{} is a value-added version of S currently sold by |
| @url{http://www.tibco.com/, TIBCO Software Inc} as `TIBCO Spotfire S+'. |
| See @url{https://en.wikipedia.org/wiki/S-PLUS} for more information. |
| |
| @node What are the differences between R and S?, Is there anything R can do that S-PLUS cannot?, What is S-PLUS?, R and S |
| @section 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; @SPLUS{} 3.x and 4.x), |
| the ``new S engine'' (S version 4; @SPLUS{} 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 @emph{engines}. |
| |
| For the remainder of this section, ``S'' refers to the S engines and not |
| the S language. |
| |
| @menu |
| * Lexical scoping:: |
| * Models:: |
| * Others:: |
| @end menu |
| |
| @node Lexical scoping, Models, What are the differences between R and S?, What are the differences between R and S? |
| @subsection 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 @emph{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: |
| |
| @example |
| @group |
| cube <- function(n) @{ |
| sq <- function() n * n |
| n * sq() |
| @} |
| @end group |
| @end example |
| |
| Under S, @code{sq()} does not ``know'' about the variable @code{n} |
| unless it is defined globally: |
| |
| @example |
| @group |
| S> cube(2) |
| Error in sq(): Object "n" not found |
| Dumped |
| S> n <- 3 |
| S> cube(2) |
| [1] 18 |
| @end group |
| @end example |
| |
| In R, the ``environment'' created when @code{cube()} was invoked is |
| also looked in: |
| |
| @example |
| @group |
| R> cube(2) |
| [1] 8 |
| @end group |
| @end example |
| |
| @c The following more `realistic' example illustrating the differences in |
| @c scoping is due to @email{tlumley@@u.washington.edu, Thomas Lumley}. |
| @c The function |
| |
| @c @example |
| @c jackknife.lm <- function(lmobj) @{ |
| @c n <- length(resid(lmobj)) |
| @c jval <- sapply(1:n, function(i) coef(update(lmobj, subset = -i))) |
| @c (n - 1) * (n - 1) * var(jval) / n |
| @c @} |
| @c @end example |
| |
| @c @noindent |
| @c does something useful in R, but does not work in S. In order to make it |
| @c work in S you need to explicitly pass the linear model object into the |
| @c function nested in @code{apply()}. If you don't and you are lucky you |
| @c will get @samp{Error: Object "lmobj" not found}. If you are unlucky |
| @c enough to have a linear model called @code{lmobj} in your global |
| @c environment you will get the wrong answer with no warning. |
| |
| @c The following version works in S. |
| |
| @c @example |
| @c jackknife.S.lm <- function(lmobj) @{ |
| @c n <- length(resid(lmobj)) |
| @c jval <- sapply(1:n, |
| @c function(i, lmobj) coef(update(lmobj, subset = -i)), |
| @c lmobj = lmobj) |
| @c (n - 1) * (n - 1) * var(jval) / n |
| @c @} |
| @c @end example |
| |
| @c (The S version was written independently by Thomas and at least three of |
| @c his fellow students over the past couple of years, causing literally |
| @c hours of confusion on each occasion.) |
| |
| As a more ``interesting'' real-world problem, suppose you want to write |
| a function which returns the density function of the @math{r}-th order |
| statistic from a sample of size @math{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 @SPLUS{} documentation for @code{call()} basically suggests the |
| following: |
| |
| @example |
| @group |
| 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 |
| @} |
| @end group |
| @end example |
| |
| @noindent 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 @code{substitute()} and seems to work |
| under both S and R is |
| |
| @example |
| @group |
| 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))) |
| @} |
| @end group |
| @end example |
| |
| @noindent |
| (the @code{eval()} is not needed in S). |
| |
| However, in R there is a much easier solution: |
| |
| @example |
| @group |
| 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) |
| @} |
| @} |
| @end group |
| @end example |
| |
| @noindent |
| 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 @emph{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 @code{MC()} (for ``make closure''): |
| |
| @example |
| @group |
| 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)) |
| @} |
| @end group |
| @end example |
| |
| 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, @code{MC()} simply is |
| |
| @example |
| MC <- function(f, env) f |
| @end example |
| |
| @noindent (lexical scope!), a version for S is |
| |
| @example |
| @group |
| 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) |
| @} |
| @end group |
| @end example |
| |
| 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 @kbd{demo("scoping")} at the R prompt. |
| Further information is provided in the standard R reference ``R: A |
| Language for Data Analysis and Graphics'' (@pxref{What documentation |
| exists for R?}) and in Robert Gentleman and Ross Ihaka (2000), ``Lexical |
| Scope and Statistical Computing'', |
| @url{http://www.amstat.org/publications/jcgs/, , @emph{Journal of |
| Computational and Graphical Statistics}}, @strong{9}, 491--508. |
| |
| Nested lexically scoped functions also imply a further major difference. |
| Whereas S stores all objects as separate files in a directory somewhere |
| (usually @file{.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 |
| @emph{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 |
| @ref{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 |
| @code{source()}. |
| |
| @quotation Note |
| If you run R from within Emacs (see @ref{R and Emacs}), you can save the |
| contents of the interaction buffer to a file and conveniently manipulate |
| it using @code{ess-transcript-mode}, as well as save source copies of |
| all functions and data used. |
| @end quotation |
| |
| @node Models, Others, Lexical scoping, What are the differences between R and S? |
| @subsection Models |
| |
| There are some differences in the modeling code, such as |
| |
| @itemize @bullet |
| @item |
| Whereas in S, you would use @code{lm(y ~ x^3)} to regress @code{y} on |
| @code{x^3}, in R, you have to insulate powers of numeric vectors (using |
| @code{I()}), i.e., you have to use @code{lm(y ~ I(x^3))}. |
| @item |
| The glm family objects are implemented differently in R and S. The same |
| functionality is available but the components have different names. |
| @item |
| Option @code{na.action} is set to @code{"na.omit"} by default in R, |
| but not set in S. |
| @item |
| 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. |
| @item |
| Finally, in R @code{y ~ x + 0} is an alternative to @code{y ~ x - 1} for |
| specifying a model with no intercept. Models with no parameters at all |
| can be specified by @code{y ~ 0}. |
| @end itemize |
| |
| @node Others, , Models, What are the differences between R and S? |
| @subsection 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. |
| |
| @itemize @bullet |
| |
| @item |
| In R, if @code{x} is a list, then @code{x[i] <- NULL} and @code{x[[i]] |
| <- NULL} remove the specified elements from @code{x}. The first of |
| these is incompatible with S, where it is a no-op. (Note that you can |
| set elements to @code{NULL} using @code{x[i] <- list(NULL)}.) |
| |
| @c @item |
| @c In R @code{x[-4]} fails if @code{x} is not @code{NULL} but has fewer |
| @c than 4 elements. In S it has no effect. |
| |
| @item |
| In S, the functions named @code{.First} and @code{.Last} in the |
| @file{.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 @option{--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 |
| @option{--no-site-file} was given. This code is loaded in package |
| @pkg{base}. Then, unless @option{--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 |
| @file{.RData} in case there is one (unless @option{--no-restore-data} or |
| @option{--no-restore} were specified). Next, a function @code{.First()} |
| is run if found on the search path. Finally, function @code{.First.sys} |
| in the @pkg{base} package is run. When terminating an R session, by |
| default a function @code{.Last} is run if found on the search path, |
| followed by @code{.Last.sys}. If needed, the functions @code{.First()} |
| and @code{.Last()} should be defined in the appropriate startup |
| profiles. See the help pages for @code{.First} and @code{.Last} for |
| more details. |
| |
| @item |
| In R, @code{T} and @code{F} are just variables being set to @code{TRUE} |
| and @code{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 @code{"T"} or @code{"F"}.) Hence, when writing code |
| you should always use @code{TRUE} and @code{FALSE}. |
| |
| @item |
| In R, @code{dyn.load()} can only load @emph{shared objects}, as created |
| for example by @kbd{R CMD SHLIB}. |
| |
| @item |
| In R, @code{attach()} currently only works for lists and data frames, |
| but not for directories. (In fact, @code{attach()} also works for R |
| data files created with @code{save()}, which is analogous to attaching |
| directories in S.) Also, you cannot attach at position 1. |
| |
| @item |
| Categories do not exist in R, and never will as they are deprecated now |
| in S. Use factors instead. |
| |
| @item |
| In R, @code{For()} loops are not necessary and hence not supported. |
| |
| @item |
| In R, @code{assign()} uses the argument @option{envir=} rather than |
| @option{where=} as in S. |
| |
| @item |
| The random number generators are different, and the seeds have different |
| length. |
| |
| @item |
| R passes integer objects to C as @code{int *} rather than @code{long *} |
| as in S. |
| |
| @item |
| R has no single precision storage mode. However, as of version 0.65.1, |
| there is a single precision interface to C/@FORTRAN{} subroutines. |
| |
| @item |
| By default, @code{ls()} returns the names of the objects in the current |
| (under R) and global (under S) environment, respectively. For example, |
| given |
| |
| @example |
| x <- 1; fun <- function() @{y <- 1; ls()@} |
| @end example |
| |
| @noindent |
| then @code{fun()} returns @code{"y"} in R and @code{"x"} (together with |
| the rest of the global environment) in S. |
| |
| @item |
| R allows for zero-extent matrices (and arrays, i.e., some elements of |
| the @code{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 @code{x} is a matrix, @code{x[, FALSE]} |
| is not @code{NULL} but a ``matrix'' with 0 columns. Hence, such objects |
| need to be tested for by checking whether their @code{length()} is zero |
| (which works in both R and S), and not using @code{is.null()}. |
| |
| @item |
