This document contains answers to some of the most frequently asked questions about R.
This document is copyright © 1998–2015 by Kurt Hornik.
This document is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version.
This document is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
Copies of the GNU General Public License versions are available at
The latest version of this document is always available from
From there, you can obtain versions converted to plain ASCII text, GNU info, HTML, PDF, as well as the Texinfo source used for creating all these formats using the GNU Texinfo system.
You can also obtain the R FAQ from the doc/FAQ subdirectory of a CRAN site (see What is CRAN?).
In publications, please refer to this FAQ as Hornik (2015), “The R FAQ”, and give the above, official URL:
@Misc{,
author = {Kurt Hornik},
title = {{R} {FAQ}},
year = {2015},
url = {https://CRAN.R-project.org/doc/FAQ/R-FAQ.html}
}
Everything should be pretty standard. ‘R>’ is used for the R prompt, and a ‘$’ for the shell prompt (where applicable).
Feedback via email to Kurt.Hornik@R-project.org is of course most welcome.
In particular, note that I do not have access to Windows or Mac systems. Features specific to the Windows and OS X ports of R are described in the “R for Windows FAQ” and the “R for Mac OS X FAQ”. If you have information on Mac or Windows systems that you think should be added to this document, please let me know.
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 (see What is S?) and Sussman’s Scheme. Whereas the resulting language is very similar in appearance to S, the underlying implementation and semantics are derived from Scheme. See 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 (see R Add-On Packages).
R was initially written by Ross Ihaka and 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, Seth Falcon, Robert Gentleman, Kurt Hornik, Stefano Iacus, Ross Ihaka, Friedrich Leisch, Uwe Ligges, Thomas Lumley, Martin Maechler, Duncan Murdoch, Paul Murrell, Martyn Plummer, Brian Ripley, Deepayan Sarkar, Duncan Temple Lang, Luke Tierney, and Simon Urbanek.
R has a home page at https://www.R-project.org/. It is free software distributed under a GNU-style copyleft, and an official part of the GNU project (“GNU S”).
R is being developed for the Unix-like, Windows and Mac families of operating systems. Support for Mac OS Classic ended with R 1.7.1.
The current version of R will configure and build under a number of common Unix-like (e.g., https://en.wikipedia.org/wiki/Unix-like) platforms including cpu-linux-gnu for the i386, amd64, alpha, arm/armel, hppa, ia64, m68k, mips/mipsel, powerpc, s390 and sparc CPUs (e.g., https://buildd.debian.org/build.php?&pkg=r-base), i386-hurd-gnu, cpu-kfreebsd-gnu for i386 and amd64, powerpc-apple-darwin, mips-sgi-irix, i386-freebsd, rs6000-ibm-aix, and sparc-sun-solaris.
If you know about other platforms, please drop us a note.
The current released version is 3.2.2. Based on this ‘major.minor.patchlevel’ numbering scheme, there are two development versions of R, a patched version of the current release (‘r-patched’) and one working towards the next minor or eventually major (‘r-devel’) releases of R, respectively. Version r-patched is for bug fixes mostly. New features are typically introduced in r-devel.
Sources, binaries and documentation for R can be obtained via CRAN, the “Comprehensive R Archive Network” (see What is CRAN?).
Sources are also available via https://svn.R-project.org/R/, the R Subversion repository, but currently not via anonymous rsync (nor CVS).
Tarballs with daily snapshots of the r-devel and r-patched development versions of R can be found at https://stat.ethz.ch/R/daily.
If R is already installed, it can be started by typing R at the shell prompt (of course, provided that the executable is in your path).
If binaries are available for your platform (see 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 What machines does R run on?). The file INSTALL that comes with the R distribution contains a brief introduction, and the “R Installation and Administration” guide (see What documentation exists for R?) has full details.
Note that you need a FORTRAN compiler or perhaps f2c in
addition to a C compiler to build R.
In the simplest case, untar the R source code, change to the directory thus created, and issue the following commands (at the shell prompt):
$ ./configure $ make
If these commands execute successfully, the R binary and a shell script front-end called R are created and copied to the bin directory. You can copy the script to a place where users can invoke it, for example to /usr/local/bin. In addition, plain text help pages as well as HTML and LaTeX versions of the documentation are built.
Use make dvi to create DVI versions of the R manuals, such as
refman.dvi (an R object reference index) and R-exts.dvi, the
“R Extension Writers Guide”, in the doc/manual subdirectory. These
files can be previewed and printed using standard programs such as
xdvi and dvips. You can also use make pdf to
build PDF (Portable Document Format) version of the manuals, and view these
using e.g. Acrobat. Manuals written in the GNU Texinfo system
can also be converted to info files suitable for reading online with Emacs
or stand-alone GNU Info; use make info to create these
versions (note that this requires Makeinfo version 4.5).
Finally, use make check to find out whether your R system works correctly.
You can also perform a “system-wide” installation using make install. By default, this will install to the following directories:
the front-end shell script
the man page
all the rest (libraries, on-line help system, …). This is the “R
Home Directory” (R_HOME) of the installed system.
In the above, prefix is determined during configuration (typically
/usr/local) and can be set by running configure with the
option
$ ./configure --prefix=/where/you/want/R/to/go
(E.g., the R executable will then be installed into /where/you/want/R/to/go/bin.)
To install DVI, info and PDF versions of the manuals, use make install-dvi, make install-info and make install-pdf, respectively.
The bin/windows directory of a CRAN site contains binaries for a base distribution and add-on packages from CRAN to run on Windows XP and later (including 64-bit versions of Windows) on ix86 and x86_64 chips. The Windows version of R was created by Robert Gentleman and Guido Masarotto, and is now being developed and maintained by Duncan Murdoch and Brian D. Ripley.
The same directory has links to snapshots of the r-patched and r-devel versions of R.
See the “R for Windows FAQ” for more details.
The bin/macosx directory of a CRAN site contains a standard Apple installer package to run on OS X 10.6 (‘Snow Leopard’) and later. Once downloaded and executed, the installer will install the current release of R and an R.app OS X GUI. This port of R for OS X is maintained by Simon Urbanek (and previously by Stefano Iacus). The “R for Mac OS X FAQ has more details.
Snapshots of the r-patched and r-devel versions of R are available as Apple installer packages at http://r.research.att.com.
The bin/linux directory of a CRAN site contains the following packages.
CPU Versions Provider Debian i386/amd64 squeeze/wheezy Johannes Ranke armel wheezy Johannes Ranke Ubuntu i386/amd64 lucid/precise/trusty Michael Rutter
Debian packages, maintained by Dirk Eddelbuettel, have long been part of the
Debian distribution, and can be accessed through APT, the Debian package
maintenance tool. Use e.g. apt-get install r-base r-recommended to
install the R environment and recommended packages. If you also want to
build R packages from source, also run apt-get install r-base-dev to
obtain the additional tools required for this. So-called “backports” of
the current R packages for at least the stable distribution of Debian
are provided by Johannes Ranke, and available from CRAN. See
https://CRAN.R-project.org/bin/linux/debian/README.html for details on
R Debian packages and installing the backports, which should also be
suitable for other Debian derivatives. Native backports for Ubuntu are
provided by Michael Rutter.
R binaries for Fedora, maintained by Tom “Spot” Callaway, are provided as
part of the Fedora distribution and can be accessed through yum,
the RPM installer/updater. Note that the “Software” application
(gnome-software), which is the default GUI for software
installation in Fedora 20, cannot be used to install R. It is therefore
recommended to use the yum command line tool. The Fedora R RPM is a
“meta-package” which installs all the user and developer components of R
(available separately as R-core and R-devel), as well as
R-java, which ensures that R is configured for use with Java. The R
RPM also installs the standalone R math library (libRmath and
libRmath-devel), although this is not necessary to use R. When a new
version of R is released, there may be a delay of up to 2 weeks until the
Fedora RPM becomes publicly available, as it must pass through the statutory
Fedora review process. RPMs for a selection of R packages are also provided
by Fedora. The Extra Packages for Enterprise Linux (EPEL) project
(https://fedoraproject.org/wiki/EPEL) provides ports of the Fedora
RPMs for RedHat Enterprise Linux and compatible distributions (e.g., Centos,
Scientific Linux, Oracle Linux).
See https://CRAN.R-project.org/bin/linux/suse/README.html for information about RPMs for openSUSE.
No other binary distributions are currently publically available via CRAN.
Online documentation for most of the functions and variables in R exists, and can be printed on-screen by typing help(name) (or ?name) at the R prompt, where name is the name of the topic help is sought for. (In the case of unary and binary operators and control-flow special forms, the name may need to be be quoted.)
This documentation can also be made available as one reference manual for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX, see How can R be installed?. An up-to-date HTML version is always available for web browsing at https://stat.ethz.ch/R-manual/.
Printed copies of the R reference manual for some version(s) are available from Network Theory Ltd, at http://www.network-theory.co.uk/R/base/. For each set of manuals sold, the publisher donates USD 10 to the R Foundation (see What is the R Foundation?).
The R distribution also comes with the following manuals.
An annotated bibliography (BibTeX format) of R-related publications can be found at
Books on R by R Core Team members include
John M. Chambers (2008), “Software for Data Analysis: Programming with R”. Springer, New York, ISBN 978-0-387-75935-7, https://statweb.stanford.edu/~jmc4/Rbook/.
Peter Dalgaard (2008), “Introductory Statistics with R”, 2nd edition. Springer, ISBN 978-0-387-79053-4, http://publicifsv.sund.ku.dk/~pd/ISwR.html.
