.C and .Fortran
dyn.load and dyn.unload
.Call and .External
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 3.1.1 (2014-07-10).
Copyright © 1999–2013 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.
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.
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.
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.
install.packages.
library(), but since the advent of package
namespaces this has been less clear: people now often talk about
loading the package's namespace and then attaching the
package so it becomes visible on the search path. Function
library performs both steps, but a package's namespace can be
loaded without the package being attached (for example by calls like
splines::ns).
The concept of lazy loading of code or data is mentioned at several points. This is part of the installation, always selected for R code but optional for data. When used the R objects of the package are created at installation time and stored in a database in the R directory of the installed package, being loaded into the session at first use. This makes the R session start up faster and use less (virtual) memory. (For technical details, see Lazy loading.)
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.
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.
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 be capitalized, 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 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’ (using the Computing Classification System of the Association for Computing Machinery, http://www.acm.org/class/), ‘Classification/JEL’ (the Journal of Economic Literature Classification System, http://www.aeaweb.org/journal/jel_class_system.html), or ‘Classification/MSC’ (the Mathematics Subject Classification of the American Mathematical Society, http://www.ams.org/msc/). 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.
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.
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
GPL-2 GPL-3 LGPL-2 LGPL-2.1 LGPL-3 AGPL-3 Artistic-2.0
BSD_2_clause BSD_3_clause MIT
as made available via http://www.R-project.org/Licenses/ and contained in subdirectory share/licenses of the R source or home directory.
Abbreviations GPL and LGPL are ambiguous and usually taken
to mean any version of the license: but it is better not to use them.
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’.
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. 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), so it is desirable that the use of suggested
packages is made conditional via
if(require(pkgname))).
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
library(pkgname) should be listed in the ‘Imports’ field
and not in the ‘Depends’ field. Packages listed in imports
or importFrom directives in the NAMESPACE file should
almost always be in ‘Imports’ and not ‘Depends’.
library(pkgname) must be listed in the ‘Depends’
field.
R CMD check on the
package must be listed in one of ‘Depends’ or ‘Suggests’ or
‘Imports’. Packages used to run examples or tests conditionally
(e.g. via if(require(pkgname))) should be listed
in ‘Suggests’ or ‘Enhances’. (This allows checkers to ensure
that all the packages needed for a complete check are installed.)
In particular, packages providing “only” data for examples or vignettes should be listed in ‘Suggests’ rather than ‘Depends’ in order to make lean installations possible.
Version dependencies in the ‘Depends’ and ‘Imports’ fields are
used by library when it loads the package, and
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.
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).
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 extensions8 .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,
an see also ‘SysDataCompression’ DESCRIPTION field.) this
will be lazy-loaded into the namespace environment – this is intended
for system datasets that are not intended to be user-accessible
via data. Also, files ending in ‘.in’ will be
allowed in the R directory to allow a configure script to
generate suitable files.
Only ASCII characters (and the control characters tab,
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 normally9 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 means10 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 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 9x11, Objective C and Objective
C++12 with associated extensions .c, .cc or
.cpp, .f, .f90 or .f95, .m, and
.mm, respectively. We recommend using .h for headers,
also for C++13 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.14
The default rules can be tweaked by setting macros15 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-arch setting since R CMD INSTALL --libs-only
is used to merge multiple 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 if 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-insensitively16
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 corresponding17 .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. This mechanism is currently used only by a very few
packages. 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).
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 standard18 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.
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 (e.g. arulesSequences), BUGS, Java, JavaScript, Matlab, Perl (FEST), php (amap), Python or Tcl, or even R. However, it seems more common to use the inst directory, for example AMA/inst/java, WriteXLS/inst/Perl, Amelia/inst/tklibs, NMF/inst/matlab and emdbook/inst/BUGS.
If your package requires one of these interpreters or an extension then this should be declared in the ‘SystemRequirements’ field of its DESCRIPTION file. Windows and Mac users should be aware that the Tcl extensions ‘BWidget’ and ‘Tktable’ which are currently included with the R for Windows and 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.
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
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,
There are some macros19 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
FLIBSPKG_LIBS: it will normally be included
automatically if the package contains FORTRAN source files.
BLAS_LIBSPKG_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 be20 included following it, as most BLAS
libraries are written at least partially in FORTRAN.
LAPACK_LIBSPKG_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_FFLAGSPKG_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.
There is some support for packages which wish to use OpenMP21. 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.22 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.
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 defined23 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.
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.
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.
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 rare24) 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
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.
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 configured25, or in a personal or site Makevars file. See Customizing package compilation. 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++ 4.8.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 experimental support for the next revision (tentatively planned for 2017) using something like -std=c++1y.
As noted above, support for C++11 varies across platforms. The default compiler26 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.
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 checkandR CMD buildrun 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, files27 ~/.R/check.Renviron and ~/.R/build.Renviron are used) to set environment variables when using these utilities.
Note to Windows users:R CMD buildmay 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).
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
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.
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.
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)
\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).
\usage
sections of Rd files are documented in the corresponding
\arguments section.
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. Windows30 users should note that the Fortran and C++ runtime libraries are examples of such external libraries.
\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.
If there is an error31 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.)
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: 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 available32). Some Windows users may need to set environment variable R_WIN_NO_JUNCTIONS to a non-empty value. The test of cyclic declarations33in 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 checks34 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.
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-insensitively35 against the file and directory names relative to the
top-level package source directory. In addition, directories from
source control systems36 or from eclipse37, 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).
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.
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
extensions38 .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 automatically39 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.40
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_HOME41.
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.
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.)
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.
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.
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.
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.
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.
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.
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 arguments42 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.
A NAMESPACE file can contain one or more useDynLib
directives which allows shared objects that need to be
loaded.43 The directive
useDynLib(foo)
registers the shared object foo44 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.
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.
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 file45 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 exportClassPattern46 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 package47, 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.
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 ., 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,48 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.
ifeq and the like), ${shell ...} and
${wildcard ...}, and the use of += and :=. Also,
the use of $< other than in implicit rules is a GNU extension, as
is the $^ macro Unfortunately makefiles which use GNU
extensions often run on other platforms but do not have the intended
results.
The use of ${shell ...} can be avoided by using backticks, e.g.
PKG_CPPFLAGS = `gsl-config --cflags`
which works in all versions of make known49 to be used with R.
If you really must assume GNU make, declare it in the DESCRIPTION file by
SystemRequirements: GNU make
Since the only viable make for Windows is GNU make, it is permissible to use GNU extensions in files Makevars.win or Makefile.win.