| Named vectors are considered vectors in R but not in S (e.g., |
| @code{is.vector(c(a = 1:3))} returns @code{FALSE} in S and @code{TRUE} |
| in R). |
| |
| @item |
| Data frames are not considered as matrices in R (i.e., if @code{DF} is a |
| data frame, then @code{is.matrix(DF)} returns @code{FALSE} in R and |
| @code{TRUE} in S). |
| |
| @item |
| 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. |
| |
| @item |
| In R, the argument of a replacement function which corresponds to the |
| right hand side must be named @samp{value}. E.g., @code{f(a) <- b} is |
| evaluated as @code{a <- "f<-"(a, value = b)}. S always takes the last |
| argument, irrespective of its name. |
| |
| @item |
| In S, @code{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. |
| |
| @item |
| In S, the index variable in a @code{for()} loop is local to the inside |
| of the loop. In R it is local to the environment where the @code{for()} |
| statement is executed. |
| |
| @item |
| In S, @code{tapply(simplify=TRUE)} returns a vector where R returns a |
| one-dimensional array (which can have named dimnames). |
| |
| @item |
| In S(-@sc{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 @code{"aA" < "Bb"} is |
| true or false). From version 1.2.0 the locale can be (re-)set in R by |
| the @code{Sys.setlocale()} function. |
| |
| @item |
| In S, @code{missing(@var{arg})} remains @code{TRUE} if @var{arg} is |
| subsequently modified; in R it doesn't. |
| |
| @item |
| From R version 1.3.0, @code{data.frame} strips @code{I()} when creating |
| (column) names. |
| |
| @item |
| In R, the string @code{"NA"} is not treated as a missing value in a |
| character variable. Use @code{as.character(NA)} to create a missing |
| character value. |
| |
| @item |
| R disallows repeated formal arguments in function calls. |
| |
| @item |
| In S, @code{dump()}, @code{dput()} and @code{deparse()} are essentially |
| different interfaces to the same code. In R from version 2.0.0, this is |
| only true if the same @code{control} argument is used, but by default it |
| is not. By default @code{dump()} tries to write code that will evaluate |
| to reproduce the object, whereas @code{dput()} and @code{deparse()} |
| default to options for producing deparsed code that is readable. |
| |
| @item |
| In R, indexing a vector, matrix, array or data frame with @code{[} using |
| a character vector index looks only for exact matches (whereas @code{[[} |
| and @code{$} allow partial matches). In S, @code{[} allows partial |
| matches. |
| |
| @item |
| S has a two-argument version of @code{atan} and no @code{atan2}. A call |
| in S such as @code{atan(x1, x2)} is equivalent to R's @code{atan2(x1, |
| x2)}. However, beware of named arguments since S's @code{atan(x = a, y |
| = b)} is equivalent to R's @code{atan2(y = a, x = b)} with the meanings |
| of @code{x} and @code{y} interchanged. (R used to have undocumented |
| support for a two-argument @code{atan} with positional arguments, but |
| this has been withdrawn to avoid further confusion.) |
| |
| @item |
| Numeric constants with no fractional and exponent (i.e., only integer) |
| part are taken as integer in @SPLUS{} 6.x or later, but as double in R. |
| |
| @end itemize |
| |
| 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. |
| |
| @node Is there anything R can do that S-PLUS cannot?, What is R-plus?, What are the differences between R and S?, R and S |
| @section Is there anything R can do that @sc{S-Plus} cannot? |
| |
| Since almost anything you can do in R has source code that you could |
| port to @SPLUS{} with little effort there will never be much you can do |
| in R that you couldn't do in @SPLUS{} if you wanted to. (Note that |
| using lexical scoping may simplify matters considerably, though.) |
| |
| R offers several graphics features that @SPLUS{} 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 @code{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'', @url{http://www.amstat.org/publications/jcgs/, , |
| @emph{Journal of Computational and Graphical Statistics}}, @strong{9}, |
| 582--599. |
| |
| @node What is R-plus?, , Is there anything R can do that S-PLUS cannot?, R and S |
| @section What is R-plus? |
| |
| For a very long time, there was no such thing. |
| |
| @url{http://www.xlsolutions-corp.com/, XLSolutions Corporation} is |
| currently beta testing a commercially supported version of R named R+ |
| (read R plus). |
| |
| @url{http://www.revolution-computing.com/, Revolution Analytics} has |
| released REvolution R, now available as Microsoft R (see |
| @url{http://blog.revolutionanalytics.com/2016/01/microsoft-r-open.html} |
| for more information). |
| |
| @c Now archived at <http://archive.today/WrgxY> |
| @c @url{http://www.random-technologies-llc.com/, Random Technologies} |
| @c offers @url{http://random-technologies-llc.com/products/RStat/rstat, |
| @c RStat}, an enterprise-strength statistical computing environment which |
| @c combines R with enterprise-level validation, documentation, software |
| @c support, and consulting services, as well as related R-based products. |
| |
| See also |
| @url{https://en.wikipedia.org/wiki/R_programming_language#Commercialized_versions_of_R} |
| for pointers to commercialized versions of R. |
| |
| @node R Web Interfaces, R Add-On Packages, R and S, Top |
| @chapter R Web Interfaces |
| |
| Please refer to the @CRAN{} task view on ``Web Technologies and |
| Services'' (@url{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'' |
| (@url{https://www.jstatsoft.org/v004/i01}), |
| David Firth (2003), |
| ``CGIwithR: Facilities for processing web forms using R'' |
| (@url{https://www.jstatsoft.org/v008/i10}), |
| and |
| Angelo Mineo and Alfredo Pontillo (2006), |
| ``Using R via PHP for Teaching Purposes: R-php'' |
| (@url{https://www.jstatsoft.org/v017/i04}). |
| |
| @node R Add-On Packages, R and Emacs, R Web Interfaces, Top |
| @chapter R Add-On Packages |
| |
| @menu |
| * Which add-on packages exist for R?:: |
| * How can add-on packages be installed?:: |
| * How can add-on packages be used?:: |
| * How can add-on packages be removed?:: |
| * How can I create an R package?:: |
| * How can I contribute to R?:: |
| @end menu |
| |
| @node Which add-on packages exist for R?, How can add-on packages be installed?, R Add-On Packages, R Add-On Packages |
| @section Which add-on packages exist for R? |
| |
| @menu |
| * Add-on packages in R:: |
| * Add-on packages from CRAN:: |
| * Add-on packages from Omegahat:: |
| * Add-on packages from Bioconductor:: |
| * Other add-on packages:: |
| @end menu |
| |
| @node Add-on packages in R, Add-on packages from CRAN, Which add-on packages exist for R?, Which add-on packages exist for R? |
| @subsection Add-on packages in R |
| |
| The R distribution comes with the following packages: |
| |
| @table @strong |
| @c <FIXME> |
| @c 3.0.0 |
| @item base |
| Base R functions (and datasets before R 2.0.0). |
| @item compiler |
| R byte code compiler (added in R 2.13.0). |
| @item datasets |
| Base R datasets (added in R 2.0.0). |
| @item grDevices |
| Graphics devices for base and grid graphics (added in R 2.0.0). |
| @c </FIXME> |
| @item graphics |
| R functions for base graphics. |
| @item grid |
| A rewrite of the graphics layout capabilities, plus some support for |
| interaction. |
| @item methods |
| Formally defined methods and classes for R objects, plus other |
| programming tools, as described in the Green Book. |
| @item parallel |
| Support for parallel computation, including by forking and by sockets, |
| and random-number generation (added in R 2.14.0). |
| @item splines |
| Regression spline functions and classes. |
| @item stats |
| R statistical functions. |
| @item stats4 |
| Statistical functions using S4 classes. |
| @item tcltk |
| Interface and language bindings to Tcl/Tk @acronym{GUI} elements. |
| @item tools |
| Tools for package development and administration. |
| @item utils |
| R utility functions. |
| @end table |
| These ``base packages'' were substantially reorganized in R 1.9.0. The |
| former @pkg{base} was split into the four packages @pkg{base}, |
| @pkg{graphics}, @pkg{stats}, and @pkg{utils}. Packages @pkg{ctest}, |
| @pkg{eda}, @pkg{modreg}, @pkg{mva}, @pkg{nls}, @pkg{stepfun} and |
| @pkg{ts} were merged into @pkg{stats}, package @pkg{lqs} returned to the |
| recommended package @CRANpkg{MASS}, and package @pkg{mle} moved to |
| @pkg{stats4}. |
| |
| @node Add-on packages from CRAN, Add-on packages from Omegahat, Add-on packages in R, Which add-on packages exist for R? |
| @subsection Add-on packages from @acronym{CRAN} |
| |
| The @CRAN{} @file{src/contrib} area contains a wealth of add-on |
| packages, including the following @emph{recommended} packages which are |
| to be included in all binary distributions of R. |
| |
| @c <FIXME> |
| @c 3.0.0 |
| @table @strong |
| @item KernSmooth |
| Functions for kernel smoothing (and density estimation) corresponding to |
| the book ``Kernel Smoothing'' by M. P. Wand and M. C. Jones, 1995. |
| @item MASS |
| Functions and datasets from the main package of Venables and Ripley, |
| ``Modern Applied Statistics with S''. |
| (Contained in the @file{VR} bundle for R versions prior to 2.10.0.) |
| @item Matrix |
| A Matrix package. |
| (Recommended for R 2.9.0 or later.) |
| @item 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. |
| @item class |
| Functions for classification (@math{k}-nearest neighbor and LVQ). |
| (Contained in the @file{VR} bundle for R versions prior to 2.10.0.) |
| @item cluster |
| Functions for cluster analysis. |
| @item codetools |
| Code analysis tools. |
| (Recommended for R 2.5.0 or later.) |
| @item foreign |
| Functions for reading and writing data stored by statistical software |
| like Minitab, S, SAS, SPSS, Stata, Systat, etc. |
| @item lattice |
| Lattice graphics, an implementation of Trellis Graphics functions. |
| @item mgcv |
| Routines for GAMs and other generalized ridge regression problems with |
| multiple smoothing parameter selection by GCV or UBRE. |
| @item nlme |
| Fit and compare Gaussian linear and nonlinear mixed-effects models. |
| @item nnet |
| Software for single hidden layer perceptrons (``feed-forward neural |
| networks''), and for multinomial log-linear models. |
| (Contained in the @file{VR} bundle for R versions prior to 2.10.0.) |
| @item rpart |
| Recursive PARTitioning and regression trees. |
| @item spatial |
| Functions for kriging and point pattern analysis from ``Modern Applied |