Robert Gentleman (2008), “R Programming for Bioinformatics”. Chapman & Hall/CRC, Boca Raton, FL, ISBN 978-1-420-06367-7, https://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, https://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html.
William N. Venables and Brian D. Ripley (2002), “Modern Applied Statistics with S” (4th edition). Springer, ISBN 0-387-95457-0, https://www.stats.ox.ac.uk/pub/MASS4/.
Jose C. Pinheiro and Douglas M. Bates (2000), “Mixed-Effects Models in S and S-Plus”. Springer, ISBN 0-387-98957-0.
Last, but not least, Ross’ and Robert’s experience in designing and implementing R is described in Ihaka & Gentleman (1996), “R: A Language for Data Analysis and Graphics”, Journal of Computational and Graphical Statistics, 5, 299–314.
To cite R in publications, use
@Manual{,
title = {R: A Language and Environment for Statistical
Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = 2015,
url = {https://www.R-project.org}
}
Citation strings (or BibTeX entries) for R and R packages can also be
obtained by citation().
Thanks to Martin Maechler, there are four mailing lists devoted to R.
R-announceA moderated list for major announcements about the development of R and the availability of new code.
R-packagesA moderated list for announcements on the availability of new or enhanced contributed packages.
R-helpThe ‘main’ R mailing list, for discussion about problems and solutions using R, announcements (not covered by ‘R-announce’ and ‘R-packages’) about the development of R and the availability of new code.
R-develThis list is for questions and discussion about code development in R.
Please read the posting guide before sending anything to any mailing list.
Note in particular that R-help is intended to be comprehensible to people who want to use R to solve problems but who are not necessarily interested in or knowledgeable about programming. Questions likely to prompt discussion unintelligible to non-programmers (e.g., questions involving C or C++) should go to R-devel.
Convenient access to information on these lists, subscription, and archives is provided by the web interface at https://stat.ethz.ch/mailman/listinfo/. One can also subscribe (or unsubscribe) via email, e.g. to R-help by sending ‘subscribe’ (or ‘unsubscribe’) in the body of the message (not in the subject!) to R-help-request@lists.R-project.org.
Send email to R-help@lists.R-project.org to send a message to everyone on the R-help mailing list. Subscription and posting to the other lists is done analogously, with ‘R-help’ replaced by ‘R-announce’, ‘R-packages’, and ‘R-devel’, respectively. Note that the R-announce and R-packages lists are gatewayed into R-help. Hence, you should subscribe to either of them only in case you are not subscribed to R-help.
It is recommended that you send mail to R-help rather than only to the R Core developers (who are also subscribed to the list, of course). This may save them precious time they can use for constantly improving R, and will typically also result in much quicker feedback for yourself.
Of course, in the case of bug reports it would be very helpful to have code which reliably reproduces the problem. Also, make sure that you include information on the system and version of R being used. See R Bugs for more details.
See https://www.R-project.org/mail.html for more information on the R mailing lists.
The R Core Team can be reached at R-core@lists.R-project.org for comments and reports.
Many of the R project’s mailing lists are also available via Gmane, from which they can be read with a web browser, using an NNTP news reader, or via RSS feeds. See http://dir.gmane.org/index.php?prefix=gmane.comp.lang.r. for the available mailing lists, and http://www.gmane.org/rss.php for details on RSS feeds.
The “Comprehensive R Archive Network” (CRAN) is a collection of sites which carry identical material, consisting of the R distribution(s), the contributed extensions, documentation for R, and binaries.
The CRAN master site at WU (Wirtschaftsuniversität Wien) in Austria can be found at the URL
Daily mirrors are available at URLs including
http://cran.at.R-project.org/ (Wirtschaftsuniversität Wien, Austria) http://cran.au.R-project.org/ (University of Melbourne, Australia) http://cran.br.R-project.org/ (Universidade Federal do Paraná, Brazil) http://cran.ch.R-project.org/ (ETH Zürich, Switzerland) http://cran.dk.R-project.org/ (dotsrc.org, Aalborg, Denmark) http://cran.es.R-project.org/ (Spanish National Research Network, Madrid, Spain) http://cran.pt.R-project.org/ (Universidade do Porto, Portugal) http://cran.uk.R-project.org/ (U of Bristol, United Kingdom)
See https://CRAN.R-project.org/mirrors.html for a complete list of mirrors. Please use the CRAN site closest to you to reduce network load.
From CRAN, you can obtain the latest official release of R, daily snapshots of R (copies of the current source trees), as gzipped and bzipped tar files, a wealth of additional contributed code, as well as prebuilt binaries for various operating systems (Linux, Mac OS Classic, OS X, and MS Windows). CRAN also provides access to documentation on R, existing mailing lists and the R Bug Tracking system.
Please always use the URL of the master site when referring to CRAN.
R is released under the GNU General Public License (GPL), version 2. If you have any questions regarding the legality of using R in any particular situation you should bring it up with your legal counsel. We are in no position to offer legal advice.
It is the opinion of the R Core Team that one can use R for commercial purposes (e.g., in business or in consulting). The GPL, like all Open Source licenses, permits all and any use of the package. It only restricts distribution of R or of other programs containing code from R. This is made clear in clause 6 (“No Discrimination Against Fields of Endeavor”) of the Open Source Definition:
The license must not restrict anyone from making use of the program in a specific field of endeavor. For example, it may not restrict the program from being used in a business, or from being used for genetic research.
It is also explicitly stated in clause 0 of the GPL, which says in part
Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program.
Most add-on packages, including all recommended ones, also explicitly allow commercial use in this way. A few packages are restricted to “non-commercial use”; you should contact the author to clarify whether these may be used or seek the advice of your legal counsel.
None of the discussion in this section constitutes legal advice. The R Core Team does not provide legal advice under any circumstances.
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’ (see What is S?).
The R Foundation is a not for profit organization working in the public interest. It was founded by the members of the R Core Team in order to provide support for the R project and other innovations in statistical computing, provide a reference point for individuals, institutions or commercial enterprises that want to support or interact with the R development community, and to hold and administer the copyright of R software and documentation. See https://www.R-project.org/foundation/ for more information.
R-Forge (https://R-Forge.R-project.org/) offers a central platform for the development of R packages, R-related software and further projects. It is based on GForge offering easy access to the best in SVN, daily built and checked packages, mailing lists, bug tracking, message boards/forums, site hosting, permanent file archival, full backups, and total web-based administration. For more information, see the R-Forge web page and Stefan Theußl and Achim Zeileis (2009), “Collaborative software development using R-Forge”, The R Journal, 1(1):9–14.
S is a very high level language and an environment for data analysis and graphics. In 1998, the Association for Computing Machinery (ACM) presented its Software System Award to John M. Chambers, the principal designer of S, for
the S system, which has forever altered the way people analyze, visualize, and manipulate data …
S is an elegant, widely accepted, and enduring software system, with conceptual integrity, thanks to the insight, taste, and effort of John Chambers.
The evolution of the S language is characterized by four books by John Chambers and coauthors, which are also the primary references for S.
This is also referred to as the “Brown Book”, and of historical interest only.
This book is often called the “Blue Book”, and introduced what is now known as S version 2.
This is also called the “White Book”, and introduced S version 3, which added structures to facilitate statistical modeling in S.
This “Green Book” describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process.
See https://statweb.stanford.edu/~jmc4/papers/96.7.ps for further information on the “Evolution of the S Language”.
There is a huge amount of user-contributed code for S, available at the S Repository at CMU.
S-PLUS is a value-added version of S currently sold by TIBCO Software Inc as ‘TIBCO Spotfire S+’. See https://en.wikipedia.org/wiki/S-PLUS for more information.
We can regard S as a language with three current implementations or “engines”, the “old S engine” (S version 3; S-PLUS 3.x and 4.x), the “new S engine” (S version 4; S-PLUS 5.x and above), and R. Given this understanding, asking for “the differences between R and S” really amounts to asking for the specifics of the R implementation of the S language, i.e., the difference between the R and S engines.
For the remainder of this section, “S” refers to the S engines and not the S language.
Contrary to other implementations of the S language, R has adopted an evaluation model in which nested function definitions are lexically scoped. This is analogous to the evaluation model in Scheme.
This difference becomes manifest when free variables occur in a function. Free variables are those which are neither formal parameters (occurring in the argument list of the function) nor local variables (created by assigning to them in the body of the function). In S, the values of free variables are determined by a set of global variables (similar to C, there is only local and global scope). In R, they are determined by the environment in which the function was created.
Consider the following function:
cube <- function(n) {
sq <- function() n * n
n * sq()
}
Under S, sq() does not “know” about the variable n unless it
is defined globally:
S> cube(2) Error in sq(): Object "n" not found Dumped S> n <- 3 S> cube(2) [1] 18
In R, the “environment” created when cube() was invoked is also
looked in:
R> cube(2) [1] 8
As a more “interesting” real-world problem, suppose you want to write a function which returns the density function of the r-th order statistic from a sample of size n from a (continuous) distribution. For simplicity, we shall use both the cdf and pdf of the distribution as explicit arguments. (Example compiled from various postings by Luke Tierney.)
The S-PLUS documentation for call() basically suggests the
following:
dorder <- function(n, r, pfun, dfun) {
f <- function(x) NULL
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
PF <- call(substitute(pfun), as.name("x"))
DF <- call(substitute(dfun), as.name("x"))
f[[length(f)]] <-
call("*", con,
call("*", call("^", PF, r - 1),
call("*", call("^", call("-", 1, PF), n - r),
DF)))
f
}
Rather tricky, isn’t it? The code uses the fact that in S, functions are just lists of special mode with the function body as the last argument, and hence does not work in R (one could make the idea work, though).
