Writing R Extensions

Table of Contents

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Writing R Extensions

This is a guide to extending R, describing the process of creating R add-on packages, writing R documentation, R’s system and foreign language interfaces, and the R API.

This manual is for R, version 3.2.0 Under development (2014-09-22).

Copyright © 1999–2014 R Core Team

Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies.

Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one.

Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.


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Acknowledgements

The contributions to early versions of this manual by Saikat DebRoy (who wrote the first draft of a guide to using .Call and .External) and Adrian Trapletti (who provided information on the C++ interface) are gratefully acknowledged.


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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 OS X and Windows) there are also binary packages, a zip file or tarball containing the files of an installed package which can be unpacked rather than installing from sources.

A package is not1 a library. The latter is used in two senses in R documentation.

There are a number of well-defined operations on source packages.

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 http://CRAN.R-project.org/banner.shtml#submitting.


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1.1 Package structure

The sources of an R package consists of a subdirectory containing a files DESCRIPTION and NAMESPACE, and the subdirectories R, data, demo, exec, inst, man, po, src, tests, tools and vignettes (some of which can be missing, but which should not be empty). The package subdirectory may also contain files INDEX, configure, cleanup, LICENSE, LICENCE and NEWS. Other files such as INSTALL (for non-standard installation instructions), README/README.md2, or ChangeLog will be ignored by R, but may be useful to end users. The utility R CMD build may add files in a build directory (but this should not be used for other purposes).

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) script files which are, respectively, executed before and (provided that option --clean was given) after installation on Unix-alikes, see Configure and cleanup. The analogues on Windows are configure.win and cleanup.win.

For the conventions for files NEWS and ChangeLog in the GNU project see http://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 OS X) 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, packages are normally distributed as tarballs, and these have a limit on path lengths: 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.

The R function package.skeleton can help to create the structure for a new package: see its help page for details.


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1.1.1 The DESCRIPTION file

The DESCRIPTION file contains basic information about the package in the following format:

Package: pkgname
Version: 0.5-1
Date: 2004-01-01
Title: My First Collection of Functions
Authors@R: c(person("Joe", "Developer", role = c("aut", "cre"),
		     email = "Joe.Developer@some.domain.net"),
	      person("Pat", "Developer", role = "aut"),
	      person("A.", "User", role = "ctb",
		     email = "A.User@whereever.net"))
Author: Joe Developer and Pat Developer, with contributions from A. User
Maintainer: Joe Developer <Joe.Developer@some.domain.net>
Depends: R (>= 1.8.0), nlme
Suggests: MASS
Description: A short (one paragraph) description of what
  the package does and why it may be useful.
License: GPL (>= 2)
URL: http://www.r-project.org, http://www.another.url
BugReports: http://pkgname.bugtracker.url

The format is that of a version of a ‘Debian Control File’ (see the help for ‘read.dcf’ and http://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.

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 ‘Description’ field should give a comprehensive description of what the package does. One can use several (complete) sentences, but only one paragraph.

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), not use any markup, not have any continuation lines, and not end in a period.

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. The roles can include ‘"aut"’ (author) for full authors, ‘"cre"’ (creator) for the package maintainer, and ‘"ctb"’ (contributor) for other contributors, ‘"cph"’ (copyright holder), 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.

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 ‘Date’ field gives the release date of the current version of the package. It is strongly recommended to use the yyyy-mm-dd format conforming to the ISO 8601 standard.

The ‘Depends’, ‘Imports’, ‘Suggests’, ‘Enhances’ and ‘LinkingTo’ 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.

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.

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.reports instead of sending an email to the maintainer.

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 code is to be byte-compiled on installation: the default is currently not to, so this may be useful for a package known to benefit particularly from byte-compilation (which can take quite a long time and increases the installed size of the package). It is used for the recommended packages, as they are byte-compiled when R is installed and for consistency should be byte-compiled when updated. This can be overridden by installing with flag --no-byte-compile.

The ‘ZipData’ logical field was used to control whether the automatic Windows build would zip up the data directory or not prior to R 2.13.0: it is now ignored.

The ‘Biarch’ logical field is used on Windows to select the INSTALL option --force-biarch for this package. (Introduced in R 3.0.0.)

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 preventing6 R CMD check from testing this. This should only be used exceptionally, for example if the PDFs include large figures which are not part of the package sources (and hence only in packages which do not have an Open Source license).

The ‘VignetteBuilder’ field names (in a comma-separated list) packages that provide an engine for building vignettes. These may include the current package, or ones listed in ‘Depends’, ‘Suggests’ or ‘Imports’. The utils package is always implicitly appended. See Non-Sweave vignettes for details.

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, latin2 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 code which 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 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, http://www.acm.org/class/; the former refers to the 1998 version), ‘Classification/JEL’ (the Journal of Economic Literature Classification System, http://www.aeaweb.org/journal/jel_class_system.html), or ‘Classification/MSC’ or ‘Classification/MSC-2010’ (the Mathematics Subject Classification of the American Mathematical Society, http://www.ams.org/msc/; 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 (http://tools.ietf.org/html/rfc5646, see also http://en.wikipedia.org/wiki/IETF_language_tag), i.e., use language subtags which in essence are 2-letter ISO 639-1 (http://en.wikipedia.org/wiki/ISO_639-1) or 3-letter ISO 639-3 (http://en.wikipedia.org/wiki/ISO_639-3) language codes.

As of R 3.2.0, 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 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 any duplicates, the last loaded definition will be used7

Note: There should be no ‘Built’ or ‘Packaged’ fields, as these are added by the package management tools.