| Statistics with S'' by W. Venables and B. Ripley. |
| (Contained in the @file{VR} bundle for R versions prior to 2.10.0.) |
| @item survival |
| Functions for survival analysis, including penalized likelihood. |
| @end table |
| @c </FIXME> |
| See the @url{https://CRAN.R-project.org/web/packages/, , @CRAN{} |
| contributed packages page} for more information. |
| |
| Many of these packages are categorized into |
| @url{https://CRAN.R-project.org/web/views/, @CRAN{} Task Views}, allowing |
| to browse packages by topic and providing tools to automatically install |
| all packages for special areas of interest. |
| |
| Some @CRAN{} packages that do not build out of the box on Windows, |
| require additional software, or are shipping third party libraries for |
| Windows cannot be made available on @CRAN{} in form of a Windows binary |
| packages. Nevertheless, some of these packages are available at the |
| ``@CRAN{} extras'' repository at |
| @url{https://www.stats.ox.ac.uk/pub/RWin/} kindly provided by Brian |
| D. Ripley. Note that this repository is a default repository for recent |
| versions of R for Windows. |
| |
| @node Add-on packages from Omegahat, Add-on packages from Bioconductor, Add-on packages from CRAN, Which add-on packages exist for R? |
| @subsection Add-on packages from Omegahat |
| |
| The @url{http://www.omegahat.net/, Omega Project for Statistical |
| Computing} provides a variety of open-source software for statistical |
| applications, with special emphasis on web-based software, Java, the |
| Java virtual machine, and distributed computing. A @acronym{CRAN} style |
| R package repository is available via @url{http://www.omegahat.net/R/}. |
| See @url{http://www.omegahat.net/} for information on most R packages |
| available from the Omega project. |
| |
| @node Add-on packages from Bioconductor, Other add-on packages, Add-on packages from Omegahat, Which add-on packages exist for R? |
| @subsection Add-on packages from Bioconductor |
| |
| @url{https://www.bioconductor.org/, Bioconductor} 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 |
| @url{https://bioconductor.org/packages/release/BiocViews.html#___Software, |
| Bioconductor software packages} |
| 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 |
| @url{https://www.bioconductor.org/download/} for available packages and a |
| complete taxonomy via BioC Views. |
| |
| @node Other add-on packages, , Add-on packages from Bioconductor, Which add-on packages exist for R? |
| @subsection 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 @url{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. |
| |
| @node How can add-on packages be installed?, How can add-on packages be used?, Which add-on packages exist for R?, R Add-On Packages |
| @section How can add-on packages be installed? |
| |
| (Unix-like only.) The add-on packages on @CRAN{} come as gzipped tar |
| files named @code{@var{pkg}_@var{version}.tar.gz}, which may in fact be |
| ``bundles'' containing more than one package. Let @var{path} be the |
| path to such a package file. Provided that @command{tar} and |
| @command{gzip} are available on your system, type |
| |
| @example |
| $ R CMD INSTALL @var{path}/@var{pkg}_@var{version}.tar.gz |
| @end example |
| |
| @noindent |
| 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 |
| @code{.libPaths()} for details on how the search path is determined). |
| |
| To install to another tree (e.g., your private one), use |
| |
| @example |
| $ R CMD INSTALL -l @var{lib} @var{path}/@var{pkg}_@var{version}.tar.gz |
| @end example |
| |
| @noindent |
| where @var{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 @code{available.packages()} for more |
| information. |
| |
| @c <COMMENT> |
| @c This is really no longer quite accurate (R_LIBS_USER is preferred to |
| @c R_LIBS), and described in ?libPaths anyways ... hence comment out. |
| @c You can use several library trees of add-on packages. The easiest way |
| @c to tell R to use these is via the environment variable @env{R_LIBS} |
| @c which should be a colon-separated list of directories at which R library |
| @c trees are rooted. You do not have to specify the default tree in |
| @c @env{R_LIBS}. E.g., to use a private tree in @file{$HOME/lib/R} and a |
| @c public site-wide tree in @file{/usr/local/lib/R-contrib}, put |
| |
| @c @example |
| @c R_LIBS="$HOME/lib/R:/usr/local/lib/R-contrib"; export R_LIBS |
| @c @end example |
| |
| @c @noindent |
| @c into your (Bourne) shell profile or even preferably, add the line |
| |
| @c @example |
| @c R_LIBS="~/lib/R:/usr/local/lib/R-contrib" |
| @c @end example |
| |
| @c @noindent |
| @c your environment (e.g., @file{~/.Renviron}) file. (Note that no |
| @c @code{export} statement is needed or allowed in this file; see the |
| @c on-line help for @code{Startup} for more information.) |
| |
| @node How can add-on packages be used?, How can add-on packages be removed?, How can add-on packages be installed?, R Add-On Packages |
| @section How can add-on packages be used? |
| |
| To find out which additional packages are available on your system, type |
| |
| @example |
| library() |
| @end example |
| |
| @noindent |
| at the R prompt. |
| |
| This produces something like |
| |
| @quotation |
| @cartouche |
| @smallexample |
| 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 |
| @end smallexample |
| @end cartouche |
| @end quotation |
| |
| You can ``load'' the installed package @var{pkg} by |
| |
| @example |
| library(@var{pkg}) |
| @end example |
| |
| You can then find out which functions it provides by typing one of |
| |
| @example |
| library(help = @var{pkg}) |
| help(package = @var{pkg}) |
| @end example |
| |
| You can unload the loaded package @var{pkg} by |
| |
| @example |
| detach("package:@var{pkg}", unload = TRUE) |
| @end example |
| |
| @noindent |
| (where @code{unload = TRUE} is needed only for packages with a |
| namespace, see @code{?unload}). |
| |
| @node How can add-on packages be removed?, How can I create an R package?, How can add-on packages be used?, R Add-On Packages |
| @section How can add-on packages be removed? |
| |
| Use |
| |
| @example |
| $ R CMD REMOVE @var{pkg_1} @dots{} @var{pkg_n} |
| @end example |
| |
| @noindent |
| to remove the packages @var{pkg_1}, @dots{}, @var{pkg_n} from the |
| library tree rooted at the first directory given in @env{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 @var{lib}, do |
| |
| @example |
| $ R CMD REMOVE -l @var{lib} @var{pkg_1} @dots{} @var{pkg_n} |
| @end example |
| |
| @node How can I create an R package?, How can I contribute to R?, How can add-on packages be removed?, R Add-On Packages |
| @section How can I create an R package? |
| |
| A package consists of a subdirectory containing a file |
| @file{DESCRIPTION} and the subdirectories @file{R}, @file{data}, |
| @file{demo}, @file{exec}, @file{inst}, @file{man}, @file{po}, |
| @file{src}, and @file{tests} (some of which can be missing). The |
| package subdirectory may also contain files @file{INDEX}, |
| @file{NAMESPACE}, @file{configure}, @file{cleanup}, @file{LICENSE}, |
| @file{LICENCE}, @file{COPYING} and @file{NEWS}. |
| |
| @ifclear UseExternalXrefs |
| See section ``Creating R packages'' in @cite{Writing R Extensions}, for |
| details. This manual is included in the R distribution, @pxref{What |
| documentation exists for R?}, and gives information on package |
| structure, the configure and cleanup mechanisms, and on automated |
| package checking and building. |
| @end ifclear |
| @ifset UseExternalXrefs |
| @xref{Creating R packages, , Creating R packages, R-exts, Writing R |
| Extensions}, for details. |
| @end ifset |
| |
| R version 1.3.0 has added the function @code{package.skeleton()} which |
| will set up directories, save data and code, and create skeleton help |
| files for a set of R functions and datasets. |
| |
| @xref{What is CRAN?}, for information on uploading a package to @CRAN{}. |
| |
| @node How can I contribute to R?, , How can I create an R package?, R Add-On Packages |
| @section 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 @url{https://developer.R-project.org/, R Developer Page} 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. |
| |
| @node R and Emacs, R Miscellanea, R Add-On Packages, Top |
| @chapter R and Emacs |
| |
| @menu |
| * Is there Emacs support for R?:: |
| * Should I run R from within Emacs?:: |
| * Debugging R from within Emacs:: |
| @end menu |
| |
| @node Is there Emacs support for R?, Should I run R from within Emacs?, R and Emacs, R and Emacs |
| @section Is there Emacs support for R? |
| |
| There is an Emacs package called @acronym{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 @SPLUS{} |
| 3.x/4.x/5.x/6.x/7.x), LispStat dialects (XLispStat, ViSta), SAS, Stata, |
| and BUGS. |
| |
| @acronym{ESS} grew out of the need for bug fixes and extensions to |
| S-mode 4.8 (which was a @acronym{GNU} Emacs interface to S/@SPLUS{} |
| 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 @acronym{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 @acronym{ESS} is available via @CRAN{} or |
| the @url{https://ESS.R-project.org/, ESS web page}. |
| |
| @acronym{ESS} comes with detailed installation instructions. |
| |
| For help with @acronym{ESS}, send email to |
| @email{ESS-help@@r-project.org}. |
| |
| Please send bug reports and suggestions on @acronym{ESS} to |
| @email{ESS-bugs@@r-project.org}. The easiest way to do this from is |
| within Emacs by typing @kbd{M-x ess-submit-bug-report} or using the |
| [ESS] or [iESS] pulldown menus. |
| |
| @node Should I run R from within Emacs?, Debugging R from within Emacs, Is there Emacs support for R?, R and Emacs |
| @section Should I run R from within Emacs? |
| |
| Yes, @emph{definitely}. Inferior R mode provides a readline/history |
| mechanism, object name completion, and syntax-based highlighting of the |
| interaction buffer using Font Lock mode, as well as a very convenient |
| interface to the R help system. |
| |
| Of course, it also integrates nicely with the mechanisms for editing R |
| source using Emacs. One can write code in one Emacs buffer and send |
| whole or parts of it for execution to R; this is helpful for both data |
| analysis and programming. One can also seamlessly integrate with a |
| revision control system, in order to maintain a log of changes in your |
| programs and data, as well as to allow for the retrieval of past |
| versions of the code. |
| |