A version which makes heavy use of substitute() and seems to work
under both S and R is
dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
eval(substitute(function(x) K * PF(x)^a * (1 - PF(x))^b * DF(x),
list(PF = substitute(pfun), DF = substitute(dfun),
a = r - 1, b = n - r, K = con)))
}
(the eval() is not needed in S).
However, in R there is a much easier solution:
dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
function(x) {
con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
}
}
This seems to be the “natural” implementation, and it works because the free variables in the returned function can be looked up in the defining environment (this is lexical scope).
Note that what you really need is the function closure, i.e., the
body along with all variable bindings needed for evaluating it. Since in
the above version, the free variables in the value function are not
modified, you can actually use it in S as well if you abstract out the
closure operation into a function MC() (for “make closure”):
dorder <- function(n, r, pfun, dfun) {
con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
MC(function(x) {
con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
},
list(con = con, pfun = pfun, dfun = dfun, r = r, n = n))
}
Given the appropriate definitions of the closure operator, this works in both R and S, and is much “cleaner” than a substitute/eval solution (or one which overrules the default scoping rules by using explicit access to evaluation frames, as is of course possible in both R and S).
For R, MC() simply is
MC <- function(f, env) f
(lexical scope!), a version for S is
MC <- function(f, env = NULL) {
env <- as.list(env)
if (mode(f) != "function")
stop(paste("not a function:", f))
if (length(env) > 0 && any(names(env) == ""))
stop(paste("not all arguments are named:", env))
fargs <- if(length(f) > 1) f[1:(length(f) - 1)] else NULL
fargs <- c(fargs, env)
if (any(duplicated(names(fargs))))
stop(paste("duplicated arguments:", paste(names(fargs)),
collapse = ", "))
fbody <- f[length(f)]
cf <- c(fargs, fbody)
mode(cf) <- "function"
return(cf)
}
Similarly, most optimization (or zero-finding) routines need some arguments to be optimized over and have other parameters that depend on the data but are fixed with respect to optimization. With R scoping rules, this is a trivial problem; simply make up the function with the required definitions in the same environment and scoping takes care of it. With S, one solution is to add an extra parameter to the function and to the optimizer to pass in these extras, which however can only work if the optimizer supports this.
Nested lexically scoped functions allow using function closures and maintaining local state. A simple example (taken from Abelson and Sussman) is obtained by typing demo("scoping") at the R prompt. Further information is provided in the standard R reference “R: A Language for Data Analysis and Graphics” (see What documentation exists for R?) and in Robert Gentleman and Ross Ihaka (2000), “Lexical Scope and Statistical Computing”, Journal of Computational and Graphical Statistics, 9, 491–508.
Nested lexically scoped functions also imply a further major difference. Whereas S stores all objects as separate files in a directory somewhere (usually .Data under the current directory), R does not. All objects in R are stored internally. When R is started up it grabs a piece of memory and uses it to store the objects. R performs its own memory management of this piece of memory, growing and shrinking its size as needed. Having everything in memory is necessary because it is not really possible to externally maintain all relevant “environments” of symbol/value pairs. This difference also seems to make R faster than S.
The down side is that if R crashes you will lose all the work for the
current session. Saving and restoring the memory “images” (the functions
and data stored in R’s internal memory at any time) can be a bit slow,
especially if they are big. In S this does not happen, because everything
is saved in disk files and if you crash nothing is likely to happen to
them. (In fact, one might conjecture that the S developers felt that the
price of changing their approach to persistent storage just to accommodate
lexical scope was far too expensive.) Hence, when doing important work, you
might consider saving often (see How can I save my workspace?) to
safeguard against possible crashes. Other possibilities are logging your
sessions, or have your R commands stored in text files which can be read in
using source().
Note: If you run R from within Emacs (see R and Emacs), you can save the contents of the interaction buffer to a file and conveniently manipulate it using
ess-transcript-mode, as well as save source copies of all functions and data used.
There are some differences in the modeling code, such as
lm(y ~ x^3) to regress y on
x^3, in R, you have to insulate powers of numeric vectors (using
I()), i.e., you have to use lm(y ~ I(x^3)).
na.action is set to "na.omit" by default in R, but not
set in S.
y ~ x + 0 is an alternative to y ~ x - 1 for
specifying a model with no intercept. Models with no parameters at all can
be specified by y ~ 0.
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.
x is a list, then x[i] <- NULL and x[[i]] <-
NULL remove the specified elements from x. The first of these is
incompatible with S, where it is a no-op. (Note that you can set elements
to NULL using x[i] <- list(NULL).)
.First and .Last in the .Data
directory can be used for customizing, as they are executed at the very
beginning and end of a session, respectively.
In R, the startup mechanism is as follows. Unless --no-environ was
given on the command line, R searches for site and user files to process for
setting environment variables. Then, R searches for a site-wide startup
profile unless the command line option --no-site-file was given.
This code is loaded in package base. Then, unless
--no-init-file was given, R searches for a user profile file, and
sources it into the user workspace. It then loads a saved image of the user
workspace from .RData in case there is one (unless
--no-restore-data or --no-restore were specified). Next,
a function .First() is run if found on the search path. Finally,
function .First.sys in the base package is run. When
terminating an R session, by default a function .Last is run if found
on the search path, followed by .Last.sys. If needed, the functions
.First() and .Last() should be defined in the appropriate
startup profiles. See the help pages for .First and .Last for
more details.
T and F are just variables being set to TRUE and
FALSE, respectively, but are not reserved words as in S and hence can
be overwritten by the user. (This helps e.g. when you have factors with
levels "T" or "F".) Hence, when writing code you should
always use TRUE and FALSE.
dyn.load() can only load shared objects, as created for
example by R CMD SHLIB.
attach() currently only works for lists and data frames, but
not for directories. (In fact, attach() also works for R data files
created with save(), which is analogous to attaching directories in
S.) Also, you cannot attach at position 1.
For() loops are not necessary and hence not supported.
assign() uses the argument envir= rather than
where= as in S.
int * rather than long * as
in S.
ls() returns the names of the objects in the current
(under R) and global (under S) environment, respectively. For example,
given
x <- 1; fun <- function() {y <- 1; ls()}
then fun() returns "y" in R and "x" (together with the
rest of the global environment) in S.
dim attribute vector can be 0). This has been determined a useful
feature as it helps reducing the need for special-case tests for empty
subsets. For example, if x is a matrix, x[, FALSE] is not
NULL but a “matrix” with 0 columns. Hence, such objects need to be
tested for by checking whether their length() is zero (which works in
both R and S), and not using is.null().
is.vector(c(a = 1:3)) returns FALSE in S and TRUE in
R).
DF is a
data frame, then is.matrix(DF) returns FALSE in R and
TRUE in S).
f(a) <- b is evaluated
as a <- "f<-"(a, value = b). S always takes the last argument,
irrespective of its name.
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.
for() loop is local to the inside of
the loop. In R it is local to the environment where the for()
statement is executed.
tapply(simplify=TRUE) returns a vector where R returns a
one-dimensional array (which can have named dimnames).
"aA" < "Bb" is true or false). From
version 1.2.0 the locale can be (re-)set in R by the Sys.setlocale()
function.
missing(arg) remains TRUE if arg is
subsequently modified; in R it doesn’t.
data.frame strips I() when creating
(column) names.
"NA" is not treated as a missing value in a
character variable. Use as.character(NA) to create a missing
character value.
dump(), dput() and deparse() are essentially
different interfaces to the same code. In R from version 2.0.0, this is
only true if the same control argument is used, but by default it is
not. By default dump() tries to write code that will evaluate to
reproduce the object, whereas dput() and deparse() default to
options for producing deparsed code that is readable.
[ using a
character vector index looks only for exact matches (whereas [[ and
$ allow partial matches). In S, [ allows partial matches.
atan and no atan2. A call in
S such as atan(x1, x2) is equivalent to R’s atan2(x1, x2).
However, beware of named arguments since S’s atan(x = a, y = b) is
equivalent to R’s atan2(y = a, x = b) with the meanings of x
and y interchanged. (R used to have undocumented support for a
two-argument atan with positional arguments, but this has been
withdrawn to avoid further confusion.)
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.
Since almost anything you can do in R has source code that you could port to S-PLUS with little effort there will never be much you can do in R that you couldn’t do in S-PLUS if you wanted to. (Note that using lexical scoping may simplify matters considerably, though.)
R offers several graphics features that S-PLUS does not, such as finer
handling of line types, more convenient color handling (via palettes), gamma
correction for color, and, most importantly, mathematical annotation in plot
texts, via input expressions reminiscent of TeX constructs. See the help
page for plotmath, which features an impressive on-line example.
More details can be found in Paul Murrell and Ross Ihaka (2000), “An
Approach to Providing Mathematical Annotation in Plots”,
Journal of
Computational and Graphical Statistics, 9, 582–599.
For a very long time, there was no such thing.
XLSolutions Corporation is currently beta testing a commercially supported version of R named R+ (read R plus).
Revolution Analytics has released REvolution R, an enterprise-class statistical analysis system based on R, suitable for deployment in professional, commercial and regulated environments.
See also http://en.wikipedia.org/wiki/R_programming_language#Commercialized_versions_of_R for pointers to commercialized versions of R.
