Information Density: The R Project for Statistical Computing – Signal Evidence & AI Readability

[H1] The R Project for Statistical Computing
[H2] Getting Started
R is a free software environment for statistical computing and
graphics. It compiles and runs on a wide variety of UNIX platforms,
Windows and MacOS. To download R,
please choose your preferred CRAN mirror.
If you have questions about R like how to download and install the
software, or what the license terms are, please read our answers to frequently asked
questions before you send an email.
[H2] News
R
version 4.6.1 (Happy Hop) prerelease versions will appear
starting Sunday 2026-06-14. Final release is scheduled for Wednesday
2026-06-24.
R version
4.5.3 (Reassured Reassurer) has been released on
2026-03-11.
The useR!
2026 conference will take place in Warsaw, Poland, July
7-9.
You can support the R Foundation with a renewable subscription as a
supporting
member.
[H2] News via Mastodon
<div class="loading-spinner"></div>
[H2] Social Media
Follow the R Foundation on
Mastodon,
BlueSky,
or
LinkedIn.

[H1] Getting Help with R
[H2] Helping Yourself
Before asking others for help, it’s generally a good idea for you to
try to help yourself. R includes extensive facilities for accessing
documentation and searching for help. There are also specialized search
engines for accessing information about R on the internet, and general
internet search engines can also prove useful (see below).
[H3] R Help: help() and
?
The help() function and ? help operator in
R provide access to the documentation pages for R functions, data sets,
and other objects, both for packages in the standard R distribution and
for contributed packages. To access documentation for the standard
lm (linear model) function, for example, enter the command
help(lm) or help("lm"), or ?lm or
?"lm" (i.e., the quotes are optional).
To access help for a function in a package that’s not
currently loaded, specify in addition the name of the package: For
example, to obtain documentation for the rlm() (robust
linear model) function in the MASS package,
help(rlm, package="MASS").
Standard names in R consist of upper- and lower-case letters,
numerals (0-9), underscores (_), and periods
(.), and must begin with a letter or a period. To obtain
help for an object with a non-standard name (such as the help
operator ?), the name must be quoted: for example,
help('?') or ?"?".
You may also use the help() function to access
information about a package in your library — for example,
help(package="MASS") — which displays an index of available
help pages for the package along with some other information.
Help pages for functions usually include a section with executable
examples illustrating how the functions work. You can execute these
examples in the current R session via the example()
command: e.g., example(lm).
[H3] Vignettes
and Code Demonstrations: browseVignettes(),
vignette() and demo()
Many packages include vignettes, which are discursive
documents meant to illustrate and explain facilities in the package. You
can discover vignettes by accessing the help page for a package, or via
the browseVignettes() function: the command
browseVignettes() opens a list of vignettes from
all of your installed packages in your browser, while
browseVignettes(package=package-name) (e.g.,
browseVignettes(package="survival")) shows the vignettes,
if any, for a particular package. vignette() is employed
similarly, but displays a list of vignettes in text form.
You can also use the vignette("vignette-name") command
to view a vignette (possibly specifying the name of the package in which
the vignette resides, if the vignette name is not unique): for example,
vignette("timedep") or
vignette("timedep", package="survival") (which are, in this
case, equivalent).
Vignettes may also be accessed from the CRAN page for the package
(e.g. survival), if you
wish to review the vignette for a package prior to installing and/or
using it.
Packages may also include extended code demonstrations (“demos”). The
command demo() lists all demos for all packages in your
library, while demo(package="package-name") (e.g.,
demo(package="stats")) lists demos in a particular package.
To run a demo, call the demo() function with the quoted
name of the demo (e.g., demo("nlm")), specifying the name
of the package if the name of the demo isn’t unique (e.g.,
demo("nlm", package="stats"), where, in this case, the
package name need not be given explicitly).
[H3] Searching for Help Within R
The help() function and ? operator are
useful only if you already know the name of the function that you wish
to use. There are also facilities in the standard R distribution for
discovering functions and other objects. The following functions cast a
progressively wider net. Use the help system to obtain complete
documentation for these functions: for example,
?apropos.
[H4] apropos()
The apropos() function searches for objects, including
functions, directly accessible in the current R session that have names
that include a specified character string. This may be a literal string
or a regular expression to be used for pattern-matching (see
?"regular expression"). By default, string matching by
apropos() is case-insensitive. For example,
apropos("^glm") returns the names of all accessible objects
that start with the (case-insensitive) characters
"glm".
[H4] help.search() and
??
The help.search() function scans the documentation for
packages installed in your library. The (first) argument to
help.search() is a character string or regular expression.
For example, help.search("^glm") searches for help pages,
vignettes, and code demos that have help “aliases,” “concepts,” or
titles that begin (case-insensitively) with the characters
"glm". The ?? operator is a synonym for
help.search(): for example, ??"^glm".
[H4] RSiteSearch()
RSiteSearch() uses an internet search engine (also see
below) to search for information
in function help pages and vignettes for all CRAN packages, and in CRAN
task views (described below). Unlike the
apropos() and help.search() functions,
RSiteSearch() requires an active internet connection and
doesn’t employ regular expressions. Braces may be used to specify
multi-word terms; otherwise matches for individual words are included.
For example, RSiteSearch("{generalized linear model}")
returns information about R functions, vignettes, and CRAN task views
related to the term "generalized linear model" without
matching the individual words "generalized",
"linear", or "model".
findfn() and ??? in the
sos package, which is not part of the standard
R distribution but is available on CRAN, provide an alternative
interface to RSiteSearch().
[H4] help.start()
help.start() starts and displays a hypertext based
version of R’s online documentation in your default browser that
provides links to locally installed versions of the R manuals, a listing
of your currently installed packages and other documentation
resources.
[H2] R Help on the Internet
There are internet search sites that are specialized for R searches,
including search.r-project.org (which is
the site used by RSiteSearch) and Rseek.org.
It is also possible to use a general search site like Google, by qualifying the search with
“R” or the name of an R package (or both). It can be particularly
helpful to paste an error message into a search engine to find out
whether others have solved a problem that you encountered.
[H3] CRAN Task Views
CRAN Task Views are documents that summarize R resources on CRAN in
particular areas of application, helping your to navigate the maze of
thousands of CRAN packages. A list of available Task
Views may be found on CRAN.
[H3] R FAQs (Frequently Asked
Questions)
There are three primary FAQ listings which are periodically updated
to reflect very commonly asked questions by R users. There is a Main R FAQ, a
Windows
specific R FAQ and a Mac OS (OS
X) specific R FAQ.
[H2] Asking for Help
If you find that you can’t answer a question or solve a problem
yourself, you can ask others for help, either locally (if you know
someone who is knowledgeable about R) or on the internet. In order to
ask a question effectively, it helps to phrase the question clearly,
and, if you’re trying to solve a problem, to include a small,
self-contained, reproducible example of the problem that others can
execute. For information on how to ask questions, see, e.g., the R
mailing list posting guide,
and the document about how
to create reproducible examples for R on Stack Overflow.
[H3] Stack Overflow
Stack Overflow is a well
organized and formatted site for help and discussions about programming.
It has excellent searchability. Topics are tagged, and “r” is a very popular tag
on the site with almost 150,000 questions (as of summer 2016). To go
directly to R-related topics, visit http://stackoverflow.com/questions/tagged/r.
For an example both of the value of the site’s organization and
information that is very useful to R users, see “How
to make a great R reproducible example?”, which is also mentioned
above.
[H3] R Email Lists
The R Project maintains a number of subscription-based email lists for posing
and answering questions about R, including the general R-help email
list, the R-devel list
for R code development, and R-package-devel
list for developers of CRAN packages; lists for announcements about R and R packages;
and a variety of more specialized lists. Before posing a question on one
of these lists, please read the R mailing list
instructions and the posting
guide.

[H1] Membership Fees & Donations
There are two ways to give money to the R Foundation. You can either
become a supporting member of the R foundation with an annual donation
of € 25.00, or you can give a one off or recurring donation of any
amount starting at € 50.00. If you have questions, please contact treasurer@R-project.org.
Funds of the R foundation are exclusively used for supporting the R Project, typically by financially
contributing to the organisation of useR! and DSC
conferences, to the Comprehensive R
Archive Network, and to other activities of the R community.
Like all activities of the R Foundation, donations are managed by
volunteers and the list of donors is typically updated once or twice per
month.
[H2] Supporting Membership
Application Form
To become a supporting member of the R Foundation, please fill out
the online membership application form below. To renew your membership,
please fill out the same form, or set-up a recurring payment.
The name or institution can be hyperlinked (with page ranking
calculations disabled via “nofollow”) on the members and donors page. Supporting members are
listed in one issue of the R
Journal.
The R Foundation reserves the right to decline or return donations,
and to decide whether or not and how to acknowledge or continue to
acknowledge donations.
[H2] One-off Donation Form
If you want to donate money directly (avoiding fees for online
payment services), please wire to The R Foundation for Statistical
Computing, Bank Austria Creditanstalt, Swift Code: BKAUATWW, IBAN: AT 93
1200 0515 8212 1701.
If you want to donate money to the R Foundation online, please fill
out the online donation form below. All donors will be listed on the R
project homepage and in one issue of the R Journal per year (unless
the respective donor prefers otherwise). The personal or company name
can be hyperlinked (with page ranking calculations disabled, see above)
on the members and donors page.
[H5] Note for US Taxpayers
The R Foundation is not registered as a charitable
organisation in the USA.

[H1] What is R?
[H2] Introduction to R
R is a language and environment for statistical computing and
graphics. It is a GNU project which is
similar to the S language and environment which was developed at Bell
Laboratories (formerly AT&T, now Lucent Technologies) by John
Chambers and colleagues. R can be considered as a different
implementation of S. There are some important differences, but much code
written for S runs unaltered under R.
R provides a wide variety of statistical (linear and nonlinear
modelling, classical statistical tests, time-series analysis,
classification, clustering, …) and graphical techniques, and is highly
extensible. The S language is often the vehicle of choice for research
in statistical methodology, and R provides an Open Source route to
participation in that activity.
One of R’s strengths is the ease with which well-designed
publication-quality plots can be produced, including mathematical
symbols and formulae where needed. Great care has been taken over the
defaults for the minor design choices in graphics, but the user retains
full control.
R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It
compiles and runs on a wide variety of UNIX platforms and similar
systems (including FreeBSD and Linux), Windows and MacOS.
[H2] The R environment
R is an integrated suite of software facilities for data
manipulation, calculation and graphical display. It includes
an effective data handling and storage facility,
a suite of operators for calculations on arrays, in particular
matrices,
a large, coherent, integrated collection of intermediate tools for
data analysis,
graphical facilities for data analysis and display either on-screen
or on hardcopy, and
a well-developed, simple and effective programming language which
includes conditionals, loops, user-defined recursive functions and input
and output facilities.
The term “environment” is intended to characterize it as a fully
planned and coherent system, rather than an incremental accretion of
very specific and inflexible tools, as is frequently the case with other
data analysis software.
R, like S, is designed around a true computer language, and it allows
users to add additional functionality by defining new functions. Much of
the system is itself written in the R dialect of S, which makes it easy
for users to follow the algorithmic choices made. For
computationally-intensive tasks, C, C++ and Fortran code can be linked
and called at run time. Advanced users can write C code to manipulate R
objects directly.
Many users think of R as a statistics system. We prefer to think of
it as an environment within which statistical techniques are
implemented. R can be extended (easily) via packages. There are
about eight packages supplied with the R distribution and many more are
available through the CRAN family of Internet sites covering a very wide
range of modern statistics.
R has its own LaTeX-like documentation format, which is used to
supply comprehensive documentation, both on-line in a number of formats
and in hardcopy.