1 Creating R packages
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()
command, 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.
1 although this is a persistent mis-usage. It seems to stem from S, whose analogues of R’s packages were officially known as library sections and later as chapters, but almost always referred to as libraries.
- A directory into which packages are installed, e.g.
/usr/lib/R/library
: in that sense it is sometimes referred to as a library directory or library tree (since the library is a directory which contains packages as directories, which themselves contain directories). - That used by the operating system, as a shared, dynamic or static library or (especially on Windows) a DLL, where the second L stands for ‘library’. Installed packages may contain compiled code in what is known on Unix-alikes as a shared object and on Windows as a DLL. The concept of a shared library (dynamic library on macOS) as a collection of compiled code to which a package might link is also used, especially for R itself on some platforms. On most platforms these concepts are interchangeable (shared objects and DLLs can both be loaded into the R process and be linked against), but macOS distinguishes between shared objects (extension
.so
) and dynamic libraries (extension.dylib
).
There are a number of well-defined operations on source packages.
- The most common is installation which takes a source package and installs it in a library using
R CMD INSTALL
orinstall.packages
. - Source packages can be built. This involves taking a source directory and creating a tarball ready for distribution, including cleaning it up and creating PDF/HTML documentation from any vignettes it may contain. Source packages (and most often tarballs) can be checked, when a test installation is done and tested (including running its examples); also, the contents of the package are tested in various ways for consistency and portability.
- Compilation is not a correct term for a package. Installing a source package which contains C, C++ or Fortran code will involve compiling that code. There is also the possibility of ‘byte’ compiling the R code in a package (using the facilities of package compiler): nowadays this is enabled by default for all packages. So compiling a package may come to mean byte-compiling its R code.
- It used to be unambiguous to talk about loading an installed package using
library()
, 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. Functionlibrary
performs both steps, but a package’s namespace can be loaded without the package being attached (for example by calls likesplines::ns
).
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.
1.1 Package structure
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.md
2, 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 purposes).
2 This seems to be commonly used for a file in ‘markdown’ format. Be aware that most users of R will not know that, nor know how to view such a file: platforms such as macOS and Windows do not have a default viewer set in their file associations. The CRAN package web pages render such files in HTML: the converter used expects the file to be encoded in UTF-8.
3 currently, top-level files .Rbuildignore
and .Rinstignore
, and vignettes/.install_extras
.
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 A-Za-z0-9._!#$%&+,;=@^(){}'[]
— we exclude space as many utilities do not accept spaces in file paths): non-English alphabetic characters cannot be guaranteed to be supported in all locales. It would be good practice to avoid the shell metacharacters (){}'[]$~
: ~
is also used as part of ‘8.3’ filenames on Windows. In addition, some applications on Windows can only work with path names of certain length, following an earlier limit in the Windows operating system. Packages are normally distributed as tarballs, and these have a limit on path lengths. So, to be friendly to users who themselves may want to use a relatively long path where they extract the package, and for maximal portability, 100 bytes.
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.
4 false positives are possible, but only a handful have been seen so far.
The R function package.skeleton
can help to create the structure for a new package: see its help page for details.
1.1.1 The DESCRIPTION
file
The DESCRIPTION
file contains basic information about the package in the following format:
: pkgname Package: 0.5-1 Version: 2015-01-01 Date: My First Collection of Functions Title: c(person("Joe", "Developer", role = c("aut", "cre"), Authors@R= "Joe.Developer@some.domain.net", email = c(ORCID = "nnnn-nnnn-nnnn-nnnn")), comment ("Pat", "Developer", role = "aut"), person("A.", "User", role = "ctb", person= "A.User@whereever.net")) email : Joe Developer [aut, cre], Author[aut], Pat Developer . User [ctb] A: Joe Developer <Joe.Developer@some.domain.net> Maintainer: R (>= 3.1.0), nlme Depends: MASS Suggests: A (one paragraph) description of what Description. the package does and why it may be useful: GPL (>= 2) License: https://www.r-project.org, http://www.another.url URL: https://pkgname.bugtracker.url BugReports
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 quotes.
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 "person"
, 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
for 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. Note that for CRAN submissions, providing Authors@R
is required, and providing ORCID identifiers (see https://orcid.org/) where possible is strongly encouraged.
5 at least if this is done in a locale which matches the package encoding.
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.
6 and required by CRAN, so checked by R CMD check --as-cran
.
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 LTO_OPT
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.
7 without a src/Makefile*
file.
8 LTO is not currently supported by the toolchain used on aarch64
.
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 INSTALL
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).
9 But it is checked for Open Source packages by R CMD check --as-cran
.
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 CITATION
file. Only encoding names latin1
and 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"
(when the package could be installed from source on any platform without additional tools). This is used by install.packages(type = "both")
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
or windows
, 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 Rd2pdf
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.
10 Duplicate definitions may trigger a warning: see User-defined macros.
Note: There should be no
Built
orPackaged
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.
11 bug.report
will try to extract an email address from a Contact
field if there is no BugReports
field.
1.1.2 Licensing
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
One of the “standard” short specifications
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.The names or abbreviations of other licenses contained in the license data base in file
share/licenses/license.db
in the R source or home directory, possibly (for versioned licenses) followed by a version restriction of the form(op v)
withop
one of the comparison operators<
,<=
,>
,>=
,==
, or!=
andv
a numeric version specification (strings of non-negative integers separated by.
), possibly combined via,
(see below for an example). For versioned licenses, one can also specify the name followed by the version, or combine an existing abbreviation and the version with a-
.Abbreviations
GPL
andLGPL
are ambiguous and usually12 taken to mean any version of the license: but it is better not to use them.One of the strings
file LICENSE
orfile LICENCE
referring to a file namedLICENSE
orLICENCE
in the package (source and installation) top-level directory.The string
Unlimited
, meaning that there are no restrictions on distribution or use other than those imposed by relevant laws (including copyright laws).
12 CRAN expands them to e.g. GPL-2 | GPL-3
.
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 available.packages
filters.
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.
1.1.3 Package Dependencies
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.
13 even one wrapped in \donttest
.
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
- A package should be listed in only one of these fields.
- Packages whose namespace only is needed to load the package using
library(pkgname)
should be listed in theImports
field and not in theDepends
field. Packages listed inimport
orimportFrom
directives in theNAMESPACE
file should almost always be inImports
and notDepends
. - Packages that need to be attached to successfully load the package using
library(pkgname)
must be listed in theDepends
field. - All packages that are needed14 to successfully run
R CMD check
on the package must be listed in one ofDepends
orSuggests
orImports
. Packages used to run examples or tests conditionally (e.g. viaif(require(pkgname))
) should be listed inSuggests
orEnhances
. (This allows checkers to ensure that all the packages needed for a complete check are installed.) - Packages needed to use datasets from the package should be in
Imports
: this includes those needed to define S4 classes used.
14 This includes all packages directly called by library
and require
calls, as well as data obtained via data(theirdata, package = "somepkg")
calls: R CMD check
will warn about all of these. But there are subtler uses which it may not detect: e.g. if package A uses package B and makes use of functionality in package B which uses package C which package B suggests or enhances, then package C needs to be in the Suggests
list for package A. Nor will undeclared uses in included files be reported, nor unconditional uses of packages listed under Enhances
. R CMD check --as-cran
will detect more of the subtler uses.
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 install.packages
checks versions for the Depends
, Imports
and (for dependencies = TRUE
) Suggests
fields.
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 ::
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 ::
calls.
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).
1.1.4 Suggested packages
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"))
is 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)) {
::plot3d(...)
rglelse {
} ## 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: plot3d
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 withAutoprint
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 _R_CHECK_SUGGESTS_ONLY_=true
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, .onLoad
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.
1.1.5 The INDEX
file
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 = pkgname)
.
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).
1.1.6 Package subdirectories
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.
15 Extensions .S
and .s
arise from code originally written for S(-PLUS), but are commonly used for assembler code. Extension .q
was used for S, which at one time was tentatively called QPE.
Extreme care is needed if top-level computations are made to depend on availability or not of other packages. In particular this applies to setMethods
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.
16 but they should be in the encoding declared in the DESCRIPTION
file.
17 This is true for OSes which implement the C
locale: Windows’ idea of the C
locale uses the WinAnsi charset.
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).
18 More precisely, they can contain the English alphanumeric characters and the symbols $ - _ . + ! ' ( ) , ; = &
.
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 .f95
,20 .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.
19 either or both of which may not be supported on particular platforms. Their main use is on macOS, but unfortunately recent versions of the macOS SDK have removed much of the support for Objective C v1.0 and Objective C++.
20 This is not accepted by the Intel Fortran compiler.
21 Using .hpp
is not guaranteed to be portable.
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 be 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__
.
22 There is also __APPLE_CC__
, but that indicates a compiler with Apple-specific features not the OS, although for historical reasons it is defined by LLVM clang
. It is used in Rinlinedfuns.h
.
23 the POSIX terminology, called ‘make variables’ by GNU make.
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
programs 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/Make |
vars.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/win |
shlib.mk` |
R_MAKEVARS_USER , ~/.R/Makevars-platform , ~/.R/Makevars R_MAKEVARS_USER , ` ~/.R/Makev |
ars.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/Make |
file.ucrt, src/Makefile.win` |
R_MAKEVARS_USER , ~/.R/Makevars-platform , ~/.R/Makevars R_MAKEVARS_USER , ` ~/.R/Makev |
ars.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 --libs-only
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
(the arch-dependent part of the path, often empty), SHLIB_EXT
(the extension of shared objects) and WINDOWS
(TRUE
on Windows, FALSE
elsewhere). Something close to the default behavior could be replicated with the following src/install.libs.R
file:
<- Sys.glob(paste0("*", SHLIB_EXT))
files <- file.path(R_PACKAGE_DIR, paste0('libs', R_ARCH))
dest 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
<- c("one", "two", "three")
execs if(WINDOWS) execs <- paste0(execs, ".exe")
if ( any(file.exists(execs)) ) {
<- file.path(R_PACKAGE_DIR, paste0('bin', R_ARCH))
dest 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 demo()
) 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 demo()
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 regexp
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).
24 The best way to generate such a file is to copy the .Rout
from a successful run of R CMD check
. If you want to generate it separately, do run R with options --vanilla --no-echo
and with environment variable LANGUAGE=en
set to get messages in English. Be careful not to use output with the option --timings
(and note that --as-cran
sets it).