Rweb is developed and maintained by Jeff Banfield. The Rweb Home Page provides access to all three versions of Rweb—a simple text entry form that returns output and graphs, a more sophisticated JavaScript version that provides a multiple window environment, and a set of point and click modules that are useful for introductory statistics courses and require no knowledge of the R language. All of the Rweb versions can analyze Web accessible datasets if a URL is provided.
The paper “Rweb: Web-based Statistical Analysis”, providing a detailed explanation of the different versions of Rweb and an overview of how Rweb works, was published in the Journal of Statistical Software (http://www.jstatsoft.org/v04/i01/).
Ulf Bartel has developed R-Online, a simple on-line programming environment for R which intends to make the first steps in statistical programming with R (especially with time series) as easy as possible. There is no need for a local installation since the only requirement for the user is a JavaScript capable browser. See http://www.osvisions.com/r_online/ for more information.
Rcgi is a CGI WWW interface to R by MJ Ray. It had the ability to use “embedded code”: you could mix user input and code, allowing the HTML author to do anything from load in data sets to enter most of the commands for users without writing CGI scripts. Graphical output was possible in PostScript or GIF formats and the executed code was presented to the user for revision. However, it is not clear if the project is still active.
There are many additional examples of web interfaces to R which basically allow to submit R code to a remote server, see for example the collection of links available from http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/StatCompCourse.
David Firth has written CGIwithR, an R add-on package available from CRAN. It provides some simple extensions to R to facilitate running R scripts through the CGI interface to a web server, and allows submission of data using both GET and POST methods. It is easily installed using Apache under Linux and in principle should run on any platform that supports R and a web server provided that the installer has the necessary security permissions. David’s paper “CGIwithR: Facilities for Processing Web Forms Using R” was published in the Journal of Statistical Software (http://www.jstatsoft.org/v08/i10/). The package is now maintained by Duncan Temple Lang and has a web page at http://www.omegahat.org/CGIwithR/.
Jeff Horner is working on the R/Apache Integration Project which embeds the R interpreter inside Apache 2 (and beyond). A tutorial and presentation are available from the project web page at http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/RApacheProject.
Rserve is a project actively developed by Simon Urbanek. It implements a TCP/IP server which allows other programs to use facilities of R. Clients are available from the web site for Java and C++ (and could be written for other languages that support TCP/IP sockets).
Two projects use PHP to provide a web interface to R. R_PHP_Online by Steve Chen (though it is unclear if this project is still active) is somewhat similar to the above Rcgi and Rweb. R-php is actively developed by Alfredo Pontillo and Angelo Mineo and provides both a web interface to R and a set of pre-specified analyses that need no R code input.
webbioc is “an integrated web interface for doing microarray analysis using several of the Bioconductor packages” and is designed to be installed at local sites as a shared computing resource.
Rwui is a web application to create user-friendly web interfaces for R scripts. All code for the web interface is created automatically. There is no need for the user to do any extra scripting or learn any new scripting techniques.
The R.rsp package by Henrik Bengtsson introduces “R Server Pages”. Analogous to Java Server Pages, an R server page is typically HTML with embedded R code that gets evaluated when the page is requested. The package includes an internal cross-platform HTTP server implemented in Tcl, so provides a good framework for including web-based user interfaces in packages. The approach is similar to the use of the brew package with Rapache with the advantage of cross-platform support and easy installation.
The Rook package by Jeffrey Horner provides a web server interface borrowing heavily from Ruby’s Rack project.
Finally, Concerto is a user friendly open-source Web Interface to R developed at the Psychometrics Centre of Cambridge University. It was designed as an online platform to design and run Computerized Adaptive Tests, but can be also used as a general-purpose R Web Interface. It allows R users with no programming or web designing background to quickly develop flexible and powerful online applications, websites, and psychometrics tests. To maximize its reliability, security, and performance, Concerto relies on the popular and reliable open-source elements such as MySQL server (exchange and storage of the data), Rstudio (R code designing and testing, file management), CKEditor (HTML Layer design), and PHP.
The R distribution comes with the following packages:
Base R functions (and datasets before R 2.0.0).
R byte code compiler (added in R 2.13.0).
Base R datasets (added in R 2.0.0).
Graphics devices for base and grid graphics (added in R 2.0.0).
R functions for base graphics.
A rewrite of the graphics layout capabilities, plus some support for interaction.
Formally defined methods and classes for R objects, plus other programming tools, as described in the Green Book.
Support for parallel computation, including by forking and by sockets, and random-number generation (added in R 2.14.0).
Regression spline functions and classes.
R statistical functions.
Statistical functions using S4 classes.
Interface and language bindings to Tcl/Tk GUI elements.
Tools for package development and administration.
R utility functions.
These “base packages” were substantially reorganized in R 1.9.0. The former base was split into the four packages base, graphics, stats, and utils. Packages ctest, eda, modreg, mva, nls, stepfun and ts were merged into stats, package lqs returned to the recommended package MASS, and package mle moved to stats4.
The CRAN src/contrib area contains a wealth of add-on packages, including the following recommended packages which are to be included in all binary distributions of R.
Functions for kernel smoothing (and density estimation) corresponding to the book “Kernel Smoothing” by M. P. Wand and M. C. Jones, 1995.
Functions and datasets from the main package of Venables and Ripley, “Modern Applied Statistics with S”. (Contained in the VR bundle for R versions prior to 2.10.0.)
A Matrix package. (Recommended for R 2.9.0 or later.)
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.
Functions for classification (k-nearest neighbor and LVQ). (Contained in the VR bundle for R versions prior to 2.10.0.)
Functions for cluster analysis.
Code analysis tools. (Recommended for R 2.5.0 or later.)
Functions for reading and writing data stored by statistical software like Minitab, S, SAS, SPSS, Stata, Systat, etc.
Lattice graphics, an implementation of Trellis Graphics functions.
Routines for GAMs and other generalized ridge regression problems with multiple smoothing parameter selection by GCV or UBRE.
Fit and compare Gaussian linear and nonlinear mixed-effects models.
Software for single hidden layer perceptrons (“feed-forward neural networks”), and for multinomial log-linear models. (Contained in the VR bundle for R versions prior to 2.10.0.)
Recursive PARTitioning and regression trees.
Functions for kriging and point pattern analysis from “Modern Applied Statistics with S” by W. Venables and B. Ripley. (Contained in the VR bundle for R versions prior to 2.10.0.)
Functions for survival analysis, including penalized likelihood.
See the CRAN contributed packages page for more information.
Many of these packages are categorized into CRAN Task Views, 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 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.
The 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 CRAN style R package repository is available via http://www.omegahat.org/R/. See http://www.omegahat.org/ for information on most R packages available from the Omega project.
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 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 https://www.bioconductor.org/download/ for available packages and a complete taxonomy via BioC Views.
Many more packages are available from places other than the three default repositories discussed above (CRAN, Bioconductor and Omegahat). In particular, R-Forge provides a CRAN style repository at https://R-Forge.R-project.org/.
More code has been posted to the R-help mailing list, and can be obtained from the mailing list archive.
(Unix-like only.) The add-on packages on CRAN come as gzipped tar files
named pkg_version.tar.gz, which may in fact be
“bundles” containing more than one package. Let path be the path to
such a package file. Provided that tar and gzip are
available on your system, type
$ R CMD INSTALL path/pkg_version.tar.gz
at the shell prompt to install to the library tree rooted at the first
directory in your library search path (see the help page for
.libPaths() for details on how the search path is determined).
To install to another tree (e.g., your private one), use
$ R CMD INSTALL -l lib path/pkg_version.tar.gz
where lib gives the path to the library tree to install to.
Even more conveniently, you can install and automatically update packages
from within R if you have access to repositories such as CRAN. See the
help page for available.packages() for more information.
To find out which additional packages are available on your system, type
library()
at the R prompt.
This produces something like
Packages in `/home/me/lib/R': mystuff My own R functions, nicely packaged but not documented Packages in `/usr/local/lib/R/library': KernSmooth Functions for kernel smoothing for Wand & Jones (1995) MASS Main Package of Venables and Ripley's MASS Matrix Sparse and Dense Matrix Classes and Methods base The R Base package boot Bootstrap R (S-Plus) Functions (Canty) class Functions for Classification cluster Functions for clustering (by Rousseeuw et al.) codetools Code Analysis Tools for R datasets The R Datasets Package foreign Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, dBase, ... grDevices The R Graphics Devices and Support for Colours and Fonts graphics The R Graphics Package grid The Grid Graphics Package lattice Lattice Graphics methods Formal Methods and Classes mgcv GAMs with GCV/AIC/REML smoothness estimation and GAMMs by PQL nlme Linear and Nonlinear Mixed Effects Models nnet Feed-forward Neural Networks and Multinomial Log-Linear Models rpart Recursive Partitioning spatial Functions for Kriging and Point Pattern Analysis splines Regression Spline Functions and Classes stats The R Stats Package stats4 Statistical functions using S4 Classes survival Survival analysis, including penalised likelihood tcltk Tcl/Tk Interface tools Tools for Package Development utils The R Utils Package
You can “load” the installed package pkg by
library(pkg)
You can then find out which functions it provides by typing one of
library(help = pkg) help(package = pkg)
You can unload the loaded package pkg by
detach("package:pkg", unload = TRUE)
(where unload = TRUE is needed only for packages with a namespace,
see ?unload).
Use
$ R CMD REMOVE pkg_1 … pkg_n
to remove the packages pkg_1, …, pkg_n from the library
tree rooted at the first directory given in R_LIBS if this is set and
non-null, and from the default library otherwise. (Versions of R prior to
1.3.0 removed from the default library by default.)
