text:
This document contains answers to some of the most frequently asked questions about R.
and D()
to work?anova()
depend on the order of factors in the model?lmer()
?Copyright © 1998–2020 Kurt Hornik
Copyright © 2021–2024 R Core Team
This document is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version.
This document is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
Copies of the GNU General Public License versions are available at
The latest version of this document is always available from
From there, you can obtain versions converted to HTML and PDF.
In publications, please refer to this FAQ as Hornik and R Core Team (2024), "The R FAQ", and give the above, official URL:
@Misc{, author = {Kurt Hornik and the R Core Team}, title = {{R} {FAQ}}, year = {2024}, url = {https://CRAN.R-project.org/doc/manuals/R-FAQ.html} }
Everything should be pretty standard. 'R>' is used for the R prompt, and a '$' for the shell prompt (where applicable).
Feedback via email to email@example.com is most welcome.
Features specific to the Windows and macOS 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 us 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, currently consisting of
John Chambers, Peter Dalgaard, Robert Gentleman, Kurt Hornik, Ross Ihaka, Tomas Kalibera, Michael Lawrence, Uwe Ligges, Thomas Lumley, Martin Maechler, Sebastian Meyer, Paul Murrell, Martyn Plummer, Brian Ripley, Deepayan Sarkar, Duncan Temple Lang, Luke Tierney, and Simon Urbanek,
plus Heiner Schwarte up to October 1999, Guido Masarotto up to June 2003, Stefano Iacus up to July 2014, Seth Falcon up to August 2015, Duncan Murdoch up to September 2017, Martin Morgan up to June 2021, Douglas Bates up to March 2024, and Friedrich Leisch up to April 2024.
R has a home page at https://www.R-project.org/. It is free software 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/x86_64, alpha, arm, arm64, hppa, mips/mipsel, powerpc, s390x and sparc CPUs (e.g., https://buildd.debian.org/build.php?&pkg=r-base), 386-hurd-gnu, cpu-kfreebsd-gnu for i386 and amd64, i386-pc-solaris, rs6000-ibm-aix, sparc-sun-solaris, x86_64-apple-darwin, aarch64-apple-darwin, x86_64-unknown-freebsd and x86_64-unknown-openbsd.
If you know about other platforms, please drop us a note.
R uses a 'major.minor.patchlevel' numbering scheme. Based on this, there are the current release version of R ('r-release') as well as two development versions of R, a patched version of the current release ('r-patched') and one working towards the next minor or eventually major ('r-devel') releases of R, respectively. New features are typically introduced in r-devel, while r-patched is for bug fixes mostly.
See https://CRAN.R-project.org/sources.html for the current versions of r-release, r-patched and r-devel.
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 90 compiler as well as a C compiler to build R.
In the simplest case, untar the R source code, change to the directory thus created, and issue the following commands (at the shell prompt):
$ ./configure $ make
If these commands execute successfully, the R binary and a shell script
front-end called R are created and copied to the bin
directory. You can copy the script to a place where users can invoke
it, for example to /usr/local/bin. In addition,
HTML versions of the R manuals (e.g., R-exts.html, the
"Writing R Extensions" manual) are built in the doc/manual
subdirectory (if a suitable texi2any
program was found).
Use make pdf to build PDF (Portable Document Format) versions of the R manuals, including fullrefman.pdf (an R object reference index). Manuals written in the GNU Texinfo system can also be converted to .info files suitable for reading online with Emacs or stand-alone GNU Info; use make info to create these files.
Finally, use make check to find out whether your R system works correctly.
You can also perform a "system-wide" installation using make install. By default, this will install to the following directories:
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
with the option
$ ./configure --prefix=/where/you/want/R/to/go
(E.g., the R executable will then be installed into /where/you/want/R/to/go/bin.)
To install info and PDF versions of the manuals, use make install-info and make install-pdf, respectively.
The bin/windows directory of a CRAN site contains binaries for a base distribution and add-on packages from CRAN to run on 64-bit versions of Windows 7 and later on x86_64 chips (R 4.1.3 was the last version of R to support 32-bit Windows). The Windows version of R was created by Robert Gentleman and Guido Masarotto; Brian Ripley and Duncan Murdoch made substantial contributions and it is now being maintained by other members of the R Core team.