There is no restriction on the use of other fields not mentioned here (but using other capitalizations of these field names would cause confusion). Fields Note, Contact (for contacting the authors/developers) and MailingList are in common use. Some repositories (including CRAN and R-forge) add their own fields.


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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., http://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

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. 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 http://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’.


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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 3.0.0 or later, include ‘R (>= 3.0.0)’ in the ‘Depends’ field. 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.

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 (since R >= 3.0.0).

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 example 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, and will be used by R CMD check.

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

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 increasingly 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 needs to declare them as a comma-separated list in the field ‘LinkingTo’ in the DESCRIPTION file. For example

LinkingTo: link1, link2

As from R 3.0.2 the ‘LinkingTo’ field can have a version requirement which is checked at installation. (In earlier versions of R it would cause the specification to be ignored.)

Specifying a package in ‘LinkingTo’ suffices if these are 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 packages BH and almost all users of RcppArmadillo and RcppEigen.

For another use of ‘LinkingTo’ see Linking to native routines in other packages.


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1.1.3.1 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 fine if that conditioning is done in examples/tests/vignettes.

However, using require for conditioning in package code is not good practice as it alters the search path for the rest of the session and relies on functions in that package not being masked by other require or library calls. It is better practice to use code like

   if (requireNamespace("rgl", quietly = TRUE)) {
      rgl::plot3d(...)
   } else {
      ## do something else not involving rgl.
   }

Note the use of rgl:: as that object would not necessarily be visible (and if it is, it need not be the one from that namespace: 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.


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1.1.4 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).


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1.1.5 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 extensions9 .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.

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 ‘SysDataCompressionDESCRIPTION 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, formfeed, LF and CR) should be used in code files. Other characters are accepted in comments, but then the comments may not be readable in e.g. a UTF-8 locale. Non-ASCII characters in object names will normally10 fail when the package is installed. Any byte will be allowed in a quoted character string but \uxxxx escapes should be used for non-ASCII characters. However, non-ASCII character strings may not be usable in some locales and may display incorrectly in others.

Various R functions in a package can be used to initialize and clean up. See Load hooks.

The man subdirectory should contain (only) documentation files for the objects in the package in R documentation (Rd) format. The documentation filenames must start with an ASCII (lower or upper case) letter or digit and have the extension .Rd (the default) or .rd. Further, the names must be valid in ‘file://’ URLs, which means11 they must be entirely ASCII and not contain ‘%’. See Writing R documentation files, for more information. Note that all user-level objects in a package should be documented; if a package pkg contains user-level objects which are for “internal” use only, it should provide a file pkg-internal.Rd which documents all such objects, and clearly states that these are not meant to be called by the user. See e.g. the sources for package grid in the R distribution. Note that packages which use internal objects extensively should not export those objects from their namespace, when they do not need to be documented (see Package namespaces).

Having a man directory containing no documentation files may give an installation error.

The man subdirectory may contain a subdirectory named macros; this will contain source for user-defined Rd macros. (See User-defined macros.) These use the Rd format, but may not contain anything but macro definitions, comments and whitespace.

The R and man subdirectories may contain OS-specific subdirectories named unix or windows.

The sources and headers for the compiled code are in src, plus optionally a file Makevars or Makefile. 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++, FORTRAN 77, Fortran 9x12, Objective C and Objective C++13 with associated extensions .c, .cc or .cpp, .f, .f90 or .f95, .m, and .mm, respectively. We recommend using .h for headers, also for C++14 or Fortran 9x include files. (Use of extension .C for C++ is no longer supported.) Files in the src directory should not be hidden (start with a dot), and hidden files will under some versions of R be ignored.

It is not portable (and may not be possible at all) to mix all these languages in a single package, and we do not support using both C++ and Fortran 9x. Because R itself uses it, we know that C and FORTRAN 77 can be used together and mixing C and C++ seems to be widely successful.

If your code needs to depend on the platform there are certain defines which can used in C or C++. On all Windows builds (even 64-bit ones) ‘_WIN32’ will be defined: on 64-bit Windows builds also ‘_WIN64’, and on OS X ‘__APPLE__’ is defined.15

The default rules can be tweaked by setting macros16 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. 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 (unless intended for only Unix-alikes or only Windows).

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:

files <- Sys.glob(paste0("*", SHLIB_EXT))
dest <- file.path(R_PACKAGE_DIR, paste0('libs', R_ARCH))
dir.create(dest, recursive = TRUE, showWarnings = FALSE)
file.copy(files, dest, overwrite = TRUE)
if(file.exists("symbols.rds"))
    file.copy("symbols.rds", dest, overwrite = TRUE)

On the other hand, executable programs could be installed along the lines of

execs <- c("one", "two", "three")
if(WINDOWS) execs <- paste0(execs, ".exe")
if ( any(file.exists(execs)) ) {
  dest <- file.path(R_PACKAGE_DIR,  paste0('bin', R_ARCH)
  dir.create(dest, recursive = TRUE, showWarnings = FALSE)
  file.copy(execs, dest, overwrite = TRUE)
}

Note the use of architecture-specific subdirectories of bin where needed.

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 white space. (Note that it is not possible to generate this index file automatically.) Note that a demo does not have a specified encoding and so should be an ASCII file (see Encoding issues). As from R 3.0.0 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-insensitively17 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 OS X 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.