To remove from library lib, do
$ R CMD REMOVE -l lib pkg_1 … pkg_n
A package consists of a subdirectory containing a file DESCRIPTION and the subdirectories R, data, demo, exec, inst, man, po, src, and tests (some of which can be missing). The package subdirectory may also contain files INDEX, NAMESPACE, configure, cleanup, LICENSE, LICENCE, COPYING and NEWS.
See section “Creating R packages” in Writing R Extensions, for details. This manual is included in the R distribution, see What documentation exists for R?, and gives information on package structure, the configure and cleanup mechanisms, and on automated package checking and building.
R version 1.3.0 has added the function package.skeleton() which will
set up directories, save data and code, and create skeleton help files for a
set of R functions and datasets.
See What is CRAN?, for information on uploading a package to CRAN.
R is in active development and there is always a risk of bugs creeping in. Also, the developers do not have access to all possible machines capable of running R. So, simply using it and communicating problems is certainly of great value.
The R Developer Page 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.
There is an Emacs package called ESS (“Emacs Speaks Statistics”) which provides a standard interface between statistical programs and statistical processes. It is intended to provide assistance for interactive statistical programming and data analysis. Languages supported include: S dialects (R, S 3/4, and S-PLUS 3.x/4.x/5.x/6.x/7.x), LispStat dialects (XLispStat, ViSta), SAS, Stata, and BUGS.
ESS grew out of the need for bug fixes and extensions to S-mode 4.8 (which was a GNU Emacs interface to S/S-PLUS version 3 only). The current set of developers desired support for XEmacs, R, S4, and MS Windows. In addition, with new modes being developed for R, Stata, and SAS, it was felt that a unifying interface and framework for the user interface would benefit both the user and the developer, by helping both groups conform to standard Emacs usage. The end result is an increase in efficiency for statistical programming and data analysis, over the usual tools.
R support contains code for editing R source code (syntactic indentation and highlighting of source code, partial evaluations of code, loading and error-checking of code, and source code revision maintenance) and documentation (syntactic indentation and highlighting of source code, sending examples to running ESS process, and previewing), interacting with an inferior R process from within Emacs (command-line editing, searchable command history, command-line completion of R object and file names, quick access to object and search lists, transcript recording, and an interface to the help system), and transcript manipulation (recording and saving transcript files, manipulating and editing saved transcripts, and re-evaluating commands from transcript files).
The latest stable version of ESS is available via CRAN or the ESS web page. The HTML version of the documentation can be found at http://stat.ethz.ch/ESS/.
ESS comes with detailed installation instructions.
For help with ESS, send email to ESS-help@stat.math.ethz.ch.
Please send bug reports and suggestions on ESS to ESS-bugs@stat.math.ethz.ch. The easiest way to do this from is within Emacs by typing M-x ess-submit-bug-report or using the [ESS] or [iESS] pulldown menus.
Yes, definitely. Inferior R mode provides a readline/history mechanism, object name completion, and syntax-based highlighting of the interaction buffer using Font Lock mode, as well as a very convenient interface to the R help system.
Of course, it also integrates nicely with the mechanisms for editing R source using Emacs. One can write code in one Emacs buffer and send whole or parts of it for execution to R; this is helpful for both data analysis and programming. One can also seamlessly integrate with a revision control system, in order to maintain a log of changes in your programs and data, as well as to allow for the retrieval of past versions of the code.
In addition, it allows you to keep a record of your session, which can also be used for error recovery through the use of the transcript mode.
To specify command line arguments for the inferior R process, use C-u M-x R for starting R.
To debug R “from within Emacs”, there are several possibilities. To use
the Emacs GUD (Grand Unified Debugger) library with the recommended debugger
GDB, type M-x gdb and give the path to the R binary as
argument. At the gdb prompt, set R_HOME and other
environment variables as needed (using e.g. set env R_HOME
/path/to/R/, but see also below), and start the binary with the desired
arguments (e.g., run --quiet).
If you have ESS, you can do C-u M-x R RET - d SPC g d b RET to start an inferior R process with arguments -d gdb.
A third option is to start an inferior R process via ESS (M-x
R) and then start GUD (M-x gdb) giving the R binary (using its full
path name) as the program to debug. Use the program ps to find
the process number of the currently running R process then use the
attach command in gdb to attach it to that process. One advantage of
this method is that you have separate *R* and *gud-gdb*
windows. Within the *R* window you have all the ESS
facilities, such as object-name completion, that we know and love.
When using GUD mode for debugging from within Emacs, you may find it most convenient to use the directory with your code in it as the current working directory and then make a symbolic link from that directory to the R binary. That way .gdbinit can stay in the directory with the code and be used to set up the environment and the search paths for the source, e.g. as follows:
set env R_HOME /opt/R set env R_PAPERSIZE letter set env R_PRINTCMD lpr dir /opt/R/src/appl dir /opt/R/src/main dir /opt/R/src/nmath dir /opt/R/src/unix
You can use
x[i] <- list(NULL)
to set component i of the list x to NULL, similarly for
named components. Do not set x[i] or x[[i]] to NULL,
because this will remove the corresponding component from the list.
For dropping the row names of a matrix x, it may be easier to use
rownames(x) <- NULL, similarly for column names.
save.image() saves the objects in the user’s .GlobalEnv to the
file .RData in the R startup directory. (This is also what happens
after q("yes").) Using save.image(file) one can save the
image under a different name.
To remove all objects in the currently active environment (typically
.GlobalEnv), you can do
rm(list = ls(all = TRUE))
(Without all = TRUE, only the objects with names not starting with a ‘.’ are removed.)
Strange things will happen if you use eval(print(x), envir = e) or
D(x^2, "x"). The first one will either tell you that "x" is
not found, or print the value of the wrong x. The other one will
likely return zero if x exists, and an error otherwise.
This is because in both cases, the first argument is evaluated in the
calling environment first. The result (which should be an object of mode
"expression" or "call") is then evaluated or differentiated.
What you (most likely) really want is obtained by “quoting” the first
argument upon surrounding it with expression(). For example,
R> D(expression(x^2), "x") 2 * x
Although this behavior may initially seem to be rather strange, it is perfectly logical. The “intuitive” behavior could easily be implemented, but problems would arise whenever the expression is contained in a variable, passed as a parameter, or is the result of a function call. Consider for instance the semantics in cases like
D2 <- function(e, n) D(D(e, n), n)
or
g <- function(y) eval(substitute(y), sys.frame(sys.parent(n = 2))) g(a * b)
See the help page for deriv() for more examples.
When a matrix with a single row or column is created by a subscripting
operation, e.g., row <- mat[2, ], it is by default turned into a
vector. In a similar way if an array with dimension, say, 2 x 3 x 1 x 4
is created by subscripting it will be coerced into a 2 x 3 x 4 array,
losing the unnecessary dimension. After much discussion this has been
determined to be a feature.
To prevent this happening, add the option drop = FALSE to the subscripting. For example,
rowmatrix <- mat[2, , drop = FALSE] # creates a row matrix colmatrix <- mat[, 2, drop = FALSE] # creates a column matrix a <- b[1, 1, 1, drop = FALSE] # creates a 1 x 1 x 1 array
The drop = FALSE option should be used defensively when programming. For example, the statement
somerows <- mat[index, ]
will return a vector rather than a matrix if index happens to have
length 1, causing errors later in the code. It should probably be rewritten
as
somerows <- mat[index, , drop = FALSE]
R has a special environment called .AutoloadEnv. Using
autoload(name, pkg), where name and pkg are
strings giving the names of an object and the package containing it, stores
some information in this environment. When R tries to evaluate name,
it loads the corresponding package pkg and reevaluates name in
the new package’s environment.
Using this mechanism makes R behave as if the package was loaded, but does not occupy memory (yet).
See the help page for autoload() for a very nice example.
The function options() allows setting and examining a variety of
global “options” which affect the way in which R computes and displays its
results. The variable .Options holds the current values of these
options, but should never directly be assigned to unless you want to drive
yourself crazy—simply pretend that it is a “read-only” variable.
For example, given
test1 <- function(x = pi, dig = 3) {
oo <- options(digits = dig); on.exit(options(oo));
cat(.Options$digits, x, "\n")
}
test2 <- function(x = pi, dig = 3) {
.Options$digits <- dig
cat(.Options$digits, x, "\n")
}
we obtain:
R> test1() 3 3.14 R> test2() 3 3.141593
What is really used is the global value of .Options, and using
options(OPT = VAL) correctly updates it. Local copies of
.Options, either in .GlobalEnv or in a function environment
(frame), are just silently disregarded.
As R uses C-style string handling, ‘\’ is treated as an escape character, so that for example one can enter a newline as ‘\n’. When you really need a ‘\’, you have to escape it with another ‘\’.
Thus, in filenames use something like "c:\\data\\money.dat". You can
also replace ‘\’ by ‘/’ ("c:/data/money.dat").
On an X11 device, plotting sometimes, e.g., when running
demo("image"), results in “Error: color allocation error”. This is
an X problem, and only indirectly related to R. It occurs when applications
started prior to R have used all the available colors. (How many colors are
available depends on the X configuration; sometimes only 256 colors can be
used.)
One application which is notorious for “eating” colors is Netscape. If the problem occurs when Netscape is running, try (re)starting it with either the -no-install (to use the default colormap) or the -install (to install a private colormap) option.
You could also set the colortype of X11() to
"pseudo.cube" rather than the default "pseudo". See the help
page for X11() for more information.