The same directory has links to snapshots of the r-patched and r-devel versions of R.
See the "R for Windows FAQ" for more details.
The bin/macosx directory of a CRAN site contains a standard Apple installer package to run on macOS 10.13 ('High Sierra') or later, and another which runs only on 'Apple Silicon' Macs under macOS 11 ('Big Sur') or later. Once downloaded and executed, the installer will install the current release of R and R.app, the macOS GUI. This port of R for macOS is maintained by Simon Urbanek (and previously by Stefano Iacus). The "R for macOS FAQ" has more details.
Snapshots of the r-patched and r-devel versions of R are available as Apple installer packages at https://mac.R-project.org.
Binary distributions of R are available on many Unix-like OSes: only some can be mentioned here so check your OS's search facilities to see if one is available for yours.
The bin/linux directory of a CRAN site contains R packages for Debian and Ubuntu.
Debian packages, maintained by Dirk Eddelbuettel, have long been part of
the Debian distribution, and can be accessed through APT, the Debian
package maintenance tool. Use e.g. apt-get install r-base
to install the R environment and recommended packages.
If you also want to build R packages from source, also run apt-get
install r-base-dev
to obtain the additional tools required for this.
So-called "backports" of the current R packages for at least the
stable distribution of Debian are provided by Johannes Ranke, and
available from CRAN. See
https://CRAN.R-project.org/bin/linux/debian/index.html for details on R
Debian packages and installing the backports, which should also be
suitable for other Debian derivatives. Native backports for Ubuntu are
provided by Michael Rutter, see
https://CRAN.R-project.org/bin/linux/ubuntu/index.html for
R binaries for Fedora, maintained by Tom "Spot" Callaway and
Iñaki Ucar, are provided as part of the Fedora distribution and can
be accessed through dnf
, the RPM installer/updater.
The Fedora R RPM is a "meta-package" which installs all the
user and developer components of R (available separately as
and R-core-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
and libRmath-devel
), although this is not
necessary to use R. When a new version of R is released, there may be a
delay of up to 2 weeks until the Fedora RPM becomes publicly
available, as it must pass through the Fedora update process.
The Extra Packages for Enterprise Linux (EPEL) project
(https://docs.fedoraproject.org/en-US/epel/) provides ports of the
Fedora RPMs for RedHat Enterprise Linux and compatible
distributions (e.g., CentOS Stream, Scientific Linux, Oracle
Linux, AlmaLinux, or Rocky Linux among others).
RPMs for selection of R packages are also provided by Fedora.
Additional RPMs for R packages are maintained by Iñaki Ucar
on Fedora Copr. See https://CRAN.R-project.org/bin/linux/fedora/
for further details and installation instructions.
No other binary distributions are currently publicly available via CRAN.
Online documentation for most of the functions and variables in R exists, and can be printed on-screen by typing help(name) (or ?name) at the R prompt, where name is the name of the topic help is sought for. (In the case of unary and binary operators and control-flow special forms, the name may need to be quoted.)
This documentation can also be made available as one reference manual for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX, see How can R be installed?. An up-to-date HTML version is always available for web browsing at https://stat.ethz.ch/R-manual/.
The R distribution also comes with the following manuals.
An 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://johnmchambers.su.domains/Rbook/.
Peter Dalgaard (2008), "Introductory Statistics with R", 2nd edition. Springer, ISBN 978-0-387-79053-4, http://publicifsv.sund.ku.dk/~pd/ISwR.html.
Robert Gentleman (2008), "R Programming for Bioinformatics". Chapman & Hall/CRC, Boca Raton, FL, ISBN 978-1-420-06367-7, https://bioconductor.org/help/publications/books/r-programming-for-bioinformatics/.
Stefano M. Iacus (2008), "Simulation and Inference for Stochastic Differential Equations: With R Examples". Springer, New York, ISBN 978-0-387-75838-1.
Deepayan Sarkar (2007), "Lattice: Multivariate Data Visualization with R". Springer, New York, ISBN 978-0-387-75968-5.
W. John Braun and Duncan J. Murdoch (2007), "A First Course in Statistical Programming with R". Cambridge University Press, Cambridge, ISBN 978-0521872652.