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 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 corresponding18 .Rout.save file, these two are compared, with differences being reported but not causing an error. The directory tests is copied to the check area, and the tests are run with the copy as the working directory and with R_LIBS set to ensure that the copy of the package installed during testing will be found by library(pkg_name). Note that the package-specific tests are run in a vanilla R session without setting the random-number seed, so tests which use random numbers will need to set the seed to obtain reproducible results (and it can be helpful to do so in all cases, to avoid occasional failures when tests are run).

If directory tests has a subdirectory Examples containing a file pkg-Ex.Rout.save, this is compared to the output file for running the examples when the latter are checked. Reference output should be produced without having the --timings option set (and note that --as-cran sets it).

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 (including Windows) 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).


Next: , Previous: Package subdirectories, Up: Package structure   [Contents][Index]

1.1.6 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 standard19 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 “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.

Images (extensions .RData 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, and look at the output of loadedNamespaces().

If your data files are large and you are not using ‘LazyData’ 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.

Some 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. 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.

Lazy-loading is not supported for very large datasets (those which when serialized exceed 2GB, the limit for the format on 32-bit platforms and all platforms prior to R 3.0.0).

The analogue for sysdata.rda is field ‘SysDataCompression’: the default (since R 2.12.2) is xz for files bigger than 1MB otherwise gzip.


Previous: Data in packages, Up: Package structure   [Contents][Index]

1.1.7 Non-R scripts in packages

Code which needs to be compiled (C, C++, FORTRAN, Fortran 95 …) 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.)

Windows and Mac users should be aware that the Tcl extensions ‘BWidget’ and ‘Tktable’ which are currently included with the R for Windows and in the OS X installers are extensions and do need to be declared. ‘Tktable’ did ship as part of the X11-based Tcl/Tk provided on CRAN for OS X prior to R 3.0.0, but you will need to tell your users how to make use of it:

> addTclPath('/usr/local/lib/Tktable2.9')
> tclRequire('Tktable')
<Tcl> 2.9

It should work with no further user action as from R 3.0.0.

BWidget’ needs to be installed by the user for OS X with R 2.x.y and on other OSes. This is fairly easy to do: first find the Tcl/Tk search path:

library(tcltk)
strsplit(tclvalue('auto_path'), " ")[[1]]

then download the sources from http://sourceforge.net/projects/tcllib/files/BWidget/ and at the command line run

tar xf bwidget-1.9.6.tar.gz
sudo mv bwidget-1.9.6 /usr/local/lib

substituting a location on the Tcl/Tk search path for /usr/local/lib if needed.


Next: , Previous: Package structure, Up: Creating R packages   [Contents][Index]

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 (Bourne 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.).

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

foo <- function(x) {
    if(!@HAVE_FOO@)
      stop("Sorry, library ‘foo’ is not available"))
    ...

From this file configure creates the actual R source file foo.R looking like

foo <- function(x) {
    if(!FALSE)
      stop("Sorry, library ‘foo’ is not available"))
    ...

if library foo was not found (with the desired functionality). In this case, the above R code effectively disables the function.

One could also use different file fragments for available and missing functionality, respectively.

You will very likely need to ensure that the same C compiler and compiler flags are used in the configure tests as when compiling R or your package. Under a Unix-alike, you can achieve this by including the following fragment early in configure.ac

: ${R_HOME=`R RHOME`}
if test -z "${R_HOME}"; then
  echo "could not determine R_HOME"
  exit 1
fi
CC=`"${R_HOME}/bin/R" CMD config CC`
CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS`
CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`

(Using ‘${R_HOME}/bin/R’ rather than just ‘R’ is necessary in order to use the correct version of R when running the script as part of R CMD INSTALL, and the quotes since ‘${R_HOME}’ might contain spaces.)

If your code does load checks then you may also need

LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS`

and packages written with C++ need to pick up the details for the C++ compiler and switch the current language to C++ by

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.

You can use R CMD config for getting 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.

To check for an external BLAS library using the ACX_BLAS macro from the official Autoconf Macro Archive, one can simply do

F77=`"${R_HOME}/bin/R" CMD config F77`
AC_PROG_F77
FLIBS=`"${R_HOME}/bin/R" CMD config FLIBS`
ACX_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 77 code works on all R platforms. Calls to the Autoconf macro AC_F77_LIBRARY_LDFLAGS, which would overwrite FLIBS, must not be used (and hence e.g. removed from ACX_BLAS). (Recent versions of Autoconf in fact allow an already set FLIBS to override the test for the FORTRAN linker flags.)

N.B.: If the configure script creates files, e.g. src/Makevars, you do need a cleanup script to remove them. Otherwise if the package has vignettes, R CMD build will 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 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 to be used on that platform. (Optionally, there can be a cleanup.win script. Both should be shell scripts to be executed by ash, which is a minimal version of Bourne-style sh.) When configure.win is run the environment variables R_HOME (which uses ‘/’ as the file separator), R_ARCH and Use R_ARCH_BIN will be set. Use R_ARCH to decide if this is a 64-bit build (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.

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. (This happens with the Java Archive files in the Omegahat SJava package.) 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 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 default on OS X) to do this is pkg-config. The configure script will need to test for the presence of the command itself (see for example package Cairo), and if present it can be asked if the software is installed, of a suitable version and for compilation/linking flags by e.g.

$ pkg-config --exists ‘QtCore >= 4.0.0’  # check the status
$ pkg-config --modversion QtCore
4.7.1
$ pkg-config --cflags QtCore
-DQT_SHARED -I/usr/include/QtCore
$ pkg-config --libs QtCore
-lQtCore

Note that pkg-config --libs gives the information required to link against the default version of that library (usually the dynamic one), and pkg-config --static is needed if the static library is to be used.