It may happen that when reading numeric data into R (usually, when reading
in a file), they come in as factors. If f is such a factor object,
you can use
as.numeric(as.character(f))
to get the numbers back. More efficient, but harder to remember, is
as.numeric(levels(f))[as.integer(f)]
In any case, do not call as.numeric() or their likes directly for the
task at hand (as as.numeric() or unclass() give the internal
codes).
The recommended package lattice (which is based on base package grid) provides graphical functionality that is compatible with most Trellis commands.
You could also look at coplot() and dotchart() which might do
at least some of what you want. Note also that the R version of
pairs() is fairly general and provides most of the functionality of
splom(), and that R’s default plot method has an argument asp
allowing to specify (and fix against device resizing) the aspect ratio of
the plot.
(Because the word “Trellis” has been claimed as a trademark we do not use it in R. The name “lattice” has been chosen for the R equivalent.)
Inside a function you may want to access variables in two additional environments: the one that the function was defined in (“enclosing”), and the one it was invoked in (“parent”).
If you create a function at the command line or load it in a package its
enclosing environment is the global workspace. If you define a function
f() inside another function g() its enclosing environment is
the environment inside g(). The enclosing environment for a function
is fixed when the function is created. You can find out the enclosing
environment for a function f() using environment(f).
The “parent” environment, on the other hand, is defined when you invoke a
function. If you invoke lm() at the command line its parent
environment is the global workspace, if you invoke it inside a function
f() then its parent environment is the environment inside
f(). You can find out the parent environment for an invocation of a
function by using parent.frame() or sys.frame(sys.parent()).
So for most user-visible functions the enclosing environment will be the
global workspace, since that is where most functions are defined. The
parent environment will be wherever the function happens to be called from.
If a function f() is defined inside another function g() it
will probably be used inside g() as well, so its parent environment
and enclosing environment will probably be the same.
Parent environments are important because things like model formulas need to be evaluated in the environment the function was called from, since that’s where all the variables will be available. This relies on the parent environment being potentially different with each invocation.
Enclosing environments are important because a function can use variables in the enclosing environment to share information with other functions or with other invocations of itself (see the section on lexical scoping). This relies on the enclosing environment being the same each time the function is invoked. (In C this would be done with static variables.)
Scoping is hard. Looking at examples helps. It is particularly instructive to look at examples that work differently in R and S and try to see why they differ. One way to describe the scoping differences between R and S is to say that in S the enclosing environment is always the global workspace, but in R the enclosing environment is wherever the function was created.
Often, it is desired to use the value of an R object in a plot label, e.g.,
a title. This is easily accomplished using paste() if the label is a
simple character string, but not always obvious in case the label is an
expression (for refined mathematical annotation). In such a case, either
use parse() on your pasted character string or use
substitute() on an expression. For example, if ahat is an
estimator of your parameter a of interest, use
title(substitute(hat(a) == ahat, list(ahat = ahat)))
(note that it is ‘==’ and not ‘=’). Sometimes bquote()
gives a more compact form, e.g.,
title(bquote(hat(a) = .(ahat)))
where subexpressions enclosed in ‘.()’ are replaced by their values.
There are more examples in the mailing list archives.
When creating data frames using data.frame() or read.table(),
R by default ensures that the variable names are syntactically valid. (The
argument check.names to these functions controls whether variable
names are checked and adjusted by make.names() if needed.)
To understand what names are “valid”, one needs to take into account that the term “name” is used in several different (but related) ways in the language:
assign() function. It
is usually a syntactic name as well, but can be any non-empty string if it
is quoted (and it is always quoted in the call to assign()).
f(trim=.5)).
Argument names are also usually syntactic names, but again can be anything
if they are quoted.
eval() or attach(), the element names become
object names.)
Package gam from CRAN implements all the Generalized Additive
Models (GAM) functionality as described in the GAM chapter of the White
Book. In particular, it implements backfitting with both local regression
and smoothing splines, and is extendable. There is a gam() function
for GAMs in package mgcv, but it is not an exact clone of what is
described in the White Book (no lo() for example). Package
gss can fit spline-based GAMs too. And if you can accept
regression splines you can use glm(). For Gaussian GAMs you can use
bruto() from package mda.
Most R commands do not generate any output. The command
1+1
computes the value 2 and returns it; the command
summary(glm(y~x+z, family=binomial))
fits a logistic regression model, computes some summary information and
returns an object of class "summary.glm" (see How should I write summary methods?).
If you type ‘1+1’ or ‘summary(glm(y~x+z, family=binomial))’ at the
command line the returned value is automatically printed (unless it is
invisible()), but in other circumstances, such as in a
source()d file or inside a function it isn’t printed unless you
specifically print it.
To print the value use
print(1+1)
or
print(summary(glm(y~x+z, family=binomial)))
instead, or use source(file, echo=TRUE).
As the help for outer() indicates, it does not work on arbitrary
functions the way the apply() family does. It requires functions
that are vectorized to work elementwise on arrays. As you can see by
looking at the code, outer(x, y, FUN) creates two large vectors
containing every possible combination of elements of x and y
and then passes this to FUN all at once. Your function probably
cannot handle two large vectors as parameters.
If you have a function that cannot handle two vectors but can handle two
scalars, then you can still use outer() but you will need to wrap
your function up first, to simulate vectorized behavior. Suppose your
function is
foo <- function(x, y, happy) {
stopifnot(length(x) == 1, length(y) == 1) # scalars only!
(x + y) * happy
}
If you define the general function
wrapper <- function(x, y, my.fun, ...) {
sapply(seq_along(x), FUN = function(i) my.fun(x[i], y[i], ...))
}
then you can use outer() by writing, e.g.,
outer(1:4, 1:2, FUN = wrapper, my.fun = foo, happy = 10)
Scalar functions can also be vectorized using Vectorize().
In a model such as ~A+B+A:B, R will report the difference in sums of
squares between the models ~1, ~A, ~A+B and
~A+B+A:B. If the model were ~B+A+A:B, R would report
differences between ~1, ~B, ~A+B, and ~A+B+A:B
. In the first case the sum of squares for A is comparing ~1
and ~A, in the second case it is comparing ~B and
~B+A. In a non-orthogonal design (i.e., most unbalanced designs)
these comparisons are (conceptually and numerically) different.
Some packages report instead the sums of squares based on comparing the full model to the models with each factor removed one at a time (the famous ‘Type III sums of squares’ from SAS, for example). These do not depend on the order of factors in the model. The question of which set of sums of squares is the Right Thing provokes low-level holy wars on R-help from time to time.
There is no need to be agitated about the particular sums of squares that R
reports. You can compute your favorite sums of squares quite easily. Any
two models can be compared with anova(model1, model2),
and drop1(model1) will show the sums of squares resulting from
dropping single terms.
Under a Unix-like, if your installation supports the type="cairo"
option to the png() device there should be no problems, and the
default settings should just work. This option is not available for
versions of R prior to 2.7.0, or without support for cairo. From R 2.7.0
png() by default uses the Quartz device on OS X, and that too works
in batch mode.
Earlier versions of the png() device used the X11 driver, which is a
problem in batch mode or for remote operation. If you have Ghostscript you
can use bitmap(), which produces a PostScript or PDF file then
converts it to any bitmap format supported by Ghostscript. On some
installations this produces ugly output, on others it is perfectly
satisfactory. Many systems now come with Xvfb from X.Org (possibly as an optional install), which is an X11 server that does
not require a screen; and there is the GDD package from CRAN,
which produces PNG, JPEG and GIF bitmaps without X11.
The Unix-like command-line interface to R can only provide the inbuilt
command line editor which allows recall, editing and re-submission of prior
commands provided that the GNU readline library is available at
the time R is configured for compilation. Note that the ‘development’
version of readline including the appropriate headers is needed: users of
Linux binary distributions will need to install packages such as
libreadline-dev (Debian) or readline-devel (Red Hat).
If you have
varname <- c("a", "b", "d")
you can do
get(varname[1]) + 2
for
a + 2
or
assign(varname[1], 2 + 2)
for
a <- 2 + 2
or
eval(substitute(lm(y ~ x + variable),
list(variable = as.name(varname[1]))))
for
lm(y ~ x + a)
At least in the first two cases it is often easier to just use a list, and then you can easily index it by name
vars <- list(a = 1:10, b = rnorm(100), d = LETTERS) vars[["a"]]
without any of this messing about.
The most likely reason is that you forgot to tell R to display the graph.
Lattice functions such as xyplot() create a graph object, but do not
display it (the same is true of ggplot2 graphics, and Trellis
graphics in S-PLUS). The print() method for the graph object
produces the actual display. When you use these functions interactively at
the command line, the result is automatically printed, but in
source() or inside your own functions you will need an explicit
print() statement.
To sort the rows within a data frame, with respect to the values in one or
more of the columns, simply use order() (e.g., DF[order(DF$a,
DF[["b"]]), ] to sort the data frame DF on columns named a
and b).
The browser-based search engine in help.start() utilizes a Java
applet. In order for this to function properly, a compatible version of
Java must installed on your system and linked to your browser, and both Java
and JavaScript need to be enabled in your browser.
There have been a number of compatibility issues with versions of Java and of browsers. For further details please consult section “Enabling search in HTML help” in R Installation and Administration. This manual is included in the R distribution, see What documentation exists for R?, and its HTML version is linked from the HTML search page.
Did you read the NEWS file? For functions that are not in the base package you need to specify the correct package namespace, since the code will be run before the packages are loaded. E.g.,
ps.options(horizontal = FALSE) help.start()
needs to be
grDevices::ps.options(horizontal = FALSE) utils::help.start()
(graphics::ps.options(horizontal = FALSE) in R 1.9.x).