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 zapsmall
.
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
).
1.1.7 Data in packages
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 save()
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.
26 People who have trouble with case are advised to use .rda
as a common error is to refer to abc.RData
as abc.Rdata
!
Images (extensions .RData
26 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
and Imports
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 Imports
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 = TRUE)
and using pkg::
to refer to objects in the namespace).
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 data()
will find, in the format foo
if data(foo)
provides foo
, or foo: bar bah
if data(foo)
provides 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 .xz
.
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 = TRUE)
(the default). Using bzip2
or xz
compression 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: checkRdaFiles
reports on the way the image was saved, and resaveRdaFiles
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
, xz
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
<- function(pkg)
CheckLazyDataCompression {
<- sub("_.*", "", pkg)
pkg_name <- tempfile(); dir.create(lib)
lib <- c("gzip", "bzip2", "xz")
zs <- integer(3); names(res) <- zs
res for (z in zs) {
<- c(paste0("--data-compress=", z),
opts "--no-libs", "--no-help", "--no-demo", "--no-exec", "--no-test-load")
.packages(pkg, lib, INSTALL_opts = opts, repos = NULL, quiet = TRUE)
install[z] <- file.size(file.path(lib, pkg_name, "data", "Rdata.rdb"))
res}
(res/1024)
ceiling}
(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 CheckLazyDataCompression()
and set the field – to gzip
in the unlikely event27 that is the best choice.
27 For all the CRAN packages tested, either gz
or bzip2
provided a very substantial reduction in installed size.
The analogue for sysdata.rda
is field SysDataCompression
: the default is xz
for files bigger than 1MB otherwise gzip
.
Lazy-loading is not supported for very large datasets (those which when serialized exceed 2GB, the limit for the format on 32-bit platforms).
1.1.8 Non-R scripts in packages
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:
28 BWidget
still is on Windows but Tktable
was not in R 4.0.0.
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)
<- !isFALSE(suppressWarnings(tclRequire('Tktable'))) have_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 https://sourceforge.net/projects/tktable/files/tktable/2.10/Tktable2.10.tar.gz/download, 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
.
1.1.9 Specifying URLs
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
.
Reserved characters should be encoded unless used in their reserved sense: see the help on URLencode()
.
The canonical URL for a CRAN package is
://cran.r-project.org/package=pkgname https
and not a version starting https://cran.r-project.org/web/packages/pkgname
.
1.2 Configure and cleanup
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 https://autotools.info/.
29 The script should only assume a POSIX-compliant /bin/sh
– see https://pubs.opengroup.org/onlinepubs/9699919799/utilities/V3_chap02.html. In particular bash
extensions must not be used, and not all R platforms have a bash
command, let alone one at /bin/bash
. All known shells used with R support the use of backticks, but not all support $(cmd)
. However, real-world shells are not fully POSIX-compliant and omissions and idiosyncrasies need to be worked around—which Autoconf will do for you. Arithmetic expansion is a known issue: see https://www.gnu.org/software/autoconf/manual/autoconf.html#Portable-Shell for this and others. Some checks can be done by the checkbashisms
Perl script at https://sourceforge.net/projects/checkbaskisms/files, also available in most Linux distributions in a package named either devscripts
or devscripts-checkbashisms
: a later version can be extracted from Debian sources such as the most recent tar.xz
in https://deb.debian.org/debian/pool/main/d/devscripts/ and has been needed for recent versions of Perl.
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 R_ARCH
(the arch-dependent part of the path, often empty).
Under a Unix-alike only, an executable (Bourne shell) script cleanup
is executed as the last thing by R CMD INSTALL
if option --clean
was given, and by R CMD build
when 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
to TRUE
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
<- function(x) {
foo if(!@HAVE_FOO@)
stop("Sorry, library 'foo' is not available")
...
From this file configure
creates the actual R source file foo.R
looking like
<- function(x) {
foo 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)
${R_HOME=`R RHOME`}
: "${R_HOME}"; then
if test -z "could not determine R_HOME"
echo
exit 1
fiCC=`"${R_HOME}/bin/R" CMD config CC`
CFLAGS=`"${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=`"${R_HOME}/bin/R" CMD config CXX`
"$CXX"; then
if test -z
AC_MSG_ERROR([No C++ compiler is available])
fiCXXFLAGS=`"${R_HOME}/bin/R" CMD config CXXFLAGS`
CPPFLAGS=`"${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
and CXXFLAGS
.
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
CC CFLAGS CXX CXXFLAGS CPPFLAGS LDFLAGS FC FCFLAGS
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
macro from the official Autoconf Macro Archive30, one can use
30 https://www.gnu.org/software/autoconf-archive/ax_blas.html. If you include macros from that archive you need to arrange for them to be included in the package sources for use by autoreconf
.
FC=`"${R_HOME}/bin/R" CMD config FC`
FCLAGS=`"${R_HOME}/bin/R" CMD config FFLAGS`
AC_PROG_FCFLIBS=`"${R_HOME}/bin/R" CMD config FLIBS`
AX_BLAS([], AC_MSG_ERROR([could not find your BLAS library], 1))
Note that FLIBS
as determined by R must be used to ensure that Fortran code works on all R platforms.
N.B.: If the configure
script creates files, e.g. src/Makevars
, you do need a cleanup
script to remove them. Otherwise R CMD build
may ship the files that are created. For example, package RODBC has
#!/bin/sh
rm -f config.* src/Makevars src/config.h
As this example shows, configure
often creates working files such as config.log
. If you use a hand-crafted script rather than one created by autoconf
, it is highly recommended that you log its actions to file config.log
.
If your configure script needs auxiliary files, it is recommended that you ship them in a tools
directory (as R itself does).
You should bear in mind that the configure script will not be used on Windows systems. If your package is to be made publicly available, please give enough information for a user on a non-Unix-alike platform to configure it manually, or provide a configure.win
script (or configure.ucrt
) to be used on that platform. (Optionally, there can be a cleanup.win
script (or cleanup.ucrt
). Both should be shell scripts to be executed by ash
, which is a minimal version of Bourne-style sh
. As from R 4.2.0, bash
is used. When configure.win
(or configure.ucrt
) is run the environment variables R_HOME
(which uses /
as the file separator), R_ARCH
and R_ARCH_BIN
will be set. Use R_ARCH
to decide if this is a 64-bit build for Intel (its value there is /x64
) and to install DLLs to the correct place (${R_HOME}/libs${R_ARCH}
). Use R_ARCH_BIN
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 R CMD SHLIB
), 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 R_PACKAGE_NAME
. (Currently the value of R_PACKAGE_DIR
is 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
may 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.
31 but it is available on the machines used to produce the CRAN binary packages: however as Apple does not ship .pc
files for its system libraries such as expat
, libcurl
, libxml2
, sqlite3
and zlib
, it may well not find information on these. Some substitutes are available from https://github.com/R-macos/recipes/tree/master/stubs/pkgconfig-darwin and are installed on the CRAN package builders.
32 It is not wise to check the version of pkg-config
as it is sometimes a link to pkgconf
, a separate project with a different version series.
-config --exists 'libtiff-4 >= 4.1.0' --print-errors # check the status
$ pkg-config --modversion libtiff-4
$ pkg4.3.0
-config --cflags libtiff-4
$ pkg-I/usr/local/include
-config --libs libtiff-4
$ pkg-L/usr/local/lib -ltiff
-config --static --libs libtiff-4
$ pkg-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.
33 but not all projects get this right when only a static library is installed, so it is often necessary to try in turn pkg-config --libs
and pkg-config --static --libs
.
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 pkg-config
is not what one might expect (e.g. libxml-2.0
even for 2.9.x). To get a complete list use
-config --list-all | sort pkg
Some external software provides a -config
command to do a similar job to pkg-config
, including
-config freetype-config gdal-config geos-config
curl-config iodbc-config libpng-config nc-config
gsl-config pcre2-config xml2-config xslt-config pcre
(curl-config
is for libcurl
not curl
. nc-config
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.ac
34 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.,
34 a decade ago Autoconf used configure.in
: this is still accepted but should be renamed and autoreconf
as used by R CMD check --as-cran
will report as such.
m4_include([tools/ax_pthread.m4])
Alternatively, Autoconf can be asked to search all .m4
files in a directory by including something like35
35 For those using autoconf
2.70 or later there is also AC_CONFIG_MACRO_DIRS
which allows multiple directories to be specified.
AC_CONFIG_MACRO_DIR([tools/m4])
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).
1.2.1 Using Makevars
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 INSTALL
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 extensions.
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
, PKG_CXXFLAGS
or PKG_FFLAGS
, 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
, CFLAGS
etc.: these should be settable by users (sites) through appropriate personal (site-wide) Makevars
files. See Customizing package compilation in R Installation and Administration for more information.
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
36 in POSIX parlance: GNU make
calls these ‘make variables’.
FLIBS
¶-
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 thesrc
directory. BLAS_LIBS
¶-
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. IfBLAS_LIBS
is included, thenFLIBS
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 addFLIBS
to the link line.37 at least on Unix-alikes: the Windows build currently resolves such dependencies to a static Fortran library when
Rblas.dll
is built. LAPACK_LIBS
¶-
A macro containing the LAPACK libraries (and paths where appropriate) used when building R. This may need to be included in
PKG_LIBS
. It may point to a dynamic librarylibRlapack
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.[
libRlapack
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 filesrc/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 2015.]For portability, the macros
BLAS_LIBS
andFLIBS
should always be included afterLAPACK_LIBS
(and in that order). SAFE_FFLAGS
¶-
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
onix86
platforms usinggfortran
. Note that this is not an additional flag to be used as part ofPKG_FFLAGS
, but a replacement forFFLAGS
. See the example later in this section.
Setting certain macros in Makevars
will prevent R CMD SHLIB
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
= @BLAS_LIBS@
PKG_LIBS
=$(R_XTRA_FFLAGS) $(FPICFLAGS) $(SHLIB_FFLAGS) $(SAFE_FFLAGS)
SLAMC_FFLAGS
: $(SHLIB)
all
.o: slamc.f
slamc(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 pmake
accept .NOTPARALLEL
as an alias for .NO_PARALLEL
.
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
to clean up (a copy of) the package sources. When it is run by build
it will have fewer macros set, in particular not $(SHLIB)
, 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
with 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 installation.
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 $(R_ARCH_BIN)
is only needed currently on Windows.