| In addition, it allows you to keep a record of your session, which can |
| also be used for error recovery through the use of the transcript mode. |
| |
| To specify command line arguments for the inferior R process, use |
| @kbd{C-u M-x R} for starting R. |
| |
| @c This prompts you for the arguments; in particular, you can increase |
| @c the memory size this way (@pxref{Why does R run out of memory?}). |
| |
| @node Debugging R from within Emacs, , Should I run R from within Emacs?, R and Emacs |
| @section 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 @kbd{M-x gdb} and give the path to the R |
| @emph{binary} as argument. At the @command{gdb} prompt, set |
| @env{R_HOME} and other environment variables as needed (using e.g.@: |
| @kbd{set env R_HOME /path/to/R/}, but see also below), and start the |
| binary with the desired arguments (e.g., @kbd{run --quiet}). |
| |
| If you have @acronym{ESS}, you can do @kbd{C-u M-x R @key{RET} - d |
| @key{SPC} g d b @key{RET}} to start an inferior R process with arguments |
| @option{-d gdb}. |
| |
| A third option is to start an inferior R process via @acronym{ESS} |
| (@kbd{M-x R}) and then start GUD (@kbd{M-x gdb}) giving the R binary |
| (using its full path name) as the program to debug. Use the program |
| @command{ps} to find the process number of the currently running R |
| process then use the @code{attach} command in gdb to attach it to that |
| process. One advantage of this method is that you have separate |
| @code{*R*} and @code{*gud-gdb*} windows. Within the @code{*R*} window |
| you have all the @acronym{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 @file{.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: |
| |
| @example |
| 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 |
| @end example |
| |
| @node R Miscellanea, R Programming, R and Emacs, Top |
| @chapter R Miscellanea |
| |
| @menu |
| * How can I set components of a list to NULL?:: |
| * How can I save my workspace?:: |
| * How can I clean up my workspace?:: |
| * How can I get eval() and D() to work?:: |
| * Why do my matrices lose dimensions?:: |
| * How does autoloading work?:: |
| * How should I set options?:: |
| * How do file names work in Windows?:: |
| * Why does plotting give a color allocation error?:: |
| * How do I convert factors to numeric?:: |
| * Are Trellis displays implemented in R?:: |
| * What are the enclosing and parent environments?:: |
| * How can I substitute into a plot label?:: |
| * What are valid names?:: |
| * Are GAMs implemented in R?:: |
| * Why is the output not printed when I source() a file?:: |
| * Why does outer() behave strangely with my function?:: |
| * Why does the output from anova() depend on the order of factors in the model?:: |
| * How do I produce PNG graphics in batch mode?:: |
| * How can I get command line editing to work?:: |
| * How can I turn a string into a variable?:: |
| * Why do lattice/trellis graphics not work?:: |
| * How can I sort the rows of a data frame?:: |
| * Why does the help.start() search engine not work?:: |
| * Why did my .Rprofile stop working when I updated R?:: |
| * Where have all the methods gone?:: |
| * How can I create rotated axis labels?:: |
| * Why is read.table() so inefficient?:: |
| * What is the difference between package and library?:: |
| * I installed a package but the functions are not there:: |
| * Why doesn't R think these numbers are equal?:: |
| * How can I capture or ignore errors in a long simulation?:: |
| * Why are powers of negative numbers wrong?:: |
| * How can I save the result of each iteration in a loop into a separate file?:: |
| * Why are p-values not displayed when using lmer()?:: |
| * Why are there unwanted borders:: |
| * Why does backslash behave strangely inside strings?:: |
| * How can I put error bars or confidence bands on my plot?:: |
| * How do I create a plot with two y-axes?:: |
| * How do I access the source code for a function?:: |
| * Why does summary() report strange results for the R^2 estimate when I fit a linear model with no intercept?:: |
| * Why is R apparently not releasing memory?:: |
| * How can I enable secure https downloads in R?:: |
| * How can I get CRAN package binaries for outdated versions of R?:: |
| @end menu |
| |
| @c @node Why does R run out of memory?, Why does sourcing a correct file fail?, R Miscellanea, R Miscellanea |
| @c @section Why does R run out of memory? |
| |
| @c Versions of R prior to 1.2.0 used a @emph{static} memory model. At |
| @c startup, R asked the operating system to reserve a fixed amount of |
| @c memory for it. The size of this chunk could not be changed |
| @c subsequently. Hence, it could happen that not enough memory was |
| @c allocated, e.g., when trying to read large data sets into R. In such |
| @c cases, it was necessary to restart R with more memory available, as |
| @c controlled by the command line options @option{--nsize} and |
| @c @option{--vsize}. |
| |
| @c R version 1.2.0 introduces a new ``generational'' garbage collector, |
| @c which will increase the memory available to R as needed. Hence, user |
| @c intervention is no longer necessary for ensuring that enough memory is |
| @c available. |
| |
| @c The new garbage collector does not move objects in memory, meaning that |
| @c it is possible for the free memory to become fragmented so that large |
| @c objects cannot be allocated even when there is apparently enough memory |
| @c for them. |
| |
| @c @node Why does sourcing a correct file fail?, How can I set components of a list to NULL?, Why does R run out of memory?, R Miscellanea |
| @c @section Why does sourcing a correct file fail? |
| |
| @c Versions of R prior to 1.2.1 may have had problems parsing files not |
| @c ending in a newline. Earlier R versions had a similar problem when |
| @c reading in data files. This should no longer happen. |
| |
| @node How can I set components of a list to NULL?, How can I save my workspace?, R Miscellanea, R Miscellanea |
| @section How can I set components of a list to NULL? |
| |
| You can use |
| |
| @example |
| x[i] <- list(NULL) |
| @end example |
| |
| @noindent |
| to set component @code{i} of the list @code{x} to @code{NULL}, similarly |
| for named components. Do not set @code{x[i]} or @code{x[[i]]} to |
| @code{NULL}, because this will remove the corresponding component from |
| the list. |
| |
| For dropping the row names of a matrix @code{x}, it may be easier to use |
| @code{rownames(x) <- NULL}, similarly for column names. |
| |
| @node How can I save my workspace?, How can I clean up my workspace?, How can I set components of a list to NULL?, R Miscellanea |
| @section How can I save my workspace? |
| |
| @code{save.image()} saves the objects in the user's @code{.GlobalEnv} to |
| the file @file{.RData} in the R startup directory. (This is also what |
| happens after @kbd{q("yes")}.) Using @code{save.image(@var{file})} one |
| can save the image under a different name. |
| |
| @node How can I clean up my workspace?, How can I get eval() and D() to work?, How can I save my workspace?, R Miscellanea |
| @section How can I clean up my workspace? |
| |
| To remove all objects in the currently active environment (typically |
| @code{.GlobalEnv}), you can do |
| |
| @example |
| rm(list = ls(all = TRUE)) |
| @end example |
| |
| @noindent |
| (Without @option{all = TRUE}, only the objects with names not starting |
| with a @samp{.} are removed.) |
| |
| @node How can I get eval() and D() to work?, Why do my matrices lose dimensions?, How can I clean up my workspace?, R Miscellanea |
| @section How can I get eval() and D() to work? |
| |
| Strange things will happen if you use @code{eval(print(x), envir = e)} |
| or @code{D(x^2, "x")}. The first one will either tell you that |
| "@code{x}" is not found, or print the value of the wrong @code{x}. |
| The other one will likely return zero if @code{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 @code{"expression"} or @code{"call"}) is then evaluated or |
| differentiated. What you (most likely) really want is obtained by |
| ``quoting'' the first argument upon surrounding it with |
| @code{expression()}. For example, |
| |
| @example |
| R> D(expression(x^2), "x") |
| 2 * x |
| @end example |
| |
| 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 |
| |
| @example |
| D2 <- function(e, n) D(D(e, n), n) |
| @end example |
| |
| @noindent |
| or |
| |
| @example |
| g <- function(y) eval(substitute(y), sys.frame(sys.parent(n = 2))) |
| g(a * b) |
| @end example |
| |
| See the help page for @code{deriv()} for more examples. |
| |
| @node Why do my matrices lose dimensions?, How does autoloading work?, How can I get eval() and D() to work?, R Miscellanea |
| @section Why do my matrices lose dimensions? |
| |
| When a matrix with a single row or column is created by a subscripting |
| operation, e.g., @code{row <- mat[2, ]}, it is by default turned into a |
| vector. In a similar way if an array with dimension, say, @w{2 x 3 x 1 |
| x 4} is created by subscripting it will be coerced into a @w{2 x 3 x 4} |
| array, losing the unnecessary dimension. After much discussion this has |
| been determined to be a @emph{feature}. |
| |
| To prevent this happening, add the option @option{drop = FALSE} to the |
| subscripting. For example, |
| |
| @example |
| rowmatrix <- mat[2, , drop = FALSE] # @r{creates a row matrix} |
| colmatrix <- mat[, 2, drop = FALSE] # @r{creates a column matrix} |
| a <- b[1, 1, 1, drop = FALSE] # @r{creates a 1 x 1 x 1 array} |
| @end example |
| |
| The @option{drop = FALSE} option should be used defensively when |
| programming. For example, the statement |
| |
| @example |
| somerows <- mat[index, ] |
| @end example |
| |
| @noindent |
| will return a vector rather than a matrix if @code{index} happens to |
| have length 1, causing errors later in the code. It should probably be |
| rewritten as |
| |
| @example |
| somerows <- mat[index, , drop = FALSE] |
| @end example |
| |
| @node How does autoloading work?, How should I set options?, Why do my matrices lose dimensions?, R Miscellanea |
| @section How does autoloading work? |
| |
| R has a special environment called @code{.AutoloadEnv}. Using |
| @kbd{autoload(@var{name}, @var{pkg})}, where @var{name} and |
| @var{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 @var{name}, it loads the corresponding package |
| @var{pkg} and reevaluates @var{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 @code{autoload()} for a very nice example. |