Many functions, particularly S3 methods, are now hidden in namespaces. This has the advantage that they cannot be called inadvertently with arguments of the wrong class, but it makes them harder to view.
To see the code for an S3 method (e.g., [.terms) use
getS3method("[", "terms")
To see the code for an unexported function foo() in the namespace of
package "bar" use bar:::foo. Don’t use these constructions to
call unexported functions in your own code—they are probably unexported
for a reason and may change without warning.
To rotate axis labels (using base graphics), you need to use text(),
rather than mtext(), as the latter does not support
par("srt").
## Increase bottom margin to make room for rotated labels par(mar = c(7, 4, 4, 2) + 0.1) ## Create plot with no x axis and no x axis label plot(1 : 8, xaxt = "n", xlab = "") ## Set up x axis with tick marks alone axis(1, labels = FALSE) ## Create some text labels labels <- paste("Label", 1:8, sep = " ") ## Plot x axis labels at default tick marks text(1:8, par("usr")[3] - 0.25, srt = 45, adj = 1, labels = labels, xpd = TRUE) ## Plot x axis label at line 6 (of 7) mtext(1, text = "X Axis Label", line = 6)
When plotting the x axis labels, we use srt = 45 for text rotation
angle, adj = 1 to place the right end of text at the tick marks, and
xpd = TRUE to allow for text outside the plot region. You can adjust
the value of the 0.25 offset as required to move the axis labels up
or down relative to the x axis. See ?par for more information.
Also see Figure 1 and associated code in Paul Murrell (2003), “Integrating grid Graphics Output with Base Graphics Output”, R News, 3/2, 7–12.
By default, read.table() needs to read in everything as character
data, and then try to figure out which variables to convert to numerics or
factors. For a large data set, this takes considerable amounts of time and
memory. Performance can substantially be improved by using the
colClasses argument to specify the classes to be assumed for the
columns of the table.
A package is a standardized collection of material extending R, e.g.
providing code, data, or documentation. A library is a place
(directory) where R knows to find packages it can use (i.e., which were
installed). R is told to use a package (to “load” it and add it to
the search path) via calls to the function library. I.e.,
library() is employed to load a package from libraries containing
packages.
See R Add-On Packages, for more details. See also Uwe Ligges (2003), “R Help Desk: Package Management”, R News, 3/3, 37–39.
To actually use the package, it needs to be loaded using
library().
See R Add-On Packages and What is the difference between package and library? for more information.
The only numbers that can be represented exactly in R’s numeric type are integers and fractions whose denominator is a power of 2. Other numbers have to be rounded to (typically) 53 binary digits accuracy. As a result, two floating point numbers will not reliably be equal unless they have been computed by the same algorithm, and not always even then. For example
R> a <- sqrt(2) R> a * a == 2 [1] FALSE R> a * a - 2 [1] 4.440892e-16
The function all.equal() compares two objects using a numeric
tolerance of .Machine$double.eps ^ 0.5. If you want much greater
accuracy than this you will need to consider error propagation carefully.
For more information, see e.g. David Goldberg (1991), “What Every Computer Scientist Should Know About Floating-Point Arithmetic”, ACM Computing Surveys, 23/1, 5–48, also available via http://www.validlab.com/goldberg/paper.pdf.
To quote from “The Elements of Programming Style” by Kernighan and Plauger:
10.0 times 0.1 is hardly ever 1.0.
Use try(), which returns an object of class "try-error"
instead of an error, or preferably tryCatch(), where the return value
can be configured more flexibly. For example
beta[i,] <- tryCatch(coef(lm(formula, data)),
error = function(e) rep(NaN, 4))
would return the coefficients if the lm() call succeeded and would
return c(NaN, NaN, NaN, NaN) if it failed (presumably there are
supposed to be 4 coefficients in this example).
You are probably seeing something like
R> -2^2 [1] -4
and misunderstanding the precedence rules for expressions in R. Write
R> (-2)^2 [1] 4
to get the square of -2.
The precedence rules are documented in ?Syntax, and to see how R
interprets an expression you can look at the parse tree
R> as.list(quote(-2^2)) [[1]] `-` [[2]] 2^2
One way is to use paste() (or sprintf()) to concatenate a stem
filename and the iteration number while file.path() constructs the
path. For example, to save results into files result1.rda, …,
result100.rda in the subdirectory Results of the current
working directory, one can use
for(i in 1:100) {
## Calculations constructing "some_object" ...
fp <- file.path("Results", paste("result", i, ".rda", sep = ""))
save(list = "some_object", file = fp)
}
Doug Bates has kindly provided an extensive response in a post to the r-help list, which can be reviewed at https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html.
This can occur when using functions such as polygon(),
filled.contour(), image() or other functions which may call
these internally. In the case of polygon(), you may observe unwanted
borders between the polygons even when setting the border argument to
NA or "transparent".
The source of the problem is the PS/PDF viewer when the plot is anti-aliased. The details for the solution will be different depending upon the viewer used, the operating system and may change over time. For some common viewers, consider the following:
There are options in Preferences to enable/disable text smoothing, image smoothing and line art smoothing. Disable line art smoothing.
There is an option in Preferences to enable/disable anti-aliasing of text and line art. Disable this option.
There are settings for Text Alpha and Graphics Alpha. Change Graphics Alpha from 4 bits to 1 bit to disable graphic anti-aliasing.
There is an option to enable/disable anti-aliasing. Disable this option.
There is not an option to disable anti-aliasing in this viewer.
There is not an option in the GUI to enable/disable anti-aliasing. From a console command line, use:
$ kwriteconfig --file okularpartrc --group 'Dlg Performance' \
--key GraphicsAntialias Disabled
Then restart Okular. Change the final word to ‘Enabled’ to restore the original setting.
This question most often comes up in relation to file names (see How do file names work in Windows?) but it also happens that people complain that they cannot seem to put a single ‘\’ character into a text string unless it happens to be followed by certain other characters.
To understand this, you have to distinguish between character strings and representations of character strings. Mostly, the representation in R is just the string with a single or double quote at either end, but there are strings that cannot be represented that way, e.g., strings that themselves contain the quote character. So
> str <- "This \"text\" is quoted" > str [1] "This \"text\" is quoted" > cat(str, "\n") This "text" is quoted
The escape sequences ‘\"’ and ‘\n’ represent a double quote
and the newline character respectively. Printing text strings, using
print() or by typing the name at the prompt will use the escape
sequences too, but the cat() function will display the string
as-is. Notice that ‘"\n"’ is a one-character string, not two; the
backslash is not actually in the string, it is just generated in the printed
representation.
> nchar("\n")
[1] 1
> substring("\n", 1, 1)
[1] "\n"
So how do you put a backslash in a string? For this, you have to escape the escape character. I.e., you have to double the backslash. as in
> cat("\\n", "\n")
\n
Some functions, particularly those involving regular expression matching, themselves use metacharacters, which may need to be escaped by the backslash mechanism. In those cases you may need a quadruple backslash to represent a single literal one.
In versions of R up to 2.4.1 an unknown escape sequence like ‘\p’ was quietly interpreted as just ‘p’. Current versions of R emit a warning.
Some functions will display a particular kind of plot with error bars, such
as the bar.err() function in the agricolae package, the
plotCI() function in the gplots package, the
plotCI() and brkdn.plot() functions in the plotrix
package and the error.bars(), error.crosses() and
error.bars.by() functions in the psych package. Within
these types of functions, some will accept the measures of dispersion (e.g.,
plotCI), some will calculate the dispersion measures from the raw
values (bar.err, brkdn.plot), and some will do both
(error.bars). Still other functions will just display error bars,
like the dispersion function in the plotrix package. Most of the
above functions use the arrows() function in the base graphics
package to draw the error bars.
The above functions all use the base graphics system. The grid and lattice
graphics systems also have specific functions for displaying error bars,
e.g., the grid.arrow() function in the grid package, and the
geom_errorbar(), geom_errorbarh(), geom_pointrange(),
geom_linerange(), geom_crossbar() and geom_ribbon()
functions in the ggplot2 package. In the lattice system, error
bars can be displayed with Dotplot() or xYplot() in the
Hmisc package and segplot() in the latticeExtra
package.
Creating a graph with two y-axes, i.e., with two sorts of data that are
scaled to the same vertical size and showing separate vertical axes on the
left and right sides of the plot that reflect the original scales of the
data, is possible in R but is not recommended. The basic approach for
constructing such graphs is to use par(new=TRUE) (see ?par);
functions twoord.plot() (in the plotrix package) and
doubleYScale() (in the latticeExtra package) automate the
process somewhat.
In most cases, typing the name of the function will print its source code. However, code is sometimes hidden in a namespace, or compiled. For a complete overview on how to access source code, see Uwe Ligges (2006), “Help Desk: Accessing the sources”, R News, 6/4, 43–45 (https://CRAN.R-project.org/doc/Rnews/Rnews_2006-4.pdf).
As described in ?summary.lm, when the intercept is zero (e.g., from
y ~ x - 1 or y ~ x + 0), summary.lm() uses the formula
R^2 = 1 - Sum(R[i]^2) / Sum((y[i])^2)
which is different from the usual
R^2 = 1 - Sum(R[i]^2) / Sum((y[i] - mean(y))^2).
There are several reasons for this:
All these come down to saying that if you know a priori that E[Y]=0 when x=0 then the ‘null’ model that you should compare to the fitted line, the model where x doesn’t explain any of the variance, is the model where E[Y]=0 everywhere. (If you don’t know a priori that E[Y]=0 when x=0, then you probably shouldn’t be fitting a line through the origin.)