Moreover, Bash extensions also need to be avoided in shell scripts, including expressions in Makefiles (which are passed to the shell for processing). Some R platforms use strictly POSIX-conformant Bourne shells, and Windows and some Unix-alike OSes use ash (http://en.wikipedia.org/wiki/Almquist_shell), a rather minimal shell with few builtins. Beware of assuming that all the POSIX command-line utilities are available, especially on Windows where only a minimal set is provided for use with R. (See The command line tools.) One particular issue is the use of echo, for which two behaviours are allowed (http://pubs.opengroup.org/onlinepubs/9699919799/utilities/echo.html) and both occur as defaults on R platforms: portable applications should not use -n (as the first argument) nor escape sequences.
g++ -Wall
-pedantic will alert you to the use of GNU extensions which fail to
compile on most other C++ compilers. If R was not configured
accordingly, one can achieve this via personal Makevars
files.
See Customizing package compilation,
Although there is a 2011 version of the C++ standard, it is not yet fully implemented and partial implementations are not universally available. Portable C++ code needs to follow the 1998 standard (and not use features from C99). See also Using C++11 code to specify a C++11 compiler.
If you use FORTRAN 77, ftnchek
(http://www.dsm.fordham.edu/~ftnchek/) provides thorough testing
of conformance to the standard.
long in C will be 32-bit
on some R platforms (including 64-bit Windows), but 64-bit on most
modern Unix and Linux platforms. It is rather unlikely that the use of
long in C code has been thought through: if you need a longer
type than int you should use a configure test for a C99 type such
as int_fast64_t (and failing that, long long 50) and typedef your own type
to be long or long long, or use another suitable type
(such as size_t).
It is not safe to assume that long and pointer types are the same
size, and they are not on 64-bit Windows. If you need to convert
pointers to and from integers use the C99 integer types intptr_t
and uintptr_t (which are defined in the header <stdint.h>
and are not required to be implemented by the C99 standard).
Note that integer in FORTRAN corresponds to int
in C on all R platforms.
abort
or exit: these terminate the user's R process, quite possibly
including all his unsaved work. One usage that could call abort
is the assert macro in C or C++ functions, which should never be
active in production code. The normal way to ensure that is to define
the macro NDEBUG, and R CMD INSTALL does so as part of
the compilation flags. If you wish to use assert during
development. you can include -UNDEBUG in PKG_CPPFLAGS.
Note that your own src/Makefile or makefiles in sub-directories
may also need to define NDEBUG.
This applies not only to your own code but to any external software you compile in or link to.
nm -pg mypkg.so
and checking if any of the symbols marked U is unexpected is a
good way to avoid this.
nm -pg), and to use names which are
clearly tied to your package (which also helps users if anything does go
wrong). Note that symbol names starting with R_ are regarded as
part of R's namespace and should not be used in packages.
.Internal, .C, .Fortran, .Call or
.External, since such interfaces are subject to change without
notice and will probably result in your code terminating the R
process.
Do be careful in what your tests actually test. Bad practice seen in distributed packages include:
If you must try to establish a tolerance empirically, configure and build R with --disable-long-double and use appropriate compiler flags (such as -ffloat-store and -fexcess-precision=standard for gcc) to mitigate the effects of extended-precision calculations.
There are a several tools available to reduce the size of PDF files: often the size can be reduced substantially with no or minimal loss in quality. Not only do large files take up space: they can stress the PDF viewer and take many minutes to print (if they can be printed at all).
qpdf (http://qpdf.sourceforge.net/) can compress losslessly. It is fairly readily available (e.g. it has binaries for Windows and packages in Debian/Ubuntu/Fedora, and is installed as part of the CRAN OS X distribution of R). R CMD build has an option to run qpdf over PDF files under inst/doc and replace them if at least 10Kb and 10% is saved. The full path to the qpdf command can be supplied as environment variable R_QPDF (and is on the CRAN binary of R for OS X). It seems MiKTeX does not use PDF object compression and so qpdf can reduce considerably the files it outputs: MiKTeX can be overridden by code in the preamble of an Sweave or LaTeX file — see how this is done for the R reference manual at https://svn.r-project.org/R/trunk/doc/manual/refman.top.
Other tools can reduce the size of PDFs containing bitmap images at
excessively high resolution. These are often best re-generated (for
example Sweave defaults to 300 ppi, and 100–150 is more
appropriate for a package manual). These tools include Adobe Acrobat
(not Reader), Apple's Preview52 and Ghostscript (which
converts PDF to PDF by
ps2pdf options -dAutoRotatePages=/None in.pdf out.pdf
and suitable options might be
-dPDFSETTINGS=/ebook
-dPDFSETTINGS=/screen
; see http://www.ghostscript.com/doc/current/Ps2pdf.htm for more such and consider all the options for image downsampling). There have been examples in CRAN packages for which Ghostscript 9.06 and later produced much better reductions than 9.05 or earlier.
We come across occasionally large PDF files containing excessively complicated figures using PDF vector graphics: such figures are often best redesigned or failing that, output as PNG files.
Option --compact-vignettes to R CMD build defaults to
value ‘qpdf’: use ‘both’ to try harder to reduce the size,
provided you have Ghostscript available (see the help for
tools::compactPDF).
There are several ways to find out where time is being spent in the check process. Start by setting the environment variable _R_CHECK_TIMINGS_ to ‘0’. This will report the total CPU times (not Windows) and elapsed times for installation and running examples, tests and vignettes, under each sub-architecture if appropriate. For tests and vignettes, it reports the time for each as well as the total.
Setting _R_CHECK_TIMINGS_ to a positive value sets a threshold (in seconds elapsed time) for reporting timings.
If you need to look in more detail at the timings for examples, use option --timings to R CMD check (this is implied by --as-cran as from R 3.0.2). This adds a summary to the check output for all the examples with CPU or elapsed time of more than 5 seconds. It produces a file mypkg.Rcheck/mypkg-Ex.timings containing timings for each help file: it is a tab-delimited file which can be read into R for further analysis.
Timings for the tests and vignette runs are given at the bottom of the corresponding log file: note that log files for successful vignette runs are only retained if environment variable _R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_ is set to a true value.
Care is needed if your package contains non-ASCII text, and in particular if it is intended to be used in more than one locale. It is possible to mark the encoding used in the DESCRIPTION file and in .Rd files, as discussed elsewhere in this manual.