P. Murrell (2005), "R Graphics", Chapman & Hall/CRC, ISBN 1-584-88486-X, https://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html.
William N. Venables and Brian D. Ripley (2002), "Modern Applied Statistics with S" (4th edition). Springer, ISBN 0-387-95457-0, https://www.stats.ox.ac.uk/pub/MASS4/.
Jose C. Pinheiro and Douglas M. Bates (2000), "Mixed-Effects Models in S and S-Plus". Springer, ISBN 0-387-98957-0.
Last, but not least, Ross' and Robert's experience in designing and implementing R is described in Ihaka & Gentleman (1996), "R: A Language for Data Analysis and Graphics", Journal of Computational and Graphical Statistics, 5, 299–314 (doi: 10.1080/10618600.1996.10474713).
To cite R in publications, use
@Manual{, title = {R: A Language and Environment for Statistical Computing}, author = {{R Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = YEAR, url = {https://www.R-project.org} }
where YEAR is the release year of the version of R used and can
determined as R.version$year
Citation strings (or BibTeX entries) for R and R packages can also be
obtained by citation()
Thanks to Martin Maechler, there are several mailing lists devoted to R, including the following:
A moderated list for major announcements about the development of R and the availability of new code.
A moderated list for announcements on the availability of new or enhanced contributed packages.
The 'main' R mailing list, for discussion about problems and solutions encountered using R, including using R packages in the standard R distribution and on CRAN; announcements (not covered by 'R-announce' or 'R-packages'); the availability of new functionality for R and documentation of R; and for posting nice examples and benchmarks.
This list is for questions and discussion about code development in R.
A list which provides a forum for those developing R packages.
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 firstname.lastname@example.com.
Send email to firstname.lastname@example.com 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 "Comprehensive R Archive Network" (CRAN) is a collection of sites which carry identical material, consisting of the R distribution(s), the contributed extensions, documentation for R, and binaries.
The CRAN main site at WU (Wirtschaftsuniversität Wien) in Austria can be found at the URL
and is mirrored daily to many sites around the world. See https://CRAN.R-project.org/mirrors.html for a complete list of mirrors. Please use the CRAN site closest to you to reduce network load.
From CRAN, you can obtain the latest official release of R, daily snapshots of R (copies of the current source trees), as gzipped and bzipped tar files, a wealth of additional contributed code, as well as prebuilt binaries for various operating systems (Linux, Mac OS Classic, macOS, and MS Windows). CRAN also provides access to documentation on R, existing mailing lists and the R Bug Tracking system.
Since March 2016, "old" material is made available from a central CRAN archive server (https://CRAN-archive.R-project.org/).
Please always use the URL of the master site when referring to CRAN.
R is released under the GNU General Public License (GPL), version 2 or version 3. If you have any questions regarding the legality of using R in any particular situation you should bring it up with your legal counsel. We are in no position to offer legal advice.
It is the opinion of the R Core Team that one can use R for commercial purposes (e.g., in business or in consulting). The GPL, like all Open Source licenses, permits all and any use of the package. It only restricts distribution of R or of other programs containing code from R. This is made clear in clause 6 ("No Discrimination Against Fields of Endeavor") of the Open Source Definition:
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://johnmchambers.su.domains/papers/96.7.ps for further information on the "Evolution of the S Language".
S-PLUS is a value-added version of S 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 distribution and density functions distribution as explicit arguments. (Example compiled from various postings by Luke Tierney.)
The S-PLUS documentation for call()
basically suggests the
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 (doi: 10.1080/10618600.2000.10474895).
Nested lexically scoped functions also imply a further major difference. Whereas S stores all objects as separate files in a directory somewhere (usually .Data under the current directory), R does not. All objects in R are stored internally. When R is started up it grabs a piece of memory and uses it to store the objects. R performs its own memory management of this piece of memory, growing and shrinking its size as needed. Having everything in memory is necessary because it is not really possible to externally maintain all relevant "environments" of symbol/value pairs. This difference also seems to make R faster than S.