Environment or make variables can be used to select different macros for Intel 64-bit code or code for other architectures, for example (GNU make
syntax, allowed on Windows)
"$(WIN)" "64"
ifeq = value for 64-bit Intel Windows
PKG_LIBS else
= value for unknown Windows architectures
PKG_LIBS 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
= -L$(XML_DIR)/lib -lxml2 PKG_LIBS
one can use
= -L$(XML_DIR)/bin -lxml2 PKG_LIBS
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 https://sourceware.org/binutils/docs-2.20/ld/WIN32.html#WIN32.
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.
1.2.2 OpenMP support
There is some support for packages which wish to use OpenMP38. The make
macros
SHLIB_OPENMP_CFLAGS
SHLIB_OPENMP_CXXFLAGS SHLIB_OPENMP_FFLAGS
are available for use in src/Makevars
, src/Makevars.win
or Makevars.ucrt
. Include the appropriate macro in PKG_CFLAGS
, PKG_CXXFLAGS
and so on, and also in PKG_LIBS
(but see below for Fortran). C/C++ code that needs to be conditioned on the use of OpenMP can be used inside #ifdef _OPENMP
: note that some toolchains used for R (including Apple’s for macOS39 and some others using clang
40) have no OpenMP support at all, not even omp.h
.
39 There are somewhat fragile workarounds: see https://mac.r-project.org/openmp/.
40 Default builds of LLVM clang
3.8.0 and later have support for OpenMP, but the libomp
run-time library may not be installed.
For example, a package with C code written for OpenMP should have in src/Makevars
the lines
= $(SHLIB_OPENMP_CFLAGS)
PKG_CFLAGS = $(SHLIB_OPENMP_CFLAGS) PKG_LIBS
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 libraries.
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 clang
runtime with GCC but not vice versa: however gfortran
>= 9 may generate calls not in the clang
runtime). For a package with Fortran code using OpenMP the appropriate lines are
= $(SHLIB_OPENMP_FFLAGS)
PKG_FFLAGS = $(SHLIB_OPENMP_CFLAGS) PKG_LIBS
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
=
USE_FC_TO_LINK = $(SHLIB_OPENMP_FFLAGS)
PKG_FFLAGS = $(SHLIB_OPENMP_FFLAGS) PKG_LIBS
in src/Makevars
, src/Makevars.win
or Makevars.ucrt
. Note however, that when this is used $(FLIBS)
should not be included in PKG_LIBS
since it is for linking Fortran-compiled code by the C compiler.
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
appears 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 -fopenmp
).
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
41 In most implementations the _OPENMP
macro has value a date which can be mapped to an OpenMP version: for example, value 201307
is the date of version 4.0 (July 2013). However this may be used to denote the latest version which is partially supported, not that which is fully implemented.
use omp_lib
Rarely, using OpenMP with clang
on Linux generates calls in libatomic
, resulting in loading messages like
: __atomic_compare_exchange
undefined symbol: __atomic_load undefined symbol
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.
42 Windows default, not MinGW-w64 default.
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
43 Which it was at the time of writing with GCC, Intel and Clang compilers. The count may include the thread running the main process.
44 Be careful not to declare nthreads
as const int
: the Oracle compiler required it to be ‘an lvalue’.
#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 KMP_THREAD_LIMIT
might take precedence.
1.2.3 Using pthreads
There is no direct support for the POSIX threads (more commonly known as pthreads
): 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
the correct specification is
= -pthread
PKG_CPPFLAGS = -pthread PKG_LIBS
(and the plural version is also accepted on some systems/versions). For other platforms the specification is
= -D_REENTRANT
PKG_CPPFLAGS = -lpthread PKG_LIBS
(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 pthreads
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 https://pubs.opengroup.org/onlinepubs/009695399/basedefs/pthread.h.html): 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 pthreads
and the known performance issues are in the latter.
1.2.4 Compiling in sub-directories
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
= $(wildcard data/*.cpp network/*.cpp utils/*.cpp model/*.cpp model/*/*.cpp model/*/*/*.cpp)
SOURCES
= siena07utilities.o siena07internals.o siena07setup.o siena07models.o $(SOURCES:.cpp=.o) OBJECTS
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
= -LCsdp/lib -lsdp $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
PKG_LIBS
(SHLIB): Csdp/lib/libsdp.a
$
/lib/libsdp.a:
Csdp(cd Csdp/lib && $(MAKE) libsdp.a \
@="$(CC)" CFLAGS="$(CFLAGS) $(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)") CC
Note the quotes: the macros can contain spaces, e.g. CC = "gcc -m64 -std=gnu99"
. Several authors have forgotten about parallel makes: the static library in the subdirectory must be made before the shared object ($(SHLIB)
) and so the latter must depend on the former. Others forget the need45 for position-independent code.
45 A few OSes (AIX, Windows) do not need special flags for such code, but most do—although compilers will often generate PIC code when not asked to do so.
We really do not recommend using src/Makefile
instead of src/Makevars
, and as the example above shows, it is not necessary.
1.2.5 Configure example
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
= @RODBC_CPPFLAGS@ PKG_CPPFLAGS = @LIBS@ PKG_LIBS
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
and ODBC_LIBS
.
1.2.6 Using modern Fortran code
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
.
46 Intel compilers do not by default but this is worked around when using packages without a src/Makefile
.
The same compiler is used for both fixed-form and free-form Fortran code (with different file extensions and possibly different flags). Macro PKG_FFLAGS
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 ifx
) default to Fortran 201847.
47 but was said to have complete support only from version 2023.0.0.
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
see https://www.intel.com/content/www/us/en/developer/articles/technical/fortran-language-and-openmp-features-in-ifx.html#Fortran%20Standards. For LLVM’s flang-new
see https://flang.llvm.org/docs/F202X.html. gfortran
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 make
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.
1.2.7 Using C++ code
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.
48 Some changes are linked from https://isocpp.org/std/standing-documents/sd-6-sg10-feature-test-recommendations: there were also additional deprecations.
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 __cplusplus
.
49 Values 201103L
, 201402L
, 201703L
and 202002L
are most commonly used for C++11, C++14, C++17 and C++20 respectively, but some compilers set 1L
. For C++23 all that can currently be assumed is a value greater than that for C++20: for example g++
12 uses 202100L
and clang++
(LLVM 15, Apple 14) uses 202101L
.
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
CXX_STD = CXX14
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.
50 Often historically used to mean ‘not C++98’
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 clang
3.4 and currently-used versions of Apple clang
.
Code needing C++14 features can check for their presence via ‘SD-6 feature tests’51. Such a check could be
51 See https://isocpp.org/std/standing-documents/sd-6-sg10-feature-test-recommendations or https://en.cppreference.com/w/cpp/experimental/feature_test. It seems a reasonable assumption that any compiler promising some C++14 conformance will provide these—e.g. g++
4.9.x did but 4.8.5 did not.
#include <memory> // header where this is defined
#if defined(__cpp_lib_make_unique) && (__cpp_lib_make_unique >= 201304)
::make_unique;
using std#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 14
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 ‘devtoolset’.
Note that C++17 or later ‘support’ does not mean complete support: use feature tests as well as resources such as https://en.cppreference.com/w/cpp/compiler_support, 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 configure
script only checks for a compiler claiming to be later than C++20.52
52 The __cplusplus
value for C++23 is not yet finalized and all but some of the latest compilers use values from earlier drafts.
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`
"$CXX17"; then
if test -z
AC_MSG_ERROR([No C++17 compiler is available])
fiCXX17STD=`"${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
CXX = ${CXX14} ${CXX14STD}
CXXFLAGS = ${CXX14FLAGS}
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
..
1.2.8 C standards
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
and false
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 https://en.cppreference.com/w/c/23.)
The configure
script in recent versions of R aims to choose a C compiler which supports C11: as the default in recent versions of gcc
, 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 configure
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 __STDC_VERSION__
. This is undefined in C89/C90 and should have values 199901L
, 201112L
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
and aligned_alloc
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
file.
1.2.9 Using cmake
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
CC CFLAGS CXX CXXFLAGS CPPFLAGS LDFLAGS
CFLAGS
and CXXFLAGS
will need to include CPICFLAGS
and CXXPICFLAGS
respectively unless (as below) cmake
is asked to generate PIC code.
Setting these (and more) as environment variables controls the behaviour of cmake
(https://cmake.org/cmake/help/latest/manual/cmake-env-variables.7.html#manual:cmake-env-variables(7)), but it may be desirable to translate these into native settings such as
CMAKE_C_COMPILER
CMAKE_C_FLAGS
CMAKE_CXX_COMPILER
CMAKE_CXX_FLAGS
CMAKE_INCLUDE_PATH
CMAKE_LIBRARY_PATH
CMAKE_SHARED_LINKER_FLAGS_INIT CMAKE_OSX_DEPLOYMENT_TARGET
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.
Use the package’s
configure
script to create a static librarysrc/build/libmyLib.a
. This can then be treated in the same way as external software, for example having insrc/Makevars
= -Ilibs/include PKG_CPPFLAGS = build/libmyLib.a PKG_LIBS
(
-Lbuild -lmyLib
could also be used but this explicit specification avoids any confusion with dynamic libraries of the same name.)The
configure
script will need to contain something like (for C code)${R_HOME=`R RHOME`} : "${R_HOME}"; then if test -z "could not determine R_HOME" echo exit 1 fiCC=`"${R_HOME}/bin/R" CMD config CC` CFLAGS=`"${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 buildcmake -S ../libs \ -DCMAKE_BUILD_TYPE=Release \-DBUILD_SHARED_LIBS:bool=OFF \ -DCMAKE_POSITION_INDEPENDENT_CODE:bool=ON ${MAKE}
Use
src/Makevars
(orsrc/Makevars.win
orMakevars.ucrt
) to build within the subdirectory. This could be something like (for C code)= -Ilibs/include PKG_CPPFLAGS = libs/libmyLib.a PKG_LIBS (SHLIB): mylibs $ : mylibs(cd libs; \ ="$(CC)" CFLAGS="$(CFLAGS)" \ CC="$(CPPFLAGS)" LDFLAGS="$(LDFLAGS)" \ CPPFLAGS. \ 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
beforesrc/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
into src/Makevars
. This also applies to the ancillary command ctest
, if used.
1.3 Checking and building packages
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).