Sometimes the name by which the software is known to pkg-config is not what one might expect (e.g. ‘gtk+-2.0’ even for 2.22). To get a complete list use

pkg-config --list-all | sort

Next: , Previous: Configure and cleanup, Up: Configure and cleanup   [Contents][Index]

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) without the use of GNU extensions.

The most common use of a Makevars file is to set additional preprocessor options (for example include paths) for C/C++ files via PKG_CPPFLAGS, and additional compiler flags by setting PKG_CFLAGS, PKG_CXXFLAGS, PKG_FFLAGS or PKG_FCFLAGS, for C, C++, FORTRAN or Fortran 9x 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.

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 Solaris compiler these are used to pass arguments to compiler phases) are all specific to GCC.

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,

There are some macros20 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

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.

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. If BLAS_LIBS is included, then FLIBS also needs to be21 included following it, as most BLAS libraries are written at least partially in FORTRAN.

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 library libRlapack which contains the main double-precision LAPACK routines as well as those double-complex LAPACK routines needed to build R, or it may point to an external LAPACK library, or may be empty if an external BLAS library also contains LAPACK.

[libRlapack includes all the double-precision LAPACK routines current in 2003: a list of which routines are included is in file src/modules/lapack/README.]

For portability, the macros BLAS_LIBS and FLIBS should always be included after LAPACK_LIBS (and in that order).

SAFE_FFLAGS

A macro containing flags which are needed to circumvent over-optimization of FORTRAN code: it is typically ‘-g -O2 -ffloat-store’ on ‘ix86’ platforms using gfortran. Note that this is not an additional flag to be used as part of PKG_FFLAGS, but a replacement for FFLAGS, and that it is intended for the FORTRAN 77 compiler ‘F77’ and not necessarily for the Fortran 90/95 compiler ‘FC’. 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

PKG_LIBS = @BLAS_LIBS@

SLAMC_FFLAGS=$(R_XTRA_FFLAGS) $(FPICFLAGS) $(SHLIB_FFLAGS) $(SAFE_FFLAGS)

all: $(SHLIB)

slamc.o: slamc.f
	$(F77) $(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
	$(F77) $(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 a 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).

Note that on Windows it is required that Makevars[.win] 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 file.

It is sometimes useful to have a target ‘clean’ in Makevars or Makevars.win: 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.

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 32- and 64-bit code, for example (GNU make syntax, allowed on Windows)

ifeq "$(WIN)" "64"
PKG_LIBS = value for 64-bit Windows
else
PKG_LIBS = value for 32-bit Windows
endif

On Windows there is normally a choice between linking to an import library or directly to a DLL. Where possible, the latter is much more reliable: import libraries are tied to a specific toolchain, and in particular on 64-bit Windows two different conventions have been commonly used. So for example instead of

PKG_LIBS = -L$(XML_DIR)/lib -lxml2

one can use

PKG_LIBS = -L$(XML_DIR)/bin -lxml2

since on Windows -lxxx will look in turn for

libxxx.dll.a
xxx.dll.a
libxxx.a
xxx.lib
libxxx.dll
xxx.dll

where the first and second are conventionally import libraries, the third and fourth often static libraries (with .lib intended for Visual C++), but might be import libraries. See for example http://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 is 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) to ship them in the same directory as the package DLL.


Next: , Previous: Using Makevars, Up: Using Makevars   [Contents][Index]

1.2.1.1 OpenMP support

There is some support for packages which wish to use OpenMP22. The make macros

SHLIB_OPENMP_CFLAGS
SHLIB_OPENMP_CXXFLAGS
SHLIB_OPENMP_FCFLAGS
SHLIB_OPENMP_FFLAGS

are available for use in src/Makevars or src/Makevars.win. Include the appropriate macro in PKG_CFLAGS, PKG_CPPFLAGS and so on, and also in PKG_LIBS. C/C++ code that needs to be conditioned on the use of OpenMP can be used inside #ifdef SUPPORT_OPENMP, a macro defined in the header Rconfig.h (see Platform and version information) or _OPENMP: note that some toolchains used for R (e.g. clang) have no OpenMP support at all, not even omp.h.

For example, a package with C code written for OpenMP should have in src/Makevars the lines

PKG_CFLAGS = $(SHLIB_OPENMP_CFLAGS)
PKG_LIBS = $(SHLIB_OPENMP_CFLAGS)

There is nothing to say what version of OpenMP is supported: version 3.0 (May 2008) is supported by recent versions of the Linux, Windows and Solaris platforms, but portable packages cannot assume that end users have recent versions. The compilers used on OS X 10.6 (‘Snow Leopard’) had partial support for OpenMP 2.5, but this is not enabled in the CRAN build of R. (Apple have discontinued support for those compilers, and their alternative as from OS X 10.9, clang, has no OpenMP support. A project to add it has been announced at http://clang-omp.github.io/, but it is unknown when or even if the Apple builds will incorporate it.)

The performance of OpenMP varies substantially between platforms. Both the Windows and the Apple OS X (where available) implementations have substantial overheads and are only beneficial if quite substantial tasks are run in parallel.

Calling any of the R API from threaded code is ‘for experts only’: they will need to read the source code to determine if it is thread-safe. In particular, code which makes use of the stack-checking mechanism must not be called from threaded code.

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. The best 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.23 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 uses

#pragma omp parallel for num_threads(nthreads) …

That way you only control your own code and not that of other OpenMP users.


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1.2.1.2 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 (hence not Windows).