First, consider carefully if you really need non-ASCII text. Many users of R will only be able to view correctly text in their native language group (e.g. Western European, Eastern European, Simplified Chinese) and ASCII.53. Other characters may not be rendered at all, rendered incorrectly, or cause your R code to give an error. For .Rd documentation, marking the encoding and including ASCII transliterations is likely to do a reasonable job. The set of characters which is commonly supported is wider than it used to be around 2000, but non-Latin alphabets (Greek, Russian, Georgian, ...) are still often problematic and those with double-width characters (Chinese, Japanese, Korean) often need specialist fonts to render correctly.
Several CRAN packages have messages in their R code in French (and a few in German). A better way to tackle this is to use the internationalization facilities discussed elsewhere in this manual.
Function showNonASCIIfile in package tools can help in
finding non-ASCII bytes in files.
There is a portable way to have arbitrary text in character strings
(only) in your R code, which is to supply them in Unicode as
\uxxxx escapes. If there are any characters not in the current
encoding the parser will encode the character string as UTF-8 and mark
it as such. This applies also to character strings in datasets: they
can be prepared using \uxxxx escapes or encoded in UTF-8 in a
UTF-8 locale, or even converted to UTF-8 via ‘iconv()’. If you do
this, make sure you have ‘R (>= 2.10)’ (or later) in the
‘Depends’ field of the DESCRIPTION file.
R sessions running in non-UTF-8 locales will if possible re-encode such strings for display (and this is done by RGui on Windows, for example). Suitable fonts will need to be selected or made available54 both for the console/terminal and graphics devices such as ‘X11()’ and ‘windows()’. Using ‘postscript’ or ‘pdf’ will choose a default 8-bit encoding depending on the language of the UTF-8 locale, and your users would need to be told how to select the ‘encoding’ argument.
If you want to run R CMD check on a Unix-alike over a package that sets a package encoding in its DESCRIPTION file you may need to specify a suitable locale via environment variable R_ENCODING_LOCALES. The default is equivalent to the value
"latin1=en_US:latin2=pl_PL:UTF-8=en_US.UTF-8:latin9=fr_FR.iso885915@euro"
(which is appropriate for a system based on glibc) except that if
the current locale is UTF-8 then the package code is translated to UTF-8
for syntax checking.
Writing portable C and C++ code is mainly a matter of observing the standards (C99, C++98 or where declared C++11) and testing that extensions (such as POSIX functions) are supported. However, some common errors are worth pointing out here. It can be helpful to look up functions at http://www.cplusplus.com/reference/ or http://en.cppreference.com/w/ and compare what is defined in the various standards.
sqrt are defined in C++ for
floating-point arguments. It is legitimate in C++ to overload these
with versions for types float, double, long double
and possibly more. This means that calling sqrt on an integer
type may have ‘overloading ambiguity’ as it could be promoted to any of the
supported floating-point types: this is commonly seen on Solaris.
(C++11 requires additional overloads for integer types.)
fabs is defined for only for floating-point types,
except in C++11 which has overloads in <cmath> for integer types.
Function abs is defined in C99's <stdlib.h> for int
and in C++98's <cstdlib> for integer types, overloaded in
<cmath> for floating-point types. C++11 has additional overloads
for abs in <cmath> for integer types.
isnan, isinf and isfinite
are not required by C++98: where compilers support them they may be only
in the std namespace or only in the main namespace. There is no
way to make use of these functions which works with all C++ compilers
currently in use on R platforms: use R's versions such as
ISNAN and R_FINITE instead.
It is an error (and make little sense, although has been seen) to call these functions for integer arguments.
CS, DS and SS defined on i586/x64 Solaris in
<sys/regset.h> and often included indirectly by <stdlib.h>
and other core headers.
Some additional information for C++ is available at http://journal.r-project.org/archive/2011-2/RJournal_2011-2_Plummer.pdf by Martyn Plummer.
If you want to distribute a binary version of a package on Windows or OS X, there are further checks you need to do to check it is portable: it is all too easy to depend on external software on your own machine that other users will not have.
For Windows, check what other DLLs your package's DLL depends on (‘imports’ from in the DLL tools' parlance). A convenient GUI-based tool to do so is ‘Dependency Walker’ (http://www.dependencywalker.com/) for both 32-bit and 64-bit DLLs – note that this will report as missing links to R's own DLLs such as R.dll and Rblas.dll. For 32-bit DLLs only, the command-line tool pedump.exe -i (in Rtools*.exe) can be used, and for the brave, the objdump tool in the appropriate toolchain will also reveal what DLLs are imported from. If you use a toolchain other than one provided by the R developers or use your own makefiles, watch out in particular for dependencies on the toolchain's runtime DLLs such as libgfortran, libstdc++ and libgcc_s.
For OS X, using R CMD otool -L on the package's shared objects in
the libs directory will show what they depend on: watch for any
dependencies in /usr/local/lib, notably
libgfortran.2.dylib.
Many people (including the CRAN package repository) will not accept source packages containing binary files as the latter are a security risk. If you want to distribute a source package which needs external software on Windows or OS X, options include
Be aware that license requirements will need to be met so you may need to supply the sources for the additional components (and will if your package has a GPL-like license).
Diagnostic messages can be made available for translation, so it is important to write them in a consistent style. Using the tools described in the next section to extract all the messages can give a useful overview of your consistency (or lack of it). Some guidelines follow.
In R error messages do not construct a message with paste (such
messages will not be translated) but via multiple arguments to
stop or warning, or via gettextf.
‘ord’ must be a positive integer, at most the number of knots
and double quotation marks when referring to an R character string or a class, such as
‘format’ must be "normal" or "short" - using "normal"
Since ASCII does not contain directional quotation marks, it
is best to use ‘'’ and let the translator (including automatic
translation) use directional quotations where available. The range of
quotation styles is immense: unfortunately we cannot reproduce them in a
portable texinfo document. But as a taster, some languages use
‘up’ and ‘down’ (comma) quotes rather than left or right quotes, and
some use guillemets (and some use what Adobe calls ‘guillemotleft’ to
start and others use it to end).