Note:
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
andR_BUILD_ENVIRON
; if unset, files53~/.R/check.Renviron
and~/.R/build.Renviron
are used) to set environment variables when using these utilities.
53 On systems which use sub-architectures, architecture-specific versions such as ~/.R/check.Renviron.x64
take precedence.
Note to Windows users:
R CMD build
may make use of the Windows toolset (see The Windows toolset in R Installation and Administration) if present and in your path, and it is required for packages which need it to install (including those withconfigure.win
,cleanup.win
,configure.ucrt
orcleanup.ucrt
scripts or asrc
directory) and e.g. need vignettes built.You may need to set the environment variable
TMPDIR
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).
1.3.1 Checking packages
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 .tar.xz
.
It is strongly recommended that the final checks are run on a tar
archive prepared by R CMD build
.
This runs a series of checks, including
The package is installed. This will warn about missing cross-references and duplicate aliases in help files.
The file names are checked to be valid across file systems and supported operating system platforms.
The files and directories are checked for sufficient permissions (Unix-alikes only).
The files are checked for binary executables, using a suitable version of
file
if available54. (There may be rare false positives.)The
DESCRIPTION
file is checked for completeness, and some of its entries for correctness. Unless installation tests are skipped, checking is aborted if the package dependencies cannot be resolved at run time. (You may need to setR_LIBS
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
andts
). Another check is that all packages mentioned inlibrary
orrequire
s or from which theNAMESPACE
file imports or are called via::
or:::
are listed (inDepends
,Imports
,Suggests
): this is not an exhaustive check of the actual imports.Available index information (in particular, for demos and vignettes) is checked for completeness.
The package subdirectories are checked for suitable file names and for not being empty. The checks on file names are controlled by the option
--check-subdirs=value
. This defaults todefault
, which runs the checks only if checking a tarball: the default can be overridden by specifying the value asyes
orno
. Further, the check on thesrc
directory is only run if the package does not contain aconfigure
script (which corresponds to the valueyes-maybe
) and there is nosrc/Makefile
orsrc/Makefile.in
.To allow a
configure
script to generate suitable files, files ending in.in
will be allowed in theR
directory.A warning is given for directory names that look like R package check directories – many packages have been submitted to CRAN containing these.
The R files are checked for syntax errors. Bytes which are non-ASCII are reported as warnings, but these should be regarded as errors unless it is known that the package will always be used in the same locale.
It is checked that the package can be loaded, first with the usual default packages and then only with package base already loaded. It is checked that the namespace can be loaded in an empty session with only the base namespace loaded. (Namespaces and packages can be loaded very early in the session, before the default packages are available, so packages should work then.)
The R files are checked for correct calls to
library.dynam
. 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 calledvalue
. All foreign function calls (.C
,.Fortran
,.Call
and.External
calls) are tested to see if they have aPACKAGE
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 oftools::checkFF("mypkg", verbose=TRUE)
, especially if the intention were to always use aPACKAGE
argument)The
Rd
files are checked for correct syntax and metadata, including the presence of the mandatory fields (\name
,\alias
,\title
and\description
). TheRd
name and title are checked for being non-empty, and there is a check for missing cross-references (links).A check is made for missing documentation entries, such as undocumented user-level objects in the package.
Documentation for functions, data sets, and S4 classes is checked for consistency with the corresponding code.
It is checked whether all function arguments given in
\usage
sections ofRd
files are documented in the corresponding\arguments
section.The
data
directory is checked for non-ASCII characters and for the use of reasonable levels of compression.C, C++ and Fortran source and header files55 are tested for portable (LF-only) line endings. If there is a
Makefile
orMakefile.in
orMakevars
orMakevars.in
file under thesrc
directory, it is checked for portable line endings and the correct use of$(BLAS_LIBS)
and$(LAPACK_LIBS)
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.Some checks are made of the contents of the
inst/doc
directory. These always include checking for files that look like leftovers, and if suitable tools (such asqpdf
) are available, checking that the PDF documentation is of minimal size.The examples provided by the package’s documentation are run. (see Writing R documentation files, for information on using
\examples
to create executable example code.) If there is a filetests/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
T
andF
redefined to generate an error unless they are set in the example: See Logical vectors in An Introduction to R.If the package sources contain a
tests
directory then the tests specified in that directory are run. (Typically they will consist of a set of.R
source files and target output files.Rout.save
.) Please note that the comparison will be done in the end user’s locale, so the target output files should be ASCII if at all possible. (The command line option--test-dir=foo
may be used to specify tests in a non-standard location. For example, unusually slow tests could be placed ininst/slowTests
and thenR 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.)The R code in package vignettes (see Writing package vignettes) is executed, and the vignettes re-made from their sources as a check of completeness of the sources (unless there is a
BuildVignettes
field in the package’sDESCRIPTION
file with a false value). If there is a target output file.Rout.save
in the vignette source directory, the output from running the code in that vignette is compared with the target output file and any differences are reported (but not recorded in the log file). (If the vignette sources are in the deprecated locationinst/doc
, do mark such target output files to not be installed in.Rinstignore
.)If there is an error57 in executing the R code in vignette
foo.ext
, a log filefoo.ext.log
is created in the check directory. The vignettes are re-made in a copy of the package sources in thevign_test
subdirectory of the check directory, so for further information on errors look in directorypkgname/vign_test/vignettes
. (It is only retained if there are errors or if environment variable_R_CHECK_CLEAN_VIGN_TEST_
is set to a false value.)The PDF version of the package’s manual is created (to check that the
Rd
files can be converted successfully). This needs LaTeX and suitable fonts and LaTeX packages to be installed. See Making the manuals in R Installation and Administration for further details.Optionally (including by
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 variableR_TIDYCMD
. Up-to-date versions can be installed from http://binaries.html-tidy.org/.
54 A suitable file.exe
is part of the Windows toolset: it checks for gfile
if a suitable file
is not found: the latter is available in the OpenCSW collection for Solaris at https://www.opencsw.org/. The source repository is http://ftp.astron.com/pub/file/.
55 An exception is made for subdirectories with names starting win
or Win
.
56 on most other platforms such runtime libraries are dynamic, but static libraries are currently used on Windows because the toolchain is not a standard part of the OS.
57 or if option --use-valgrind
is used or environment variable _R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_
is set to a true value or if there are differences from a target output file
58 for the most comprehensive checking this should be 5.8.0 or later: any for which tidy --version
does not report a version number will be too old – this includes the 2006 version shipped with macOS.
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
="suppressLocalUnused=FALSE" _R_CHECK_CODETOOLS_PROFILE_
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 https://pandoc.org/installing.html.
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 check
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
-l libdir --install=check:pkg.log pkg Rdev CMD check
(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
.)
1.3.2 Building package tarballs
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 eclipse
61, directories with names check
, chm
, or ending .Rcheck
or Old
or old
and files GNUMakefile
62, 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 names.
60 called CVS
or .svn
or .arch-ids
or .bzr
or .git
(but not files called .git
) or .hg
.
61 called .metadata
.
62 which is an error: GNU make uses GNUmakefile
.
63 see tools:::.hidden_file_exclusions
and tools:::get_exclude_patterns()
for further excluded files and file patterns, respectively.
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 \Sexpr
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
or xz
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 gzip
. 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 tools::compactPDF
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 _R_BUILD_COMPACT_VIGNETTES_
) 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 build
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).
1.3.3 Building binary packages
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:
- If the installation is successful, it will overwrite any existing installation of the same package.
- The default library tree must have write permission; if not, the package will not install and the binary will not be created.
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.
1.4 Writing package vignettes
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 Sweave
vignette in package utils for details on the source document format.
64 and to avoid problems with case-insensitive file systems, lower-case versions of all these extensions.
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
65 unless inhibited by using BuildVignettes: no
in the DESCRIPTION
file.
66 provided the conditions of the package’s license are met: many, including CRAN, see the omission of source components as incompatible with an Open Source license.
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 texi2pdf
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
67. Finally, if there is a Makefile
and it has a clean:
target, make clean
is run.
67 R_HOME/bin
is prepended to the PATH
so that references to R
or Rscript
in the Makefile
do make use of the currently running version of R.
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
: pdf clean
all
: ABC-intro.pdf ABC-details.pdf
pdf
%.pdf: %.tex
--pdf $*
texi2dvi
:
clean*.tex ABC-details-*.pdf rm
which will start removing the source files whilst pdflatex
is working.
Metadata lines can be placed in the source file, preferably in LaTeX comments in the preamble. One such is a \VignetteIndexEntry
of the form
%\VignetteIndexEntry{Using Animal}
Others you may see are \VignettePackage
(currently ignored), \VignetteDepends
(a comma-separated list of package names) and \VignetteKeyword
(which replaced \VignetteKeywords
). These are processed at package installation time to create the saved data frame Meta/vignette.rds
. The \VignetteEngine
statement is described in Non-Sweave vignettes. Vignette metadata can be extracted from a source file using tools::vignetteInfo
.
At install time an HTML index for all vignettes in the package is automatically created from the \VignetteIndexEntry
statements 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 .R
.
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.)
1.4.1 Encodings and vignettes
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.
Stangle()
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.
Sweave()
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
\usepackage[utf8]{inputenc}
(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
%\VignetteEncoding{UTF-8}
If the encoding is UTF-8, this can also be declared using the declaration
%\SweaveUTF8
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.
Sweave()
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 Sweave()
: this can be circumvented by setting options(useFancyQuotes="UTF-8")
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.
1.4.2 Non-Sweave vignettes
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 Stangle()
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
%\VignetteEngine{knitr::knitr}
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
directory).
Package writers who would like to supply vignette engines need to register those engines in the package .onLoad
function. 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.
1.5 Package namespaces
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 library
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.
68 Note that lazy-loaded datasets are not in the package’s namespace so need to be accessed via ::
, e.g. survival::survexp.us
.
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).
1.5.1 Specifying imports and exports
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 exportPattern
. The directive
exportPattern("^[^.]")
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
and importFrom
. 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 importFrom
. 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
and baz
. 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
will 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.
1.5.2 Registering S3 methods
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 print
method for class foo
, then the directive
(print, foo) S3method
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
must be.)
It is possible to specify a third argument to S3method, the function to be used as the method, for example
(print, check_so_symbols, .print.via.format) S3method
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 cls
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.
1.5.3 Load hooks
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
and .onAttach
. They both take arguments69 libname
and pkgname
; they should be defined in the namespace but not exported.