| |
| @node How should I set options?, How do file names work in Windows?, How does autoloading work?, R Miscellanea |
| @section How should I set options? |
| |
| The function @code{options()} allows setting and examining a variety of |
| global ``options'' which affect the way in which R computes and displays |
| its results. The variable @code{.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 |
| |
| @example |
| 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") |
| @} |
| @end example |
| |
| @noindent |
| we obtain: |
| |
| @example |
| R> test1() |
| 3 3.14 |
| R> test2() |
| 3 3.141593 |
| @end example |
| |
| What is really used is the @emph{global} value of @code{.Options}, and |
| using @kbd{options(OPT = VAL)} correctly updates it. Local copies of |
| @code{.Options}, either in @code{.GlobalEnv} or in a function |
| environment (frame), are just silently disregarded. |
| |
| @node How do file names work in Windows?, Why does plotting give a color allocation error?, How should I set options?, R Miscellanea |
| @section How do file names work in Windows? |
| |
| As R uses C-style string handling, @samp{\} is treated as an escape |
| character, so that for example one can enter a newline as @samp{\n}. |
| When you really need a @samp{\}, you have to escape it with another |
| @samp{\}. |
| |
| Thus, in filenames use something like @code{"c:\\data\\money.dat"}. You |
| can also replace @samp{\} by @samp{/} (@code{"c:/data/money.dat"}). |
| |
| @node Why does plotting give a color allocation error?, How do I convert factors to numeric?, How do file names work in Windows?, R Miscellanea |
| @section Why does plotting give a color allocation error? |
| |
| On an X11 device, plotting sometimes, e.g., when running |
| @code{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 @option{-no-install} (to use the default colormap) or the |
| @option{-install} (to install a private colormap) option. |
| |
| You could also set the @code{colortype} of @code{X11()} to |
| @code{"pseudo.cube"} rather than the default @code{"pseudo"}. See the |
| help page for @code{X11()} for more information. |
| |
| @c @node Is R Y2K-compliant?, How do I convert factors to numeric?, Why does plotting give a color allocation error?, R Miscellanea |
| @c @section Is R Y2K-compliant? |
| |
| @c We expect R to be Y2K compliant when compiled and run on a Y2K compliant |
| @c system. In particular R does not internally represent or manipulate |
| @c dates as two-digit quantities. However, no guarantee of Y2K compliance |
| @c is provided for R. R is free software and comes with @emph{no warranty |
| @c whatsoever}. |
| |
| @c R, like any other programming language, can be used to write programs |
| @c and manipulate data in ways that are not Y2K compliant. |
| |
| @node How do I convert factors to numeric?, Are Trellis displays implemented in R?, Why does plotting give a color allocation error?, R Miscellanea |
| @section 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 @code{f} is such a |
| factor object, you can use |
| |
| @example |
| as.numeric(as.character(f)) |
| @end example |
| |
| @noindent |
| to get the numbers back. More efficient, but harder to remember, is |
| |
| @example |
| as.numeric(levels(f))[as.integer(f)] |
| @end example |
| |
| In any case, do not call @code{as.numeric()} or their likes directly for |
| the task at hand (as @code{as.numeric()} or @code{unclass()} give the |
| internal codes). |
| |
| @node Are Trellis displays implemented in R?, What are the enclosing and parent environments?, How do I convert factors to numeric?, R Miscellanea |
| @section Are Trellis displays implemented in R? |
| |
| The recommended package @CRANpkg{lattice} (which is based on base |
| package @pkg{grid}) provides graphical functionality that is compatible |
| with most Trellis commands. |
| |
| You could also look at @code{coplot()} and @code{dotchart()} which might |
| do at least some of what you want. Note also that the R version of |
| @code{pairs()} is fairly general and provides most of the functionality |
| of @code{splom()}, and that R's default plot method has an argument |
| @code{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.) |
| |
| @node What are the enclosing and parent environments?, How can I substitute into a plot label?, Are Trellis displays implemented in R?, R Miscellanea |
| @section 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 |
| @code{f()} inside another function @code{g()} its enclosing environment |
| is the environment inside @code{g()}. The enclosing environment for a |
| function is fixed when the function is created. You can find out the |
| enclosing environment for a function @code{f()} using |
| @code{environment(f)}. |
| |
| The ``parent'' environment, on the other hand, is defined when you |
| invoke a function. If you invoke @code{lm()} at the command line its |
| parent environment is the global workspace, if you invoke it inside a |
| function @code{f()} then its parent environment is the environment |
| inside @code{f()}. You can find out the parent environment for an |
| invocation of a function by using @code{parent.frame()} or |
| @code{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 @code{f()} is defined inside another function |
| @code{g()} it will probably be used inside @code{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 @emph{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 |
| @emph{always} the global workspace, but in R the enclosing environment |
| is wherever the function was created. |
| |
| @node How can I substitute into a plot label?, What are valid names?, What are the enclosing and parent environments?, R Miscellanea |
| @section 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 @code{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 @code{parse()} on your pasted character string or use |
| @code{substitute()} on an expression. For example, if @code{ahat} is an |
| estimator of your parameter @math{a} of interest, use |
| |
| @example |
| title(substitute(hat(a) == ahat, list(ahat = ahat))) |
| @end example |
| |
| @noindent |
| (note that it is @samp{==} and not @samp{=}). Sometimes @code{bquote()} |
| gives a more compact form, e.g., |
| |
| @example |
| title(bquote(hat(a) = .(ahat))) |
| @end example |
| |
| @noindent |
| where subexpressions enclosed in @samp{.()} are replaced by their |
| values. |
| |
| There are more examples in the mailing list archives. |
| |
| @node What are valid names?, Are GAMs implemented in R?, How can I substitute into a plot label?, R Miscellanea |
| @section What are valid names? |
| |
| When creating data frames using @code{data.frame()} or |
| @code{read.table()}, R by default ensures that the variable names are |
| syntactically valid. (The argument @option{check.names} to these |
| functions controls whether variable names are checked and adjusted by |
| @code{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: |
| |
| @enumerate |
| @item |
| A @emph{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. |
| @item |
| An @emph{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 @code{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 |
| @code{assign()}). |
| |
| @item |
| An @emph{argument name} is what appears to the left of the equals sign |
| when supplying an argument in a function call (for example, |
| @code{f(trim=.5)}). Argument names are also usually syntactic names, |
| but again can be anything if they are quoted. |
| |
| @item |
| An @emph{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 @samp{$} 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 @code{eval()} or @code{attach()}, the |
| element names become object names.) |
| |
| @item |
| Finally, a @emph{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''. |
| @end enumerate |
| |
| @node Are GAMs implemented in R?, Why is the output not printed when I source() a file?, What are valid names?, R Miscellanea |
| @section Are GAMs implemented in R? |
| |
| Package @CRANpkg{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 |
| @code{gam()} function for GAMs in package @CRANpkg{mgcv}, but it is not |
| an exact clone of what is described in the White Book (no @code{lo()} |
| for example). Package @CRANpkg{gss} can fit spline-based GAMs too. And |
| if you can accept regression splines you can use @code{glm()}. For |
| Gaussian GAMs you can use @code{bruto()} from package @CRANpkg{mda}. |
| |
| @node Why is the output not printed when I source() a file?, Why does outer() behave strangely with my function?, Are GAMs implemented in R?, R Miscellanea |
| @section Why is the output not printed when I source() a file? |
| |
| Most R commands do not generate any output. The command |
| |
| @example |
| 1+1 |
| @end example |
| |
| @noindent |
| computes the value 2 and returns it; the command |
| |
| @example |
| summary(glm(y~x+z, family=binomial)) |
| @end example |
| |
| @noindent |
| fits a logistic regression model, computes some summary information and |
| returns an object of class @code{"summary.glm"} (@pxref{How should I |
| write summary methods?}). |
| |
| If you type @samp{1+1} or @samp{summary(glm(y~x+z, family=binomial))} at |
| the command line the returned value is automatically printed (unless it |
| is @code{invisible()}), but in other circumstances, such as in a |
| @code{source()}d file or inside a function it isn't printed unless you |
| specifically print it. |
| |
| To print the value use |
| |
| @example |
| print(1+1) |
| @end example |
| |
| @noindent |
| or |
| |
| @example |
| print(summary(glm(y~x+z, family=binomial))) |
| @end example |
| |
| @noindent |
| instead, or use @code{source(@var{file}, echo=TRUE)}. |
| |
| @node Why does outer() behave strangely with my function?, Why does the output from anova() depend on the order of factors in the model?, Why is the output not printed when I source() a file?, R Miscellanea |
| @section Why does outer() behave strangely with my function? |
| |
| As the help for @code{outer()} indicates, it does not work on arbitrary |
| functions the way the @code{apply()} family does. It requires functions |
| that are vectorized to work elementwise on arrays. As you can see by |
| looking at the code, @code{outer(x, y, FUN)} creates two large vectors |
| containing every possible combination of elements of @code{x} and |
| @code{y} and then passes this to @code{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 @code{outer()} but you will need to wrap |