The down side is that if R crashes you will lose all the work for the
current session. Saving and restoring the memory "images" (the
functions and data stored in R's internal memory at any time) can be a
bit slow, especially if they are big. In S this does not happen,
because everything is saved in disk files and if you crash nothing is
likely to happen to them. (In fact, one might conjecture that the S
developers felt that the price of changing their approach to persistent
storage just to accommodate lexical scope was far too expensive.)
Hence, when doing important work, you might consider saving often (see
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
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
, 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
, in R, you have to insulate powers of numeric vectors (using
), i.e., you have to use lm(y ~ I(x^3))
is set to "na.omit"
by default in R,
but not set in S.
y ~ x + 0
is an alternative to y ~ x - 1
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.
is a list, then x[i] <- NULL
and x[[i]]
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)
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
more details.
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
can only load shared objects, as created
for example by R CMD SHLIB.
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.
loops are not necessary and hence not supported.
uses the argument envir= rather than
where= as in S.
int *
rather than long *
as in S.
returns the names of the objects in the current
(under R) and global (under S) environment, respectively. For example,
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.
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).
is a
data frame, then is.matrix(DF)
returns FALSE
in R and
in S).
f(a) <- b
evaluated as a <- "f<-"(a, value = b)
. S always takes the last
argument, irrespective of its name.
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.
loop is local to the inside
of the loop. In R it is local to the environment where the for()
statement is executed.
returns a vector where R returns a
one-dimensional array (which can have named dimnames).
"aA" < "Bb"
true or false). From version 1.2.0 the locale can be (re-)set in R by
the Sys.setlocale()
remains TRUE
if arg is
subsequently modified; in R it doesn't.
strips I()
when creating
(column) names.
is not treated as a missing value in a
character variable. Use as.character(NA)
to create a missing
character value.
, 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.
a character vector index looks only for exact matches (whereas [[
and $
allow partial matches). In S, [
allows partial
and no atan2
. A call
in S such as atan(x1, x2)
is equivalent to R's atan2(x1,
. 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
(doi: 10.1080/10618600.2000.10474900).
For a very long time, there was no such thing.
Revolution Analytics has released REvolution R, now available as Microsoft R (see https://blog.revolutionanalytics.com/2016/01/microsoft-r-open.html for more information).
See also https://en.wikipedia.org/wiki/R_programming_language#Commercialized_versions_of_R for pointers to commercialized versions of R.
Please refer to the CRAN task view on "Web Technologies and Services" (https://CRAN.R-project.org/view=WebTechnologies), specifically section "Web and Server Frameworks", for up-to-date information on R web interface packages.
Early references on R web interfaces include Jeff Banfield (1999), "Rweb: Web-based Statistical Analysis" (doi: 10.18637/jss.v004.i01), David Firth (2003), "CGIwithR: Facilities for processing web forms using R" (doi: 10.18637/jss.v008.i10), and Angelo Mineo and Alfredo Pontillo (2006), "Using R via PHP for Teaching Purposes: R-php" (doi: 10.18637/jss.v017.i04).
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.
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".
Support for spares and dense matrices
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).
Functions for cluster analysis.
Code analysis tools.
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.
Recursive PARTitioning and regression trees.
Functions for kriging and point pattern analysis from "Modern Applied Statistics with S" by W. Venables and B. Ripley.
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.
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://bioconductor.org/install/ for available packages and a complete taxonomy via BioC Views.
Many more packages are available from places other than the default repositories discussed above (CRAN and Bioconductor). In particular, R-Forge provides a CRAN style repository at https://R-Forge.R-project.org/.
More code has been posted to the R-help mailing list, and can be obtained from the mailing list archive.
(Unix-like only.) The add-on packages on CRAN come as gzipped tar
files named pkg_version.tar.gz
. Let path be
the path to such a package file. Provided that tar
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
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
To find out which additional packages are available on your system, type
at the R prompt.