69 they will be called with two unnamed arguments, in that order.
Packages can use a .onDetach
or .Last.lib
function (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 unloadNamespace
, 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 exported.
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 setHook
.
These hooks are often used incorrectly. People forget to export .Last.lib
. Compiled code should be loaded in .onLoad
(or via a useDynLb
directive: see below) and unloaded in .onUnload
. 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.
1.5.4 useDynLib
A NAMESPACE
file can contain one or more useDynLib
directives which allows shared objects that need to be loaded.70 The directive
70 NB: this will only be read in all versions of R if the package contains R code in a R
directory.
useDynLib(foo)
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 useDynLib
directive instead.
71 Note that this is the basename of the shared object, and the appropriate extension (.so
or .dll
) will be added.
The useDynLib
directive also accepts the names of the native routines that are to be used in R via the .C
, .Call
, .Fortran
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
, .Fortran
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 myRoutine
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 = dllName
format used above for mapping symbols to R variables. For example, if we wanted to assign the DLL reference for the DLL foo
in the example above to the variable myDLL
, we would use the following directive in the NAMESPACE
file:
= useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine) myDLL
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 useDynLib
, 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[] = {
, INTSXP, STRSXP, LGLSXP
REALSXP};
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
, bar_sym
, 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
<- function()
R_version
{.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 F_bkde
and so on, and so avoid clashes with R code in the namespace.
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.
1.5.5 An example
As an example consider two packages named foo and bar. The R code for package foo in file foo.R
is
<- 1 x <- function(y) c(x,y) f <- function(x) .Call("foo", x, PACKAGE="foo") foo .foo <- function(x, ...) cat("<a foo>\n") print
Some C code defines a C function compiled into DLL foo
(with an appropriate extension). The NAMESPACE
file for this package is
(foo) useDynLib(f, foo) export(print, foo) S3method
The second package bar has code file bar.R
<- function(...) sum(...) c <- function(y) f(c(y, 7)) g <- function(y) y+9 h
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
produces
> 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 sum
, but in foo the variable c
refers to the standard function c
in base.
1.5.6 Namespaces with S4 classes and methods
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)
or 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
72 Imports: methods
may suffice, but package code is little exercised without the methods package on the search path and may not be fully robust to this scenario.
(mle) # exporting methods implicitly exports the generic
export("stats", approx, optim, pchisq, predict, qchisq, qnorm, spline)
importFrom## For these, we define methods or (AIC, BIC, nobs) an implicit generic:
("stats", AIC, BIC, coef, confint, logLik, nobs, profile,
importFrom, vcov)
update(mle, profile.mle, summary.mle)
exportClasses## All methods for imported generics:
(coef, confint, logLik, plot, profile, summary,
exportMethods, update, vcov)
show## implicit generics which do not have any methods here
(AIC, BIC, nobs) export
All S4 classes to be used outside the package need to be listed in an exportClasses
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.
73 This defaults to the same pattern as exportPattern
: use something like exportClassPattern("^$")
to override this.
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
or exportMethods
, 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
74 if it does, there will be opaque warnings about replacing imports if the classes/methods are also imported.
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
files
export(f1, ng1) exportMethods("[") exportClasses(c1)
and
importFrom(A, ng1) importClassesFrom(A, c1) importMethodsFrom(A, f1) export(f4, f5) exportMethods(f6, "[") exportClasses(c1, c2)
respectively.
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
from 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.
1.6 Writing portable packages
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()
.
75 People use dev.new()
to open a device at a particular size: that is not portable but using dev.new(noRStudioGD = TRUE)
helps.
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.
If your package has a
configure
script, provide aconfigure.win
orconfigure.ucrt
script to be used on Windows (an emptyconfigure.win
file if no actions are needed).If your package has a
Makevars
orMakefile
file, make sure that you use only portable make features. Such files should be LF-terminated76 (including the final line of the file) and not make use of GNU extensions. (The POSIX specification is available at https://pubs.opengroup.org/onlinepubs/9699919799/utilities/make.html; anything not documented there should be regarded as an extension to be avoided. Further advice can be found at https://www.gnu.org/software/autoconf/manual/autoconf.html#Portable-Make. ) Commonly misused GNU extensions are conditional inclusions (ifeq
and the like),${shell ...}
,${wildcard ...}
and similar, and the use of+=
77 and:=
. Also, the use of$<
other than in implicit rules is a GNU extension, as is the$^
macro. 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 formake
is not included in the POSIX specification and is not implemented by some of themake
s which have been used with R. However, it is more commonly implemented (e.g. by FreeBSDmake
) than the GNU-specific--directory=
.You should not rely on built-in/default
make
rules, even when specified by POSIX, as somemake
s do not have the POSIX ones and others have altered them.The use of
${shell ...}
can be avoided by using backticks, e.g.= `gsl-config --cflags` PKG_CPPFLAGS
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 bySystemRequirements: GNU make
and ensure that you use the value of environment variable
MAKE
(and not justmake
) in your scripts. (On some platforms GNU make is available under a name such asgmake
, and thereSystemRequirements
is used to setMAKE
.) Yourconfigure
script (or similar) does need to check that the executable pointed to byMAKE
is indeed GNU 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 calledGNUmakefile
rather thanMakefile
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
orMakefile.ucrt
.If you use
src/Makevars
to compile code in a subdirectory, ensure that you have followed all the advice above. In particular- Anticipate a parallel
make
. See UsingMakevars
. - Pass macros down to the makefile in the subdirectory, including all the needed compiler flags (including PIC and visibility flags). If they are used (even by a default rule) in the subdirectory’s Makefile, this includes macros
AR
andRANLIB
. See Compiling in sub-directories, which has a C example. A C++ example:makefile pkg/libpkg.a: (cd pkg && $(MAKE) -f make_pkg libpkg.a \ CXX="$(CXX)" CXXFLAGS="$(CXXFLAGS) $(CXXPICFLAGS) $(C_VISIBILITY)" \ AR="$(AR)" RANLIB="$(RANLIB)")
- Ensure that cleanup will be performed by
R CMD build
, for example in acleanup
script or aclean
target.
- Anticipate a parallel
If your package uses a
src/Makefile
file to compile code to be linked into R, ensure that it uses exactly the same compiler and flag settings that R uses when compiling such code: people often forgetPIC
flags. IfR CMD config
is used, this needs something like (for C++)makefile RBIN = `"${R_HOME}/bin/R"` CXX = `"${RBIN}" CMD config CXX` CXXFLAGS = `"${RBIN}" CMD config CXXFLAGS` `"${RBIN}" CMD config CXXPICFLAGS`
Names of source files including
=
(such assrc/complex_Sig=gen.c
) will confuse somemake
programs and should be avoided.Bash extensions also need to be avoided in shell scripts, including expressions in Makefiles (which are passed to the shell for processing). Some R platforms use strict79 Bourne shells: an earlier R toolset on Windows80 and some Unix-alike OSes use
ash
(https://en.wikipedia.org/wiki/Almquist_shell, a ’lightweight shell with few builtins) or derivatives such asdash
. Beware of assuming that all the POSIX command-line utilities are available, especially on Windows where only a subset (which has changed by version ofRtools
) is provided for use with R. One particular issue is the use ofecho
, for which two behaviours are allowed (https://pubs.opengroup.org/onlinepubs/9699919799/utilities/echo.html) 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 forecho -n
is the commandprintf
. Another common issue is the construction=value export FOO
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.,
-f src/*.{o,so,d} rm
is not portable.
The
-o
flag forset
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 asdarwin19.0
:uname
is often what is intended (with common valuesDarwin
,Linux
andSunOS
).On macOS which shell
/bin/sh
invokes is user- and platform-dependent: it might bebash
version 3.2,dash
orzsh
(for new accounts it iszsh
, for accounts ported from Mojave or earlier it is usuallybash
).R is not built by default as a shared library on non-Windows platforms (although it commonly is on macOS to support the GUI), so there need not be a file
libR.so
norlibR.dylib
. Users ofcmake
orrust
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 for more information.Make use of the abilities of your compilers to check the standards-conformance of your code. For example,
gcc
,clang
andgfortran
82 can be used with options-Wall -pedantic
to alert you to potential problems. This is particularly important for C++, whereg++ -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 personalMakevars
files. See Customizing package compilation in R Installation and Administration for more information.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,gfortran
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 withgcc
orclang
. If your package has a (autoconf
-generated)configure script
, try installing it whilst using this flag, and read through theconfig.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 insrc/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 aresrc/Makefile
files and makefiles in sub-directories. As a comment in the code saysIt 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 inflang-new
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 customizesrc/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 fornative
). People assume this is a ‘minimum’ CPU specification, but that is not how it is documented forgcc
(it is accepted byclang
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 evengcc
supportsnative
for all CPUs, and it can do surprising things if it finds a CPU released later than its version.Do be very careful with passing arguments between R, C and Fortran code. In particular,
long
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 oflong
in C code has been thought through: if you need a longer type thanint
you should use a configure test for a C99/C++11 type such asint_fast64_t
(and failing that,long long
) and typedef your own type, or use another suitable type (such assize_t
, but beware that is unsigned andssize_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 typesintptr_t
anduintptr_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 toint
in C on all R platforms. There is no such guarantee for Fortranlogical
, and recentgfortran
maps it toint_least32_t
on most platforms.Under no circumstances should your compiled code ever call
abort
orexit
83: these terminate the user’s R process, quite possibly losing all unsaved work. One usage that could callabort
is theassert
macro in C or C++ functions, which should never be active in production code. The normal way to ensure that is to define the macroNDEBUG
, andR 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 useassert
during development. you can include-UNDEBUG
inPKG_CPPFLAGS
or#undef
it in your headers or code files. Note that your ownsrc/Makefile
or makefiles in sub-directories may also need to defineNDEBUG
.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
norEXIT
(a GNU extension).Compiled code should not write to
stdout
orstderr
and C++ and Fortran I/O should not be used. As with the previous item such calls may come from external software and may never be called, but package authors are often mistaken about that.Compiled code should not call the system random number generators such as
rand
,drand48
andrandom
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. viasrand
), they will interfere with each other. This applies also to Fortran 90’srandom_number
andrandom_seed
, and Fortran 2018’srandom_init
. And to GNU Fortran’srand
,irand
andsrand
. Except fordrand48
, what PRNG these functions use is implementation-dependent.Nor should the C++11 random number library be used nor any other third-party random number generators such as those in GSL.