For reasonably recent versions of gcc and clang the correct specification is

PKG_CPPFLAGS = -pthread
PKG_LIBS = -pthread

(and the plural version is also accepted on some systems/versions). For other platforms the specification is

PKG_CPPFLAGS = -D_REENTRANT
PKG_LIBS = -lpthread

(and note that the library name is singular). This is what -pthread does on all known current platforms (although earlier versions of OpenBSD used a different library name).

For a tutorial see https://computing.llnl.gov/tutorials/pthreads/.

POSIX threads are not normally used on Windows, which has its own native concepts of threads. However, there are two projects implementing pthreads on top of Windows, pthreads-w32 and winpthreads (a recent part of the MinGW-w64 project).

Whether Windows toolchains implement pthreads is up to the toolchain provider: the currently recommended toolchain does by default provide it. A make variable SHLIB_PTHREAD_FLAGS is available: this should be included in both PKG_CPPFLAGS (or the Fortran or F9x equivalents) and PKG_LIBS.

The presence of a working pthreads implementation cannot be unambiguously determined without testing for yourself: however, that ‘_REENTRANT’ is defined24 in C/C++ code is a good indication.

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.


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1.2.1.3 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

SOURCES = $(wildcard data/*.cpp network/*.cpp utils/*.cpp model/*.cpp model/*/*.cpp model/*/*/*.cpp)

OBJECTS = siena07utilities.o siena07internals.o siena07setup.o siena07models.o $(SOURCES:.cpp=.o)

One problem with that approach is that unless GNU make extensions are used, the source files need to be listed and kept up-to-date. As in the following from CRAN package lossDev:

OBJECTS.samplers = samplers/ExpandableArray.o samplers/Knots.o \
  samplers/RJumpSpline.o samplers/RJumpSplineFactory.o \
  samplers/RealSlicerOV.o samplers/SliceFactoryOV.o samplers/MNorm.o
OBJECTS.distributions = distributions/DSpline.o \
  distributions/DChisqrOV.o distributions/DTOV.o \
  distributions/DNormOV.o distributions/DUnifOV.o distributions/RScalarDist.o
OBJECTS.root = RJump.o

OBJECTS = $(OBJECTS.samplers) $(OBJECTS.distributions) $(OBJECTS.root)

Where the subdirectory is self-contained code with a suitable makefile, the best approach is something like

PKG_LIBS = -LCsdp/lib -lsdp $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)

$(SHLIB): Csdp/lib/libsdp.a

Csdp/lib/libsdp.a
	@(cd Csdp/lib && $(MAKE) libsdp.a \
	  CC="$(CC)" CFLAGS="$(CFLAGS) $(CPICFLAGS)" AR="$(AR)" RANLIB="$(RANLIB)")

Note the quotes: the macros can contain spaces, e.g. CC = "gcc -m64 -std=gnu99". Several authors have forgotten about parallel makes: the static library in the subdirectory must be made before the shared object ($(SHLIB)) and so the latter must depend on the former. Others forget the need for position-independent code.

We really do not recommend using src/Makefile instead of src/Makevars, and as the example above shows, it is not necessary.


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1.2.2 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=`"${R_HOME}/bin/R" CMD config CC`
CPP=`"${R_HOME}/bin/R" CMD config CPP`
CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS`
CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS`
AC_PROG_CC
AC_PROG_CPP


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

PKG_CPPFLAGS = @RODBC_CPPFLAGS@
PKG_LIBS = @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.


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1.2.3 Using F95 code

R assumes that source files with extension .f are FORTRAN 77, and passes them to the compiler specified by ‘F77’. On most but not all platforms that compiler will accept Fortran 90/95 code: some platforms have a separate Fortran 90/95 compiler and a few (by now quite rare25) platforms have no Fortran 90/95 support.

This means that portable packages need to be written in correct FORTRAN 77, which will also be valid Fortran 95. See http://developer.R-project.org/Portability.html for reference resources. In particular, free source form F95 code is not portable.

On some systems an alternative F95 compiler is available: from the gcc family this might be gfortran or g95. Configuring R will try to find a compiler which (from its name) appears to be a Fortran 90/95 compiler, and set it in macro ‘FC’. Note that it does not check that such a compiler is fully (or even partially) compliant with Fortran 90/95. Packages making use of Fortran 90/95 features should use file extension .f90 or .f95 for the source files: the variable PKG_FCFLAGS specifies any special flags to be used. There is no guarantee that compiled Fortran 90/95 code can be mixed with any other type of compiled code, nor that a build of R will have support for such packages.

Some (but not) all compilers specified by the ‘FC’ macro will accept Fortran 2003 or 2008 code: such code should still use file extension .f90 or .f95. For platforms using gfortran, you may need to include -std=f2003 or -std=f2008 in PKG_FCFLAGS: the default is ‘GNU Fortran’, Fortran 95 with non-standard extensions. The Solaris f95 compiler ‘accepts some Fortran 2003 features’. Note that the compiler used for OS X <= 10.8 is gfortran 4.2.3 which has limited Fortran 2003 support (http://gcc.gnu.org/onlinedocs/gcc-4.2.3/gfortran/).

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

Previous: Using F95 code, Up: Configure and cleanup   [Contents][Index]

1.2.4 Using C++11 code

R can be built without a C++ compiler although one is available (but not necessarily installed) on all known R platforms. For full portability across platforms, all that can be assumed is approximate support for the C++98 standard (the widely used g++ deviates considerably from the standard). Some compilers have a concept of ‘C++03’ (‘essentially a bug fix’) or ‘C++ Technical Report 1’ (TR1), an optional addition to the ‘C++03’ revision which was published in 2007. Finally a revised standard was published in 2011 and compilers with fairly complete implementations are becoming available. C++11 added all of the C99 features which are not otherwise implemented in C++, and C++ compilers commonly accept C99 extensions to C++98. A minor update to C++11 (sometimes known as C++14) was approved in August 2014.