In R messages it is also possible to use sQuote or dQuote as in
stop(gettextf("object must be of class %s or %s",
dQuote("manova"), dQuote("maov")),
domain = NA)
library
if((length(nopkgs) > 0) && !missing(lib.loc)) {
if(length(nopkgs) > 1)
warning("libraries ",
paste(sQuote(nopkgs), collapse = ", "),
" contain no packages")
else
warning("library ", paste(sQuote(nopkgs)),
" contains no package")
}
and was replaced by
if((length(nopkgs) > 0) && !missing(lib.loc)) {
pkglist <- paste(sQuote(nopkgs), collapse = ", ")
msg <- sprintf(ngettext(length(nopkgs),
"library %s contains no packages",
"libraries %s contain no packages",
domain = "R-base"),
pkglist)
warning(msg, domain=NA)
}
Note that it is much better to have complete clauses as here, since in another language one might need to say ‘There is no package in library %s’ or ‘There are no packages in libraries %s’.
There are mechanisms to translate the R- and C-level error and warning messages. There are only available if R is compiled with NLS support (which is requested by configure option --enable-nls, the default).
The procedures make use of msgfmt and xgettext which are
part of GNU gettext and this will need to be installed:
Windows users can find pre-compiled binaries at
http://www.stats.ox.ac.uk/pub/Rtools/goodies/gettext-tools.zip.
The process of enabling translations is
#include <R.h> /* to include Rconfig.h */
#ifdef ENABLE_NLS
#include <libintl.h>
#define _(String) dgettext ("pkg", String)
/* replace pkg as appropriate */
#else
#define _(String) (String)
#endif
_(...),
for example
error(_("'ord' must be a positive integer"));
If you want to use different messages for singular and plural forms, you need to add
#ifndef ENABLE_NLS
#define dngettext(pkg, String, StringP, N) (N > 1 ? StringP : String)
#endif
and mark strings by
dngettext(("pkg", <singular string>, <plural string>, n)
xgettext --keyword=_ -o pkg.pot *.c
The file src/pkg.pot is the template file, and conventionally this is shipped as po/pkg.pot.
Mechanisms are also available to support the automatic translation of
R stop, warning and message messages. They make
use of message catalogs in the same way as C-level messages, but using
domain R-pkg rather than pkg. Translation of
character strings inside stop, warning and message
calls is automatically enabled, as well as other messages enclosed in
calls to gettext or gettextf. (To suppress this, use
argument domain=NA.)
Tools to prepare the R-pkg.pot file are provided in package
tools: xgettext2pot will prepare a file from all strings
occurring inside gettext/gettextf, stop,
warning and message calls. Some of these are likely to be
spurious and so the file is likely to need manual editing.
xgettext extracts the actual calls and so is more useful when
tidying up error messages.
The R function ngettext provides an interface to the C
function of the same name: see example in the previous section. It is
safest to use domain="R-pkg" explicitly in calls to
ngettext, and necessary for earlier versions of R unless they
are calls directly from a function in the package.
Once the template files have been created, translations can be made. Conventional translations have file extension .po and are placed in the po subdirectory of the package with a name that is either ‘ll.po’ or ‘R-ll.po’ for translations of the C and R messages respectively to language with code ‘ll’.
See Localization of messages, for details of language codes.
There is an R function, update_pkg_po in package tools,
to automate much of the maintenance of message translations. See its
help for what it does in detail.
If this is called on a package with no existing translations, it creates the directory pkgdir/po, creates a template file of R messages, pkgdir/po/R-pkg.pot, within it, creates the ‘en@quot’ translation and installs that. (The ‘en@quot’ pseudo-language interprets quotes in their directional forms in suitable (e.g. UTF-8) locales.)
If the package has C source files in its src directory that are marked for translation, use
touch pkgdir/po/pkg.pot
to create a dummy template file, then call update_pkg_po again
(this can also be done before it is called for the first time).
When translations to new languages are added in the pkgdir/po directory, running the same command will check and then install the translations.
If the package sources are updated, the same command will update the template files, merge the changes into the translation .po files and then installed the updated translations. You will often see that merging marks translations as ‘fuzzy’ and this is reported in the coverage statistics. As fuzzy translations are not used, this is an indication that the translation files need human attention.
The merged translations are run through tools::checkPofile to
check that C-style formats are used correctly: if not the mismatches are
reported and the broken translations are not installed.
This function needs the GNU gettext-tools installed and on the path: see its help page.
An installed file named CITATION will be used by the
citation() function. (To be installed, it needed to be in the
inst subdirectory of the package sources.)
The CITATION file is parsed as R code (in the package's
declared encoding, or in ASCII if none is declared). If no
such file is present, citation auto-generates citation
information from the package DESCRIPTION metadata, and an example
of what that would look like as a CITATION file can be seen in
recommended package nlme (see below): recommended packages
boot, cluster and mgcv have further
examples.
A CITATION file will contain calls to function bibentry.
Here is that for nlme:
year <- sub("-.*", "", meta$Date)
note <- sprintf("R package version %s", meta$Version)
bibentry(bibtype = "Manual",
title = "{nlme}: Linear and Nonlinear Mixed Effects Models",
author = c(person("Jose", "Pinheiro"),
person("Douglas", "Bates"),
person("Saikat", "DebRoy"),
person("Deepayan", "Sarkar"),
person("R Core Team")),
year = year,
note = note,
url = "http://CRAN.R-project.org/package=nlme")
Note the way that information that may need to be updated is picked up
from the DESCRIPTION file – it is tempting to hardcode such
information, but it normally then gets outdated. See ?bibentry
for further details of the information which can be provided.
In case a bibentry contains LaTeX markup (e.g., for accented
characters or mathematical symbols), it may be necessary to provide a
text representation to be used for printing via the textVersion
argument to bibentry. E.g., earlier versions of
nlme additionally used
textVersion =
paste0("Jose Pinheiro, Douglas Bates, Saikat DebRoy,",
"Deepayan Sarkar and the R Core Team (",
year,
"). nlme: Linear and Nonlinear Mixed Effects Models. ",
note, ".")
The CITATION file should itself produce no output when
source-d.
The DESCRIPTION file has an optional field Type which if
missing is assumed to be ‘Package’, the sort of extension discussed
so far in this chapter. Currently one other type is recognized; there
used also to be a ‘Translation’ type.
This is a rather general mechanism, designed for adding new front-ends
such as the former gnomeGUI package (see the Archive area on
CRAN). If a configure file is found in the top-level
directory of the package it is executed, and then if a Makefile
is found (often generated by configure), make is called.
If R CMD INSTALL --clean is used make clean is called. No
other action is taken.
R CMD build can package up this type of extension, but R
CMD check will check the type and skip it.
Many packages of this type need write permission for the R installation directory.
Several members of the R project have set up services to assist those writing R packages, particularly those intended for public distribution.
win-builder.r-project.org offers the automated preparation of (32/64-bit) Windows binaries from well-tested source packages.