Use of
sprintf
andvsprintf
is regarded as a potential security risk and warned about on some platforms.85R CMD check
reports if any calls are found.Errors in memory allocation and reading/writing outside arrays are very common causes of crashes (e.g., segfaults) on some machines. See Checking memory access for tools which can be used to look for this.
Many platforms will allow unsatisfied entry points in compiled code, but will crash the application (here R) if they are ever used. Some (notably Windows) will not. Looking at the output of
-pg mypkg.so nm
and checking if any of the symbols marked
U
is unexpected is a good way to avoid this.Linkers have a lot of freedom in how to resolve entry points in dynamically-loaded code, so the results may differ by platform. One area that has caused grief is packages including copies of standard system software such as
libz
(especially those already linked into R). In the case in point, entry pointgzgets
was 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 namemyprintf
, 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 matchliblz4
. And-L.
only appends to the list of searched locations, andliblz4
might be found in an earlier-searched location (and has been). The only safe way is to give an explicit path, for example/libLZ4.a .
Conflicts between symbols in DLLs are handled in very platform-specific ways. Good ways to avoid trouble are to make as many symbols as possible static (check with
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 withR_
are regarded as part of R’s namespace and should not be used in packages.It is good practice for DLLs to register their symbols (see Registering native routines), restrict visibility (see Controlling visibility) and not allow symbol search (see Registering native routines). It should be possible for a DLL to have only one visible symbol,
R_init_pkgname
, on suitable platforms87, which would completely avoid symbol conflicts.It is not portable to call compiled code in R or other packages via
.Internal
,.C
,.Fortran
,.Call
or.External
, since such interfaces are subject to change without notice and will probably result in your code terminating the R process.Do not use (hard or symbolic) file links in your package sources. Where possible
R CMD build
will replace them by copies.If you do not yourself have a Windows system, consider submitting your source package to WinBuilder (https://win-builder.r-project.org/) before distribution. If you need to check on an M1 Mac, there is a check service at https://mac.r-project.org/macbuilder/submit.html.
It is bad practice for package code to alter the search path using
library
,require
orattach
and this often does not work as intended. For alternatives, see Suggested packages andwith()
.Examples can be run interactively via
example
as well as in batch mode when checking. So they should behave appropriately in both scenarios, conditioning byinteractive()
the parts which need an operator or observer. For instance, progress bars88 are only appropriate in interactive use, as is displaying help pages or callingView()
(see below).Be careful with the order of entries in macros such as
PKG_LIBS
. 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
.Care is needed with the use of
LinkingTo
. 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 theCPPFLAGS
macro 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 packageinclude
directories should be avoided, both between packages and between a package and system and third-party software.The
ar
utility is often used in makefiles to make static libraries. Its modifieru
is defined by POSIX but is disabled in GNUar
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$(RANLIB)
if needed (which is system-dependent: on most systems90ar
always 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 systemsar -cr
must have at least one file specified.The
s
modifier (to replace a separate call toranlib
) is required by X/OPEN but not POSIX, soar -crs
is not portable.For portability the
AR
andRANLIB
macros should always be used – some builds require wrappers such asgcc-ar
or extra arguments to specify plugins.The
strip
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 forstrip
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
invokesstrip --strip-debug
for GNUld
(and similarly on macOS) but is not portable: in particular on Solaris it did something completely different and took an argument.Some people have a need to set a locale. Locale names are not portable, and e.g.
fr_FR.utf8
is commonly used on Linux but not accepted on macOS.fr_FR.UTF-8
is more portable, being accepted on recent Linux, AIX, FreeBSD, macOS and Solaris (at least). However, some Linux distributions micro-package, so locales defined by glibc (including these examples) may not be installed.Avoid spaces in file names, not least as they can cause difficulties for external tools. An example was a package with a knitr vignette that used spaces in plot names: this caused some older versions of
pandoc
to fail with a baffling error message.Non-ASCII filenames can also cause problems (particularly in non-UTF-8 locales).
Take care in naming LaTeX macros (also known as ‘commands’) in vignette sources: if these are also defined in a future version of one of the LaTeX packages used there will be a fatal error. One instance in 2021 was package
hyperref
newly defining\C
,\F
,\G
,\U
and\textapprox
. If you are confident that your definitions will be the only ones relevant you can use\renewcommand
but it is better to use names clearly associated with your package.Make sure that any version requirement for Java code is both declared in the
SystemRequirements
field91 and tested at runtime (not least as the Java installation when the package is installed might not be the same as when the package is run and will not be for binary packages).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 isSystemRequirements: Java (>= 11)
A suitable test for Java at least version 8 for packages using rJava would be something like
.jinit() <- .jcall("java/lang/System", "S", "getProperty", "java.runtime.version") jv if(substr(jv, 1L, 2L) == "1.") { <- as.numeric(paste0(strsplit(jv, "[.]")[[1L]][1:2], collapse = ".")) jvn 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 gavejv
as11+28
whereas Java 11.0.11 gave11.0.11+9
. (https://openjdk.org:443/jeps/322 details the current scheme. Note that it is necessary to allow for pre-releases like11-ea+22
.)Note too that the compiler used to produce a
jar
can impose a minimum Java version, often resulting in an arcane message like: ... Unsupported major.minor version 52.0 java.lang.UnsupportedClassVersionError
(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 utilityjavap
, if necessary after extracting the.class
files from a.jar
archive. For example,jar xvf some.jar-verbose path/to/some.class | grep major javap
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 wasA package with a hard-to-satisfy system requirement is by definition not portable, annoyingly so if this is not declared in the
SystemRequirements
field. The most common example is the use ofpandoc
, 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 ofpandoc
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
andrustc
).Usage of external commands should always be conditional on a test for presence (perhaps using
Sys.which
), as well as declared in theSystemRequirements
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 ifcmake
is not found on the path.Be sure to use portable encoding names: none of
utf8
,mac
andmacroman
is portable. See the help forfile
for more details.Do not invoke R by plain
R
,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
can contain spaces).Do not use
R_HOME
in makefiles except when passing them to the shell. Specifically, do not useR_HOME
in the argument toinclude
, asR_HOME
can contain spaces. Quoting the argument toinclude
does not help. A portable and the recommended way to avoid the problem of spaces in${R_HOME}
is using option-f
ofmake
. This is easy to do with recursive invocation ofmake
, which is also the only usual situation whenR_HOME
is needed in the argument forinclude
.makefile $(MAKE) -f "${R_HOME}/etc${R_ARCH}/Makeconf" -f Makefile.inner
If distributing datasets involving date-times, consider if a time zone needs to be specified. The most portable way to distribute date-times is as objects of class
"POSIXct"
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"tzone"
attribute. Dates (e.g, birthdays) are conventionally considered independently of time zone.If at all possible avoid any Internet access during package installation. Installation and use may well be on different machines/accounts and those allowed to install software may have no Internet access, and being self-contained helps ensure long-term reproducibility.
76 Solaris make
did not accept CRLF-terminated Makefiles; Solaris warned about and some other make
s ignore incomplete final lines.
77 This was apparently introduced in SunOS 4, and is available elsewhere provided it is surrounded by spaces.
78 GNU make, BSD make and other variants of pmake
in FreeBSD, NetBSD and formerly in macOS, and formerly AT&T make as implemented on Solaris and ‘Distributed Make’ (dmake
), part of Oracle Developer Studio and available in other versions including from Apache OpenOffice.
79 For example, test
options -a
and -e
are not portable, and not supported in the AT&T Bourne shell used on Solaris 10/11, even though they are in the POSIX standard. Nor did Solaris support $(cmd)
.
80 as from R 4.0.0 the default is bash
.
81 it was not in the Bourne shell, and was not supported by Solaris 10.
82 https://fortranwiki.org/fortran/show/Modernizing+Old+Fortran may help explain some of the warnings from gfortran -Wall -pedantic
.
83 or where supported the variants _Exit
and _exit
.
84 This and srandom
are in any case not portable. They are in POSIX but not in the C99 standard, and not available on Windows.
85 including macOS as from version 13.
86 in libselinux
.
87 At least Linux and Windows, but not macOS.
88 except perhaps the simplest kind as used by download.file()
in non-interactive use.
89 Whereas the GNU linker reorders so -L
options are processed first, the Solaris one did not.
90 some versions of macOS did not.
91 If a Java interpreter is required directly (not via rJava) this must be declared and its presence tested like any other external command.
92 For example, the ability to handle https://
URLs.
Do be careful in what your tests (and examples) actually test. Bad practice seen in distributed packages include:
It is not reasonable to test the time taken by a command: you cannot know how fast or how heavily loaded an R platform might be. At best you can test a ratio of times, and even that is fraught with difficulties and not advisable: for example, the garbage collector may trigger at unpredictable times following heuristics that may change without notice.
Do not test the exact format of R messages (from R itself or from other packages): They change, and they can be translated.
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.Do not test for the absence of warnings (something users of testthat are fond of). Future changes in either R or packages you make use of can create new warnings, and your tests should not make these into errors. (Some deprecation notices may be intended to remain as warnings for a long time.)
If you use functions such as
View
, remember that in testing there is no one to look at the output. It is better to use something like one ofif(interactive()) View(obj) else print(head(obj)) if(interactive()) View(obj) else str(obj)
Be careful when comparing file paths. There can be multiple paths to a single file, and some of these can be very long character strings. If possible canonicalize paths before comparisons, but study
?normalizePath
to be aware of the pitfalls.Only test the accuracy of results if you have done a formal error analysis. Things such as checking that probabilities numerically sum to one are silly: numerical tests should always have a tolerance. That the tests on your platform achieve a particular tolerance says little about other platforms. R is configured by default to make use of long doubles where available, but they may not be available or be too slow for routine use. Most R platforms use
ix86
orx86_64
CPUs: these may use extended precision registers on some but not all of their FPU instructions. Thus the achieved precision can depend on the compiler version and optimization flags—our experience is that 32-bit builds tend to be less precise than 64-bit ones. But not all platforms use those CPUs, and not all93 which use them configure them to allow the use of extended precision. In particular, current ARM CPUs do not have extended precision nor long doubles, andclang
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 forlong 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
forgcc
, depending on the CPU type94) to mitigate the effects of extended-precision calculations. The platform most often seen to give different numerical results isarm64
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.
Tests should use
options(warn = 1)
as reporting22 warnings (use warnings() to see them) There were
is pointless, especially for automated checking systems.