From version 3.1.0, R provides support for C++11 in packages, in addition to C++98. This support is not uniform across platforms as it depends on the capabilities of the compiler (see below). When R is configured, it will determine whether the C++ compiler supports C++11 and which compiler flags, if any, are required to enable C++11 support. For example, recent versions of g++ or clang++ accept the compiler flag -std=c++11, and earlier versions support a flag -std=c++0x, but the latter only provides partial support for the C++11 standard.

In order to use C++11 code in a package, the package’s Makevars file (or Makevars.win on Windows) should include the line

CXX_STD = CXX11

Compilation and linking will then be done with the C++11 compiler. If any other value is given to the ‘CXX_STD’ macro it will be ignored. (Further options may become available in the future as the C++ standard evolves.)

Packages without a Makevars file may specify that they require C++11 by including ‘C++11’ in the ‘SystemRequirements’ field of the DESCRIPTION file, e.g.

SystemRequirements: C++11

If a package does have a Makevars[.win] file then setting the make variable ‘CXX_STD’ is preferred, as it allows R CMD SHLIB to work correctly in the package’s src directory.

The C++11 compiler will be used systematically by R for all C++ code if the environment variable USE_CXX1X is defined (with any value). Hence this environment variable should be defined when invoking R CMD SHLIB in the absence of a Makevars file (or Makevars.win on Windows) if a C++11 compiler is required.

Further control over compilation of C++11 code can be obtained by specifying the macros ‘CXX1X’ and ‘CXX1XSTD’ when R is configured26, or in a personal or site Makevars file. See Customizing package compilation in R Installation and Administration. If C++11 support is not available then these macros are both empty. Otherwise, ‘CXX1X’ defaults to the same value as the C++ compiler ‘CXX’ and the flag ‘CXX1XSTD’ defaults to -std=c++11 or -std=c++0x (the latter on Windows). It is possible to specify ‘CXX1X’ to be a distinct compiler just for C++11–using packages, e.g. g++ 4.9.x on Solaris. Note however that different C++ compilers (and even different versions of the same compiler) often differ in their ABI so their outputs can rarely be mixed. By setting ‘CXX1XSTD’ it is also possible to choose a different dialect of the standard, such as -std=gnu++11, or enable support for the 2014 revision using something like -std=c++14 or -std=c++1y.

As noted above, support for C++11 varies across platforms. The default compiler27 for OS X (<= 10.8) is based on GCC 4.2.1 and has no support for anything other than the GNU version of C++98 and GNU extensions (which include TR1). The default compiler on Windows is GCC 4.6.x and supports the -std=c++0x flag and some C++11 features (see http://gcc.gnu.org/gcc-4.6/cxx0x_status.html). On these platforms, it is necessary to select a different compiler for C++11, via personal or site Makevars files.


Next: , Previous: Configure and cleanup, Up: Creating R packages   [Contents][Index]

1.3 Checking and building packages

Before using these tools, please check that your package can be installed (which checked it can be loaded). 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 and R CMD build run R processes with --vanilla in which none of the user’s startup files are read. If you need R_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 variables R_CHECK_ENVIRON and R_BUILD_ENVIRON; if unset, files28 ~/.R/check.Renviron and ~/.R/build.Renviron are used) to set environment variables when using these utilities.

Note to Windows users: R CMD build may make use of the Windows toolset (see the “R Installation and Administration” manual) if present and in your path, and it is required for packages which need it to install (including those with configure.win or cleanup.win scripts or a src 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).


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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

  1. The package is installed. This will warn about missing cross-references and duplicate aliases in help files.
  2. The file names are checked to be valid across file systems and supported operating system platforms.
  3. The files and directories are checked for sufficient permissions (Unix-alikes only).
  4. The files are checked for binary executables, using a suitable version of file if available29. (There may be rare false positives.)
  5. 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 set R_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’ and ‘ts’). Another check is that all packages mentioned in library or requires or from which the NAMESPACE file imports or are called via :: or ::: are listed (in ‘Depends’, ‘Imports’, ‘Suggests’): this is not an exhaustive check of the actual imports.
  6. Available index information (in particular, for demos and vignettes) is checked for completeness.
  7. 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 to ‘default’, which runs the checks only if checking a tarball: the default can be overridden by specifying the value as ‘yes’ or ‘no’. Further, the check on the src directory is only run if the package does not contain a configure script (which corresponds to the value ‘yes-maybe’) and there is no src/Makefile or src/Makefile.in.

    To allow a configure script to generate suitable files, files ending in ‘.in’ will be allowed in the R directory.

    A warning is given for directory names that look like R package check directories – many packages have been submitted to CRAN containing these.

  8. 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.
  9. 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 this 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.)
  10. 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 arguments of the corresponding generic, and whether the final argument of replacement functions is called ‘value’. All foreign function calls (.C, .Fortran, .Call and .External calls) are tested to see if they have a PACKAGE argument, and if not, whether the appropriate DLL might be deduced from the namespace of the package. Any other calls are reported. (The check is generous, and users may want to supplement this by examining the output of tools::checkFF("mypkg", verbose=TRUE), especially if the intention were to always use a PACKAGE argument)
  11. The Rd files are checked for correct syntax and metadata, including the presence of the mandatory fields (\name, \alias, \title and \description). The Rd name and title are checked for being non-empty, and there is a check for missing cross-references (links).
  12. A check is made for missing documentation entries, such as undocumented user-level objects in the package.
  13. Documentation for functions, data sets, and S4 classes is checked for consistency with the corresponding code.
  14. It is checked whether all function arguments given in \usage sections of Rd files are documented in the corresponding \arguments section.
  15. The data directory is checked for non-ASCII characters and for the use of reasonable levels of compression.
  16. C, C++ and FORTRAN source and header files30 are tested for portable (LF-only) line endings. If there is a Makefile or Makefile.in or Makevars or Makevars.in file under the src 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. Windows31 users should note that the Fortran and C++ runtime libraries are examples of such external libraries.