R-Forge (R-Forge.r-project.org) and
RForge (www.rforge.net) are similar
services with similar names. Both provide source-code management
through SVN, daily building and checking, mailing lists and a repository
that can be accessed via install.packages (they can be
selected by setRepositories and the GUI menus that use it).
Package developers have the opportunity to present their work on the
basis of project websites or news announcements. Mailing lists, forums
or wikis provide useRs with convenient instruments for discussions and
for exchanging information between developers and/or interested useRs.
R objects are documented in files written in “R documentation” (Rd) format, a simple markup language much of which closely resembles (La)TeX, which can be processed into a variety of formats, including LaTeX, HTML and plain text. The translation is carried out by functions in the tools package called by the script Rdconv in R_HOME/bin and by the installation scripts for packages.
The R distribution contains more than 1300 such files which can be found in the src/library/pkg/man directories of the R source tree, where pkg stands for one of the standard packages which are included in the R distribution.
As an example, let us look at a simplified version of
src/library/base/man/load.Rd which documents the R function
load.
% File src/library/base/man/load.Rd \name{load} \alias{load} \title{Reload Saved Datasets} \description{ Reload the datasets written to a file with the function \code{save}. } \usage{ load(file, envir = parent.frame()) } \arguments{ \item{file}{a connection or a character string giving the name of the file to load.} \item{envir}{the environment where the data should be loaded.} } \seealso{ \code{\link{save}}. } \examples{ ## save all data save(list = ls(), file= "all.RData") ## restore the saved values to the current environment load("all.RData") ## restore the saved values to the workspace load("all.RData", .GlobalEnv) } \keyword{file}
An Rd file consists of three parts. The header gives basic information about the name of the file, the topics documented, a title, a short textual description and R usage information for the objects documented. The body gives further information (for example, on the function's arguments and return value, as in the above example). Finally, there is an optional footer with keyword information. The header is mandatory.
Information is given within a series of sections with standard names (and user-defined sections are also allowed). Unless otherwise specified55 these should occur only once in an Rd file (in any order), and the processing software will retain only the first occurrence of a standard section in the file, with a warning.
See “Guidelines for Rd files” for guidelines for writing documentation in Rd format
which should be useful for package writers.
The R
generic function prompt is used to construct a bare-bones Rd
file ready for manual editing. Methods are defined for documenting
functions (which fill in the proper function and argument names) and
data frames. There are also functions promptData,
promptPackage, promptClass, and promptMethods for
other types of Rd file.
The general syntax of Rd files is summarized below. For a detailed technical discussion of current Rd syntax, see “Parsing Rd files”.
Rd files consists of three types of text input. The most common
is LaTeX-like, with the backslash used as a prefix on markup
(e.g. \alias), and braces used to indicate arguments
(e.g. {load}). The least common type of text is verbatim text,
where no markup is processed. The third type is R-like, intended for
R code, but allowing some embedded macros. Quoted strings within
R-like text are handled specially: regular character escapes such as
\n may be entered as-is. Only markup starting with \l
(e.g. \link) or \v (e.g. \var) will be recognized
within quoted strings. The rarely used vertical tab \v must be
entered as \\v.
Each macro defines the input type for its argument. For example, the
file initially uses LaTeX-like syntax, and this is also used in the
\description section, but the \usage section uses
R-like syntax, and the \alias macro uses verbatim syntax.
Comments run from a percent symbol % to the end of the line in
all types of text (as on the first line of the load example).
Because backslashes, braces and percent symbols have special meaning, to enter them into text sometimes requires escapes using a backslash. In general balanced braces do not need to be escaped, but percent symbols always do. For the complete list of macros and rules for escapes, see “Parsing Rd files”.
The basic markup commands used for documenting R objects (in particular, functions) are given in this subsection.
\name{name}\name entry in a
file, and it must not contain any markup. Entries in the package manual
will be in alphabetic57 order
of the \name entries.
\alias{topic}\alias sections specify all “topics” the file documents.
This information is collected into index data bases for lookup by the
on-line (plain text and HTML) help systems. The topic can
contain spaces, but (for historical reasons) leading and trailing spaces
will be stripped. Percent and left brace need to be escaped by
a backslash.
There may be several \alias entries. Quite often it is
convenient to document several R objects in one file. For example,
file Normal.Rd documents the density, distribution function,
quantile function and generation of random variates for the normal
distribution, and hence starts with
\name{Normal}
\alias{Normal}
\alias{dnorm}
\alias{pnorm}
\alias{qnorm}
\alias{rnorm}
Also, it is often convenient to have several different ways to refer to
an R object, and an \alias does not need to be the name of an
object.
Note that the \name is not necessarily a topic documented, and if
so desired it needs to have an explicit \alias entry (as in this
example).
\title{Title}Markup is supported in the text, but use of characters other than English text and punctuation (e.g., ‘<’) may limit portability.
There must be one (and only one) \title section in a help file.
\description{...}\usage{fun(arg1, arg2, ...)}The usage information specified should match the function definition exactly (such that automatic checking for consistency between code and documentation is possible).
It is no longer advisable to use \synopsis for the actual
synopsis and show modified synopses in the \usage. Support for
\synopsis will be removed in \R 3.1.0. To indicate that a
function can be used in several different ways, depending on the named
arguments specified, use section \details. E.g.,
abline.Rd contains
\details{
Typical usages are
\preformatted{abline(a, b, untf = FALSE, \dots)
......
}
Use \method{generic}{class} to indicate the name
of an S3 method for the generic function generic for objects
inheriting from class "class". In the printed versions,
this will come out as generic (reflecting the understanding that
methods should not be invoked directly but via method dispatch), but
codoc() and other QC tools always have access to the full name.
For example, print.ts.Rd contains
\usage{
\method{print}{ts}(x, calendar, \dots)
}
which will print as
Usage:
## S3 method for class ‘ts’:
print(x, calendar, ...)
Usage for replacement functions should be given in the style of
dim(x) <- value rather than explicitly indicating the name of the
replacement function ("dim<-" in the above). Similarly, one
can use \method{generic}{class}(arglist) <-
value to indicate the usage of an S3 replacement method for the generic
replacement function "generic<-" for objects inheriting
from class "class".