If your package uses dates/times, ensure that it works in all timezones, especially those near boundaries (problems have most often be seen in
Europe/London
(zero offset in Winter) andPacific/Auckland
, near enough the International Date line) and with offsets not in whole hours (Adelaide, Chatham Islands, …). More extreme examples areAfrica/Conakry
(permanent UTC),Asia/Calcutta
(no DST, permanent half-hour offset) andPacific/Kiritimati
(no DST, more than 12 hours ahead of UTC).
93 Not doing so is the default on Windows, overridden for the R executables.
94 These are not needed for the default compiler settings on x86_64
but are likely to be needed on ix86
.
1.6.1 PDF size
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).
qpdf
(https://qpdf.sourceforge.io/) can compress losslessly. It is fairly readily available (e.g. it is included in rtools
, 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 https://svn.r-project.org/R/trunk/doc/manual/refman.top.
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
95 Select ‘Save as’, and select ‘Reduce file size’ from the ‘Quartz filter’ menu’: this can be accessed in other ways, for example by Automator.
-dAutoRotatePages=/None -dPrinted=false in.pdf out.pdf ps2pdf options
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
to 9.56
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 tools::compactPDF
).
1.6.2 Check timing
There are several ways to find out where time is being spent in the check process. Start by setting the environment variable _R_CHECK_TIMINGS_
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 _R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_
is set to a true value.
1.6.3 Encoding issues
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.
96 except perhaps some special characters such as backslash and hash which may be taken over for currency symbols.
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 iconv()
. 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.
97 Typically on a Unix-alike this is done by telling fontconfig
where to find suitable fonts to select glyphs from.
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
"latin1=en_US:latin2=pl_PL:UTF-8=en_US.UTF-8:latin9=fr_FR.iso885915@euro"
(which is appropriate for a system based on glibc
: macOS requires latin9=fr_FR.ISO8859-15
) 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.
1.6.4 Portable C and C++ code
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.)
98 which it is on all known platforms, and is required as from R 4.0.0
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.
99 Ubuntu provides 5 years of support (but people were running 14.04 after 7 years) and RHEL provides 10 years full support and up to 14 with extended support.
100 This is seen on Linux, Solaris and FreeBSD, although each has other ways to turn on all extensions, e.g. defining _GNU_SOURCE
, __EXTENSIONS__
or _BSD_SOURCE
: the GCC compilers by default define _GNU_SOURCE
unless a strict standard such as -std=c99
is used. On macOS extensions are declared unless one of these macros is given too small a value.
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
or
#ifdef __GLIBC__
# define _POSIX_C_SOURCE 200809L
#endif
before any headers. (strdup
, strncasecmp
and strnlen
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
page 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 functionality.
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 (clang
and flang
) Intel and Oracle Developer Studio suites have been used with R, and both LLVM clang
and 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.
Mathematical functions such as
sqrt
are defined in C++11 for floating-point arguments:float
,double
,long double
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 forpow
also seen on macOS and other platforms usingclang++
.A not-uncommonly-seen problem is to mistakenly call
floor(x/y)
orceil(x/y)
forint
argumentsx
andy
. Sincex/y
does integer division, the result is of typeint
and ‘overloading ambiguity’ may be reported. Some people have (pointlessly) calledfloor
andceil
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 constant1e-3
. Note that there are constants such asM_SQRT2
defined viaRmath.h
101 forsqrt(2.0)
, frequently mis-coded assqrt(2)
.Function
fabs
is defined only for floating-point types, except in C++11 and later which have overloads forstd::fabs
in<cmath>
for integer types. Functionabs
is defined in C99’s<stdlib.h>
forint
and in C++’s<cstdlib>
for integer types, overloaded in<cmath>
for floating-point types. C++11 has additional overloads forstd::abs
in<cmath>
for integer types. The effect of callingabs
with a floating-point type is implementation-specific: it may truncate to an integer. For clarity and to avoid compiler warnings, useabs
for integer types andfabs
for double values, and when using C++ include<cmath>
and use thestd::
prefix.It is an error (and make little sense, although has been seen) to call macros/functions
isnan
,isinf
andisfinite
for integer arguments: a few compilers give a compilation error. Functionfinite
is obsolete, and some compilers will warn about its use102.The GNU C/C++ compilers support a large number of non-portable extensions. For example,
INFINITY
(which is a float value in C99 and C++11), for which R provides the portable double valueR_PosInf
(andR_NegInf
for-INFINITY
). AndNAN
103 is just one NaN float value: for use with R,NA_REAL
is often what is intended, butR_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 underext
.Including C-style headers in C++ code is not portable. Including the legacy header104
math.h
in C++ code may conflict withcmath
which may be included by other headers. In C++11, functions likesqrt
andisnan
are defined fordouble
arguments inmath.h
and for a range of types includingdouble
incmath
. Similar issues have been seen forstdlib.h
andcstdlib
. Including the C++ header first used to be a sufficient workaround but for some 2016 compilers only one could be included.Be careful to include the headers which define the functions you use. Some compilers/OSes include other system headers in their headers which are not required by the standards, and so code may compile on such systems and not on others. (A prominent example is the C++ header
<random>
which is indirectly included by<algorithm>
byg++
. Another issue is the C header<time.h>
which is included by other headers on Linux and Windows but not macOS.)g++
11 often needs explicit inclusion of the C++ headers<limits>
(fornumeric_limits
) or<exception>
(forset_terminate
and similar), whereas earlier versions included these in other headers.g++
13 requires the explicit inclusion of<cstdint>
for types such asuint32_t
which was previously included implicitly. (For more such, see https://gcc.gnu.org/gcc-13/porting_to.html.)Note that
malloc
,calloc
,realloc
andfree
are defined by C99 in the headerstdlib.h
and (in thestd::
namespace) by C++ headercstdlib
. Some earlier implementations used a headermalloc.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 headersunistd.h
andsys/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 instdint.h
alongsidesize_t
.Some headers are not portable: we have just mentioned
malloc.h
and often CRAN submissions attempt to useendian.h
. The latter is aglibc
extension: some OSes havemachine/endian.h
orsys/endian.h
but some have neither.Use
#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,types.h
.For C++ code, be careful to specify namespaces where needed. Many functions are defined by the standards to be in the
std
namespace, butg++
puts many such also in the C++ main namespace. One way to do so is to use declarations such as::floor; using std
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 havebind
in the main namespace. Also seen has been typesize_t
defined only in thestd
namespace.NB: These functions are only guaranteed to be in the
std
namespace if the correct C++ header is included, e.g.<cmath>
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 andstd::byte
,std::data
,std::sample
andstd::size
from C++17.In C++ code
using namespace 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
std::
prefixes for all functions declared by the C++ standard to be in that namespace. It is an error to useusing namespace std
before including any C++ headers, and some recent compilers will warn if this is done.Some C++ compilers refuse to compile constructs such as
if(ptr > 0) { ....}
which compares a pointer to the integer
0
. This could just useif(ptr)
(pointer addresses cannot be negative) but if needed pointers can be tested againstnullptr
(C++11) orNULL
.Macros defined by the compiler/OS can cause problems. Identifiers starting with an underscore followed by an upper-case letter or another underscore are reserved for system macros and should not be used in portable code (including not as guards in C/C++ headers). Other macros, typically upper-case, may be defined by the compiler or system headers and can cause problems. Some of these can be avoided by defining
_POSIX_C_SOURCE
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 name.typedef
s in OS headers can conflict with those in the package: examples have includedulong
,index_t
,single
andthread
. (Note that these may conflict with other uses as identifiers, e.g. defining a C++ function calledsingle
.) The POSIX standard reserves (in §2.2.2) all identifiers ending in_t
.Some compilers do not allow a space between
-D
and the macro to be defined. Similarly for-U
.If you use OpenMP, check carefully that you have followed the advice in the subsection on OpenMP support. In particular, any use of OpenMP in C/C++ code will need to use
#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: ignoring #pragma omp parallel [-Wunknown-pragmas] warning
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.Package authors commonly assume things are part of C/C++ when they are not: the most common example is POSIX105 function
strdup
. 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 forstrdup
you need#define _POSIX_C_SOURCE 200809L ... #include <string.h> ... (s) strdup call
where the appropriate value can be found by
man strdup
on Linux. (Use ofstrncasecmp
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
andM_LN2
, which are part of the X/Open standard: to use these define_XOPEN_SOURCE
before including any headers, or include the R headerRmath.h
.Using
alloca
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
Compiler writers feel free to implement features from later standards than the one specified, so for example they may implement or warn on C++14/17/20 features when C++11 is specified. Portable code will not use such features – it can be hard to know what they are but the most common warnings are
'register' storage class specifier is deprecated and incompatible with C++17 ++11 does not allow conversion from string literal to 'char *' ISO C
(where conversion should be to
const char *
). Keywordregister
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 includebind1st
/bind2nd
(usestd::bind
or lambdas106)std::auto_ptr
(replaced bystd::unique_ptr
),std::mem_fun_ref
andstd::ptr_fun
.Later versions of standards may add reserved words: for example
bool
,false
andtrue
became keywords in C23 and are no longer available as variable names. As noted above, C++17 usesbyte
,data
,sample
andsize
.So avoid common words and keywords from other programming languages.
Be careful about including C headers in C++ code. Issues include
- Use of the
register
storage class specifier (see the previous but one item). - The C99 keyword
restrict
is not part of107 any C++ standard and is rejected by some C++ compilers. - Inclusion by such headers of C-style headers such as
math.h
(see above).
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).