  17. 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 as qpdf) are available, checking that the PDF documentation is of minimal size.
  18. 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 file tests/Examples/pkg-Ex.Rout.save, the output of running the examples is compared to that file.

    Of course, released packages should be able to run at least their own examples. Each example is run in a ‘clean’ environment (so earlier examples cannot be assumed to have been run), and with the variables T and F redefined to generate an error unless they are set in the example: See Logical vectors in An Introduction to R.

  19. 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.
  20. The code in package vignettes (see Writing package vignettes) is executed, and the vignette PDFs re-made from their sources as a check of completeness of the sources (unless there is a ‘BuildVignettes’ field in the package’s DESCRIPTION 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 location inst/doc, do mark such target output files to not be installed in .Rinstignore.)

    If there is an error32 in executing the R code in vignette foo.ext, a log file foo.ext.log is created in the check directory. The vignette PDFs are re-made in a copy of the package sources in the vign_test subdirectory of the check directory, so for further information on errors look in directory pkgname/vign_test/vignettes. (It is only retained if there are errors or if environment variable _R_CHECK_CLEAN_VIGN_TEST_ is set to a false value.)

  21. 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.

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 available33). Some Windows users may need to set environment variable R_WIN_NO_JUNCTIONS to a non-empty value. The test of cyclic declarations34in DESCRIPTION files needs repositories (including CRAN) set: do this in ~/.Rprofile, by e.g

options(repos = c(CRAN="http://cran.r-project.org"))

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.

Multiple sub-architectures: On systems which support multiple sub-architectures (principally Windows), R CMD check will install and check a package which contains compiled code under all available sub-architectures. (Use option --force-multiarch to force this for packages without compiled code, which are otherwise only checked under the main sub-architecture.) This will run the loading tests, examples and tests directory under each installed sub-architecture in turn, and give an error if any fail. Where environment variables (including perhaps PATH) need to be set differently for each sub-architecture, these can be set in architecture-specific files such as R_HOME/etc/i386/Renviron.site.

An alternative approach is to use R CMD check --no-multiarch to check the primary sub-architecture, and then to use something like R --arch=x86_64 CMD check --extra-arch or (Windows) /path/to/R/bin/x64/Rcmd check --extra-arch to run for each additional sub-architecture just the checks35 which differ by sub-architecture. (This approach is required for packages which are installed by R CMD INSTALL --merge-multiarch.)

Where packages need additional commands to install all the sub-architectures these can be supplied by e.g. --install-args=--force-biarch.


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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 Windows and OS X platforms.

Using R CMD build, the R package builder, one can build R package tarballs from their sources (for example, for subsequent release).

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-insensitively36 against the file and directory names relative to the top-level package source directory. In addition, directories from source control systems37 or from eclipse38, directories with names ending .Rcheck or Old or old and files GNUMakefile, Read-and-delete-me or with base names starting with ‘.#’, or starting and ending with ‘#’, or ending in ‘~’, ‘.bak’ or ‘.swp’, are excluded by default. In addition, those files in the R, demo and man directories which are flagged by R CMD check as having invalid names will be excluded.

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 (http://qpdf.sourceforge.net/) 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).


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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 OS X platform the default binary package file extension is .tgz.

The recommended method of building binary packages is to use

R CMD INSTALL --build pkg where pkg is either the name of a source tarball (in the usual .tar.gz format) or the location of the directory of the package source to be built. This operates by first installing the package and then packing the installed binaries into the appropriate binary package file for the particular platform.

By default, R CMD INSTALL --build will attempt to install the package into the default library tree for the local installation of R. This has two implications:

To prevent changes to the present working installation or to provide an install location with write access, create a suitably located directory with write access and use the -l option to build the package in the chosen location. The usage is then

R CMD INSTALL -l location --build pkg

where location is the chosen directory with write access. The package will be installed as a subdirectory of location, and the package binary will be created in the current directory.

Other options for R CMD INSTALL can be found using R CMD INSTALL --help, and platform-specific details for special cases (e.g. handling Fortran sources on OS X) 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 http://win-builder.R-project.org/) is able to build 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.


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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 (La)TeX). Vignette engines do this work, using “tangle” and “weave” functions respectively. Sweave, provided by the R distribution, is the default engine. Since R version 3.0.0, 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 extensions39 .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.

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 build will automatically40 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 his machine.41

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_HOME42. Finally, if there is a Makefile and it has a ‘clean:’ target, make clean is run.

All the usual caveats about including a Makefile apply. It must be portable (no GNU extensions), use LF line endings and must work correctly with a parallel make: too many authors have written things like

## BAD EXAMPLE
all: pdf clean

pdf: ABC-intro.pdf ABC-details.pdf

%.pdf:  %.tex
	texi2dvi --pdf $*

clean:
	rm *.tex ABC-details-*.pdf

which will start removing the source files whilst pdflatex 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 and \VignetteKeyword (which replaced \VignetteKeywords). These are processed at package installation time to create the saved data frame Meta/vignette.rds, but only the \VignetteIndexEntry and \VignetteKeyword statements are currently used. The \VignetteEngine statement is described in Non-Sweave vignettes.