Usage for S3 methods for extracting or replacing parts of an object, S3 methods for members of the Ops group, and S3 methods for user-defined (binary) infix operators (‘%xxx%’) follows the above rules, using the appropriate function names. E.g., Extract.factor.Rd contains
\usage{
\method{[}{factor}(x, \dots, drop = FALSE)
\method{[[}{factor}(x, \dots)
\method{[}{factor}(x, \dots) <- value
}
which will print as
Usage:
## S3 method for class ‘factor’:
x[..., drop = FALSE]
## S3 method for class ‘factor’:
x[[...]]
## S3 replacement method for class ‘factor’:
x[...] <- value
\arguments{...} \item{arg_i}{Description of arg_i.}
for each element of the argument list. (Note that there is
no whitespace between the three parts of the entry.) There may be
optional text outside the \item entries, for example to give
general information about groups of parameters.
\details{...}\description
slot.
\value{...}If a list with multiple values is returned, you can use entries of the form
\item{comp_i}{Description of comp_i.}
for each component of the list returned. Optional text may
precede58 this
list (see for example the help for rle). Note that \value
is implicitly a \describe environment, so that environment should
not be used for listing components, just individual \item{}{}
entries.
\references{...}\url{} or
\href{}{} for web pointers.
\note{...}\note sections are allowed, but might be confusing to the end users.
For example, pie.Rd contains
\note{
Pie charts are a very bad way of displaying information.
The eye is good at judging linear measures and bad at
judging relative areas.
......
}
\author{...}\email{} without extra delimiters (such as ‘( )’ or
‘< >’) to specify email addresses, or \url{} or
\href{}{} for web pointers.
\seealso{...}\code{\link{...}} to
refer to them (\code is the correct markup for R object names,
and \link produces hyperlinks in output formats which support
this. See Marking text, and Cross-references).
\examples{...}example() unless marked otherwise (see below).
Examples are not only useful for documentation purposes, but also
provide test code used for diagnostic checking of R code. By
default, text inside \examples{} will be displayed in the
output of the help page and run by example() and by R CMD
check. You can use \dontrun{}
for text that should only be shown, but not run, and
\dontshow{}
for extra commands for testing that should not be shown to users, but
will be run by example(). (Previously this was called
\testonly, and that is still accepted.)
Text inside \dontrun{} is verbatim, but the other parts
of the \examples section are R-like text.
For example,
x <- runif(10) # Shown and run. \dontrun{plot(x)} # Only shown. \dontshow{log(x)} # Only run.
Thus, example code not included in \dontrun must be executable!
In addition, it should not use any system-specific features or require
special facilities (such as Internet access or write permission to
specific directories). Text included in \dontrun is indicated by
comments in the processed help files: it need not be valid R code but
the escapes must still be used for %, \ and unpaired
braces as in other verbatim text.
Example code must be capable of being run by example, which uses
source. This means that it should not access stdin,
e.g. to scan() data from the example file.
Data needed for making the examples executable can be obtained by random
number generation (for example, x <- rnorm(100)), or by using
standard data sets listed by data() (see ?data for more
info).
Finally, there is \donttest, used (at the beginning of a separate
line) to mark code that should be run by examples() but not by
R CMD check. This should be needed only occasionally but can be
used for code which might fail in circumstances that are hard to test
for, for example in some locales. (Use e.g. capabilities() to
test for features needed in the examples wherever possible, and you can
also use try() or tryCatch().)
\keyword{key}\keyword sections per file.
Each \keyword section should specify a single keyword, preferably
one of the standard keywords as listed in file KEYWORDS in the
R documentation directory (default R_HOME/doc). Use
e.g. RShowDoc("KEYWORDS") to inspect the standard keywords from
within R. There can be more than one \keyword entry if the R
object being documented falls into more than one category, or none.
Do strongly consider using \concept (see Indices) instead of
\keyword if you are about to use more than very few non standard
keywords.
The special keyword ‘internal’ marks a page of internal objects
that are not part of the package's API. If the help page for object
foo has keyword ‘internal’, then help(foo) gives this
help page, but foo is excluded from several object indices,
including the alphabetical list of objects in the HTML help system.
help.search() can search by keyword, including user-defined
values: however the ‘Search Engine & Keywords’ HTML page accessed
via help.start() provides single-click access only to a
pre-defined list of keywords.
The structure of Rd files which document R data sets is slightly
different. Sections such as \arguments and \value are not
needed but the format and source of the data should be explained.
As an example, let us look at src/library/datasets/man/rivers.Rd
which documents the standard R data set rivers.
\name{rivers} \docType{data} \alias{rivers} \title{Lengths of Major North American Rivers} \description{ This data set gives the lengths (in miles) of 141 \dQuote{major} rivers in North America, as compiled by the US Geological Survey. } \usage{rivers} \format{A vector containing 141 observations.} \source{World Almanac and Book of Facts, 1975, page 406.} \references{ McNeil, D. R. (1977) \emph{Interactive Data Analysis}. New York: Wiley. } \keyword{datasets}
This uses the following additional markup commands.
\docType{...}promptMethods()) and ‘class’ (from
promptClass()).
\format{...}\source{...}\references could give secondary sources and
usages.
Note also that when documenting data set bar,
\usage entry is always bar or (for packages
which do not use lazy-loading of data) data(bar). (In
particular, only document a single data object per Rd file.)
\keyword entry should always be ‘datasets’.
If bar is a data frame, documenting it as a data set can
be initiated via prompt(bar). Otherwise, the promptData
function may be used.
There are special ways to use the ‘?’ operator, namely
‘class?topic’ and ‘methods?topic’, to access
documentation for S4 classes and methods, respectively. This mechanism
depends on conventions for the topic names used in \alias
entries. The topic names for S4 classes and methods respectively are of
the form
class-class
generic,signature_list-method
where signature_list contains the names of the classes in the
signature of the method (without quotes) separated by ‘,’ (without
whitespace), with ‘ANY’ used for arguments without an explicit
specification. E.g., ‘genericFunction-class’ is the topic name for
documentation for the S4 class "genericFunction", and
‘coerce,ANY,NULL-method’ is the topic name for documentation for
the S4 method for coerce for signature c("ANY", "NULL").
Skeletons of documentation for S4 classes and methods can be generated
by using the functions promptClass() and promptMethods()
from package methods. If it is necessary or desired to provide an
explicit function declaration (in a \usage section) for an S4
method (e.g., if it has “surprising arguments” to be mentioned
explicitly), one can use the special markup
\S4method{generic}{signature_list}(argument_list)
(e.g., ‘\S4method{coerce}{ANY,NULL}(from, to)’).