This question is often asked in different flavors along the lines of “I
have removed objects in R and run gc() and yet ps/top
still shows the R process using a lot of memory”, often on Linux machines.
This is an artifact of the way the operating system (OS) allocates memory.
In general it is common that the OS is not capable of releasing all unused
memory. In extreme cases it is possible that even if R frees almost all its
memory, the OS can not release any of it due to its design and thus tools
such as ps or top will report substantial amount of resident
RAM used by the R process even though R has released all that memory. In
general such tools do not report the actual memory usage of the
process but rather what the OS is reserving for that process.
The short answer is that this is a limitation of the memory allocator in the operating system and there is nothing R can do about it. That space is simply kept by the OS in the hope that R will ask for it later. The following paragraph gives more in-depth answer with technical details on how this happens.
Most systems use two separate ways to allocate memory. For allocation of
large chunks they will use mmap to map memory into the process
address space. Such chunks can be released immediately when they are
completely free, because they can reside anywhere in the virtual memory.
However, this is a relatively expensive operation and many OSes have a limit
on the number of such allocated chunks, so this is only used for allocating
large memory regions. For smaller allocations the system can expand the
data segment of the process (historically using the brk system call),
but this whole area is always contiguous. The OS can only move the end of
this space, it cannot create any “holes”. Since this operation is fairly
cheap, it is used for allocations of small pieces of memory. However, the
side-effect is that even if there is just one byte that is in use at the end
of the data segment, the OS cannot release any memory at all, because it
cannot change the address of that byte. This is actually more common than
it may seem, because allocating a lot of intermediate objects, then
allocating a result object and removing all intermediate objects is a very
common practice. Since the result is allocated at the end it will prevent
the OS from releasing any memory used by the intermediate objects. In
practice, this is not necessarily a problem, because modern operating
systems can page out unused portions of the virtual memory so it does not
necessarily reduce the amount of real memory available for other
applications. Typically, small objects such as strings or pairlists will be
affected by this behavior, whereas large objects such as long vectors will
be allocated using mmap and thus not affected. On Linux (and
possibly other Unix-like systems) it is possible to use the mallinfo
system call (also see the mallinfo
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.
When R transfers files over HTTP (e.g., using the
install.packages() or download.file() function), a download
method is chosen based on the download.file.method option. There
are several methods available and the default behavior if no option is
explicitly specified is to use R’s internal HTTP implementation.
In most circumstances this internal method will not support HTTPS
URLs so you will need to override the default: this is done automatically
for such URLs as from R 3.2.2.
R versions 3.2.0 and greater include two download methods ("libcurl"
and "wininet") that both support HTTPS connections: we
recommend that you use these methods. The requisite code to add to
.Rprofile or Rprofile.site is:
options(download.file.method = "wininet", url.method = "wininet") (Windows) options(download.file.method = "libcurl", url.method = "libcurl") (Linux and OS X)
(Method "wininet" is the default on Windows as from R 3.2.2.)
Note that the "libcurl" method may or may not have been compiled in.
In the case that it was not, i.e.. capabilities("libcurl") == FALSE,
we recommend method "wget" on Linux and "curl" on OS X. It is
possible that system versions of "libcurl", wget or
curl may have been compiled without HTTPS support, but
this is unlikely. As from R 3.3.0 "libcurl" with HTTPS
support is required except on Windows.
Suppose you want to provide a summary method for class "foo". Then
summary.foo() should not print anything, but return an object of
class "summary.foo", and you should write a method
print.summary.foo() which nicely prints the summary information and
invisibly returns its object. This approach is preferred over having
summary.foo() print summary information and return something useful,
as sometimes you need to grab something computed by summary() inside
a function or similar. In such cases you don’t want anything printed.
Roughly speaking, you need to start R inside the debugger, load the code, send an interrupt, and then set the required breakpoints.
See section “Finding entry points in dynamically loaded code” in Writing R Extensions. This manual is included in the R distribution, see What documentation exists for R?.
The most convenient way is to call R_PV from the symbolic debugger.
See section “Inspecting R objects when debugging” in Writing R Extensions.
Suppose you have C code file for dynloading into R, but you want to use
R CMD SHLIB with compilation flags other than the default ones (which
were determined when R was built).
Starting with R 2.1.0, users can provide personal Makevars configuration
files in $HOME/.R to override the default flags.
See section “Add-on packages” in R Installation and Administration.
For earlier versions of R, you could change the file
R_HOME/etc/Makeconf to reflect your preferences, or (at least
for systems using GNU Make) override them by the environment
variable MAKEFLAGS.
See section “Creating shared objects” in Writing R Extensions.
Use the trace() function with argument signature= to add calls
to the browser or any other code to the method that will be dispatched for
the corresponding signature. See ?trace for details.
If R executes an illegal instruction, or dies with an operating system error
message that indicates a problem in the program (as opposed to something
like “disk full”), then it is certainly a bug. If you call .C(),
.Fortran(), .External() or .Call() (or
.Internal()) yourself (or in a function you wrote), you can always
crash R by using wrong argument types (modes). This is not a bug.
Taking forever to complete a command can be a bug, but you must make certain that it was really R’s fault. Some commands simply take a long time. If the input was such that you know it should have been processed quickly, report a bug. If you don’t know whether the command should take a long time, find out by looking in the manual or by asking for assistance.
If a command you are familiar with causes an R error message in a case where
its usual definition ought to be reasonable, it is probably a bug. If a
command does the wrong thing, that is a bug. But be sure you know for
certain what it ought to have done. If you aren’t familiar with the
command, or don’t know for certain how the command is supposed to work, then
it might actually be working right. For example, people sometimes think
there is a bug in R’s mathematics because they don’t understand how
finite-precision arithmetic works. Rather than jumping to conclusions, show
the problem to someone who knows for certain. Unexpected results of
comparison of decimal numbers, for example 0.28 * 100 != 28 or
0.1 + 0.2 != 0.3, are not a bug. See 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.
When you decide that there is a bug, it is important to report it and to report it in a way which is useful. What is most useful is an exact description of what commands you type, starting with the shell command to run R, until the problem happens. Always include the version of R, machine, and operating system that you are using; type version in R to print this.
The most important principle in reporting a bug is to report facts, not hypotheses or categorizations. It is always easier to report the facts, but people seem to prefer to strain to posit explanations and report them instead. If the explanations are based on guesses about how R is implemented, they will be useless; others will have to try to figure out what the facts must have been to lead to such speculations. Sometimes this is impossible. But in any case, it is unnecessary work for the ones trying to fix the problem.
For example, suppose that on a data set which you know to be quite large the command
R> data.frame(x, y, z, monday, tuesday)
never returns. Do not report that data.frame() fails for large data
sets. Perhaps it fails when a variable name is a day of the week. If this
is so then when others got your report they would try out the
data.frame() command on a large data set, probably with no day of the
week variable name, and not see any problem. There is no way in the world
that others could guess that they should try a day of the week variable
name.
Or perhaps the command fails because the last command you used was a method
for "["() that had a bug causing R’s internal data structures to be
corrupted and making the data.frame() command fail from then on.
This is why others need to know what other commands you have typed (or read
from your startup file).
It is very useful to try and find simple examples that produce apparently the same bug, and somewhat useful to find simple examples that might be expected to produce the bug but actually do not. If you want to debug the problem and find exactly what caused it, that is wonderful. You should still report the facts as well as any explanations or solutions. Please include an example that reproduces (e.g., https://en.wikipedia.org/wiki/Reproducibility) the problem, preferably the simplest one you have found.
Invoking R with the --vanilla option may help in isolating a bug. This ensures that the site profile and saved data files are not read.
Before you actually submit a bug report, you should check whether the bug has already been reported and/or fixed. First, try the “Show open bugs new-to-old” or the search facility on https://bugs.R-project.org/. Second, consult https://svn.R-project.org/R/trunk/doc/NEWS.Rd, which records changes that will appear in the next release of R, including bug fixes that do not appear on the Bug Tracker. Third, if possible try the current r-patched or r-devel version of R. If a bug has already been reported or fixed, please do not submit further bug reports on it. Finally, check carefully whether the bug is with R, or a contributed package. Bug reports on contributed packages should be sent first to the package maintainer, and only submitted to the R-bugs repository by package maintainers, mentioning the package in the subject line.
A bug report can be generated using the function bug.report(). For
reports on R this will open the Web page at
http://bugs.R-project.org/: for a contributed package it will open the
package’s bug tracker Web page or help you compose an email to the
maintainer.
There is a section of the bug repository for suggestions for enhancements for R labelled ‘wishlist’. Suggestions can be submitted in the same ways as bugs, but please ensure that the subject line makes clear that this is for the wishlist and not a bug report, for example by starting with ‘Wishlist:’.
Comments on and suggestions for the Windows port of R should be sent to R-windows@R-project.org.
Corrections to and comments on message translations should be sent to the last translator (listed at the top of the appropriate ‘.po’ file) or to the translation team as listed at https://developer.R-project.org/TranslationTeams.html.
Of course, many many thanks to Robert and Ross for the R system, and to the package writers and porters for adding to it.
Special thanks go to Doug Bates, Peter Dalgaard, Paul Gilbert, Stefano Iacus, Fritz Leisch, Jim Lindsey, Thomas Lumley, Martin Maechler, Brian D. Ripley, Anthony Rossini, and Andreas Weingessel for their comments which helped me improve this FAQ.
More to come soon …