This produces something like
Packages in library '/home/me/lib/R': mystuff My own R functions, nicely packaged but not documented Packages in library '/usr/local/lib/R/library': KernSmooth Functions for Kernel Smoothing Supporting Wand & Jones (1995) MASS Support Functions and Datasets for Venables and Ripley's MASS Matrix Sparse and Dense Matrix Classes and Methods base The R Base Package boot Bootstrap Functions (Originally by Angelo Canty for S) class Functions for Classification cluster "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al. codetools Code Analysis Tools for R compiler The R Compiler Package datasets The R Datasets Package foreign Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', ... grDevices The R Graphics Devices and Support for Colours and Fonts graphics The R Graphics Package grid The Grid Graphics Package lattice Trellis Graphics for R methods Formal Methods and Classes mgcv Mixed GAM Computation Vehicle with Automatic Smoothness Estimation nlme Linear and Nonlinear Mixed Effects Models nnet Feed-Forward Neural Networks and Multinomial Log-Linear Models parallel Support for Parallel Computation in R rpart Recursive Partitioning and Regression Trees spatial Functions for Kriging and Point Pattern Analysis splines Regression Spline Functions and Classes stats The R Stats Package stats4 Statistical Functions using S4 Classes survival Survival Analysis tcltk Tcl/Tk Interface tools Tools for Package Development utils The R Utils Package
You can "load" the installed package pkg by
You can then find out which functions it provides by typing one of
library(help = pkg) help(package = pkg)
You can remove the loaded package pkg from the search()
path by
(which does not by default unload the namespace, see ?detach
$ R CMD REMOVE pkg_1 … pkg_n
to remove the packages pkg_1, …, pkg_n from the
library tree rooted at the first directory given in R_LIBS
if this
is set and non-null, and from the default library otherwise.
To remove from library lib, do
$ R CMD REMOVE -l lib pkg_1 … pkg_n
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 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()
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.
ESS comes with detailed installation instructions.
For help with ESS, send email to email@example.com.
Please send bug reports and suggestions on ESS to firstname.lastname@example.com. 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, instead of just running it in a console, definitely. As an alternative to other IDEs such as RStudio, possibly, notably if you are interested to use Emacs for other computer interaction. You'd be using ESS, Emacs Speaks Statistics, see previous FAQ.
Inferior R mode provides a readline/history mechanism, object name completion, and syntax-based highlighting of the interaction buffer using Font Lock mode, as well as a very convenient interface to the R help system.
Of course, it also integrates nicely with the mechanisms for editing R source using Emacs. One can write code in one Emacs buffer and send whole or parts of it for execution to R; this is helpful for both data analysis and programming. One can also seamlessly integrate with a revision control system, in order to maintain a log of changes in your programs and data, as well as to allow for the retrieval of past versions of the code.
In addition, it allows you to keep a record of your session, which can also be used for error recovery through the use of the transcript mode.
To specify command line arguments for the inferior R process, use C-u M-x R for starting R.
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
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
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
and *gud-gdb*
windows. Within the *R*
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
and D()
to work?anova()
depend on the order of factors in the model?lmer()
?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]]
, 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.
saves the objects in the user's .GlobalEnv
the file .RData in the R startup directory. (This is also what
happens after q("yes").) Using save.image(file)
can save the image under a different name.
To remove all objects in the currently active environment (typically
), you can do
rm(list = ls(all.names = TRUE))
(Without all = TRUE, only the objects with names not starting with a '.' are removed.)
and D()
to work?Strange things will happen if you use eval(print(x), envir = e)
or D(x^2, "x")
. The first one will either tell you that
" is not found, or print the value of the wrong x
The other one will likely return zero if x
exists, and an error
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
. 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)
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]
Autoloading is rarely used since packages became lazy-loaded.
R has a special environment called .AutoloadEnv
. Using
autoload(name, pkg), where name and
pkg are strings giving the names of an object and the package
containing it, stores some information in this environment. When R
tries to evaluate name, it loads the corresponding package
pkg and reevaluates name in the new package's
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"
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
, 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"
This is about a problem rarely seen with modern X11 installations.
On an X11 device, plotting sometimes, e.g., when running
, results in "Error: color allocation error".
This is an X problem, and only indirectly related to R. It occurs when
applications started prior to R have used all the available colors.
(How many colors are available depends on the X configuration; sometimes
only 256 colors can be used.)
You could also set the colortype
of X11()
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
to get the numbers back. More efficient, but harder to remember, is
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
is fairly general and provides most of the functionality
of splom()
, and that R's default plot method has an argument
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
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()
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()
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
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
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()
, 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
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:
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
). Argument names are also usually syntactic names,
but again can be anything if they are quoted.