To make full use of the potential of the on-line documentation system,
all user-visible S4 classes and methods in a package should at least
have a suitable \alias entry in one of the package's Rd files.
If a package has methods for a function defined originally somewhere
else, and does not change the underlying default method for the
function, the package is responsible for documenting the methods it
creates, but not for the function itself or the default method.
An S4 replacement method is documented in the same way as an S3 one: see
the description of \method in Documenting functions.
See help("Documentation", package = "methods") for more information on using and creating on-line documentation for S4 classes and methods.
Packages may have an overview help page with an \alias
pkgname-package, e.g. ‘utils-package’ for the
utils package, when package?pkgname will open that
help page. If a topic named pkgname does not exist in
another Rd file, it is helpful to use this as an additional
\alias.
Skeletons of documentation for a package can be generated using the
function promptPackage(). If the final = TRUE argument
is used, then the Rd file will be generated in final form, containing
the information that would be produced up to
library(help = pkgname). Otherwise (the default) comments
will be inserted giving suggestions for content.
Apart from the mandatory \name and \title and the
pkgname-package alias, the only requirement for the package
overview page is that it include a \docType{package} statement.
All other content is optional. We suggest that it should be a short
overview, to give a reader unfamiliar with the package enough
information to get started. More extensive documentation is better
placed into a package vignette (see Writing package vignettes) and
referenced from this page, or into individual man pages for the
functions, datasets, or classes.
To begin a new paragraph or leave a blank line in an example, just
insert an empty line (as in (La)TeX). To break a line, use
\cr.
In addition to the predefined sections (such as \description{},
\value{}, etc.), you can “define” arbitrary ones by
\section{section_title}{...}.
For example
\section{Warning}{
You must not call this function unless ...
}
For consistency with the pre-assigned sections, the section name (the
first argument to \section) should be capitalized (but not all
upper case). Whitespace between the first and second braced expressions
is not allowed. Markup (e.g. \code) within the section title
may cause problems with the latex conversion (depending on the version
of macro packages such as ‘hyperref’) and so should be avoided.
The \subsection macro takes arguments in the same format as
\section, but is used within a section, so it may be used to
nest subsections within sections or other subsections. There is no
predefined limit on the nesting level, but formatting is not designed
for more than 3 levels (i.e. subsections within subsections within
sections).
Note that additional named sections are always inserted at a fixed
position in the output (before \note, \seealso and the
examples), no matter where they appear in the input (but in the same
order amongst themselves as in the input).
The following logical markup commands are available for emphasizing or quoting text.
\emph{text}\strong{text}\strong is regarded as stronger (more emphatic).
\bold{text}\sQuote{text}\dQuote{text}Each of the above commands takes LaTeX-like input, so other macros may be used within text.
The following logical markup commands are available for indicating specific kinds of text. Except as noted, these take verbatim text input, and so other macros may not be used within them. Some characters will need to be escaped (see Insertions).
\code{text}typewriter font
if possible. Macros \var and \link are interpreted within
text.
\preformatted{text}typewriter font if possible. Formatting,
e.g. line breaks, is preserved. (Note that this includes a line break
after the initial {, so typically text should start on the same line as
the command.)
Due to limitations in LaTeX as of this writing, this macro may not be
nested within other markup macros other than \dQuote and
\sQuote, as errors or bad formatting may result.
\kbd{keyboard-characters}\samp{text}typewriter font if possible.
\verb{text}\var, but which will be included
within word-wrapped text. Displayed using typewriter font if
possible.
\pkg{package_name}\file{file_name}\email{email_address}typewriter font if possible.
\url{uniform_resource_locator}typewriter font if
possible.
\href{uniform_resource_locator}{text}\var{metasyntactic_variable}\env{environment_variable}typewriter font if possible
\option{option}typewriter font if possible.
\command{command_name}\var is
interpreted. Displayed using typewriter font if possible.
\dfn{term}\cite{reference}\link
(see Cross-references), such as the name of a book. LaTeX-like.
\acronym{acronym}
The \itemize and \enumerate commands take a single
argument, within which there may be one or more \item commands.
The text following each \item is formatted as one or more
paragraphs, suitably indented and with the first paragraph marked with a
bullet point (\itemize) or a number (\enumerate).
Note that unlike argument lists, \item in these formats is
followed by a space and the text (not enclosed in braces). For example
\enumerate{
\item A database consists of one or more records, each with one or
more named fields.
\item Regular lines start with a non-whitespace character.
\item Records are separated by one or more empty lines.
}
\itemize and \enumerate commands may be nested.
The \describe command is similar to \itemize but allows
initial labels to be specified. Each \item takes two arguments,
the label and the body of the item, in exactly the same way as an
argument or value \item. \describe commands are mapped to
<DL> lists in HTML and \description lists in LaTeX.
The \tabular command takes two arguments. The first gives for
each of the columns the required alignment (‘l’ for
left-justification, ‘r’ for right-justification or ‘c’ for
centring.) The second argument consists of an arbitrary number of
lines separated by \cr, and with fields separated by \tab.
For example:
\tabular{rlll}{
[,1] \tab Ozone \tab numeric \tab Ozone (ppb)\cr
[,2] \tab Solar.R \tab numeric \tab Solar R (lang)\cr
[,3] \tab Wind \tab numeric \tab Wind (mph)\cr
[,4] \tab Temp \tab numeric \tab Temperature (degrees F)\cr
[,5] \tab Month \tab numeric \tab Month (1--12)\cr
[,6] \tab Day \tab numeric \tab Day of month (1--31)
}
There must be the same number of fields on each line as there are
alignments in the first argument, and they must be non-empty (but can
contain only spaces). (There is no whitespace between \tabular
and the first argument, nor between the two arguments.)
The markup \link{foo} (usually in the combination
\code{\link{foo}}) produces a hyperlink to the help for
foo. Here foo is a topic, that is the argument of
\alias markup in another Rd file (possibly in another package).
Hyperlinks are supported in some of the formats to which Rd files are
converted, for example HTML and PDF, but ignored in others, e.g.
the text format.
One main usage of \link is in the \seealso section of the
help page, see Rd format.
Note that whereas leading and trailing spaces are stripped when
extracting a topic from a \alias, they are not stripped when
looking up the topic of a \link.
You can specify a link to a different topic than its name by
\link[=dest]{name} which links to topic dest
with name name. This can be used to refer to the documentation
for S3/4 classes, for example \code{"\link[=abc-class]{abc}"}
would be a way to refer to the documentation of an S4 class "abc"
defined in your package, and
\code{"\link[=terms.object]{terms}"} to the S3 "terms"
class (in package stats). To make these easy to read in the
source file, \code{"\linkS4class{abc}"} expands to the form
given above.