| your function up first, to simulate vectorized behavior. Suppose your |
| function is |
| |
| @example |
| foo <- function(x, y, happy) @{ |
| stopifnot(length(x) == 1, length(y) == 1) # scalars only! |
| (x + y) * happy |
| @} |
| @end example |
| |
| @noindent |
| If you define the general function |
| |
| @example |
| wrapper <- function(x, y, my.fun, ...) @{ |
| sapply(seq_along(x), FUN = function(i) my.fun(x[i], y[i], ...)) |
| @} |
| @end example |
| |
| @noindent |
| then you can use @code{outer()} by writing, e.g., |
| |
| @example |
| outer(1:4, 1:2, FUN = wrapper, my.fun = foo, happy = 10) |
| @end example |
| |
| Scalar functions can also be vectorized using @code{Vectorize()}. |
| |
| @node Why does the output from anova() depend on the order of factors in the model?, How do I produce PNG graphics in batch mode?, Why does outer() behave strangely with my function?, R Miscellanea |
| @section Why does the output from anova() depend on the order of factors in the model? |
| |
| In a model such as @code{~A+B+A:B}, R will report the difference in sums |
| of squares between the models @code{~1}, @code{~A}, @code{~A+B} and |
| @code{~A+B+A:B}. If the model were @code{~B+A+A:B}, R would report |
| differences between @code{~1}, @code{~B}, @code{~A+B}, and |
| @code{~A+B+A:B} . In the first case the sum of squares for @code{A} is |
| comparing @code{~1} and @code{~A}, in the second case it is comparing |
| @code{~B} and @code{~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 @code{anova(@var{model1}, |
| @var{model2})}, and @code{drop1(@var{model1})} will show the sums of |
| squares resulting from dropping single terms. |
| |
| @node How do I produce PNG graphics in batch mode?, How can I get command line editing to work?, Why does the output from anova() depend on the order of factors in the model?, R Miscellanea |
| @section How do I produce PNG graphics in batch mode? |
| |
| Under a Unix-like, if your installation supports the |
| @code{type="cairo"} option to the @code{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 @code{png()} by default uses the Quartz device |
| on macOS, and that too works in batch mode. |
| |
| Earlier versions of the @code{png()} device used the X11 driver, which |
| is a problem in batch mode or for remote operation. If you have |
| Ghostscript you can use @code{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 |
| @url{http://www.x.org/, X.Org} (possibly as an optional |
| install), which is an X11 server that does not require a screen; and |
| there is the @CRANpkg{GDD} package from @CRAN{}, which produces PNG, |
| JPEG and GIF bitmaps without X11. |
| |
| @node How can I get command line editing to work?, How can I turn a string into a variable?, How do I produce PNG graphics in batch mode?, R Miscellanea |
| @section 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 @acronym{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 @code{libreadline-dev} (Debian) or |
| @code{readline-devel} (Red Hat). |
| |
| @node How can I turn a string into a variable?, Why do lattice/trellis graphics not work?, How can I get command line editing to work?, R Miscellanea |
| @section How can I turn a string into a variable? |
| |
| If you have |
| |
| @example |
| varname <- c("a", "b", "d") |
| @end example |
| |
| @noindent |
| you can do |
| |
| @example |
| get(varname[1]) + 2 |
| @end example |
| |
| @noindent |
| for |
| |
| @example |
| a + 2 |
| @end example |
| |
| @noindent |
| or |
| |
| @example |
| assign(varname[1], 2 + 2) |
| @end example |
| |
| @noindent |
| for |
| |
| @example |
| a <- 2 + 2 |
| @end example |
| |
| @noindent |
| or |
| |
| @example |
| eval(substitute(lm(y ~ x + variable), |
| list(variable = as.name(varname[1])))) |
| @end example |
| |
| @noindent |
| for |
| |
| @example |
| lm(y ~ x + a) |
| @end example |
| |
| 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 |
| |
| @example |
| vars <- list(a = 1:10, b = rnorm(100), d = LETTERS) |
| vars[["a"]] |
| @end example |
| |
| @noindent |
| without any of this messing about. |
| |
| @node Why do lattice/trellis graphics not work?, How can I sort the rows of a data frame?, How can I turn a string into a variable?, R Miscellanea |
| @section 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 @code{xyplot()} create a graph object, |
| but do not display it (the same is true of @CRANpkg{ggplot2} graphics, |
| and Trellis graphics in @SPLUS{}). The @code{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 @code{source()} or inside your own functions you will need an |
| explicit @code{print()} statement. |
| |
| @node How can I sort the rows of a data frame?, Why does the help.start() search engine not work?, Why do lattice/trellis graphics not work?, R Miscellanea |
| @section 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 @code{order()} (e.g., |
| @code{DF[order(DF$a, DF[["b"]]), ]} to sort the data frame @code{DF} on |
| columns named @code{a} and @code{b}). |
| |
| @node Why does the help.start() search engine not work?, Why did my .Rprofile stop working when I updated R?, How can I sort the rows of a data frame?, R Miscellanea |
| @section Why does the help.start() search engine not work? |
| |
| The browser-based search engine in @code{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 @emph{and} JavaScript need to be enabled in your browser. |
| |
| There have been a number of compatibility issues with versions of Java |
| and of browsers. |
| @ifclear UseExternalXrefs |
| For further details please consult section ``Enabling search in HTML |
| help'' in @cite{R Installation and Administration}. This manual is |
| included in the R distribution, @pxref{What documentation exists for |
| R?}, and its @acronym{HTML} version is linked from the @acronym{HTML} |
| search page. |
| @end ifclear |
| @ifset UseExternalXrefs |
| @xref{Enabling search in HTML help, , Enabling search in HTML help, |
| R-admin, R Installation and Administration}, for further details. |
| @end ifset |
| |
| @node Why did my .Rprofile stop working when I updated R?, Where have all the methods gone?, Why does the help.start() search engine not work?, R Miscellanea |
| @section Why did my .Rprofile stop working when I updated R? |
| |
| Did you read the @file{NEWS} file? For functions that are not in the |
| @pkg{base} package you need to specify the correct package namespace, |
| since the code will be run @emph{before} the packages are loaded. E.g., |
| |
| @example |
| ps.options(horizontal = FALSE) |
| help.start() |
| @end example |
| |
| @noindent |
| needs to be |
| |
| @example |
| grDevices::ps.options(horizontal = FALSE) |
| utils::help.start() |
| @end example |
| |
| @c <FIXME> |
| @c 3.0.0 |
| @noindent |
| (@code{graphics::ps.options(horizontal = FALSE)} in R 1.9.x). |
| @c </FIXME> |
| |
| @node Where have all the methods gone?, How can I create rotated axis labels?, Why did my .Rprofile stop working when I updated R?, R Miscellanea |
| @section 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., @code{[.terms}) use |
| |
| @example |
| getS3method("[", "terms") |
| @end example |
| |
| @noindent |
| To see the code for an unexported function @code{foo()} in the namespace |
| of package @code{"bar"} use @code{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. |
| |
| @node How can I create rotated axis labels?, Why is read.table() so inefficient?, Where have all the methods gone?, R Miscellanea |
| @section How can I create rotated axis labels? |
| |
| To rotate axis labels (using base graphics), you need to use |
| @code{text()}, rather than @code{mtext()}, as the latter does not |
| support @code{par("srt")}. |
| |
| @example |
| ## @r{Increase bottom margin to make room for rotated labels} |
| par(mar = c(7, 4, 4, 2) + 0.1) |
| ## @r{Create plot with no x axis and no x axis label} |
| plot(1 : 8, xaxt = "n", xlab = "") |
| ## @r{Set up x axis with tick marks alone} |
| axis(1, labels = FALSE) |
| ## @r{Create some text labels} |
| labels <- paste("Label", 1:8, sep = " ") |
| ## @r{Plot x axis labels at default tick marks} |
| text(1:8, par("usr")[3] - 0.25, srt = 45, adj = 1, |
| labels = labels, xpd = TRUE) |
| ## @r{Plot x axis label at line 6 (of 7)} |
| mtext(1, text = "X Axis Label", line = 6) |
| @end example |
| |
| @noindent |
| When plotting the x axis labels, we use @code{srt = 45} for text |
| rotation angle, @code{adj = 1} to place the right end of text at the |
| tick marks, and @code{xpd = TRUE} to allow for text outside the plot |
| region. You can adjust the value of the @code{0.25} offset as required |
| to move the axis labels up or down relative to the x axis. See |
| @code{?par} for more information. |
| |
| Also see Figure 1 and associated code in Paul Murrell (2003), |
| ``Integrating grid Graphics Output with Base Graphics Output'', |
| @emph{R News}, @strong{3/2}, 7--12. |
| |
| @node Why is read.table() so inefficient?, What is the difference between package and library?, How can I create rotated axis labels?, R Miscellanea |
| @section Why is read.table() so inefficient? |
| |
| By default, @code{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 |
| @code{colClasses} argument to specify the classes to be assumed for the |
| columns of the table. |
| |
| @node What is the difference between package and library?, I installed a package but the functions are not there, Why is read.table() so inefficient?, R Miscellanea |
| @section What is the difference between package and library? |
| |
| A @dfn{package} is a standardized collection of material extending R, |
| e.g.@: providing code, data, or documentation. A @dfn{library} is a |
| place (directory) where R knows to find packages it can use (i.e., which |
| were @dfn{installed}). R is told to use a package (to ``load'' it and |
| add it to the search path) via calls to the function @code{library}. |
| I.e., @code{library()} is employed to load a package from libraries |
| containing packages. |
| |
| @xref{R Add-On Packages}, for more details. See also Uwe Ligges (2003), |
| ``R Help Desk: Package Management'', @emph{R News}, @strong{3/3}, |
| 37--39. |
| |
| @node I installed a package but the functions are not there, Why doesn't R think these numbers are equal?, What is the difference between package and library?, R Miscellanea |
| @section I installed a package but the functions are not there |
| |
| To actually @emph{use} the package, it needs to be @emph{loaded} using |
| @code{library()}. |
| |