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
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
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
d file or inside a function it isn't printed unless you
specifically print it.
To print the value use
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 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()
depend on the order of factors in the model?In a model such as ~A+B+A:B
, R will report the difference in sums
of squares between the models ~1
. If the model were ~B+A+A:B
, R would report
differences between ~1
, and
. In the first case the sum of squares for A
comparing ~1
and ~A
, in the second case it is comparing
and ~B+A
. In a non-orthogonal design (i.e., most
unbalanced designs) these comparisons are (conceptually and numerically)
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,
, and drop1(model1)
will show the sums of
squares resulting from dropping single terms.
Under a Unix-like, if your installation supports the
option to the png()
device there should be no
problems, and the default settings should just work. This option is not
available for versions of R prior to 2.7.0, or without support for
cairo. From R 2.7.0 png()
by default uses the Quartz device
on macOS, and that too works in batch mode.
Earlier versions of the png()
device used the X11 driver, which
is a problem in batch mode or for remote operation. If you have
Ghostscript you can use bitmap()
, which produces a PostScript or
PDF file then converts it to any bitmap format supported by Ghostscript.
On some installations this produces ugly output, on others it is
perfectly satisfactory. Many systems now come with Xvfb from
X.Org (possibly as an optional
install), which is an X11 server that does not require a screen.
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
(Red Hat).
If you have
varname <- c("a", "b", "d")
you can do
get(varname[1]) + 2
a + 2
assign(varname[1], 2 + 2)
a <- 2 + 2
eval(substitute(lm(y ~ x + variable), list(variable = as.name(varname[1]))))
lm(y ~ x + a)
At least in the first two cases it is often easier to just use a list, and then you can easily index it by name
vars <- list(a = 1:10, b = rnorm(100), d = LETTERS) vars[["a"]]
without any of this messing about. This becomes especially true if you
are finding yourself creating and trying to programmatically access
groups of related variables such as result1
, result2
, and so on: instead of fighting against the language to
assign(paste0("result", i), process(get(paste0("dataset", i))))
it is much easier to put the related variables in lists and use
result[[i]] <- process(dataset[[i]])
and, eventually,
result <- lapply(dataset, process)
which is easy to replace with parLapply
for parallel processing.
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()
To sort the rows within a data frame, with respect to the values in one
or more of the columns, simply use order()
DF[order(DF$a, DF[["b"]]), ]
to sort the data frame DF
columns named a
and b
From R 4.4.0, sort_by()
provides a less verbose alternative
with a formula interface (e.g., sort_by(DF, ~a + b)
Since R 2.10.0, the browser-based search engine in help.start()
is an HTML interface to help.search()
, and should always work.
Before that, the engine utilized a Java applet. In order for this to
function properly, one needed a compatible version of Java installed on
the system and linked to the browser, and both Java and
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()
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
, 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
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
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
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. All other numbers are internally rounded to (typically) 53 binary digits accuracy. As a result, two floating point numbers will not reliably be equal unless they have been computed by the same algorithm, and not always even then. For example
R> a <- sqrt(2) R> a * a == 2 [1] FALSE R> a * a - 2 [1] 4.440892e-16 R> print(a * a, digits = 18) [1] 2.00000000000000044
The function all.equal()
compares two objects using a numeric
tolerance of .Machine$double.eps ^ 0.5
. If you want much greater
accuracy than this you will need to consider error propagation
A discussion with many easily followed examples is in Appendix G "Computational Precision and Floating Point Arithmetic", pages 753–771 of Statistical Analysis and Data Display: An Intermediate Course with Examples in R, Richard M. Heiberger and Burt Holland (Springer 2015, second edition). This appendix is a free download from https://link.springer.com/content/pdf/bbm:978-1-4939-2122-5/1.pdf.
For more information, see e.g. David Goldberg (1991), "What Every Computer Scientist Should Know About Floating-Point Arithmetic", ACM Computing Surveys, 23/1, 5–48, also available via https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html.