- Use of the
Include only what is essential in
extern "C" {}
blocks in C++ code. In particular it is not portable to include R headers in such blocks (although they are themselves C code, they may include C++ system headers and the public ones already enclose their declarations in such a block). And maintainers have include R headers from other headers included in such a block.reinterpret_cast
in C++ is not safe for pointers: for example the types may have different alignment requirements. Usememcpy
to copy the contents to a fresh variable of the destination type.Avoid platform-specific code if at all possible, but if you need to test for a platform ensure that all platforms are covered. For example,
__unix__
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
but
#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
configure
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).Headers in subdirectories are often not portable. For C++, this includes
bits/
,tr1/
andtr2/
, none of which exist on macOS (andext/
exists there but with different content fromg++
-based platforms). Headerbits/stdc++.h
is both not portable and not recommended for end-user code even on platforms which include it.Be careful if using
malloc
orcalloc
. First, their return value must always be checked to see if the allocation succeeded – it is almost always easier to use R’sR_Calloc
, which does check. Second, the first argument is of typesize_t
108 and some recent compilers warn about passingint
(signed) arguments (which could get promoted to ridiculously large values).For C code, consider using the flag
-Wstrict-prototypes
which is supported bygcc
and LLVM and Appleclang
. 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 Appleclang
and for LLVMclang
in C23 mode.) Note that usingf()
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 uncertainty.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.Several C entry points are warned against in their
man
pages on most systems, often in very strong terms such as ‘Do not use these functions’. macOS has started to warn109 if these are used forsprintf
,vsprintf
,gets
,mktemp
,tempmam
andtmpnam
. 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 example200809L
before including the (C or C++) header which defines them. (However, this may hide other extensions.)Compilers may interpret comments in source code, so it is necessary to remove any intended for a compiler to interpret. The main example has been comments for Visual Fortran (as the Intel Fortran compiler has been known on Windows110) like
!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
has similar forms starting with!GCC$
.The C++
new
operator takes argumentstd::size_t size
, which is unsigned. Using a signed integer type such asint
may lead to compiler warnings such as: argument 1 value '18446744073709551615' exceeds maximum object warning9223372036854775807 [-Walloc-size-larger-than=] size
(especially if LTO is used). So don’t do that!
Some authors feel the need to print (using
Rprintf
or similar) vector lengths or indices which are of typeR_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; R_xlen_t nelem; SEXP Robj("Actual: %0.f; Expected %0.f\n", (double) XLENGTH(Robj), (double) nelem); error
(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 theint
,long
orlong long
type on current platforms and even on 64-bit ones need not be the same type asint64_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).Variadic macros in C or C++ only portably allow a non-zero number of arguments, although some compilers have allowed zero, often with a warning. The latter was standardized in C++20 using the
__VA_OPT__
macro. C23 will also allow zero arguments in a similar way.
101 often taken from the toolchain’s headers.
102 at the time of writing arm64
macOS both warned and did not supply a prototype in math.h
which resulted in a compilation error.
103 also part of C++11 and later.
104 which often is the same as the header included by the C compiler, but some compilers have wrappers for some of the C headers.
105 Although this is expected to be part of C23, full support of that is years away.
107 it is allowed but ignored in system headers.
108 an unsigned 64-bit integer type on recent R platforms.
109 when using the macOS 13 SDK with a deployment target of macOS 13.
110 and at one time as DEC Fortran, hence the DEC
.
Some additional information for C++ is available at https://journal.r-project.org/archive/2011-2/RJournal_2011-2_Plummer.pdf by Martyn Plummer.
1.6.5 Common symbols
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, gcc
10111 and LLVM clang
11112 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 extern
. 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 seen113 by using compiler flag -fno-common
as part of CFLAGS
in earlier versions of gcc
and clang
.
113 In principle this could depend on the OS, but has been checked on Linux and macOS.
-fno-common
is said to be particularly beneficial for ARM CPUs.
This is not pertinent to C++ which does not permit tentative definitions.
1.6.6 C++17 issues
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
-D_HAS_AUTO_PTR_ETC=0
to PKG_CPPLAGS
in src/Makevars
, src/Makevars.win
and src/Makevars.ucrt
. This covers the removal of
::auto_ptr
std::unary_function
std::binary_function
std::random_shuffle
std::binder1st
std::binder2nd std
with most issues seen with code that includes boost/functional.hpp
, usually indirectly.
A rarer issue is the use of illegal values for enum
types, usually negative ones such as
BOOST_MPL_AUX_STATIC_CAST(AUX_WRAPPER_VALUE_TYPE, (value - 1));
in boost/mpl/aux_/integral_wrapper.hpp
. Adding
-Wno-error=enum-constexpr-conversion
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
test.
Pre=built versions of current clang
/libc++
are 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 Rtools
and the macOS ones are unsigned). To select libc++
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 Rtools
can be found at https://github.com/mstorsjo/llvm-mingw: this uses libc++
.
1.6.7 Portable Fortran code
For many years almost all known R platforms used gfortran
as their Fortran compiler, but now there are LLVM and ‘classic’ flang
and the Intel compilers ifort
114 and ifx
are now free-of-change.
114 discontinued in 2023.
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 gfortran
this can be checked by adding -std=f2018
to FFLAGS
. The most commonly seen issues are
The use of
DFLOAT
, which was superseded byDBLE
in Fortran 77. Also, use ofDCMPLX
,DCONJG
,DIMAG
and similar.Use of what
gfortran
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- Arithmetic
IF
statements. DO
loops which are not terminated with aEND DO
orCONTINUE
statement. (UnlabelledDO
loops terminated byEND DO
are preferred for readability.)- Labelled
DO
loops sharing a terminatingCONTINUE
statement.
- Arithmetic
The use of GNU Fortran extensions. Some are listed at https://gcc.gnu.org/onlinedocs/gfortran/Extensions-implemented-in-GNU-Fortran.html. Others which have caused problems include
etime
,getpid
,isnan
115 andsizeof
.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.
115 There is a portable way to do this in Fortran 2003 (ieee_is_nan()
in module ieee_arithmetic
), but that was not supported in the versions 4.x of GNU Fortran. A pretty robust alternative is to test if(my_var /= my_var)
.
Unfortunately this flags extensions such as DOUBLE COMPLEX
and COMPLEX*16
. R has tested that DOUBLE COMPLEX
works and so is preferred to COMPLEX*16
. (One can also use something like COMPLEX(KIND=KIND(0.0D0))
.)
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
The use of Fortran types such as
REAL(KIND=8)
is very far from portable. According to the standards this merely enumerates different supported types, soDOUBLE PRECISION
might beREAL(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 examplegfortran
supports values of 4 and 8 on all current platforms and 10 and 16 on a few (but not for example on allarm
CPUs).The same applies to
INTEGER(KIND=4)
andCOMPLEX(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 thatINTEGER
andDOUBLE PRECISION
correspond to the C typesint
anddouble
. To make this explicit, from Fortran 2003 one can use the named constantsc_int
,c_double
andc_double_complex
from moduleiso_c_binding
.The Intel compilers only recognize the extensions
.f
(fixed-form) and.f90
(free-form) and not.f95
.R CMD INSTALL
works around this for packages without asrc/Makefile
.Use of extensions
.F
and.F90
to indicate source code to be preprocessed: the preprocessor used is compiler-specific and may or may not becpp
. Compilers may even preprocess files with extension.f
or.f90
(Intel does).Fixed form Fortran (with extension
.f
) should only use 72 columns, and free-form at most 132 columns. This includes trailing comments. Over-long lines may be silently truncated or give a warning.Tabs are not part of the Fortran character set: compilers tend to accept them but how they are interpreted is compiler-specific.
Fortran-66-style Hollerith constants.
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
ENTTRY
statements.FORALL
statements.- Labelled
DO
statements. COMMON
andEQUIVALENCE
statements, andBLOCK DATA
units.- Computed
GOTO
statements, replaced bySELECT CASE
. - Statement functions.
DATA
statements after executable statements.- Specific (rather than generic) names for intrinsic functions.
gfortran
with option -std=f2018
will warn about these: R will report only in the installation log.
1.6.8 Binary distribution
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 objdump
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 libquadmath.0.dylib
. (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
- To arrange for installation of the package to download the additional software from a URL, as e.g. package Cairo used to.
- To negotiate with Tomas Kalibera to include Windows software in
Rtools
or with Simon Urbanek to include macOS software in his ‘recipes’ system. - (For CRAN.) To negotiate with Uwe Ligges to host the additional components on WinBuilder, and write a
configure.win
file to install them.
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).
1.7 Diagnostic messages
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.
Messages are sentence fragments, and not viewed in isolation. So it is conventional not to capitalize the first word and not to end with a period (or other punctuation).
Try not to split up messages into small pieces. In C error messages use a single format string containing all English words in the messages.
In R error messages do not construct a message with
paste
(such messages will not be translated) but via multiple arguments tostop
orwarning
, or viagettextf
.Do not use colloquialisms such as “can’t” and “don’t”.
Conventionally single quotation marks are used for quotations such as
'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
ordQuote
as instop(gettextf("object must be of class %s or %s", dQuote("manova"), dQuote("maov")), domain = NA)
Occasionally messages need to be singular or plural (and in other languages there may be no such concept or several plural forms – Slovenian has four). So avoid constructions such as was once used in
library
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)) { <- paste(sQuote(nopkgs), collapse = ", ") pkglist <- sprintf(ngettext(length(nopkgs), msg "library %s contains no packages", "libraries %s contain no packages", = "R-base"), domain ) pkglist(msg, domain=NA) warning}
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’.
1.8 Internationalization
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 https://www.stats.ox.ac.uk/pub/Rtools/goodies/gettext-tools.zip.
1.8.1 C-level messages
The process of enabling translations is
In a header file that will be included in all the C (or C++ or Objective C/C++) files containing messages that should be translated, declare
#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 each message that should be translated, wrap it in
_(...)
, for example(_("'ord' must be a positive integer")); error
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)
In the package’s
src
directory runr xgettext --keyword=_ -o pkg.pot *.c
The file src/pkg.pot
is the template file, and conventionally this is shipped as po/pkg.pot
.
1.8.2 R messages
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
/gettextf
, stop
, warning
and message
calls. Some of these are likely to be spurious and so the file is likely to need manual editing. xgettext
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 ngettext
, and necessary for earlier versions of R unless they are calls directly from a function in the package.
1.8.3 Preparing translations
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
again (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
to 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.
1.9 CITATION files
An installed file named CITATION
will be used by the citation()
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 textVersion
argument to bibentry
. E.g., earlier versions of nlme additionally used something like
=
textVersion ("Jose Pinheiro, Douglas Bates, Saikat DebRoy, ",
paste0"Deepayan Sarkar and the R Core Team (",
("-.*", "", meta$Date),
sub"). nlme: Linear and Nonlinear Mixed Effects Models. ",
("R package version %s", meta$Version), ".") sprintf
The CITATION
file should itself produce no output when source
-d.
It is desirable (and essential for CRAN) that the CITATION
file does not contain calls to functions such as packageDescription
which assume the package is installed in a library tree on the package search path.
1.10 Package types
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.
1.10.1 Frontend
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.
1.11 Services
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.
Footnotes