| See @ref{R Add-On Packages} and @ref{What is the difference between |
| package and library?} for more information. |
| |
| @node Why doesn't R think these numbers are equal?, How can I capture or ignore errors in a long simulation?, I installed a package but the functions are not there, R Miscellanea |
| @section 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 |
| |
| @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 |
| @end example |
| |
| The function @code{all.equal()} compares two objects using a numeric |
| tolerance of @code{.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 @emph{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 |
| @url{http://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'', |
| @emph{ACM Computing Surveys}, @strong{23/1}, 5--48, also available via |
| @url{http://www.validlab.com/goldberg/paper.pdf}. |
| |
| Here is another example, this time using addition: |
| |
| @example |
| 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 |
| @end example |
| |
| |
| @node How can I capture or ignore errors in a long simulation?, Why are powers of negative numbers wrong?, Why doesn't R think these numbers are equal?, R Miscellanea |
| @section How can I capture or ignore errors in a long simulation? |
| |
| Use @code{try()}, which returns an object of class @code{"try-error"} |
| instead of an error, or preferably @code{tryCatch()}, where the return |
| value can be configured more flexibly. For example |
| |
| @example |
| beta[i,] <- tryCatch(coef(lm(formula, data)), |
| error = function(e) rep(NaN, 4)) |
| @end example |
| |
| @noindent |
| would return the coefficients if the @code{lm()} call succeeded and |
| would return @code{c(NaN, NaN, NaN, NaN)} if it failed (presumably there |
| are supposed to be 4 coefficients in this example). |
| |
| @node Why are powers of negative numbers wrong?, How can I save the result of each iteration in a loop into a separate file?, How can I capture or ignore errors in a long simulation?, R Miscellanea |
| @section Why are powers of negative numbers wrong? |
| |
| You are probably seeing something like |
| |
| @example |
| R> -2^2 |
| [1] -4 |
| @end example |
| |
| @noindent |
| and misunderstanding the precedence rules for expressions in R. |
| Write |
| |
| @example |
| R> (-2)^2 |
| [1] 4 |
| @end example |
| |
| @noindent |
| to get the square of @math{-2}. |
| |
| The precedence rules are documented in @code{?Syntax}, and to see how R |
| interprets an expression you can look at the parse tree |
| |
| @example |
| R> as.list(quote(-2^2)) |
| [[1]] |
| `-` |
| |
| [[2]] |
| 2^2 |
| @end example |
| |
| @node How can I save the result of each iteration in a loop into a separate file?, Why are p-values not displayed when using lmer()?, Why are powers of negative numbers wrong?, R Miscellanea |
| @section How can I save the result of each iteration in a loop into a separate file? |
| |
| One way is to use @code{paste()} (or @code{sprintf()}) to concatenate a |
| stem filename and the iteration number while @code{file.path()} |
| constructs the path. For example, to save results into files |
| @file{result1.rda}, @dots{}, @file{result100.rda} in the subdirectory |
| @file{Results} of the current working directory, one can use |
| |
| @example |
| for(i in 1:100) @{ |
| ## Calculations constructing "some_object" ... |
| fp <- file.path("Results", paste("result", i, ".rda", sep = "")) |
| save(list = "some_object", file = fp) |
| @} |
| @end example |
| |
| @node Why are p-values not displayed when using lmer()?, Why are there unwanted borders, How can I save the result of each iteration in a loop into a separate file?, R Miscellanea |
| @section Why are @math{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 |
| @uref{https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html}. |
| |
| @node Why are there unwanted borders, Why does backslash behave strangely inside strings?, Why are p-values not displayed when using lmer()?, R Miscellanea |
| @section 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 @code{polygon()}, |
| @code{filled.contour()}, @code{image()} or other functions which may |
| call these internally. In the case of @code{polygon()}, you may observe |
| unwanted borders between the polygons even when setting the |
| @code{border} argument to @code{NA} or @code{"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: |
| |
| @ftable @asis |
| @item Acrobat Reader (cross platform) |
| There are options in Preferences to enable/disable text smoothing, image |
| smoothing and line art smoothing. |
| Disable line art smoothing. |
| @item Preview (macOS) |
| There is an option in Preferences to enable/disable anti-aliasing of |
| text and line art. |
| Disable this option. |
| @item 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. |
| @item gv (Unix-like X) |
| There is an option to enable/disable anti-aliasing. |
| Disable this option. |
| @item Evince (Linux/GNOME) |
| There is not an option to disable anti-aliasing in this viewer. |
| @item Okular (Linux/KDE) |
| There is not an option in the GUI to enable/disable anti-aliasing. |
| From a console command line, use: |
| @smallexample |
| $ kwriteconfig --file okularpartrc --group 'Dlg Performance' \ |
| --key GraphicsAntialias Disabled |
| @end smallexample |
| Then restart Okular. Change the final word to @samp{Enabled} to restore |
| the original setting. |
| @end ftable |
| |
| @node Why does backslash behave strangely inside strings?, How can I put error bars or confidence bands on my plot?, Why are there unwanted borders, R Miscellanea |
| @section Why does backslash behave strangely inside strings? |
| |
| This question most often comes up in relation to file names (see |
| @ref{How do file names work in Windows?}) but it also happens that |
| people complain that they cannot seem to put a single @samp{\} 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 @emph{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 |
| |
| @example |
| > str <- "This \"text\" is quoted" |
| > str |
| [1] "This \"text\" is quoted" |
| > cat(str, "\n") |
| This "text" is quoted |
| @end example |
| |
| @noindent |
| The @emph{escape sequences} @samp{\"} and @samp{\n} represent a double |
| quote and the newline character respectively. Printing text strings, |
| using @code{print()} or by typing the name at the prompt will use the |
| escape sequences too, but the @code{cat()} function will display the |
| string as-is. Notice that @samp{"\n"} is a one-character string, not |
| two; the backslash is not actually in the string, it is just generated |
| in the printed representation. |
| |
| @example |
| > nchar("\n") |
| [1] 1 |
| > substring("\n", 1, 1) |
| [1] "\n" |
| @end example |
| |
| 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 |
| |
| @example |
| > cat("\\n", "\n") |
| \n |
| @end example |
| |
| 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 @emph{quadruple} |
| backslash to represent a single literal one. |
| |
| In versions of R up to 2.4.1 an unknown escape sequence like @samp{\p} |
| was quietly interpreted as just @samp{p}. Current versions of R emit a |
| warning. |
| |
| @node How can I put error bars or confidence bands on my plot?, How do I create a plot with two y-axes?, Why does backslash behave strangely inside strings?, R Miscellanea |
| @section 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 @code{bar.err()} function in the @CRANpkg{agricolae} |
| package, the @code{plotCI()} function in the @CRANpkg{gplots} package, |
| the @code{plotCI()} and @code{brkdn.plot()} functions in the |
| @CRANpkg{plotrix} package and the @code{error.bars()}, |
| @code{error.crosses()} and @code{error.bars.by()} functions in the |
| @CRANpkg{psych} package. Within these types of functions, some will |
| accept the measures of dispersion (e.g., @code{plotCI}), some will |
| calculate the dispersion measures from the raw values (@code{bar.err}, |
| @code{brkdn.plot}), and some will do both (@code{error.bars}). Still |
| other functions will just display error bars, like the dispersion |
| function in the @CRANpkg{plotrix} package. Most of the above functions |
| use the @code{arrows()} function in the base @pkg{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 @code{grid.arrow()} function in the @pkg{grid} |
| package, and the @code{geom_errorbar()}, @code{geom_errorbarh()}, |
| @code{geom_pointrange()}, @code{geom_linerange()}, |
| @code{geom_crossbar()} and @code{geom_ribbon()} functions in the |
| @CRANpkg{ggplot2} package. In the lattice system, error bars can be |
| displayed with @code{Dotplot()} or @code{xYplot()} in the |
| @CRANpkg{Hmisc} package and @code{segplot()} in the |
| @CRANpkg{latticeExtra} package. |
| |
| @node How do I create a plot with two y-axes?, How do I access the source code for a function?, How can I put error bars or confidence bands on my plot?, R Miscellanea |
| @section 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 @code{par(new=TRUE)} (see |
| @code{?par}); functions @code{twoord.plot()} (in the @CRANpkg{plotrix} |
| package) and @code{doubleYScale()} (in the @CRANpkg{latticeExtra} |
| package) automate the process somewhat. |
| @c See |
| @c @url{http://rwiki.sciviews.org/@/doku.php?id=tips:graphics-base:2yaxes} |
| @c for more information, including strong arguments against this sort of |
| @c graph. |
| |
| @node How do I access the source code for a function?, Why does summary() report strange results for the R^2 estimate when I fit a linear model with no intercept?, How do I create a plot with two y-axes?, R Miscellanea |
| @section 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'', @emph{R News}, @strong{6/4}, |
| 43--45 (@url{https://CRAN.R-project.org/doc/Rnews/Rnews_2006-4.pdf}). |
| |
| @node Why does summary() report strange results for the R^2 estimate when I fit a linear model with no intercept?, Why is R apparently not releasing memory?, How do I access the source code for a function?, R Miscellanea |
| @section Why does summary() report strange results for the R^2 estimate when I fit a linear model with no intercept? |
| |
| As described in @code{?summary.lm}, when the intercept is zero (e.g., |
| from @code{y ~ x - 1} or @code{y ~ x + 0}), @code{summary.lm()} uses the |
| formula |
| @ifnottex |
| R^2 = 1 - Sum(R[i]^2) / Sum((y[i])^2) |
| @end ifnottex |
| @tex |
| $ R^2 = 1 - \sum_i R_i^2 / \sum_i y_i^2 $ |
| @end tex |
| which is different from the usual |
| @ifnottex |
| R^2 = 1 - Sum(R[i]^2) / Sum((y[i] - mean(y))^2). |