Here is another example, this time using addition:
R> .3 + .6 == .9 [1] FALSE R> .3 + .6 - .9 [1] -1.110223e-16 R> print(matrix(c(.3, .6, .9, .3 + .6)), digits = 18) [,1] [1,] 0.299999999999999989 [2,] 0.599999999999999978 [3,] 0.900000000000000022 [4,] 0.899999999999999911
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
and misunderstanding the precedence rules for expressions in R. Write
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", paste0("result", i, ".rda")) 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()
, 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
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()
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
), 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_linerange()
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)
); 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
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
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
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.
From R 4.2.0, "libcurl"
download method is always available and
used for HTTPS by default on all platforms. It has been used
since R 3.3.0 everywhere but Windows where the default method
also supported HTTPS.
So nothing needs to be done to access 'https://' websites in recent versions of R.
Since March 2016, Windows and macOS binaries of CRAN packages for old
versions of R (released more than 5 years ago) are made available from a
central CRAN archive server instead of the CRAN mirrors. To get
these, one should set the CRAN "mirror" element of the repos
option accordingly, by something like
local({r <- getOption("repos") r["CRAN"] <- "http://CRAN-archive.R-project.org" options(repos = r) })
(see ?options
for more information).
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 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
See Inspecting R objects when debugging in Writing R Extensions.
Suppose you have C code file for dynloading into R, but you want to use
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 Add-on packages in R Installation and Administration.
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
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
, .Fortran()
, .External()
or .Call()
) yourself (or in a function you wrote), you can
always crash R by using wrong argument types (modes). This is not a
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.
See also "Making sure it's a bug" in Bug Reporting in R for more information.
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
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()
fail from then on. This is why others need to know what other commands
you have typed (or read from your startup file).
It is very useful to try and find simple examples that produce apparently the same bug, and somewhat useful to find simple examples that might be expected to produce the bug but actually do not. If you want to debug the problem and find exactly what caused it, that is wonderful. You should still report the facts as well as any explanations or solutions. Please include an example that reproduces (e.g., https://en.wikipedia.org/wiki/Reproducibility) the problem, preferably the simplest one you have found.
Invoking R with the --vanilla option may help in isolating a bug. This ensures that the site profile and saved data files are not read.
Before you actually submit a bug report, you should check whether the bug has already been reported and/or fixed. First, try the "Show open bugs new-to-old" or the search facility on https://bugs.R-project.org/. Second, consult https://svn.R-project.org/R/trunk/doc/NEWS.Rd, which records changes that will appear in the next release of R, including bug fixes that do not appear on the Bug Tracker. Third, if possible try the current r-patched or r-devel version of R. If a bug has already been reported or fixed, please do not submit further bug reports on it. Finally, check carefully whether the bug is with R, or a contributed package. Bug reports on contributed packages should be sent first to the package maintainer, and only submitted to the R-bugs repository by package maintainers, mentioning the package in the subject line.
A bug report can be generated using the function bug.report()
For reports on R this will open the R Bugzilla page at
https://bugs.R-project.org/: for a contributed package it will open
the package's bug tracker Web page or help you compose an email to the
maintainer. Since 2016, only "members" (including all who have
previously submitted bugs) can submit new bugs on the R Bugzilla. See
"Where to submit bug reports and patches" on
Bug Reporting in R for more
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 firstname.lastname@example.com.
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 …


text:
DND DC Duo 100% Pure Gel Pack: 1 Gel Lacquer .5 oz + 1 Lacquer .5oz Matching ColorWith the new formula, Daisy Gel Nail Polish system needs only Gel Color and Top Coat, eliminating the use of Base Coat and Bond. It lasts to 21 days. LED and UV cured. Made in USA.
- Gently remove the shine from nail bed with 180/240 buffer and clean the oil/dirt from nail bed.
- Apply 1st thin layer of Daisy soak-off gel color. Be sure to seal the free edge foe each step
- Cure under UV Lamp for 2 mins or LED for 60 seconds
- Apply 2nd layer Daisy Soak-Off Gel Color. Cure under UV Lamp or LED lamp with the same time as step 3
- Apply Daisy Top Gel. Cure under UV Lamp or LED with the same time as step 3
- Use a lint free pad/nail wipes with nail cleanser to remove the tacky residue on the nail
Additional Details
- 24704321

subset: commoncrawl