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.)


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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 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.) If the encoding is UTF-8, this can also be declared using the declaration

%!\SweaveUTF8

but be aware that LaTeX may require the ‘usepackage’ declaration. R CMD check will warn about any non-ASCII vignettes it finds which do not have one of these declarations.

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.


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1.4.2 Non-Sweave vignettes

R 3.0.0 and later allow vignettes in formats other than Sweave by means of “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.


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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 mechanism for specifying a namespace for a package is to place a 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 visible43.

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. Sealing also 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.

Prior to R 2.14.0, namespaces were optional in packages: a default namespace was generated on installation in 2.14.x and 2.15.x. As from 3.0.0 a namespace is mandatory.


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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 the code nowadays 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.

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.

Using foo:::f instead of foo::f allows access to unexported objects. This is generally not recommended, as the semantics of unexported objects may be changed by the package author in routine maintenance.


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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

S3method(print, foo)

ensures that the method is registered and available for UseMethod dispatch, and the function print.foo does not need to be exported. Since the generic print is defined in base it does not need to be imported explicitly.

(Note that function and class names may be quoted, and reserved words and non-standard names such as [<- and function must be.)

It is possible to specify a third argument to S3method, the function to be used as the method, for example

S3method(print, check_so_symbols, .print.via.format)

when print.check_so_symbols is not needed.

There used to be a limit on the number of S3method directives: it was 500 prior to R 3.0.2.


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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 arguments44 libname and pkgname; they should be defined in the namespace but not exported.

Packages can use a .onDetach (as from R 3.0.0) 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. .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.


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1.5.4 useDynLib

A NAMESPACE file can contain one or more useDynLib directives which allows shared objects that need to be loaded.45 The directive

useDynLib(foo)

registers the shared object foo46 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.

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 several compiled DLLs.

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:

myDLL = useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)

Then, the R variable myDLL is in the package’s namespace and available for calls such as myDLL$dynRoutine to access routines that are not explicitly resolved at load time.

If the package has registration information (see Registering native routines), then we can use that directly rather than specifying the list of symbols again in the useDynLib directive in the NAMESPACE file. Each routine in the registration information is specified by giving a name by which the routine is to be specified along with the address of the routine and any information about the number and type of the parameters. Using the .registration argument of 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:

R_CMethodDef cMethods[] = {
   {"foo", (DL_FUNC) &foo, 4, {REALSXP, INTSXP, STRSXP, LGLSXP}},
   {"bar_sym", (DL_FUNC) &bar, 0},
   {NULL, NULL, 0}
};

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

R_version <- function()
{
  .Call(R_version_sym)
}

Using argument .fixes allows an automatic prefix to be added to the registered symbols, which can be useful when working with an existing package. For example, package KernSmooth has

useDynLib(KernSmooth, .registration = TRUE, .fixes = "F_")

which makes the R variables corresponding to the FORTRAN symbols F_bkde and so on, and so avoid clashes with R code in the namespace.


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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

x <- 1
f <- function(y) c(x,y)
foo <- function(x) .Call("foo", x, PACKAGE="foo")
print.foo <- function(x, ...) cat("<a foo>\n")

Some C code defines a C function compiled into DLL foo (with an appropriate extension). The NAMESPACE file for this package is

useDynLib(foo)
export(f, foo)
S3method(print, foo)

The second package bar has code file bar.R

c <- function(...) sum(...)
g <- function(y) f(c(y, 7))
h <- function(y) y+9

and NAMESPACE file

import(foo)
export(g, h)

Calling library(bar) loads bar and attaches its exports to the search path. Package foo is also loaded but not attached to the search path. A call to g 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.


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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 in its DESCRIPTION file47 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

export(mle) # exporting methods implicitly exports the generic
importFrom("graphics", plot)
importFrom("stats", optim, qchisq)
## For these, we define methods or (AIC, BIC, nobs) an implicit generic:
importFrom("stats", AIC, BIC, coef, confint, logLik, nobs, profile,
	   update, vcov)
exportClasses(mle, profile.mle, summary.mle)
## All methods for imported generics:
exportMethods(coef, confint, logLik, plot, profile, summary,
	      show, update, vcov)
## implicit generics which do not have any methods here
export(AIC, BIC, nobs)

All S4 classes to be used outside the package need to be listed in an exportClasses directive. Alternatively, they can be specified using exportClassPattern48 in the same style as for exportPattern. To export methods for generics from other packages an exportMethods directive can be used.

Note that exporting methods on a generic in the namespace will also export the generic, and exporting a generic in the namespace will also export its methods. If the generic function is not local to this package, either because it was imported as a generic function or because the non-generic version has been made generic solely to add S4 methods to it (as for functions such as plot 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 package49, it needs to import the classes and methods explicitly, with directives

importClassesFrom(package, ...)
importMethodsFrom(package, ...)

listing the classes and functions with methods respectively. Suppose we had two small packages A and B with B using A. Then they could have NAMESPACE 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 plot from graphics 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.


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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 which are mentioned above.

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,50 use dev.new().

Use R CMD build to make the release .tar.gz file.

R CMD check provides a basic set of checks, but often further problems emerge when people try to install and use packages submitted to CRAN – many of these involve compiled code. Here are some further checks that you can do to make your package more portable.

Do be careful in what your tests (and examples) actually test. Bad practice seen in distributed packages include: