text:
This is a guide to extending R, describing the process of creating R add-on packages, writing R documentation, R's system and foreign language interfaces, and the R API.
This manual is for R, version 4.4.1 Patched (2024-07-08).
Copyright © 1999–2024 R Core Team
Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies.
Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one.
Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
and .Fortran
and dyn.unload
and .External
The contributions to early versions of this manual by Saikat DebRoy
(who wrote the first draft of a guide to using .Call
) and Adrian Trapletti (who provided information on the
C++ interface) are gratefully acknowledged.
Packages provide a mechanism for loading optional code, data and documentation as needed. The R distribution itself includes about 30 packages.
In the following, we assume that you know the library()
including its lib.loc
argument, and we also assume basic
knowledge of the R CMD INSTALL
utility. Otherwise, please
look at R's help pages on
?library ?INSTALL
before reading on.
For packages which contain code to be compiled, a computing environment including a number of tools is assumed; the "R Installation and Administration" manual describes what is needed for each OS.
Once a source package is created, it must be installed by
the command R CMD INSTALL
See Add-on-packages in R Installation and Administration.
Other types of extensions are supported (but rare): See Package types.
Some notes on terminology complete this introduction. These will help with the reading of this manual, and also in describing concepts accurately when asking for help.
A package is a directory of files which extend R, a
source package (the master files of a package), or a tarball
containing the files of a source package, or an installed
package, the result of running R CMD INSTALL
on a source
package. On some platforms (notably macOS and 'x86_64' Windows)
there are also binary packages, a zip file or tarball containing
the files of an installed package which can be unpacked rather than
installing from sources.
A package is not1 a library. The latter is used in two senses in R documentation.
There are a number of well-defined operations on source packages.
, but since the advent of package
namespaces this has been less clear: people now often talk about
loading the package's namespace and then attaching the
package so it becomes visible on the search path. Function
performs both steps, but a package's namespace can be
loaded without the package being attached (for example by calls like
The concept of lazy loading of code or data is mentioned at several points. This is part of the installation, always selected for R code but optional for data. When used the R objects of the package are created at installation time and stored in a database in the R directory of the installed package, being loaded into the session at first use. This makes the R session start up faster and use less (virtual) memory. (For technical details, see Lazy loading in R Internals.)
CRAN is a network of WWW sites holding the R distributions and contributed code, especially R packages. Users of R are encouraged to join in the collaborative project and to submit their own packages to CRAN: current instructions are linked from https://CRAN.R-project.org/banner.shtml#submitting.
The sources of an R package consist of a subdirectory containing the
files DESCRIPTION and NAMESPACE, and the subdirectories
R, data, demo, exec, inst,
man, po, src, tests, tools and
vignettes (some of which can be missing, but which should not be
empty). The package subdirectory may also contain files INDEX,
configure, cleanup, LICENSE, LICENCE and
NEWS. Other files such as INSTALL (for non-standard
installation instructions), README/README.md2, or ChangeLog will be ignored by R, but may
be useful to end users. The utility R CMD build
may add files
in a build directory (but this should not be used for other
Except where specifically mentioned,3 packages should not contain Unix-style 'hidden' files/directories (that is, those whose name starts with a dot).
The DESCRIPTION and INDEX files are described in the subsections below. The NAMESPACE file is described in the section on Package namespaces.
The optional files configure and cleanup are (Bourne) shell scripts which are, respectively, executed before and (if option --clean was given) after installation on Unix-alikes, see Configure and cleanup. The analogues on Windows are configure.win and cleanup.win. Since R 4.2.0 on Windows, configure.ucrt and cleanup.ucrt are supported and take precedence over configure.win and cleanup.win. They can hence be used to provide content specific to UCRT or Rtools42 and newer, if needed, but the support for .ucrt files may be removed in future when building packages from source on the older versions of R will no longer be needed, and hence the files may be renamed back to .win.
For the conventions for files NEWS and ChangeLog in the GNU project see https://www.gnu.org/prep/standards/standards.html#Documentation.
The package subdirectory should be given the same name as the package. Because some file systems (e.g., those on Windows and by default on macOS) are not case-sensitive, to maintain portability it is strongly recommended that case distinctions not be used to distinguish different packages. For example, if you have a package named foo, do not also create a package named Foo.
To ensure that file names are valid across file systems and supported
operating systems, the ASCII control characters as well as the
characters '"', '*', ':', '/', '<', '>',
'?', '\', and '|' are not allowed in file names. In
addition, files with names 'con', 'prn', 'aux',
'clock$', 'nul', 'com1' to 'com9', and 'lpt1'
to 'lpt9' after conversion to lower case and stripping possible
"extensions" (e.g., 'lpt5.foo.bar'), are disallowed. Also, file
names in the same directory must not differ only by case (see the
previous paragraph). In addition, the basenames of '.Rd' files may
be used in URLs and so must be ASCII and not contain %
For maximal portability filenames should only contain only
ASCII characters not excluded already (that is
— we exclude space as many
utilities do not accept spaces in file paths): non-English alphabetic
characters cannot be guaranteed to be supported in all locales. It
would be good practice to avoid the shell metacharacters
: ~
is also used as part of '8.3' filenames on
Windows. In addition, packages are normally distributed as tarballs,
and these have a limit on path lengths: for maximal portability 100
A source package if possible should not contain binary executable files:
they are not portable, and a security risk if they are of the
appropriate architecture. R CMD check
will warn about
them4 unless they are listed (one filepath per line) in a file
BinaryFiles at the top level of the package. Note that
CRAN will not accept submissions containing binary files
even if they are listed.
The R function package.skeleton
can help to create the
structure for a new package: see its help page for details.
The DESCRIPTION file contains basic information about the package in the following format:
Package: pkgname Version: 0.5-1 Date: 2015-01-01 Title: My First Collection of Functions Authors@R: c(person("Joe", "Developer", role = c("aut", "cre"), email = "[email protected]"), person("Pat", "Developer", role = "aut"), person("A.", "User", role = "ctb", email = "[email protected]")) Author: Joe Developer [aut, cre], Pat Developer [aut], A. User [ctb] Maintainer: Joe Developer <[email protected]> Depends: R (>= 3.1.0), nlme Suggests: MASS Description: A (one paragraph) description of what the package does and why it may be useful. License: GPL (>= 2) URL: https://www.r-project.org, http://www.another.url BugReports: https://pkgname.bugtracker.url
The format is that of a version of a 'Debian Control File' (see the help for 'read.dcf' and https://www.debian.org/doc/debian-policy/ch-controlfields.html: R does not require encoding in UTF-8 and does not support comments starting with '#'). Fields start with an ASCII name immediately followed by a colon: the value starts after the colon and a space. Continuation lines (for example, for descriptions longer than one line) start with a space or tab. Field names are case-sensitive: all those used by R are capitalized.
For maximal portability, the DESCRIPTION file should be written entirely in ASCII — if this is not possible it must contain an 'Encoding' field (see below).
Several optional fields take logical values: these can be specified as 'yes', 'true', 'no' or 'false': capitalized values are also accepted.
The 'Package', 'Version', 'License', 'Description', 'Title', 'Author', and 'Maintainer' fields are mandatory, all other fields are optional. Fields 'Author' and 'Maintainer' can be auto-generated from 'Authors@R', and may be omitted if the latter is provided: however if they are not ASCII we recommend that they are provided.
The mandatory 'Package' field gives the name of the package. This should contain only (ASCII) letters, numbers and dot, have at least two characters and start with a letter and not end in a dot. If it needs explaining, this should be done in the 'Description' field (and not the 'Title' field).
The mandatory 'Version' field gives the version of the package.
This is a sequence of at least two (and usually three)
non-negative integers separated by single '.' or '-'
characters. The canonical form is as shown in the example, and a
version such as '0.01' or '0.01.0' will be handled as if it
were '0.1-0'. It is not a decimal number, so for example
0.9 < 0.75
since 9 < 75
The mandatory 'License' field is discussed in the next subsection.
The mandatory 'Title' field should give a short description
of the package. Some package listings may truncate the title to 65
characters. It should use title case (that is, use capitals for
the principal words: tools::toTitleCase
can help you with this),
not use any markup, not have any continuation lines, and not end in a
period (unless part of …). Do not repeat the package name: it is
often used prefixed by the name. Refer to other packages and external
software in single quotes, and to book titles (and similar) in double
The mandatory 'Description' field should give a comprehensive description of what the package does. One can use several (complete) sentences, but only one paragraph. It should be intelligible to all the intended readership (e.g. for a CRAN package to all CRAN users). It is good practice not to start with the package name, 'This package' or similar. As with the 'Title' field, double quotes should be used for quotations (including titles of books and articles), and single quotes for non-English usage, including names of other packages and external software. This field should also be used for explaining the package name if necessary. URLs should be enclosed in angle brackets, e.g. '<https://www.r-project.org>': see also Specifying URLs.
The mandatory 'Author' field describes who wrote the package. It is a plain text field intended for human readers, but not for automatic processing (such as extracting the email addresses of all listed contributors: for that use 'Authors@R'). Note that all significant contributors must be included: if you wrote an R wrapper for the work of others included in the src directory, you are not the sole (and maybe not even the main) author.
The mandatory 'Maintainer' field should give a single name
followed by a valid (RFC 2822) email address in angle brackets. It
should not end in a period or comma. This field is what is reported by
the maintainer
function and used by bug.report
. For a
CRAN package it should be a person, not a mailing list
and not a corporate entity: do ensure that it is valid and will remain
valid for the lifetime of the package.
Note that the display name (the part before the address in angle brackets) should be enclosed in double quotes if it contains non-alphanumeric characters such as comma or period. (The current standard, RFC 5322, allows periods but RFC 2822 did not.)
Both 'Author' and 'Maintainer' fields can be omitted if a
suitable 'Authors@R' field is given. This field can be used to
provide a refined and machine-readable description of the package
"authors" (in particular specifying their precise roles),
via suitable R code. It should create an object of class
, by either a call to person
or a series of calls
(one per "author") concatenated by c()
: see the example
DESCRIPTION file above. The roles can include '"aut"'
(author) for full authors, '"cre"' (creator) for the package
maintainer, and '"ctb"' (contributor) for other contributors,
'"cph"' (copyright holder, which should be the legal name for an
institution or corporate body), among others. See ?person
more information. Note that no role is assumed by default.
Auto-generated package citation information takes advantage of this
specification. The 'Author' and 'Maintainer' fields are
auto-generated from it if needed when building5 or installing.
An optional 'Copyright' field can be used where the copyright holder(s) are not the authors. If necessary, this can refer to an installed file: the convention is to use file inst/COPYRIGHTS.
The optional 'Date' field gives the release date of the current version of the package. It is strongly recommended6 to use the 'yyyy-mm-dd' format conforming to the ISO 8601 standard.
The 'Depends', 'Imports', 'Suggests', 'Enhances', 'LinkingTo' and 'Additional_repositories' fields are discussed in a later subsection.
Dependencies external to the R system should be listed in the
'SystemRequirements' field, possibly amplified in a separate
README file. This includes specifying a non-default C++ standard
and the need for GNU make
The 'URL' field may give a list of URLs separated by commas or whitespace, for example the homepage of the author or a page where additional material describing the software can be found. These URLs are converted to active hyperlinks in CRAN package listings. See Specifying URLs.
The 'BugReports' field may contain a single URL to which
bug reports about the package should be submitted. This URL
will be used by bug.report
instead of sending an email to the
maintainer. A browser is opened for a 'http://' or 'https://'
URL. To specify another email address for bug reports, use
'Contact' instead: however bug.report
will try to extract an
email address (preferably from a 'mailto:' URL or enclosed in angle
brackets) from 'BugReports'.
Base and recommended packages (i.e., packages contained in the R source distribution or available from CRAN and recommended to be included in every binary distribution of R) have a 'Priority' field with value 'base' or 'recommended', respectively. These priorities must not be used by other packages.
A 'Collate' field can be used for controlling the collation order for the R code files in a package when these are processed for package installation. The default is to collate according to the 'C' locale. If present, the collate specification must list all R code files in the package (taking possible OS-specific subdirectories into account, see Package subdirectories) as a whitespace separated list of file paths relative to the R subdirectory. Paths containing white space or quotes need to be quoted. An OS-specific collation field ('Collate.unix' or 'Collate.windows') will be used in preference to 'Collate'.
The 'LazyData' logical field controls whether the R datasets use lazy-loading. A 'LazyLoad' field was used in versions prior to 2.14.0, but now is ignored.
The 'KeepSource' logical field controls if the package code is sourced
using keep.source = TRUE
or FALSE
: it might be needed
exceptionally for a package designed to always be used with
keep.source = TRUE
The 'ByteCompile' logical field controls if the package R code is to be byte-compiled on installation: the default is to byte-compile. This can be overridden by installing with flag --no-byte-compile.
The 'UseLTO' logical field is used to indicate if source code in
the package7 is to be
compiled with Link-Time Optimization (see Using Link-time Optimization) if R was installed with --enable-lto (default
true) or --enable-lto=R (default false) (or on
Windows8 if
is set in MkRules). This can be overridden by the
flags --use-LTO and --no-use-LTO. LTO is said to give
most size and performance improvements for large and complex (heavily
templated) C++ projects.
The 'StagedInstall' logical field controls if package installation is 'staged', that is done to a temporary location and moved to the final location when successfully completed. This field was introduced in R 3.6.0 and it true by default: it is considered to be a temporary measure which may be withdrawn in future.
The 'ZipData' logical field has been ignored since R 2.13.0.
The 'Biarch' logical field is used on Windows to select the
option --force-biarch for this package. Not
currently relevant.
The 'BuildVignettes' logical field can be set to a false value to
stop R CMD build
from attempting to build the vignettes, as
well as preventing9 R CMD check
from testing
this. This should only be used exceptionally, for example if the PDFs
include large figures which are not part of the package sources (and
hence only in packages which do not have an Open Source license).
The 'VignetteBuilder' field names (in a comma-separated list) packages that provide an engine for building vignettes. These may include the current package, or ones listed in 'Depends', 'Suggests' or 'Imports'. The utils package is always implicitly appended. See Non-Sweave vignettes for details. Note that if, for example, a vignette has engine 'knitr::rmarkdown', then knitr provides the engine but both knitr and rmarkdown are needed for using it, so both these packages need to be in the 'VignetteBuilder' field and at least suggested (as rmarkdown is only suggested by knitr, and hence not available automatically along with it). Many packages using knitr also need the package formatR which it suggests and so the user package needs to do so too and include this in 'VignetteBuilder'.
If the DESCRIPTION file is not entirely in ASCII it
should contain an 'Encoding' field specifying an encoding. This is
used as the encoding of the DESCRIPTION file itself and of the
R and NAMESPACE files, and as the default encoding of
.Rd files. The examples are assumed to be in this encoding when
running R CMD check
, and it is used for the encoding of the
file. Only encoding names latin1
and UTF-8
are known to be portable. (Do not specify an encoding
unless one is actually needed: doing so makes the package less
portable. If a package has a specified encoding, you should run
R CMD build
etc in a locale using that encoding.)
The 'NeedsCompilation' field should be set to "yes"
if the
package contains native code which needs to be compiled, otherwise "no"
the package could be installed from source on any platform without
additional tools). This is used by install.packages(type =
in R >= 2.15.2 on platforms where binary packages are the
norm: it is normally set by R CMD build
or the repository
assuming compilation is required if and only if the package has a
src directory.
The 'OS_type' field specifies the OS(es) for which the
package is intended. If present, it should be one of unix
, and indicates that the package can only be installed
on a platform with '.Platform$OS.type' having that value.
The 'Type' field specifies the type of the package: see Package types.
One can add subject classifications for the content of the package using the fields 'Classification/ACM' or 'Classification/ACM-2012' (using the Computing Classification System of the Association for Computing Machinery, https://www.acm.org/publications/class-2012; the former refers to the 1998 version), 'Classification/JEL' (the Journal of Economic Literature Classification System, https://www.aeaweb.org/econlit/jelCodes.php, or 'Classification/MSC' or 'Classification/MSC-2010' (the Mathematics Subject Classification of the American Mathematical Society, https://mathscinet.ams.org/msc/msc2010.html; the former refers to the 2000 version). The subject classifications should be comma-separated lists of the respective classification codes, e.g., 'Classification/ACM: G.4, H.2.8, I.5.1'.
A 'Language' field can be used to indicate if the package documentation is not in English: this should be a comma-separated list of standard (not private use or grandfathered) IETF language tags as currently defined by RFC 5646 (https://www.rfc-editor.org/rfc/rfc5646, see also https://en.wikipedia.org/wiki/IETF_language_tag), i.e., use language subtags which in essence are 2-letter ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) or 3-letter ISO 639-3 (https://en.wikipedia.org/wiki/ISO_639-3) language codes.
An 'RdMacros' field can be used to hold a comma-separated list of
packages from which the current package will import Rd macro
definitions. These package should also be listed in 'Imports'
(or 'Depends'). The macros in these packages will be
imported after the system macros, in the
order listed in the 'RdMacros' field, before any macro definitions
in the current package are loaded. Macro definitions in individual
.Rd files in the man directory are loaded last, and are
local to later parts of that file. In case of duplicates, the last
loaded definition will be used.10 Both R CMD
and R CMD Rdconv
have an optional flag
--RdMacros=pkglist. The option is also a comma-separated list
of package names, and has priority over the value given in
DESCRIPTION. Packages using Rd macros should depend on
R 3.2.0 or later.
Note: There should be no 'Built' or 'Packaged' fields, as these are added by the package management tools.
There is no restriction on the use of other fields not mentioned here
(but using other capitalizations of these field names would cause
confusion). Fields Note
, Contact
(for contacting the
authors/developers11) and MailingList
are in common
use. Some repositories (including CRAN and R-forge) add their
own fields.
Licensing for a package which might be distributed is an important but potentially complex subject.
It is very important that you include license information! Otherwise, it may not even be legally correct for others to distribute copies of the package, let alone use it.
The package management tools use the concept of 'free or open source software' (FOSS, e.g., https://en.wikipedia.org/wiki/FOSS) licenses: the idea being that some users of R and its packages want to restrict themselves to such software. Others need to ensure that there are no restrictions stopping them using a package, e.g. forbidding commercial or military use. It is a central tenet of FOSS software that there are no restrictions on users nor usage.
Do not use the 'License' field for information on copyright holders: if needed, use a 'Copyright' field.
The mandatory 'License' field in the DESCRIPTION file should specify the license of the package in a standardized form. Alternatives are indicated via vertical bars. Individual specifications must be one of
GPL-2 GPL-3 LGPL-2 LGPL-2.1 LGPL-3 AGPL-3 Artistic-2.0 BSD_2_clause BSD_3_clause MIT
as made available via https://www.R-project.org/Licenses/ and contained in subdirectory share/licenses of the R source or home directory.
Abbreviations GPL
and LGPL
are ambiguous and
usually12 taken to mean any version of the license: but it is better
not to use them.
Multiple licences can be specified separated by '|' (surrounded by spaces) in which case the user can choose any of the alternatives.
If a package license restricts a base license (where permitted, e.g., using GPL-3 or AGPL-3 with an attribution clause), the additional terms should be placed in file LICENSE (or LICENCE), and the string '+ file LICENSE' (or '+ file LICENCE', respectively) should be appended to the corresponding individual license specification (preferably with the '+' surrounded by spaces). Note that several commonly used licenses do not permit restrictions: this includes GPL-2 and hence any specification which includes it.
Examples of standardized specifications include
License: GPL-2 License: LGPL (>= 2.0, < 3) | Mozilla Public License License: GPL-2 | file LICENCE License: GPL (>= 2) | BSD_3_clause + file LICENSE License: Artistic-2.0 | AGPL-3 + file LICENSE
Please note in particular that "Public domain" is not a valid license, since it is not recognized in some jurisdictions.
Please ensure that the license you choose also covers any dependencies (including system dependencies) of your package: it is particularly important that any restrictions on the use of such dependencies are evident to people reading your DESCRIPTION file.
Fields 'License_is_FOSS' and 'License_restricts_use' may be
added by repositories where information cannot be computed from the name
of the license. 'License_is_FOSS: yes' is used for licenses which
are known to be FOSS, and 'License_restricts_use' can have values
'yes' or 'no' if the LICENSE file is known to restrict
users or usage, or known not to. These are used by, e.g., the
The optional file LICENSE/LICENCE contains a copy of the license of the package. To avoid any confusion only include such a file if it is referred to in the 'License' field of the DESCRIPTION file.
Whereas you should feel free to include a license file in your source distribution, please do not arrange to install yet another copy of the GNU COPYING or COPYING.LIB files but refer to the copies on https://www.R-project.org/Licenses/ and included in the R distribution (in directory share/licenses). Since files named LICENSE or LICENCE will be installed, do not use these names for standard license files. To include comments about the licensing rather than the body of a license, use a file named something like LICENSE.note.
A few "standard" licenses are rather license templates which need additional information to be completed via '+ file LICENSE' (with the '+' surrounded by spaces). Where the additional information is 'COPYRIGHT HOLDER', this must give the actual legal entities (not something vague like 'Name-of-package authors'): if more than one they should be listed in decreasing order of contribution.
The 'Depends' field gives a comma-separated list of package names
which this package depends on. Those packages will be attached before
the current package when library
or require
is called.
Each package name may be optionally followed by a comment in parentheses
specifying a version requirement. The comment should contain a
comparison operator, whitespace and a valid version number,
e.g. 'MASS (>= 3.1-20)'.
The 'Depends' field can also specify a dependence on a certain version of R — e.g., if the package works only with R version 4.0.0 or later, include 'R (>= 4.0)' in the 'Depends' field. (As here, trailing zeroes can be dropped and it is recommended that they are.) You can also require a certain SVN revision for R-devel or R-patched, e.g. 'R (>= 2.14.0), R (>= r56550)' requires a version later than R-devel of late July 2011 (including released versions of 2.14.0).
It makes no sense to declare a dependence on R
without a version
specification, nor on the package base: this is an R package
and package base is always available.
A package or 'R' can appear more than once in the 'Depends' field, for example to give upper and lower bounds on acceptable versions.
It is inadvisable to use a dependence on R with patch level (the third digit) other than zero. Doing so with packages which others depend on will cause the other packages to become unusable under earlier versions in the series, and e.g. versions 4.x.1 are widely used throughout the Northern Hemisphere academic year.
Both library
and the R package checking facilities use this
field: hence it is an error to use improper syntax or misuse the
'Depends' field for comments on other software that might be
needed. The R INSTALL
facilities check if the version of
R used is recent enough for the package being installed, and the list
of packages which is specified will be attached (after checking version
requirements) before the current package.
The 'Imports' field lists packages whose namespaces are imported from (as specified in the NAMESPACE file) but which do not need to be attached. Namespaces accessed by the '::' and ':::' operators must be listed here, or in 'Suggests' or 'Enhances' (see below). Ideally this field will include all the standard packages that are used, and it is important to include S4-using packages (as their class definitions can change and the DESCRIPTION file is used to decide which packages to re-install when this happens). Packages declared in the 'Depends' field should not also be in the 'Imports' field. Version requirements can be specified and are checked when the namespace is loaded.
The 'Suggests' field uses the same syntax as 'Depends' and lists packages that are not necessarily needed. This includes packages used only in examples, tests or vignettes (see Writing package vignettes), and packages loaded in the body of functions. E.g., suppose an example13 from package foo uses a dataset from package bar. Then it is not necessary to have bar use foo unless one wants to execute all the examples/tests/vignettes: it is useful to have bar, but not necessary. Version requirements can be specified but should be checked by the code which uses the package.
Finally, the 'Enhances' field lists packages "enhanced" by the package at hand, e.g., by providing methods for classes from these packages, or ways to handle objects from these packages (so several packages have 'Enhances: chron' because they can handle datetime objects from chron even though they prefer R's native datetime functions). Version requirements can be specified, but are currently not used. Such packages cannot be required to check the package: any tests which use them must be conditional on the presence of the package. (If your tests use e.g. a dataset from another package it should be in 'Suggests' and not 'Enhances'.)
The general rules are
should be listed in the 'Imports' field
and not in the 'Depends' field. Packages listed in import
or importFrom
directives in the NAMESPACE file should
almost always be in 'Imports' and not 'Depends'.
must be listed in the 'Depends'
R CMD check
on the
package must be listed in one of 'Depends' or 'Suggests' or
'Imports'. Packages used to run examples or tests conditionally
(e.g. via if(require(pkgname))
) should be listed
in 'Suggests' or 'Enhances'. (This allows checkers to ensure
that all the packages needed for a complete check are installed.)
In particular, packages providing "only" data for examples or vignettes should be listed in 'Suggests' rather than 'Depends' in order to make lean installations possible.
Version dependencies in the 'Depends' and 'Imports' fields are
used by library
when it loads the package, and
checks versions for the 'Depends',
'Imports' and (for dependencies = TRUE
) 'Suggests'
It is important that the information in these fields is complete and accurate: it is for example used to compute which packages depend on an updated package and which packages can safely be installed in parallel.
This scheme was developed before all packages had namespaces (R 2.14.0 in October 2011), and good practice changed once that was in place.
Field 'Depends' should nowadays be used rarely, only for packages which are intended to be put on the search path to make their facilities available to the end user (and not to the package itself): for example it makes sense that a user of package latticeExtra would want the functions of package lattice made available.
Almost always packages mentioned in 'Depends' should also be imported from in the NAMESPACE file: this ensures that any needed parts of those packages are available when some other package imports the current package.
The 'Imports' field should not contain packages which are not
imported from (via the NAMESPACE file or ::
operators), as all the packages listed in that field need to
be installed for the current package to be installed. (This is checked
by R CMD check
R code in the package should call library
or require
only exceptionally. Such calls are never needed for packages listed in
'Depends' as they will already be on the search path. It used to
be common practice to use require
calls for packages listed in
'Suggests' in functions which used their functionality, but
nowadays it is better to access such functionality via ::
A package that wishes to make use of header files in other packages to compile its C/C++ code needs to declare them as a comma-separated list in the field 'LinkingTo' in the DESCRIPTION file. For example
LinkingTo: link1, link2
The 'LinkingTo' field can have a version requirement which is checked at installation.
Specifying a package in 'LinkingTo' suffices if these are C/C++ headers containing source code or static linking is done at installation: the packages do not need to be (and usually should not be) listed in the 'Depends' or 'Imports' fields. This includes CRAN package BH and almost all users of RcppArmadillo and RcppEigen. Note that 'LinkingTo' applies only to installation: if a packages wishes to use headers to compile code in tests or vignettes the package providing them needs to be listed in 'Suggests' or perhaps 'Depends'.
For another use of 'LinkingTo' see Linking to native routines in other packages.
The 'Additional_repositories' field is a comma-separated list of
repository URLs where the packages named in the other fields may be
found. It is currently used by R CMD check
to check that the
packages can be found, at least as source packages (which can be
installed on any platform).
Note that someone wanting to run the examples/tests/vignettes may not
have a suggested package available (and it may not even be possible to
install it for that platform). The recommendation used to be to make
their use conditional via if(require("pkgname"))
this is OK if that conditioning is done in examples/tests/vignettes,
although using if(requireNamespace("pkgname"))
preferred, if possible.
However, using require
for conditioning in package code is
not good practice as it alters the search path for the rest of the
session and relies on functions in that package not being masked by
other require
or library
calls. It is better practice to
use code like
if (requireNamespace("rgl", quietly = TRUE)) { rgl::plot3d(...) } else { ## do something else not involving rgl. }
Note the use of rgl::
as that object would not necessarily be
visible (and if it is, it need not be the one from that namespace:
occurs in several other packages). If the intention is to
give an error if the suggested package is not available, simply use
e.g. rgl::plot3d
If the conditional code produces print
output, function
can be useful.
Note that the recommendation to use suggested packages conditionally in tests does also apply to packages used to manage test suites: a notorious example was testthat which in version 1.0.0 contained illegal C++ code and hence could not be installed on standards-compliant platforms.
Some people have assumed that a 'recommended' package in 'Suggests' can safely be used unconditionally, but this is not so. (R can be installed without recommended packages, and which packages are 'recommended' may change.)
As noted above, packages in 'Enhances' must be used
conditionally and hence objects within them should always be accessed
via ::
On most systems, R CMD check
can be run with only those
packages declared in 'Depends' and 'Imports' by setting
environment variable _R_CHECK_DEPENDS_ONLY_=true
, whereas setting
also allows suggested packages, but
not those in 'Enhances' nor those not mentioned in the
DESCRIPTION file. It is recommended that a package is checked
with each of these set, as well as with neither.
WARNING: Be extremely careful if you do things which would be
run at installation time depending on whether suggested packages are
available or not—this includes top-level code in R code files,
functions and the definitions of S4 classes and methods.
The problem is that once a namespace of a suggested package is loaded,
references to it may be captured in the installed package (most commonly
in S4 methods), but the suggested package may not be available when the
installed package is used (which especially for binary packages might be
on a different machine). Even worse, the problems might not be confined
to your package, for the namespaces of your suggested packages will also
be loaded whenever any package which imports yours is installed and so
may be captured there.
The optional file INDEX contains a line for each sufficiently
interesting object in the package, giving its name and a description
(functions such as print methods not usually called explicitly might not
be included). Normally this file is missing and the corresponding
information is automatically generated from the documentation sources
(using tools::Rdindex()
) when installing from source.
The file is part of the information given by library(help =
Rather than editing this file, it is preferable to put customized information about the package into an overview help page (see Documenting packages) and/or a vignette (see Writing package vignettes).
The R subdirectory contains R code files, only. The code
files to be installed must start with an ASCII (lower or upper
case) letter or digit and have one of the extensions15 .R,
.S, .q, .r, or .s. We recommend using
.R, as this extension seems to be not used by any other software.
It should be possible to read in the files using source()
, so
R objects must be created by assignments. Note that there need be no
connection between the name of the file and the R objects created by
it. Ideally, the R code files should only directly assign R
objects and definitely should not call functions with side effects such
as require
and options
. If computations are required to
create objects these can use code 'earlier' in the package (see the
'Collate' field) plus functions in the 'Depends' packages
provided that the objects created do not depend on those packages except
via namespace imports.
Extreme care is needed if top-level computations are made to depend on
availability or not of other packages. In particular this applies to
and setClass
calls. Nor should they depend on
the availability of external resources such as downloads.
Two exceptions are allowed: if the R subdirectory contains a file
sysdata.rda (a saved image of one or more R objects: please
use suitable compression as suggested by tools::resaveRdaFiles
and see also the 'SysDataCompression' DESCRIPTION field.)
this will be lazy-loaded into the namespace environment – this is
intended for system datasets that are not intended to be user-accessible
via data
. Also, files ending in '.in' will be
allowed in the R directory to allow a configure script to
generate suitable files.
Only ASCII characters (and the control characters tab, form feed, LF and CR) should be used in code files. Other characters are accepted in comments16, but then the comments may not be readable in e.g. a UTF-8 locale. Non-ASCII characters in object names will normally17 fail when the package is installed. Any byte will be allowed in a quoted character string but '\uxxxx' escapes should be used for non-ASCII characters. However, non-ASCII character strings may not be usable in some locales and may display incorrectly in others.
Various R functions in a package can be used to initialize and clean up. See Load hooks.
The man subdirectory should contain (only) documentation files for the objects in the package in R documentation (Rd) format. The documentation filenames must start with an ASCII (lower or upper case) letter or digit and have the extension .Rd (the default) or .rd. Further, the names must be valid in 'file://' URLs, which means18 they must be entirely ASCII and not contain '%'. See Writing R documentation files, for more information. Note that all user-level objects in a package should be documented; if a package pkg contains user-level objects which are for "internal" use only, it should provide a file pkg-internal.Rd which documents all such objects, and clearly states that these are not meant to be called by the user. See e.g. the sources for package grid in the R distribution. Note that packages which use internal objects extensively should not export those objects from their namespace, when they do not need to be documented (see Package namespaces).
Having a man directory containing no documentation files may give an installation error.
The man subdirectory may contain a subdirectory named macros; this will contain source for user-defined Rd macros. (See User-defined macros.) These use the Rd format, but may not contain anything but macro definitions, comments and whitespace.
The R and man subdirectories may contain OS-specific subdirectories named unix or windows.
The sources and headers for the compiled code are in src, plus
optionally a file Makevars or Makefile (or for use on
Windows, with extension .win or .ucrt). When a package is
installed using R CMD INSTALL
, make
is used to control
compilation and linking into a shared object for loading into R.
There are default make
variables and rules for this
(determined when R is configured and recorded in
R_HOME/etcR_ARCH/Makeconf), providing support for C,
C++, fixed- or free-form Fortran, Objective C and Objective
C++19 with associated extensions .c,
.cc or .cpp, .f, .f90 or
.m, and .mm, respectively. We recommend using .h
for headers, also for C++21 or Fortran include files. (Use of extension
.C for C++ is no longer supported.) Files in the src
directory should not be hidden (start with a dot), and hidden files will
under some versions of R be ignored.
It is not portable (and may not be possible at all) to mix all these languages in a single package. Because R itself uses it, we know that C and fixed-form Fortran can be used together, and mixing C, C++ and Fortran usually work for the platform's native compilers.
If your code needs to depend on the platform there are certain defines which can used in C or C++. On all Windows builds (even 64-bit ones) '_WIN32' will be defined: on 64-bit Windows builds also '_WIN64'. For Windows on ARM, test for '_M_ARM64' or both '_WIN32' and '__aarch64__'. On macOS '__APPLE__' is defined22; for an 'Apple Silicon' platform, test for both '__APPLE__' and '__arm64__'.
The default rules can be tweaked by setting macros23 in a file
src/Makevars (see Using Makevars). Note that this mechanism
should be general enough to eliminate the need for a package-specific
src/Makefile. If such a file is to be distributed, considerable
care is needed to make it general enough to work on all R platforms.
If it has any targets at all, it should have an appropriate first target
named 'all' and a (possibly empty) target 'clean' which
removes all files generated by running make
(to be used by
'R CMD INSTALL --clean' and 'R CMD INSTALL --preclean').
There are platform-specific file names on Windows:
src/Makevars.win takes precedence over src/Makevars and
src/Makefile.win must be used. Since R 4.2.0,
src/Makevars.ucrt takes precedence over
src/Makevars.win and src/Makefile.ucrt takes precedence
over src/Makefile.win. src/Makevars.ucrt and
src/Makefile.ucrt will be ignored by earlier versions of R, and
hence can be used to provide content specific to UCRT or Rtools42 and newer,
but the support for .ucrt files may be removed in the future when
building packages from source on the older versions of R will no longer
be needed, and hence the files may be renamed back to .win.
Some make
require makefiles to have a complete final line, including a newline.
A few packages use the src directory for purposes other than making a shared object (e.g. to create executables). Such packages should have files src/Makefile and src/Makefile.win or src/Makefile.ucrt (unless intended for only Unix-alikes or only Windows). Note that on Unix such makefiles are included after R_HOME/etc/R_ARCH/Makeconf so all the usual R macros and make rules are available – for example C compilation will by default use the C compiler and flags with which R was configured. This also applies on Windows as from R 4.3.0: packages intended to be used with earlier versions should include that file themselves.
The order of inclusion of makefiles for a package which does not have a src/Makefile file is
Unix-alike | Windows |
src/Makevars | src/Makevars.ucrt, src/Makevars.win |
R_HOME/etc/R_ARCH/Makeconf | R_HOME/etc/R_ARCH/Makeconf |
R_MAKEVARS_SITE , R_HOME/etc/R_ARCH/Makevars.site | R_MAKEVARS_SITE , R_HOME/etc/R_ARCH/Makevars.site |
R_HOME/share/make/shlib.mk | R_HOME/share/make/winshlib.mk |
R_MAKEVARS_USER , ~/.R/Makevars-platform,
~/.R/Makevars | R_MAKEVARS_USER , ~/.R/Makevars.ucrt,
~/.R/Makevars.win64, ~/.R/Makevars.win |
For those which do, it is
R_HOME/etc/R_ARCH/Makeconf | R_HOME/etc/R_ARCH/Makeconf |
R_MAKEVARS_SITE , R_HOME/etc/R_ARCH/Makevars.site | R_MAKEVARS_SITE , R_HOME/etc/R_ARCH/Makevars.site |
src/Makefile | src/Makefile.ucrt, src/Makefile.win |
R_MAKEVARS_USER , ~/.R/Makevars-platform,
~/.R/Makevars | R_MAKEVARS_USER , ~/.R/Makevars.ucrt,
~/.R/Makevars.win64, ~/.R/Makevars.win |
Items in capitals are environment variables: those separated by commas are alternatives looked for in the order shown.
In very special cases packages may create binary files other than the
shared objects/DLLs in the src directory. Such files will not be
installed in a multi-architecture setting since R CMD INSTALL
is used to merge multiple sub-architectures and it only
copies shared objects/DLLs. If a package wants to install other
binaries (for example executable programs), it should provide an R
script src/install.libs.R which will be run as part of the
installation in the src
build directory instead of copying
the shared objects/DLLs. The script is run in a separate R
environment containing the following variables: R_PACKAGE_NAME
(the name of the package), R_PACKAGE_SOURCE
(the path to the
source directory of the package), R_PACKAGE_DIR
(the path of the
target installation directory of the package), R_ARCH
arch-dependent part of the path, often empty), SHLIB_EXT
extension of shared objects) and WINDOWS
on Windows,
elsewhere). Something close to the default behavior could
be replicated with the following src/install.libs.R file:
files <- Sys.glob(paste0("*", SHLIB_EXT)) dest <- file.path(R_PACKAGE_DIR, paste0('libs', R_ARCH)) dir.create(dest, recursive = TRUE, showWarnings = FALSE) file.copy(files, dest, overwrite = TRUE) if(file.exists("symbols.rds")) file.copy("symbols.rds", dest, overwrite = TRUE)
On the other hand, executable programs could be installed along the lines of
execs <- c("one", "two", "three") if(WINDOWS) execs <- paste0(execs, ".exe") if ( any(file.exists(execs)) ) { dest <- file.path(R_PACKAGE_DIR, paste0('bin', R_ARCH)) dir.create(dest, recursive = TRUE, showWarnings = FALSE) file.copy(execs, dest, overwrite = TRUE) }
Note the use of architecture-specific subdirectories of bin where needed. (Executables should installed under a bin directory and not under libs. It is good practice to check that they can be executed as part of the installation script, so a broken package is not installed.)
The data subdirectory is for data files: See Data in packages.
The demo subdirectory is for R scripts (for running via
) that demonstrate some of the functionality of the
package. Demos may be interactive and are not checked automatically, so
if testing is desired use code in the tests directory to achieve
this. The script files must start with a (lower or upper case) letter
and have one of the extensions .R or .r. If present, the
demo subdirectory should also have a 00Index file with one
line for each demo, giving its name and a description separated by a tab
or at least three spaces. (This index file is not generated
automatically.) Note that a demo does not have a specified encoding and
so should be an ASCII file (see Encoding issues). Function
will use the package encoding if there is one, but this is
mainly useful for non-ASCII comments.
The contents of the inst subdirectory will be copied recursively
to the installation directory. Subdirectories of inst should not
interfere with those used by R (currently, R, data,
demo, exec, libs, man, help,
html and Meta, and earlier versions used latex,
R-ex). The copying of the inst happens after src
is built so its Makefile can create files to be installed. To
exclude files from being installed, one can specify a list of exclude
patterns in file .Rinstignore in the top-level source directory.
These patterns should be Perl-like regular expressions (see the help for
in R for the precise details), one per line, to be
matched case-insensitively against the file and directory paths, e.g.
doc/.*[.]png$ will exclude all PNG files in inst/doc based
on the extension.
Note that with the exceptions of INDEX,
LICENSE/LICENCE and NEWS, information files at the
top level of the package will not be installed and so not be
known to users of Windows and macOS compiled packages (and not seen
by those who use R CMD INSTALL
or install.packages()
on the tarball). So any information files you wish an end user to see
should be included in inst. Note that if the named exceptions
also occur in inst, the version in inst will be that seen
in the installed package.
Things you might like to add to inst are a CITATION file
for use by the citation
function, and a NEWS.Rd file for
use by the news
function. See its help page for the specific
format restrictions of the NEWS.Rd file.
Another file sometimes needed in inst is AUTHORS or COPYRIGHTS to specify the authors or copyright holders when this is too complex to put in the DESCRIPTION file.
Subdirectory tests is for additional package-specific test code,
similar to the specific tests that come with the R distribution.
Test code can either be provided directly in a .R (or .r
as from R 3.4.0) file, or via a .Rin file containing
code which in turn creates the corresponding .R file (e.g., by
collecting all function objects in the package and then calling them
with the strangest arguments). The results of running a .R file
are written to a .Rout file. If there is a
corresponding24 .Rout.save file, these two are
compared, with differences being reported but not causing an error. The
directory tests is copied to the check area, and the tests are
run with the copy as the working directory and with R_LIBS
set to
ensure that the copy of the package installed during testing will be
found by library(pkg_name)
. Note that the package-specific
tests are run in a vanilla R session without setting the
random-number seed, so tests which use random numbers will need to set
the seed to obtain reproducible results (and it can be helpful to do so
in all cases, to avoid occasional failures when tests are run).
If directory tests has a subdirectory Examples containing
a file pkg-Ex.Rout.save
, this is compared to the output
file for running the examples when the latter are checked. Reference
output should be produced without having the --timings option
set (and note that --as-cran sets it).
If reference output is included for examples, tests or vignettes do make
sure that it is fully reproducible, as it will be compared verbatim to
that produced in a check run, unless the 'IGNORE_RDIFF' markup is
used. Things which trip up maintainers include displayed version
numbers from loading other packages, printing numerical results to an
unreproducibly high precision and printing timings. Another trap is
small values which are in fact rounding error from zero: consider using
Subdirectory exec could contain additional executable scripts the
package needs, typically scripts for interpreters such as the shell,
Perl, or Tcl. NB: only files (and not directories) under exec
are installed (and those with names starting with a dot are ignored),
and they are all marked as executable (mode 755
, moderated by
'umask') on POSIX platforms. Note too that this is not suitable
for executable programs since some platforms support multiple
architectures using the same installed package directory.
Subdirectory po is used for files related to localization: see Internationalization.
Subdirectory tools is the preferred place for auxiliary files
needed during configuration, and also for sources need to re-create
scripts (e.g. M4 files for autoconf
: some prefer to put
those in a subdirectory m4 of tools).
The data subdirectory is for data files, either to be made
available via lazy-loading or for loading using data()
(The choice is made by the 'LazyData' field in the
DESCRIPTION file: the default is not to do so.) It should not be
used for other data files needed by the package, and the convention has
grown up to use directory inst/extdata for such files.
Data files can have one of three types as indicated by their extension:
plain R code (.R or .r), tables (.tab,
.txt, or .csv, see ?data
for the file formats, and
note that .csv is not the standard25 CSV format), or
images (.RData or .rda). The files should
not be hidden (have names starting with a dot). Note that R code
should be if possible "self-sufficient" and not make use of extra
functionality provided by the package, so that the data file can also be
used without having to load the package or its namespace: it should run
as silently as possible and not change the search()
path by
attaching packages or other environments.
Images (extensions .RData26 or .rda) can contain
references to the namespaces of packages that were used to create them.
Preferably there should be no such references in data files, and in any
case they should only be to packages listed in the Depends
fields, as otherwise it may be impossible to install the
package. To check for such references, load all the images into a
vanilla R session, run str()
on all the datasets, and look at
the output of loadedNamespaces()
Particular care is needed where a dataset or one of its components is of
an S4 class, especially if the class is defined in a different package.
First, the package containing the class definition has to be available
to do useful things with the dataset, so that package must be listed in
or Depends
(even if this gives a check warning
about unused imports). Second, the definition of an S4 class can
change, and often is unnoticed when in a package with a different
author. So it may be wiser to use the .R form and use that to
create the dataset object when needed (loading package namespaces but
not attaching them by using requireNamespace(pkg, quietly =
and using pkg::
to refer to objects in the
If you are not using 'LazyData' and either your data files are large
or e.g., you use data/foo.R scripts to produce your data, loading
your namespace, you
can speed up installation by providing a file datalist in the
data subdirectory. This should have one line per topic that
will find, in the format 'foo' if data(foo)
provides 'foo', or 'foo: bar bah' if data(foo)
'bar' and 'bah'. R CMD build
will automatically add
a datalist file to data directories of over 1Mb, using the
function tools::add_datalist
Tables (.tab, .txt, or .csv files) can be
compressed by gzip
, bzip2
or xz
optionally with additional extension .gz, .bz2 or
If your package is to be distributed, do consider the resource
implications of large datasets for your users: they can make packages
very slow to download and use up unwelcome amounts of storage space, as
well as taking many seconds to load. It is normally best to distribute
large datasets as .rda images prepared by save(, compress =
(the default). Using bzip2
or xz
will usually reduce the size of both the package tarball and the
installed package, in some cases by a factor of two or more.
Package tools has a couple of functions to help with data images:
reports on the way the image was saved, and
will re-save with a different type of compression,
including choosing the best type for that particular image.
Many packages using 'LazyData' will benefit from using a form of
compression other than gzip
in the installed lazy-loading
database. This can be selected by the --data-compress option
to R CMD INSTALL
or by using the 'LazyDataCompression'
field in the DESCRIPTION file. Useful values are bzip2
and the default, gzip
: value none
is also
accepted. The only way to discover which is best is to try them all and
look at the size of the pkgname/data/Rdata.rdb file. A
function to do that (quoting sizes in KB) is
CheckLazyDataCompression <- function(pkg) { pkg_name <- sub("_.*", "", pkg) lib <- tempfile(); dir.create(lib) zs <- c("gzip", "bzip2", "xz") res <- integer(3); names(res) <- zs for (z in zs) { opts <- c(paste0("--data-compress=", z), "--no-libs", "--no-help", "--no-demo", "--no-exec", "--no-test-load") install.packages(pkg, lib, INSTALL_opts = opts, repos = NULL, quiet = TRUE) res[z] <- file.size(file.path(lib, pkg_name, "data", "Rdata.rdb")) } ceiling(res/1024) }
(applied to a source package without any 'LazyDataCompression'
field). R CMD check
will warn if it finds a
pkgname/data/Rdata.rdb file of more than 5MB without
'LazyDataCompression' being set. If you see that, run
and set the field – to gzip
the unlikely event27 that is the best choice.
The analogue for sysdata.rda is field 'SysDataCompression':
the default is xz
for files bigger than 1MB otherwise
Lazy-loading is not supported for very large datasets (those which when serialized exceed 2GB, the limit for the format on 32-bit platforms).
Code which needs to be compiled (C, C++, Fortran …) is included in the src subdirectory and discussed elsewhere in this document.
Subdirectory exec could be used for scripts for interpreters such as the shell, BUGS, JavaScript, Matlab, Perl, PHP (amap), Python or Tcl (Simile), or even R. However, it seems more common to use the inst directory, for example WriteXLS/inst/Perl, NMF/inst/m-files, RnavGraph/inst/tcl, RProtoBuf/inst/python and emdbook/inst/BUGS and gridSVG/inst/js.
Java code is a special case: except for very small programs, .java files should be byte-compiled (to a .class file) and distributed as part of a .jar file: the conventional location for the .jar file(s) is inst/java. It is desirable (and required under an Open Source license) to make the Java source files available: this is best done in a top-level java directory in the package—the source files should not be installed.
If your package requires one of these interpreters or an extension then this should be declared in the 'SystemRequirements' field of its DESCRIPTION file. (Users of Java most often do so via rJava, when depending on/importing that suffices unless there is a version requirement on Java code in the package.)
Windows and Mac users should be aware that the Tcl extensions 'BWidget' and 'Tktable' (which have sometimes been included in the Windows28 and macOS R installers) are extensions and do need to be declared (and that 'Tktable' is less widely available than it used to be, including not in the main repositories for major Linux distributions). 'BWidget' needs to be installed by the user on other OSes. This is fairly easy to do: first find the Tcl search path:
library(tcltk) strsplit(tclvalue('auto_path'), " ")[[1]]
then download the sources from https://sourceforge.net/projects/tcllib/files/BWidget/ and in a terminal run something like
tar xf bwidget-1.9.14.tar.gz sudo mv bwidget-1.9.14 /usr/local/lib
substituting a location on the Tcl search path for /usr/local/lib if
needed. (If no location on that search path is writeable, you will need
to add one each time 'BWidget' is to be used with tcltk::addTclPath()
To (silently) test for the presence of 'Tktable' one can use
library(tcltk) have_tktable <- !isFALSE(suppressWarnings(tclRequire('Tktable')))
Installing 'Tktable' needs a C compiler and the Tk headers (not
necessarily installed with Tcl/Tk). At the time of writing the latest
sources (from 2008) were available from
but needed patching for current Tk (8.6.11, but not 8.6.10) – a patch
can be found at https://www.stats.ox.ac.uk/pub/bdr/Tktable/. For
a system installation of Tk you may need to install 'Tktable' as
'root' as on e.g. Fedora all the locations on auto_path
are owned by 'root'.
URLs in many places in the package documentation will be converted to clickable hyperlinks in at least some of their renderings. So care is needed that their forms are correct and portable.
The full URL should be given, including the scheme (often 'http://' or 'https://') and a final '/' for references to directories.
Spaces in URLs are not portable and how they are handled does vary by HTTP server and by client. There should be no space in the host part of an 'http://' URL, and spaces in the remainder should be encoded, with each space replaced by '%20'.
Other characters may benefit from being encoded: see the help on
The canonical URL for a CRAN package is
and not a version starting 'https://cran.r-project.org/web/packages/pkgname'.
Note that most of this section is specific to Unix-alikes: see the comments later on about the Windows port of R.
If your package needs some system-dependent configuration before
installation you can include an executable (Bourne29 shell script configure
in your package which (if present) is executed by R CMD INSTALL
before any other action is performed. This can be a script created by
the Autoconf mechanism, but may also be a script written by yourself.
Use this to detect if any nonstandard libraries are present such that
corresponding code in the package can be disabled at install time rather
than giving error messages when the package is compiled or used. To
summarize, the full power of Autoconf is available for your extension
package (including variable substitution, searching for libraries,
etc.). Background and useful tips on Autoconf and related tools
(including pkg-config
described below) can be found at
A configure
script is run in an environment which has all the
environment variables set for an R session (see
R_HOME/etc/Renviron) plus R_PACKAGE_NAME
(the name of
the package), R_PACKAGE_DIR
(the path of the target installation
directory of the package, a temporary location for staged installs) and
(the arch-dependent part of the path, often empty).
Under a Unix-alike only, an executable (Bourne shell) script
is executed as the last thing by R CMD INSTALL
option --clean was given, and by R CMD build
preparing the package for building from its source.
As an example consider we want to use functionality provided by a (C or
Fortran) library foo
. Using Autoconf, we can create a configure
script which checks for the library, sets variable HAVE_FOO
if it was found and to FALSE
otherwise, and then
substitutes this value into output files (by replacing instances of
'@HAVE_FOO@' in input files with the value of HAVE_FOO
For example, if a function named bar
is to be made available by
linking against library foo
(i.e., using -lfoo), one
could use
AC_CHECK_LIB(foo, fun, [HAVE_FOO=TRUE], [HAVE_FOO=FALSE]) AC_SUBST(HAVE_FOO) ...... AC_CONFIG_FILES([foo.R]) AC_OUTPUT
in configure.ac (assuming Autoconf 2.50 or later).
The definition of the respective R function in foo.R.in could be
foo <- function(x) { if(!@HAVE_FOO@) stop("Sorry, library 'foo' is not available") ...
From this file configure
creates the actual R source file
foo.R looking like
foo <- function(x) { if(!FALSE) stop("Sorry, library 'foo' is not available") ...
if library foo
was not found (with the desired functionality).
In this case, the above R code effectively disables the function.
One could also use different file fragments for available and missing functionality, respectively.
You will very likely need to ensure that the same C compiler and
compiler flags are used in the configure tests as when compiling
R or your package. Under a Unix-alike, you can achieve this by
including the following fragment early in configure.ac
(before calling AC_PROG_CC
or anything which calls it)
: ${R_HOME=`R RHOME`} if test -z "${R_HOME}"; then echo "could not determine R_HOME" exit 1 fi CC=`"${R_HOME}/bin/R" CMD config CC` CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS` CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`
(Using '${R_HOME}/bin/R' rather than just 'R' is necessary
in order to use the correct version of R when running the script as
part of R CMD INSTALL
, and the quotes since '${R_HOME}'
might contain spaces.)
If your code does load checks (for example, to check for an entry point in a library or to run code) then you will also need
LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS`
Packages written with C++ need to pick up the details for the C++ compiler and switch the current language to C++ by something like
CXX=`"${R_HOME}/bin/R" CMD config CXX` if test -z "$CXX"; then AC_MSG_ERROR([No C++ compiler is available]) fi CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXXFLAGS` CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` AC_LANG(C++)
The latter is important, as for example C headers may not be available
to C++ programs or may not be written to avoid C++ name-mangling. Note
that an R installation is not required to have a C++ compiler so
'CXX' may be empty. If the package specifies a non-default C++
standard, use the config
variable names (such as CXX17
appropriate to the standard, but still set CXX
You can use R CMD config
to get the value of the basic
configuration variables, and also the header and library flags necessary
for linking a front-end executable program against R, see R CMD
config --help for details. If you do, it is essential that you use
both the command and the appropriate flags, so that for example
'CC' must always be used with 'CFLAGS' and (for code to be
linked into a shared library) 'CPICFLAGS'. For Fortran, be careful
to use 'FC FFLAGS FPICFLAGS' for fixed-form Fortran and
'FC FCFLAGS FPICFLAGS' for free-form Fortran.
As from R 4.3.0, variables
are set in the environment (if not already set) when configure
is called from R CMD INSTALL
, in case the script forgets to
set them as described above. This includes making use of the selected C
standard (but not the C++ standard as that is selected at a later stage
by R CMD SHLIB
To check for an external BLAS library using the AX_BLAS
from the official Autoconf Macro
Archive30, one
can use
FC=`"${R_HOME}/bin/R" CMD config FC` FCLAGS=`"${R_HOME}/bin/R" CMD config FFLAGS` AC_PROG_FC FLIBS=`"${R_HOME}/bin/R" CMD config FLIBS` AX_BLAS([], AC_MSG_ERROR([could not find your BLAS library], 1))
Note that FLIBS
as determined by R must be used to ensure that
Fortran code works on all R platforms.
N.B.: If the configure
script creates files, e.g.
src/Makevars, you do need a cleanup
script to remove
them. Otherwise R CMD build
may ship the files that are
created. For example, package RODBC has
#!/bin/sh rm -f config.* src/Makevars src/config.h
As this example shows, configure
often creates working files
such as config.log. If you use a hand-crafted script
rather than one created by autoconf
, it is highly recommended
that you log its actions to file config.log.
If your configure script needs auxiliary files, it is recommended that you ship them in a tools directory (as R itself does).
You should bear in mind that the configure script will not be used on
Windows systems. If your package is to be made publicly available,
please give enough information for a user on a non-Unix-alike platform
to configure it manually, or provide a configure.win script (or
configure.ucrt) to be used on that platform. (Optionally, there
can be a cleanup.win script (or cleanup.ucrt). Both
should be shell scripts to be executed by ash
, which is a
minimal version of Bourne-style sh
. As from R 4.2.0,
is used. When configure.win (or
configure.ucrt) is run the environment variables R_HOME
(which uses '/' as the file separator), R_ARCH
will be set. Use R_ARCH
to decide if this is a
64-bit build for Intel (its value there is '/x64') and to install DLLs to the
correct place (${R_HOME}/libs${R_ARCH}). Use
to find the correct place under the bin
directory, e.g. ${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe. If
a configure.win script does compilation (including calling
), most of the considerations above apply.
As the scripts on Windows are executed as sh ./configure.win
and similar, any 'shebang' first line (such as #! /bin/bash
) is
treated as a comment.
In some rare circumstances, the configuration and cleanup scripts need
to know the location into which the package is being installed. An
example of this is a package that uses C code and creates two shared
object/DLLs. Usually, the object that is dynamically loaded by R
is linked against the second, dependent, object. On some systems, we
can add the location of this dependent object to the object that is
dynamically loaded by R. This means that each user does not have to
set the value of the LD_LIBRARY_PATH
(or equivalent) environment
variable, but that the secondary object is automatically resolved.
Another example is when a package installs support files that are
required at run time, and their location is substituted into an R
data structure at installation time.
The names of the top-level library directory (i.e., specifiable
via the '-l' argument) and the directory of the package
itself are made available to the installation scripts via the two
shell/environment variables R_LIBRARY_DIR
and R_PACKAGE_DIR
Additionally, the name of the package (e.g. 'survival' or
'MASS') being installed is available from the environment variable
. (Currently the value of R_PACKAGE_DIR
always ${R_LIBRARY_DIR}/${R_PACKAGE_NAME}
, but this used not to
be the case when versioned installs were allowed. Its main use is in
configure.win (or configure.ucrt) scripts for the installation path of external
software's DLLs.) Note that the value of R_PACKAGE_DIR
contain spaces and other shell-unfriendly characters, and so should be
quoted in makefiles and configure scripts.
One of the more tricky tasks can be to find the headers and libraries of
external software. One tool which is increasingly available on
Unix-alikes (but not by default31 on macOS) to
do this is pkg-config
. The configure script will need
to test for the presence of the command itself32
(see for example package tiff), and if present it can be
asked if the software is installed, of a suitable version and for
compilation/linking flags by e.g.
$ pkg-config --exists 'libtiff-4 >= 4.1.0' --print-errors # check the status $ pkg-config --modversion libtiff-4 4.3.0 $ pkg-config --cflags libtiff-4 -I/usr/local/include $ pkg-config --libs libtiff-4 -L/usr/local/lib -ltiff $ pkg-config --static --libs libtiff-4 -L/usr/local/lib -ltiff -lwebp -llzma -ljpeg -lz
Note that pkg-config --libs
gives the information required to
link against the default version33 of that library (usually the dynamic one), and
pkg-config --static --libs
may be needed if the static library is
to be used.
Static libraries are commonly used on macOS and Windows to facilitate
bundling external software with binary distributions of packages. This
means that portable (source) packages need to allow for this. It is
not safe to just use pkg-config --static --libs
, as
that will often include further libraries that are not necessarily
installed on the user's system (or maybe only the versioned library such
as libjbig.so.2.1 is installed and not libjbig.so which
would be needed to use -ljbig
sometimes included in
pkg-config --static --libs libtiff-4
Another issue is that pkg-config --exists
may not be reliable.
It checks not only that the 'module' is available but all of the
dependencies, including those in principle needed for static linking.
(XQuartz 2.8.x only distributed dynamic libraries and not some of the
.pc files needed for --exists
Sometimes the name by which the software is known to
is not what one might expect (e.g.
'libxml-2.0' even for 2.9.x). To get a complete list use
pkg-config --list-all | sort
Some external software provides a -config command to do a similar
job to pkg-config
, including
curl-config freetype-config gdal-config geos-config gsl-config iodbc-config libpng-config nc-config pcre-config pcre2-config xml2-config xslt-config
is for libcurl
not curl
is for netcdf
.) Most have an option to use
static libraries.
N.B. These commands indicate what header paths and libraries are needed, but they do not obviate the need to check that the recipes they give actually work. (This is especially necessary for platforms which use static linking.)
If using Autoconf it is good practice to include all the Autoconf
sources in the package (and required for an Open Source package and
tested by R CMD check --as-cran
). This will include the file
configure.ac34 in the top-level directory of the package. If
extensions written in m4
are needed, these should be included
under the directory tools and included from configure.ac
via e.g.,
Alternatively, Autoconf can be asked to search all .m4 files in a directory by including something like35
One source of such extensions is the 'Autoconf Archive' (https://www.gnu.org/software/autoconf-archive/. It is not safe to assume this is installed on users' machines, so the extension should be shipped with the package (taking care to comply with its licence).
Sometimes writing your own configure script can be avoided by supplying a file Makevars: also one of the most common uses of a configure script is to make Makevars from Makevars.in.
A Makevars file is a makefile and is used as one of several
makefiles by R CMD SHLIB
(which is called by R CMD
to compile code in the src directory). It should be
written if at all possible in a portable style, in particular (except
for Makevars.win and Makevars.ucrt) without the use of GNU
The most common use of a Makevars file is to set additional
preprocessor options (for example include paths and definitions) for
C/C++ files via PKG_CPPFLAGS
, and additional compiler
flags by setting PKG_CFLAGS
, for C, C++ or Fortran respectively (see Creating shared objects).
N.B.: Include paths are preprocessor options, not compiler
options, and must be set in PKG_CPPFLAGS
as otherwise
platform-specific paths (e.g. '-I/usr/local/include') will take
precedence. PKG_CPPFLAGS
should contain '-I', '-D',
'-U' and (where supported) '-include' and '-pthread'
options: everything else should be a compiler flag. The order of flags
matters, and using '-I' in PKG_CFLAGS
or PKG_CXXFLAGS
has led to hard-to-debug platform-specific errors.
Makevars can also be used to set flags for the linker, for
example '-L' and '-l' options, via PKG_LIBS
When writing a Makevars file for a package you intend to
distribute, take care to ensure that it is not specific to your
compiler: flags such as -O2 -Wall -pedantic (and all other
-W flags: for the Oracle compilers these were used to pass
arguments to compiler phases) are all specific to GCC (and compilers such
as clang
which aim to be options-compatible with it).
Also, do not set variables such as CPPFLAGS
these should be settable by users (sites) through appropriate personal
(site-wide) Makevars files.
See Customizing package compilation in R Installation and Administration,
There are some macros36 which are set whilst configuring the building of R itself and are stored in R_HOME/etcR_ARCH/Makeconf. That makefile is included as a Makefile after Makevars[.win], and the macros it defines can be used in macro assignments and make command lines in the latter. These include
¶A macro containing the set of libraries need to link Fortran code. This
may need to be included in PKG_LIBS
: it will normally be included
automatically if the package contains Fortran source files in the
src directory.
¶A macro containing the BLAS libraries used when building R. This may
need to be included in PKG_LIBS
. Beware that if it is empty then
the R executable will contain all the double-precision and
double-complex BLAS routines, but no single-precision nor complex
routines. If BLAS_LIBS
is included, then FLIBS
also needs
to be37 included following it, as most BLAS
libraries are written at least partially in Fortran. However, it can
be omitted if the package contains Fortran source code as that will add
to the link line.
¶A macro containing the LAPACK libraries (and paths where appropriate)
used when building R. This may need to be included in
. It may point to a dynamic library libRlapack
which contains the main double-precision LAPACK routines as well as
those double-complex LAPACK routines needed to build R, or it may
point to an external LAPACK library, or may be empty if an external BLAS
library also contains LAPACK.
includes all the double-precision LAPACK routines
which were current in 2003 and a few more recent ones: a list of which
routines are included is in file src/modules/lapack/README. Note
that an external LAPACK/BLAS library need not do so, as some were
'deprecated' (and not compiled by default) in LAPACK 3.6.0 in late
For portability, the macros BLAS_LIBS
and FLIBS
always be included after LAPACK_LIBS
(and in that order).
¶A macro containing flags which are needed to circumvent
over-optimization of FORTRAN code: it is might be '-g -O2
-ffloat-store' or '-g -O2 -msse2 -mfpmath=sse' on 'ix86'
platforms using gfortran
. Note that this is not an
additional flag to be used as part of PKG_FFLAGS
, but a
replacement for FFLAGS
. See the example later in this section.
Setting certain macros in Makevars will prevent R CMD
setting them: in particular if Makevars sets
'OBJECTS' it will not be set on the make
command line.
This can be useful in conjunction with implicit rules to allow other
types of source code to be compiled and included in the shared object.
It can also be used to control the set of files which are compiled,
either by excluding some files in src or including some files in
subdirectories. For example
OBJECTS = 4dfp/endianio.o 4dfp/Getifh.o R4dfp-object.o
Note that Makevars should not normally contain targets, as it is
included before the default makefile and make
will call the
first target, intended to be all
in the default makefile. If you
really need to circumvent that, use a suitable (phony) target all
before any actual targets in Makevars.[win]: for example package
fastICA used to have
PKG_LIBS = @BLAS_LIBS@ SLAMC_FFLAGS=$(R_XTRA_FFLAGS) $(FPICFLAGS) $(SHLIB_FFLAGS) $(SAFE_FFLAGS) all: $(SHLIB) slamc.o: slamc.f $(FC) $(SLAMC_FFLAGS) -c -o slamc.o slamc.f
needed to ensure that the LAPACK routines find some constants without infinite looping. The Windows equivalent was
all: $(SHLIB) slamc.o: slamc.f $(FC) $(SAFE_FFLAGS) -c -o slamc.o slamc.f
(since the other macros are all empty on that platform, and R's
internal BLAS was not used). Note that the first target in
Makevars will be called, but for back-compatibility it is best
named all
If you want to create and then link to a library, say using code in a subdirectory, use something like
.PHONY: all mylibs all: $(SHLIB) $(SHLIB): mylibs mylibs: (cd subdir; $(MAKE))
Be careful to create all the necessary dependencies, as there is no
guarantee that the dependencies of all
will be run in a
particular order (and some of the CRAN build machines use
multiple CPUs and parallel makes). In particular,
all: mylibs
does not suffice. GNU make does allow the construct
.NOTPARALLEL: all all: mylibs $(SHLIB)
but that is not portable. dmake
and pmake
allow the
similar .NO_PARALLEL
, also not portable: some variants of
accept .NOTPARALLEL
as an alias for
Note that on Windows it is required that Makevars[.win, .ucrt] does create a DLL: this is needed as it is the only reliable way to ensure that building a DLL succeeded. If you want to use the src directory for some purpose other than building a DLL, use a Makefile.win or Makefile.ucrt file.
It is sometimes useful to have a target 'clean' in Makevars,
Makevars.win or Makevars.ucrt:
this will be used by R CMD build
clean up (a copy of) the package sources. When it is run by
it will have fewer macros set, in particular not
, nor $(OBJECTS)
unless set in the file itself.
It would also be possible to add tasks to the target 'shlib-clean'
which is run by R CMD INSTALL
and R CMD SHLIB
options --clean and --preclean.
Avoid the use of default (also known as 'implicit' rules) in makefiles,
as these are make
-specific. Even when mandated by POSIX –
GNU make
does not comply and this has broken package
An unfortunately common error is to have
all: $(SHLIB) clean
which asks make
to clean in parallel with compiling the code.
Not only does this lead to hard-to-debug installation errors, it wipes
out all the evidence of any error (from a parallel make or not). It is
much better to leave cleaning to the end user using the facilities in
the previous paragraph.
If you want to run R code in Makevars, e.g. to find
configuration information, please do ensure that you use the correct
copy of R
or Rscript
: there might not be one in the path
at all, or it might be the wrong version or architecture. The correct
way to do this is via
"$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" filename "$(R_HOME)/bin$(R_ARCH_BIN)/Rscript" -e 'R expression'
where $(R_ARCH_BIN)
is only needed currently on Windows.
Environment or make variables can be used to select different macros for
Intel 64-bit code or code for other architectures, for example
(GNU make
syntax, allowed on Windows)
ifeq "$(WIN)" "64" PKG_LIBS = value for 64-bit Intel Windows else PKG_LIBS = value for unknown Windows architectures endif
On Windows there is normally a choice between linking to an import library or directly to a DLL. Where possible, the latter is much more reliable: import libraries are tied to a specific toolchain, and in particular on 64-bit Windows two different conventions have been commonly used. So for example instead of
PKG_LIBS = -L$(XML_DIR)/lib -lxml2
one can use
PKG_LIBS = -L$(XML_DIR)/bin -lxml2
since on Windows -lxxx
will look in turn for
libxxx.dll.a xxx.dll.a libxxx.a xxx.lib libxxx.dll xxx.dll
where the first and second are conventionally import libraries, the
third and fourth often static libraries (with .lib
intended for
Visual C++), but might be import libraries. See for example
The fly in the ointment is that the DLL might not be named libxxx.dll, and in fact on 32-bit Windows there was a libxml2.dll whereas on one build for 64-bit Windows the DLL is called libxml2-2.dll. Using import libraries can cover over these differences but can cause equal difficulties.
If static libraries are available they can save a lot of problems with run-time finding of DLLs, especially when binary packages are to be distributed and even more when these support both architectures. Where using DLLs is unavoidable we normally arrange (via configure.win or configure.ucrt) to ship them in the same directory as the package DLL.
There is some support for packages which wish to use
OpenMP38. The
are available for use in src/Makevars, src/Makevars.win or
Makevars.ucrt. Include the appropriate macro in
and so on, and also in
(but see below for Fortran). C/C++ code that needs to
be conditioned on the use of OpenMP can be used inside #ifdef
: note that some toolchains used for R (including Apple's for
macOS39 and some others using
40) have no OpenMP support at all, not even
For example, a package with C code written for OpenMP should have in src/Makevars the lines
Note that the macro SHLIB_OPENMP_CXXFLAGS
applies to the default
C++ compiler and not necessarily to the C++17/20/23 compiler: users of the
latter should do their own configure
checks. If you do use
your own checks, make sure that OpenMP support is complete by compiling
and linking an OpenMP-using program: on some platforms the runtime
library is optional and on others that library depends on other optional
Some care is needed when compilers are from different families which may
use different OpenMP runtimes (e.g. clang
vs GCC
including gfortran
, although it is often possible to use the
runtime with GCC but not vice versa: however
>= 9 may generate calls not in the clang
runtime). For a package with Fortran code using OpenMP the appropriate
lines are
as the C compiler will be used to link the package code. There are platforms on which this does not work for some OpenMP-using code and installation will fail. Since R >= 3.6.2 the best alternative for a package with only Fortran sources using OpenMP is to use
in src/Makevars, src/Makevars.win or Makevars.ucrt.
Note however, that
when this is used $(FLIBS)
should not be included in
since it is for linking Fortran-compiled code by the C
Common platforms may inline all OpenMP calls and so tolerate the
omission of the OpenMP flag from PKG_LIBS
, but this usually
results in an installation failure with a different compiler or
compilation flags. So cross-check that e.g. -fopenmp
in the linking line in the installation logs.
It is not portable to use OpenMP with more than one of C, C++ and Fortran in a single package since it is not uncommon that the compilers are of different families.
For portability, any C/C++ code using the omp_*
functions should
include the omp.h header: some compilers (but not all) include it
when OpenMP mode is switched on (e.g. via flag
There is nothing41 to say what
version of OpenMP is supported: version 4.0 (and much of 4.5 or 5.0) is
supported by recent versions of the Linux and Windows platforms, but
portable packages cannot assume that end users have recent versions.
Apple clang
on macOS has no OpenMP support.
https://www.openmp.org/resources/openmp-compilers-tools/ gives
some idea of what compilers support what versions. Note that support
for Fortran compilers is often less up-to-date and that page suggests it
is unwise to rely on a version later than 3.1. Which introduced a
Fortran OpenMP module, so Fortran users of OpenMP should include
use omp_lib
Rarely, using OpenMP with clang
on Linux generates calls in
, resulting in loading messages like
undefined symbol: __atomic_compare_exchange undefined symbol: __atomic_load
The workaround is to link with -latomic
(having checked it exists).
The performance of OpenMP varies substantially between platforms. The Windows implementation has substantial overheads, so is only beneficial if quite substantial tasks are run in parallel. Also, on Windows new threads are started with the default42 FPU control word, so computations done on OpenMP threads will not make use of extended-precision arithmetic which is the default for the main process.
Do not include these macros unless your code does make use of OpenMP (possibly for C++ via included external headers): this can result in the OpenMP runtime being linked in, threads being started, ….
Calling any of the R API from threaded code is 'for experts only' and strongly discouraged. Many functions in the R API modify internal R data structures and might corrupt these data structures if called simultaneously from multiple threads. Most R API functions can signal errors, which must only happen on the R main thread. Also, external libraries (e.g. LAPACK) may not be thread-safe.
Packages are not standard-alone programs, and an R process could
contain more than one OpenMP-enabled package as well as other components
(for example, an optimized BLAS) making use of OpenMP. So careful
consideration needs to be given to resource usage. OpenMP works with
parallel regions, and for most implementations the default is to use as
many threads as 'CPUs' for such regions. Parallel regions can be
nested, although it is common to use only a single thread below the
first level. The correctness of the detected number of 'CPUs' and the
assumption that the R process is entitled to use them all are both
dubious assumptions. One way to limit resources is to limit the overall
number of threads available to OpenMP in the R process: this can be
done via environment variable OMP_THREAD_LIMIT
, where
implemented.43 Alternatively, the number of threads per
region can be limited by the environment variable OMP_NUM_THREADS
or API call omp_set_num_threads
, or, better, for the regions in
your code as part of their specification. E.g. R uses44
#pragma omp parallel for num_threads(nthreads) ...
That way you only control your own code and not that of other OpenMP users.
Note that setting environment variables to control OpenMP is
implementation-dependent and may need to be done outside the R
process or before any use of OpenMP (which might be by another process
or R itself). Also, implementation-specific variables such as
might take precedence.
There is no direct support for the POSIX threads (more commonly known as
): by the time we considered adding it several packages
were using it unconditionally so it seems that nowadays it is
universally available on POSIX operating systems.
For reasonably recent versions of gcc
and clang
correct specification is
PKG_CPPFLAGS = -pthread PKG_LIBS = -pthread
(and the plural version is also accepted on some systems/versions). For other platforms the specification is
PKG_CPPFLAGS = -D_REENTRANT PKG_LIBS = -lpthread
(and note that the library name is singular). This is what -pthread does on all known current platforms (although earlier versions of OpenBSD used a different library name).
For a tutorial see https://hpc-tutorials.llnl.gov/posix/.
POSIX threads are not normally used on Windows which has its own native
concepts of threads: however, recent toolchains do provide the
header and library.
The presence of a working pthreads
implementation cannot be
unambiguously determined without testing for yourself: however, that
'_REENTRANT' is defined in C/C++ code is a good indication.
Note that not all pthreads
implementations are equivalent as parts
are optional (see
for example, macOS lacks the 'Barriers' option.
See also the comments on thread-safety and performance under OpenMP: on
all known R platforms OpenMP is implemented via
and the known performance issues are in the latter.
Package authors fairly often want to organize code in sub-directories of src, for example if they are including a separate piece of external software to which this is an R interface.
One simple way is simply to set OBJECTS
to be all the objects
that need to be compiled, including in sub-directories. For example,
CRAN package RSiena has
SOURCES = $(wildcard data/*.cpp network/*.cpp utils/*.cpp model/*.cpp model/*/*.cpp model/*/*/*.cpp) OBJECTS = siena07utilities.o siena07internals.o siena07setup.o siena07models.o $(SOURCES:.cpp=.o)
One problem with that approach is that unless GNU make extensions are used, the source files need to be listed and kept up-to-date. As in the following from CRAN package lossDev:
OBJECTS.samplers = samplers/ExpandableArray.o samplers/Knots.o \ samplers/RJumpSpline.o samplers/RJumpSplineFactory.o \ samplers/RealSlicerOV.o samplers/SliceFactoryOV.o samplers/MNorm.o OBJECTS.distributions = distributions/DSpline.o \ distributions/DChisqrOV.o distributions/DTOV.o \ distributions/DNormOV.o distributions/DUnifOV.o distributions/RScalarDist.o OBJECTS.root = RJump.o OBJECTS = $(OBJECTS.samplers) $(OBJECTS.distributions) $(OBJECTS.root)
Where the subdirectory is self-contained code with a suitable makefile, the best approach is something like
PKG_LIBS = -LCsdp/lib -lsdp $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) $(SHLIB): Csdp/lib/libsdp.a Csdp/lib/libsdp.a: @(cd Csdp/lib && $(MAKE) libsdp.a \ CC="$(CC)" CFLAGS="$(CFLAGS) $(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)")
Note the quotes: the macros can contain spaces, e.g. CC = "gcc
-m64 -std=gnu99"
. Several authors have forgotten about parallel makes:
the static library in the subdirectory must be made before the shared
object ($(SHLIB)
) and so the latter must depend on the former.
Others forget the need45 for
position-independent code.
We really do not recommend using src/Makefile instead of src/Makevars, and as the example above shows, it is not necessary.
It may be helpful to give an extended example of using a configure script to create a src/Makevars file: this is based on that in the RODBC package.
The configure.ac file follows: configure is created from
this by running autoconf
in the top-level package directory
(containing configure.ac).
AC_INIT([RODBC], 1.1.8) dnl package name, version dnl A user-specifiable option odbc_mgr="" AC_ARG_WITH([odbc-manager], AC_HELP_STRING([--with-odbc-manager=MGR], [specify the ODBC manager, e.g. odbc or iodbc]), [odbc_mgr=$withval]) if test "$odbc_mgr" = "odbc" ; then AC_PATH_PROGS(ODBC_CONFIG, odbc_config) fi dnl Select an optional include path, from a configure option dnl or from an environment variable. AC_ARG_WITH([odbc-include], AC_HELP_STRING([--with-odbc-include=INCLUDE_PATH], [the location of ODBC header files]), [odbc_include_path=$withval]) RODBC_CPPFLAGS="-I." if test [ -n "$odbc_include_path" ] ; then RODBC_CPPFLAGS="-I. -I${odbc_include_path}" else if test [ -n "${ODBC_INCLUDE}" ] ; then RODBC_CPPFLAGS="-I. -I${ODBC_INCLUDE}" fi fi dnl ditto for a library path AC_ARG_WITH([odbc-lib], AC_HELP_STRING([--with-odbc-lib=LIB_PATH], [the location of ODBC libraries]), [odbc_lib_path=$withval]) if test [ -n "$odbc_lib_path" ] ; then LIBS="-L$odbc_lib_path ${LIBS}" else if test [ -n "${ODBC_LIBS}" ] ; then LIBS="-L${ODBC_LIBS} ${LIBS}" else if test -n "${ODBC_CONFIG}"; then odbc_lib_path=`odbc_config --libs | sed s/-lodbc//` LIBS="${odbc_lib_path} ${LIBS}" fi fi fi dnl Now find the compiler and compiler flags to use : ${R_HOME=`R RHOME`} if test -z "${R_HOME}"; then echo "could not determine R_HOME" exit 1 fi CC=`"${R_HOME}/bin/R" CMD config CC` CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS` CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` if test -n "${ODBC_CONFIG}"; then RODBC_CPPFLAGS=`odbc_config --cflags` fi CPPFLAGS="${CPPFLAGS} ${RODBC_CPPFLAGS}" dnl Check the headers can be found AC_CHECK_HEADERS(sql.h sqlext.h) if test "${ac_cv_header_sql_h}" = no || test "${ac_cv_header_sqlext_h}" = no; then AC_MSG_ERROR("ODBC headers sql.h and sqlext.h not found") fi dnl search for a library containing an ODBC function if test [ -n "${odbc_mgr}" ] ; then AC_SEARCH_LIBS(SQLTables, ${odbc_mgr}, , AC_MSG_ERROR("ODBC driver manager ${odbc_mgr} not found")) else AC_SEARCH_LIBS(SQLTables, odbc odbc32 iodbc, , AC_MSG_ERROR("no ODBC driver manager found")) fi dnl for 64-bit ODBC need SQL[U]LEN, and it is unclear where they are defined. AC_CHECK_TYPES([SQLLEN, SQLULEN], , , [# include <sql.h>]) dnl for unixODBC header AC_CHECK_SIZEOF(long, 4) dnl substitute RODBC_CPPFLAGS and LIBS AC_SUBST(RODBC_CPPFLAGS) AC_SUBST(LIBS) AC_CONFIG_HEADERS([src/config.h]) dnl and do substitution in the src/Makevars.in and src/config.h AC_CONFIG_FILES([src/Makevars]) AC_OUTPUT
where src/Makevars.in would be simply
A user can then be advised to specify the location of the ODBC driver manager files by options like (lines broken for easier reading)
R CMD INSTALL \ --configure-args='--with-odbc-include=/opt/local/include \ --with-odbc-lib=/opt/local/lib --with-odbc-manager=iodbc' \ RODBC
or by setting the environment variables ODBC_INCLUDE
R assumes that source files with extension .f are fixed-form Fortran 90 (which includes Fortran 77), and passes them to the compiler specified by macro 'FC'. The Fortran compiler will also accept free-form Fortran 90/95 code with extension .f90 or (most46) .f95.
The same compiler is used for both fixed-form and free-form Fortran code
(with different file extensions and possibly different flags). Macro
can be used for package-specific flags: for the
un-encountered case that both are included in a single package and that
different flags are needed for the two forms, macro PKG_FCFLAGS
is also available for free-form Fortran.
The code used to build R allows a 'Fortran 90' compiler to be selected as 'FC', so platforms might be encountered which only support Fortran 90. However, Fortran 95 is supported on all known platforms.
Most compilers specified by 'FC' will accept most Fortran 2003, 2008 or
2018 code: such code should still use file extension .f90. Most
current platforms use gfortran
where you might need to include
-std=f2003, -std=f2008 or (from version 8)
-std=f2018 in PKG_FFLAGS
or PKG_FCFLAGS
: the
default is 'GNU Fortran', currently Fortran 2018 (but Fortran 95 prior
to gfortran
8) with non-standard extensions. The other
compilers in current use (LLVM's flang-new
and Intel's
) default to Fortran 201847.
It is good practice to describe a Fortran version requirement in
DESCRIPTION's 'SystemRequirements' field. Note that this is
purely for information: the package also needs a configure
script to determine the compiler and set appropriate option(s) and test
that the features needed from the standard are actually supported.
The Fortran 2023 released in Nov 2023: as usual compiler vendors are
introducing support incrementally.
For Intel's ifx
For LLVM's flang-new
does not have complete support even for the 2008 and
2018 standards, but the option -std=f2023 is supported from
version 14.1.
Modern versions of Fortran support modules, whereby compiling one source
file creates a module file which is then included in others. (Module
files typically have a .mod extension: they do depend on the
compiler used and so should never be included in a package.) This
creates a dependence which make
will not know about and often
causes installation with a parallel make to fail. Thus it is necessary
to add explicit dependencies to src/Makevars to tell
the constraints on the order of compilation. For
example, if file iface.f90 creates a module 'iface' used by
files cmi.f90 and dmi.f90 then src/Makevars needs
to contain something like
cmi.o dmi.o: iface.o
Some maintainers have found it difficult to find all the module
dependencies which leads to hard-to-reproduce installation failures.
There are tools available to find these, including the Intel compiler's
flag -gen-dep and makedepf90
Note that it is not portable (although some platforms do accept it) to define a module of the same name in multiple source files.
R can be built without a C++ compiler although one is available (but not necessarily installed) on all known R platforms. As from R 4.0.0 a C++ compiler will be selected only if it conforms to the 2011 standard ('C++11'). A minor update48 ('C++14') was published in December 2014 and was used by default as from R 4.1.0 if supported. Further revisions 'C++17' (in December 2017) and 'C++20' (with many new features in December 2020) have been published since. The next revision, 'C++23', is expected in 2024 and several compilers already have extensive partial support for the final draft.
The default standard for compiling R packages was changed to C++17 in R 4.3.0 if supported, and from R 4.4.0 only a C++17 compiler will be selected as the default C++ compiler.
What standard a C++ compiler aims to support can be hard to determine:
the value49 of __cplusplus
may help but
some compilers use it to denote a standard which is partially supported
and some the latest standard which is (almost) fully supported. On a
Unix-alike configure
will try to identify a compiler and flags
for each of the standards: this relies heavily on the reported values of
The webpage https://en.cppreference.com/w/cpp/compiler_support gives some information on which compilers are known to support recent C++ features.
C++ standards have deprecated and later removed features. Be aware that
some current compilers still accept removed features in C++17 mode,
such as std::unary_function
(deprecated in C++11, removed in C++17).
Different versions of R have used different default C++ standards, so for maximal portability a package should specify the standard it requires. In order to specify C++14 code in a package with a Makevars file (or Makevars.win or Makevars.ucrt on Windows) should include the line
Compilation and linking will then be done with the C++14 compiler (if any). Analogously for other standards (details below). On the other hand, specifying C++1150 when the code is valid under C++14 or C++17 reduces future portability.
Packages without a src/Makevars or src/Makefile file may specify a C++ standard for code in the src directory by including something like 'C++14' in the 'SystemRequirements' field of the DESCRIPTION file, e.g.
SystemRequirements: C++14
If a package does have a src/Makevars[.win] file then also
setting the make variable 'CXX_STD' there is recommended, as it
allows R CMD SHLIB
to work correctly in the package's
src directory.
A requirement of C++17 or later should always be declared in the 'SystemRequirements' field (as well as in src/Makevars or src/Makefile) so this is shown on the package's summary pages on CRAN or similar. This is also good practice for a requirement of C++14. Note that support of C++14 or C++17 is only available from R 3.4.0, so if the package has an R version requirement it needs to take that into account.
Essentially complete C++14 support is available from GCC 5, LLVM
3.4 and currently-used versions of Apple
Code needing C++14 features can check for their presence via 'SD-6 feature tests'51. Such a check could be
#include <memory> // header where this is defined #if defined(__cpp_lib_make_unique) && (__cpp_lib_make_unique >= 201304) using std::make_unique; #else // your emulation #endif
C++17 (as from R 3.4.0), C++20 (as from R 4.0.0) and C++23 (as
from R 4.3.0) can be specified in an analogous way (replacing
by 17
, 20
or 23
) but compiler/OS support
is platform-dependent. Some C++17 and C++20 support is available with
the default builds of R on macOS and Windows as from R 4.0.0.
Much of g++
's support for C++17 needs version 7 or later: that
is more recent than some still-current Linux distributions but often
packages for later compilers are available: for RHEL/Centos 7 look for
Note that C++17 or later 'support' does not mean complete support: use
feature tests as well as resources such as
https://gcc.gnu.org/projects/cxx-status.html and
https://clang.llvm.org/cxx_status.html to see if the features you
want to use are widely implemented. In particular, for C++23 R's
script only checks for a compiler claiming to be
later than C++20.52
Attempts to specify an unknown C++ standard are silently ignored: recent versions of R throw an error for C++98 and for known standards for which no compiler+flags has been detected.
If a package using C++ has a configure
script it is essential
that the script selects the correct C++ compiler and standard,
via something like
CXX17=`"${R_HOME}/bin/R" CMD config CXX17` if test -z "$CXX17"; then AC_MSG_ERROR([No C++17 compiler is available]) fi CXX17STD=`"${R_HOME}/bin/R" CMD config CXX17STD` CXX="${CXX17} ${CXX17STD}" CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXX17FLAGS` ## for an configure.ac file AC_LANG(C++)
if C++17 was specified, but using
CXX=`"${R_HOME}/bin/R" CMD config CXX` CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXXFLAGS` ## for an configure.ac file AC_LANG(C++)
if no standard was specified.
If you want to compile C++ code in a subdirectory, make sure you pass down the macros to specify the appropriate compiler, e.g. in src/Makevars
sublibs: @(cd libs && $(MAKE) \ CXX="$(CXX17) $(CXX17STD)" CXXFLAGS="$(CXX17FLAGS) $(CXX17PICFLAGS)")
The discussion above is about the standard R ways of compiling C++:
it will not apply to packages using src/Makefile or building in a
subdirectory that do not set the C++ standard. And compilers' default
C++ standards varies widely and gets changed frequently by vendors –
for example Apple clang up to at least 16 defaults to C++98, LLVM
clang 14–15 to C++14, LLVM clang 16–18 to C++17 and g++
11–14 to C++17.
For a package with a src/Makefile (or a Windows analogue), a non-default C++ compiler can be selected by including something like
CXX14 = `"${R_HOME}/bin/R" CMD config CXX14` CXX14STD = `"${R_HOME}/bin/R" CMD config CXX14STD` CXX = ${CXX14} ${CXX14STD} CXXFLAGS = `"${R_HOME}/bin/R" CMD config CXX14FLAGS` CXXPICFLAGS = `"${R_HOME}/bin/R" CMD config CXX14PICFLAGS` SHLIB_LD = "${R_HOME}/bin/R" CMD config SHLIB_CXX14LD` SHLIB_LDFLAGS = "${R_HOME}/bin/R" CMD config SHLIB_CXX14LDFLAGS`
and ensuring these values are used in relevant compilations, after checking they are non-empty. A common use of src/Makefile is to compile an executable, when likely something like (for example for C++14)
CXX14 = `"${R_HOME}/bin/R" CMD config CXX14` CXX14STD = `"${R_HOME}/bin/R" CMD config CXX14STD` CXX = ${CXX14} ${CXX14STD} CXXFLAGS = `"${R_HOME}/bin/R" CMD config CXX14FLAGS`
suffices. On Unix (and on Windows from R 4.3.0) this can be simplified to
On a Unix-alike C++ compilation defaulted to C++11 from R 3.6.0, to C++14 from R 4.1.0 and to C++17 from R 4.3.0. However, only 'if available', so platforms using very old OSes might have used the previous default. Even older versions of R defaulted to the compiler's default, almost certainly C++98 for compilers of comparable vintage.
On Windows the default was changed from C++98 to C++11 in R 3.6.2, to C++14 in R 4.2.3 and to C++17 in R 4.3.0.
The C++11 standard could be specified as from R 3.1.0 and C++14 or C++17 as from R 3.4.0, for C++20 from R 4.0.0 and for C++23 from R 4.3.0 (although they may not be supported by the compilers in use). C++11 support became mandatory in R 4.0.0 and C++17 support in R 4.4.0.
The .so/.dll in a package may need to be linked by the
C++ compiler if it or any library it links to contains compiled C++
code. Dynamic linking usually brings in the C++ runtime library
(commonly libstdc++
but can be, for example, libc++
) but
static linking (as used for external libraries on Windows and macOS)
will not. R CMD INSTALL
will link with the C++ compiler if
there are any top-level C++ files in src, but not if these are
all in subdirectories. The simplest way to force linking by the C++
compiler is to include an empty C++ file in src..
C has had standards C89/C90, C99, C11, C17 (also known as C18), and C23
is in final draft and expected to be published in 2024. C11 was a
minor change to C99 which introduced some new features and made others
optional, and C17 is a 'bug-fix' update to C11. On the other hand, C23
makes extensive changes, including making bool
, true
reserved words, finally disallowing K&R-style function
declarations and clarifying the formerly deprecated meaning of function
declarations with an empty parameter list to mean zero
parameters. (There are many other additions: see for example
The configure
script in recent versions of R aims to choose
a C compiler which supports C11: as the default in recent versions of
, LLVM clang
and Apple clang
is C17,
that is what is likely to be chosen. On the other hand, until R 4.3.0
the makefiles for the Windows build specified C99. They now use the
compiler default which for the recommended compiler is C17.
Packages may want to either avoid or embrace the changes in C23, and can
do so via specifying 'USE_Cnn' for 17, 23, 90 or 99 in the
'SystemRequirements' field of their DESCRIPTION file of a
package depending on 'R (>= 4.3.0)'. Those using a
script should set the corresponding compiler and
flags, for example using
CC=`"${R_HOME}/bin/R" CMD config CC23` CFLAGS=`"${R_HOME}/bin/R" CMD config C23FLAGS` CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS`
The (claimed) C standard in use can be checked by the macro
. This is undefined in C89/C90 and should have
values 199901L
and 201710L
for C99, C11
and C17. As C23 is not yet published there is as yet no definitive
value: compilers are currently using 202000L
C23 has macros similar to C++ 'feature tests' for many of its changes,
for example __STDC_VERSION_LIMITS_H__
However, note the 'claimed' as no compiler had 100% conformance, and it
is better to use configure
to test for the feature you want to
use than to condition on the value of __STDC_VERSION__
. In
particular, C11 alignment functionality such as _Alignas
is not implemented on Windows.
End users can specify a standard by something like R CMD
INSTALL --use-C17
. This overrides the 'SystemRequirements' field,
but not for any configure
¶Packages often wish to include the sources of other software and compile that for inclusion in their .so or .dll, which is normally done by including (or unpacking) the sources in a subdirectory of src, as considered above.
Further issues arise when the external software uses another build
system such as cmake
, principally to ensure that
all the settings for compilers, include and load paths etc
are made. This section has already mentioned the need to set
at least some of
and CXXFLAGS
will need to include CPICFLAGS
and CXXPICFLAGS
respectively unless (as below) cmake
asked to generate PIC code.
Setting these (and more) as environment variables controls the behaviour
of cmake
but it may be desirable to translate these into native settings such as
and it is often necessary to ensure a static library of PIC code is built by
-DBUILD_SHARED_LIBS:bool=OFF -DCMAKE_POSITION_INDEPENDENT_CODE:bool=ON
If R is to be detected or used, this must be the build being used for
package installation – "${R_HOME}"/bin/R
To fix ideas, consider a package with sources for a library myLib under src/libs. Two approaches have been used. It is often most convenient to build the external software in a directory other than its sources (particularly during development when the build directory can be removed between builds rather than attempting to clean the sources) – this is illustrated in the first approach.
PKG_CPPFLAGS = -Ilibs/include PKG_LIBS = build/libmyLib.a
(-Lbuild -lmyLib
could also be used but this explicit
specification avoids any confusion with dynamic libraries of the same
The configure script will need to contain something like (for C code)
: ${R_HOME=`R RHOME`} if test -z "${R_HOME}"; then echo "could not determine R_HOME" exit 1 fi CC=`"${R_HOME}/bin/R" CMD config CC` CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS` CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS` cd src mkdir build && cd build cmake -S ../libs \ -DCMAKE_BUILD_TYPE=Release \ -DBUILD_SHARED_LIBS:bool=OFF \ -DCMAKE_POSITION_INDEPENDENT_CODE:bool=ON ${MAKE}
PKG_CPPFLAGS = -Ilibs/include PKG_LIBS = libs/libmyLib.a $(SHLIB): mylibs mylibs: (cd libs; \ CC="$(CC)" CFLAGS="$(CFLAGS)" \ CPPFLAGS="$(CPPFLAGS)" LDFLAGS="$(LDFLAGS)" \ cmake . \ -DCMAKE_BUILD_TYPE=Release \ -DBUILD_SHARED_LIBS:bool=OFF \ -DCMAKE_POSITION_INDEPENDENT_CODE:bool=ON; \ $(MAKE))
the compiler and other settings having been set as Make variables by an
R makefile included by INSTALL
before src/Makevars.
A complication is that on macOS cmake
(where installed) is
commonly not on the path but at
/Applications/CMake.app/Contents/bin/cmake. One way to work
around this is for the package's configure script to include
if test -z "$CMAKE"; then CMAKE="`which cmake`"; fi if test -z "$CMAKE"; then CMAKE=/Applications/CMake.app/Contents/bin/cmake; fi if test -f "$CMAKE"; then echo "no 'cmake' command found"; exit 1; fi
and for the second approach to substitute CMAKE
src/Makevars. This also applies to the ancillary command
, if used.
Before using these tools, please check that your package can be
installed. R CMD check
will inter alia do this, but you
may get more detailed error messages doing the install directly.
If your package specifies an encoding in its DESCRIPTION file, you should run these tools in a locale which makes use of that encoding: they may not work at all or may work incorrectly in other locales (although UTF-8 locales will most likely work).
R CMD check
andR CMD build
run R processes with --vanilla in which none of the user's startup files are read. If you needR_LIBS
set (to find packages in a non-standard library) you can set it in the environment: also you can use the check and build environment files (as specified by the environment variablesR_CHECK_ENVIRON
; if unset, files53 ~/.R/check.Renviron and ~/.R/build.Renviron are used) to set environment variables when using these utilities.
Note to Windows users:
R CMD build
may make use of the Windows toolset (see the "R Installation and Administration" manual) if present and in your path, and it is required for packages which need it to install (including those with configure.win, cleanup.win, configure.ucrt or cleanup.ucrt scripts or a src directory) and e.g. need vignettes built.You may need to set the environment variable
to point to a suitable writable directory with a path not containing spaces – use forward slashes for the separators. Also, the directory needs to be on a case-honouring file system (some network-mounted file systems are not).
Using R CMD check
, the R package checker, one can test whether
source R packages work correctly. It can be run on one or
more directories, or compressed package tar
archives with
extension .tar.gz, .tgz, .tar.bz2 or
It is strongly recommended that the final checks are run on a
archive prepared by R CMD build
This runs a series of checks, including
if available54. (There may be
rare false positives.)
in the environment if
dependent packages are in a separate library tree.) One check is that
the package name is not that of a standard package, nor one of the
defunct standard packages ('ctest', 'eda', 'lqs',
'mle', 'modreg', 'mva', 'nls', 'stepfun' and
'ts'). Another check is that all packages mentioned in
or require
s or from which the NAMESPACE
file imports or are called via ::
or :::
are listed
(in 'Depends', 'Imports', 'Suggests'): this is not an
exhaustive check of the actual imports.
To allow a configure script to generate suitable files, files ending in '.in' will be allowed in the R directory.
A warning is given for directory names that look like R package check directories – many packages have been submitted to CRAN containing these.
Package startup functions are checked for correct argument lists and
(incorrect) calls to functions which modify the search path or
inappropriately generate messages. The R code is checked for
possible problems using codetools. In addition, it is checked
whether S3 methods have all the arguments of the corresponding generic, and
whether the final argument of replacement functions is called
'value'. All foreign function calls (.C
, .Fortran
and .External
calls) are tested to see if they have
a PACKAGE
argument, and if not, whether the appropriate DLL might
be deduced from the namespace of the package. Any other calls are
reported. (The check is generous, and users may want to supplement this
by examining the output of tools::checkFF("mypkg", verbose=TRUE)
especially if the intention were to always use a PACKAGE
, \alias
and \description
). The Rd name and
title are checked for being non-empty, and there is a check for missing
cross-references (links).
sections of Rd files are documented in the corresponding
Compiled code is checked for symbols corresponding to functions which might terminate R or write to stdout/stderr instead of the console. Note that the latter might give false positives in that the symbols might be pulled in with external libraries and could never be called. Windows56 users should note that the Fortran and C++ runtime libraries are examples of such external libraries.
) are available, checking that the
PDF documentation is of minimal size.
to create executable example code.) If there is a file
tests/Examples/pkg-Ex.Rout.save, the output of running the
examples is compared to that file.
Of course, released packages should be able to run at least their own
examples. Each example is run in a 'clean' environment (so earlier
examples cannot be assumed to have been run), and with the variables
and F
redefined to generate an error unless they are set
in the example:
See Logical vectors in An Introduction to R.
be used to specify tests in a non-standard location. For example,
unusually slow tests could be placed in inst/slowTests and then
R CMD check --test-dir=inst/slowTests
would be used to run them.
Other names that have been suggested are, for example,
inst/testWithOracle for tests that require Oracle to be installed,
inst/randomTests for tests which use random values and may
occasionally fail by chance, etc.)
If there is an error57 in executing the R code in vignette foo.ext, a log
file foo.ext.log is created in the check directory. The
vignettes are re-made in a copy of the package sources in the
vign_test subdirectory of the check directory, so for further
information on errors look in directory
pkgname/vign_test/vignettes. (It is only retained if there
are errors or if environment variable _R_CHECK_CLEAN_VIGN_TEST_
set to a false value.)
R CMD check --as-cran
) the HTML
version of the manual is created and checked for compliance with the
HTML5 standard. This requires a recent version58 of 'HTML
Tidy', either on the path or at a location specified by environment
variable R_TIDYCMD
. Up-to-date versions can be installed from
All these tests are run with collation set to the C
locale, and
for the examples and tests with environment variable LANGUAGE=en
this is to minimize differences between platforms.
Use R CMD check --help to obtain more information about the usage
of the R package checker. A subset of the checking steps can be
selected by adding command-line options. It also allows customization by
setting environment variables _R_CHECK_*_
as described in
Tools in R Internals:
a set of these customizations similar to those used by CRAN
can be selected by the option --as-cran (which works best if
Internet access is available). Some Windows users may
need to set environment variable R_WIN_NO_JUNCTIONS
to a non-empty
value. The test of cyclic declarations59in DESCRIPTION files needs
repositories (including CRAN) set: do this in
~/.Rprofile, by e.g.
options(repos = c(CRAN="https://cran.r-project.org"))
One check customization which can be revealing is
which reports unused local assignments. Not only does this point out
computations which are unnecessary because their results are unused, it
also can uncover errors. (Two such are to intend to update an object by
assigning a value but mistype its name or assign in the wrong scope,
for example using <-
where <<-
was intended.) This can
give false positives, most commonly because of non-standard evaluation
for formulae and because the intention is to return objects in the
environment of a function for later use.
Complete checking of a package which contains a file README.md
needs a reasonably current version of pandoc
installed: see
You do need to ensure that the package is checked in a suitable locale
if it contains non-ASCII characters. Such packages are likely
to fail some of the checks in a C
locale, and R CMD
will warn if it spots the problem. You should be able to check
any package in a UTF-8 locale (if one is available). Beware that
although a C
locale is rarely used at a console, it may be the
default if logging in remotely or for batch jobs.
Often R CMD check
will need to consult a CRAN repository to
check details of uninstalled packages. Normally this defaults to the
CRAN main site, but a mirror can be specified by setting environment
variables R_CRAN_WEB
and (rarely needed) R_CRAN_SRC
to the
URL of a CRAN mirror.
It is possible to install a package and then check the installed package. To do so first install the package and keep a log of the installation:
R CMD INSTALL -l libdir pkg > pkg.log 2>&1
and then use
Rdev CMD check -l libdir --install=check:pkg.log pkg
(Specifying the library is required: it ensures that the just-installed
package is the one checked. If you know for sure only one copy is
installed you can use --install=skip: this is used for R
installation's make check-recommended
Packages may be distributed in source form as "tarballs" (.tar.gz files) or in binary form. The source form can be installed on all platforms with suitable tools and is the usual form for Unix-like systems; the binary form is platform-specific, and is the more common distribution form for the macOS and 'x86_64' Windows platforms.
Using R CMD build
, the R package builder, one can build
R package tarballs from their sources (for example, for subsequent
release). It is recommended that packages are built for release by the
current release version of R or 'r-patched', to avoid
inadvertently picking up new features of a development version of R.
Prior to actually building the package in the standard gzipped tar file format, a few diagnostic checks and cleanups are performed. In particular, it is tested whether object indices exist and can be assumed to be up-to-date, and C, C++ and Fortran source files and relevant makefiles in a src directory are tested and converted to LF line-endings if necessary.
Run-time checks whether the package works correctly should be performed
using R CMD check
prior to invoking the final build procedure.
To exclude files from being put into the package, one can specify a list
of exclude patterns in file .Rbuildignore in the top-level source
directory. These patterns should be Perl-like regular expressions (see
the help for regexp
in R for the precise details), one per
line, to be matched case-insensitively against the file and directory
names relative to the top-level package source directory. In addition,
directories from source control systems60 or from
61, directories with
names check, chm, or ending .Rcheck or Old
or old and files
GNUMakefile62, Read-and-delete-me or with base names
starting with '.#', or starting and ending with '#', or ending
in '~', '.bak' or '.swp', are excluded by
default63. In addition,
same-package tarballs (from previous builds) and their binary forms will
be excluded from the top-level directory, as well as
those files in the R, demo and man
directories which are flagged by R CMD check
as having invalid
Use R CMD build --help to obtain more information about the usage of the R package builder.
Unless R CMD build is invoked with the --no-build-vignettes option (or the package's DESCRIPTION contains 'BuildVignettes: no' or similar), it will attempt to (re)build the vignettes (see Writing package vignettes) in the package. To do so it installs the current package into a temporary library tree, but any dependent packages need to be installed in an available library tree (see the Note: at the top of this section).
Similarly, if the .Rd documentation files contain any
macros (see Dynamic pages), the package will be
temporarily installed to execute them. Post-execution binary copies of
those pages containing build-time macros will be saved in
build/partial.rdb. If there are any install-time or render-time
macros, a .pdf version of the package manual will be built and
installed in the build subdirectory. (This allows
CRAN or other repositories to display the manual even if they
are unable to install the package.) This can be suppressed by the
option --no-manual or if package's DESCRIPTION contains
'BuildManual: no' or similar.
One of the checks that R CMD build
runs is for empty source
directories. These are in most (but not all) cases unintentional, if
they are intentional use the option --keep-empty-dirs (or set
the environment variable _R_BUILD_KEEP_EMPTY_DIRS_
to 'TRUE',
or have a 'BuildKeepEmpty' field with a true value in the
DESCRIPTION file).
The --resave-data option allows saved images (.rda and
.RData files) in the data directory to be optimized for
size. It will also compress tabular files and convert .R files
to saved images. It can take values no
, gzip
(the default
if this option is not supplied, which can be changed by setting the
environment variable _R_BUILD_RESAVE_DATA_
) and best
(equivalent to giving it without a value), which chooses the most
effective compression. Using best
adds a dependence on R
(>= 2.10)
to the DESCRIPTION file if bzip2
compression is selected for any of the files. If this is
thought undesirable, --resave-data=gzip (which is the default
if that option is not supplied) will do what compression it can with
. A package can control how its data is resaved by
supplying a 'BuildResaveData' field (with one of the values given
earlier in this paragraph) in its DESCRIPTION file.
The --compact-vignettes option will run
over the PDF files in inst/doc (and its
subdirectories) to losslessly compress them. This is not enabled by
default (it can be selected by environment variable
) and needs qpdf
(https://qpdf.sourceforge.io/) to be available.
It can be useful to run R CMD check --check-subdirs=yes
on the
built tarball as a final check on the contents.
Where a non-POSIX file system is in use which does not utilize execute
permissions, some care is needed with permissions. This applies on
Windows and to e.g. FAT-formatted drives and SMB-mounted file systems
on other OSes. The 'mode' of the file recorded in the tarball will be
whatever file.info()
returns. On Windows this will record only
directories as having execute permission and on other OSes it is likely
that all files have reported 'mode' 0777
. A particular issue is
packages being built on Windows which are intended to contain executable
scripts such as configure and cleanup: R CMD
ensures those two are recorded with execute permission.
Directory build of the package sources is reserved for use by
R CMD build
: it contains information which may not easily be
created when the package is installed, including index information on
the vignettes and, rarely, information on the help pages and perhaps a
copy of the PDF reference manual (see above).
Binary packages are compressed copies of installed versions of packages. They contain compiled shared libraries rather than C, C++ or Fortran source code, and the R functions are included in their installed form. The format and filename are platform-specific; for example, a binary package for Windows is usually supplied as a .zip file, and for the macOS platform the default binary package file extension is .tgz.
The recommended method of building binary packages is to use
R CMD INSTALL --build pkg
where pkg is either the name of a source tarball (in the usual .tar.gz format) or the location of the directory of the package source to be built. This operates by first installing the package and then packing the installed binaries into the appropriate binary package file for the particular platform.
By default, R CMD INSTALL --build
will attempt to install the
package into the default library tree for the local installation of
R. This has two implications:
To prevent changes to the present working installation or to provide an
install location with write access, create a suitably located directory
with write access and use the -l
option to build the package
in the chosen location. The usage is then
R CMD INSTALL -l location --build pkg
where location is the chosen directory with write access. The package will be installed as a subdirectory of location, and the package binary will be created in the current directory.
Other options for R CMD INSTALL
can be found using R
CMD INSTALL --help
, and platform-specific details for special cases are
discussed in the platform-specific FAQs.
Finally, at least one web-based service is available for building binary packages from (checked) source code: WinBuilder (see https://win-builder.R-project.org/) is able to build 'x86_64' Windows binaries. Note that this is intended for developers on other platforms who do not have access to Windows but wish to provide binaries for the Windows platform.
In addition to the help files in Rd format, R packages allow the inclusion of documents in arbitrary other formats. The standard location for these is subdirectory inst/doc of a source package, the contents will be copied to subdirectory doc when the package is installed. Pointers from package help indices to the installed documents are automatically created. Documents in inst/doc can be in arbitrary format, however we strongly recommend providing them in PDF format, so users on almost all platforms can easily read them. To ensure that they can be accessed from a browser (as an HTML index is provided), the file names should start with an ASCII letter and be comprised entirely of ASCII letters or digits or hyphen or underscore.
A special case is package vignettes. Vignettes are documents in PDF or HTML format obtained from plain-text literate source files from which R knows how to extract R code and create output (in PDF/HTML or intermediate LaTeX). Vignette engines do this work, using "tangle" and "weave" functions respectively. Sweave, provided by the R distribution, is the default engine. Other vignette engines besides Sweave are supported; see Non-Sweave vignettes.
Package vignettes have their sources in subdirectory vignettes of
the package sources. Note that the location of the vignette sources
only affects R CMD build
and R CMD check
: the
tarball built by R CMD build
includes in inst/doc the
components intended to be installed.
Sweave vignette sources are normally given the file extension
.Rnw or .Rtex, but for historical reasons
extensions64 .Snw and
.Stex are also recognized. Sweave allows the integration of
LaTeX documents: see the Sweave
help page in R and the
vignette in package utils for details on the
source document format.
Package vignettes are tested by R CMD check
by executing all R
code chunks they contain (except those marked for non-evaluation, e.g.,
with option eval=FALSE
for Sweave). The R working directory
for all vignette tests in R CMD check
is a copy of the
vignette source directory. Make sure all files needed to run the R
code in the vignette (data sets, …) are accessible by either
placing them in the inst/doc hierarchy of the source package or
by using calls to system.file()
. All other files needed to
re-make the vignettes (such as LaTeX style files, BibTeX input
files and files for any figures not created by running the code in the
vignette) must be in the vignette source directory. R CMD check
will check that vignette production has succeeded by comparing
modification times of output files in inst/doc with
the source in vignettes.
R CMD build
will automatically65 create the
(PDF or HTML versions of the) vignettes in inst/doc for
distribution with the package sources. By including the vignette
outputs in the package sources it is not necessary that these can be
re-built at install time, i.e., the package author can use private R
packages, screen snapshots and LaTeX extensions which are only
available on their machine.66
By default R CMD build
will run Sweave
on all Sweave
vignette source files in vignettes. If Makefile is found
in the vignette source directory, then R CMD build
will try to
run make
after the Sweave
runs, otherwise
is run on each .tex file produced.
The first target in the Makefile should take care of both
creation of PDF/HTML files and cleaning up afterwards (including
after Sweave
), i.e., delete all files that shall not appear in
the final package archive. Note that if the make
step runs R
it needs to be careful to respect the environment values of R_LIBS
and R_HOME
Finally, if there is a Makefile and it has a 'clean:'
target, make clean
is run.
All the usual caveats about including a Makefile apply.
It must be portable (no GNU extensions), use LF line endings
and must work correctly with a parallel make
: too many authors
have written things like
## BAD EXAMPLE all: pdf clean pdf: ABC-intro.pdf ABC-details.pdf %.pdf: %.tex texi2dvi --pdf $* clean: rm *.tex ABC-details-*.pdf
which will start removing the source files whilst pdflatex
Metadata lines can be placed in the source file, preferably in LaTeX
comments in the preamble. One such is a \VignetteIndexEntry
the form
%\VignetteIndexEntry{Using Animal}
Others you may see are \VignettePackage
(currently ignored),
(a comma-separated list of package names)
and \VignetteKeyword
(which replaced
). These are processed at package installation
time to create the saved data frame Meta/vignette.rds.
The \VignetteEngine
is described in Non-Sweave vignettes.
Vignette metadata can be extracted from a source file using
At install time an HTML index for all vignettes in the package is
automatically created from the \VignetteIndexEntry
unless a file index.html exists in directory
inst/doc. This index is linked from the HTML help index for
the package. If you do supply a inst/doc/index.html file it
should contain relative links only to files under the installed
doc directory, or perhaps (not really an index) to HTML help
files or to the DESCRIPTION file, and be valid HTML as
confirmed via the W3C Markup
Validation Service or Validator.nu.
Sweave/Stangle allows the document to specify the split=TRUE
option to create a single R file for each code chunk: this will not
work for vignettes where it is assumed that each vignette source
generates a single file with the vignette extension replaced by
Do watch that PDFs are not too large – one in a CRAN package was 72MB! This is usually caused by the inclusion of overly detailed figures, which will not render well in PDF viewers. Sometimes it is much better to generate fairly high resolution bitmap (PNG, JPEG) figures and include those in the PDF document.
When R CMD build
builds the vignettes, it copies these and
the vignette sources from directory vignettes to inst/doc.
To install any other files from the vignettes directory, include
a file vignettes/.install_extras which specifies these as
Perl-like regular expressions on one or more lines. (See the
description of the .Rinstignore file for full details.)
Vignettes will in general include descriptive text, R input, R output and figures, LaTeX include files and bibliographic references. As any of these may contain non-ASCII characters, the handling of encodings can become very complicated.
The vignette source file should be written in ASCII or contain a declaration of the encoding (see below). This applies even to comments within the source file, since vignette engines process comments to look for options and metadata lines. When an engine's weave and tangle functions are called on the vignette source, it will be converted to the encoding of the current R session.
will produce an R code file in the current locale's
encoding: for a non-ASCII vignette what that is is recorded in a
comment at the top of the file.
will produce a .tex file in the current
encoding, or in UTF-8 if that is declared. Non-ASCII encodings
need to be declared to LaTeX via a line like
(It is also possible to use the more recent 'inputenx' LaTeX package.) For files where this line is not needed (e.g. chapters included within the body of a larger document, or non-Sweave vignettes), the encoding may be declared using a comment like
If the encoding is UTF-8, this can also be declared using the declaration
If no declaration is given in the vignette, it will be assumed to be in the encoding declared for the package. If there is no encoding declared in either place, then it is an error to use non-ASCII characters in the vignette.
In any case, be aware that LaTeX may require the 'usepackage' declaration.
will also parse and evaluate the R code in each
chunk. The R output will also be in the current locale (or UTF-8
if so declared), and should
be covered by the 'inputenc' declaration. One thing people often
forget is that the R output may not be ASCII even for
ASCII R sources, for many possible reasons. One common one
is the use of 'fancy' quotes: see the R help on sQuote
: note
carefully that it is not portable to declare UTF-8 or CP1252 to cover
such quotes, as their encoding will depend on the locale used to run
: this can be circumvented by setting
in the vignette.
The final issue is the encoding of figures – this applies only to PDF
figures and not PNG etc. The PDF figures will contain declarations for
their encoding, but the Sweave option pdf.encoding
may need to be
set appropriately: see the help for the pdf()
graphics device.
As a real example of the complexities, consider the fortunes
package version '1.4-0'. That package did not have a declared
encoding, and its vignette was in ASCII. However, the data it
displays are read from a UTF-8 CSV file and will be assumed to be in the
current encoding, so fortunes.tex will be in UTF-8 in any locale.
Had read.table
been told the data were UTF-8, fortunes.tex
would have been in the locale's encoding.
Vignettes in formats other than Sweave are supported via
"vignette engines". For example knitr version 1.1 or later
can create .tex files from a variation on Sweave format, and
.html files from a variation on "markdown" format. These
engines replace the Sweave()
function with other functions to
convert vignette source files into LaTeX files for processing into
.pdf, or directly into .pdf or .html files. The
function is replaced with a function that extracts the
R source from a vignette.
R recognizes non-Sweave vignettes using filename extensions specified
by the engine. For example, the knitr package supports
the extension .Rmd (standing for
"R markdown"). The user indicates the vignette engine
within the vignette source using a \VignetteEngine
line, for example
This specifies the name of a package and an engine to use in place of
Sweave in processing the vignette. As Sweave
is the only engine
supplied with the R distribution, the package providing any other
engine must be specified in the 'VignetteBuilder' field of the
package DESCRIPTION file, and also specified in the
'Suggests', 'Imports' or 'Depends' field (since its
namespace must be available to build or check your package). If more
than one package is specified as a builder, they will be searched in the
order given there. The utils package is always implicitly
appended to the list of builder packages, but may be included earlier
to change the search order.
Note that a package with non-Sweave vignettes should always have a
'VignetteBuilder' field in the DESCRIPTION file, since this
is how R CMD check
recognizes that there are vignettes to be
checked: packages listed there are required when the package is checked.
The vignette engine can produce .tex, .pdf, or .html
files as output. If it produces .tex files, R will
call texi2pdf
to convert them to .pdf for display
to the user (unless there is a Makefile in the vignettes
Package writers who would like to supply vignette engines need
to register those engines in the package .onLoad
For example, that function could make the call
tools::vignetteEngine("knitr", weave = vweave, tangle = vtangle, pattern = "[.]Rmd$", package = "knitr")
(The actual registration in knitr is more complicated, because
it supports other input formats.) See the ?tools::vignetteEngine
help topic for details on engine registration.
R has a namespace management system for code in packages. This system allows the package writer to specify which variables in the package should be exported to make them available to package users, and which variables should be imported from other packages.
The namespace for a package is specified by the
NAMESPACE file in the top level package directory. This file
contains namespace directives describing the imports and exports
of the namespace. Additional directives register any shared objects to
be loaded and any S3-style methods that are provided. Note that
although the file looks like R code (and often has R-style
comments) it is not processed as R code. Only very simple
conditional processing of if
statements is implemented.
Packages are loaded and attached to the search path by calling
or require
. Only the exported variables are
placed in the attached frame. Loading a package that imports variables
from other packages will cause these other packages to be loaded as well
(unless they have already been loaded), but they will not be
placed on the search path by these implicit loads. Thus code in the
package can only depend on objects in its own namespace and its imports
(including the base namespace) being visible68.
Namespaces are sealed once they are loaded. Sealing means that imports and exports cannot be changed and that internal variable bindings cannot be changed. Sealing allows a simpler implementation strategy for the namespace mechanism and allows code analysis and compilation tools to accurately identify the definition corresponding to a global variable reference in a function body.
The namespace controls the search strategy for variables used by functions in the package. If not found locally, R searches the package namespace first, then the imports, then the base namespace and then the normal search path (so the base namespace precedes the normal search rather than being at the end of it).
Exports are specified using the export
directive in the
NAMESPACE file. A directive of the form
export(f, g)
specifies that the variables f
and g
are to be exported.
(Note that variable names may be quoted, and reserved words and
non-standard names such as [<-.fractions
must be.)
For packages with many variables to export it may be more convenient to
specify the names to export with a regular expression using
. The directive
exports all variables that do not start with a period. However, such broad patterns are not recommended for production code: it is better to list all exports or use narrowly-defined groups. (This pattern applies to S4 classes.) Beware of patterns which include names starting with a period: some of these are internal-only variables and should never be exported, e.g. '.__S3MethodsTable__.' (and loading excludes known cases).
Packages implicitly import the base namespace.
Variables exported from other packages with namespaces need to be
imported explicitly using the directives import
. The import
directive imports all exported
variables from the specified package(s). Thus the directives
import(foo, bar)
specifies that all exported variables in the packages foo and
bar are to be imported. If only some of the exported variables
from a package are needed, then they can be imported using
. The directive
importFrom(foo, f, g)
specifies that the exported variables f
and g
of the
package foo are to be imported. Using importFrom
selectively rather than import
is good practice and recommended
notably when importing from packages with more than a dozen exports and
especially from those written by others (so what they export can change
in future).
To import every symbol from a package but for a few exceptions,
pass the except
argument to import
. The directive
import(foo, except=c(bar, baz))
imports every symbol from foo except bar
. The value of except
should evaluate to something
coercible to a character vector, after substituting each symbol for
its corresponding string.
It is possible to export variables from a namespace which it has
imported from other namespaces: this has to be done explicitly and not
via exportPattern
If a package only needs a few objects from another package it can use a
fully qualified variable reference in the code instead of a formal
import. A fully-qualified reference to the function f
in package
foo is of the form foo::f
. This is slightly less efficient
than a formal import and also loses the advantage of recording all
dependencies in the NAMESPACE file (but they still need to be
recorded in the DESCRIPTION file). Evaluating foo::f
cause package foo to be loaded, but not attached, if it was not
loaded already—this can be an advantage in delaying the loading of a
rarely used package. However, if foo is listed only in
'Suggests' or 'Enhances' this also delays the check that it is
installed: it is good practice to use such imports conditionally (e.g.
via requireNamespace("foo", quietly = TRUE)
Using the foo::f
form will be necessary when a package needs to
use a function of the same name from more than one namespace.
Using foo:::f
instead of foo::f
allows access to
unexported objects. This is generally not recommended, as the existence
or semantics of unexported objects may be changed by the package author
in routine maintenance.
The standard method for S3-style UseMethod
dispatching might fail
to locate methods defined in a package that is imported but not attached
to the search path. To ensure that these methods are available the
packages defining the methods should ensure that the generics are
imported and register the methods using S3method
directives. If
a package defines a function print.foo
intended to be used as a
method for class foo
, then the directive
S3method(print, foo)
ensures that the method is registered and available for UseMethod
dispatch, and the function print.foo
does not need to be exported.
Since the generic print
is defined in base it does not need
to be imported explicitly.
(Note that function and class names may be quoted, and reserved words
and non-standard names such as [<-
and function
It is possible to specify a third argument to S3method, the function to be used as the method, for example
S3method(print, check_so_symbols, .print.via.format)
when print.check_so_symbols
is not needed.
As from R 3.6.0 one can also use S3method()
directives to
perform delayed registration. With
if(getRversion() >= "3.6.0") { S3method(pkg::gen, cls) }
function gen.cls
will get registered as an S3 method for class
and generic gen
from package pkg
only when the
namespace of pkg
is loaded. This can be employed to deal with
situations where the method is not "immediately" needed, and having to
pre-load the namespace of pkg
(and all its strong dependencies)
in order to perform immediate registration is considered too onerous.
There are a number of hooks called as packages are loaded, attached,
detached, and unloaded. See help(".onLoad")
for more details.
Since loading and attaching are distinct operations, separate hooks are
provided for each. These hook functions are called .onLoad
. They both take arguments69 libname
; they should be defined in the namespace but not
Packages can use a .onDetach
or .Last.lib
(provided the latter is exported from the namespace) when detach
is called on the package. It is called with a single argument, the full
path to the installed package. There is also a hook .onUnload
which is called when the namespace is unloaded (via a call to
, perhaps called by detach(unload = TRUE)
with argument the full path to the installed package's directory.
Functions .onUnload
and .onDetach
should be defined in the
namespace and not exported, but .Last.lib
does need to be
Packages are not likely to need .onAttach
(except perhaps for a
start-up banner); code to set options and load shared objects should be
placed in a .onLoad
function, or use made of the useDynLib
directive described next.
User-level hooks are also available: see the help on function
These hooks are often used incorrectly. People forget to export
. Compiled code should be loaded in .onLoad
via a useDynLb
directive: see below) and unloaded in
. Do remember that a package's namespace can be loaded
without the namespace being attached (e.g. by pkgname::fun
) and
that a package can be detached and re-attached whilst its namespace
remains loaded.
It is good practice for these functions to be quiet. Any messages
should use packageStartupMessage
so users (include check scripts)
can suppress them if desired.
¶A NAMESPACE file can contain one or more useDynLib
directives which allows shared objects that need to be
loaded.70 The directive
registers the shared object foo
71 for loading with library.dynam
Loading of registered object(s) occurs after the package code has been
loaded and before running the load hook function. Packages that would
only need a load hook function to load a shared object can use the
directive instead.
The useDynLib
directive also accepts the names of the native
routines that are to be used in R via the .C
, .Call
and .External
interface functions. These are given as
additional arguments to the directive, for example,
useDynLib(foo, myRoutine, myOtherRoutine)
By specifying these names in the useDynLib
directive, the native
symbols are resolved when the package is loaded and R variables
identifying these symbols are added to the package's namespace with
these names. These can be used in the .C
, .Call
and .External
calls in place of the name of the
routine and the PACKAGE
argument. For instance, we can call the
routine myRoutine
from R with the code
.Call(myRoutine, x, y)
rather than
.Call("myRoutine", x, y, PACKAGE = "foo")
There are at least two benefits to this approach. Firstly, the symbol lookup is done just once for each symbol rather than each time the routine is invoked. Secondly, this removes any ambiguity in resolving symbols that might be present in more than one DLL. However, this approach is nowadays deprecated in favour of supplying registration information (see below).
In some circumstances, there will already be an R variable in the
package with the same name as a native symbol. For example, we may have
an R function in the package named myRoutine
. In this case,
it is necessary to map the native symbol to a different R variable
name. This can be done in the useDynLib
directive by using named
arguments. For instance, to map the native symbol name myRoutine
to the R variable myRoutine_sym
, we would use
useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)
We could then call that routine from R using the command
.Call(myRoutine_sym, x, y)
Symbols without explicit names are assigned to the R variable with that name.
In some cases, it may be preferable not to create R variables in the
package's namespace that identify the native routines. It may be too
costly to compute these for many routines when the package is loaded
if many of these routines are not likely to be used. In this case,
one can still perform the symbol resolution correctly using the DLL,
but do this each time the routine is called. Given a reference to the
DLL as an R variable, say dll
, we can call the routine
using the expression
.Call(dll$myRoutine, x, y)
The $
operator resolves the routine with the given name in the
DLL using a call to getNativeSymbol
. This is the same
computation as above where we resolve the symbol when the package is
loaded. The only difference is that this is done each time in the case
of dll$myRoutine
In order to use this dynamic approach (e.g., dll$myRoutine
), one
needs the reference to the DLL as an R variable in the package. The
DLL can be assigned to a variable by using the variable =
format used above for mapping symbols to R variables. For
example, if we wanted to assign the DLL reference for the DLL
in the example above to the variable myDLL
, we would
use the following directive in the NAMESPACE file:
myDLL = useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)
Then, the R variable myDLL
is in the package's namespace and
available for calls such as myDLL$dynRoutine
to access routines
that are not explicitly resolved at load time.
If the package has registration information (see Registering native routines), then we can use that directly rather than specifying the
list of symbols again in the useDynLib
directive in the
NAMESPACE file. Each routine in the registration information is
specified by giving a name by which the routine is to be specified along
with the address of the routine and any information about the number and
type of the parameters. Using the .registration
argument of
, we can instruct the namespace mechanism to create
R variables for these symbols. For example, suppose we have the
following registration information for a DLL named myDLL
static R_NativePrimitiveArgType foo_t[] = { REALSXP, INTSXP, STRSXP, LGLSXP }; static const R_CMethodDef cMethods[] = { {"foo", (DL_FUNC) &foo, 4, foo_t}, {"bar_sym", (DL_FUNC) &bar, 0}, {NULL, NULL, 0, NULL} }; static const R_CallMethodDef callMethods[] = { {"R_call_sym", (DL_FUNC) &R_call, 4}, {"R_version_sym", (DL_FUNC) &R_version, 0}, {NULL, NULL, 0} };
Then, the directive in the NAMESPACE file
useDynLib(myDLL, .registration = TRUE)
causes the DLL to be loaded and also for the R variables foo
, R_call_sym
and R_version_sym
to be
defined in the package's namespace.
Note that the names for the R variables are taken from the entry in
the registration information and do not need to be the same as the name
of the native routine. This allows the creator of the registration
information to map the native symbols to non-conflicting variable names
in R, e.g. R_version
to R_version_sym
for use in an
R function such as
R_version <- function() { .Call(R_version_sym) }
Using argument .fixes
allows an automatic prefix to be added to
the registered symbols, which can be useful when working with an
existing package. For example, package KernSmooth has
useDynLib(KernSmooth, .registration = TRUE, .fixes = "F_")
which makes the R variables corresponding to the Fortran symbols
and so on, and so avoid clashes with R code in the
NB: Using these arguments for a package which does not register native symbols merely slows down the package loading (although many CRAN packages have done so). Once symbols are registered, check that the corresponding R variables are not accidentally exported by a pattern in the NAMESPACE file.
As an example consider two packages named foo and bar. The R code for package foo in file foo.R is
x <- 1 f <- function(y) c(x,y) foo <- function(x) .Call("foo", x, PACKAGE="foo") print.foo <- function(x, ...) cat("<a foo>\n")
Some C code defines a C function compiled into DLL foo
(with an
appropriate extension). The NAMESPACE file for this package is
useDynLib(foo) export(f, foo) S3method(print, foo)
The second package bar has code file bar.R
c <- function(...) sum(...) g <- function(y) f(c(y, 7)) h <- function(y) y+9
and NAMESPACE file
import(foo) export(g, h)
Calling library(bar)
loads bar and attaches its exports to
the search path. Package foo is also loaded but not attached to
the search path. A call to g
> g(6) [1] 1 13
This is consistent with the definitions of c
in the two settings:
in bar the function c
is defined to be equivalent to
, but in foo the variable c
refers to the
standard function c
in base.
Some additional steps are needed for packages which make use of formal
(S4-style) classes and methods (unless these are purely used
internally). The package should have
Depends: methods
72 in its DESCRIPTION and import(methods)
importFrom(methods, ...)
plus any classes and methods which are
to be exported need to be declared in the NAMESPACE file. For
example, the stats4 package has
export(mle) # exporting methods implicitly exports the generic importFrom("stats", approx, optim, pchisq, predict, qchisq, qnorm, spline) ## For these, we define methods or (AIC, BIC, nobs) an implicit generic: importFrom("stats", AIC, BIC, coef, confint, logLik, nobs, profile, update, vcov) exportClasses(mle, profile.mle, summary.mle) ## All methods for imported generics: exportMethods(coef, confint, logLik, plot, profile, summary, show, update, vcov) ## implicit generics which do not have any methods here export(AIC, BIC, nobs)
All S4 classes to be used outside the package need to be listed in an
directive. Alternatively, they can be specified
using exportClassPattern
73 in the same style as
for exportPattern
. To export methods for generics from other
packages an exportMethods
directive can be used.
Note that exporting methods on a generic in the namespace will also
export the generic, and exporting a generic in the namespace will also
export its methods. If the generic function is not local to this
package, either because it was imported as a generic function or because
the non-generic version has been made generic solely to add S4 methods
to it (as for functions such as coef
in the example above), it
can be declared via either or both of export
, but the latter is clearer (and is used in the
stats4 example above). In particular, for primitive functions
there is no generic function, so export
would export the
primitive, which makes no sense. On the other hand, if the generic is
local to this package, it is more natural to export the function itself
using export()
, and this must be done if an implicit
generic is created without setting any methods for it (as is the case
for AIC
in stats4).
A non-local generic function is only exported to ensure that calls to
the function will dispatch the methods from this package (and that is
not done or required when the methods are for primitive functions). For
this reason, you do not need to document such implicitly created generic
functions, and undoc
in package tools will not report them.
If a package uses S4 classes and methods exported from another package, but does not import the entire namespace of the other package74, it needs to import the classes and methods explicitly, with directives
importClassesFrom(package, ...) importMethodsFrom(package, ...)
listing the classes and functions with methods respectively. Suppose we
had two small packages A and B with B using A.
Then they could have NAMESPACE
export(f1, ng1) exportMethods("[") exportClasses(c1)
importFrom(A, ng1) importClassesFrom(A, c1) importMethodsFrom(A, f1) export(f4, f5) exportMethods(f6, "[") exportClasses(c1, c2)
Note that importMethodsFrom
will also import any generics defined
in the namespace on those methods.
It is important if you export S4 methods that the corresponding generics
are available. You may for example need to import coef
stats to make visible a function to be converted into its
implicit generic. But it is better practice to make use of the generics
exported by stats4 as this enables multiple packages to
unambiguously set methods on those generics.
This section contains advice on writing packages to be used on multiple platforms or for distribution (for example to be submitted to a package repository such as CRAN).
Portable packages should have simple file names: use only alphanumeric
ASCII characters and period (.
), and avoid those names
not allowed under Windows (see Package structure).
Many of the graphics devices are platform-specific: even X11()
(aka x11()
) which although emulated on Windows may not be
available on a Unix-alike (and is not the preferred screen device on OS
X). It is rarely necessary for package code or examples to open a new
device, but if essential,75 use dev.new()
Use R CMD build
to make the release .tar.gz file.
R CMD check
provides a basic set of checks, but often further
problems emerge when people try to install and use packages submitted to
CRAN – many of these involve compiled code. Here are some
further checks that you can do to make your package more portable.
and the like), ${shell ...}
, ${wildcard ...}
similar, and the use of +=
77 and :=
. Also, the use of $<
than in implicit rules is a GNU extension, as is the $^
As is the use of .PHONY
(some other makes ignore it).
Unfortunately makefiles which use GNU extensions often run on other
platforms but do not have the intended results.
Note that the -C flag for make
is not included in the
POSIX specification and is not implemented by some of the
s which have been used with R. However, it is more
commonly implemented (e.g. by FreeBSD make
) than the GNU-specific
You should not rely on built-in/default make
rules, even when
specified by POSIX, as some make
s do not have the POSIX ones
and others have altered them.
The use of ${shell ...}
can be avoided by using backticks, e.g.
PKG_CPPFLAGS = `gsl-config --cflags`
which works in all versions of make
known78 to be
used with R.
If you really must require GNU make, declare it in the DESCRIPTION file by
SystemRequirements: GNU make
and ensure that you use the value of environment variable MAKE
(and not just make
) in your scripts. (On some platforms GNU
make is available under a name such as gmake
, and there
is used to set MAKE
If you only need GNU make for parts of the package which are rarely needed (for example to create bibliography files under vignettes), use a file called GNUmakefile rather than Makefile as GNU make (only) will use the former.
macOS has used GNU make for many years (it previously used BSD make), but the version has been frozen at 3.81 (from 2006).
Since the only viable make for Windows is GNU make, it is permissible to use GNU extensions in files Makevars.win, Makevars.ucrt, Makefile.win or Makefile.ucrt.
. See Using Makevars.
pkg/libpkg.a: (cd pkg && $(MAKE) -f make_pkg libpkg.a \ CXX="$(CXX)" CXXFLAGS="$(CXXFLAGS) $(CXXPICFLAGS) $(C_VISIBILITY)" \ AR="$(AR)" RANLIB="$(RANLIB)")
R CMD build
, for
example in a cleanup
script or a 'clean' target.
R CMD config
is used, this needs something
like (for C++)
RBIN = `"${R_HOME}/bin/R"` CXX = `"${RBIN}" CMD config CXX` CXXFLAGS = `"${RBIN}" CMD config CXXFLAGS` `"${RBIN}" CMD config CXXPICFLAGS`
and should be avoided.
a 'lightweight shell with few builtins) or derivatives such as dash
Beware of assuming that all the POSIX command-line utilities are
available, especially on Windows where only a subset (which has changed
by version of Rtools) is provided for use with R. One
particular issue is the use of echo
, for which two behaviours
are allowed
and both have occurred as defaults on R platforms: portable
applications should use neither -n (as the first argument) nor
escape sequences. The recommended replacement for echo -n
the command printf
. Another common issue is the construction
export FOO=value
which is bash
-specific (first set the variable then export it
by name).
Using test -e
(or [ -e ]
) in shell scripts is not fully
portable81: -f
is normally what is intended. Flags
-a and -o are nowadays declared obsolescent by POSIX
and should not be used.
Use of 'brace expansion', e.g.,
rm -f src/*.{o,so,d}
is not portable.
The -o flag for set
in shell scripts is optional in
POSIX and not supported on all the platforms R is used on.
The variable 'OSTYPE' is shell-specific and its values are
rather unpredictable and may include a version such as
'darwin19.0': `uname`
is often what is intended (with
common values 'Darwin', 'Linux' and 'SunOS').
On macOS which shell /bin/sh invokes is user- and
platform-dependent: it might be bash
version 3.2,
or zsh
(for new accounts it is zsh
for accounts ported from Mojave or earlier it is usually
or rust
have all too frequently assumed
otherwise, so do ensure your package is checked under a vanilla R build.
See Configuration options in R Installation and Administration.
82 can be used with options -Wall -pedantic to alert
you to potential problems. This is particularly important for C++,
where g++ -Wall -pedantic
will alert you to the use of some of
the GNU extensions which fail to compile on most other C++ compilers. If
R was not configured accordingly, one can achieve this via
personal Makevars files.
See Customizing package compilation in R Installation and Administration,
Portable C++ code needs to follow all of the 2011, 2014 and 2017 standards (including not using deprecated/removed features) or to specify C+11/14/17/20/23 where available (which is not the case on all R platforms). Currently C++20 support is patchy across R platforms.
If using Fortran with the GNU compiler, use the flags -std=f95
-Wall -pedantic which reject most GNU extensions and features from
later standards. (Although R only requires Fortran 90,
does not have a way to specify that standard.) Also
consider -std=f2008 as some recent compilers have Fortran 2008
or even 2018 as the minimum supported standard.
As from macOS 11 (late 2020), its C compiler sets the flag
-Werror=implicit-function-declaration by default which forces
stricter conformance to C99. This can be used on other platforms with
or clang
. If your package has a
-generated) configure script
, try
installing it whilst using this flag, and read through the
config.log file — compilation warnings and errors can lead to
features which are present not being detected. (If possible do this on
several platforms.)
R CMD check
performs some checks for non-portable
compiler/linker flags in src/Makevars. However, it cannot check
the meaning of such flags, and some are commonly accepted but with
compiler-specific meanings. There are other non-portable flags which
are not checked, nor are src/Makefile files and makefiles in
sub-directories. As a comment in the code says
It is hard to think of anything apart from -I* and -D* that is safe for general use …
although -pthread is pretty close to portable. (Option -U is portable but little use on the command line as it will only cancel built-in defines (not portable) and those defined earlier on the command line (R does not use any).)
The GNU option -pipe used to be widely accepted by
C/C++/Fortran compilers, but has been removed in
18. In any case, it should not be used in
distributed code as it may lead to excessive memory use.
People have used configure
to customize src/Makevars,
including for specific compilers. This is unsafe for several reasons.
First, unintended compilers might meet the check—for example, several
compilers other than GCC identify themselves as 'GCC' whilst being only
partially conformant. Second, future versions of compilers may behave
differently (including updates to quite old series) so for example
-Werror (and specializations) can make a package
non-installable under a future version. Third, using flags to suppress
diagnostic messages can hide important information for debugging on a
platform not tested by the package maintainer. (R CMD check
can optionally report on unsafe flags which were used.)
Avoid the use of -march and especially -march=native.
This allows the compiler to generate code that will only run on a
particular class of CPUs (that of the compiling machine for
'native'). People assume this is a 'minimum' CPU specification,
but that is not how it is documented for gcc
(it is accepted
by clang
but apparently it is undocumented what precisely it
does, and it can be accepted and may be ignored for other compilers).
(For personal use -mtune is safer, but still not portable
enough to be used in a public package.) Not even gcc
'native' for all CPUs, and it can do surprising things if it finds
a CPU released later than its version.
in C will be 32-bit on some R
platforms (including 64-bit Windows), but 64-bit on most modern Unix and
Linux platforms. It is rather unlikely that the use of long
in C
code has been thought through: if you need a longer type than int
you should use a configure test for a C99/C++11 type such as
(and failing that, long long
) and typedef
your own type, or use another suitable type (such as size_t
, but
beware that is unsigned and ssize_t
is not portable).
It is not safe to assume that long
and pointer types are the same
size, and they are not on 64-bit Windows. If you need to convert
pointers to and from integers use the C99/C++11 integer types
and uintptr_t
(in the headers <stdint.h>
and <cstdint>
: they are not required to be implemented by the
standards but are used in C code by R itself).
Note that integer
in Fortran corresponds to int
in C on
all R platforms. There is no such guarantee for Fortran
, and recent gfortran
maps it to
on most platforms.
or exit
83: these terminate the user's R process, quite possibly
losing all unsaved work. One usage that could call abort
is the
macro in C or C++ functions, which should never be active
in production code. The normal way to ensure that is to define the
macro NDEBUG
, and R CMD INSTALL
does so as part of the
compilation flags. Beware of including headers (including from other
packages) which could undefine it, now or in future versions. If you
wish to use assert
during development. you can include
in PKG_CPPFLAGS
or #undef
it in your
headers or code files. Note that your own src/Makefile or
makefiles in sub-directories may also need to define NDEBUG
This applies not only to your own code but to any external software you compile in or link to.
Nor should Fortran code call STOP
nor EXIT
(a GNU extension).
, drand48
and random
84, but rather use the
interfaces to R's RNGs described in Random number generation. In
particular, if more than one package initializes a system RNG (e.g.
via srand
), they will interfere with each other. This
applies also to Fortran 90's random_number
, and Fortran 2018's random_init
. And to GNU
Fortran's rand
, irand
and srand
. Except for
, what PRNG these functions use is
Nor should the C++11 random number library be used nor any other third-party random number generators such as those in GSL.
and vsprintf
is regarded as a potential
security risk and warned about on some platforms.85 R CMD check
reports if any calls
are found.
nm -pg mypkg.so
and checking if any of the symbols marked U
is unexpected is a
good way to avoid this.
(especially those already linked
into R). In the case in point, entry point gzgets
sometimes resolved against the old version compiled into the package,
sometimes against the copy compiled into R and sometimes against the
system dynamic library. The only safe solution is to rename the entry
points in the copy in the package. We have even seen problems with
entry point name myprintf
, which is a system entry
point86 on some Linux systems.
A related issue is the naming of libraries built as part of the package installation. macOS and Windows have case-insensitive file systems, so using
-L. -lLZ4
in PKG_LIBS
will match liblz4
. And -L.
appends to the list of searched locations, and liblz4
might be
found in an earlier-searched location (and has been). The only safe way
is to give an explicit path, for example
nm -pg
), and to use names which are
clearly tied to your package (which also helps users if anything does go
wrong). Note that symbol names starting with R_
are regarded as
part of R's namespace and should not be used in packages.
, on
suitable platforms87,
which would completely avoid symbol conflicts.
, .Fortran
, .Call
, since such interfaces are subject to change without
notice and will probably result in your code terminating the R
R CMD build
will replace them by copies.
, require
or attach
and this often does not
work as intended. For alternatives, see Suggested packages and
as well as
in batch mode when checking. So they should behave appropriately in
both scenarios, conditioning by interactive()
the parts which
need an operator or observer. For instance, progress
bars88 are only appropriate in
interactive use, as is displaying help pages or calling View()
(see below).
Some linkers will re-order the entries, and behaviour can differ between
dynamic and static libraries. Generally -L options should
precede89 the libraries (typically
specified by -l options) to be found from those directories,
and libraries are searched once in the order they are specified. Not
all linkers allow a space after -L .
. This puts one or more
directories on the include search path ahead of system headers but
(prior to R 3.4.0) after those specified in the CPPFLAGS
of the R build (which normally includes -I/usr/local/include
but most platforms ignore that and include it with the system headers).
Any confusion would be avoided by having LinkingTo
headers in a
directory named after the package. In any case, name conflicts of
headers and directories under package include directories should
be avoided, both between packages and between a package and system and
third-party software.
utility is often used in makefiles to make static
libraries. Its modifier u
is defined by POSIX but is disabled in
GNU ar
on some Linux distributions which use
'deterministic mode'. The safest way to make a static library is to first
remove any existing file of that name then use $(AR) -cr
and then
if needed (which is system-dependent: on most
systems90 ar
maintains a symbol table). The POSIX standard says options should be
preceded by a hyphen (as in -cr), although most OSes accept
them without.
Note that on some systems ar -cr
must have at least one file
The s
modifier (to replace a separate call to ranlib
is required by X/OPEN but not POSIX, so ar -crs
is not
For portability the AR
and RANLIB
macros should always be
used – some builds require wrappers such as gcc-ar
or extra
arguments to specify plugins.
utility is platform-specific (and CRAN
prohibits removing debug symbols). For example the options
--strip-debug and --strip-unneeded of the GNU version
are not supported on macOS: the POSIX standard for strip
does not mention any options, and what calling it without options does
is platform-dependent. Stripping a .so file could even prevent
it being dynamically loaded into R on an untested platform.
ld -S
invokes strip --strip-debug
for GNU
(and similarly on macOS) but is not portable: in particular
on Solaris it did something completely different and took an argument.
to fail with a baffling error message.
Non-ASCII filenames can also cause problems (particularly in non-UTF-8 locales).
When specifying a minimum Java version please use the official version names, which are (confusingly)
1.1 1.2 1.3 1.4 5.0 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
and as from 2018 a year.month scheme such as '18.9' is also in use. Fortunately only the integer values are likely to be relevant. If at all possible, use one of the LTS versions (8, 11, 17, 21 …) as the minimum version. The preferred form of version specification is
SystemRequirements: Java (>= 11)
A suitable test for Java at least version 8 for packages using rJava would be something like
.jinit() jv <- .jcall("java/lang/System", "S", "getProperty", "java.runtime.version") if(substr(jv, 1L, 2L) == "1.") { jvn <- as.numeric(paste0(strsplit(jv, "[.]")[[1L]][1:2], collapse = ".")) if(jvn < 1.8) stop("Java >= 8 is needed for this package but not available") }
Java 9 changed the format of this string (which used to be something
like '1.8.0_292-b10'); Java 11 gave jv
as '11+28'
whereas Java 11.0.11 gave '11.0.11+9'.
(https://openjdk.org:443/jeps/322 details the current scheme.
Note that it is necessary to allow for pre-releases like
Note too that the compiler used to produce a jar
can impose a minimum
Java version, often resulting in an arcane message like
java.lang.UnsupportedClassVersionError: ... Unsupported major.minor version 52.0
(Where https://en.wikipedia.org/wiki/Java_class_file maps
class-file version numbers to Java versions.) Compile with something
like javac -target 11
to ensure this is avoided. Note this
also applies to packages distributing (or even downloading) compiled
Java code produced by others, so their requirements need to be checked
(they are often not documented accurately) and accounted for. It should
be possible to check the class-file version via command-line
utility javap
, if necessary after extracting the .class
files from a .jar archive. For example,
jar xvf some.jar javap -verbose path/to/some.class | grep major
Some packages have stated a requirement on a particular JDK, but a package should only be requiring a JRE unless providing its own Java interface.
Java 8 is still in widespread use (and may remain so because of licence changes and support on older OSes: OpenJDK has security support until March 2026). On the other hand, newer platforms may only have support for recent versions of Java: for 'arm64' macOS the first officially supported version was 17.
, which is only available for a very limited range of
platforms (and has onerous requirements to install from source) and has
capabilities92 that vary by build but are not documented. Several recent
versions of pandoc
for macOS did not work on R's then
target of High Sierra (and this too was undocumented). Another example
is the Rust compilation system (cargo
and rustc
Usage of external commands should always be conditional on a test for
presence (perhaps using Sys.which
), as well as declared in the
'SystemRequirements' field. A package should pass its checks
without warnings nor errors without the external command being present.
An external command can be a (possibly optional) requirement for an imported or suggested package but needed for examples, tests or vignettes in the package itself. Such usages should always be declared and conditional.
Interpreters for scripting languages such as Perl, Python and Ruby need to be declared as system requirements and used conditionally: for example macOS 10.16 was announced not to have them (but released as macOS 11 with them); later it was announced that macOS 12.3 does not have Python 2 and only a minimal install of Python 3 is included. Python 2 has passed end-of-life and been removed from many major distributions. Support for Rust or Go cannot be assumed.
Command cmake
is not commonly installed, and where it is, it
might not be on the path. In particular, the most common location on
macOS is /Applications/CMake.app/Contents/bin/cmake and that
should be looked for if cmake
is not found on the path.
, mac
and macroman
is portable. See the help for file
for more
, Rscript
or (on
Windows) Rterm
in your examples, tests, vignettes, makefiles
or other scripts. As pointed out in several places earlier in this
manual, use something like
"$(R_HOME)/bin/Rscript" "$(R_HOME)/bin$(R_ARCH_BIN)/Rterm"
with appropriate quotes (as, although not recommended, R_HOME
contain spaces).
in makefiles except when passing them to the shell.
Specifically, do not use R_HOME
in the argument to include
as R_HOME
can contain spaces. Quoting the argument to include
does not help. A portable and the recommended way to avoid the problem of spaces in
is using option -f
of make
. This is
easy to do with recursive invocation of make
, which is also the
only usual situation when R_HOME
is needed in the argument for
$(MAKE) -f "${R_HOME}/etc${R_ARCH}/Makeconf" -f Makefile.inner
and as these record the time in
UTC, the time represented is independent of the time zone: but how it is
printed may not be. Objects of class "POSIXlt"
should have a
attribute. Dates (e.g, birthdays) are conventionally
considered independently of time zone.
Do be careful in what your tests (and examples) actually test. Bad practice seen in distributed packages include:
Packages have even tested the exact format of system error messages,
which are platform-dependent and perhaps locale-dependent. For example,
in late 2021 libcurl
changed its warning/error messages,
including when URLs are not found.
, remember that in testing there
is no one to look at the output. It is better to use something like one of
if(interactive()) View(obj) else print(head(obj)) if(interactive()) View(obj) else str(obj)
to be aware of the pitfalls.
currently has long
double the same as double on all ARM CPUs. On the other hand some CPUs
have higher-precision modes which may be used for long double
notably 64-bit PowerPC and Sparc.
If you must try to establish a tolerance empirically, configure and
build R with --disable-long-double and use appropriate
compiler flags (such as -ffloat-store and
-fexcess-precision=standard for gcc
, depending on the
CPU type94) to
mitigate the effects of extended-precision calculations. The platform
most often seen to give different numerical results is 'arm64' macOS,
so be sure to include that in any empirical determination.
Tests which involve random inputs or non-deterministic algorithms should normally set a seed or be tested for many seeds.
options(warn = 1)
as reporting
There were 22 warnings (use warnings() to see them)
is pointless, especially for automated checking systems.
There are a several tools available to reduce the size of PDF files: often the size can be reduced substantially with no or minimal loss in quality. Not only do large files take up space: they can stress the PDF viewer and take many minutes to print (if they can be printed at all).
(https://qpdf.sourceforge.io/) can compress
losslessly. It is fairly readily available (e.g. it is included in
, has packages in Debian/Ubuntu/Fedora, and is
installed as part of the CRAN macOS distribution of R).
R CMD build
has an option to run qpdf
over PDF files
under inst/doc and replace them if at least 10Kb and 10% is
saved. The full path to the qpdf
command can be supplied as
environment variable R_QPDF
(and is on the CRAN binary
of R for macOS). It seems MiKTeX does not use PDF object
compression and so qpdf
can reduce considerably the sizes of
files it outputs: MiKTeX's defaults can be overridden by code in the
preamble of an Sweave or LaTeX file — see how this is done for the
R reference manual at
Other tools can reduce the size of PDFs containing bitmap images at
excessively high resolution. These are often best re-generated (for
example Sweave
defaults to 300 ppi, and 100–150 is more
appropriate for a package manual). These tools include Adobe Acrobat
(not Reader), Apple's Preview95 and Ghostscript (which
converts PDF to PDF by
ps2pdf options -dAutoRotatePages=/None -dPrinted=false in.pdf out.pdf
and suitable options might be
-dPDFSETTINGS=/ebook -dPDFSETTINGS=/screen
See https://ghostscript.readthedocs.io/en/latest/VectorDevices.html for
more such and consider all the options for image downsampling). There
have been examples in CRAN packages for which current versions
of Ghostscript produced much bigger reductions than earlier ones (e.g.
at the upgrades from 9.50
to 9.52
, from 9.55
and then to 10.00.0
We come across occasionally large PDF files containing excessively complicated figures using PDF vector graphics: such figures are often best redesigned or failing that, output as PNG files.
Option --compact-vignettes to R CMD build
defaults to
value 'qpdf': use 'both' to try harder to reduce the size,
provided you have Ghostscript available (see the help for
There are several ways to find out where time is being spent in the
check process. Start by setting the environment variable
to '0'. This will report the total CPU
times (not Windows) and elapsed times for installation and running
examples, tests and vignettes, under each sub-architecture if
appropriate. For tests and vignettes, it reports the time for each as
well as the total.
Setting _R_CHECK_TIMINGS_
to a positive value sets a threshold (in
seconds elapsed time) for reporting timings.
If you need to look in more detail at the timings for examples, use
option --timings to R CMD check
(this is set by
--as-cran). This adds a summary to the check output for all
the examples with CPU or elapsed time of more than 5 seconds. It
produces a file mypkg.Rcheck/mypkg-Ex.timings
containing timings for each help file: it is a tab-delimited file which
can be read into R for further analysis.
Timings for the tests and vignette runs are given at the bottom of the
corresponding log file: note that log files for successful vignette runs
are only retained if environment variable
is set to a true value.
The issues in this subsection have been much alleviated by the change in R 4.2.0 to running the Windows port of R in a UTF-8 locale where available. However, Windows users might be running an earlier version of R on an earlier version of Windows which does not support UTF-8 locales.
Care is needed if your package contains non-ASCII text, and in particular if it is intended to be used in more than one locale. It is possible to mark the encoding used in the DESCRIPTION file and in .Rd files, as discussed elsewhere in this manual.
First, consider carefully if you really need non-ASCII text. Some users of R will only be able to view correctly text in their native language group (e.g. Western European, Eastern European, Simplified Chinese) and ASCII.96. Other characters may not be rendered at all, rendered incorrectly, or cause your R code to give an error. For .Rd documentation, marking the encoding and including ASCII transliterations is likely to do a reasonable job. The set of characters which is commonly supported is wider than it used to be around 2000, but non-Latin alphabets (Greek, Russian, Georgian, …) are still often problematic and those with double-width characters (Chinese, Japanese, Korean, emoji) often need specialist fonts to render correctly.
Several CRAN packages have messages in their R code in French (and a few in German). A better way to tackle this is to use the internationalization facilities discussed elsewhere in this manual.
Function showNonASCIIfile
in package tools can help in
finding non-ASCII bytes in files.
There is a portable way to have arbitrary text in character strings
(only) in your R code, which is to supply them in Unicode as
'\uxxxx' escapes (or, rarely needed except for emojis,
'\Uxxxxxxxx' escapes). If there are any characters not in the
current encoding the parser will encode the character string as UTF-8
and mark it as such. This applies also to character strings in
datasets: they can be prepared using '\uxxxx' escapes or encoded in
UTF-8 in a UTF-8 locale, or even converted to UTF-8 via
. If you do this, make sure you have 'R (>= 2.10)'
(or later) in the 'Depends' field of the DESCRIPTION file.
R sessions running in non-UTF-8 locales will if possible re-encode
such strings for display (and this is done by RGui
on older
versions of Windows, for example). Suitable fonts will need to be
selected or made available97 both for the console/terminal and graphics devices
such as 'X11()' and 'windows()'. Using 'postscript' or
'pdf' will choose a default 8-bit encoding depending on the
language of the UTF-8 locale, and your users would need to be told how
to select the 'encoding' argument.
Note that the previous two paragraphs only apply to character strings in R code. Non-ASCII characters are particularly prevalent in comments (in the R code of the package, in examples, tests, vignettes and even in the NAMESPACE file) but should be avoided there. Most commonly people use the Windows extensions to Latin-1 (often directional single and double quotes, ellipsis, bullet and en and em dashes) which are not supported in strict Latin-1 locales nor in CJK locales on Windows. A surprisingly common misuse is to use a right quote in 'don't' instead of the correct apostrophe.
Datasets can include marked UTF-8 or Latin-1 character strings. As R is nowadays unlikely to be run in a Latin-1 or Windows' CP1252 locale, for performance reasons these should be converted to UTF-8.
If you want to run R CMD check
on a Unix-alike over a package
that sets a package encoding in its DESCRIPTION file and do
not use a UTF-8 locale you may need to specify a suitable locale
via environment variable R_ENCODING_LOCALES
. The default
is equivalent to the value
(which is appropriate for a system based on glibc
: macOS requires
) except that if the current locale is
UTF-8 then the package code is translated to UTF-8 for syntax checking,
so it is strongly recommended to check in a UTF-8 locale.
Writing portable C and C++ code is mainly a matter of observing the standards (C99, C++14 or where declared C++11/17/20) and testing that extensions (such as POSIX functions) are supported. Do make maximal use of your compiler diagnostics — this typically means using flags -Wall and -pedantic for both C and C++ and additionally -Werror=implicit-function-declaration and -Wstrict-prototypes for C (on some platforms and compiler versions) these are part of -Wall or -pedantic).
C++ standards: From version 3.6.0 (3.6.2 on Windows), R defaulted to C++11 where available98; from R 4.1.0 to C++14 and from R 4.3.0 to C++17 (where available). However, in earlier versions the default standard was that of the compiler used, often C++98 or C++14, and the default is likely to change in future. For maximal portability a package should either specify a standard (see Using C++ code) or be tested under all of C++11, C++98, C++14 and C++17. (Specifying C++14 or later will limit portability.)
Note that the 'TR1' C++ extensions are not part of any of these
standards and the <tr1/name>
headers are not supplied by some of
the compilers used for R, including on macOS. (Use the C++11
versions instead.)
A common error is to assume recent versions of compilers or OSes. In production environments 'long term support' versions of OSes may be in use for many years,99 and their compilers may not be updated during that time. For example, GCC 4.8 was still in use in 2022 and could be (in RHEL 7) until 2028: that supports neither C++14 nor C++17.
The POSIX standards only require recently-defined functions to be declared if certain macros are defined with large enough values, and on some compiler/OS combinations100 they are not declared otherwise. So you may need to include something like one of
#define _XOPEN_SOURCE 600
#ifdef __GLIBC__ # define _POSIX_C_SOURCE 200809L #endif
before any headers. (strdup
, strncasecmp
are such functions – there were several older platforms
which did not have the POSIX 2008 function strnlen
However, some common errors are worth pointing out here. It can be helpful to look up functions at https://cplusplus.com/reference/ or https://en.cppreference.com/w/ and compare what is defined in the various standards.
More care is needed for functions such as mallinfo
which are not
specified by any of these standards—hopefully the man
on your system will tell you so. Searching online for such pages for
various OSes (preferably at least Linux and macOS, and the FreeBSD
manual pages at https://man.freebsd.org/cgi/man.cgi allow you to
select many OSes) should reveal useful information but a
configure script is likely to be needed to check availability and
Both the compiler and OS (via system header files, which may
differ by architecture even for nominally the same OS) affect the
compilability of C/C++ code. Compilers from the GCC, LLVM
and flang
) Intel and Oracle Developer Studio
suites have been used with R, and both LLVM clang
Oracle have more than one implementation of C++ headers and library.
The range of possibilities makes comprehensive empirical checking
impossible, and regrettably compilers are patchy at best on warning
about non-standard code.
are defined in C++11 for
floating-point arguments: float
, double
, long
and possibly more. The standard specifies what happens with an
argument of integer type but this is not always implemented, resulting
in a report of 'overloading ambiguity': this was commonly seen on
Solaris, but for pow
also seen on macOS and other platforms
using clang++
A not-uncommonly-seen problem is to mistakenly call floor(x/y)
for int
arguments x
and y
. Since
does integer division, the result is of type int
'overloading ambiguity' may be reported. Some people have (pointlessly)
called floor
and ceil
on arguments of integer type, which
may have an 'overloading ambiguity'.
A surprising common misuse is things like pow(10, -3)
: this
should be the constant 1e-3
. Note that there are constants such
as M_SQRT2
defined via Rmath.h101 for sqrt(2.0)
, frequently mis-coded as
is defined only for floating-point types, except in
C++11 and later which have overloads for std::fabs
<cmath> for integer types. Function abs
is defined in
C99's <stdlib.h> for int
and in C++'s <cstdlib> for
integer types, overloaded in <cmath> for floating-point types.
C++11 has additional overloads for std::abs
in <cmath> for
integer types. The effect of calling abs
with a floating-point
type is implementation-specific: it may truncate to an integer. For
clarity and to avoid compiler warnings, use abs
for integer types
and fabs
for double values, and when using C++ include
<cmath> and use the std::
, isinf
and isfinite
integer arguments: a few compilers give a compilation error. Function
is obsolete, and some compilers will warn about its
(which is a float value
in C99 and C++11), for which R provides the portable double value
(and R_NegInf
for -INFINITY
). And
103 is just one NaN
float value: for use with R, NA_REAL
is often what is
intended, but R_NaN
is also available.
Some (but not all) extensions are listed at https://gcc.gnu.org/onlinedocs/gcc/C-Extensions.html and https://gcc.gnu.org/onlinedocs/gcc/C_002b_002b-Extensions.html.
Other GNU extensions which have bitten package writers are the use of non-portable characters such as '$' in identifiers and use of C++ headers under ext.
Including C-style headers in C++ code is not portable. Including the
legacy header104 math.h in C++ code may conflict with cmath which
may be included by other headers. In C++11, functions like sqrt
and isnan
are defined for double
arguments in
math.h and for a range of types including double
cmath. Similar issues have been seen for stdlib.h and
cstdlib. Including the C++ header first used to be a sufficient
workaround but for some 2016 compilers only one could be included.
which is indirectly included by <algorithm>
. Another issue is the C header <time.h>
which is
included by other headers on Linux and Windows but not macOS.)
11 often needs explicit inclusion of the C++ headers
(for numeric_limits
) or <exception>
and similar), whereas earlier versions included
these in other headers. g++
13 requires the
explicit inclusion of <cstdint>
for types such as uint32_t
which was previously included implicitly. (For more such, see
Note that malloc
, calloc
, realloc
and free
are defined by C99 in the header stdlib.h and (in the
namespace) by C++ header cstdlib. Some earlier
implementations used a header malloc.h, but that is not portable
and does not exist on macOS.
This also applies to types such as ssize_t
. The POSIX standards
say that is declared in headers unistd.h
and sys/types.h
and the latter is often included indirectly by other headers on some
but not all systems.
Similarly for constants: for example SIZE_MAX
is defined in
alongside size_t
extension: some OSes have
machine/endian.h or sys/endian.h but some have neither.
#include "my.h"
not #include <my.h>
for headers in
your package. The second form is intended for system headers and the
search order for such headers is platform-dependent (and may not include
the current directory). For extra safety, name headers in a way that
cannot be confused with a system header so not, for example,
namespace, but g++
puts many such also in the C++ main
namespace. One way to do so is to use declarations such as
using std::floor;
but it is usually preferable to use explicit namespace prefixes in the code.
Examples seen in CRAN packages include
abs acos atan bind calloc ceil div exp fabs floor fmod free log malloc memcpy memset pow printf qsort round sin sprintf sqrt strcmp strcpy strerror strlen strncmp strtol tan trunc
This problem is less common than it used to be, but in 2019
LLVM clang
did not have bind
in the main namespace. Also
seen has been type size_t
defined only in the std
NB: These functions are only guaranteed to be in the
namespace if the correct C++ header is included, e.g.
rather than <math.h>
If you define functions in C++ which are inspired by later standards, put
them in a namespace and refer to them using the namespace. We have seen
conflicts with std::make_unique
from C++14 and std::byte
, std::sample
and std::size
from C++17.
using namesapace std;
is not good practice, and has caused platform-dependent errors if
included before headers, especially system headers (which may be
included by other headers). The best practice is to use explicit
prefixes for all functions declared by the C++ standard to
be in that namespace.
if(ptr > 0) { ....}
which compares a pointer to the integer 0
. This could just use
(pointer addresses cannot be negative) but if needed
pointers can be tested against nullptr
(C++11) or NULL
before including any system headers, but it is better to only use
all-upper-case names which have a unique prefix such as the package
s in OS headers can conflict with those in the package:
examples have included ulong
, index_t
, single
. (Note that these may conflict with other uses as
identifiers, e.g. defining a C++ function called single
The POSIX standard reserves (in §2.2.2) all identifiers ending in
and the macro to
be defined. Similarly for -U
#ifdef _OPENMP # include <omp.h> #endif
Any use of OpenMP functions, e.g. omp_set_num_threads
, also
needs to be conditioned. To avoid incessant warnings such as
warning: ignoring #pragma omp parallel [-Wunknown-pragmas]
uses of such pragmas should also be conditioned (or commented out if they are used in code in a package not enabling OpenMP on any platform).
Do not hardcode -lgomp: not only is that specific to the GCC family of compilers, using the correct linker flag often sets up the run-time path to the library.
. The most common C library on Linux, glibc
, will
hide the declarations of such extensions unless a 'feature-test macro'
is defined before (almost) any system header is included. So
for strdup
you need
#define _POSIX_C_SOURCE 200809L ... #include <string.h> ... strdup call(s)
where the appropriate value can be found by man strdup
Linux. (Use of strncasecmp
is similar.)
However, modes of gcc
with 'GNU EXTENSIONS' (which are the
default, either -std=gnu99 or -std=gnu11) declare
enough macros to ensure that missing declarations are rarely seen.
This applies also to constants such as M_PI
and M_LN2
which are part of the X/Open standard: to use these define
before including any headers, or include the R
header Rmath.h.
portably is tricky: it is neither an ISO C/C++ nor a
POSIX function. An adequately portable preamble is
#ifdef __GNUC__ /* Includes GCC, clang and Intel compilers */ # undef alloca # define alloca(x) __builtin_alloca((x)) #elif defined(__sun) || defined(_AIX) /* this was necessary (and sufficient) for Solaris 10 and AIX 6: */ # include <alloca.h> #endif
'register' storage class specifier is deprecated and incompatible with C++17 ISO C++11 does not allow conversion from string literal to 'char *'
(where conversion should be to const char *
). Keyword
was not mentioned in C++98, deprecated in C++11 and
removed in C++17.
There are quite a lot of other C++98 features deprecated in C++11 and
removed in C++17, and LLVM clang
9 and later warn about them
(and as from version 16 they have been removed). Examples include
(use std::bind
(replaced by std::unique_ptr
and std::ptr_fun
, false
and true
became keywords in C23 and are
no longer available as variable names. As noted above, C++17 uses
, data
, sample
and size
So avoid common words and keywords from other programming languages.
storage class specifier (see the previous but
one item).
is not part of107 any C++ standard and is rejected by some
C++ compilers.
The most portable way to interface to other software with a C API is to use C code (which can normally be mixed with C++ code in a package).
in C++ is not safe for pointers: for example the types
may have different alignment requirements. Use memcpy
to copy
the contents to a fresh variable of the destination type.
is not defined on all Unix-alikes, in particular not on
macOS. A reasonably portable way to condition code for a Unix-alike is
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) #endif
#ifdef _WIN32 // Windows-specific code # if defined(_M_ARM64) || defined(__aarch64__) // for ARM # else // for Intel # endif #else // Unix-alike code #endif
would be better. For a Unix-alike it is much better to use
to test for the functionality needed than make
assumptions about OSes (and people all too frequently forget R is
used on platforms other than Linux, Windows and macOS — and some
forget macOS).
-based platforms). Header bits/stdc++.h is both not
portable and not recommended for end-user code even on platforms which
include it.
or calloc
. First, their return
value must always be checked to see if the allocation succeeded – it is
almost always easier to use R's R_Calloc
, which does check.
Second, the first argument is of type size_t
and recent compilers
are warning about passing signed arguments (which could get promoted to
ridiculously large values).
and LLVM and Apple clang
. This
has found quite a number of errors where functions have been declared
without arguments and is likely to become the default in future
compilers. (It already is for LLVM clang
in C23 mode.) Note
that using f()
for a function without any parameters was
deprecated in C99 and C11, but it expected to be non-deprecated in C23.
However, f(void)
is supported by all standards and avoids any
LLVM clang
has a separate warning
-Wdeprecated-non-prototype which is enabled by
-Wstrict-prototypes. This warns on K&R-style usage, which will
not be accepted in C23.
on most systems, often in very strong terms such as 'Do not use
these functions'. macOS has started to warn108 if these are used
for sprintf
, vsprintf
, gets
, mktemp
and tmpnam
. It is highly recommended that you use
safer alternatives (on any platform) but the warning can be avoided by
defining '_POSIX_C_SOURCE' to for example '200809L' before
including the (C or C++) header which defines them. (However, this may
hide other extensions.)
!DEC$ ATTRIBUTES DLLEXPORT,C,REFERENCE,ALIAS:'kdenestmlcvb' :: kdenestmlcvb
which are interpreted by Intel Fortran on all platforms (and are
inappropriate for use with R on Windows). gfortran
similar forms starting with !GCC$
operator takes argument std::size_t size
which is unsigned. Using a signed integer type such as int
lead to compiler warnings such as
warning: argument 1 value '18446744073709551615' exceeds maximum object size 9223372036854775807 [-Walloc-size-larger-than=]
(especially if LTO is used). So don't do that!
similar) vector lengths or indices which are of type R_xlen_t
That may be a 32-bit or (most commonly) 64-bit type but which integer
type it is mapped to is platform-specific. The safest way is to cast
the length to double and use a double format. So one could use
something like
SEXP Robj; R_xlen_t nelem; error("Actual: %0.f; Expected %0.f\n", (double) XLENGTH(Robj), (double) nelem);
(This could print to full precision, lengths well beyond the address space limits of current OSes, let alone practical limits.)
If you do want to use an integer format, be aware that R_xlen_t
is implemented by the int
, long
or long long
on current platforms and even on 64-bit ones need not be the same type
as int64_t
So the values will need to be cast to the type assumed by the format
(and %lld
was not supported on Windows until R 4.2.0).
Some additional information for C++ is available at https://journal.r-project.org/archive/2011-2/RJournal_2011-2_Plummer.pdf by Martyn Plummer.
Most OSes (including all those commonly used for R) have the concept
of 'tentative definitions' where global C variables are defined without
an initializer. Traditionally the linker resolves all tentative
definitions of the same variable in different object files to the same
object, or to a non-tentative definition. However,
10110 and
LLVM clang
changed their default so that tentative definitions cannot be
merged and the linker will give an error if the same variable is defined
in more than one object file. To avoid this, all but one of the C
source files should declare the variable extern
— which means
that any such variables included in header files need to be declared
. A commonly used idiom (including by R itself) is to
define all global variables as extern
in a header, say
globals.h (and nowhere else), and then in one (and one only)
source file use
#define extern # include "globals.h" #undef extern
A cleaner approach is not to have global variables at all, but to place
in a single file common variables (declared static
) followed by
all the functions which make use of them: this may result in more
efficient code.
The 'modern' behaviour can be seen112 by using
compiler flag -fno-common as part of 'CFLAGS' in earlier
versions of gcc
and clang
-fno-common is said to be particularly beneficial for ARM CPUs.
This is not pertinent to C++ which does not permit tentative definitions.
R 4.3.0 and later default to C++17 when compiling C++, and that
finally removed many C++98 features which were deprecated as long ago as
C++11. Compiler/runtime authors have been slow to remove these, but
LLVM clang
with its libc++
runtime library finally
started to do so in 2023 – some others warn but some do not.
The principal offender is the 'Boost' collection of C++ headers and libraries. There are two little-documented ways to work around aspects of its outdated code. One is to add
to PKG_CPPLAGS
in src/Makevars, src/Makevars.win
and src/Makevars.ucrt. This covers
the removal of
std::auto_ptr std::unary_function std::binary_function std::random_shuffle std::binder1st std::binder2nd
with most issues seen with code that includes boost/functional.hpp, usually indirectly.
A rarer issue is the use of illegal values for enum
usually negative ones such as
BOOST_MPL_AUX_STATIC_CAST(AUX_WRAPPER_VALUE_TYPE, (value - 1));
in boost/mpl/aux_/integral_wrapper.hpp. Adding
to PKG_CXXFLAGS
will allow this, but that flag is only accepted
by recent versions of LLVM clang
(and will not be in future)
so needs a configure
Pre=built versions of current clang
usually available from
https://github.com/llvm/llvm-project/releases for a wide range of
platforms (but the Windows builds there are not compatible with
and the macOS ones are unsigned). To select
add -stdlib=libc++ to CXX
, for example
by having
CXX="/path/to/clang/clang++ -std=gnu++17 -stdlib=libc++"
in ~/.R/Makevars.
Another build for Windows which may be sufficiently compatible with
can be found at
https://github.com/mstorsjo/llvm-mingw: this uses libc++
For many years almost all known R platforms used gfortran
as their Fortran compiler, but now there are LLVM and 'classic'
and the Intel compilers
113 and ifx
are now
There is still a lot of Fortran code in CRAN packages which
predates Fortran 77. Modern Fortran compilers are being written to
target a minimum standard of Fortran 2018. and it is desirable that
Fortran code in packages complies with that standard. For
this can be checked by adding -std=f2018 to
. The most commonly seen issues are
, which was superseded by DBLE
in Fortran
77. Also, use of DCMPLX
calls 'Fortran 2018 deleted features',
although most were 'deleted' in earlier standards: those itemized here
were deleted in Fortran 2008. (In the Fortran standards 'deleted' means
features that compilers are not required to implement.) These include
loops which are not terminated with a END DO
statement. (Unlabelled DO
loops terminated by
are preferred for readability.)
loops sharing a terminating CONTINUE
, getpid
114 and sizeof
One that frequently catches package writers is that it allows out-of-order declarations: in standard-conformant Fortran variables must be declared (explicitly or implicitly) before use in other declarations such as dimensions.
Unfortunately this flags extensions such as DOUBLE COMPLEX
. R has tested that DOUBLE COMPLEX
works and
so is preferred to COMPLEX*16
. (One can also use something like
GNU Fortran 10 and later give a compilation error for the previously widespread practice of passing a Fortran array element where an array is expected, or a scalar instead of a length-one array. See https://gcc.gnu.org/gcc-10/porting_to.html. As do the Intel Fortran compilers, and they can be stricter.
The use of IMPLICIT NONE
is highly recommended – Intel compilers
with -warn will warn on variables without an explicit type.
Common non-portable constructions include
is very far from
portable. According to the standards this merely enumerates different
supported types, so DOUBLE PRECISION
might be REAL(KIND=3)
(and is on an actual compiler). Even if for a particular compiler the
value indicates the size in bytes, which values are supported is
platform-specific — for example gfortran
supports values of 4
and 8 on all current platforms and 10 and 16 on a few (but not for
example on all 'arm' CPUs).
The same applies to INTEGER(KIND=4)
and COMPLEX(KIND=16)
Many uses of integer and real variable in Fortran code in packages will
interwork with C (for example .Fortran
is written in C), and R
has checked that INTEGER
and DOUBLE PRECISION
correspond to
the C types int
and double
. To make this explicit, from
Fortran 2003 one can use the named constants c_int
and c_double_complex
from module
works around this for packages without a src/Makefile.
to indicate source code to
be preprocessed: the preprocessor used is compiler-specific and may or
may not be cpp
. Compilers may even preprocess files with
extension .f or .f90 (Intel does).
As well as 'deleted features', Fortran standards have 'obsolescent features'. These are similar to 'deprecated' in other languages, but the Fortran standards committee has said it will only move them to 'deleted' status when they are no longer much used. These include
and EQUIVALENCE
statements, and BLOCK DATA
statements, replaced by SELECT CASE
statements after executable statements.
with option -std=f2018 will warn about these:
R will report only in the installation log.
If you want to distribute a binary version of a package on Windows or macOS, there are further checks you need to do to check it is portable: it is all too easy to depend on external software on your own machine that other users will not have.
For Windows, check what other DLLs your package's DLL depends on
('imports' from in the DLL tools' parlance). A convenient GUI-based
tool to do so is 'Dependency Walker'
(https://www.dependencywalker.com/) for both 32-bit and 64-bit
DLLs – note that this will report as missing links to R's own DLLs
such as R.dll and Rblas.dll. The command-line tool
in the appropriate toolchain will also reveal what
DLLs are imported from. If you use a toolchain other than one provided
by the R developers or use your own makefiles, watch out in
particular for dependencies on the toolchain's runtime DLLs such as
libgfortran, libstdc++ and libgcc_s.
For macOS, using R CMD otool -L
on the package's shared object(s)
in the libs directory will show what they depend on: watch for
any dependencies in /usr/local/lib or
/usr/local/gfortran/lib, notably libgfortran.?.dylib and
(For ways to fix these,
see Building binary-packages in R Installation and Administration.)
Many people (including the CRAN package repository) will not accept source packages containing binary files as the latter are a security risk. If you want to distribute a source package which needs external software on Windows or macOS, options include
or with Simon Urbanek to include macOS software in his
'recipes' system.
Be aware that license requirements you may require you to supply the sources for the additional components (and will if your package has a GPL-like license).
Diagnostic messages can be made available for translation, so it is important to write them in a consistent style. Using the tools described in the next section to extract all the messages can give a useful overview of your consistency (or lack of it). Some guidelines follow.
In R error messages do not construct a message with paste
messages will not be translated) but via multiple arguments to
or warning
, or via gettextf
'ord' must be a positive integer, at most the number of knots
and double quotation marks when referring to an R character string or a class, such as
'format' must be "normal" or "short" - using "normal"
Since ASCII does not contain directional quotation marks, it
is best to use ''' and let the translator (including automatic
translation) use directional quotations where available. The range of
quotation styles is immense: unfortunately we cannot reproduce them in a
portable texinfo
document. But as a taster, some languages use
'up' and 'down' (comma) quotes rather than left or right quotes, and
some use guillemets (and some use what Adobe calls 'guillemotleft' to
start and others use it to end).
In R messages it is also possible to use sQuote
or dQuote
as in
stop(gettextf("object must be of class %s or %s", dQuote("manova"), dQuote("maov")), domain = NA)
if((length(nopkgs) > 0) && !missing(lib.loc)) { if(length(nopkgs) > 1) warning("libraries ", paste(sQuote(nopkgs), collapse = ", "), " contain no packages") else warning("library ", paste(sQuote(nopkgs)), " contains no package") }
and was replaced by
if((length(nopkgs) > 0) && !missing(lib.loc)) { pkglist <- paste(sQuote(nopkgs), collapse = ", ") msg <- sprintf(ngettext(length(nopkgs), "library %s contains no packages", "libraries %s contain no packages", domain = "R-base"), pkglist) warning(msg, domain=NA) }
Note that it is much better to have complete clauses as here, since in another language one might need to say 'There is no package in library %s' or 'There are no packages in libraries %s'.
There are mechanisms to translate the R- and C-level error and warning
messages. There are only available if R is compiled with NLS support
(which is requested by configure
option --enable-nls,
the default).
The procedures make use of msgfmt
and xgettext
which are
part of GNU gettext
and this will need to be installed:
'x86_64' Windows users can find pre-compiled binaries at
The process of enabling translations is
#include <R.h> /* to include Rconfig.h */ #ifdef ENABLE_NLS #include <libintl.h> #define _(String) dgettext ("pkg", String) /* replace pkg as appropriate */ #else #define _(String) (String) #endif
for example
error(_("'ord' must be a positive integer"));
If you want to use different messages for singular and plural forms, you need to add
#ifndef ENABLE_NLS #define dngettext(pkg, String, StringP, N) (N == 1 ? String : StringP) #endif
and mark strings by
dngettext("pkg", <singular string>, <plural string>, n)
xgettext --keyword=_ -o pkg.pot *.c
The file src/pkg.pot is the template file, and conventionally this is shipped as po/pkg.pot.
Mechanisms are also available to support the automatic translation of
R stop
, warning
and message
messages. They make
use of message catalogs in the same way as C-level messages, but using
domain R-pkg
rather than pkg
. Translation of
character strings inside stop
, warning
and message
calls is automatically enabled, as well as other messages enclosed in
calls to gettext
or gettextf
. (To suppress this, use
argument domain=NA
Tools to prepare the R-pkg.pot file are provided in package
tools: xgettext2pot
will prepare a file from all strings
occurring inside gettext
, stop
and message
calls. Some of these are likely to be
spurious and so the file is likely to need manual editing.
extracts the actual calls and so is more useful when
tidying up error messages.
The R function ngettext
provides an interface to the C
function of the same name: see example in the previous section. It is
safest to use domain="R-pkg"
explicitly in calls to
, and necessary for earlier versions of R unless they
are calls directly from a function in the package.
Once the template files have been created, translations can be made. Conventional translations have file extension .po and are placed in the po subdirectory of the package with a name that is either 'll.po' or 'R-ll.po' for translations of the C and R messages respectively to language with code 'll'.
See Localization of messages in R Installation and Administration, for details of language codes.
There is an R function, update_pkg_po
in package tools,
to automate much of the maintenance of message translations. See its
help for what it does in detail.
If this is called on a package with no existing translations, it creates the directory pkgdir/po, creates a template file of R messages, pkgdir/po/R-pkg.pot, within it, creates the 'en@quot' translation and installs that. (The 'en@quot' pseudo-language interprets quotes in their directional forms in suitable (e.g. UTF-8) locales.)
If the package has C source files in its src directory that are marked for translation, use
touch pkgdir/po/pkg.pot
to create a dummy template file, then call update_pkg_po
(this can also be done before it is called for the first time).
When translations to new languages are added in the pkgdir/po directory, running the same command will check and then install the translations.
If the package sources are updated, the same command will update the template files, merge the changes into the translation .po files and then installed the updated translations. You will often see that merging marks translations as 'fuzzy' and this is reported in the coverage statistics. As fuzzy translations are not used, this is an indication that the translation files need human attention.
The merged translations are run through tools::checkPofile
check that C-style formats are used correctly: if not the mismatches are
reported and the broken translations are not installed.
This function needs the GNU gettext-tools
installed and on the
path: see its help page.
An installed file named CITATION will be used by the
function. (It should be in the inst
subdirectory of the package sources.)
The CITATION file is parsed as R code (in the package's
declared encoding, or in ASCII if none is declared).
It will contain calls to function bibentry
Here is that for nlme:
## R package reference generated from DESCRIPTION metadata citation(auto = meta) ## NLME book bibentry(bibtype = "Book", title = "Mixed-Effects Models in S and S-PLUS", author = c(person(c("José", "C."), "Pinheiro"), person(c("Douglas", "M."), "Bates")), year = "2000", publisher = "Springer", address = "New York", doi = "10.1007/b98882")
Note how the first call auto-generates citation information
from object meta
, a parsed version of the DESCRIPTION file
– it is tempting to hardcode such information, but it normally then
gets outdated. How the first entry would look like as a bibentry
call can be seen from
print(citation("pkgname", auto = TRUE), style = "R")
for any installed package. Auto-generated information is
returned by default if no CITATION file is present.
See ?bibentry
for further details of the
information which can be provided.
In case a bibentry contains LaTeX markup (e.g., for accented
characters or mathematical symbols), it may be necessary to provide a
text representation to be used for printing via the
argument to bibentry
. E.g., earlier versions
of nlme additionally used something like
textVersion = paste0("Jose Pinheiro, Douglas Bates, Saikat DebRoy, ", "Deepayan Sarkar and the R Core Team (", sub("-.*", "", meta$Date), "). nlme: Linear and Nonlinear Mixed Effects Models. ", sprintf("R package version %s", meta$Version), ".")
The CITATION file should itself produce no output when
It is desirable (and essential for CRAN) that the
CITATION file does not contain calls to functions such as
which assume the package is installed in a
library tree on the package search path.
The DESCRIPTION file has an optional field Type
which if
missing is assumed to be 'Package', the sort of extension discussed
so far in this chapter. Currently one other type is recognized; there
used also to be a 'Translation' type.
This is a rather general mechanism, designed for adding new front-ends
such as the former gnomeGUI package (see the Archive area on
CRAN). If a configure file is found in the top-level
directory of the package it is executed, and then if a Makefile
is found (often generated by configure), make
is called.
If R CMD INSTALL --clean
is used make clean
is called. No
other action is taken.
R CMD build
can package up this type of extension, but R
CMD check
will check the type and skip it.
Many packages of this type need write permission for the R installation directory.
Several members of the R project have set up services to assist those writing R packages, particularly those intended for public distribution.
win-builder.r-project.org offers the automated preparation of ('x86_64') Windows binaries from well-tested source packages.
R-Forge (R-Forge.r-project.org) and
RForge (www.rforge.net) are similar
services with similar names. Both provide source-code management
through SVN, daily building and checking, mailing lists and a repository
that can be accessed via install.packages
(they can be
selected by setRepositories
and the GUI menus that use it).
Package developers have the opportunity to present their work on the
basis of project websites or news announcements. Mailing lists, forums
or wikis provide useRs with convenient instruments for discussions and
for exchanging information between developers and/or interested useRs.
R objects are documented in files written in "R documentation"
(Rd) format, a simple markup language much of which closely resembles
(La)TeX, which can be processed into a variety of formats,
including LaTeX, HTML and plain text. The translation is
carried out by functions in the tools package called by the
script Rdconv
in R_HOME/bin and by the
installation scripts for packages.
The R distribution contains more than 1400 such files which can be found in the src/library/pkg/man directories of the R source tree, where pkg stands for one of the standard packages which are included in the R distribution.
As an example, let us look at a simplified version of
src/library/base/man/load.Rd which documents the R function
% File src/library/base/man/load.Rd \name{load} \alias{load} \title{Reload Saved Datasets} \description{ Reload datasets written with the function \code{save}. } \usage{ load(file, envir = parent.frame(), verbose = FALSE) } \arguments{ \item{file}{a (readable binary-mode) \link{connection} or a character string giving the name of the file to load (when \link{tilde expansion} is done).} \item{envir}{the environment where the data should be loaded.} \item{verbose}{should item names be printed during loading?} } \value{ A character vector of the names of objects created, invisibly. } \seealso{ \code{\link{save}}. } \examples{ ## save all data save(list = ls(all.names = TRUE), file = "all.RData") ## restore the saved values to the current environment load("all.RData") } \keyword{file}
An Rd file consists of three parts. The header gives basic information about the name of the file, the topics documented, a title, a short textual description and R usage information for the objects documented. The body gives further information (for example, on the function's arguments and return value, as in the above example). Finally, there is an optional footer with keyword information. The header is mandatory.
Information is given within a series of sections with standard names (and user-defined sections are also allowed). Unless otherwise specified115 these should occur only once in an Rd file (in any order), and the processing software will retain only the first occurrence of a standard section in the file, with a warning.
See "Guidelines for Rd
files" for guidelines for writing documentation in Rd format
which should be useful for package writers.
The R
generic function prompt
is used to construct a bare-bones Rd
file ready for manual editing. Methods are defined for documenting
functions (which fill in the proper function and argument names) and
data frames. There are also functions promptData
, promptClass
, and promptMethods
other types of Rd files.
The general syntax of Rd files is summarized below. For a detailed technical discussion of current Rd syntax, see "Parsing Rd files".
Rd files consist of four types of text input. The most common
is LaTeX-like, with the backslash used as a prefix on markup
(e.g. \alias
), and braces used to indicate arguments
(e.g. {load}
). The least common type of text is 'verbatim'
text, where no markup other than the comment marker (%
) is
processed. There is also a rare variant of 'verbatim' text
(used in \eqn
, \deqn
, \figure
and \newcommand
) where comment markers need not be escaped.
The final type is R-like, intended for R code, but allowing some
embedded macros. Quoted strings within R-like text are handled
specially: regular character escapes such as \n
may be entered
as-is. Only markup starting with \l
(e.g. \link
) or
(e.g. \var
) will be recognized within quoted strings.
The rarely used vertical tab \v
must be entered as \\v
Each macro defines the input type for its argument. For example, the
file initially uses LaTeX-like syntax, and this is also used in the
section, but the \usage
section uses
R-like syntax, and the \alias
macro uses 'verbatim' syntax.
Comments run from a percent symbol %
to the end of the line in
all types of text except the rare 'verbatim' variant
(as on the first line of the load
Because backslashes, braces and percent symbols have special meaning, to enter them into text sometimes requires escapes using a backslash. In general balanced braces do not need to be escaped, but percent symbols always do, except in the 'verbatim' variant. For the complete list of macros and rules for escapes, see "Parsing Rd files".
The basic markup commands used for documenting R objects (in particular, functions) are given in this subsection.
¶name typically116 is the basename of
the Rd file containing the documentation. It is the "name" of
the Rd object represented by the file and has to be unique in a
package. To avoid problems with indexing the package manual, it may not
contain '!' '|' nor '@'.
(LaTeX special characters are allowed, but may not be collated
correctly in the index.) There can only be one \name
entry in a
file, and it must not contain any markup and should only contain
printable ASCII characters. Entries in the package manual
will be in alphabetic117 order
of the \name
¶The \alias
sections specify all "topics" the file documents.
This information is collected into index data bases for lookup by the
on-line (plain text and HTML) help systems. The topic can
contain spaces, but (for historical reasons) leading and trailing spaces
will be stripped. Percent and left brace need to be escaped by
a backslash.
There may be several \alias
entries. Quite often it is
convenient to document several R objects in one file. For example,
file Normal.Rd documents the density, distribution function,
quantile function and generation of random variates for the normal
distribution, and hence starts with
\name{Normal} \alias{Normal} \alias{dnorm} \alias{pnorm} \alias{qnorm} \alias{rnorm}
Also, it is often convenient to have several different ways to refer to
an R object, and an \alias
does not need to be the name of an
Note that the \name
is not necessarily a topic documented, and if
so desired it needs to have an explicit \alias
entry (as in this
¶Title information for the Rd file. This should be capitalized and not end in a period; try to limit its length to at most 65 characters for widest compatibility.
Markup is supported in the text, but use of characters other than English text and punctuation (e.g., '<') may limit portability.
There must be one (and only one) \title
section in a help file.
¶A short description of what the function(s) do(es) (one paragraph, a few lines only). (If a description is too long and cannot easily be shortened, the file probably tries to document too much at once.) This is mandatory except for package-overview files.
\usage{fun(arg1, arg2, …)}
¶One or more lines showing the synopsis of the function(s) and variables documented in the file. These are set in typewriter font. This is an R-like command.
The usage information specified should match the function definition exactly (such that automatic checking for consistency between code and documentation is possible).
To indicate that a function can be used in several different ways,
depending on the named arguments specified, use section \details
E.g., abline.Rd contains
\details{ Typical usages are \preformatted{abline(a, b, ...) ...... }
Use \method{generic}{class}
to indicate the name
of an S3 method for the generic function generic for objects
inheriting from class "class"
. In the printed versions,
this will come out as generic (reflecting the understanding that
methods should not be invoked directly but via method dispatch), but
and other QC tools always have access to the full name.
For example, print.ts.Rd contains
\usage{ \method{print}{ts}(x, calendar, \dots) }
which will print as
Usage: ## S3 method for class 'ts': print(x, calendar, ...)
Usage for replacement functions should be given in the style of
dim(x) <- value
rather than explicitly indicating the name of the
replacement function ("dim<-"
in the above). Similarly, one
can use \method{generic}{class}(arglist) <-
to indicate the usage of an S3 replacement method for the generic
replacement function "generic<-"
for objects inheriting
from class "class"
Usage for S3 methods for extracting or replacing parts of an object, S3 methods for members of the Ops group, and S3 methods for user-defined (binary) infix operators ('%xxx%') follows the above rules, using the appropriate function names. E.g., Extract.factor.Rd contains
\usage{ \method{[}{factor}(x, \dots, drop = FALSE) \method{[[}{factor}(x, \dots) \method{[}{factor}(x, \dots) <- value }
which will print as
Usage: ## S3 method for class 'factor': x[..., drop = FALSE] ## S3 method for class 'factor': x[[...]] ## S3 replacement method for class 'factor': x[...] <- value
is accepted as an alternative to \method
¶Description of the function's arguments, using an entry of the form
\item{arg_i}{Description of arg_i.}
for each element of the argument list. (Note that there is
no whitespace between the three parts of the entry.) Arguments can also
be described jointly by separating their names with commas (and optional
whitespace) in the \item
label. There may be
optional text outside the \item
entries, for example to give
general information about groups of parameters.
¶A detailed if possible precise description of the functionality
provided, extending the basic information in the \description
¶Description of the function's return value.
If a list with multiple values is returned, you can use entries of the form
\item{comp_i}{Description of comp_i.}
for each component of the list returned. There may be
optional text outside the \item
(see for example the joint help for rle
and inverse.rle
or the sets of items in l10n_info
). Note that \value
is implicitly a \describe
environment, so that environment should
not be used for listing components, just individual \item{}{}
¶A section with references to the literature. Use \url{}
for web pointers, and \doi{}
for DOIs
(this needs R >= 3.3, see User-defined macros for more info).
¶Use this for a special note you want to have pointed out. Multiple
sections are allowed, but might be confusing to the end users.
For example, pie.Rd contains
\note{ Pie charts are a very bad way of displaying information. The eye is good at judging linear measures and bad at judging relative areas. ...... }
¶Information about the author(s) of the Rd file. Use
without extra delimiters (such as '( )' or
'< >') to specify email addresses, or \url{}
for web pointers.
¶Pointers to related R objects, using \code{\link{...}}
refer to them (\code
is the correct markup for R object names,
and \link
produces hyperlinks in output formats which support
this. See Marking text, and Cross-references).
¶Examples of how to use the function. Code in this section is set
in typewriter font without reformatting and is run by
unless marked otherwise (see below).
Examples are not only useful for documentation purposes, but also
provide test code used for diagnostic checking of R code. By
default, text inside \examples{}
will be displayed in the
output of the help page and run by example()
and by R CMD
. You can use \dontrun{}
for text that should only be shown, but not run, and
for extra commands for testing that should not be shown to users, but
will be run by example()
. (Previously this was called
, and that is still accepted.)
Text inside \dontrun{}
is 'verbatim', but the other parts
of the \examples
section are R-like text.
For example,
x <- runif(10) # Shown and run. \dontrun{plot(x)} # Only shown. \dontshow{log(x)} # Only run.
Thus, example code not included in \dontrun
must be executable!
In addition, it should not use any system-specific features or require
special facilities (such as Internet access or write permission to
specific directories). Text included in \dontrun
is indicated by
comments in the processed help files: it need not be valid R code but
the escapes must still be used for %
, \
and unpaired
braces as in other 'verbatim' text.
Example code must be capable of being run by example
, which uses
. This means that it should not access stdin,
e.g. to scan()
data from the example file.
Data needed for making the examples executable can be obtained by random
number generation (for example, x <- rnorm(100)
), or by using
standard data sets listed by data()
(see ?data
for more
Finally, there is \donttest
, used (at the beginning of a separate
line) to mark code that should be run by example()
but not by
R CMD check
(by default: the option --run-donttest can
be used). This should be needed only occasionally but can be used for
code which might fail in circumstances that are hard to test for, for
example in some locales. (Use e.g. capabilities()
to test for features needed in
the examples wherever possible, and you can also use try()
. Use interactive()
to condition examples which
need someone to interact with.) Note that code included in
must be correct R code, and any packages used should
be declared in the DESCRIPTION file. It is good practice to
include a comment in the \donttest
section explaining why it is
Output from code marked with \dontdiff
(requires R >= 4.4.0)
or between comment lines
is ignored when comparing check output to reference output (a pkg-Ex.Rout.save file). The comment-based markup can also be used for scripts under tests.
¶There can be zero or more \keyword
sections per file.
Each \keyword
section should specify a single keyword, preferably
one of the standard keywords as listed in file KEYWORDS in the
R documentation directory (default R_HOME/doc). Use
e.g. RShowDoc("KEYWORDS")
to inspect the standard keywords from
within R. There can be more than one \keyword
entry if the R
object being documented falls into more than one category, or none.
Do strongly consider using \concept
(see Indices) instead of
if you are about to use more than very few non-standard
The special keyword 'internal' marks a page of internal topics
(typically, objects that are not part of the package's API).
If the help page for topic
has keyword 'internal', then help(foo)
gives this
help page, but foo
is excluded from several topic indices,
including the alphabetical list of topics in the HTML help system.
can search by keyword, including user-defined
values: however the 'Search Engine & Keywords' HTML page accessed
via help.start()
provides single-click access only to a
pre-defined list of keywords.
The structure of Rd files which document R data sets is slightly
different. Sections such as \arguments
and \value
are not
needed but the format and source of the data should be explained.
As an example, let us look at src/library/datasets/man/rivers.Rd
which documents the standard R data set rivers
\name{rivers} \docType{data} \alias{rivers} \title{Lengths of Major North American Rivers} \description{ This data set gives the lengths (in miles) of 141 \dQuote{major} rivers in North America, as compiled by the US Geological Survey. } \usage{rivers} \format{A vector containing 141 observations.} \source{World Almanac and Book of Facts, 1975, page 406.} \references{ McNeil, D. R. (1977) \emph{Interactive Data Analysis}. New York: Wiley. } \keyword{datasets}
This uses the following additional markup commands.
Indicates the "type" of the documentation object. Always 'data'
for data sets, and 'package' for pkg-package.Rd
overview files. Documentation for S4 methods and classes uses
'methods' (from promptMethods()
) and 'class' (from
¶A description of the format of the data set (as a vector, matrix, data frame, time series, …). For matrices and data frames this should give a description of each column, preferably as a list or table. See Lists and tables, for more information.
¶Details of the original source (a reference or URL,
see Specifying URLs). In addition, section \references
give secondary sources and usages.
Note also that when documenting data set bar,
entry is always bar
or (for packages
which do not use lazy-loading of data) data(bar)
. (In
particular, only document a single data object per Rd file.)
entry should always be 'datasets'.
If bar
is a data frame, documenting it as a data set can
be initiated via prompt(bar)
. Otherwise, the promptData
function may be used.
There are special ways to use the '?' operator, namely
'class?topic' and 'methods?topic', to access
documentation for S4 classes and methods, respectively. This mechanism
depends on conventions for the topic names used in \alias
entries. The topic names for S4 classes and methods respectively are of
the form
class-class generic,signature_list-method
where signature_list contains the names of the classes in the
signature of the method (without quotes) separated by ',' (without
whitespace), with 'ANY' used for arguments without an explicit
specification. E.g., 'genericFunction-class' is the topic name for
documentation for the S4 class "genericFunction"
, and
'coerce,ANY,NULL-method' is the topic name for documentation for
the S4 method for coerce
for signature c("ANY", "NULL")
Skeletons of documentation for S4 classes and methods can be generated
by using the functions promptClass()
and promptMethods()
from package methods. If it is necessary or desired to provide an
explicit function declaration (in a \usage
section) for an S4
method (e.g., if it has "surprising arguments" to be mentioned
explicitly), one can use the special markup
(e.g., '\S4method{coerce}{ANY,NULL}(from, to)').
To make full use of the potential of the on-line documentation system,
all user-visible S4 classes and methods in a package should at least
have a suitable \alias
entry in one of the package's Rd files.
If a package has methods for a function defined originally somewhere
else, and does not change the underlying default method for the
function, the package is responsible for documenting the methods it
creates, but not for the function itself or the default method.
An S4 replacement method is documented in the same way as an S3 one: see
the description of \method
in Documenting functions.
See help("Documentation", package = "methods")
for more
information on using and creating on-line documentation for S4 classes and
Packages may have an overview help page with an \alias
, e.g. 'utils-package' for the
utils package, when package?pkgname
will open that
help page. If a topic named pkgname
does not exist in
another Rd file, it is helpful to use this as an additional
Skeletons of documentation for a package can be generated using the
function promptPackage()
. If the final = TRUE
is used, then the Rd file will be generated in final form, containing
only basic information from the DESCRIPTION file. Otherwise (the
default) comments will be inserted giving suggestions for content.
Apart from the mandatory \name
and \title
and the
alias, the only requirement for the package
overview page is that it include a \docType{package}
All other content is optional. We suggest that it should be a short
overview, to give a reader unfamiliar with the package enough
information to get started. More extensive documentation is better
placed into a package vignette (see Writing package vignettes) and
referenced from this page, or into individual man pages for the
functions, datasets, or classes.
To begin a new paragraph or leave a blank line in an example, just
insert an empty line (as in (La)TeX). To break a line, use
In addition to the predefined sections (such as \description{}
, etc.), you can "define" arbitrary ones by
For example
\section{Warning}{ You must not call this function unless ... }
For consistency with the pre-assigned sections, the section name (the
first argument to \section
) should be capitalized (but not all
upper case) and not end in a period.
Whitespace between the first and second braced expressions
is not allowed. Markup (e.g. \code
) within the section title
may cause problems with the latex conversion (depending on the version
of macro packages such as 'hyperref') and so should be avoided.
The \subsection
macro takes arguments in the same format as
, but is used within a section, so it may be used to
nest subsections within sections or other subsections. There is no
predefined limit on the nesting level, but formatting is not designed
for more than 3 levels (i.e. subsections within subsections within
Note that additional named sections are always inserted at a fixed
position in the output (before \note
, \seealso
and the
examples), no matter where they appear in the input (but in the same
order amongst themselves as in the input).
The following logical markup commands are available for emphasizing or quoting text.
Emphasize text using italic and bold font if
possible; \strong
is regarded as stronger (more emphatic).
¶Set text in bold font where possible.
Portably single or double quote text (without hard-wiring the characters used for quotation marks).
Each of the above commands takes LaTeX-like input, so other macros may be used within text.
The following logical markup commands are available for indicating specific kinds of text. Except as noted, these take 'verbatim' text input, and so other macros may not be used within them. Some characters will need to be escaped (see Insertions).
¶Indicate text that is a literal example of a piece of an R program,
e.g., a fragment of R code or the name of an R object. Text is
entered in R-like syntax, and displayed using typewriter
where possible. Macros \var
and \link
are interpreted within
¶Indicate text that is a literal example of a piece of a program. Text
is displayed using typewriter
font where possible. Formatting,
e.g. line breaks, is preserved. (Note that this includes a line break
after the initial {, so typically text should start on the same line as
the command.)
Due to limitations in LaTeX as of this writing, this macro may not be
nested within other markup macros other than \dQuote
, as errors or bad formatting may result.
¶Indicate keyboard input, using slanted typewriter font if possible, so users can distinguish the characters they are supposed to type from computer output. Text is entered 'verbatim'.
¶Indicate text that is a literal example of a sequence of characters,
entered 'verbatim', to be included within word-wrapped text. Displayed
within single quotation marks and
using typewriter
font where possible.
¶Indicate text that is a literal example of a sequence of characters,
entered 'verbatim'. No wrapping or reformatting will occur. Displayed
using typewriter
font where possible.
¶Indicate the name of an R package. LaTeX-like.
¶Indicate the name of a file. Text is LaTeX-like, so backslash needs to be escaped. Displayed using a distinct font where possible.
¶Indicate an electronic mail address. LaTeX-like, will be rendered as
a hyperlink in HTML and PDF conversion. Displayed using
font where possible.
¶Indicate a uniform resource locator (URL) for the World Wide Web. The argument is handled as 'verbatim' text (with percent and braces escaped by backslash), and rendered as a hyperlink in HTML and PDF conversion. Line feeds are removed, and leading and trailing whitespace119 is removed. See Specifying URLs.
Displayed using typewriter
font where possible.
¶Indicate a hyperlink to the World Wide Web. The first argument is handled as 'verbatim' text (with percent and braces escaped by backslash) and is used as the URL in the hyperlink, with the second argument of LaTeX-like text displayed to the user. Line feeds are removed from the first argument, and leading and trailing whitespace is removed.
Note that RFC3986-encoded URLs (e.g. using '%28VS.85%29' in
place of '(VS.85)') may not work correctly in versions of R
before 3.1.3 and are best avoided—use URLdecode()
to decode
¶Indicate a metasyntactic variable. In most cases this will be rendered distinctly, e.g. in italic (PDF/HTML) or wrapped in '<…>' (text), but not in all120. LaTeX-like.
¶Indicate an environment variable. 'Verbatim'.
Displayed using typewriter
font where possible
¶Indicate a command-line option. 'Verbatim'.
Displayed using typewriter
font where possible.
¶Indicate the name of a command. LaTeX-like, so \var
interpreted. Displayed using typewriter
font where possible.
¶Indicate the introductory or defining use of a term. LaTeX-like.
¶Indicate a reference without a direct cross-reference via \link
(see Cross-references), such as the name of a book. LaTeX-like.
¶Indicate an acronym (an abbreviation written in all capital letters), such as GNU. LaTeX-like.
¶Indicates an abbreviation. LaTeX-like.
The \itemize
and \enumerate
commands take a single
argument, within which there may be one or more \item
The text following each \item
is formatted as one or more
paragraphs, suitably indented and with the first paragraph marked with a
bullet point (\itemize
) or a number (\enumerate
Note that unlike argument lists, \item
in these formats is
followed by a space and the text (not enclosed in braces). For example
\enumerate{ \item A database consists of one or more records, each with one or more named fields. \item Regular lines start with a non-whitespace character. \item Records are separated by one or more empty lines. }
and \enumerate
commands may be nested.
The \describe
command is similar to \itemize
but allows
initial labels to be specified. Each \item
takes two arguments,
the label and the body of the item, in exactly the same way as an
argument or value \item
. \describe
commands are mapped to
lists in HTML and \description
lists in LaTeX.
Using these without any \item
s may cause problems with some
conversions and makes little sense.
The \tabular
command takes two arguments. The first gives for
each of the columns the required alignment ('l' for
left-justification, 'r' for right-justification or 'c' for
centring.) The second argument consists of an arbitrary number of
lines separated by \cr
, and with fields separated by \tab
For example:
\tabular{rlll}{ [,1] \tab Ozone \tab numeric \tab Ozone (ppb)\cr [,2] \tab Solar.R \tab numeric \tab Solar R (lang)\cr [,3] \tab Wind \tab numeric \tab Wind (mph)\cr [,4] \tab Temp \tab numeric \tab Temperature (degrees F)\cr [,5] \tab Month \tab numeric \tab Month (1--12)\cr [,6] \tab Day \tab numeric \tab Day of month (1--31) }
There must be the same number of fields on each line as there are
alignments in the first argument, and they must be non-empty (but can
contain only spaces). (There is no whitespace between \tabular
and the first argument, nor between the two arguments.)
The markup \link{foo}
(usually in the combination
) produces a hyperlink to the help for
foo. Here foo is a topic, that is the argument of
markup in another Rd file (possibly in another package).
Hyperlinks are supported in some of the formats to which Rd files are
converted, for example HTML and PDF, but ignored in others, e.g.
the text format.
One main usage of \link
is in the \seealso
section of the
help page, see Rd format.
Note that whereas leading and trailing spaces are stripped when
extracting a topic from a \alias
, they are not stripped when
looking up the topic of a \link
You can specify a link to a different topic than its name by
which links to topic dest
with name name. This can be used to refer to the documentation
for S3/4 classes, for example \code{"\link[=abc-class]{abc}"}
would be a way to refer to the documentation of an S4 class "abc"
defined in your package, and
to the S3 "terms"
class (in package stats). To make these easy to read in the
source file, \code{"\linkS4class{abc}"}
expands to the form
given above.
There are two other forms with an optional 'anchor' argument, specified as
, to link to topics
and bar
respectively in the package
pkg. They are currently only used in HTML help (and
ignored for hyperlinks in LaTeX conversions of help pages). One
should be careful about topics containing special characters (such as
arithmetic operators) as they may result in unresolvable links, and
preferably use a safer alias in the same help page.
Historically (before R version 4.1.0), links of the form
used to be interpreted as links
to files foo.html and bar.html in
package pkg, respectively. For this reason, the HTML help
system looks for file foo.html in package pkg
if it does not find topic foo
, and then searches for the
topic in other installed packages. To test that links work both with
both old and new systems, the pre-4.1.0 behaviour can be restored by
setting the environment variable _R_HELP_LINKS_TO_TOPICS_=false
Packages referred to by these 'other forms' should be declared in the DESCRIPTION file, in the 'Depends', 'Imports', 'Suggests' or 'Enhances' fields.
Mathematical formulae should be set beautifully for printed
documentation and in KaTeX/MathJax-enhanced HTML help (as from
R 4.2.0) yet we still want something useful for plain-text (and
legacy HTML) help. To this end, the two commands
are used. Whereas \eqn
is used for "inline" formulae (corresponding to TeX's
), \deqn
gives "displayed equations" (as in
LaTeX's displaymath
environment, or TeX's
). Both arguments are treated as 'verbatim' text.
Both commands can also be used as \eqn{latexascii}
one argument) which then is used for both latex and
ascii. No whitespace is allowed between command and the first
argument, nor between the first and second arguments.
The following example is from Poisson.Rd:
\deqn{p(x) = \frac{\lambda^x e^{-\lambda}}{x!}}{% p(x) = \lambda^x exp(-\lambda)/x!} for \eqn{x = 0, 1, 2, \ldots}.
In plain-text help we get
p(x) = lambda^x exp(-lambda)/x! for x = 0, 1, 2, ....
In legacy HTML help, Greek letters (both cases) will be rendered if preceded by a
backslash, \dots
and \ldots
will be rendered as ellipses
and \sqrt
, \ge
and \le
as mathematical symbols.
Note that only basic LaTeX can be used, there being no provision to specify LaTeX style files, but AMS extensions are supported as from R 4.2.2.
To include figures in help pages, use the \figure
markup. There
are three forms.
The two commonly used simple forms are \figure{filename}
and \figure{filename}{alternate text}
. This will
include a copy of the figure in either HTML or LaTeX output. In text
output, the alternate text will be displayed instead. (When the second
argument is omitted, the filename will be used.) Both the filename and
the alternate text will be parsed verbatim, and should not include
special characters that are significant in HTML or LaTeX.
The expert form is \figure{filename}{options:
. (The word 'options:' must be typed exactly as
shown and followed by at least one space.) In this form, the
string is copied into the HTML img
tag as attributes
following the src
attribute, or into the second argument of the
macro in LaTeX, which by default is used as options to
an \includegraphics
call. As it is unlikely that any single
string would suffice for both display modes, the expert form would
normally be wrapped in conditionals. It is up to the author to make
sure that legal HTML/LaTeX is used. For example, to include a
logo in both HTML (using the simple form) and LaTeX (using the
expert form), the following could be used:
\if{html}{\figure{Rlogo.svg}{options: width=100 alt="R logo"}} \if{latex}{\figure{Rlogo.pdf}{options: width=0.5in}}
The files containing the figures should be stored in the directory
man/figures. Files with extensions .jpg, .jpeg,
.pdf, .png and .svg from that directory will be
copied to the help/figures directory at install time. (Figures in
PDF format will not display in most HTML browsers, but might be the
best choice in reference manuals.) Specify the filename relative to
man/figures in the \figure
Use \R
for the R system itself. The \dots
macro is a historical alternative to using literal '...'
for the dots in function argument lists; use
for ellipsis dots in ordinary text.121 These macros can be followed by
, and should be unless followed by whitespace.
After an unescaped '%', you can put your own comments regarding the help text. The rest of the line (but not the newline at the end) will be completely disregarded. Therefore, you can also use it to make part of the "help" invisible.
You can produce a backslash ('\') by escaping it by another
backslash. (Note that \cr
is used for generating line breaks.)
The "comment" character '%' and unpaired braces122 almost always need to be escaped by '\', and '\\' can be used for backslash and needs to be when there are two or more adjacent backslashes. In R-like code quoted strings are handled slightly differently; see "Parsing Rd files" for details – in particular braces should not be escaped in quoted strings.
All of '% { } \' should be escaped in LaTeX-like text.
Text which might need to be represented differently in different
encodings should be marked by \enc
, e.g.
(with no whitespace between the
braces) where the first argument will be used where encodings are
allowed and the second should be ASCII (and is used for e.g.
the text conversion in locales that cannot represent the encoded form).
(This is intended to be used for individual words, not whole sentences
or paragraphs.)
The \alias
command (see Documenting functions) is used to
specify the "topics" documented, which should include all R
objects in a package such as functions and variables, data sets, and S4
classes and methods (see Documenting S4 classes and methods). The
on-line help system searches the index data base consisting of all
alias topics.
In addition, it is possible to provide "concept index entries" using
, which can be used for help.search()
E.g., file cor.test.Rd in the standard package stats
\concept{Kendall correlation coefficient} \concept{Pearson correlation coefficient} \concept{Spearman correlation coefficient}
so that e.g. ??Spearman will succeed in finding the help page for the test for association between paired samples using Spearman's rho.
(Note that help.search()
only uses "sections" of documentation
objects with no additional markup.)
Each \concept
entry should give a single index term (word
or phrase), and not use any Rd markup.
If you want to cross reference such items from other help files via
, you need to use \alias
and not \concept
Sometimes the documentation needs to differ by platform. Currently two OS-specific options are available, 'unix' and 'windows', and lines in the help source file can be enclosed in
#ifdef OS ... #endif
#ifndef OS ... #endif
for OS-specific inclusion or exclusion. Such blocks should not be nested, and should be entirely within a block (that, is between the opening and closing brace of a section or item), or at top-level contain one or more complete sections.
If the differences between platforms are extensive or the R objects documented are only relevant to one platform, platform-specific Rd files can be put in a unix or windows subdirectory.
Occasionally the best content for one output format is different from
the best content for another. For this situation, the
is used. Here format is a comma separated list of formats in
which the text should be rendered. The alternate will be
rendered if the format does not match. Both text and
alternate may be any sequence of text and markup.
Currently the following formats are recognized: example
, latex
and text
. These select output for
the corresponding targets. (Note that example
refers to
extracted example code rather than the displayed example in some other
format.) Also accepted are TRUE
(matching all formats) and
(matching no formats). These could be the output
of the \Sexpr
macro (see Dynamic pages).
The \out{literal}
macro would usually be used within
the text part of \if{format}{text}
. It
causes the renderer to output the literal text exactly, with no
attempt to escape special characters. For example, use
the following to output the markup necessary to display the Greek letter in
LaTeX or HTML, and the text string alpha
in other formats:
Two macros supporting dynamically generated man pages are \Sexpr
and \RdOpts
. These are modelled after Sweave, and are intended
to contain executable R expressions in the Rd file.
The main argument to \Sexpr
must be valid R code that can be
executed. It may also take options in square brackets before the main
argument. Depending on the options, the code may be executed at
package build time, package install time, or man page rendering time.
The options follow the same format as in Sweave, but different options are supported. Currently the allowed options and their defaults are:
Whether the R code should be evaluated.
Whether the R code should be echoed. If TRUE
and results=verbatim
, a display will
be given in a preformatted block. For example,
\Sexpr[echo=TRUE,results=verbatim]{ x <- 1 }
will be displayed as
> x <- 1
Whether to keep the author's formatting when displaying the
code, or throw it away and use a deparsed version.
How should the results be displayed? The possibilities
Apply as.character()
to the result of the code, and insert it
as a text element.
Print the results of the code just as if it was executed at the console,
and include the printed results verbatim. (Invisible results will not print.)
The result is assumed to be a character vector containing markup to be
passed to parse_Rd()
, with the result inserted in place. This
could be used to insert computed aliases, for instance.
is called first with fragment = FALSE
to allow
a single Rd section macro to be inserted. If that fails, it is called
again with fragment = TRUE
, the older behavior.
Insert no output.
Remove leading and trailing blank lines in verbatim
output if strip.white=true
(or TRUE
). With
, remove all blank lines.
Control when this macro is run. Possible values are
The macro is run when building a source tarball.
The macro is run when installing from source.
The macro is run when displaying the help page.
Conditionals such as #ifdef
(see Platform-specific documentation) are applied after the
macros but before the install
macros. In some
situations (e.g. installing directly from a source directory without a
tarball, or building a binary package) the above description is not
literally accurate, but authors can rely on the sequence being
, #ifdef
, install
, render
, with all
stages executed.
Code is only run once in each stage, so a \Sexpr[results=rd]
macro can output an \Sexpr
macro designed for a later stage,
but not for the current one or any earlier stage.
width, height, fig
These options are currently allowed but ignored.
The \RdOpts
macro is used to set new defaults for options to apply
to following uses of \Sexpr
For more details, see the online document "Parsing Rd files".
The \newcommand
and \renewcommand
macros allow new macros
to be defined within an Rd file. These are similar but not identical to
the same-named LaTeX macros.
They each take two arguments which are parsed verbatim. The first is
the name of the new macro including the initial backslash, and the
second is the macro definition. As in LaTeX, \newcommand
requires that the new macro not have been previously defined, whereas
allows existing macros (including all built-in
ones) to be replaced. (This test is disabled by default, but may be
enabled by setting the environment variable
to a true value.)
Also as in LaTeX, the new macro may be defined to take arguments,
and numeric placeholders such as #1
are used in the macro
definition. However, unlike LaTeX, the number of arguments is
determined automatically from the highest placeholder number seen in
the macro definition. For example, a macro definition containing
and #3
(but no other placeholders) will define a
three argument macro (whose second argument will be ignored). As in
LaTeX, at most 9 arguments may be defined. If the #
character is followed by a non-digit it will have no special
significance. All arguments to user-defined macros will be parsed as
verbatim text, and simple text-substitution will be used to replace
the place-holders, after which the replacement text will be parsed.
A number of macros are defined in the file share/Rd/macros/system.Rd of the R source or home directory, and these will normally be available in all .Rd files. For example, that file contains the definition
which defines \PR
to be a single argument macro; then code
(typically used in the NEWS.Rd file) like
will expand to
when parsed.
Some macros that might be of general use are:
¶A package on CRAN
¶A single space (used after a period that does not end a sentence).
¶A digital object identifier (DOI).
See the system.Rd file in share/Rd/macros for more details
and macro definitions, including macros \packageTitle
, \packageAuthor
, \packageMaintainer
and \packageIndices
Packages may also define their own common macros; these would be stored in an .Rd file in man/macros in the package source and will be installed into help/macros when the package is installed. A package may also use the macros from a different package by listing the other package in the 'RdMacros' field in the DESCRIPTION file.
Rd files are text files and so it is impossible to deduce the encoding
they are written in unless ASCII: files with 8-bit characters
could be UTF-8, Latin-1, Latin-9, KOI8-R, EUC-JP, etc. So an
section must be used to specify the encoding if it
is not ASCII. (The \encoding{}
section must be on a
line by itself, and in particular one containing no non-ASCII
characters. The encoding declared in the DESCRIPTION file will
be used if none is declared in the file.) The Rd files are
converted to UTF-8 before parsing and so the preferred encoding for the
files themselves is now UTF-8.
Wherever possible, avoid non-ASCII chars in Rd files, and
even symbols such as '<', '>', '$', '^', '&',
'|', '@', '~', and '*' outside 'verbatim'
environments (since they may disappear in fonts designed to render
text). (Function showNonASCIIfile
in package tools can help
in finding non-ASCII bytes in the files.)
For convenience, encoding names 'latin1' and 'latin2' are
always recognized: these and 'UTF-8' are likely to work fairly
widely. However, this does not mean that all characters in UTF-8 will
be recognized, and the coverage of non-Latin characters123 is fairly low. Using LaTeX
(see ?Rd2pdf
in R) will give greater coverage
of UTF-8.
The \enc
command (see Insertions) can be used to provide
transliterations which will be used in conversions that do not support
the declared encoding.
The LaTeX conversion converts the file to UTF-8 from the declared encoding, and includes a
command, and this needs to be matched by a suitable invocation of the
command. The R utility R
CMD Rd2pdf
looks at the converted code and includes the encodings used:
it might for example use
(Use of utf8
as an encoding requires LaTeX dated 2003/12/01 or
later. Also, the use of Cyrillic characters in 'UTF-8' appears to
also need '\usepackage[T2A]{fontenc}', and R CMD Rd2pdf
includes this conditionally on the file t2aenc.def being present
and environment variable _R_CYRILLIC_TEX_
being set.)
Note that this mechanism works best with Latin letters: the coverage of UTF-8 in LaTeX is quite low.
There are several commands to process Rd files from the system command line.
Using R CMD Rdconv
one can convert R documentation format to
other formats, or extract the executable examples for run-time testing.
The currently supported conversions are to plain text, HTML and
LaTeX as well as extraction of the examples.
R CMD Rd2pdf
generates PDF output from documentation in Rd
files, which can be specified either explicitly or by the path to a
directory with the sources of a package. In the latter case, a
reference manual for all documented objects in the package is created,
including the information in the DESCRIPTION files.
R CMD Sweave
and R CMD Stangle
process vignette-like
documentation files (e.g. Sweave vignettes with extension
'.Snw' or '.Rnw', or other non-Sweave vignettes).
R CMD Stangle
is used to extract the R code fragments.
The exact usage and a detailed list of available options for all of
these commands can be obtained by running R CMD command
, e.g., R CMD Rdconv --help. All available commands can be
listed using R --help (or Rcmd --help under Windows).
All of these work under Windows. You may need to have installed the the tools to build packages from source as described in the "R Installation and Administration" manual, although typically all that is needed is a LaTeX installation.
It can be very helpful to prepare .Rd files using a editor which knows about their syntax and will highlight commands, indent to show the structure and detect mis-matched braces, and so on.
The system most commonly used for this is some version of
(including XEmacs
) with the ESS
package (https://ESS.R-project.org/: it is often is installed with
but may need to be loaded, or even installed,
Another is the Eclipse IDE with the Stat-ET plugin (https://projects.eclipse.org/projects/science.statet), and (on Windows only) Tinn-R (https://sourceforge.net/projects/tinn-r/).
People have also used LaTeX mode in a editor, as .Rd files are rather similar to LaTeX files.
Some R front-ends provide editing support for .Rd files, for example RStudio (https://posit.co/).
R code which is worth preserving in a package and perhaps making available for others to use is worth documenting, tidying up and perhaps optimizing. The last two of these activities are the subject of this chapter.
R treats function code loaded from packages and code entered by users differently. By default code entered by users has the source code stored internally, and when the function is listed, the original source is reproduced. Loading code from a package (by default) discards the source code, and the function listing is re-created from the parse tree of the function.
Normally keeping the source code is a good idea, and in particular it avoids comments being removed from the source. However, we can make use of the ability to re-create a function listing from its parse tree to produce a tidy version of the function, for example with consistent indentation and spaces around operators. If the original source does not follow the standard format this tidied version can be much easier to read.
We can subvert the keeping of source in two ways.
can be set to FALSE
before the code
is loaded into R.
function, for example by
myfun <- removeSource(myfun)
In each case if we then list the function we will get the standard layout.
Suppose we have a file of functions myfuns.R that we want to tidy up. Create a file tidy.R containing
source("myfuns.R", keep.source = FALSE) dump(ls(all.names = TRUE), file = "new.myfuns.R")
and run R with this as the source file, for example by R --vanilla < tidy.R or by pasting into an R session. Then the file new.myfuns.R will contain the functions in alphabetical order in the standard layout. Warning: comments in your functions will be lost.
The standard format provides a good starting point for further tidying. Although the deparsing cannot do so, we recommend the consistent use of the preferred assignment operator '<-' (rather than '=') for assignment. Many package authors use a version of Emacs (on a Unix-alike or Windows) to edit R code, using the ESS[S] mode of the ESS Emacs package. See R coding standards in R Internals for style options within the ESS[S] mode recommended for the source code of R itself.
It is possible to profile R code on Windows and most124 Unix-alike versions of R.
The command Rprof
is used to control profiling, and its help
page can be consulted for full details. Profiling works by recording at
fixed intervals125 (by default every
20 msecs) which line in which R function is being used, and recording
the results in a file (default Rprof.out in the working
directory). Then the function summaryRprof
or the command-line
utility R CMD Rprof Rprof.out
can be used to summarize the
As an example, consider the following code (from Venables & Ripley, 2002, pp. 225–6).
library(MASS); library(boot) storm.fm <- nls(Time ~ b*Viscosity/(Wt - c), stormer, start = c(b=30.401, c=2.2183)) st <- cbind(stormer, fit=fitted(storm.fm)) storm.bf <- function(rs, i) { st$Time <- st$fit + rs[i] tmp <- nls(Time ~ (b * Viscosity)/(Wt - c), st, start = coef(storm.fm)) tmp$m$getAllPars() } rs <- scale(resid(storm.fm), scale = FALSE) # remove the mean Rprof("boot.out") storm.boot <- boot(rs, storm.bf, R = 4999) # slow enough to profile Rprof(NULL)
Having run this we can summarize the results by
R CMD Rprof boot.out Each sample represents 0.02 seconds. Total run time: 22.52 seconds. Total seconds: time spent in function and callees. Self seconds: time spent in function alone.
% total % self total seconds self seconds name 100.0 25.22 0.2 0.04 "boot" 99.8 25.18 0.6 0.16 "statistic" 96.3 24.30 4.0 1.02 "nls" 33.9 8.56 2.2 0.56 "<Anonymous>" 32.4 8.18 1.4 0.36 "eval" 31.8 8.02 1.4 0.34 ".Call" 28.6 7.22 0.0 0.00 "eval.parent" 28.5 7.18 0.3 0.08 "model.frame" 28.1 7.10 3.5 0.88 "model.frame.default" 17.4 4.38 0.7 0.18 "sapply" 15.0 3.78 3.2 0.80 "nlsModel" 12.5 3.16 1.8 0.46 "lapply" 12.3 3.10 2.7 0.68 "assign" ...
% self % total self seconds total seconds name 5.7 1.44 7.5 1.88 "inherits" 4.0 1.02 96.3 24.30 "nls" 3.6 0.92 3.6 0.92 "$" 3.5 0.88 28.1 7.10 "model.frame.default" 3.2 0.80 15.0 3.78 "nlsModel" 2.8 0.70 9.8 2.46 "qr.coef" 2.7 0.68 12.3 3.10 "assign" 2.5 0.64 2.5 0.64 ".Fortran" 2.5 0.62 7.1 1.80 "qr.default" 2.2 0.56 33.9 8.56 "<Anonymous>" 2.1 0.54 5.9 1.48 "unlist" 2.1 0.52 7.9 2.00 "FUN" ...
This often produces surprising results and can be used to identify bottlenecks or pieces of R code that could benefit from being replaced by compiled code.
Two warnings: profiling does impose a small performance penalty, and the output files can be very large if long runs are profiled at the default sampling interval.
Profiling short runs can sometimes give misleading results. R from
time to time performs garbage collection to reclaim unused
memory, and this takes an appreciable amount of time which profiling
will charge to whichever function happens to provoke it. It may be
useful to compare profiling code immediately after a call to gc()
with a profiling run without a preceding call to gc
More detailed analysis of the output can be achieved by the tools in the CRAN packages proftools and profr: in particular these allow call graphs to be studied.
Measuring memory use in R code is useful either when the code takes more memory than is conveniently available or when memory allocation and copying of objects is responsible for slow code. There are three ways to profile memory use over time in R code. The second and third require R to have been compiled with --enable-memory-profiling, which is not the default, but is currently used for the macOS and Windows binary distributions. All can be misleading, for different reasons.
In understanding the memory profiles it is useful to know a little more
about R's memory allocation. Looking at the results of gc()
shows a division of memory into Vcells
used to store the contents
of vectors and Ncells
used to store everything else, including
all the administrative overhead for vectors such as type and length
information. In fact the vector contents are divided into two
pools. Memory for small vectors (by default 128 bytes or less) is
obtained in large chunks and then parcelled out by R; memory for
larger vectors is obtained directly from the operating system.
Some memory allocation is obvious in interpreted code, for example,
y <- x + 1
allocates memory for a new vector y
. Other memory allocation is
less obvious and occurs because R
is forced to make good on its
promise of 'call-by-value' argument passing. When an argument is
passed to a function it is not immediately copied. Copying occurs (if
necessary) only when the argument is modified. This can lead to
surprising memory use. For example, in the 'survey' package we have
print.svycoxph <- function (x, ...) { print(x$survey.design, varnames = FALSE, design.summaries = FALSE, ...) x$call <- x$printcall NextMethod() }
It may not be obvious that the assignment to x$call
will cause
the entire object x
to be copied. This copying to preserve the
call-by-value illusion is usually done by the internal C function
The main reason that memory-use profiling is difficult is garbage collection. Memory is allocated at well-defined times in an R program, but is freed whenever the garbage collector happens to run.
¶The sampling profiler Rprof
described in the previous section can
be given the option memory.profiling=TRUE
. It then writes out the
total R memory allocation in small vectors, large vectors, and cons
cells or nodes at each sampling interval. It also writes out the number
of calls to the internal function duplicate
, which is called to
copy R objects. summaryRprof
provides summaries of this
information. The main reason that this can be misleading is that the
memory use is attributed to the function running at the end of the
sampling interval. A second reason is that garbage collection can make
the amount of memory in use decrease, so a function appears to use
little memory. Running under gctorture
helps with both problems:
it slows down the code to effectively increase the sampling frequency
and it makes each garbage collection release a smaller amount of memory.
The second method of memory profiling uses a memory-allocation
profiler, Rprofmem()
, which writes out a stack trace to an
output file every time a large vector is allocated (with a
user-specified threshold for 'large') or a new page of memory is
allocated for the R heap. Summary functions for this output are still
being designed.
Running the example from the previous section with
> Rprofmem("boot.memprof",threshold=1000) > storm.boot <- boot(rs, storm.bf, R = 4999) > Rprofmem(NULL)
shows that apart from some initial and final work in boot
are no vector allocations over 1000 bytes.
The third method of memory profiling involves tracing copies made of a
specific (presumably large) R object. Calling tracemem
on an
object marks it so that a message is printed to standard output when
the object is copied via duplicate
or coercion to another type,
or when a new object of the same size is created in arithmetic
operations. The main reason that this can be misleading is that
copying of subsets or components of an object is not tracked. It may
be helpful to use tracemem
on these components.
In the example above we can run tracemem
on the data frame
> tracemem(st) [1] "<0x9abd5e0>" > storm.boot <- boot(rs, storm.bf, R = 4) memtrace[0x9abd5e0->0x92a6d08]: statistic boot memtrace[0x92a6d08->0x92a6d80]: $<-.data.frame $<- statistic boot memtrace[0x92a6d80->0x92a6df8]: $<-.data.frame $<- statistic boot memtrace[0x9abd5e0->0x9271318]: statistic boot memtrace[0x9271318->0x9271390]: $<-.data.frame $<- statistic boot memtrace[0x9271390->0x9271408]: $<-.data.frame $<- statistic boot memtrace[0x9abd5e0->0x914f558]: statistic boot memtrace[0x914f558->0x914f5f8]: $<-.data.frame $<- statistic boot memtrace[0x914f5f8->0x914f670]: $<-.data.frame $<- statistic boot memtrace[0x9abd5e0->0x972cbf0]: statistic boot memtrace[0x972cbf0->0x972cc68]: $<-.data.frame $<- statistic boot memtrace[0x972cc68->0x972cd08]: $<-.data.frame $<- statistic boot memtrace[0x9abd5e0->0x98ead98]: statistic boot memtrace[0x98ead98->0x98eae10]: $<-.data.frame $<- statistic boot memtrace[0x98eae10->0x98eae88]: $<-.data.frame $<- statistic boot
The object is duplicated fifteen times, three times for each of the
calls to storm.bf
. This is surprising, since none of the duplications happen inside nls
. Stepping through storm.bf
in the debugger shows that all three happen in the line
st$Time <- st$fit + rs[i]
Data frames are slower than matrices and this is an example of why.
Using tracemem(st$Viscosity)
does not reveal any additional
Profiling compiled code is highly system-specific, but this section contains some hints gleaned from various R users. Some methods need to be different for a compiled executable and for dynamic/shared libraries/objects as used by R packages.
This chapter is based on reports from users and the information may not be current.
Options include using sprof
for a shared object, and
(see https://oprofile.sourceforge.io/news/)
and perf
https://perf.wiki.kernel.org/index.php/Tutorial) for any
executable or shared object. These seem less widely supplied than they
used to be. There is also 'Google Performance Tools', also known as
gperftools or google-perftools.
All of these work best when R and any packages have been built with debugging symbols.
¶This seems the most widely distributed tool.
Here is an example on x86_64
Linux using R 4.3.1 built with LTO.
At its simplest
perf record R -f tests/Examples/stats-Ex.R perf report --sort=dso perf report --sort=srcfile rm perf.data*
The first report is
75.67% R 9.25% libc.so.6 4.87% [unknown] 3.75% libz.so.1.2.11 3.37% stats.so 1.17% libm.so.6 0.63% libtirpc.so.3.0.0 0.41% graphics.so 0.30% grDevices.so 0.20% libRblas.so 0.09% libpcre2-8.so.0.11.0 0.07% methods.so ...
which shows which shared libraries (DSOs) the time was spent in.
perf annotate
can be used on an application built with GCC and
-ggdb: it interleaves disassembled and source code.
and operf
¶The oprofile
project has two modes of operation. Since
version 0.9.8 (August 2012), the preferred mode is to use
, so we discuss only that.
Let us look at the boot example from §3.2 on x86_64
using R 4.3.1.
This can be run under operf
and analysed by commands like
operf R -f boot.R opreport opreport -l /path/to/R_HOME/bin/exec/R opreport -l /path/to/R_HOME/library/stats/src/stats.so opannotate --source /path/to/R_HOME/bin/exec/R
The first line had to be done as root.
The first report shows in which library (etc) the time was spent:
CPU_CLK_UNHALT...| samples| %| ------------------ 278341 91.9947 R 18290 6.0450 libc.so.6 2277 0.7526 kallsyms 1426 0.4713 stats.so 739 0.2442 libRblas.so 554 0.1831 libz.so.1.2.11 373 0.1233 libm.so.6 352 0.1163 libtirpc.so.3.0.0 153 0.0506 ld-linux-x86-64.so.2 12 0.0040 methods.so
is the kernel.)
The rest of the output is voluminous, and only extracts are shown.
Most of the time within R is spent in
samples % image name symbol name 52955 19.0574 R bcEval.lto_priv.0 16484 5.9322 R Rf_allocVector3 14224 5.1189 R Rf_findVarInFrame3 12581 4.5276 R CONS_NR 8289 2.9830 R Rf_matchArgs_NR 8034 2.8913 R Rf_cons 7114 2.5602 R R_gc_internal.lto_priv.0 6552 2.3579 R Rf_eval 5969 2.1481 R VECTOR_ELT 5684 2.0456 R Rf_applyClosure 5497 1.9783 R findVarLocInFrame.part.0.lto_priv.0 4827 1.7371 R Rf_mkPROMISE 4609 1.6587 R Rf_install 4317 1.5536 R Rf_findFun3 4035 1.4521 R getvar.lto_priv.0 3491 1.2563 R SETCAR 3179 1.1441 R Rf_defineVar 2892 1.0408 R duplicate1.lto_priv.0
and in stats.so
samples % image name symbol name 285 24.4845 stats.so termsform 284 24.3986 stats.so numeric_deriv 213 18.2990 stats.so modelframe 114 9.7938 stats.so nls_iter 55 4.7251 stats.so ExtractVars 47 4.0378 stats.so EncodeVars 37 3.1787 stats.so getListElement 32 2.7491 stats.so TrimRepeats 25 2.1478 stats.so InstallVar 20 1.7182 stats.so MatchVar 20 1.7182 stats.so isZeroOne 15 1.2887 stats.so ConvInfoMsg.isra.0
The profiling data is by default stored in sub-directory oprofile_data of the current directory, which can be removed at the end of the session.
¶You can select shared objects to be profiled with sprof
setting the environment variable LD_PROFILE
. For example
% setenv LD_PROFILE /path/to/R_HOME/library/stats/libs/stats.so % R -f boot.R % sprof /path/to/R_HOME/library/stats/libs/stats.so \ /var/tmp/path/to/R_HOME/library/stats/libs/stats.so.profile Flat profile: Each sample counts as 0.01 seconds. % cumulative self self total time seconds seconds calls us/call us/call name 76.19 0.32 0.32 0 0.00 numeric_deriv 16.67 0.39 0.07 0 0.00 nls_iter 7.14 0.42 0.03 0 0.00 getListElement ... to clean up ... rm /var/tmp/path/to/R_HOME/library/stats/libs/stats.so.profile
It is possible that root access will be needed to create the directories used for the profile data.
Developers have recommended Instruments
(part of Xcode
see https://help.apple.com/instruments/mac/current/), This had a
command-line version prior to macOS 12.
Very Sleepy
(https://github.com/VerySleepy/verysleepy) has been used on
'x86_64' Windows. There were
problems with accessing the debug information, but the best results which
included function names were obtained by attaching the profiler to an
existing Rterm
process, either via GUI or using /a:
(PID obtained via Sys.getpid()
This chapter covers the debugging of R extensions, starting with the ways to get useful error information and moving on to how to deal with errors that crash R.
Most of the R-level debugging facilities are based around the
built-in browser. This can be used directly by inserting a call to
into the code of a function (for example, using
). When code execution reaches that point in
the function, control returns to the R console with a special prompt.
For example
> fix(summary.data.frame) ## insert browser() call after for() loop > summary(women) Called from: summary.data.frame(women) Browse[1]> ls() [1] "digits" "i" "lbs" "lw" "maxsum" "ncw" "nm" "nr" [9] "nv" "object" "sms" "z" Browse[1]> maxsum [1] 7 Browse[1]> c height weight Min. :58.0 Min. :115.0 1st Qu.:61.5 1st Qu.:124.5 Median :65.0 Median :135.0 Mean :65.0 Mean :136.7 3rd Qu.:68.5 3rd Qu.:148.0 Max. :72.0 Max. :164.0 > rm(summary.data.frame)
At the browser prompt one can enter any R expression, so for example
lists the objects in the current frame, and entering the
name of an object will126 print it. The following commands are
also accepted
Enter 'step-through' mode. In this mode, hitting return executes the
next line of code (more precisely one line and any continuation lines).
Typing c
will continue to the end of the current context, e.g.
to the end of the current loop or function.
In normal mode, this quits the browser and continues execution, and just
return works in the same way. cont
is a synonym.
This prints the call stack. For example
> summary(women) Called from: summary.data.frame(women) Browse[1]> where where 1: summary.data.frame(women) where 2: summary(women) Browse[1]>
Quit both the browser and the current expression, and return to the top-level prompt.
Errors in code executed at the browser prompt will normally return
control to the browser prompt. Objects can be altered by assignment,
and will keep their changed values when the browser is exited. If
really necessary, objects can be assigned to the workspace from the
browser prompt (by using <<-
if the name is not already in
Suppose your R program gives an error message. The first thing to
find out is what R was doing at the time of the error, and the most
useful tool is traceback()
. We suggest that this is run whenever
the cause of the error is not immediately obvious. Errors are often
reported to the R mailing lists as being in some package when
would show that the error was being reported by some
other package or base R. Here is an example from the regression
> success <- c(13,12,11,14,14,11,13,11,12) > failure <- c(0,0,0,0,0,0,0,2,2) > resp <- cbind(success, failure) > predictor <- c(0, 5^(0:7)) > glm(resp ~ 0+predictor, family = binomial(link="log")) Error: no valid set of coefficients has been found: please supply starting values > traceback() 3: stop("no valid set of coefficients has been found: please supply starting values", call. = FALSE) 2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, mustart = mustart, offset = offset, family = family, control = control, intercept = attr(mt, "intercept") > 0) 1: glm(resp ~ 0 + predictor, family = binomial(link ="log"))
The calls to the active frames are given in reverse order (starting with
the innermost). So we see the error message comes from an explicit
check in glm.fit
. (traceback()
shows you all the lines of
the function calls, which can be limited by setting option
Sometimes the traceback will indicate that the error was detected inside
compiled code, for example (from ?nls
Error in nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321), trace = TRUE) : step factor 0.000488281 reduced below 'minFactor' of 0.000976563 > traceback() 2: .Call(R_nls_iter, m, ctrl, trace) 1: nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321), trace = TRUE)
This will be the case if the innermost call is to .C
, .Call
, .External
or .Internal
, but
as it is also possible for such code to evaluate R expressions, this
need not be the innermost call, as in
> traceback() 9: gm(a, b, x) 8: .Call(R_numeric_deriv, expr, theta, rho, dir) 7: numericDeriv(form[[3]], names(ind), env) 6: getRHS() 5: assign("rhs", getRHS(), envir = thisEnv) 4: assign("resid", .swts * (lhs - assign("rhs", getRHS(), envir = thisEnv)), envir = thisEnv) 3: function (newPars) { setPars(newPars) assign("resid", .swts * (lhs - assign("rhs", getRHS(), envir = thisEnv)), envir = thisEnv) assign("dev", sum(resid^2), envir = thisEnv) assign("QR", qr(.swts * attr(rhs, "gradient")), envir = thisEnv) return(QR$rank < min(dim(QR$qr))) }(c(-0.00760232418963883, 1.00119632515036)) 2: .Call(R_nls_iter, m, ctrl, trace) 1: nls(yeps ~ gm(a, b, x), start = list(a = 0.12345, b = 0.54321))
Occasionally traceback()
does not help, and this can be the case
if S4 method dispatch is involved. Consider the following example
> xyd <- new("xyloc", x=runif(20), y=runif(20)) Error in as.environment(pkg) : no item called "package:S4nswv" on the search list Error in initialize(value, ...) : S language method selection got an error when called from internal dispatch for function 'initialize' > traceback() 2: initialize(value, ...) 1: new("xyloc", x = runif(20), y = runif(20))
which does not help much, as there is no call to as.environment
in initialize
(and the note "called from internal dispatch"
tells us so). In this case we searched the R sources for the quoted
call, which occurred in only one place,
. So now we knew where the
error was occurring. (This was an unusually opaque example.)
The error message
evaluation nested too deeply: infinite recursion / options(expressions=)?
can be hard to handle with the default value (5000). Unless you know that there actually is deep recursion going on, it can help to set something like
and re-run the example showing the error.
Sometimes there is warning that clearly is the precursor to some later
error, but it is not obvious where it is coming from. Setting
options(warn = 2)
(which turns warnings into errors) can help here.
Once we have located the error, we have some choices. One way to proceed
is to find out more about what was happening at the time of the crash by
looking a post-mortem dump. To do so, set
and run the code again. Then invoke
and explore the dump. Continuing our example:
> options(error = dump.frames) > glm(resp ~ 0 + predictor, family = binomial(link ="log")) Error: no valid set of coefficients has been found: please supply starting values
which is the same as before, but an object called last.dump
appeared in the workspace. (Such objects can be large, so remove it
when it is no longer needed.) We can examine this at a later time by
calling the function debugger
> debugger() Message: Error: no valid set of coefficients has been found: please supply starting values Available environments had calls: 1: glm(resp ~ 0 + predictor, family = binomial(link = "log")) 2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, mus 3: stop("no valid set of coefficients has been found: please supply starting values Enter an environment number, or 0 to exit Selection:
which gives the same sequence of calls as traceback
, but in
outer-first order and with only the first line of the call, truncated to
the current width. However, we can now examine in more detail what was
happening at the time of the error. Selecting an environment opens the
browser in that frame. So we select the function call which spawned the
error message, and explore some of the variables (and execute two
function calls).
Enter an environment number, or 0 to exit Selection: 2 Browsing in the environment with call: glm.fit(x = X, y = Y, weights = weights, start = start, etas Called from: debugger.look(ind) Browse[1]> ls() [1] "aic" "boundary" "coefold" "control" "conv" [6] "dev" "dev.resids" "devold" "EMPTY" "eta" [11] "etastart" "family" "fit" "good" "intercept" [16] "iter" "linkinv" "mu" "mu.eta" "mu.eta.val" [21] "mustart" "n" "ngoodobs" "nobs" "nvars" [26] "offset" "start" "valideta" "validmu" "variance" [31] "varmu" "w" "weights" "x" "xnames" [36] "y" "ynames" "z" Browse[1]> eta 1 2 3 4 5 0.000000e+00 -2.235357e-06 -1.117679e-05 -5.588393e-05 -2.794197e-04 6 7 8 9 -1.397098e-03 -6.985492e-03 -3.492746e-02 -1.746373e-01 Browse[1]> valideta(eta) [1] TRUE Browse[1]> mu 1 2 3 4 5 6 7 8 1.0000000 0.9999978 0.9999888 0.9999441 0.9997206 0.9986039 0.9930389 0.9656755 9 0.8397616 Browse[1]> validmu(mu) [1] FALSE Browse[1]> c Available environments had calls: 1: glm(resp ~ 0 + predictor, family = binomial(link = "log")) 2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart 3: stop("no valid set of coefficients has been found: please supply starting v Enter an environment number, or 0 to exit Selection: 0 > rm(last.dump)
Because last.dump
can be looked at later or even in another R
session, post-mortem debugging is possible even for batch usage of R.
We do need to arrange for the dump to be saved: this can be done either
using the command-line flag --save to save the workspace at the
end of the run, or via a setting such as
> options(error = quote({dump.frames(to.file=TRUE); q()}))
See the help on dump.frames
for further options and a worked
An alternative error action is to use the function recover()
> options(error = recover) > glm(resp ~ 0 + predictor, family = binomial(link = "log")) Error: no valid set of coefficients has been found: please supply starting values Enter a frame number, or 0 to exit 1: glm(resp ~ 0 + predictor, family = binomial(link = "log")) 2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart Selection:
which is very similar to dump.frames
. However, we can examine
the state of the program directly, without dumping and re-loading the
dump. As its help page says, recover
can be routinely used as
the error action in place of dump.calls
and dump.frames
since it behaves like dump.frames
in non-interactive use.
Post-mortem debugging is good for finding out exactly what went wrong,
but not necessarily why. An alternative approach is to take a closer
look at what was happening just before the error, and a good way to do
that is to use debug
. This inserts a call to the browser
at the beginning of the function, starting in step-through mode. So in
our example we could use
> debug(glm.fit) > glm(resp ~ 0 + predictor, family = binomial(link ="log")) debugging in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, mustart = mustart, offset = offset, family = family, control = control, intercept = attr(mt, "intercept") > 0) debug: { ## lists the whole function Browse[1]> debug: x <- as.matrix(x) ... Browse[1]> start [1] -2.235357e-06 debug: eta <- drop(x %*% start) Browse[1]> eta 1 2 3 4 5 0.000000e+00 -2.235357e-06 -1.117679e-05 -5.588393e-05 -2.794197e-04 6 7 8 9 -1.397098e-03 -6.985492e-03 -3.492746e-02 -1.746373e-01 Browse[1]> debug: mu <- linkinv(eta <- eta + offset) Browse[1]> mu 1 2 3 4 5 6 7 8 1.0000000 0.9999978 0.9999888 0.9999441 0.9997206 0.9986039 0.9930389 0.9656755 9 0.8397616
(The prompt Browse[1]>
indicates that this is the first level of
browsing: it is possible to step into another function that is itself
being debugged or contains a call to browser()
can be used for hidden functions and S3 methods by
e.g. debug(stats:::predict.Arima)
. (It cannot be used for S4
methods, but an alternative is given on the help page for debug
Sometimes you want to debug a function defined inside another function,
e.g. the function arimafn
defined inside arima
. To do so,
set debug
on the outer function (here arima
) and
step through it until the inner function has been defined. Then
call debug
on the inner function (and use c
to get out of
step-through mode in the outer function).
To remove debugging of a function, call undebug
with the argument
previously given to debug
; debugging otherwise lasts for the rest
of the R session (or until the function is edited or otherwise
can be used to temporarily insert debugging code into a
function, for example to insert a call to browser()
just before
the point of the error. To return to our running example
## first get a numbered listing of the expressions of the function > page(as.list(body(glm.fit)), method="print") > trace(glm.fit, browser, at=22) Tracing function "glm.fit" in package "stats" [1] "glm.fit" > glm(resp ~ 0 + predictor, family = binomial(link ="log")) Tracing glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, .... step 22 Called from: eval(expr, envir, enclos) Browse[1]> n ## and single-step from here. > untrace(glm.fit)
For your own functions, it may be as easy to use fix
to insert
temporary code, but trace
can help with functions in a namespace
(as can fixInNamespace
). Alternatively, use
to insert code visually.
Errors in memory allocation and reading/writing outside arrays are very common causes of crashes (e.g., segfaults) on some machines. Often the crash appears long after the invalid memory access: in particular damage to the structures which R itself has allocated may only become apparent at the next garbage collection (or even at later garbage collections after objects have been deleted).
Note that memory access errors may be seen with LAPACK, BLAS, OpenMP and Java-using packages: some at least of these seem to be intentional, and some are related to passing characters to Fortran.
Some of these tools can detect mismatched allocation and deallocation.
C++ programmers should note that memory allocated by new []
be freed by delete []
, other uses of new
by delete
and memory allocated by malloc
, calloc
and realloc
by free
. Some platforms will tolerate mismatches (perhaps with
memory leaks) but others will segfault.
¶We can help to detect memory problems in R objects earlier by running
garbage collection as often as possible. This is achieved by
, which as described on its help page
Provokes garbage collection on (nearly) every memory allocation. Intended to ferret out memory protection bugs. Also makes R run very slowly, unfortunately.
The reference to 'memory protection' is to missing C-level calls to
(see Handling the effects of garbage collection) which if
missing allow R objects to be garbage-collected when they are still
in use. But it can also help with other memory-related errors.
Normally running under gctorture(TRUE)
will just produce a crash
earlier in the R program, hopefully close to the actual cause. See
the next section for how to decipher such crashes.
It is possible to run all the examples, tests and vignettes covered by
R CMD check
under gctorture(TRUE)
by using the option
The function gctorture2
provides more refined control over the GC
torture process. Its arguments step
, wait
are documented on its help page. Environment
variables can also be used at the start of the R session to turn on
GC torture: R_GCTORTURE
corresponds to the step
argument to
to wait
, and
to inhibit_release
If R is configured with --enable-strict-barrier then a variety of tests for the integrity of the write barrier are enabled. In addition tests to help detect protect issues are enabled:
on creation.
are marked
as type FREESXP
and their previous type is recorded.
inputs and
outputs and signal an error if a FREESXP
is found.
The address of the node and the old type are included in the error
R CMD check --use-gct
can be set to use
rather than gctorture(TRUE)
by setting
environment variable _R_CHECK_GCT_N_
to a positive integer value
to be used as n
Used with a debugger and with gctorture
or gctorture2
mechanism can be helpful in isolating memory protect problems.
If you have access to Linux on a common CPU type or supported versions
of FreeBSD or Solaris127 you can use valgrind
(https://valgrind.org/, pronounced to rhyme with 'tinned') to
check for possible problems. To run some examples under valgrind
use something like
R -d valgrind --vanilla < mypkg-Ex.R R -d "valgrind --tool=memcheck --leak-check=full" --vanilla < mypkg-Ex.R
where mypkg-Ex.R is a set of examples, e.g. the file created in
mypkg.Rcheck by R CMD check
. Occasionally this reports
memory reads of 'uninitialised values' that are the result of compiler
optimization, so can be worth checking under an unoptimized compile: for
maximal information use a build with debugging symbols. We know there
will be some small memory leaks from readline
and R itself —
these are memory areas that are in use right up to the end of the R
session. Expect this to run around 20x slower than without
, and in some cases much slower than that. Several
versions of valgrind
were not happy with some optimized BLAS libraries
that use CPU-specific instructions so you may need to build a
version of R specifically to use with valgrind
On platforms where valgrind
and its headers128
are installed you can build a version of R with extra instrumentation
to help valgrind
detect errors in the use of memory allocated
from the R heap. The configure
option is
--with-valgrind-instrumentation=level, where level
is 0, 1 or 2. Level 0 is the default and does not add anything. Level
1 will detect some uses129 of uninitialised memory and has little impact on speed
(compared to level 0). Level 2 will detect many other memory-use
bugs130 but make R much slower when running under
. Using this in conjunction with gctorture
can be
even more effective (and even slower).
An example of valgrind
output is
==12539== Invalid read of size 4 ==12539== at 0x1CDF6CBE: csc_compTr (Mutils.c:273) ==12539== by 0x1CE07E1E: tsc_transpose (dtCMatrix.c:25) ==12539== by 0x80A67A7: do_dotcall (dotcode.c:858) ==12539== by 0x80CACE2: Rf_eval (eval.c:400) ==12539== by 0x80CB5AF: R_execClosure (eval.c:658) ==12539== by 0x80CB98E: R_execMethod (eval.c:760) ==12539== by 0x1B93DEFA: R_standardGeneric (methods_list_dispatch.c:624) ==12539== by 0x810262E: do_standardGeneric (objects.c:1012) ==12539== by 0x80CAD23: Rf_eval (eval.c:403) ==12539== by 0x80CB2F0: Rf_applyClosure (eval.c:573) ==12539== by 0x80CADCC: Rf_eval (eval.c:414) ==12539== by 0x80CAA03: Rf_eval (eval.c:362) ==12539== Address 0x1C0D2EA8 is 280 bytes inside a block of size 1996 alloc'd ==12539== at 0x1B9008D1: malloc (vg_replace_malloc.c:149) ==12539== by 0x80F1B34: GetNewPage (memory.c:610) ==12539== by 0x80F7515: Rf_allocVector (memory.c:1915) ...
This example is from an instrumented version of R, while tracking
down a bug in the Matrix package in 2006. The first line
indicates that R has tried to read 4 bytes from a memory address that
it does not have access to. This is followed by a C stack trace showing
where the error occurred. Next is a description of the memory that was
accessed. It is inside a block allocated by malloc
, called from
, that is, in the internal R heap. Since this
memory all belongs to R, valgrind
would not (and did not)
detect the problem in an uninstrumented build of R. In this example
the stack trace was enough to isolate and fix the bug, which was in
, and in this example running under
did not provide any additional information.
is good at spotting the use of uninitialized values:
use option --track-origins=yes to show where these originated
from. What it cannot detect is the misuse of arrays allocated on the
stack: this includes C automatic variables and some131
Fortran arrays.
It is possible to run all the examples, tests and vignettes covered by
R CMD check
under valgrind
by using the option
--use-valgrind. If you do this you will need to select the
options some other way, for example by having a
~/.valgrindrc file containing
--leak-check=full --track-origins=yes
or setting the environment variable VALGRIND_OPTS
. As from R
4.2.0, --use-valgrind also uses valgrind
re-building the vignettes.
This section has described the use of memtest
, the default
(and most useful) of valgrind
's tools. There are others
described in its documentation: helgrind
can be useful for
threaded programs.
('ASan') is a tool with similar aims to the
memory checker in valgrind
. It is available with suitable
builds132 of gcc
on common Linux and macOS platforms. See
https://clang.llvm.org/docs/AddressSanitizer.html and
More thorough checks of C++ code are done if the C++ library has been
'annotated': at the time of writing this applied to std::vector
in libc++
for use with clang
and gives rise to
It requires code to have been compiled and linked with
-fsanitize=address and compiling with -fno-omit-frame-pointer
will give more legible reports. It has a runtime penalty of 2–3x,
extended compilation times and uses substantially more memory, often
1–2GB, at run time. On 64-bit platforms it reserves (but does not
allocate) 16–20TB of virtual memory: restrictive shell settings can
cause problems. It can be helpful to increase the stack size, for
example to 40MB.
By comparison with valgrind
, ASan can
detect misuse of stack and global variables but not the use of
uninitialized memory.
Recent versions return symbolic addresses for the location of the error
provided llvm-symbolizer
134 is on the path: if it is available but not
on the path or has been renamed135, one can use an environment variable, e.g.
An alternative is to pipe the output through
136 and perhaps
then (for compiled C++ code) c++filt
. (On macOS, you may need
to run dsymutil
to get line-number reports.)
The simplest way to make use of this is to build a version of R with something like
CC="gcc -std=gnu99 -fsanitize=address" CFLAGS="-fno-omit-frame-pointer -g -O2 -Wall -pedantic -mtune=native"
which will ensure that the libasan
run-time library is compiled
into the R executable. However this check can be enabled on a
per-package basis by using a ~/.R/Makevars file like
CC = gcc -std=gnu99 -fsanitize=address -fno-omit-frame-pointer CXX = g++ -fsanitize=address -fno-omit-frame-pointer FC = gfortran -fsanitize=address
(Note that -fsanitize=address
has to be part of the compiler
specification to ensure it is used for linking. These settings will not
be honoured by packages which ignore ~/.R/Makevars.) It will
be necessary to build R with
MAIN_LDFLAGS = -fsanitize=address
to link the runtime libraries into the R executable if it was not specified as part of 'CC' when R was built. (For some builds without OpenMP, -pthread is also required.)
For options available via the environment variable
With gcc
additional control is available via the
--param flag: see its man
For more detailed information on an error, R can be run under a
debugger with a breakpoint set before the address sanitizer report is
produced: for gdb
or lldb
you could use
break __asan_report_error
(See https://github.com/google/sanitizers/wiki/AddressSanitizerAndDebugger.)
More recent versions137 added the flag -fsanitize-address-use-after-scope: see https://github.com/google/sanitizers/wiki/AddressSanitizerUseAfterScope.
One of the checks done by ASan is that malloc/free
and in C++
and new[]/delete[]
are used consistently
(rather than say free
being used to deallocate memory allocated by
). This matters on some systems but not all: unfortunately
on some of those where it does not matter, system libraries138 are not consistent. The
check can be suppressed by including 'alloc_dealloc_mismatch=0' in
ASan also checks system calls and sometimes reports can refer to problems in the system software and not the package nor R. A couple of reports have been of 'heap-use-after-free' errors in the X11 libraries called from Tcl/Tk.
For x86_64
Linux there is a leak sanitizer, 'LSan': see
This is available on recent versions of gcc
and clang
, and
where available is compiled in as part of ASan.
One way to invoke this from an ASan-enabled build is by the environment variable
However, this was made the default as from clang
3.5 and
When LSan is enabled, leaks give the process a failure error status (by
default 23
). For an R package this means the R process,
and as the parser retains some memory to the end of the process, if R
itself was built against ASan all runs will have a failure error status
(which may include running R as part of building R itself).
To disable this, allocation-mismatch checking and some strict C++ checking use
setenv ASAN_OPTIONS 'alloc_dealloc_mismatch=0:detect_leaks=0:detect_odr_violation=0'
LSan also has a 'stand-alone' mode where it is compiled in using -fsanitize=leak and avoids the run-time overhead of ASan.
'Undefined behaviour' is where the language standard does not require
particular behaviour from the compiler. Examples include division by
zero (where for doubles R requires the
ISO/IEC 60559 behaviour but C/C++ do not), use
of zero-length arrays, shifts too far for signed types (e.g. int
x, y; y = x << 31;
), out-of-range coercion, invalid C++ casts and
mis-alignment. Not uncommon examples of out-of-range coercion in R
packages are attempts to coerce a NaN
or infinity to type
or NA_INTEGER
to an unsigned type such as
. Also common is y[x - 1]
forgetting that x
might be NA_INTEGER
'UBSanitizer' is a tool for C/C++ source code selected by
-fsanitize=undefined in suitable builds139 of clang
and GCC. Its (main) runtime library is
linked into each package's DLL, so it is less often needed to be
included in MAIN_LDFLAGS
. Platforms supported by clang
are listed at
CRAN uses it for C/C++ with both GCC and clang
'x86_64' Linux: the two toolchains often highlight different
things with more reports from clang
than GCC.
This sanitizer may be combined with the Address Sanitizer by
-fsanitize=undefined,address (where both are supported, and we
have seen library conflicts for clang
17 and later).
Finer control of what is checked can be achieved by other options.
For clang
The current set is (on a single line):
-fsanitize=alignment,bool,bounds,builtin,enum,float-cast-overflow, float-divide-by-zero,function,implicit-unsigned-integer-truncation, implicit-signed-integer-truncation,implicit-integer-sign-change, integer-divide-by-zero,nonnull-attribute,null,nullability-arg, nullability-assign,nullability-return,object-size, pointer-overflow,return,returns-nonnull-attribute,shift, signed-integer-overflow,unreachable,unsigned-integer-overflow, unsigned-shift-base,vla-bound,vptr
(plus the more specific versions array-bounsds
, shift-base
and shift-exponent
), or
use something like
-fsanitize=undefined -fno-sanitize=float-divide-by-zero
where in recent versions -fno-sanitize=float-divide-by-zero
is the
Options return
and vptr
apply only to C++: to
use vptr
its run-time library needs to be linked into the main
R executable by building the latter with something like
MAIN_LD="clang++ -fsanitize=undefined"
Option float-divide-by-zero
is undesirable for use with R
which allow such divisions as part of IEC 60559
arithmetic, and in versions of clang
since June 2019 it is no
longer part of -fsanitize=undefined.
There are also groups of options implicit-integer-truncation
, integer
and nullability
For GCC see https://gcc.gnu.org/onlinedocs/gcc/Instrumentation-Options.html (or the manual for your version of GCC, installed or via https://gcc.gnu.org/onlinedocs/: look for 'Program Instrumentation Options') for the options supported by GCC: versions 13.x supported
-fsanitize=alignment,bool,bounds,builtin,enum,integer-divide-by-zero, nonnull-attribute,null,object-size,pointer-overflow,return, returns-nonnull-attribute,shift,signed-integer-overflow, unreachable,vla-bound,vptr
plus the more specific versions shift-base
and non-default options
where float-divide-by-zero
is not desirable for R uses and
is an extension of bounds
Other useful flags include
which causes the first report to be fatal (it always is for the
and return
suboptions). For more detailed
information on where the runtime error occurs, using
setenv UBSAN_OPTIONS 'print_stacktrace=1'
will include a traceback in the report. Beyond that, R can
be run under a debugger with a breakpoint set before the sanitizer
report is produced: for gdb
or lldb
you could use
break __ubsan_handle_float_cast_overflow break __ubsan_handle_float_cast_overflow_abort
or similar (there are handlers for each type of undefined behaviour).
There are also the compiler flags -fcatch-undefined-behavior
and -ftrapv, said to be more reliable in clang
For more details on the topic see https://blog.regehr.org/archives/213 and https://blog.llvm.org/2011/05/what-every-c-programmer-should-know.html (which has 3 parts).
It may or may not be possible to build R itself with
-fsanitize=undefined: problems have in the past been seen with
OpenMP-using code with gcc
but there has been success
with clang
up to version 16.. However, problems have been
seen with clang
17 and later, including missing entry points
and R builds hanging. What has succeeded is to use UBSAN just for
the package under test (and not in combination with ASAN). To do so,
check with an unaltered R, using a custom Makevars file
something like
CC = clang -fsanitize=undefined -fno-sanitize=float-divide-by-zero -fno-omit-frame-pointer CXX = clang++ -fsanitize=undefined -fno-sanitize=float-divide-by-zero -fno-omit-frame-pointer -frtti UBSAN_DIR = /path/to/LLVM18/lib/clang/18/lib/x86_64-unknown-linux-gnu SAN_LIBS = $(UBSAN_DIR)/libclang_rt.ubsan_standalone.a $(UBSAN_DIR)/libclang_rt.ubsan_standalone_cxx.a
which links the UBSAN libraries statically into the package-under-test's DSO. It is also possible to use the dynamic library via
SAN_LIBS = -L$(UBSAN_DIR) -Wl,-rpath,$(UBSAN_DIR) -lclang_rt.ubsan_standalone
provided UBSAN_DIR
is added to the runtime library path (as shown
or using LD_LIBRARY_PATH
). N.B.: The details, especially
the paths used, have changed several times recently.
Recent versions of clang
on 'x86_64' Linux have
'ThreadSanitizer' (https://github.com/google/sanitizers/wiki#threadsanitizer),
a 'data race detector for C/C++ programs', and 'MemorySanitizer'
for the detection of uninitialized memory. Both are based on and
provide similar functionality to tools in valgrind
has a 'Static Analyzer' which can be run on the source
files during compilation: see https://clang-analyzer.llvm.org/.
GCC 10 introduced a new flag -fanalyzer which does static analysis during compilation, currently for C code. It is regarded as experimental and it may slow down computation considerably when problems are found (and use many GB of resident memory). There is some overlap with problems detected by the Undefined Behaviour sanitizer, but some issues are only reported by this tool and as it is a static analysis, it does not rely on code paths being exercised.
(or the documentation for your version of gcc
if later) and
'Dr. Memory' from https://drmemory.org/ is a memory checker for
(currently) Windows, Linux and macOS with similar aims to
. It works with unmodified executables140
and detects memory access errors, uninitialized reads and memory leaks.
Most of the Fortran compilers used with R allow code to be compiled
with checking of array bounds: for example gfortran
has option
-fbounds-check. This will give an error when the upper or
lower bound is exceeded, e.g.
At line 97 of file .../src/appl/dqrdc2.f Fortran runtime error: Index '1' of dimension 1 of array 'x' above upper bound of 0
One does need to be aware that lazy programmers often specify Fortran
dimensions as 1
rather than *
or a real bound and these
will be reported (as may *
It is easy to arrange to use this check on just the code in your
package: add to ~/.R/Makevars something like (for
FFLAGS = -g -O2 -mtune=native -fbounds-check
when you run R CMD check
This may report errors with the way that Fortran character variables are passed, particularly when Fortran subroutines are called from C code and character lengths are not passed (see Fortran character strings).
Sooner or later programmers will be faced with the need to debug
compiled code loaded into R. This section is geared to platforms
using gdb
with code compiled by gcc
, but similar things
are possible with other debuggers such as lldb
(https://lldb.llvm.org/, used on macOS) and Sun's dbx
some debuggers have graphical front-ends available.
Consider first 'crashes', that is when R terminated unexpectedly with an illegal memory access (a 'segfault' or 'bus error'), illegal instruction or similar. Unix-alike versions of R use a signal handler which aims to give some basic information. For example
*** caught segfault *** address 0x20000028, cause 'memory not mapped' Traceback: 1: .identC(class1[[1]], class2) 2: possibleExtends(class(sloti), classi, ClassDef2 = getClassDef(classi, where = where)) 3: validObject(t(cu)) 4: stopifnot(validObject(cu <- as(tu, "dtCMatrix")), validObject(t(cu)), validObject(t(tu))) Possible actions: 1: abort (with core dump) 2: normal R exit 3: exit R without saving workspace 4: exit R saving workspace Selection: 3
Since the R process may be damaged, the only really safe options are the first or third. (Note that a core dump is only produced where enabled: a common default in a shell is to limit its size to 0, thereby disabling it.)
A fairly common cause of such crashes is a package which uses .C
or .Fortran
and writes beyond (at either end) one of the
arguments it is passed. There is a good way to detect this: using
options(CBoundsCheck = TRUE)
(which can be selected via
the environment variable R_C_BOUNDS_CHECK=yes)
changes the way
and .Fortran
work to check if the compiled code writes
in the 64 bytes at either end of an argument.
Another cause of a 'crash' is to overrun the C stack. R tries to track that in its own code, but it may happen in third-party compiled code. For modern POSIX-compliant OSes R can safely catch that and return to the top-level prompt, so one gets something like
> .C("aaa") Error: segfault from C stack overflow >
However, C stack overflows are fatal under Windows and normally defeat attempts at debugging on that platform. Further, the size of the stack is set when R is compiled on Windows, whereas on POSIX OSes it can be set in the shell from which R is launched.
If you have a crash which gives a core dump you can use something like
gdb /path/to/R/bin/exec/R core.12345
to examine the core dump. If core dumps are disabled or to catch errors that do not generate a dump one can run R directly under a debugger by for example
$ R -d gdb --vanilla ... gdb> run
at which point R will run normally, and hopefully the debugger will catch the error and return to its prompt. This can also be used to catch infinite loops or interrupt very long-running code. For a simple example
> for(i in 1:1e7) x <- rnorm(100) [hit Ctrl-C] Program received signal SIGINT, Interrupt. 0x00397682 in _int_free () from /lib/tls/libc.so.6 (gdb) where #0 0x00397682 in _int_free () from /lib/tls/libc.so.6 #1 0x00397eba in free () from /lib/tls/libc.so.6 #2 0xb7cf2551 in R_gc_internal (size_needed=313) at /users/ripley/R/svn/R-devel/src/main/memory.c:743 #3 0xb7cf3617 in Rf_allocVector (type=13, length=626) at /users/ripley/R/svn/R-devel/src/main/memory.c:1906 #4 0xb7c3f6d3 in PutRNGstate () at /users/ripley/R/svn/R-devel/src/main/RNG.c:351 #5 0xb7d6c0a5 in do_random2 (call=0x94bf7d4, op=0x92580e8, args=0x9698f98, rho=0x9698f28) at /users/ripley/R/svn/R-devel/src/main/random.c:183 ...
In many cases it is possible to attach a debugger to a running process: this is helpful if an alternative front-end is in use or to investigate a task that seems to be taking far too long. This is done by something like
gdb -p pid
where pid
is the id of the R executable or front-end
process and can be found from within a running R process by calling
or from a process monitor. This stops the process
so its state can be examined: use continue
to resume execution.
Some "tricks" worth knowing follow:
Under most compilation environments, compiled code dynamically loaded into R cannot have breakpoints set within it until it is loaded. To use a symbolic debugger on such dynamically loaded code under Unix-alikes use
or library
to load your
shared object.
Under Windows signals may not be able to be used, and if so the procedure is more complicated. See the rw-FAQ.
The key to inspecting R objects from compiled code is the function
PrintValue(SEXP s)
which uses the normal R printing
mechanisms to print the R object pointed to by s, or the safer
version R_PV(SEXP s)
which will only print 'objects'.
One way to make use of PrintValue
is to insert suitable calls
into the code to be debugged.
Another way is to call R_PV
from the symbolic debugger.
is hidden as Rf_PrintValue
.) For example,
from gdb
we can use
(gdb) p R_PV(ab)
using the object ab
from the convolution example, if we have
placed a suitable breakpoint in the convolution C code.
To examine an arbitrary R object we need to work a little harder. For example, let
R> DF <- data.frame(a = 1:3, b = 4:6)
By setting a breakpoint at do_get
and typing get("DF") at
the R prompt, one can find out the address in memory of DF
, for
Value returned is $1 = (SEXPREC *) 0x40583e1c (gdb) p *$1 $2 = { sxpinfo = {type = 19, obj = 1, named = 1, gp = 0, mark = 0, debug = 0, trace = 0, = 0}, attrib = 0x40583e80, u = { vecsxp = { length = 2, type = {c = 0x40634700 "0>X@D>X@0>X@", i = 0x40634700, f = 0x40634700, z = 0x40634700, s = 0x40634700}, truelength = 1075851272, }, primsxp = {offset = 2}, symsxp = {pname = 0x2, value = 0x40634700, internal = 0x40203008}, listsxp = {carval = 0x2, cdrval = 0x40634700, tagval = 0x40203008}, envsxp = {frame = 0x2, enclos = 0x40634700}, closxp = {formals = 0x2, body = 0x40634700, env = 0x40203008}, promsxp = {value = 0x2, expr = 0x40634700, env = 0x40203008} } }
(Debugger output reformatted for better legibility).
Using R_PV()
one can "inspect" the values of the various
elements of the SEXP
, for example,
(gdb) p R_PV($1->attrib) $names [1] "a" "b" $row.names [1] "1" "2" "3" $class [1] "data.frame" $3 = void
To find out where exactly the corresponding information is stored, one needs to go "deeper":
(gdb) set $a = $1->attrib (gdb) p $a->u.listsxp.tagval->u.symsxp.pname->u.vecsxp.type.c $4 = 0x405d40e8 "names" (gdb) p $a->u.listsxp.carval->u.vecsxp.type.s[1]->u.vecsxp.type.c $5 = 0x40634378 "b" (gdb) p $1->u.vecsxp.type.s[0]->u.vecsxp.type.i[0] $6 = 1 (gdb) p $1->u.vecsxp.type.s[1]->u.vecsxp.type.i[1] $7 = 5
Another alternative is the R_inspect
function which shows the
low-level structure of the objects recursively (addresses differ from
the above as this example is created on another machine):
(gdb) p R_inspect($1) @100954d18 19 VECSXP g0c2 [OBJ,NAM(2),ATT] (len=2, tl=0) @100954d50 13 INTSXP g0c2 [NAM(2)] (len=3, tl=0) 1,2,3 @100954d88 13 INTSXP g0c2 [NAM(2)] (len=3, tl=0) 4,5,6 ATTRIB: @102a70140 02 LISTSXP g0c0 [] TAG: @10083c478 01 SYMSXP g0c0 [MARK,NAM(2),gp=0x4000] "names" @100954dc0 16 STRSXP g0c2 [NAM(2)] (len=2, tl=0) @10099df28 09 CHARSXP g0c1 [MARK,gp=0x21] "a" @10095e518 09 CHARSXP g0c1 [MARK,gp=0x21] "b" TAG: @100859e60 01 SYMSXP g0c0 [MARK,NAM(2),gp=0x4000] "row.names" @102a6f868 13 INTSXP g0c1 [NAM(2)] (len=2, tl=1) -2147483648,-3 TAG: @10083c948 01 SYMSXP g0c0 [MARK,gp=0x4000] "class" @102a6f838 16 STRSXP g0c1 [NAM(2)] (len=1, tl=1) @1008c6d48 09 CHARSXP g0c2 [MARK,gp=0x21,ATT] "data.frame"
In general the representation of each object follows the format:
@<address> <type-nr> <type-name> <gc-info> [<flags>] ...
For a more fine-grained control over the depth of the recursion
and the output of vectors R_inspect3
takes additional two character()
parameters: maximum depth and the maximal number of elements that will
be printed for scalar vectors. The defaults in R_inspect
currently -1 (no limit) and 5 respectively.
To debug code in a package it is easiest to unpack it in a directory and install it with
R CMD INSTALL --dsym pkgname
as macOS does not store debugging symbols in the .so file. (It
is not necessary to have R built with debugging symbols, although
compiling the package should be done including -g in
Security measures may prevent running a CRAN binary
distribution of R under lldb
or attaching this as a
although both were possible on High Sierra and are again from R
4.2.0. This can also affect locally compiled builds, where attaching to
an interactive R session under Big Sur or Monterey worked in 2022
after giving administrator permission via a popup-up. (To debug
in what Apple deems a non-interactive session, e.g. logged in remotely,
see man DevToolsSecurity
Debugging a local build of R on macOS can raise additional hurdles as
environment variables such as DYLD_FALLBACK_LIBRARY_PATH
are not
usually passed through141 the lldb
process, resulting in messages
R -d lldb ... (lldb) run Process 16828 launched: '/path/to/bin/exec/R' (x86_64) dyld: Library not loaded: libR.dylib Referenced from: /path/to/bin/exec/R
A quick workaround is to symlink the dylibs under R_HOME/lib to
somewhere where they will be found such as the current working
directory. It would be possible to do as the distribution
does142 and
use install_name_tool
, but that would have to be done for all
the dylibs including those in packages.
It may be simplest to attach the debugger to a running process (see above). Specifically, run R and when it is at the prompt just before a command that is to be debugged, at a terminal
ps -ef | grep exec/R # identify the PID pid for the next command: it is the second item lldb -p pid (lldb) continue
and then return to the R console.
For non-interactive use, one may need lldb --batch
Where supported, link time optimization provides a comprehensive
way to check the consistency of calls between Fortran files or between C
and Fortran. Use this via R CMD INSTALL --use-LTO
that does not apply if there is a src/Makefile file or a Windows
To set up support on a Unix-alike, see Link-Time Optimization in R Installation and Administration. On Linux using GCC without building R with LTO support, it should suffice to set
LTO_OPT = -flto LTO_FC_OPT = -flto AR = gcc-ar NM = gcc-nm
in a personal (or site) Makevars file: See Customizing package compilation in R Installation and Administration,
For Windows, first edit file etc/${R_ARCH}/Makeconf to give
the value -flto
or do so in a personal/site
Makevars file; see also file
src/gnuwin32/README.compilation in the sources.
For example:
boot.f:61: warning: type of 'ddot' does not match original declaration [-Wlto-type-mismatch] y(j,i)=ddot(p,x(j,1),n,b(1,j,i),1) crq.f:1023: note: return value type mismatch
where the package author forgot to declare
double precision ddot external ddot
in boot.f. That package had its own copy of ddot
: to
detect misuse of the one in R's BLAS library would have needed R
configured with --enable-lto=check.
Further examples:
rkpk2.f:77:5: warning: type of 'dstup' does not match original declaration [-Wlto-type-mismatch] *info, wk) rkpk1.f:2565:5: note: type mismatch in parameter 14 subroutine dstup (s, lds, nobs, nnull, qraux, jpvt, y, q, ldqr, rkpk1.f:2565:5: note: 'dstup' was previously declared here
where the fourteenth argument dum
was missing in the call.
reg.f:78:33: warning: type of 'dqrdc' does not match original declaration [-Wlto-type-mismatch] call dqrdc (sr, nobs, nobs, nnull, wk, dum, dum, 0) dstup.f:20: note: 'dqrdc' was previously declared here call dqrdc (s, lds, nobs, nnull, qraux, jpvt, work, 1)
is a LINPACK routine from R, jpvt
is an integer
array and work
is a double precision one so dum
match both. (If --enable-lto=check had been used the
comparison would have been with the definition in R.)
For Fortran files all in the package, most inconsistencies can be detected by concatenating the Fortran files and compiling the result, sometimes with clearer diagnostics than provided by LTO. For our last two examples this gives
all.f:2966:72: *info, work1) 1 Warning: Missing actual argument for argument 'dum' at (1)
all.f:1663:72: *ipvtwk), wk(ikwk), wk(iwork1), wk(iwork2), info) 1 Warning: Type mismatch in argument 'jpvt' at (1); passed REAL(8) to INTEGER(4)
On a Unix-alike for a package with a src/Makefile file, LTO can be enabled by including suitable flags in that file, for example
and ensuring these are used for compilation, for example as part of
or FCFLAGS
. If R CMD
is used for compilation, add --use-LTO to its call.
On Windows for a package with a src/Makefile.ucrt or src/Makefile.win file which includes '"${R_HOME}/etc${R_ARCH}/Makeconf"', include
or to always use LTO however R was built,
LTO = -flto
and .Fortran
and dyn.unload
and .External
Access to operating system functions is via the R functions
and system2
The details will differ by platform (see the on-line help), and about
all that can safely be assumed is that the first argument will be a
string command
that will be passed for execution (not necessarily
by a shell) and the second argument to system
will be
which if true will collect the output of the command
into an R character vector.
On POSIX-compliant OSes these commands pass a command-line to a shell:
Windows is not POSIX-compliant and there is a separate function
to do so.
The function system.time
is available for timing. Timing on child processes is only available on
Unix-alikes, and may not be reliable there.
and .Fortran
¶These two functions provide an interface to compiled code that has been
linked into R, either at build time or via dyn.load
(see dyn.load
and dyn.unload
). They are primarily intended for
compiled C and Fortran code respectively, but the .C
function can
be used with other languages which can generate C interfaces, for
example C++ (see Interfacing C++ code).
The first argument to each function is a character string specifying the
symbol name as known143 to C or
Fortran, that is the function or subroutine name. (That the symbol is
loaded can be tested by, for example, is.loaded("cg")
. Use the
name you pass to .C
or .Fortran
rather than the translated
symbol name.)
There can be up to 65 further arguments giving R objects to be passed to compiled code. Normally these are copied before being passed in, and copied again to an R list object when the compiled code returns. If the arguments are given names, these are used as names for the components in the returned list object (but not passed to the compiled code).
The following table gives the mapping between the modes of R atomic vectors and the types of arguments to a C function or Fortran subroutine.
R storage mode C type Fortran type logical
int *
int *
double *
Rcomplex *
char **
unsigned char *
On all R platforms int
and INTEGER
are 32-bit. Code
ported from S-PLUS (which uses long *
for logical
) will not work on all 64-bit platforms (although it may
appear to work on some, including 'x86_64' Windows). Note also
that if your compiled code is a mixture of C functions and Fortran
subprograms the argument types must match as given in the table above.
C type Rcomplex
is a structure with double
and i
defined in the header file
R_ext/Complex.h.144 (On most platforms this is stored in a way compatible
with the C99 double complex
type: however, it may not be possible
to pass Rcomplex
to a C99 function expecting a double
argument. Nor need it be compatible with a C++ complex
type. Moreover, the compatibility can depend on the optimization level
set for the compiler.)
Only a single character string of fixed length can be passed to or from
Fortran (the length is not passed), and the success of this is
compiler-dependent: its use was formally deprecated in 2019. Other R
objects can be passed to .C
, but it is much better to use one of
the other interfaces.
It is possible to pass numeric vectors of storage mode double
C as float *
or to Fortran as REAL
by setting the
attribute Csingle
, most conveniently by using the R functions
, single
or mode
. This is intended only
to be used to aid interfacing existing C or Fortran code.
Logical values are sent as 0
), 1
) or INT_MIN = -2147483648
, but only if
is true), and the compiled code should return one of these
three values. (Non-zero values other than INT_MIN
are mapped to
.) Note that the use of int *
for Fortran logical is
not guaranteed to be portable (although people have gotten away with it
for many years): it is better to pass integers and convert to/from
Fortran logical in a Fortran wrapper.
Unless formal argument NAOK
is true, all the other arguments are
checked for missing values NA
and for the IEEE special
values NaN
, Inf
and -Inf
, and the presence of any
of these generates an error. If it is true, these values are passed
Argument PACKAGE
confines the search for the symbol name to a
specific shared object (or use "base"
for code compiled into
R). Its use is highly desirable, as there is no way to avoid two
package writers using the same symbol name, and such name clashes are
normally sufficient to cause R to crash. (If it is not present and
the call is from the body of a function defined in a package namespace,
the shared object loaded by the first (if any) useDynLib
directive will be used.)
Note that the compiled code should not return anything except through
its arguments: C functions should be of type void
and Fortran
subprograms should be subroutines.
To fix ideas, let us consider a very simple example which convolves two
finite sequences. (This is hard to do fast in interpreted R code, but
easy in C code.) We could do this using .C
void convolve(double *a, int *na, double *b, int *nb, double *ab) { int nab = *na + *nb - 1; for(int i = 0; i < nab; i++) ab[i] = 0.0; for(int i = 0; i < *na; i++) for(int j = 0; j < *nb; j++) ab[i + j] += a[i] * b[j]; }
called from R by
conv <- function(a, b) .C("convolve", as.double(a), as.integer(length(a)), as.double(b), as.integer(length(b)), ab = double(length(a) + length(b) - 1))$ab
Note that we take care to coerce all the arguments to the correct R
storage mode before calling .C
; mistakes in matching the types
can lead to wrong results or hard-to-catch errors.
Special care is needed in handling character
vector arguments in
C (or C++). On entry the contents of the elements are duplicated and
assigned to the elements of a char **
array, and on exit the
elements of the C array are copied to create new elements of a character
vector. This means that the contents of the character strings of the
char **
array can be changed, including to \0
to shorten
the string, but the strings cannot be lengthened. It is
possible145 to allocate a new string via
and replace an entry in the char **
array by the
new string. However, when character vectors are used other than in a
read-only way, the .Call
interface is much to be preferred.
Passing character strings to Fortran code needs even more care, is deprecated and should be avoided where possible. Only the first element of the character vector is passed in, as a fixed-length (255) character array. Up to 255 characters are passed back to a length-one character vector. How well this works (or even if it works at all) depends on the C and Fortran compilers on each platform (including on their options). Often what is being passed to Fortran is one of a small set of possible values (a factor in R terms) which could alternatively be passed as an integer code: similarly Fortran code that wants to generate diagnostic messages could pass an integer code to a C or R wrapper which would convert it to a character string.
It is possible to pass some R objects other than atomic vectors via
, but this is only supported for historical compatibility: use
the .Call
or .External
interfaces for such objects. Any
C/C++ code that includes Rinternals.h should be called via
or .External
is primarily intended for Fortran 77 code, and long
precedes any support for 'modern' Fortran. Nowadays implementations of
Fortran support the Fortran 2003 module iso_c_binding
, a better
way to interface modern Fortran code to R is to use .C
write a C interface using use iso_c_binding
and dyn.unload
¶Compiled code to be used with R is loaded as a shared object (Unix-alikes including macOS, see Creating shared objects for more information) or DLL (Windows).
The shared object/DLL is loaded by dyn.load
and unloaded by
. Unloading is not normally necessary and is not safe
in general, but it is needed to allow the DLL to be re-built on some
platforms, including Windows. Unloading a DLL and then re-loading a DLL
of the same name may not work: Solaris used the first version loaded. A
DLL that registers C finalizers, but fails to unregister them when
unloaded, may cause R to crash after unloading.
The first argument to both functions is a character string giving the path to the object. Programmers should not assume a specific file extension for the object/DLL (such as .so) but use a construction like
file.path(path1, path2, paste0("mylib", .Platform$dynlib.ext))
for platform independence. On Unix-alike systems the path supplied to
can be an absolute path, one relative to the current
directory or, if it starts with '~', relative to the user's home
Loading is most often done automatically based on the useDynLib()
declaration in the NAMESPACE file, but may be done
explicitly via a call to library.dynam
This has the form
library.dynam("libname", package, lib.loc)
where libname
is the object/DLL name with the extension
omitted. Note that the first argument, chname
, should
not be package
since this will not work if the package
is installed under another name.
Under some Unix-alike systems there is a choice of how the symbols are
resolved when the object is loaded, governed by the arguments
and now
. Only use these if really necessary: in
particular using now=FALSE
and then calling an unresolved symbol
will terminate R unceremoniously.
R provides a way of executing some code automatically when a object/DLL
is either loaded or unloaded. This can be used, for example, to
register native routines with R's dynamic symbol mechanism, initialize
some data in the native code, or initialize a third party library. On
loading a DLL, R will look for a routine within that DLL named
where lib is the name of the DLL file with
the extension removed. For example, in the command
library.dynam("mylib", package, lib.loc)
R looks for the symbol named R_init_mylib
. Similarly, when
unloading the object, R looks for a routine named
, e.g., R_unload_mylib
. In either case,
if the routine is present, R will invoke it and pass it a single
argument describing the DLL. This is a value of type DllInfo
which is defined in the Rdynload.h file in the R_ext
Note that there are some implicit restrictions on this mechanism as the
basename of the DLL needs to be both a valid file name and valid as part
of a C entry point (e.g. it cannot contain '.'): for portable
code it is best to confine DLL names to be ASCII alphanumeric
plus underscore. If entry point R_init_lib
is not found it
is also looked for with '.' replaced by '_'.
The following example shows templates for the initialization and
unload routines for the mylib
#include <R_ext/Rdynload.h> void R_init_mylib(DllInfo *info) { /* Register routines, allocate resources. */ } void R_unload_mylib(DllInfo *info) { /* Release resources. */ }
If a shared object/DLL is loaded more than once the most recent version
is used.146 More generally, if the same symbol name
appears in several shared objects, the most recently loaded occurrence
is used. The PACKAGE
argument and registration (see the next
section) provide good ways to avoid any ambiguity in which occurrence is
On Unix-alikes the paths used to resolve dynamically linked dependent
libraries are fixed (for security reasons) when the process is launched,
so dyn.load
will only look for such libraries in the locations
set by the R shell script (via etc/ldpaths) and in
the OS-specific defaults.
Windows allows more control (and less security) over where dependent
DLLs are looked for. On all versions this includes the PATH
environment variable, but with lowest priority: note that it does not
include the directory from which the DLL was loaded. It is possible to
add a single path with quite high priority via the DLLpath
argument to dyn.load
. This is (by default) used by
to include the package's libs/x64 directory (on
Intel) in the DLL search path.
By 'native' routine, we mean an entry point in compiled code.
In calls to .C
, .Call
, .Fortran
, R must locate the specified native routine by
looking in the appropriate shared object/DLL. By default, R uses the
operating-system-specific dynamic loader to lookup the symbol in
all147 loaded DLLs and the R executable
or libraries it is linked to. Alternatively, the author of the DLL can
explicitly register routines with R and use a single,
platform-independent mechanism for finding the routines in the DLL. One
can use this registration mechanism to provide additional information
about a routine, including the number and type of the arguments, and
also make it available to R programmers under a different name.
Registering routines has two main advantages: it provides a faster148 way to find the address of the entry point via tables stored in the DLL at compilation time, and it provides a run-time check that the entry point is called with the right number of arguments and, optionally, the right argument types.
To register routines with R, one calls the C routine
. This is typically done when the DLL is first
loaded within the initialization routine R_init_dll name
described in dyn.load
and dyn.unload
. R_registerRoutines
takes 5 arguments. The first is the DllInfo
object passed by
R to the initialization routine. This is where R stores the
information about the methods. The remaining 4 arguments are arrays
describing the routines for each of the 4 different interfaces:
, .Call
, .Fortran
and .External
. Each
argument is a NULL
-terminated array of the element types given in
the following table:
Currently, the R_ExternalMethodDef
type is the same as
type and contains fields for the name of the
routine by which it can be accessed in R, a pointer to the actual
native symbol (i.e., the routine itself), and the number of arguments
the routine expects to be passed from R. For example, if we had a
routine named myCall
defined as
SEXP myCall(SEXP a, SEXP b, SEXP c);
we would describe this as
static const R_CallMethodDef callMethods[] = { {"myCall", (DL_FUNC) &myCall, 3}, {NULL, NULL, 0} };
along with any other routines for the .Call
interface. For
routines with a variable number of arguments invoked via the
interface, one specifies -1
for the number of
arguments which tells R not to check the actual number passed.
Routines for use with the .C
and .Fortran
interfaces are
described with similar data structures, which have one optional
additional field for describing the type of each argument. If
specified, this field should be an array with the SEXP
describing the expected type of each argument of the routine.
(Technically, the elements of the types array are of type
which is just an unsigned integer.)
The R types and corresponding type identifiers are provided in the
following table:
Consider a C routine, myC
, declared as
void myC(double *x, int *n, char **names, int *status);
We would register it as
static R_NativePrimitiveArgType myC_type[] = { REALSXP, INTSXP, STRSXP, LGLSXP }; static const R_CMethodDef cMethods[] = { {"myC", (DL_FUNC) &myC, 4, myC_type}, {NULL, NULL, 0, NULL} };
If registering types, check carefully that the number of types matches
the number of arguments: as the type array (here myC_type
) is
passed as a pointer in C, the registration mechanism cannot check this
for you.
Note that .Fortran
entry points are mapped to lowercase, so
registration should use lowercase only.
Having created the arrays describing each routine, the last step is to
actually register them with R. We do this by calling
. For example, if we have the descriptions
above for the routines accessed by the .C
and .Call
we would use the following code:
void R_init_myLib(DllInfo *info) { R_registerRoutines(info, cMethods, callMethods, NULL, NULL); }
This routine will be invoked when R loads the shared object/DLL named
. The last two arguments in the call to
are for the routines accessed by
and .External
interfaces. In our example, these
are given as NULL
since we have no routines of these types.
When R unloads a shared object/DLL, its registrations are removed. There is no other facility for unregistering a symbol.
Examples of registering routines can be found in the different packages in the R source tree (e.g., stats and graphics). Also, there is a brief, high-level introduction in R News (volume 1/3, September 2001, pages 20–23, https://www.r-project.org/doc/Rnews/Rnews_2001-3.pdf).
Once routines are registered, they can be referred to as R objects if
this is arranged in the useDynLib
call in the package's
NAMESPACE file (see useDynLib
). So for example the
stats package has
# Refer to all C/Fortran routines by their name prefixed by C_ useDynLib(stats, .registration = TRUE, .fixes = "C_")
in its NAMESPACE file, and then ansari.test
's default
methods can contain
pansari <- function(q, m, n) .C(C_pansari, as.integer(length(q)), p = as.double(q), as.integer(m), as.integer(n))$p
This avoids the overhead of looking up an entry point each time it is
used, and ensures that the entry point in the package is the one used
(without a PACKAGE = "pkg"
routines are often of the form
void attribute_visible R_init_mypkg(DllInfo *dll) { R_registerRoutines(dll, CEntries, CallEntries, FortEntries, ExternalEntries); R_useDynamicSymbols(dll, FALSE); R_forceSymbols(dll, TRUE); ... }
The R_useDynamicSymbols
call says the DLL is not to be searched
for entry points specified by character strings so .C
etc calls
will only find registered symbols: the R_forceSymbols
call only
allows .C
etc calls which specify entry points by R objects
such as C_pansari
(and not by character strings). Each provides
some protection against accidentally finding your entry points when
people supply a character string without a package, and avoids slowing
down such searches. (For the visibility attribute see Controlling visibility.)
In more detail, if a package mypkg
contains entry points
and unreg
and the first is registered as a 0-argument
routine, we could use (from code in the package)
.Call("reg") .Call("unreg")
Without or with registration, these will both work. If
calls R_useDynamicSymbols(dll, FALSE)
, only
the first will work. If in addition to registration the
NAMESPACE file contains
useDynLib(mypkg, .registration = TRUE, .fixes = "C_")
then we can call .Call(C_reg)
. Finally, if R_init_mypkg
also calls R_forceSymbols(dll, TRUE)
, only .Call(C_reg)
will work (and not .Call("reg")
). This is usually what we want:
it ensures that all of our own .Call
calls go directly to the
intended code in our package and that no one else accidentally finds our
entry points. (Should someone need to call our code from outside the
package, for example for debugging, they can use
Sometimes registering native routines or using a PACKAGE
can make a large difference. The results can depend quite markedly on
the OS (and even if it is 32- or 64-bit), on the version of R and
what else is loaded into R at the time.
To fix ideas, first consider x86_64
OS 10.7 and R 2.15.2. A
simple .Call
function might be
foo <- function(x) .Call("foo", x)
with C code
#include <Rinternals.h> SEXP foo(SEXP x) { return x; }
If we compile with by R CMD SHLIB foo.c
, load the code by
and run foo(pi)
it took around 22
microseconds (us). Specifying the DLL by
foo2 <- function(x) .Call("foo", x, PACKAGE = "foo")
reduced the time to 1.7 us.
Now consider making these functions part of a package whose
NAMESPACE file uses useDynlib(foo)
. This immediately
reduces the running time as "foo"
will be preferentially looked
for foo.dll. Without specifying PACKAGE
it took about 5
us (it needs to fathom out the appropriate DLL each time it is invoked
but it does not need to search all DLLs), and with the PACKAGE
argument it is again about 1.7 us.
Next suppose the package has registered the native routine foo
Then foo()
still has to find the appropriate DLL but can get to
the entry point in the DLL faster, in about 4.2 us. And foo2()
now takes about 1 us. If we register the symbols in the
NAMESPACE file and use
foo3 <- function(x) .Call(C_foo, x)
then the address for the native routine is looked up just once when the
package is loaded, and foo3(pi)
takes about 0.8 us.
Versions using .C()
rather than .Call()
took about 0.2 us
These are all quite small differences, but C routines are not uncommonly invoked millions of times for run times of a few microseconds each, and those doing such things may wish to be aware of the differences.
On Linux and Solaris there is a smaller overhead in looking up symbols.
Symbol lookup on Windows used to be far slower, so R maintains a small cache. If the cache is currently empty enough that the symbol can be stored in the cache then the performance is similar to Linux and Solaris: if not it may be slower. R's own code always uses registered symbols and so these never contribute to the cache: however many other packages do rely on symbol lookup.
In more recent versions of R all the standard packages register
native symbols and do not allow symbol search, so in a new session
can only look in foo.so and may be as fast as
. This will no longer apply when many contributed packages
are loaded, and generally those last loaded are searched first. For
example, consider R 3.3.2 on x86_64 Linux. In an empty R session,
both foo()
and foo2()
took about 0.75 us; however after
packages igraph and spatstat had been loaded (which
loaded another 12 DLLs), foo()
took 3.6 us but foo2()
still took about 0.80 us. Using registration in a package reduced this
to 0.55 us and foo3()
took 0.40 us, times which were unchanged
when further packages were loaded.
The splines package was converted to use symbol registration in 2001, but we can use it as an example149 of what needs to be done for a small package.
nm -g /path/to/splines.so | grep " T " 0000000000002670 T _spline_basis 0000000000001ec0 T _spline_value
This indicates that there are two relevant entry points. (They may or
may not have a leading underscore, as here. Fortran entry points will
have a trailing underscore.) Check in the R code that they are
called by the package and how: in this case they are used by
Alternatively, examine the package's R code for all .C
, .Call
and .External
extern "C"
#include <stdlib.h> // for NULL #include <R_ext/Rdynload.h> #define CALLDEF(name, n) {#name, (DL_FUNC) &name, n} static const R_CallMethodDef R_CallDef[] = { CALLDEF(spline_basis, ?), CALLDEF(spline_value, ?), {NULL, NULL, 0} }; void R_init_splines(DllInfo *dll) { R_registerRoutines(dll, NULL, R_CallDef, NULL, NULL); }
and then replace the ?
in the skeleton with the actual numbers of
arguments. You will need to add declarations (also known as
'prototypes') of the functions unless appending to the only C source
file. Some packages will already have these in a header file, or you
could create one and include it in init.c, for example
splines.h containing
#include <Rinternals.h> // for SEXP extern SEXP spline_basis(SEXP knots, SEXP order, SEXP xvals, SEXP derivs); extern SEXP spline_value(SEXP knots, SEXP coeff, SEXP order, SEXP x, SEXP deriv);
Tools are available to extract declarations, at least for C and C++
code: see the help file for
in package
tools. Here we could have used
cproto -I/path/to/R/include -e splines.c
For examples of registering other types of calls, see packages
graphics and stats. In particular, when registering entry
points for .Fortran
one needs declarations as if called from C,
such as
#include <R_ext/RS.h> void F77_NAME(supsmu)(int *n, double *x, double *y, double *w, int *iper, double *span, double *alpha, double *smo, double *sc, double *edf);
8.4, 9.2 and later can help generate such prototypes
with its flag -fc-prototypes-external (although one will need
to replace the hard-coded trailing underscore with the F77_NAME
One can get away with inaccurate argument lists in the declarations: it
is easy to specify the arguments for .Call
(all SEXP
) and
(one SEXP
) and as the arguments for .C
and .Fortran
are all pointers, specifying them as void *
suffices. (For most platforms one can omit all the arguments, although
link-time optimization will warn, as will compilers set up to warn on
strict prototypes – and C23 will require correct arguments.)
Using -fc-prototypes-external will give a prototype using
int_least32_t *lgl
for Fortran LOGICAL LGL
, but this is
not portable and traditionally it has been assumed that the C/C++
equivalent was int *lgl
. If adding a declaration just to
register a .Fortran
call, the most portable version is void
etc to use the
symbols you chose to register by editing src/init.c to contain
void R_init_splines(DllInfo *dll) { R_registerRoutines(dll, NULL, R_CallDef, NULL, NULL); R_useDynamicSymbols(dll, FALSE); }
A skeleton for the steps so far can be made using
in package
tools. This will optionally create declarations based on the
usage in the R code.
The remaining steps are optional but recommended.
useDynLib(splines, .registration = TRUE, .fixes = "C_")
temp <- .Call("spline_basis", knots, ord, x, derivs, PACKAGE = "splines") y[accept] <- .Call("spline_value", knots, coeff, ord, x[accept], deriv, PACKAGE = "splines") y = .Call("spline_value", knots, coef(object), ord, x, deriv, PACKAGE = "splines")
temp <- .Call(C_spline_basis, knots, ord, x, derivs) y[accept] <- .Call(C_spline_value, knots, coeff, ord, x[accept], deriv) y = .Call(C_spline_value, knots, coef(object), ord, x, deriv)
Check that there is no exportPattern
directive which
unintentionally exports the newly created R objects.
to use the R symbols by editing
src/init.c to contain
void R_init_splines(DllInfo *dll) { R_registerRoutines(dll, NULL, R_CallDef, NULL, NULL); R_useDynamicSymbols(dll, FALSE); R_forceSymbols(dll, TRUE); }
nm -g /path/to/splines.so | grep " T " 0000000000002e00 T _R_init_splines 00000000000025e0 T _spline_basis 0000000000001e20 T _spline_value
If there were any entry points not intended to be used by the package we
should try to avoid exporting them, for example by making them
. Now that the two relevant entry points are only accessed
via the registration table, we can hide them. There are two ways
to do so on some Unix-alikes. We can hide individual entry points
#include <R_ext/Visibility.h> SEXP attribute_hidden spline_basis(SEXP knots, SEXP order, SEXP xvals, SEXP derivs) ... SEXP attribute_hidden spline_value(SEXP knots, SEXP coeff, SEXP order, SEXP x, SEXP deriv) ...
Alternatively, we can change the default visibility for all C symbols by including
in src/Makevars, and then we need to allow registration by
declaring R_init_splines
to be visible:
#include <R_ext/Visibility.h> void attribute_visible R_init_splines(DllInfo *dll) ...
See Controlling visibility for more details, including using Fortran code and ways to restrict visibility on Windows.
#include <stdlib.h> #include <R_ext/Rdynload.h> #include <R_ext/Visibility.h> // optional #include "splines.h" #define CALLDEF(name, n) {#name, (DL_FUNC) &name, n} static const R_CallMethodDef R_CallDef[] = { CALLDEF(spline_basis, 4), CALLDEF(spline_value, 5), {NULL, NULL, 0} }; void attribute_visible // optional R_init_splines(DllInfo *dll) { R_registerRoutines(dll, NULL, R_CallDef, NULL, NULL); R_useDynamicSymbols(dll, FALSE); R_forceSymbols(dll, TRUE); }
In addition to registering C routines to be called by R, it can at times be useful for one package to make some of its C routines available to be called by C code in another package. The interface consists of two routines declared in header R_ext/Rdynload.h as
void R_RegisterCCallable(const char *package, const char *name, DL_FUNC fptr); DL_FUNC R_GetCCallable(const char *package, const char *name);
A package packA that wants to make a C routine myCfun
available to C code in other packages would include the call
R_RegisterCCallable("packA", "myCfun", myCfun);
in its initialization function R_init_packA
. A package
packB that wants to use this routine would retrieve the function
pointer with a call of the form
p_myCfun = R_GetCCallable("packA", "myCfun");
As the type DL_FUNC
is only appropriate for functions with no
arguments, other users will need to cast to an appropriate type. For
typedef SEXP (*na_omit_xts_func) (SEXP x); ... na_omit_xts_func fun = (na_omit_xts_func) R_GetCCallable("xts", "na_omit_xts"); return fun(x);
The author of packB is responsible for ensuring that
has an appropriate declaration. In the future R may
provide some automated tools to simplify exporting larger numbers of
A package that wishes to make use of header files in other packages needs to declare them as a comma-separated list in the field 'LinkingTo' in the DESCRIPTION file. This then arranges for the include directories in the installed linked-to packages to be added to the include paths for C and C++ code.
It must specify150 'Imports' or 'Depends' of those packages, for they have to be loaded151 prior to this one (so the path to their compiled code has been registered).
CRAN examples of the use of this mechanism include coxme linking to bdsmatrix and xts linking to zoo.
NB: this mechanism is fragile, as changes to the interface provided by packA have to be recognised by packB. The consequences of not doing so have included serious corruption to the memory pool of the R session. Either packB has to depend on the exact version of packA or there needs to be a mechanism for packB to test at runtime the version of packA it is linked to matches that it was compiled against.
Suppose we have the following hypothetical C++ library, consisting of
the two files X.h and X.cpp, and implementing the two
classes X
and Y
which we want to use in R.
// X.h class X { public: X (); ~X (); }; class Y { public: Y (); ~Y (); };
// X.cpp #include <R.h> #include "X.h" static Y y; X::X() { REprintf("constructor X\n"); } X::~X() { REprintf("destructor X\n"); } Y::Y() { REprintf("constructor Y\n"); } Y::~Y() { REprintf("destructor Y\n"); }
To use with R, the only thing we have to do is writing a wrapper function and ensuring that the function is enclosed in
extern "C" { }
For example,
// X_main.cpp: #include "X.h" extern "C" { void X_main () { X x; } } // extern "C"
Compiling and linking should be done with the C++ compiler-linker
(rather than the C compiler-linker or the linker itself); otherwise, the
C++ initialization code (and hence the constructor of the static
variable Y
) are not called. On a properly configured system, one
can simply use
R CMD SHLIB X.cpp X_main.cpp
to create the shared object, typically X.so (the file name extension may be different on your platform). Now starting R yields
R version 2.14.1 Patched (2012-01-16 r58124) Copyright (C) 2012 The R Foundation for Statistical Computing ... Type "q()" to quit R.
R> dyn.load(paste("X", .Platform$dynlib.ext, sep = "")) constructor Y R> .C("X_main") constructor X destructor X list() R> q() Save workspace image? [y/n/c]: y destructor Y
The R for Windows FAQ (rw-FAQ) contains details of how to compile this example under Windows.
Earlier versions of this example used C++ iostreams: this is best avoided. There is no guarantee that the output will appear in the R console, and indeed it will not on the R for Windows console. Use R code or the C entry points (see Printing) for all I/O if at all possible. Examples have been seen where merely loading a DLL that contained calls to C++ I/O upset R's own C I/O (for example by resetting buffers on open files).
Most R header files can be included within C++ programs but they
should not be included within an extern "C"
block (as
they include system headers152).
Quite a lot of external C++ software is header-only (e.g. most of the Boost 'libraries' including all those supplied by package BH, and most of Armadillo as supplied by package RcppArmadillo) and so is compiled when an R package which uses it is installed. This causes few problems.
A small number of external libraries used in R packages have a C++ interface to a library of compiled code, e.g. packages sf and rjags. This raises many more problems! The C++ interface uses name-mangling and the ABI153 may depend on the compiler, version and even C++ defines154, so requires the package C++ code to be compiled in exactly the same way as the library (and what that was is often undocumented).
Even fewer external libraries use C++ internally but present a C interface, such as rgeos. These require the C++ runtime library to be linked into the package's shared object/DLL, and this is best done by including a dummy C++ file in the package sources.
There is a trend to link to the C++ interfaces offered by C software such as hdf5, pcre and ImageMagick. Their C interfaces are much preferred for portability (and can be used from C++ code). Also, the C++ interfaces are often optional in the software build or packaged separately and so users installing from package sources are less likely to already have them installed.
We have already warned against the use of C++ iostreams not least
because output is not guaranteed to appear on the R console, and this
warning applies equally to Fortran output to units *
and 6
. See Printing from Fortran, which describes workarounds.
In the past most Fortran compilers implemented I/O on top of the C I/O
system and so the two interworked successfully. This was true of
, but it is less true of gfortran
as used in
4 and later. In particular, any package that makes use of
Fortran I/O will when compiled on Windows interfere with C I/O: when the
Fortran I/O support code is initialized (typically when the package is
loaded) the C stdout
and stderr
are switched to LF line
endings. (Function init
in file
src/modules/lapack/init_win.c shows how to mitigate this. In a
package this would look something like
#ifdef _WIN32 # include <fcntl.h> #endif void R_init_mypkgname(DllInfo *dll) { // Native symbol registration calls #ifdef _WIN32 // gfortran I/O initialization sets these to _O_BINARY setmode(1, _O_TEXT); /* stdout */ setmode(2, _O_TEXT); /* stderr */ #endif }
in the file used for native symbol registration.)
It is not in general possible to link a DLL in package packA to a
DLL provided by package packB (for the security reasons mentioned
in dyn.load
and dyn.unload
, and also because some platforms
distinguish between shared objects and dynamic libraries), but it is on
Note that there can be tricky versioning issues here, as package packB could be re-installed after package packA — it is desirable that the API provided by package packB remains backwards-compatible.
Shipping a static library in package packB for other packages to link to avoids most of the difficulties.
It is possible to link a shared object in package packA to a library provided by package packB under limited circumstances on a Unix-alike OS. There are severe portability issues, so this is not recommended for a distributed package.
This is easiest if packB provides a static library
packB/lib/libpackB.a. (Note using directory lib rather
than libs is conventional, and architecture-specific
sub-directories may be needed and are assumed in the sample code
below. The code in the static library will need to be compiled with
flags on platforms where it matters.) Then as the code from
package packB is incorporated when package packA is
installed, we only need to find the static library at install time for
package packA. The only issue is to find package packB, and
for that we can ask R by something like (long lines broken for
display here)
PKGB_PATH='echo 'library(packB); cat(system.file("lib", package="packB", mustWork=TRUE))' \ | "${R_HOME}/bin/R" --vanilla --no-echo` PKG_LIBS="$(PKGB_PATH)$(R_ARCH)/libpackB.a"
For a dynamic library packB/lib/libpackB.so (packB/lib/libpackB.dylib on macOS: note that you cannot link to a shared object, .so, on that platform) we could use
PKGB_PATH='echo 'library(packB); cat(system.file("lib", package="packB", mustWork=TRUE))' \ | "${R_HOME}/bin/R" --vanilla --no-echo` PKG_LIBS=-L"$(PKGB_PATH)$(R_ARCH)" -lpackB
This will work for installation, but very likely not when package
is loaded, as the path to package packB's lib
directory is not in the ld.so
155 search path. You can arrange to
put it there before R is launched by setting (on some
platforms) LD_RUN_PATH
or LD_LIBRARY_PATH
or adding to the
cache (see man ldconfig
). On platforms that
support it, the path to the directory containing the dynamic library can
be hardcoded at install time (which assumes that the location of package
packB will not be changed nor the package updated to a changed
API). On systems with the gcc
or clang
and the
GNU linker (e.g. Linux) and some others this can be done by
PKGB_PATH='echo 'library(packB); cat(system.file("lib", package="packB", mustWork=TRUE)))' \ | "${R_HOME}/bin/R" --vanilla --no-echo` PKG_LIBS=-L"$(PKGB_PATH)$(R_ARCH)" -Wl,-rpath,"$(PKGB_PATH)$(R_ARCH)" -lpackB
Some other systems (e.g. Solaris with its native linker) use -Rdir rather than -rpath,dir (and this is accepted by the compiler as well as the linker).
It may be possible to figure out what is required semi-automatically
from the result of R CMD libtool --config
(look for
Making headers provided by package packB available to the code to
be compiled in package packA can be done by the LinkingTo
mechanism (see Registering native routines).
Suppose package packA wants to make use of compiled code provided by packB in DLL packB/libs/exB.dll, possibly the package's DLL packB/libs/packB.dll. (This can be extended to linking to more than one package in a similar way.) There are three issues to be addressed:
This is done by the LinkingTo
mechanism (see Registering native routines).
to link to packB/libs/exB.dll.
This needs an entry in Makevars.win or Makevars.ucrt of the form
PKG_LIBS= -L<something> -lexB
and one possibility is that <something>
is the path to the
installed pkgB/libs directory. To find that we need to ask R
where it is by something like
PKGB_PATH='echo 'library(packB); cat(system.file("libs", package="packB", mustWork=TRUE))' \ | rterm --vanilla --no-echo` PKG_LIBS= -L"$(PKGB_PATH)$(R_ARCH)" -lexB
Another possibility is to use an import library, shipping with package packA an exports file exB.def. Then Makevars.win (or Makevars.ucrt) could contain
PKG_LIBS= -L. -lexB all: $(SHLIB) before before: libexB.dll.a libexB.dll.a: exB.def
and then installing package packA will make and use the import library for exB.dll. (One way to prepare the exports file is to use pexports.exe.)
If exB.dll
was used by package packB (because it is in fact
packB.dll or packB.dll depends on it) and packB has
been loaded before packA, then nothing more needs to be done as
exB.dll will already be loaded into the R executable. (This
is the most common scenario.)
More generally, we can use the DLLpath
argument to
to ensure that exB.dll
is found, for example
by setting
library.dynam("packA", pkg, lib, DLLpath = system.file("libs", package="packB"))
Note that DLLpath
can only set one path, and so for linking to
two or more packages you would need to resort to setting environment
variable PATH
Using C code to speed up the execution of an R function is often very
fruitful. Traditionally this has been done via the .C
function in R. However, if a user wants to write C code using
internal R data structures, then that can be done using the
and .External
functions. The syntax for the calling
function in R in each case is similar to that of .C
, but the
two functions have different C interfaces. Generally the .Call
interface is simpler to use, but .External
is a little more
A call to .Call
is very similar to .C
, for example
.Call("convolve2", a, b)
The first argument should be a character string giving a C symbol name of code that has already been loaded into R. Up to 65 R objects can passed as arguments. The C side of the interface is
#include <R.h> #include <Rinternals.h> SEXP convolve2(SEXP a, SEXP b) ...
A call to .External
is almost identical
.External("convolveE", a, b)
but the C side of the interface is different, having only one argument
#include <R.h> #include <Rinternals.h> SEXP convolveE(SEXP args) ...
Here args
is a LISTSXP
, a Lisp-style pairlist from which
the arguments can be extracted.
In each case the R objects are available for manipulation via
a set of functions and macros defined in the header file
Rinternals.h or some S-compatibility macros156 See
Interface functions .Call
and .External
for details on
and .External
Before you decide to use .Call
or .External
, you should
look at other alternatives. First, consider working in interpreted R
code; if this is fast enough, this is normally the best option. You
should also see if using .C
is enough. If the task to be
performed in C is simple enough involving only atomic vectors and
requiring no call to R, .C
suffices. A great deal of useful
code was written using just .C
before .Call
were available. These interfaces allow much more
control, but they also impose much greater responsibilities so need to
be used with care. Neither .Call
nor .External
copy their
arguments: you should treat arguments you receive through these
interfaces as read-only.
To handle R objects from within C code we use the macros and functions that have been used to implement the core parts of R. A public157 subset of these is defined in the header file Rinternals.h in the directory R_INCLUDE_DIR (default R_HOME/include) that should be available on any R installation.
A substantial amount of R, including the standard packages, is implemented using the functions and macros described here, so the R source code provides a rich source of examples and "how to do it": do make use of the source code for inspirational examples.
It is necessary to know something about how R objects are handled in
C code. All the R objects you will deal with will be handled with
the type SEXP158, which is a
pointer to a structure with typedef SEXPREC
. Think of this
structure as a variant type that can handle all the usual types
of R objects, that is vectors of various modes, functions,
environments, language objects and so on. The details are given later
in this section and in
R Internal Structures in R Internals,
but for most
purposes the programmer does not need to know them. Think rather of a
model such as that used by Visual Basic, in which R objects are
handed around in C code (as they are in interpreted R code) as the
variant type, and the appropriate part is extracted for, for example,
numerical calculations, only when it is needed. As in interpreted R
code, much use is made of coercion to force the variant object to the
right type.
We need to know a little about the way R handles memory allocation. The memory allocated for R objects is not freed by the user; instead, the memory is from time to time garbage collected. That is, some or all of the allocated memory not being used is freed or marked as re-usable.
The R object types are represented by a C structure defined by a
typedef SEXPREC
in Rinternals.h. It contains several
things among which are pointers to data blocks and to other
s. A SEXP
is simply a pointer to a SEXPREC
If you create an R object in your C code, you must tell R that you
are using the object by using the PROTECT
macro on a pointer to
the object. This tells R that the object is in use so it is not
destroyed during garbage collection. Notice that it is the object which
is protected, not the pointer variable. It is a common mistake to
believe that if you invoked PROTECT(p)
at some point then
p is protected from then on, but that is not true once a new
object is assigned to p.
Protecting an R object automatically protects all the R objects
pointed to in the corresponding SEXPREC
, for example all elements
of a protected list are automatically protected.
The programmer is solely responsible for housekeeping the calls to
. There is a corresponding macro UNPROTECT
takes as argument an int
giving the number of objects to
unprotect when they are no longer needed. The protection mechanism is
stack-based, so UNPROTECT(n)
unprotects the last n
objects which were protected. The calls to PROTECT
must balance when the user's code returns and should
balance in all functions. R will warn about
"stack imbalance in .Call"
(or .External
) if the
housekeeping is wrong.
Here is a small example of creating an R numeric vector in C code:
#include <R.h> #include <Rinternals.h> SEXP ab; .... ab = PROTECT(allocVector(REALSXP, 2)); REAL(ab)[0] = 123.45; REAL(ab)[1] = 67.89; UNPROTECT(1);
Now, the reader may ask how the R object could possibly get removed
during those manipulations, as it is just our C code that is running.
As it happens, we can do without the protection in this example, but in
general we do not know (nor want to know) what is hiding behind the R
macros and functions we use, and any of them might cause memory to be
allocated, hence garbage collection and hence our object ab
to be
removed. It is usually wise to err on the side of caution and assume
that any of the R macros and functions might remove the object.
In some cases it is necessary to keep better track of whether protection
is really needed. Be particularly aware of situations where a large
number of objects are generated. The pointer protection stack has a
fixed size (default 10,000) and can become full. It is not a good idea
then to just PROTECT
everything in sight and UNPROTECT
several thousand objects at the end. It will almost invariably be
possible to either assign the objects as part of another object (which
automatically protects them) or unprotect them immediately after use.
There is a less-used macro UNPROTECT_PTR(s)
that unprotects the
object pointed to by the SEXP
s, even if it is not the top item
on the pointer protection stack. This macro was introduced for use in the
parser, where the code interfacing with the R heap is generated and the
generator cannot be configured to insert proper calls to PROTECT
. However, UNPROTECT_PTR
is dangerous to use in
combination with UNPROTECT
when the same object has been protected
multiple times. It has been superseded by multi-set based functions
and R_ReleaseFromMSet
, which protect objects
in a multi-set created by R_NewPreciousMSet
and typically itself
protected using PROTECT
. These functions should not be needed
outside parsers.