| @end ifnottex |
| @tex |
| $ R^2 = 1 - \sum R_i^2 / \sum_i (y_i - \hbox{mean}(y))^2. $ |
| @end tex |
| There are several reasons for this: |
| @itemize |
| @item |
| Otherwise the @math{R^2} could be negative (because the model with zero |
| intercept can fit @emph{worse} than the constant-mean model it is |
| implicitly compared to). |
| @item |
| If you set the slope to zero in the model with a line through the |
| origin you get fitted values y*=0 |
| @item |
| The model with constant, non-zero mean is not nested in the model |
| with a line through the origin. |
| @end itemize |
| |
| All these come down to saying that if you know @emph{a priori} that |
| @math{E[Y]=0} when @math{x=0} then the `null' model that you should |
| compare to the fitted line, the model where @math{x} doesn't explain any |
| of the variance, is the model where @math{E[Y]=0} everywhere. (If you |
| don't know a priori that @math{E[Y]=0} when @math{x=0}, then you |
| probably shouldn't be fitting a line through the origin.) |
| |
| @node Why is R apparently not releasing memory?, How can I enable secure https downloads in R?, Why does summary() report strange results for the R^2 estimate when I fit a linear model with no intercept?, R Miscellanea |
| @section 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 @code{gc()} and yet |
| @code{ps}/@code{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 @code{ps} or @code{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 |
| @emph{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 @code{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 @code{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 @code{mmap} and thus not affected. On Linux (and possibly other |
| Unix-like systems) it is possible to use the @code{mallinfo} system call |
| (also see the @url{https://rforge.net/mallinfo, 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. |
| |
| @node How can I enable secure https downloads in R?, How can I get CRAN package binaries for outdated versions of R?, Why is R apparently not releasing memory?, R Miscellanea |
| @section How can I enable secure https downloads in R? |
| |
| @c This should be re-phrased for 3.3.0. |
| |
| When R transfers files over @acronym{HTTP} (e.g., using the |
| @code{install.packages()} or @code{download.file()} function), a |
| download method is chosen based on the @option{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 @acronym{HTTP} |
| implementation. In most circumstances this internal method will not |
| support @acronym{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 |
| (@code{"libcurl"} and @code{"wininet"}) that both support |
| @acronym{HTTPS} connections: we recommend that you use these methods. |
| The requisite code to add to @file{.Rprofile} or @file{Rprofile.site} is: |
| |
| @example |
| options(download.file.method = "wininet", url.method = "wininet") |
| @r{(Windows)} |
| options(download.file.method = "libcurl", url.method = "libcurl") |
| @r{(Linux and macOS)} |
| @end example |
| |
| @noindent |
| (Method @code{"wininet"} is the default on Windows as from R 3.2.2.) |
| |
| Note that the @code{"libcurl"} method may or may not have been compiled |
| in. In the case that it was not, i.e.@: @code{capabilities("libcurl") == |
| FALSE}, we recommend method @code{"wget"} on Linux and @code{"curl"} on |
| macOS. It is possible that system versions of @code{"libcurl"}, |
| @command{wget} or @command{curl} may have been compiled without |
| @acronym{HTTPS} support, but this is unlikely. As from R 3.3.0 |
| @code{"libcurl"} with @acronym{HTTPS} support is required except on |
| Windows. |
| |
| @node How can I get CRAN package binaries for outdated versions of R?, , How can I enable secure https downloads in R?, R Miscellanea |
| @section 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 @code{repos} |
| option accordingly, by something like |
| @example |
| local(@{r <- getOption("repos") |
| r["CRAN"] <- "http://CRAN-archive.R-project.org" |
| options(repos = r) |
| @}) |
| @end example |
| @noindent |
| (see @code{?options} for more information). |
| |
| @node R Programming, R Bugs, R Miscellanea, Top |
| @chapter R Programming |
| |
| @menu |
| * How should I write summary methods?:: |
| * How can I debug dynamically loaded code?:: |
| * How can I inspect R objects when debugging?:: |
| * How can I change compilation flags?:: |
| * How can I debug S4 methods?:: |
| @end menu |
| |
| @node How should I write summary methods?, How can I debug dynamically loaded code?, R Programming, R Programming |
| @section How should I write summary methods? |
| |
| Suppose you want to provide a summary method for class @code{"foo"}. |
| Then @code{summary.foo()} should not print anything, but return an |
| object of class @code{"summary.foo"}, @emph{and} you should write a |
| method @code{print.summary.foo()} which nicely prints the summary |
| information and invisibly returns its object. This approach is |
| preferred over having @code{summary.foo()} print summary information and |
| return something useful, as sometimes you need to grab something |
| computed by @code{summary()} inside a function or similar. In such |
| cases you don't want anything printed. |
| |
| @node How can I debug dynamically loaded code?, How can I inspect R objects when debugging?, How should I write summary methods?, R Programming |
| @section 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. |
| |
| @ifclear UseExternalXrefs |
| See section ``Finding entry points in dynamically loaded code'' in |
| @cite{Writing R Extensions}. This manual is included in the R |
| distribution, @pxref{What documentation exists for R?}. |
| @end ifclear |
| @ifset UseExternalXrefs |
| @xref{Finding entry points, , Finding entry points in dynamically loaded |
| code, R-exts, Writing R Extensions}. |
| @end ifset |
| |
| @node How can I inspect R objects when debugging?, How can I change compilation flags?, How can I debug dynamically loaded code?, R Programming |
| @section How can I inspect R objects when debugging? |
| |
| The most convenient way is to call @code{R_PV} from the symbolic |
| debugger. |
| |
| @ifclear UseExternalXrefs |
| See section ``Inspecting R objects when debugging'' in @cite{Writing R |
| Extensions}. |
| @end ifclear |
| @ifset UseExternalXrefs |
| @xref{Inspecting R objects, , Inspecting R objects when debugging, |
| R-exts, Writing R Extensions}. |
| @end ifset |
| |
| @node How can I change compilation flags?, How can I debug S4 methods?, How can I inspect R objects when debugging?, R Programming |
| @section How can I change compilation flags? |
| |
| Suppose you have C code file for dynloading into R, but you want to use |
| @code{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 @file{$@env{HOME}/.R} to override the default flags. |
| @ifclear UseExternalXrefs |
| See section ``Add-on packages'' in @cite{R Installation and |
| Administration}. |
| @end ifclear |
| @ifset UseExternalXrefs |
| @xref{Add-on packages, , Add-on packages, R-admin, |
| R Installation and Administration}. |
| @end ifset |
| |
| For earlier versions of R, you could change the file |
| @file{@var{R_HOME}/etc/Makeconf} to reflect your preferences, or (at |
| least for systems using @acronym{GNU} Make) override them by the |
| environment variable @env{MAKEFLAGS}. |
| @ifclear UseExternalXrefs |
| See section ``Creating shared objects'' in @cite{Writing R Extensions}. |
| @end ifclear |
| @ifset UseExternalXrefs |
| @xref{Creating shared objects, , Creating shared objects, R-exts, |
| Writing R Extensions}. |
| @end ifset |
| |
| @node How can I debug S4 methods?, , How can I change compilation flags?, R Programming |
| @section How can I debug S4 methods? |
| |
| Use the @code{trace()} function with argument @code{signature=} to add |
| calls to the browser or any other code to the method that will be |
| dispatched for the corresponding signature. See @code{?trace} for |
| details. |
| |
| @node R Bugs, Acknowledgments, R Programming, Top |
| @chapter R Bugs |
| |
| @menu |
| * What is a bug?:: |
| * How to report a bug:: |
| @end menu |
| |
| @node What is a bug?, How to report a bug, R Bugs, R Bugs |
| @section 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 |
| @code{.C()}, @code{.Fortran()}, @code{.External()} or @code{.Call()} (or |
| @code{.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 @emph{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 |
| @code{0.28 * 100 != 28} or @code{0.1 + 0.2 != 0.3}, are not a bug. |
| @xref{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. |
| |
| @node How to report a bug, , What is a bug?, R Bugs |
| @section 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 @kbd{version} in |
| R to print this. |
| |
| The most important principle in reporting a bug is to report |
| @emph{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 |
| |
| @example |
| R> data.frame(x, y, z, monday, tuesday) |
| @end example |
| |
| @noindent |
| never returns. Do not report that @code{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 |
| @code{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 @code{"["()} that had a bug causing R's internal data |
| structures to be corrupted and making the @code{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., @url{https://en.wikipedia.org/wiki/Reproducibility}) the problem, |
| preferably the simplest one you have found. |
| |
| Invoking R with the @option{--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 |
| @url{https://bugs.R-project.org/}. Second, consult |
| @url{https://svn.R-project.org/@/R/@/trunk/@/doc/@/NEWS.Rd}, which |
| records changes that will appear in the @emph{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 @code{bug.report()}. |
| For reports on R this will open the Web page at |
| @url{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 @samp{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 @samp{Wishlist:}. |
| |
| Comments on and suggestions for the Windows port of R should be sent to |
| @email{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 @samp{.po} file) |
| or to the translation team as listed at |
| @url{https://developer.R-project.org/TranslationTeams.html}. |
| |
| @node Acknowledgments, , R Bugs, Top |
| @chapter 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 @acronym{FAQ}. |
| |
| More to come soon @dots{} |
| |
| @bye |
| |
| @c Local Variables: *** |
| @c mode: TeXinfo *** |
| @c End: *** |