Sometimes an object is changed (for example duplicated, coerced or
grown) yet the current value needs to be protected. For these cases
saves an index of the protection location that
can be used to replace the protected value using REPROTECT
For example (from the internal code for optim
PROTECT_INDEX ipx; .... PROTECT_WITH_INDEX(s = eval(OS->R_fcall, OS->R_env), &ipx); REPROTECT(s = coerceVector(s, REALSXP), ipx);
Note that it is dangerous to mix UNPROTECT_PTR
also with
, as the former changes the protection
locations of objects that were protected after the one being
There is another way to avoid the effects of garbage collection: a call
to R_PreserveObject
adds an object to an internal list of objects
not to be collects, and a subsequent call to R_ReleaseObject
removes it from that list. This provides a way for objects which are
not returned as part of R objects to be protected across calls to
compiled code: on the other hand it becomes the user's responsibility to
release them when they are no longer needed (and this often requires the
use of a finalizer). It is less efficient than the normal protection
mechanism, and should be used sparingly.
For functions from packages as well as R to safely co-operate in protecting objects, certain rules have to be followed:
and UNPROTECT
should balance in each function. A function may only call UNPROTECT
on objects it has itself protected. Note that the pointer
protection stack balance is restored automatically on non-local transfer of
control (See Condition handling and cleanup code.), as if a call to
was invoked with the right argument.
and UNPROTECT
It is always safe and recommended to follow those rules. In fact, several
R functions and macros protect their own arguments and some functions do
not allocate or do not allocate when used in a certain way, but that is
subject to change, so relying on that may be fragile. PROTECT
can be safely called with unprotected arguments
and UNPROTECT
does not allocate.
For many purposes it is sufficient to allocate R objects and
manipulate those. There are quite a few allocXxx
defined in Rinternals.h—you may want to explore them.
One that is commonly used is allocVector
, the C-level equivalent
of R-level vector()
and its wrappers such as integer()
and character()
. One distinction is that whereas the R
functions always initialize the elements of the vector,
only does so for lists, expressions and character
vectors (the cases where the elements are themselves R objects).
If storage is required for C objects during the calculations this is
best allocating by calling R_alloc
; see Memory allocation.
All of these memory allocation routines do their own error-checking, so
the programmer may assume that they will raise an error and not return
if the memory cannot be allocated.
Users of the Rinternals.h macros will need to know how the R types are known internally. The different R data types are represented in C by SEXPTYPE. Some of these are familiar from R and some are internal data types. The usual R object modes are given in the table.
SEXPTYPE R equivalent REALSXP
numeric with storage mode double
integer CPLXSXP
complex LGLSXP
logical STRSXP
character VECSXP
list (generic vector) LISTSXP
pairlist DOTSXP
a '…' object NILSXP
name/symbol CLOSXP
function or function closure ENVSXP
Among the important internal SEXPTYPE
s are LANGSXP
, etc. (N.B.: although it is
possible to return objects of internal types, it is unsafe to do so as
assumptions are made about how they are handled which may be violated at
user-level evaluation.) More details are given in
R Internal Structures in R Internals.
Unless you are very sure about the type of the arguments, the code
should check the data types. Sometimes it may also be necessary to
check data types of objects created by evaluating an R expression in
the C code. You can use functions like isReal
, isInteger
and isString
to do type checking.
Other such functions declared in the header file Rinternals.h
include isNull
, isSymbol
, isLogical
, isExpression
, and isEnvironment
All of these take a SEXP
as argument and return 1 or 0 to
indicate TRUE or FALSE.
What happens if the SEXP
is not of the correct type? Sometimes
you have no other option except to generate an error. You can use the
function Rf_error
for this. It is usually better to coerce the
object to the correct type. For example, if you find that an
is of the type INTEGER
, but you need a REAL
object, you can change the type by using
newSexp = PROTECT(coerceVector(oldSexp, REALSXP));
Protection is needed as a new object is created; the object
formerly pointed to by the SEXP
is still protected but now
All the coercion functions do their own error-checking, and generate
s with a warning or stop with an error as appropriate.
Note that these coercion functions are not the same as calling
(and so on) in R code, as they do not dispatch on
the class of the object. Thus it is normally preferable to do the
coercion in the calling R code.
So far we have only seen how to create and coerce R objects from C code, and how to extract the numeric data from numeric R vectors. These can suffice to take us a long way in interfacing R objects to numerical algorithms, but we may need to know a little more to create useful return objects.
Many R objects have attributes: some of the most useful are classes
and the dim
and dimnames
that mark objects as matrices or
arrays. It can also be helpful to work with the names
of vectors.
To illustrate this, let us write code to take the outer product of two
vectors (which outer
and %o%
already do). As usual the
R code is simple
out <- function(x, y) { storage.mode(x) <- storage.mode(y) <- "double" .Call("out", x, y) }
where we expect x
and y
to be numeric vectors (possibly
integer), possibly with names. This time we do the coercion in the
calling R code.
C code to do the computations is
#include <R.h> #include <Rinternals.h> SEXP out(SEXP x, SEXP y) { int nx = length(x), ny = length(y); SEXP ans = PROTECT(allocMatrix(REALSXP, nx, ny)); double *rx = REAL(x), *ry = REAL(y), *rans = REAL(ans); for(int i = 0; i < nx; i++) { double tmp = rx[i]; for(int j = 0; j < ny; j++) rans[i + nx*j] = tmp * ry[j]; } UNPROTECT(1); return ans; }
Note the way REAL
is used: as it is a function call it can be
considerably faster to store the result and index that.
However, we would like to set the dimnames
of the result. We can use
#include <R.h> #include <Rinternals.h>
SEXP out(SEXP x, SEXP y) { int nx = length(x), ny = length(y); SEXP ans = PROTECT(allocMatrix(REALSXP, nx, ny)); double *rx = REAL(x), *ry = REAL(y), *rans = REAL(ans); for(int i = 0; i < nx; i++) { double tmp = rx[i]; for(int j = 0; j < ny; j++) rans[i + nx*j] = tmp * ry[j]; } SEXP dimnames = PROTECT(allocVector(VECSXP, 2)); SET_VECTOR_ELT(dimnames, 0, getAttrib(x, R_NamesSymbol)); SET_VECTOR_ELT(dimnames, 1, getAttrib(y, R_NamesSymbol)); setAttrib(ans, R_DimNamesSymbol, dimnames);
UNPROTECT(2); return ans; }
This example introduces several new features. The getAttrib
functions get and set individual attributes. Their second argument is a
defining the name in the symbol table of the attribute we
want; these and many such symbols are defined in the header file
There are shortcuts here too: the functions namesgets
and dimnamesgets
are the internal versions of the
default methods of names<-
, dim<-
and dimnames<-
(for vectors and arrays), and there are functions such as
, GetRowNames
, GetMatrixDimnames
What happens if we want to add an attribute that is not pre-defined? We
need to add a symbol for it via a call to
. Suppose for illustration we wanted to add an attribute
with value 3.0
. We could use
SEXP version; version = PROTECT(allocVector(REALSXP, 1)); REAL(version)[0] = 3.0; setAttrib(ans, install("version"), version); UNPROTECT(1);
Using install
when it is not needed is harmless and provides a
simple way to retrieve the symbol from the symbol table if it is already
installed. However, the lookup takes a non-trivial amount of time, so
consider code such as
static SEXP VerSymbol = NULL; ... if (VerSymbol == NULL) VerSymbol = install("version");
if it is to be done frequently.
This example can be simplified by another convenience function:
SEXP version = PROTECT(ScalarReal(3.0)); setAttrib(ans, install("version"), version); UNPROTECT(1);
In R the class is just the attribute named "class"
so it can
be handled as such, but there is a shortcut classgets
. Suppose
we want to give the return value in our example the class "mat"
We can use
#include <R.h> #include <Rinternals.h> .... SEXP ans, dim, dimnames, class; .... class = PROTECT(allocVector(STRSXP, 1)); SET_STRING_ELT(class, 0, mkChar("mat")); classgets(ans, class); UNPROTECT(4); return ans; }
As the value is a character vector, we have to know how to create that
from a C character array, which we do using the function
Some care is needed with lists, as R moved early on from using
LISP-like lists (now called "pairlists") to S-like generic vectors.
As a result, the appropriate test for an object of mode list
, and we need allocVector(VECSXP, n
) and
not allocList(n)
List elements can be retrieved or set by direct access to the elements of the generic vector. Suppose we have a list object
a <- list(f = 1, g = 2, h = 3)
Then we can access a$g
as a[[2]]
double g; .... g = REAL(VECTOR_ELT(a, 1))[0];
This can rapidly become tedious, and the following function (based on one in package stats) is very useful:
/* get the list element named str, or return NULL */ SEXP getListElement(SEXP list, const char *str) { SEXP elmt = R_NilValue, names = getAttrib(list, R_NamesSymbol);
for (int i = 0; i < length(list); i++) if(strcmp(CHAR(STRING_ELT(names, i)), str) == 0) { elmt = VECTOR_ELT(list, i); break; } return elmt; }
and enables us to say
double g; g = REAL(getListElement(a, "g"))[0];
R character vectors are stored as STRSXP
s, a vector type like
where every element is of type CHARSXP
. The
elements of STRSXP
s are accessed using
and SET_STRING_ELT
s are read-only objects and must never be modified. In
particular, the C-style string contained in a CHARSXP
should be
treated as read-only and for this reason the CHAR
function used
to access the character data of a CHARSXP
returns (const
char *)
(this also allows compilers to issue warnings about improper
use). Since CHARSXP
s are immutable, the same CHARSXP
be shared by any STRSXP
needing an element representing the same
string. R maintains a global cache of CHARSXP
s so that there
is only ever one CHARSXP
representing a given string in memory.
You can obtain a CHARSXP
by calling mkChar
and providing a
NUL-terminated C-style string. This function will return a pre-existing
if one with a matching string already exists, otherwise
it will create a new one and add it to the cache before returning it to
you. The variant mkCharLen
can be used to create a
from part of a buffer and will ensure null-termination.
Note that R character strings are restricted to 2^31 - 1
bytes, and hence so should the input to mkChar
be (C allows
longer strings on 64-bit platforms).
It will be usual that all the R objects needed in our C computations
are passed as arguments to .Call
or .External
, but it is
possible to find the values of R objects from within the C given
their names. The following code is the equivalent of get(name,
envir = rho)
SEXP getvar(SEXP name, SEXP rho) { SEXP ans; if(!isString(name) || length(name) != 1) error("name is not a single string"); if(!isEnvironment(rho)) error("rho should be an environment"); ans = findVar(installChar(STRING_ELT(name, 0)), rho); Rprintf("first value is %f\n", REAL(ans)[0]); return R_NilValue; }
The main work is done by
, but to use it we need to install name
as a name
in the symbol table. As we wanted the value for internal use, we return
Similar functions with syntax
void defineVar(SEXP symbol, SEXP value, SEXP rho) void setVar(SEXP symbol, SEXP value, SEXP rho)
can be used to assign values to R variables. defineVar
creates a new binding or changes the value of an existing binding in the
specified environment frame; it is the analogue of assign(symbol,
value, envir = rho, inherits = FALSE)
, but unlike assign
does not make a copy of the object
.160 setVar
searches for an existing
binding for symbol
in rho
or its enclosing environments.
If a binding is found, its value is changed to value
. Otherwise,
a new binding with the specified value is created in the global
environment. This corresponds to assign(symbol, value, envir =
rho, inherits = TRUE)
At times it may also be useful to create a new environment frame in C code.
is a C version of the R function new.env
SEXP R_NewEnv(SEXP enclos, int hash, int size)
Some operations are done so frequently that there are convenience functions to handle them. (All these are provided via the header file Rinternals.h.)
Suppose we wanted to pass a single logical argument
: we could use
int ign = asLogical(ignore_quotes); if(ign == NA_LOGICAL) error("'ignore_quotes' must be TRUE or FALSE");
which will do any coercion needed (at least from a vector argument), and
return NA_LOGICAL
if the value passed was NA
or coercion
failed. There are also asInteger
, asReal
. The function asChar
returns a CHARSXP
All of these functions ignore any elements of an input vector after the
To return a length-one real vector we can use
double x; ... return ScalarReal(x);
and there are versions of this for all the atomic vector types (those for
a length-one character vector being ScalarString
with argument a
and mkString
with argument const char *
SEXP ScalarReal(double); SEXP ScalarInteger(int); SEXP ScalarLogical(int) SEXP ScalarRaw(Rbyte); SEXP ScalarComplex(Rcomplex); SEXP ScalarString(SEXP); SEXP mkString(const char *);
Some of the isXXXX
functions differ from their apparent
R-level counterparts: for example isVector
is true for any
atomic vector type (isVectorAtomic
) and for lists and expressions
) (with no check on attributes). isMatrix
a test of a length-2 "dim"
Rboolean isVector(SEXP); Rboolean isVectorAtomic(SEXP); Rboolean isVectorList(SEXP); Rboolean isMatrix(SEXP); Rboolean isPairList(SEXP); Rboolean isPrimitive(SEXP); Rboolean isTs(SEXP); Rboolean isNumeric(SEXP); Rboolean isArray(SEXP); Rboolean isFactor(SEXP); Rboolean isObject(SEXP); Rboolean isFunction(SEXP); Rboolean isLanguage(SEXP); Rboolean isNewList(SEXP); Rboolean isList(SEXP); Rboolean isOrdered(SEXP); Rboolean isUnordered(SEXP);
There are a series of small macros/functions to help construct pairlists
and language objects (whose internal structures just differ by
). Function CONS(u, v)
is the basic building
block: it constructs a pairlist from u
followed by v
(which is a pairlist or R_NilValue
is a variant
that constructs a language object. Functions list1
construct a pairlist from one to six items, and
to lang6
do the same for a language object (a
function to call plus zero to five arguments).
Functions elt
and lastElt
find the i-th element and
the last element of a pairlist, and nthcdr
returns a pointer to
the n-th position in the pairlist (whose CAR
is the
n-th item).
Functions str2type
and type2str
map R
length-one character strings to and from SEXPTYPE
numbers, and
maps numbers to C character strings.
There is quite a collection of functions that may be used in your C code if you are willing to adapt to rare "API" changes. These typically contain "workhorses" of their R counterparts.
Functions any_duplicated
and any_duplicated3
are fast
versions of R's any(duplicated(.))
Function R_compute_identical
corresponds to R's identical
[The NAMED
mechanism has been replaced by reference counting.]
When assignments are done in R such as
x <- 1:10 y <- x
the named object is not necessarily copied, so after those two
assignments y
and x
are bound to the same SEXPREC
(the structure a SEXP
points to). This means that any code which
alters one of them has to make a copy before modifying the copy if the
usual R semantics are to apply. Note that whereas .C
do copy their arguments (unless the dangerous dup
is used), .Call
and .External
do not. So
is commonly called on arguments to .Call
modifying them.
However, at least some of this copying is unneeded. In the first
assignment shown, x <- 1:10
, R first creates an object with
value 1:10
and then assigns it to x
but if x
modified no copy is necessary as the temporary object with value
cannot be referred to again. R distinguishes between
named and unnamed objects via a field in a SEXPREC
can be accessed via the macros NAMED
and SET_NAMED
. This
can take values
The object is not bound to any symbol
The object has been bound to exactly one symbol
>= 2
The object has potentially been bound to two or more symbols, and one
should act as if another variable is currently bound to this value.
The maximal value is NAMEDMAX
Note the past tenses: R does not do currently do full reference counting and there may currently be fewer bindings.
It is safe to modify the value of any SEXP
for which
is zero, and if NAMED(foo)
is two or more, the
value should be duplicated (via a call to duplicate
before any modification. Note that it is the responsibility of the
author of the code making the modification to do the duplication, even
if it is x
whose value is being modified after y <- x
The case NAMED(foo) == 1
allows some optimization, but it can be
ignored (and duplication done whenever NAMED(foo) > 0
). (This
optimization is not currently usable in user code.) It is intended
for use within replacement functions. Suppose we used
x <- 1:10 foo(x) <- 3
which is computed as
x <- 1:10 x <- "foo<-"(x, 3)
Then inside "foo<-"
the object pointing to the current value of
will have NAMED(foo)
as one, and it would be safe to
modify it as the only symbol bound to it is x
and that will be
rebound immediately. (Provided the remaining code in "foo<-"
make no reference to x
, and no one is going to attempt a direct
call such as y <- "foo<-"(x)
This mechanism was replaced in R 4.0.0. To
support future changes, package code should use the macros
, and
. These currently correspond to
NAMED(x) > 0
NAMED(x) > 1
SET_NAMED(x, NAMEDMAX)
and .External
¶In this section we consider the details of the R/C interfaces.
These two interfaces have almost the same functionality. .Call
based on the interface of the same name in S version 4, and
is based on R's .Internal
. .External
is more complex but allows a variable number of arguments.
¶Let us convert our finite convolution example to use .Call
. The
calling function in R is
conv <- function(a, b) .Call("convolve2", a, b)
which could hardly be simpler, but as we shall see all the type coercion is transferred to the C code, which is
#include <R.h> #include <Rinternals.h> SEXP convolve2(SEXP a, SEXP b) { int na, nb, nab; double *xa, *xb, *xab; SEXP ab; a = PROTECT(coerceVector(a, REALSXP)); b = PROTECT(coerceVector(b, REALSXP)); na = length(a); nb = length(b); nab = na + nb - 1; ab = PROTECT(allocVector(REALSXP, nab)); xa = REAL(a); xb = REAL(b); xab = REAL(ab); for(int i = 0; i < nab; i++) xab[i] = 0.0; for(int i = 0; i < na; i++) for(int j = 0; j < nb; j++) xab[i + j] += xa[i] * xb[j]; UNPROTECT(3); return ab; }
¶We can use the same example to illustrate .External
. The R
code changes only by replacing .Call
by .External
conv <- function(a, b) .External("convolveE", a, b)
but the main change is how the arguments are passed to the C code, this time as a single SEXP. The only change to the C code is how we handle the arguments.
#include <R.h> #include <Rinternals.h> SEXP convolveE(SEXP args) { int i, j, na, nb, nab; double *xa, *xb, *xab; SEXP a, b, ab; a = PROTECT(coerceVector(CADR(args), REALSXP)); b = PROTECT(coerceVector(CADDR(args), REALSXP)); ... }
Once again we do not need to protect the arguments, as in the R side of the interface they are objects that are already in use. The macros
first = CADR(args); second = CADDR(args); third = CADDDR(args); fourth = CAD4R(args); fifth = CAD5R(args);
provide convenient ways to access the first four arguments. More
generally we can use the
and CAR
macros as in
args = CDR(args); a = CAR(args); args = CDR(args); b = CAR(args);
which clearly allows us to extract an unlimited number of arguments
(whereas .Call
has a limit, albeit at 65 not a small one).
More usefully, the .External
interface provides an easy way to
handle calls with a variable number of arguments, as length(args)
will give the number of arguments supplied (of which the first is
ignored). We may need to know the names ('tags') given to the actual
arguments, which we can by using the TAG
macro and using
something like the following example, that prints the names and the first
value of its arguments if they are vector types.
SEXP showArgs(SEXP args) { args = CDR(args); /* skip 'name' */ for(int i = 0; args != R_NilValue; i++, args = CDR(args)) { const char *name = isNull(TAG(args)) ? "" : CHAR(PRINTNAME(TAG(args))); SEXP el = CAR(args); if (length(el) == 0) { Rprintf("[%d] '%s' R type, length 0\n", i+1, name); continue; }
switch(TYPEOF(el)) { case REALSXP: Rprintf("[%d] '%s' %f\n", i+1, name, REAL(el)[0]); break;
case LGLSXP: case INTSXP: Rprintf("[%d] '%s' %d\n", i+1, name, INTEGER(el)[0]); break;
case CPLXSXP: { Rcomplex cpl = COMPLEX(el)[0]; Rprintf("[%d] '%s' %f + %fi\n", i+1, name, cpl.r, cpl.i); } break;
case STRSXP: Rprintf("[%d] '%s' %s\n", i+1, name, CHAR(STRING_ELT(el, 0))); break;
default: Rprintf("[%d] '%s' R type\n", i+1, name); } } return R_NilValue; }
This can be called by the wrapper function
showArgs <- function(...) invisible(.External("showArgs", ...))
Note that this style of programming is convenient but not necessary, as an alternative style is
showArgs1 <- function(...) invisible(.Call("showArgs1", list(...)))
The (very similar) C code is in the scripts.
Additional functions for accessing pairlist components are CAAR
, and CDDDR
These components can be modified with SETCAR
, and SETCAD4R
One piece of error-checking the .C
call does (unless NAOK
is true) is to check for missing (NA
) and IEEE special
values (Inf
, -Inf
and NaN
) and give an error if any
are found. With the .Call
interface these will be passed to our
code. In this example the special values are no problem, as
IEC 60559 arithmetic will handle them correctly. In the current
implementation this is also true of NA
as it is a type of
, but it is unwise to rely on such details. Thus we will
re-write the code to handle NA
s using macros defined in
R_ext/Arith.h included by R.h.
The code changes are the same in any of the versions of convolve2
or convolveE
... for(int i = 0; i < na; i++) for(int j = 0; j < nb; j++) if(ISNA(xa[i]) || ISNA(xb[j]) || ISNA(xab[i + j])) xab[i + j] = NA_REAL; else xab[i + j] += xa[i] * xb[j]; ...
Note that the ISNA
macro, and the similar macros ISNAN
(which checks for NaN
or NA
) and R_FINITE
(which is
false for NA
and all the special values), only apply to numeric
values of type double
. Missingness of integers, logicals and
character strings can be tested by equality to the constants
and NA_STRING
. These and
can be used to set elements of R vectors to NA
The constants R_NaN
, R_PosInf
and R_NegInf
can be
used to set double
s to the special values.
The main function we will use is
SEXP eval(SEXP expr, SEXP rho);
the equivalent of the interpreted R code eval(expr, envir =
(so rho
must be an environment), although we can also make
use of findVar
, defineVar
and findFun
restricts the search to functions).
To see how this might be applied, here is a simplified internal version
of lapply
for expressions, used as
a <- list(a = 1:5, b = rnorm(10), test = runif(100)) .Call("lapply", a, quote(sum(x)), new.env())
with C code
SEXP lapply(SEXP list, SEXP expr, SEXP rho) { int n = length(list); SEXP ans; if(!isNewList(list)) error("'list' must be a list"); if(!isEnvironment(rho)) error("'rho' should be an environment"); ans = PROTECT(allocVector(VECSXP, n)); for(int i = 0; i < n; i++) { defineVar(install("x"), VECTOR_ELT(list, i), rho); SET_VECTOR_ELT(ans, i, eval(expr, rho)); } setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol)); UNPROTECT(1); return ans; }
It would be closer to lapply
if we could pass in a function
rather than an expression. One way to do this is via interpreted
R code as in the next example, but it is possible (if somewhat
obscure) to do this in C code. The following is based on the code in
SEXP lapply2(SEXP list, SEXP fn, SEXP rho) { int n = length(list); SEXP R_fcall, ans; if(!isNewList(list)) error("'list' must be a list"); if(!isFunction(fn)) error("'fn' must be a function"); if(!isEnvironment(rho)) error("'rho' should be an environment"); R_fcall = PROTECT(lang2(fn, R_NilValue)); ans = PROTECT(allocVector(VECSXP, n)); for(int i = 0; i < n; i++) { SETCADR(R_fcall, VECTOR_ELT(list, i)); SET_VECTOR_ELT(ans, i, eval(R_fcall, rho)); } setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol)); UNPROTECT(2); return ans; }
used by
.Call("lapply2", a, sum, new.env())
Function lang2
creates an executable pairlist of two elements, but
this will only be clear to those with a knowledge of a LISP-like
As a more comprehensive example of constructing an R call in C code
and evaluating, consider the following fragment. Similar code appears in
the definition of do_docall
in src/main/coerce.c.
SEXP s, t; t = s = PROTECT(allocLang(3)); SETCAR(t, install("print")); t = CDR(t); SETCAR(t, CAR(a)); t = CDR(t); SETCAR(t, ScalarInteger(digits)); SET_TAG(t, install("digits")); eval(s, env); UNPROTECT(1);
The function allocLang
is available as of R 4 4.1; for older
versions replace allocLang(3)
LCONS(R_NilValue, allocList(2))
At this point CAR(a)
is the R object to be printed, the
current attribute. There are three steps: the call is constructed as
a pairlist of length 3, the list is filled in, and the expression
represented by the pairlist is evaluated.
A pairlist is quite distinct from a generic vector list, the only
user-visible form of list in R. A pairlist is a linked list (with
computing the next entry), with items (accessed by
) and names or tags (set by SET_TAG
). In this call
there are to be three items, a symbol (pointing to the function to be
called) and two argument values, the first unnamed and the second named.
Setting the type to LANGSXP
makes this a call which can be evaluated.
Customarily, the evaluation environment is passed from the calling
R code (see rho
above). In special cases it is possible that
the C code may need to obtain the current evaluation environment
which can be done via R_GetCurrentEnv()
In this section we re-work the example of Becker, Chambers & Wilks (1988, pp.~205–10) on finding a zero of a univariate function. The R code and an example are
zero <- function(f, guesses, tol = 1e-7) { f.check <- function(x) { x <- f(x) if(!is.numeric(x)) stop("Need a numeric result") as.double(x) } .Call("zero", body(f.check), as.double(guesses), as.double(tol), new.env()) } cube1 <- function(x) (x^2 + 1) * (x - 1.5) zero(cube1, c(0, 5))
where this time we do the coercion and error-checking in the R code. The C code is
SEXP mkans(double x) { // no need for PROTECT() here, as REAL(.) does not allocate: SEXP ans = allocVector(REALSXP, 1); REAL(ans)[0] = x; return ans; }
double feval(double x, SEXP f, SEXP rho) { // a version with (too) much PROTECT()ion .. "better safe than sorry" SEXP symbol, value; PROTECT(symbol = install("x")); PROTECT(value = mkans(x)); defineVar(symbol, value, rho); UNPROTECT(2); return(REAL(eval(f, rho))[0]); }
SEXP zero(SEXP f, SEXP guesses, SEXP stol, SEXP rho) { double x0 = REAL(guesses)[0], x1 = REAL(guesses)[1], tol = REAL(stol)[0]; double f0, f1, fc, xc;
if(tol <= 0.0) error("non-positive tol value"); f0 = feval(x0, f, rho); f1 = feval(x1, f, rho); if(f0 == 0.0) return mkans(x0); if(f1 == 0.0) return mkans(x1); if(f0*f1 > 0.0) error("x[0] and x[1] have the same sign");
for(;;) { xc = 0.5*(x0+x1); if(fabs(x0-x1) < tol) return mkans(xc); fc = feval(xc, f, rho); if(fc == 0) return mkans(xc); if(f0*fc > 0.0) { x0 = xc; f0 = fc; } else { x1 = xc; f1 = fc; } } }
We will use a longer example (by Saikat DebRoy) to illustrate the use of
evaluation and .External
. This calculates numerical derivatives,
something that could be done as effectively in interpreted R code but
may be needed as part of a larger C calculation.
An interpreted R version and an example are
numeric.deriv <- function(expr, theta, rho=sys.frame(sys.parent())) { eps <- sqrt(.Machine$double.eps) ans <- eval(substitute(expr), rho) grad <- matrix(, length(ans), length(theta), dimnames=list(NULL, theta)) for (i in seq_along(theta)) { old <- get(theta[i], envir=rho) delta <- eps * max(1, abs(old)) assign(theta[i], old+delta, envir=rho) ans1 <- eval(substitute(expr), rho) assign(theta[i], old, envir=rho) grad[, i] <- (ans1 - ans)/delta } attr(ans, "gradient") <- grad ans } omega <- 1:5; x <- 1; y <- 2 numeric.deriv(sin(omega*x*y), c("x", "y"))
where expr
is an expression, theta
a character vector of
variable names and rho
the environment to be used.
For the compiled version the call from R will be
.External("numeric_deriv", expr, theta, rho)
with example usage
.External("numeric_deriv", quote(sin(omega*x*y)), c("x", "y"), .GlobalEnv)
Note the need to quote the expression to stop it being evaluated in the caller.
Here is the complete C code which we will explain section by section.
#include <R.h> #include <Rinternals.h> #include <float.h> /* for DBL_EPSILON */ SEXP numeric_deriv(SEXP args) { SEXP theta, expr, rho, ans, ans1, gradient, par, dimnames; double tt, xx, delta, eps = sqrt(DBL_EPSILON), *rgr, *rans; int i, start;
expr = CADR(args); if(!isString(theta = CADDR(args))) error("theta should be of type character"); if(!isEnvironment(rho = CADDDR(args))) error("rho should be an environment");
ans = PROTECT(coerceVector(eval(expr, rho), REALSXP)); gradient = PROTECT(allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta))); rgr = REAL(gradient); rans = REAL(ans);
for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) { par = PROTECT(findVar(installChar(STRING_ELT(theta, i)), rho)); tt = REAL(par)[0]; xx = fabs(tt); delta = (xx < 1) ? eps : xx*eps; REAL(par)[0] += delta; ans1 = PROTECT(coerceVector(eval(expr, rho), REALSXP)); for(int j = 0; j < LENGTH(ans); j++) rgr[j + start] = (REAL(ans1)[j] - rans[j])/delta; REAL(par)[0] = tt; UNPROTECT(2); /* par, ans1 */ }
dimnames = PROTECT(allocVector(VECSXP, 2)); SET_VECTOR_ELT(dimnames, 1, theta); dimnamesgets(gradient, dimnames); setAttrib(ans, install("gradient"), gradient); UNPROTECT(3); /* ans gradient dimnames */ return ans; }
The code to handle the arguments is
expr = CADR(args); if(!isString(theta = CADDR(args))) error("theta should be of type character"); if(!isEnvironment(rho = CADDDR(args))) error("rho should be an environment");
Note that we check for correct types of theta
and rho
do not check the type of expr
. That is because eval
handle many types of R objects other than EXPRSXP
. There is
no useful coercion we can do, so we stop with an error message if the
arguments are not of the correct mode.
The first step in the code is to evaluate the expression in the
environment rho
, by
ans = PROTECT(coerceVector(eval(expr, rho), REALSXP));
We then allocate space for the calculated derivative by
gradient = PROTECT(allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));
The first argument to allocMatrix
gives the SEXPTYPE
the matrix: here we want it to be REALSXP
. The other two
arguments are the numbers of rows and columns. (Note that LENGTH
is intended to be used for vectors: length
is more generally
for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) { par = PROTECT(findVar(installChar(STRING_ELT(theta, i)), rho));
Here, we are entering a for loop. We loop through each of the
variables. In the for
loop, we first create a symbol
corresponding to the i
-th element of the STRSXP
. Here, STRING_ELT(theta, i)
accesses the
-th element of the STRSXP
. Macro
extracts the actual character
representation161 of it: it returns a pointer. We then
install the name and use findVar
to find its value.
tt = REAL(par)[0]; xx = fabs(tt); delta = (xx < 1) ? eps : xx*eps; REAL(par)[0] += delta; ans1 = PROTECT(coerceVector(eval(expr, rho), REALSXP));
We first extract the real value of the parameter, then calculate
, the increment to be used for approximating the numerical
derivative. Then we change the value stored in par
environment rho
) by delta
and evaluate expr
environment rho
again. Because we are directly dealing with
original R memory locations here, R does the evaluation for the
changed parameter value.
for(int j = 0; j < LENGTH(ans); j++) rgr[j + start] = (REAL(ans1)[j] - rans[j])/delta; REAL(par)[0] = tt; UNPROTECT(2); }
Now, we compute the i
-th column of the gradient matrix. Note how
it is accessed: R stores matrices by column (like Fortran).
dimnames = PROTECT(allocVector(VECSXP, 2)); SET_VECTOR_ELT(dimnames, 1, theta); dimnamesgets(gradient, dimnames); setAttrib(ans, install("gradient"), gradient); UNPROTECT(3); return ans; }
First we add column names to the gradient matrix. This is done by
allocating a list (a VECSXP
) whose first element, the row names,
is NULL
(the default) and the second element, the column names,
is set as theta
. This list is then assigned as the attribute
having the symbol R_DimNamesSymbol
. Finally we set the gradient
matrix as the gradient attribute of ans
, unprotect the remaining
protected locations and return the answer ans
Suppose an R extension want to accept an R expression from the user and evaluate it. The previous section covered evaluation, but the expression will be entered as text and needs to be parsed first. A small part of R's parse interface is declared in header file R_ext/Parse.h162.
An example of the usage can be found in the (example) Windows package windlgs included in the R source tree. The essential part is
#include <R.h> #include <Rinternals.h> #include <R_ext/Parse.h> SEXP menu_ttest3() { char cmd[256]; SEXP cmdSexp, cmdexpr, ans = R_NilValue; ParseStatus status; ... if(done == 1) { cmdSexp = PROTECT(allocVector(STRSXP, 1)); SET_STRING_ELT(cmdSexp, 0, mkChar(cmd)); cmdexpr = PROTECT(R_ParseVector(cmdSexp, -1, &status, R_NilValue)); if (status != PARSE_OK) { UNPROTECT(2); error("invalid call %s", cmd); } /* Loop is needed here as EXPSEXP will be of length > 1 */ for(int i = 0; i < length(cmdexpr); i++) ans = eval(VECTOR_ELT(cmdexpr, i), R_GlobalEnv); UNPROTECT(2); } return ans; }
Note that a single line of text may give rise to more than one R expression.
is essentially the code used to implement
at R level. The first argument is a character
vector (corresponding to text
) and the second the maximal
number of expressions to parse (corresponding to n
). The third
argument is a pointer to a variable of an enumeration type, and it is
normal (as parse
does) to regard all values other than
as an error. Other values which might be returned are
(an incomplete expression was found) and
(a syntax error), in both cases the value returned
being R_NilValue
. The fourth argument is a length one character
vector to be used as a filename in error messages, a srcfile
object or the R NULL
object (as in the example above). If a
object was used, a srcref
attribute would be
attached to the result, containing a list of srcref
objects of
the same length as the expression, to allow it to be echoed with its
original formatting.
The source references added by the parser are recorded by R's evaluator as it evaluates code. Two functions make these available to debuggers running C code:
SEXP R_GetCurrentSrcref(int skip);
This function checks R_Srcref
and the current evaluation stack
for entries that contain source reference information. The
argument tells how many source references to skip before
returning the SEXP
of the srcref
object, counting from
the top of the stack. If skip < 0
, abs(skip)
are counted up from the bottom of the stack. If too few or no source
references are found, NULL
is returned.
SEXP R_GetSrcFilename(SEXP srcref);
This function extracts the filename from the source reference for
display, returning a length 1 character vector containing the
filename. If no name is found, ""
is returned.
The SEXPTYPE
s EXTPTRSXP
and WEAKREFSXP
can be
encountered at R level, but are created in C code.
External pointer SEXP
s are intended to handle references to C
structures such as 'handles', and are used for this purpose in package
RODBC for example. They are unusual in their copying semantics in
that when an R object is copied, the external pointer object is not
duplicated. (For this reason external pointers should only be used as
part of an object with normal semantics, for example an attribute or an
element of a list.)
An external pointer is created by
SEXP R_MakeExternalPtr(void *p, SEXP tag, SEXP prot);
where p
is the pointer (and hence this cannot portably be a
function pointer), and tag
and prot
are references to
ordinary R objects which will remain in existence (be protected from
garbage collection) for the lifetime of the external pointer object. A
useful convention is to use the tag
field for some form of type
identification and the prot
field for protecting the memory that
the external pointer represents, if that memory is allocated from the
R heap. Both tag
and prot
can be R_NilValue
and often are.
An alternative way to create an external pointer from a function pointer is
typedef void * (*R_DL_FUNC)(); SEXP R_MakeExternalPtrFn(R_DL_FUNC p, SEXP tag, SEXP prot);
The elements of an external pointer can be accessed and set via
void *R_ExternalPtrAddr(SEXP s); DL_FUNC R_ExternalPtrAddrFn(SEXP s); SEXP R_ExternalPtrTag(SEXP s); SEXP R_ExternalPtrProtected(SEXP s); void R_ClearExternalPtr(SEXP s); void R_SetExternalPtrAddr(SEXP s, void *p); void R_SetExternalPtrTag(SEXP s, SEXP tag); void R_SetExternalPtrProtected(SEXP s, SEXP p);
Clearing a pointer sets its value to the C NULL
An external pointer object can have a finalizer, a piece of code to be run when the object is garbage collected. This can be R code or C code, and the various interfaces are, respectively.
void R_RegisterFinalizer(SEXP s, SEXP fun); void R_RegisterFinalizerEx(SEXP s, SEXP fun, Rboolean onexit); typedef void (*R_CFinalizer_t)(SEXP); void R_RegisterCFinalizer(SEXP s, R_CFinalizer_t fun); void R_RegisterCFinalizerEx(SEXP s, R_CFinalizer_t fun, Rboolean onexit);
The R function indicated by fun
should be a function of a
single argument, the object to be finalized. R does not perform a
garbage collection when shutting down, and the onexit
argument of
the extended forms can be used to ask that the finalizer be run during a
normal shutdown of the R session. It is suggested that it is good
practice to clear the pointer on finalization.
The only R level function for interacting with external pointers is
which can be used to set a finalizer.
It is probably not a good idea to allow an external pointer to be
d and then reloaded, but if this happens the pointer will be
set to the C NULL
Finalizers can be run at many places in the code base and much of it, including the R interpreter, is not re-entrant. So great care is needed in choosing the code to be run in a finalizer. Finalizers are marked to be run at garbage collection but only run at a somewhat safe point thereafter.
Weak references are used to allow the programmer to maintain information on entities without preventing the garbage collection of the entities once they become unreachable.
A weak reference contains a key and a value. The value is reachable is
if it either reachable directly or via weak references with reachable
keys. Once a value is determined to be unreachable during garbage
collection, the key and value are set to R_NilValue
and the
finalizer will be run later in the garbage collection.
Weak reference objects are created by one of
SEXP R_MakeWeakRef(SEXP key, SEXP val, SEXP fin, Rboolean onexit); SEXP R_MakeWeakRefC(SEXP key, SEXP val, R_CFinalizer_t fin, Rboolean onexit);
where the R or C finalizer are specified in exactly the same way as for an external pointer object (whose finalization interface is implemented via weak references).
The parts can be accessed via
SEXP R_WeakRefKey(SEXP w); SEXP R_WeakRefValue(SEXP w); void R_RunWeakRefFinalizer(SEXP w);
A toy example of the use of weak references can be found at https://homepage.stat.uiowa.edu/~luke/R/references/weakfinex.html, but that is used to add finalizers to external pointers which can now be done more directly. At the time of writing no CRAN or Bioconductor package uses weak references.
Package RODBC uses external pointers to maintain its
channels, connections to databases. There can be several
connections open at once, and the status information for each is stored
in a C structure (pointed to by thisHandle
in the code extract
below) that is returned via an external pointer as part of the
'channel' (as the "handle_ptr"
attribute). The external pointer
is created by
SEXP ans, ptr; ans = PROTECT(allocVector(INTSXP, 1)); ptr = R_MakeExternalPtr(thisHandle, install("RODBC_channel"), R_NilValue); PROTECT(ptr); R_RegisterCFinalizerEx(ptr, chanFinalizer, TRUE); ... /* return the channel no */ INTEGER(ans)[0] = nChannels; /* and the connection string as an attribute */ setAttrib(ans, install("connection.string"), constr); setAttrib(ans, install("handle_ptr"), ptr); UNPROTECT(3); return ans;
Note the symbol given to identify the usage of the external pointer, and
the use of the finalizer. Since the final argument when registering the
finalizer is TRUE
, the finalizer will be run at the end of the
R session (unless it crashes). This is used to close and clean up
the connection to the database. The finalizer code is simply
static void chanFinalizer(SEXP ptr) { if(!R_ExternalPtrAddr(ptr)) return; inRODBCClose(R_ExternalPtrAddr(ptr)); R_ClearExternalPtr(ptr); /* not really needed */ }
Clearing the pointer and checking for a NULL
pointer avoids any
possibility of attempting to close an already-closed channel.
R's connections provide another example of using external pointers, in that case purely to be able to use a finalizer to close and destroy the connection if it is no longer is use.
The vector accessors like REAL
, and VECTOR_ELT
are functions
when used in R extensions. (For efficiency they may be macros or
inline functions when used in the R source code, apart from
and SET_VECTOR_ELT
which are always
The accessor functions check that they are being used on an appropriate
type of SEXP
Formerly it was possible for packages to obtain internal versions of some accessors by defining 'USE_RINTERNALS' before including Rinternals.h. This is no longer the case. Defining 'USE_RINTERNALS' now has no effect.
s can be marked as coming from a known encoding (Latin-1
or UTF-8). This is mainly intended for human-readable output, and most
packages can just treat such CHARSXP
s as a whole. However, if
they need to be interpreted as characters or output at C level then it
would normally be correct to ensure that they are converted to the
encoding of the current locale: this can be done by accessing the data
in the CHARSXP
by translateChar
rather than by
. If re-encoding is needed this allocates memory with
which thus persists to the end of the
call unless vmaxset
is used
(see Transient storage allocation).
There is a similar function translateCharUTF8
which converts to
UTF-8: this has the advantage that a faithful translation is almost
always possible (whereas only a few languages can be represented in the
encoding of the current locale unless that is UTF-8).
Both translateChar
and translateCharUTF8
will translate
any input, using escapes such as '<A9>' and '<U+0093>' to
represent untranslatable parts of the input.
There is a public interface to the encoding marked on CHARSXPs
typedef enum {CE_NATIVE, CE_UTF8, CE_LATIN1, CE_BYTES, CE_SYMBOL, CE_ANY} cetype_t; cetype_t getCharCE(SEXP); SEXP mkCharCE(const char *, cetype_t);
Only CE_UTF8
and CE_LATIN1
are marked on CHARSXPs
(and so Rf_getCharCE
will only return one of the first three),
and these should only be used on non-ASCII strings. Value
is used to make CHARSXP
s which should be regarded
as a set of bytes and not translated. Value CE_SYMBOL
is used
internally to indicate Adobe Symbol encoding. Value CE_ANY
used to indicate a character string that will not need re-encoding –
this is used for character strings known to be in ASCII, and
can also be used as an input parameter where the intention is that the
string is treated as a series of bytes. (See the comments under
about the length of input allowed.)
const char *reEnc(const char *x, cetype_t ce_in, cetype_t ce_out, int subst);
can be used to re-encode character strings: like translateChar
returns a string allocated by R_alloc
. This can translate from
to CE_UTF8
, but not conversely. Argument
controls what to do with untranslatable characters or
invalid input: this is done byte-by-byte with 1
indicates to
output hex of the form <a0>
, and 2
to replace by .
with any other value causing the byte to produce no output.
There is also
SEXP mkCharLenCE(const char *, int, cetype_t);
to create marked character strings of a given length.
There are a large number of entry points in the R executable/DLL that can be called from C code (and some that can be called from Fortran code). Only those documented here are stable enough that they will only be changed with considerable notice.
The recommended procedure to use these is to include the header file R.h in your C code by
#include <R.h>
This will include several other header files from the directory R_INCLUDE_DIR/R_ext, and there are other header files there that can be included too, but many of the features they contain should be regarded as undocumented and unstable.
Most of these header files, including all those included by R.h, can be used from C++ code.
Note: Because R re-maps many of its external names to avoid clashes with system or user code, it is essential to include the appropriate header files when using these entry points.
This remapping can cause problems163,
and can be eliminated by defining R_NO_REMAP
(before including
any R headers) and prepending 'Rf_' to all the function
names used from Rinternals.h and R_ext/Error.h. These
problems can usually be avoided by including other headers (such as
system headers and those for external software used by the package)
before any R headers. (Headers from other packages may include R
headers directly or via inclusion from further packages, and may
define R_NO_REMAP
with or without including Rinternals.h.)
Some of these entry points are declared in header Rmath.h, most
of which are remapped there. That remapping can be eliminated by
defining R_NO_REMAP_RMATH
(before including any R headers) and
prepending 'Rf_' to the function names used from that header except
exp_rand norm_rand unif_rand signrank_free wilcox_free
We can classify the entry points as
Entry points which are documented in this manual and declared in an installed header file. These can be used in distributed packages and will only be changed after deprecation.
Entry points declared in an installed header file that are exported on all R platforms but are not documented and subject to change without notice.
Entry points that are used when building R and exported on all R platforms but are not declared in the installed header files. Do not use these in distributed code.
Entry points that are where possible (Windows and some modern Unix-alike compilers/loaders when using R as a shared library) not exported.
There are two types of memory allocation available to the C programmer, one in which R manages the clean-up and the other in which user has full control (and responsibility).
These functions are declared in header R_ext/RS.h which is included by R.h.
Here R will reclaim the memory at the end of the call to .C
or .External
. Use
char *R_alloc(size_t n, int size)
which allocates n units of size bytes each. A typical usage (from package stats) is
x = (int *) R_alloc(nrows(merge)+2, sizeof(int));
is defined in stddef.h which the header defining
There is a similar call, S_alloc
(named for compatibility with older
versions of S) which zeroes the memory allocated,
char *S_alloc(long n, int size)
char *S_realloc(char *p, long new, long old, int size)
which (for new > old
) changes the allocation size
from old to new units, and zeroes the additional units. NB:
these calls are best avoided as long
is insufficient for large
memory allocations on 64-bit Windows (where it is limited to 2^31-1
This memory is taken from the heap, and released at the end of the
, .Call
or .External
call. Users can also manage
it, by noting the current position with a call to vmaxget
subsequently clearing memory allocated by a call to vmaxset
. An
example might be
void *vmax = vmaxget() // a loop involving the use of R_alloc at each iteration vmaxset(vmax)
This is only recommended for experts.
Note that this memory will be freed on error or user interrupt (if allowed: see Allowing interrupts).
The memory returned is only guaranteed to be aligned as required for
pointers: take precautions if casting to a pointer which
needs more. There is also
long double *R_allocLD(size_t n)
which is guaranteed to have the 16-byte alignment needed for long
pointers on some platforms.
These functions should only be used in code called by .C
never from front-ends. They are not thread-safe.
The other form of memory allocation is an interface to malloc
the interface providing R error signaling. This memory lasts until
freed by the user and is additional to the memory allocated for the R
The interface macros are
type* R_Calloc(size_t n, type) type* R_Realloc(any *p, size_t n, type) void R_Free(any *p)
providing analogues of calloc
, realloc
and free
If there is an error during allocation it is handled by R, so if
these return the memory has been successfully allocated or freed.
will set the pointer p to NULL
. (Some but not
all versions of S did so.)
Users should arrange to R_Free
this memory when no longer needed,
including on error or user interrupt. This can often be done most
conveniently from an on.exit
action in the calling R function
– see pwilcox
for an example.
Do not assume that memory allocated by R_Calloc
comes from the same pool as used by malloc
:164 in particular do not use
or strdup
with it.
Memory obtained by these macros should be aligned in the same way as
, that is 'suitably aligned for any kind of variable'.
Historically the macros Calloc
, Free
and Realloc
were used, and these remain available unless STRICT_R_HEADERS
was defined prior to the inclusion of the header.
char * CallocCharBuf(size_t n) void * Memcpy(q, p, n) void * Memzero(p, n)
is shorthand for R_Calloc(n+1, char)
to allow
for the nul
terminator. Memcpy
and Memzero
items from array p
and copy them to array q
zero them respectively.
The basic error signaling routines are the equivalents of stop
in R code, and use the same interface.
void error(const char * format, ...); void warning(const char * format, ...); void errorcall(SEXP call, const char * format, ...); void warningcall(SEXP call, const char * format, ...); void warningcall_immediate(SEXP call, const char * format, ...);
These have the same call sequences as calls to printf
, but in the
simplest case can be called with a single character string argument
giving the error message. (Don't do this if the string contains '%'
or might otherwise be interpreted as a format.)
These are defined in header R_ext/Error.h included by R.h.
NB: when R_NO_REMAP
is defined (as is recommeneded for
C++ code), Rf_error
etc must be used.
There are two interface function provided to call Rf_error
from Fortran code, in each case with a simple character
string argument. They are defined as
subroutine rexit(message) subroutine rwarn(message)
Messages of more than 255 characters are truncated, with a warning.
The interface to R's internal random number generation routines is
double unif_rand(); double norm_rand(); double exp_rand(); double R_unif_index(double);
giving one uniform, normal or exponential pseudo-random variate. However, before these are used, the user must call
and after all the required variates have been generated, call
These essentially read in (or create) .Random.seed
and write it
out after use.
These are defined in header R_ext/Random.h.
The random number generator is private to R; there is no way to select the kind of RNG nor set the seed except by evaluating calls to the R functions.
The C code behind R's rxxx
functions can be accessed by
including the header file Rmath.h; See Distribution functions.
Those calls should also be preceded and followed by calls to
and PutRNGstate
It was explained earlier that Fortran random-number generators should not be used in R packages, not least as packages cannot safely initialize them. Rather a package should call R's built in generators: one way to do so is to use C wrappers like
#include <R_ext/RS.h> #include <R_ext/Random.h> void F77_SUB(getRNGseed)(void) { GetRNGstate(); } void F77_SUB(putRNGseed)(void) { PutRNGstate(); } double F77_SUB(unifRand)(void) { return(unif_rand()); }
called from Fortran code like
... double precision X call getRNGseed() X = unifRand() ... call putRNGseed()
Alternatively one could use Fortran 2003's iso_c_binding
by something like (fixed-form Fortran 90 code):
module rngfuncs use iso_c_binding interface double precision * function unifRand() bind(C, name = "unif_rand") end function unifRand subroutine getRNGseed() bind(C, name = "GetRNGstate") end subroutine getRNGseed subroutine putRNGseed() bind(C, name = "PutRNGstate") end subroutine putRNGseed end interface end module rngfuncs subroutine testit use rngfuncs double precision X call getRNGseed() X = unifRand() print *, X call putRNGSeed() end subroutine testit
A set of functions is provided to test for NA
, Inf
and NaN
. These functions are accessed via macros:
ISNA(x) True for R'sNA
only ISNAN(x) True for R'sNA
and IEEENaN
R_FINITE(x) False forInf
and via function R_IsNaN
which is true for NaN
but not
Do use R_FINITE
rather than isfinite
or finite
; the
latter is often mendacious and isfinite
is only available on a
some platforms, on which R_FINITE
is a macro expanding to
Currently in C code ISNAN
is a macro calling isnan
(Since this gives problems on some C++ systems, if the R headers is
called from C++ code a function call is used.)
You can check for Inf
or -Inf
by testing equality to
or R_NegInf
, and set (but not test) an NA
as NA_REAL
All of the above apply to double variables only. For integer
variables there is a variable accessed by the macro NA_INTEGER
which can used to set or test for missingness.
These are defined in header R_ext/Arith.h included by R.h.
The most useful function for printing from a C routine compiled into
R is Rprintf
. This is used in exactly the same way as
, but is guaranteed to write to R's output (which might
be a GUI console rather than a file, and can be re-directed by
). It is wise to write complete lines (including the
) before returning to R. It is defined in
The function REprintf
is similar but writes on the error stream
) which may or may not be different from the standard
output stream.
Functions Rvprintf
and REvprintf
are analogues using the
interface. Because that is a C99165 interface, they are only defined by R_ext/Print.h in C++
code if the macro R_USE_C99_IN_CXX
is defined before it is
included or (as from R 4.0.0) a C++11 compiler is used.
Another circumstance when it may be important to use these functions is when using parallel computation on a cluster of computational nodes, as their output will be re-directed/logged appropriately.
On many systems Fortran write
and print
statements can be
used, but the output may not interleave well with that of C, and may be
invisible on GUI interfaces. They are not portable and best
Some subroutines are provided to ease the output of information from Fortran code.
subroutine dblepr(label, nchar, data, ndata) subroutine realpr(label, nchar, data, ndata) subroutine intpr (label, nchar, data, ndata)
subroutine labelpr(label, nchar) subroutine dblepr1(label, nchar, var) subroutine realpr1(label, nchar, var) subroutine intpr1 (label, nchar, var)
Here label is a character label of up to 255 characters,
nchar is its length (which can be -1
if the whole label is
to be used), data is an array of length at least ndata of
the appropriate type (double precision
, real
respectively) and var is a (scalar) variable.
These routines print the label on one line and then print data or
var as if it were an R vector on subsequent line(s). Note that
some compilers will give an error or warning unless data is an
array: others will accept a scalar when ndata has value one or
zero. NB: There is no check on the type of data or
var, so using real
(including a real constant) instead of
double precision
will give incorrect answers.
works with zero ndata so can be used to print a
label in earlier versions of R.
Naming conventions for symbols generated by Fortran differ by platform: it is not safe to assume that Fortran names appear to C with a trailing underscore. To help cover up the platform-specific differences there is a set of macros166 that should be used.
to define a function in C to be called from Fortran
to declare a Fortran routine in C before use
to call a Fortran routine from C
to declare a Fortran common block in C
to access a Fortran common block from C
On most current platforms these are all the same, but it is unwise to rely on this. Note that names containing underscores were not legal in Fortran 77, and are not portably handled by the above macros. (Also, all Fortran names for use by R are lower case, but this is not enforced by the macros.)
For example, suppose we want to call R's normal random numbers from Fortran. We need a C wrapper along the lines of
#include <R.h> void F77_SUB(rndstart)(void) { GetRNGstate(); } void F77_SUB(rndend)(void) { PutRNGstate(); } double F77_SUB(normrnd)(void) { return norm_rand(); }
to be called from Fortran as in
subroutine testit() double precision normrnd, x call rndstart() x = normrnd() call dblepr("X was", 5, x, 1) call rndend() end
Note that this is not guaranteed to be portable, for the return conventions might not be compatible between the C and Fortran compilers used. (Passing values via arguments is safer.)
The standard packages, for example stats, are a rich source of further examples.
Where supported, link time optimization provides a reliable way
to check the consistency of calls to C from Fortran or vice
See Using Link-time Optimization.
One place where this occurs is the registration of .Fortran
in C code (see Registering native routines). For example
init.c:10:13: warning: type of 'vsom_' does not match original declaration [-Wlto-type-mismatch] extern void F77_NAME(vsom)(void *, void *, void *, void *, void *, void *, void *, void *, void *); vsom.f90:20:33: note: type mismatch in parameter 9 subroutine vsom(neurons,dt,dtrows,dtcols,xdim,ydim,alpha,train) vsom.f90:20:33: note: 'vsom' was previously declared here
shows that a subroutine has been registered with 9 arguments (as that is
what the .Fortran
call used) but only has 8.
Passing character strings from C to Fortran or vice versa is
not portable, but can be done with care. The internal representations
are different: a character array in C (or C++) is NUL-terminated so its
length can be computed by strlen
. Fortran character arrays are
typically stored as an array of bytes and a length. This matters when
passing strings from C to Fortran or vice versa: in many cases
one has been able to get away with passing the string but not the
length. However, in 2019 this changed for gfortran
, starting
with version 9 but backported to versions 7 and 8. Several months
later, gfortran
9.2 introduced an option
and made it the current default but said it might be withdrawn in future.
Suppose we want a function to report a message from Fortran to R's
console (one could use labelpr
, or intpr
with dummy data,
but this might be the basis of a custom reporting function). Suppose the
equivalent in Fortran would be
subroutine rmsg(msg) character*(*) msg print *.msg end
in file rmsg.f. Using gfortran
9.2 and later we can
extract the C view by
gfortran -c -fc-prototypes-external rmsg.f
which gives
void rmsg_ (char *msg, size_t msg_len);
(where size_t
applies to version 8 and later). We could re-write
that portably in C as
#ifndef USE_FC_LEN_T # define USE_FC_LEN_T #endif #include <Rconfig.h> // included by R.h, so define USE_FC_LEN_T early void F77_NAME(rmsg)(char *msg, FC_LEN_T msg_len) { char cmsg[msg_len+1]; strncpy(cmsg, msg, msg_len); cmsg[msg_len] = '\0'; // nul-terminate the string, to be sure // do something with 'cmsg' }
in code depending on R(>= 3.6.2)
. For earlier versions of R we
could just assume that msg
is NUL-terminated (not guaranteed, but
people have been getting away with it for many years), so the complete C
side might be
#ifndef USE_FC_LEN_T # define USE_FC_LEN_T #endif #include <Rconfig.h> #ifdef FC_LEN_T void F77_NAME(rmsg)(char *msg, FC_LEN_T msg_len) { char cmsg[msg_len+1]; strncpy(cmsg, msg, msg_len); cmsg[msg_len] = '\0'; // do something with 'cmsg' } #else void F77_NAME(rmsg)(char *msg) { // do something with 'msg' } #endif
is the default as from R 4.3.0.)
An alternative is to use Fortran 2003 features to set up the Fortran routine to pass a C-compatible character string. We could use something like
module cfuncs use iso_c_binding, only: c_char, c_null_char interface subroutine cmsg(msg) bind(C, name = 'cmsg') use iso_c_binding, only: c_char character(kind = c_char):: msg(*) end subroutine cmsg end interface end module subroutine rmsg(msg) use cfuncs character(*) msg call cmsg(msg//c_null_char) ! need to concatenate a nul terminator end subroutine rmsg
where the C side is simply
void cmsg(const char *msg) { // do something with nul-terminated string 'msg' }
If you use bind
to a C function as here, the only way to check
that the bound definition is correct is to compile the package with LTO
(which requires compatible C and Fortran compilers, usually
and gfortran
Passing a variable-length string from C to Fortran is trickier, but https://www.intel.com/content/www/us/en/docs/fortran-compiler/developer-guide-reference/2023-0/bind-c.html provides a recipe. However, all the uses in BLAS and LAPACK are of a single character, and for these we can write a wrapper in Fortran along the lines of
subroutine c_dgemm(transa, transb, m, n, k, alpha, + a, lda, b, ldb, beta, c, ldc) + bind(C, name = 'Cdgemm') use iso_c_binding, only : c_char, c_int, c_double character(c_char), intent(in) :: transa, transb integer(c_int), intent(in) :: m, n, k, lda, ldb, ldc real(c_double), intent(in) :: alpha, beta, a(lda, *), b(ldb, *) real(c_double), intent(out) :: c(ldc, *) call dgemm(transa, transb, m, n, k, alpha, + a, lda, b, ldb, beta, c, ldc) end subroutine c_dgemm
which is then called from C with declaration
void Cdgemm(const char *transa, const char *transb, const int *m, const int *n, const int *k, const double *alpha, const double *a, const int *lda, const double *b, const int *ldb, const double *beta, double *c, const int *ldc);
Alternatively, do as R does as from version 3.6.2 and pass the character length(s) from C to Fortran. A portable way to do this is
// before any R headers, or define in PKG_CPPFLAGS #ifndef USE_FC_LEN_T # define USE_FC_LEN_T #endif #include <Rconfig.h> #include <R_ext/BLAS.h> #ifndef FCONE # define FCONE #endif ... F77_CALL(dgemm)("N", "T", &nrx, &ncy, &ncx, &one, x, &nrx, y, &nry, &zero, z, &nrx FCONE FCONE);
(Note there is no comma before or between the FCONE
It is strongly recommended that packages which call from C/C++
BLAS/LAPACK routines with character arguments adopt this approach:
packages not using will fail to install as from R 4.3.0.
Passing Fortran LOGICAL variables to/from C/C++ is potentially
compiler-dependent. Fortran compilers have long used a 32-bit integer
type so it is pretty portable to use int *
on the C/C++ side.
However, recent versions of gfortran
via the option
-fc-prototypes-external say the C equivalent is
int_least32_t *
: 'Link-Time Optimization' will report int
as a mismatch. It is possible to use iso_c_binding
in Fortran
2003 to map LOGICAL variables to the C99 type _Bool
, but it is
usually simpler to pass integers to and fro.
A number of packages call C functions passed as arguments to Fortran code along the lines of
c subroutine fcn(m,n,x,fvec,iflag) c integer m,n,iflag c double precision x(n),fvec(m) ... subroutine lmdif(fcn, ...
where the C declaration and call are
void fcn_lmdif(int *m, int *n, double *par, double *fvec, int *iflag); void F77_NAME(lmdif)(void (*fcn_lmdif)(int *m, int *n, double *par, double *fvec, int *iflag), ... F77_CALL(lmdif)(&fcn_lmdif, ...
This works on most platforms but depends on the C and Fortran compilers
agreeing on calling conventions: this have been seen to fail. The most
portable solution seems to be to convert the Fortran code to C, perhaps
using f2c
R contains a large number of mathematical functions for its own use, for example numerical linear algebra computations and special functions.
The header files R_ext/BLAS.h, R_ext/Lapack.h and R_ext/Linpack.h contains declarations of the BLAS, LAPACK and LINPACK linear algebra functions included in R. These are expressed as calls to Fortran subroutines, and they will also be usable from users' Fortran code. Although not part of the official API, this set of subroutines is unlikely to change (but might be supplemented).
The header file Rmath.h lists many other functions that are available and documented in the following subsections. Many of these are C interfaces to the code behind R functions, so the R function documentation may give further details.
The routines used to calculate densities, cumulative distribution functions and quantile functions for the standard statistical distributions are available as entry points.
The arguments for the entry points follow the pattern of those for the normal distribution:
double dnorm(double x, double mu, double sigma, int give_log); double pnorm(double x, double mu, double sigma, int lower_tail, int give_log); double qnorm(double p, double mu, double sigma, int lower_tail, int log_p); double rnorm(double mu, double sigma);
That is, the first argument gives the position for the density and CDF
and probability for the quantile function, followed by the
distribution's parameters. Argument lower_tail should be
(or 1
) for normal use, but can be FALSE
) if the probability of the upper tail is desired or specified.
Finally, give_log should be non-zero if the result is required on log scale, and log_p should be non-zero if p has been specified on log scale.
Note that you directly get the cumulative (or "integrated") hazard function, H(t) = - log(1 - F(t)), by using
- pdist(t, ..., /*lower_tail = */ FALSE, /* give_log = */ TRUE)
or shorter (and more cryptic) - pdist(t, ..., 0, 1)
The random-variate generation routine rnorm
returns one normal
variate. See Random number generation, for the protocol in using the
random-variate routines.
Note that these argument sequences are (apart from the names and that
has no n) mainly the same as the corresponding R
functions of the same name, so the documentation of the R functions
can be used. Note that the exponential and gamma distributions are
parametrized by scale
rather than rate
For reference, the following table gives the basic name (to be prefixed by 'd', 'p', 'q' or 'r' apart from the exceptions noted) and distribution-specific arguments for the complete set of distributions.
beta beta
non-central beta nbeta
binomial binom
Cauchy cauchy
chi-squared chisq
non-central chi-squared nchisq
exponential exp
(and notrate
) F f
non-central F nf
gamma gamma
geometric geom
hypergeometric hyper
logistic logis
lognormal lnorm
negative binomial nbinom
normal norm
Poisson pois
Student's t t
non-central t nt
Studentized range tukey
uniform unif
Weibull weibull
Wilcoxon rank sum wilcox
Wilcoxon signed rank signrank
Entries marked with an asterisk only have 'p' and 'q' functions available, and none of the non-central distributions have 'r' functions.
(If remapping is suppressed, the Normal distribution names are
, Rf_pnorm5
and Rf_qnorm5
Additionally, a multivariate RNG for the multinomial distribution is
void rmultinom(int n, double* prob, int K, int* rN)
where K = length(prob)
sum(prob[.]) == 1
and rN
must point to a length-K
integer vector
n1 n2 .. nK where each entry
is "filled" by a random binomial from
Bin(n; prob[j]),
constrained to sum(rN[.]) == n.
After calls to dwilcox
, pwilcox
or qwilcox
function wilcox_free()
should be called, and similarly
for the signed rank functions.
Since wilcox_free()
and signrank_free()
were only added to
Rmath.h in R 4.2.0, their use requires something like
#include "Rmath.h" #include "Rversion.h" #if R_VERSION < R_Version(4, 2, 0) extern void wilcox_free(void); extern void signrank_free(void); #endif
For the negative binomial distribution ('nbinom'), in addition to the
(size, prob)
parametrization, the alternative (size, mu)
parametrization is provided as well by functions '[dpqr]nbinom_mu()',
see ?NegBinomial in R.
Functions dpois_raw(x, *)
and dbinom_raw(x, *)
are versions of the
Poisson and binomial probability mass functions which work continuously in
, whereas dbinom(x,*)
and dpois(x,*)
only return non
zero values for integer x
double dbinom_raw(double x, double n, double p, double q, int give_log) double dpois_raw (double x, double lambda, int give_log)
Note that dbinom_raw()
returns both p and q = 1-p which may be advantageous when one of them is close to 1.
gammafn (double x)
lgammafn (double x)
digamma (double x)
trigamma (double x)
tetragamma (double x)
pentagamma (double x)
psigamma (double x, double deriv)
dpsifn (double x, int n, int kode, int m, double* ans, int* nz, int* ierr)
¶The Gamma function, the natural logarithm of its absolute value and
first four derivatives and the n-th derivative of Psi, the digamma
function, which is the derivative of lgammafn
. In other words,
is the same as psigamma(x,0)
trigamma(x) == psigamma(x,1)
, etc.
The underlying workhorse, dpsifn()
, is useful, e.g., when several derivatives of
log Gamma=lgammafn
are desired. It computes and
returns in ans[]
the length-m sequence
(-1)^(k+1) / gamma(k+1) * psi(k;x) for
k = n ... n+m-1, where psi(k;x)
is the k-th derivative of Psi(x), i.e.,
. For more details, see the comments in
beta (double a, double b)
lbeta (double a, double b)
¶The (complete) Beta function and its natural logarithm.
choose (double n, double k)
lchoose (double n, double k)
¶The number of combinations of k items chosen from n and the natural logarithm of its absolute value, generalized to arbitrary real n. k is rounded to the nearest integer (with a warning if needed).
bessel_i (double x, double nu, double expo)
bessel_j (double x, double nu)
bessel_k (double x, double nu, double expo)
bessel_y (double x, double nu)
¶Bessel functions of types I, J, K and Y with index nu. For
and bessel_k
there is the option to return
exp(-x) I(x; nu) or exp(x) K(x; nu) if expo is 2. (Use expo == 1
for unscaled
There are a few other numerical utility functions available as entry points.
R_pow (double x, double y)
R_pow_di (double x, int i)
pow1p (double x, double y)
¶R_pow(x, y)
and R_pow_di(x, i)
compute x^y
and x^i
, respectively
using R_FINITE
checks and returning the proper result (the same
as R) for the cases where x, y or i are 0 or
missing or infinite or NaN
pow1p(x, y)
computes (1 + x)^y
, accurately
even for small x, i.e., |x| << 1.
log1p (double x)
¶Computes log(1 + x)
(log 1 plus x), accurately
even for small x, i.e., |x| << 1.
This should be provided by your platform, in which case it is not included in Rmath.h, but is (probably) in math.h which Rmath.h includes (except under C++, so it may not be declared for C++98).
log1pmx (double x)
¶Computes log(1 + x) - x
(log 1 plus x minus x),
accurately even for small x, i.e., |x| << 1.
log1pexp (double x)
¶Computes log(1 + exp(x))
(log 1 plus exp),
accurately, notably for large x, e.g., x > 720.
log1mexp (double x)
¶Computes log(1 - exp(-x))
(log 1 minus exp),
accurately, carefully for two regions of x, optimally cutting
off at log 2 (= 0.693147..), using
((-x) > -M_LN2 ? log(-expm1(-x)) : log1p(-exp(-x)))
expm1 (double x)
¶Computes exp(x) - 1
(exp x minus 1), accurately
even for small x, i.e., |x| << 1.
This should be provided by your platform, in which case it is not included in Rmath.h, but is (probably) in math.h which Rmath.h includes (except under C++, so it may not be declared for C++98).
lgamma1p (double x)
¶Computes log(gamma(x + 1))
(log(gamma(1 plus x))),
accurately even for small x, i.e., 0 < x < 0.5.
cospi (double x)
¶Computes cos(pi * x)
(where pi
is 3.14159...),
accurately, notably for half integer x.
This might be provided by your platform167, in which case it is not included in Rmath.h, but is in math.h which Rmath.h includes. (Ensure that neither math.h nor cmath is included before Rmath.h or define
#define __STDC_WANT_IEC_60559_FUNCS_EXT__ 1
before the first inclusion.)
sinpi (double x)
¶Computes sin(pi * x)
accurately, notably for (half) integer x.
This might be provided by your platform, in which case it is not
included in Rmath.h, but is in math.h which Rmath.h
includes (but see the comments for cospi
Rtanpi (double x)
¶Computes tan(pi * x)
accurately, notably for integer x, giving
NaN for half integer x and exactly +1 or -1 for (non half)
quarter integers.
tanpi (double x)
¶Computes tan(pi * x)
accurately for integer x with possibly
platform dependent behavior for half (and quarter) integers.
This might be provided by your platform, in which case it is not included
in Rmath.h, but is in math.h which Rmath.h includes
(but see the comments for cospi
logspace_add (double logx, double logy)
logspace_sub (double logx, double logy)
logspace_sum (const double* logx, int n)
¶Compute the log of a sum or difference from logs of terms, i.e., "x +
y" as log (exp(logx) + exp(logy))
and "x - y" as
log (exp(logx) - exp(logy))
and "sum_i x[i]" as log (sum[i = 1:n exp(logx[i])] )
without causing unnecessary overflows or throwing away too much accuracy.
imax2 (int x, int y)
imin2 (int x, int y)
fmax2 (double x, double y)
fmin2 (double x, double y)
¶Return the larger (max
) or smaller (min
) of two integer or
double numbers, respectively. Note that fmax2
and fmin2
differ from C99/C++11's fmax
and fmin
when one of the
arguments is a NaN
: these versions return NaN
sign (double x)
¶Compute the signum function, where sign(x) is 1, 0, or
-1, when x is positive, 0, or negative, respectively, and
if x
is a NaN
fsign (double x, double y)
¶Performs "transfer of sign" and is defined as |x| * sign(y).
fprec (double x, double digits)
¶Returns the value of x rounded to digits significant decimal digits.
This is the function used by R's signif()
fround (double x, double digits)
¶Returns the value of x rounded to digits decimal digits (after the decimal point).
This is the function used by R's round()
. (Note that C99/C++11
provide a round
function but C++98 need not.)
ftrunc (double x)
¶Returns the value of x truncated (to an integer value) towards zero.
R has a set of commonly used mathematical constants encompassing
constants defined by POSIX and usually found in headers math.h
and cmath, as well as further ones that are used in statistical
computations. These are defined to (at least) 30 digits accuracy in
Rmath.h. The following definitions use ln(x)
for the
natural logarithm (log(x)
in R).
Name Definition ( ln = log
)round(value, 7) M_E
e 2.7182818 M_LOG2E
log2(e) 1.4426950 M_LOG10E
log10(e) 0.4342945 M_LN2
ln(2) 0.6931472 M_LN10
ln(10) 2.3025851 M_PI
pi 3.1415927 M_PI_2
pi/2 1.5707963 M_PI_4
pi/4 0.7853982 M_1_PI
1/pi 0.3183099 M_2_PI
2/pi 0.6366198 M_2_SQRTPI
2/sqrt(pi) 1.1283792 M_SQRT2
sqrt(2) 1.4142136 M_SQRT1_2
1/sqrt(2) 0.7071068 M_SQRT_3
sqrt(3) 1.7320508 M_SQRT_32
sqrt(32) 5.6568542 M_LOG10_2
log10(2) 0.3010300 M_2PI
2*pi 6.2831853 M_SQRT_PI
sqrt(pi) 1.7724539 M_1_SQRT_2PI
1/sqrt(2*pi) 0.3989423 M_SQRT_2dPI
sqrt(2/pi) 0.7978846 M_LN_SQRT_PI
ln(sqrt(pi)) 0.5723649 M_LN_SQRT_2PI
ln(sqrt(2*pi)) 0.9189385 M_LN_SQRT_PId2
ln(sqrt(pi/2)) 0.2257914
There are a set of constants (PI
) (and so on)
defined (unless STRICT_R_HEADERS
is defined) in the included
header R_ext/Constants.h, mainly for compatibility with S.
All but PI
are deprecated and should be replaced by the C99/C++11
versions used in that file.
Further, the included header R_ext/Boolean.h has enumeration
constants TRUE
and FALSE
of type Rboolean
order to provide a way of using "logical" variables in C consistently.
This can conflict with other software: for example it conflicts with the
headers in IJG's jpeg-9
(but not earlier versions).
The C code underlying optim
can be accessed directly. The user
needs to supply a function to compute the function to be minimized, of
the type
typedef double optimfn(int n, double *par, void *ex);
where the first argument is the number of parameters in the second argument. The third argument is a pointer passed down from the calling routine, normally used to carry auxiliary information.
Some of the methods also require a gradient function
typedef void optimgr(int n, double *par, double *gr, void *ex);
which passes back the gradient in the gr
argument. No function
is provided for finite-differencing, nor for approximating the Hessian
at the result.
The interfaces (defined in header R_ext/Applic.h) are
void nmmin(int n, double *xin, double *x, double *Fmin, optimfn fn, int *fail, double abstol, double intol, void *ex, double alpha, double beta, double gamma, int trace, int *fncount, int maxit);
void vmmin(int n, double *x, double *Fmin, optimfn fn, optimgr gr, int maxit, int trace, int *mask, double abstol, double reltol, int nREPORT, void *ex, int *fncount, int *grcount, int *fail);
void cgmin(int n, double *xin, double *x, double *Fmin, optimfn fn, optimgr gr, int *fail, double abstol, double intol, void *ex, int type, int trace, int *fncount, int *grcount, int maxit);
void lbfgsb(int n, int lmm, double *x, double *lower, double *upper, int *nbd, double *Fmin, optimfn fn, optimgr gr, int *fail, void *ex, double factr, double pgtol, int *fncount, int *grcount, int maxit, char *msg, int trace, int nREPORT);
void samin(int n, double *x, double *Fmin, optimfn fn, int maxit, int tmax, double temp, int trace, void *ex);
Many of the arguments are common to the various methods. n
the number of parameters, x
or xin
is the starting
parameters on entry and x
the final parameters on exit, with
final value returned in Fmin
. Most of the other parameters can
be found from the help page for optim
: see the source code
src/appl/lbfgsb.c for the values of nbd
, which
specifies which bounds are to be used.
The C code underlying integrate
can be accessed directly. The
user needs to supply a vectorizing C function to compute the
function to be integrated, of the type
typedef void integr_fn(double *x, int n, void *ex);
where x[]
is both input and output and has length n
, i.e.,
a C function, say fn
, of type integr_fn
must basically do
for(i in 1:n) x[i] := f(x[i], ex)
. The vectorization requirement
can be used to speed up the integrand instead of calling it n
times. Note that in the current implementation built on QUADPACK,
will be either 15 or 21. The ex
argument is a pointer
passed down from the calling routine, normally used to carry auxiliary
There are interfaces (defined in header R_ext/Applic.h) for integrals over finite and infinite intervals (or "ranges" or "integration boundaries").
void Rdqags(integr_fn f, void *ex, double *a, double *b, double *epsabs, double *epsrel, double *result, double *abserr, int *neval, int *ier, int *limit, int *lenw, int *last, int *iwork, double *work);
void Rdqagi(integr_fn f, void *ex, double *bound, int *inf, double *epsabs, double *epsrel, double *result, double *abserr, int *neval, int *ier, int *limit, int *lenw, int *last, int *iwork, double *work);
Only the 3rd and 4th argument differ for the two integrators; for the
finite range integral using Rdqags
, a
and b
are the
integration interval bounds, whereas for an infinite range integral using
, bound
is the finite bound of the integration (if
the integral is not doubly-infinite) and inf
is a code indicating
the kind of integration range,
inf = 1
corresponds to (bound, +Inf),
inf = -1
corresponds to (-Inf, bound),
inf = 2
corresponds to (-Inf, +Inf),
and ex
define the integrand function, see above;
and epsrel
specify the absolute and relative
accuracy requested, result
, abserr
and last
are the
output components value
, abs.err
and subdivisions
of the R function integrate, where neval
gives the number of
integrand function evaluations, and the error code ier
translated to R's integrate() $ message
, look at that function
definition. limit
corresponds to integrate(...,
subdivisions = *)
. It seems you should always define the two work
arrays and the length of the second one as
lenw = 4 * limit; iwork = (int *) R_alloc(limit, sizeof(int)); work = (double *) R_alloc(lenw, sizeof(double));
The comments in the source code in src/appl/integrate.c give
more details, particularly about reasons for failure (ier >= 1
R has a fairly comprehensive set of sort routines which are made available to users' C code. The following is declared in header file Rinternals.h.
R_orderVector (int* indx, int n, SEXP arglist, Rboolean nalast, Rboolean decreasing)
R_orderVector1 (int* indx, int n, SEXP x, Rboolean nalast, Rboolean decreasing)
corresponds to R's order(..., na.last, decreasing)
More specifically, indx <- order(x, y, na.last, decreasing)
corresponds to
R_orderVector(indx, n, Rf_lang2(x, y), nalast, decreasing)
and for
three vectors, Rf_lang3(x,y,z)
is used as arglist.
Both R_orderVector
and R_orderVector1
assume the vector
to be allocated to length >= n. On return,
contains a permutation of 0:(n-1)
, i.e., 0-based C
indices (and not 1-based R indices, as R's order()
When ordering only one vector, R_orderVector1
is faster and
corresponds (but is 0-based) to R's indx <- order(x, na.last,
. It was added in R 3.3.0.
All other sort routines are declared in header file R_ext/Utils.h (included by R.h) and include the following.
R_isort (int* x, int n)
R_rsort (double* x, int n)
R_csort (Rcomplex* x, int n)
rsort_with_index (double* x, int* index, int n)
¶The first three sort integer, real (double) and complex data
respectively. (Complex numbers are sorted by the real part first then
the imaginary part.) NA
s are sorted last.
sorts on x, and applies the same
permutation to index. NA
s are sorted last.
revsort (double* x, int* index, int n)
¶Is similar to rsort_with_index
but sorts into decreasing order,
and NA
s are not handled.
iPsort (int* x, int n, int k)
rPsort (double* x, int n, int k)
cPsort (Rcomplex* x, int n, int k)
¶These all provide (very) partial sorting: they permute x so that
is in the correct place with smaller values to
the left, larger ones to the right.
R_qsort (double *v, size_t i, size_t j)
R_qsort_I (double *v, int *I, int i, int j)
R_qsort_int (int *iv, size_t i, size_t j)
R_qsort_int_I (int *iv, int *I, int i, int j)
¶These routines sort v[i:j]
(using 1-indexing, i.e.,
is the first element) calling the quicksort algorithm
as used by R's sort(v, method = "quick")
and documented on the
help page for the R function sort
. The ..._I()
versions also return the sort.index()
vector in I
. Note
that the ordering is not stable, so tied values may be permuted.
Note that NA
s are not handled (explicitly) and you should
use different sorting functions if NA
s can be present.
qsort4 (double precision v, integer indx, integer ii, integer jj)
qsort3 (double precision v, integer ii, integer jj)
¶The Fortran interface routines for sorting double precision vectors are
and qsort4
, equivalent to R_qsort
, respectively.
R_max_col (double* matrix, int* nr, int* nc, int* maxes, int* ties_meth)
¶Given the nr by nc matrix matrix
in column-major
order, R_max_col()
returns in maxes[i-1]
column number of the maximal element in the i-th row (the same as
R's max.col()
function). In the case of ties (multiple maxima),
is an integer code in 1:3
determining the method:
1 = "random", 2 = "first" and 3 = "last".
See R's help page ?max.col
findInterval (double* xt, int n, double x, Rboolean rightmost_closed, Rboolean all_inside, int ilo, int* mflag)
findInterval2(double* xt, int n, double x, Rboolean rightmost_closed, Rboolean all_inside, Rboolean left_open, int ilo, int* mflag)
¶Given the ordered vector xt of length n, return the interval
or index of x in xt[]
, typically max(i; 1 <= i <= n & xt[i] <=
x) where we use 1-indexing as in R and Fortran (but not C). If
rightmost_closed is true, also returns n-1 if x
equals xt[n]. If all_inside is not 0, the
result is coerced to lie in 1:(n-1)
even when x is
outside the xt[] range. On return, *mflag
-1 if x < xt[1], +1 if x >=
xt[n], and 0 otherwise.
The algorithm is particularly fast when ilo is set to the last
result of findInterval()
and x is a value of a sequence which
is increasing or decreasing for subsequent calls.
is a generalization of findInterval()
with an extra Rboolean
argument left_open. Setting
left_open = TRUE
basically replaces all left-closed right-open
intervals t) by left-open ones t], see the help page
of R function findInterval
for details.
There is also an F77_CALL(interv)()
version of
with the same arguments, but all pointers.
A system-independent interface to produce the name of a temporary file is provided as
char *
R_tmpnam (const char *prefix, const char *tmpdir)
¶char *
R_tmpnam2 (const char *prefix, const char *tmpdir, const char *fileext)
R_free_tmpnam (char *name)
¶Return a pathname for a temporary file with name beginning with
prefix and ending with fileext in directory tmpdir.
prefix or extension is replaced by ""
. Note that
the return value is dynamically allocated and should be freed using
when no longer needed (unlike the
system call tmpnam
). Freeing the result using free
is no
longer recommended.
R_atof (const char* str)
R_strtod (const char* str, char ** end)
¶Implementations of the C99/POSIX functions atof
and strtod
which guarantee platform-dependent behaviour, including always using the
period as the decimal point aka 'radix character' and converting
to R's NA_REAL_
There is also the internal function used to expand file names in several
R functions, and called directly by path.expand
const char *
R_ExpandFileName (const char *fn)
¶Expand a path name fn by replacing a leading tilde by the user's
home directory (if defined). The precise meaning is platform-specific;
it will usually be taken from the environment variable HOME
this is defined.
For historical reasons there are Fortran interfaces to functions
and I1MACH
. These can be called from C code as
e.g. F77_CALL(d1mach)(4)
. Note that these are emulations of
the original functions by Fox, Hall and Schryer on Netlib at
https://netlib.org/slatec/src/ for IEC 60559 arithmetic
(required by R).
R has its own C-level interface to the encoding conversion
capabilities provided by iconv
because there are
incompatibilities between the declarations in different implementations
of iconv
These are declared in header file R_ext/Riconv.h.
void *
Riconv_open (const char *to, const char *from)
¶Set up a pointer to an encoding object to be used to convert between two
encodings: ""
indicates the current locale.
Riconv (void *cd, const char **inbuf, size_t *inbytesleft, char **outbuf, size_t *outbytesleft)
¶Convert as much as possible of inbuf
to outbuf
. Initially
the size_t
variables indicate the number of bytes available in the
buffers, and they are updated (and the char
pointers are updated
to point to the next free byte in the buffer). The return value is the
number of characters converted, or (size_t)-1
is usually an unsigned type). It should be safe to assume
that an error condition sets errno
to one of E2BIG
output buffer is full), EILSEQ
(the input cannot be converted,
and might be invalid in the encoding specified) or EINVAL
input does not end with a complete multi-byte character).
Riconv_close (void * cd)
¶Free the resources of an encoding object.
Three functions are available for establishing condition handlers from within C code:
#include <Rinternals.h> SEXP R_tryCatchError(SEXP (*fun)(void *data), void *data, SEXP (*hndlr)(SEXP cond, void *hdata), void *hdata); SEXP R_tryCatch(SEXP (*fun)(void *data), void *data, SEXP, SEXP (*hndlr)(SEXP cond, void *hdata), void *hdata, void (*clean)(void *cdata), void *cdata); SEXP R_withCallingErrorHandler(SEXP (*fun)(void *data), void *data, SEXP (*hndlr)(SEXP cond, void *hdata), void *hdata)
establishes an exiting handler for conditions
inheriting form class error
can be used to establish a handler for other
conditions and to register a cleanup action. The conditions to be
handled are specified as a character vector (STRSXP
pointer can be passed as fun
or clean
if condition handling or cleanup are not needed.
These are currently implemented using the R-level tryCatch
mechanism so are subject to some overhead.
establishes a calling handler for
conditions inheriting from class error
. It establishes the
handler without calling back into R and will therefore be more
The function R_UnwindProtect
can be used to ensure that a cleanup
action takes place on ordinary return as well as on a non-local transfer
of control, which R implements as a longjmp
SEXP R_UnwindProtect(SEXP (*fun)(void *data), void *data, void (*clean)(void *data, Rboolean jump), void *cdata, SEXP cont);
can be used in two ways. The simper usage,
suitable for use in C code, passes NULL
for the cont
argument. R_UnwindProtect
will call fun(data)
. If
returns a value, then R_UnwindProtect
clean(cleandata, FALSE)
before returning the value returned by
. If fun
executes a non-local transfer of control, then
clean(cleandata, TRUE)
is called, and the non-local transfer of
control is resumed.
The second use pattern, suitable to support C++ stack unwinding, uses two additional functions:
SEXP R_MakeUnwindCont(); NORET void R_ContinueUnwind(SEXP cont);
allocates a continuation token
to pass to R_UnwindProtect
. This token should be
protected with PROTECT
before calling
. When the clean
function is called with
jump == TRUE
, indicating that R is executing a non-local transfer
of control, it can throw a C++ exception to a C++ catch
the C++ code to be unwound, and then use the continuation token in the a
call R_ContinueUnwind(cont)
to resume the non-local transfer of
control within R.
No part of R can be interrupted whilst running long computations in compiled code, so programmers should make provision for the code to be interrupted at suitable points by calling from C
#include <R_ext/Utils.h> void R_CheckUserInterrupt(void);
and from Fortran
subroutine rchkusr()
These check if the user has requested an interrupt, and if so branch to R's error signaling functions.
Note that it is possible that the code behind one of the entry points defined here if called from your C or Fortran code could be interruptible or generate an error and so not return to your code.
The header files define USING_R
, which can be used to test if
the code is indeed being used with R.
Header file Rconfig.h (included by R.h) is used to define
platform-specific macros that are mainly for use in other header files.
The macro WORDS_BIGENDIAN
is defined on
systems (e.g. most OSes on Sparc and PowerPC hardware) and not on
little-endian systems (nowadays all the commoner R platforms). It
can be useful when manipulating binary files. NB: these macros apply
only to the C compiler used to build R, not necessarily to another C
or C++ compiler.
Header file Rversion.h (not included by R.h)
defines a macro R_VERSION
giving the version number encoded as an
integer, plus a macro R_Version
to do the encoding. This can be
used to test if the version of R is late enough, or to include
back-compatibility features. For protection against very old versions
of R which did not have this macro, use a construction such as
#if defined(R_VERSION) && R_VERSION >= R_Version(3, 1, 0) ... #endif
More detailed information is available in the macros R_MAJOR
and R_DAY
: see the
header file Rversion.h for their format. Note that the minor
version includes the patch level (as in '2.2').
Packages which use alloca
need to ensure it is defined: as it is
part of neither C nor POSIX there is no standard way to do so. One can
#include <Rconfig.h> // for HAVE_ALLOCA_H #ifdef __GNUC__ // this covers gcc, clang, icc # undef alloca # define alloca(x) __builtin_alloca((x)) #elif defined(HAVE_ALLOCA_H) // needed for native compilers on Solaris and AIX # include <alloca.h> #endif
(and this should be included before standard C headers such as stdlib.h, since on some platforms these include malloc.h which may have a conflicting definition), which suffices for known R platforms.
The C99 keyword inline
should be recognized by all compilers
nowadays used to build R. Portable code which might be used with
earlier versions of R can be written using the macro R_INLINE
(defined in file Rconfig.h included by R.h), as for
example from package cluster
#include <R.h> static R_INLINE int ind_2(int l, int j) { ... }
Be aware that using inlining with functions in more than one compilation
unit is almost impossible to do portably, see
https://www.greenend.org.uk/rjk/tech/inline.html, so this usage
is for static
functions as in the example. All the R
configure code has checked is that R_INLINE
can be used in a
single C file with the compiler used to build R. We recommend that
packages making extensive use of inlining include their own configure
Header R_ext/Visibility.h has some definitions for controlling the visibility of entry points. These are only effective when 'HAVE_VISIBILITY_ATTRIBUTE' is defined – this is checked when R is configured and recorded in header Rconfig.h (included by R_ext/Visibility.h). It is often defined on modern Unix-alikes with a recent compiler169, but not supported on macOS nor Windows. Minimizing the visibility of symbols in a shared library will both speed up its loading (unlikely to be significant) and reduce the possibility of linking to other entry points of the same name.
C/C++ entry points prefixed by attribute_hidden
will not be
visible in the shared object. There is no comparable mechanism for
Fortran entry points, but there is a more comprehensive scheme used by,
for example package stats. Most compilers which allow control of
visibility will allow control of visibility for all symbols via a
flag, and where known the flag is encapsulated in the macros
'C_VISIBILITY', 'CXX_VISIBILITY'170 and 'F_VISIBILITY' for C, C++ and
Fortran compilers.171 These are defined in etc/Makeconf and so
available for normal compilation of package code. For example,
src/Makevars could include some of
This would end up with no visible entry points, which would be
pointless. However, the effect of the flags can be overridden by using
the attribute_visible
prefix. A shared object which registers
its entry points needs only for have one visible entry point, its
initializer, so for example package stats has
void attribute_visible R_init_stats(DllInfo *dll) { R_registerRoutines(dll, CEntries, CallEntries, FortEntries, NULL); R_useDynamicSymbols(dll, FALSE); ... }
Because the 'C_VISIBILITY' mechanism is only useful in conjunction
with attribute_visible
, it is not enabled unless
'HAVE_VISIBILITY_ATTRIBUTE' is defined. The usual visibility flag
is -fvisibility=hidden: some compilers also support
-fvisibility-inlines-hidden which can be used by overriding
'C_VISIBILITY' and 'CXX_VISIBILITY' in config.site when
building R, or editing etc/Makeconf in the R installation.
Note that configure
only checks that visibility attributes and
flags are accepted, not that they actually hide symbols.
The visibility mechanism is not available on Windows, but there is an equally effective way to control which entry points are visible, by supplying a definitions file pkgnme/src/pkgname-win.def: only entry points listed in that file will be visible. Again using stats as an example, it has
LIBRARY stats.dll EXPORTS R_init_stats
It is possible to build Mathlib
, the R set of mathematical
functions documented in Rmath.h, as a standalone library
libRmath under both Unix-alikes and Windows. (This includes the
functions documented in Numerical analysis subroutines as from
that header file.)
The library is not built automatically when R is installed, but can be built in the directory src/nmath/standalone in the R sources: see the file README there. To use the code in your own C program include
#define MATHLIB_STANDALONE #include <Rmath.h>
and link against '-lRmath' (and perhaps '-lm'). There is an example file test.c.
A little care is needed to use the random-number routines. You will need to supply the uniform random number generator
double unif_rand(void)
or use the one supplied (and with a dynamic library or DLL you will have to use the one supplied, which is the Marsaglia-multicarry with an entry points
set_seed(unsigned int, unsigned int)
to set its seeds and
get_seed(unsigned int *, unsigned int *)
to read the seeds).
The header files which R installs are in directory R_INCLUDE_DIR (default R_HOME/include). This currently includes
R.h includes many other files Rinternals.h definitions for using R's internal structures Rdefines.h macros for an S-like interface to the above (no longer maintained) Rmath.h standalone math library Rversion.h R version information Rinterface.h for add-on front-ends (Unix-alikes only) Rembedded.h for add-on front-ends R_ext/Applic.h optimization and integration R_ext/BLAS.h C definitions for BLAS routines R_ext/Callbacks.h C (and R function) top-level task handlers R_ext/GetX11Image.h X11Image interface used by package trkplot R_ext/Lapack.h C definitions for some LAPACK routines R_ext/Linpack.h C definitions for some LINPACK routines, not all of which are included in R R_ext/Parse.h a small part of R's parse interface: not part of the stable API. R_ext/RStartup.h for add-on front-ends R_ext/Rdynload.h needed to register compiled code in packages R_ext/Riconv.h interface to iconv
R_ext/Visibility.h definitions controlling visibility R_ext/eventloop.h for add-on front-ends and for packages that need to share in the R event loops (not Windows)
The following headers are included by R.h:
Rconfig.h configuration info that is made available R_ext/Arith.h handling for NA
R_ext/Boolean.h TRUE
type R_ext/Complex.h C typedefs for R's complex
R_ext/Constants.h constants R_ext/Error.h error signaling R_ext/Memory.h memory allocation R_ext/Print.h Rprintf
and variations. R_ext/RS.h definitions common to R.h and the former S.h, including F77_CALL
etc. R_ext/Random.h random number generation R_ext/Utils.h sorting and other utilities R_ext/libextern.h definitions for exports from R.dll on Windows.
The graphics systems are exposed in headers R_ext/GraphicsEngine.h, R_ext/GraphicsDevice.h (which it includes) and R_ext/QuartzDevice.h. Facilities for defining custom connection implementations are provided in R_ext/Connections.h, but make sure you consult the file before use.
Let us re-iterate the advice to include in C++ code system headers
before the R header files, especially Rinternals.h (included
by Rdefines.h) and Rmath.h, which redefine names which may
be used in system headers, or (preferably) to define R_NO_REMAP
Setting the environment variable _R_CXX_USE_NO_REMAP_
to a true
value allows the need for this to be tested, as it causes R CMD
to compile C++ code defining R_NO_REMAP
. This is
planned to become the default in future.
R programmers will often want to add methods for existing generic functions, and may want to add new generic functions or make existing functions generic. In this chapter we give guidelines for doing so, with examples of the problems caused by not adhering to them.
This chapter only covers the 'informal' class system copied from S3, and not with the S4 (formal) methods of package methods.
First, a caveat: a function named gen.cl
be invoked by the generic gen
for class cl
, so
do not name functions in this style unless they are intended to be
The key function for methods is NextMethod
, which dispatches the
next method. It is quite typical for a method function to make a few
changes to its arguments, dispatch to the next method, receive the
results and modify them a little. An example is
t.data.frame <- function(x) { x <- as.matrix(x) NextMethod("t") }
Note that the example above works because there is a next method, the default method, not that a new method is selected when the class is changed.
Any method a programmer writes may be invoked from another method
by NextMethod
, with the arguments appropriate to the
previous method. Further, the programmer cannot predict which method
will pick (it might be one not yet dreamt of), and the
end user calling the generic needs to be able to pass arguments to the
next method. For this to work
A method must have all the arguments of the generic, including
if the generic does.
It is a grave misunderstanding to think that a method needs only to
accept the arguments it needs. The original S version of
did not have a …
argument, although
did. It soon became clear that predict.glm
an argument dispersion
to handle over-dispersion. As
had neither a dispersion
nor a …
argument, NextMethod
could no longer be used. (The legacy, two
direct calls to predict.lm
, lives on in predict.glm
R, which is based on the workaround for S3 written by Venables &
Further, the user is entitled to use positional matching when calling
the generic, and the arguments to a method called by UseMethod
are those of the call to the generic. Thus
A method must have arguments in exactly the same order as the generic.
To see the scale of this problem, consider the generic function
, defined as
scale <- function (x, center = TRUE, scale = TRUE) UseMethod("scale")
Suppose an unthinking package writer created methods such as
scale.foo <- function(x, scale = FALSE, ...) { }
Then for x
of class "foo"
the calls
scale(x, , TRUE) scale(x, scale = TRUE)
would most likely do different things, to the justifiable consternation of the end user.
To add a further twist, which default is used when a user calls
in our example? What if
scale.bar <- function(x, center, scale = TRUE) NextMethod("scale")
and x
has class c("bar", "foo")
? It is the default
specified in the method that is used, but the default
specified in the generic may be the one the user sees.
This leads to the recommendation:
If the generic specifies defaults, all methods should use the same defaults.
An easy way to follow these recommendations is to always keep generics simple, e.g.
scale <- function(x, ...) UseMethod("scale")
Only add parameters and defaults to the generic if they make sense in all possible methods implementing it.
When creating a new generic function, bear in mind that its argument
list will be the maximal set of arguments for methods, including those
written elsewhere years later. So choosing a good set of arguments may
well be an important design issue, and there need to be good arguments
not to include a …
If a …
argument is supplied, some thought should be given
to its position in the argument sequence. Arguments which follow
must be named in calls to the function, and they must be
named in full (partial matching is suppressed after …
Formal arguments before …
can be partially matched, and so
may 'swallow' actual arguments intended for …
. Although it
is commonplace to make the …
argument the last one, that is
not always the right choice.
Sometimes package writers want to make generic a function in the base package, and request a change in R. This may be justifiable, but making a function generic with the old definition as the default method does have a small performance cost. It is never necessary, as a package can take over a function in the base package and make it generic by something like
foo <- function(object, ...) UseMethod("foo") foo.default <- function(object, ...) base::foo(object)
Earlier versions of this manual suggested assigning foo.default <-
. This is not a good idea, as it captures the base
function at the time of installation and it might be changed as R is
patched or updated.
The same idea can be applied for functions in other packages.
There are a number of ways to build front-ends to R: we take this to mean a GUI or other application that has the ability to submit commands to R and perhaps to receive results back (not necessarily in a text format). There are other routes besides those described here, for example the package Rserve (from CRAN, see also https://www.rforge.net/Rserve/) and connections to Java in 'JRI' (part of the rJava package on CRAN).
Note that the APIs described in this chapter are only intended to be
used in an alternative front-end: they are not part of the API made
available for R packages and can be dangerous to use in a
conventional package (although packages may contain alternative
front-ends). Conversely some of the functions from the API (such as
) should not be used in front-ends.
R can be built as a shared library172 if configured with --enable-R-shlib. This shared library can be used to run R from alternative front-end programs. We will assume this has been done for the rest of this section. Also, it can be built as a static library if configured with --enable-R-static-lib, and that can be used in a very similar way (at least on Linux: on other platforms one needs to ensure that all the symbols exported by libR.a are linked into the front-end).
The command-line R front-end, R_HOME/bin/exec/R, is one such example, and the former GNOME (see package gnomeGUI on CRAN's 'Archive' area) and macOS consoles are others. The source for R_HOME/bin/exec/R is in file src/main/Rmain.c and is very simple
int Rf_initialize_R(int ac, char **av); /* in ../unix/system.c */ void Rf_mainloop(); /* in main.c */ extern int R_running_as_main_program; /* in ../unix/system.c */ int main(int ac, char **av) { R_running_as_main_program = 1; Rf_initialize_R(ac, av); Rf_mainloop(); /* does not return */ return 0; }
indeed, misleadingly simple. Remember that R_HOME/bin/exec/R is run from a shell script R_HOME/bin/R which sets up the environment for the executable, and this is used for
and checking it is valid, as well as the path
and R_DOC_DIR
to the installed share and
doc directory trees. Also setting R_ARCH
if needed.
to include the directories used in linking
R. This is recorded as the default setting of
in the shell script
The first two of these can be achieved for your front-end by running it
via R CMD
. So, for example
R CMD /usr/local/lib/R/bin/exec/R R CMD exec/R
will both work in a standard R installation. (R CMD
first for executables in R_HOME/bin. These command-lines
need modification if a sub-architecture is in use.) If you do not want
to run your front-end in this way, you need to ensure that R_HOME
is set and LD_LIBRARY_PATH
is suitable. (The latter might well
be, but modern Unix/Linux systems do not normally include
/usr/local/lib (/usr/local/lib64 on some architectures),
and R does look there for system components.)
The other senses in which this example is too simple are that all the
internal defaults are used and that control is handed over to the
R main loop. There are a number of small examples173 in the
tests/Embedding directory. These make use of
in src/main/Rembedded.c, and essentially
#include <Rembedded.h> int main(int ac, char **av) { /* do some setup */ Rf_initEmbeddedR(argc, argv); /* do some more setup */ /* submit some code to R, which is done interactively via run_Rmainloop(); A possible substitute for a pseudo-console is R_ReplDLLinit(); while(R_ReplDLLdo1() > 0) { /* add user actions here if desired */ } */ Rf_endEmbeddedR(0); /* final tidying up after R is shutdown */ return 0; }
If you do not want to pass R arguments, you can fake an argv
array, for example by
char *argv[]= {"REmbeddedPostgres", "--silent"}; Rf_initEmbeddedR(sizeof(argv)/sizeof(argv[0]), argv);
However, to make a GUI we usually do want to run run_Rmainloop
after setting up various parts of R to talk to our GUI, and arranging
for our GUI callbacks to be called during the R mainloop.
One issue to watch is that on some platforms Rf_initEmbeddedR
change the settings of the FPU (e.g. to allow
errors to be trapped and to make use of extended precision registers).
The standard code sets up a session temporary directory in the usual
way, unless R_TempDir
is set to a non-NULL value before
is called. In that case the value is assumed to
contain an existing writable directory, and it is not
cleaned up when R is shut down.
sets R to be in interactive mode: you can set
(defined in Rinterface.h) subsequently to
change this.
Note that R expects to be run with the locale category
'LC_NUMERIC' set to its default value of C
, and so should
not be embedded into an application which changes that.
It is the user's responsibility to attempt to initialize only once. To
protect the R interpreter, Rf_initialize_R
will exit the
process if re-initialization is attempted.
Suitable flags to compile and link against the R (shared or static) library can be found by
R CMD config --cppflags R CMD config --ldflags
(These apply only to an uninstalled copy or a standard install.)
If R is installed, pkg-config
is available and neither
sub-architectures nor a macOS framework have been used, alternatives for
a shared R library are
pkg-config --cflags libR pkg-config --libs libR
and for a static R library
pkg-config --cflags libR pkg-config --static --libs libR
(This may work for an installed OS framework if pkg-config
taught where to look for libR.pc: it is installed inside the
However, a more comprehensive way is to set up a Makefile to compile the front-end. Suppose file myfe.c is to be compiled to myfe. A suitable Makefile might be
## WARNING: does not work when ${R_HOME} contains spaces include ${R_HOME}/etc${R_ARCH}/Makeconf all: myfe ## The following is not needed, but avoids PIC flags. myfe.o: myfe.c $(CC) $(ALL_CPPFLAGS) $(CFLAGS) -c myfe.c -o $@ ## replace $(LIBR) $(LIBS) by $(STATIC_LIBR) if R was build with a static libR myfe: myfe.o $(MAIN_LINK) -o $@ myfe.o $(LIBR) $(LIBS)
invoked as
R CMD make R CMD myfe
Even though not recommended, ${R_HOME}
may contain spaces. In
that case, it cannot be passed as an argument to include
in the
makefile. Instead, one can instruct make
using the -f
option to include Makeconf, for example via recursive
invocation of make
, see Writing portable packages.
all: $(MAKE) -f "${R_HOME}/etc${R_ARCH}/Makeconf" -f Makefile.inner
Additional flags which $(MAIN_LINK)
includes are, amongst others,
those to select OpenMP and --export-dynamic for the GNU linker
on some platforms. In principle $(LIBS)
is not needed
when using a shared R library as libR is linked against
those libraries, but some platforms need the executable also linked
against them.
For Unix-alikes there is a public header file Rinterface.h that
makes it possible to change the standard callbacks used by R in a
documented way. This defines pointers (if R_INTERFACE_PTRS
extern void (*ptr_R_Suicide)(const char *); extern void (*ptr_R_ShowMessage)(const char *); extern int (*ptr_R_ReadConsole)(const char *, unsigned char *, int, int); extern void (*ptr_R_WriteConsole)(const char *, int); extern void (*ptr_R_WriteConsoleEx)(const char *, int, int); extern void (*ptr_R_ResetConsole)(); extern void (*ptr_R_FlushConsole)(); extern void (*ptr_R_ClearerrConsole)(); extern void (*ptr_R_Busy)(int); extern void (*ptr_R_CleanUp)(SA_TYPE, int, int); extern int (*ptr_R_ShowFiles)(int, const char **, const char **, const char *, Rboolean, const char *); extern int (*ptr_R_ChooseFile)(int, char *, int); extern int (*ptr_R_EditFile)(const char *); extern void (*ptr_R_loadhistory)(SEXP, SEXP, SEXP, SEXP); extern void (*ptr_R_savehistory)(SEXP, SEXP, SEXP, SEXP); extern void (*ptr_R_addhistory)(SEXP, SEXP, SEXP, SEXP); extern int (*ptr_R_EditFiles)(int, const char **, const char **, const char *); extern SEXP (*ptr_do_selectlist)(SEXP, SEXP, SEXP, SEXP); extern SEXP (*ptr_do_dataentry)(SEXP, SEXP, SEXP, SEXP); extern SEXP (*ptr_do_dataviewer)(SEXP, SEXP, SEXP, SEXP); extern void (*ptr_R_ProcessEvents)();
which allow standard R callbacks to be redirected to your GUI. What these do is generally documented in the file src/unix/system.txt.
R_ShowMessage (char *message)
¶This should display the message, which may have multiple lines: it should be brought to the user's attention immediately.
R_Busy (int which)
¶This function invokes actions (such as change of cursor) when R
embarks on an extended computation (which=1
) and when such
a state terminates (which=0
R_ReadConsole (const char *prompt, unsigned char *buf, int buflen, int hist)
R_WriteConsole (const char *buf, int buflen)
R_WriteConsoleEx (const char *buf, int buflen, int otype)
R_ResetConsole ()
R_FlushConsole ()