There are two other forms of optional argument specified as
\link[pkg]{foo} and
\link[pkg:bar]{foo} to link to the package
pkg, to files foo.html and
bar.html respectively. These are rarely needed, perhaps to
refer to not-yet-installed packages (but there the HTML help system
will resolve the link at run time) or in the normally undesirable event
that more than one package offers help on a topic60 (in
which case the present package has precedence so this is only needed to
refer to other packages). They are currently only used in HTML help
(and ignored for hyperlinks in LaTeX conversions of help pages), and
link to the file rather than the topic (since there is no way to know
which topics are in which files in an uninstalled package). The
only reason to use these forms for base and recommended
packages is to force a reference to a package that might be further down
the search path. Because they have been frequently misused, the HTML
help system looks for topic foo in package pkg
if it does not find file foo.html.
Mathematical formulae should be set beautifully for printed
documentation yet we still want something useful for text and HTML
online help. To this end, the two commands
\eqn{latex}{ascii} and
\deqn{latex}{ascii} are used. Whereas \eqn
is used for “inline” formulae (corresponding to TeX's
$...$), \deqn gives “displayed equations” (as in
LaTeX's displaymath environment, or TeX's
$$...$$). Both arguments are treated as verbatim text.
Both commands can also be used as \eqn{latexascii} (only
one argument) which then is used for both latex and
ascii. No whitespace is allowed between command and the first
argument, nor between the first and second arguments.
The following example is from Poisson.Rd:
\deqn{p(x) = \frac{\lambda^x e^{-\lambda}}{x!}}{%
p(x) = \lambda^x exp(-\lambda)/x!}
for \eqn{x = 0, 1, 2, \ldots}.
For text on-line help we get
p(x) = lambda^x exp(-lambda)/x! for x = 0, 1, 2, ....
Greek letters (both cases) will be rendered in HTML if preceded by a
backslash, \dots and \ldots will be rendered as ellipses
and \sqrt, \ge and \le as mathematical symbols.
Note that only basic LaTeX can be used, there being no provision to specify LaTeX style files such as the AMS extensions.
To include figures in help pages, use the \figure markup. There
are three forms.
The two commonly used simple forms are \figure{filename}
and \figure{filename}{alternate text}. This will
include a copy of the figure in either HTML or LaTeX output. In text
output, the alternate text will be displayed instead. (When the second
argument is omitted, the filename will be used.) Both the filename and
the alternate text will be parsed verbatim, and should not include
special characters that are significant in HTML or LaTeX.
The expert form is \figure{filename}{options:
string}. (The word ‘options:’ must be typed exactly as
shown and followed by at least one space.) In this form, the
string is copied into the HTML img tag as attributes
following the src attribute, or into the second argument of the
\Figure macro in LaTeX, which by default is used as options to
an \includegraphics call. As it is unlikely that any single
string would suffice for both display modes, the expert form would
normally be wrapped in conditionals. It is up to the author to make
sure that legal HTML/LaTeX is used. For example, to include a
logo in both HTML (using the simple form) and LaTeX (using the
expert form), the following could be used:
\if{html}{\figure{logo.jpg}{Our logo}}
\if{latex}{\figure{logo.jpg}{options: width=0.5in}}
The files containing the figures should be stored in the directory
man/figures. Files with extensions .jpg, .jpeg,
.pdf, .png and .svg from that directory will be
copied to the help/figures directory at install time. (Figures in
PDF format will not display in most HTML browsers, but might be the
best choice in reference manuals.) Specify the filename relative to
man/figures in the \figure directive.
Use \R for the R system itself. Use \dots
for the dots in function argument lists ‘...’, and
\ldots
for ellipsis dots in ordinary text.61 These can be followed by
{}, and should be unless followed by whitespace.
After an unescaped ‘%’, you can put your own comments regarding the help text. The rest of the line (but not the newline at the end) will be completely disregarded. Therefore, you can also use it to make part of the “help” invisible.
You can produce a backslash (‘\’) by escaping it by another
backslash. (Note that \cr is used for generating line breaks.)
The “comment” character ‘%’ and unpaired braces62 almost always need to be escaped by ‘\’, and ‘\\’ can be used for backslash and needs to be when there two or more adjacent backslashes). In R-like code quoted strings are handled slightly differently; see “Parsing Rd files” for details – in particular braces should not be escaped in quoted strings.
All of ‘% { } \’ should be escaped in LaTeX-like text.
Text which might need to be represented differently in different
encodings should be marked by \enc, e.g.
\enc{Jöreskog}{Joreskog} (with no whitespace between the
braces) where the first argument will be used where encodings are
allowed and the second should be ASCII (and is used for e.g.
the text conversion in locales that cannot represent the encoded form).
(This is intended to be used for individual words, not whole sentences
or paragraphs.)
The \alias command (see Documenting functions) is used to
specify the “topics” documented, which should include all R
objects in a package such as functions and variables, data sets, and S4
classes and methods (see Documenting S4 classes and methods). The
on-line help system searches the index data base consisting of all
alias topics.
In addition, it is possible to provide “concept index entries” using
\concept, which can be used for help.search() lookups.
E.g., file cor.test.Rd in the standard package stats
contains
\concept{Kendall correlation coefficient}
\concept{Pearson correlation coefficient}
\concept{Spearman correlation coefficient}
so that e.g. ??Spearman will succeed in finding the help page for the test for association between paired samples using Spearman's rho.
(Note that help.search() only uses “sections” of documentation
objects with no additional markup.)
If you want to cross reference such items from other help files via
\link, you need to use \alias and not \concept.
Sometimes the documentation needs to differ by platform. Currently two OS-specific options are available, ‘unix’ and ‘windows’, and lines in the help source file can be enclosed in
#ifdef OS
...
#endif
or
#ifndef OS
...
#endif
for OS-specific inclusion or exclusion. Such blocks should not be nested, and should be entirely within a block (that, is between the opening and closing brace of a section or item), or at top-level contain one or more complete sections.
If the differences between platforms are extensive or the R objects documented are only relevant to one platform, platform-specific Rd files can be put in a unix or windows subdirectory.
Occasionally the best content for one output format is different from
the best content for another. For this situation, the
\if{format}{text} or
\ifelse{format}{text}{alternate} markup
is used. Here format is a comma separated list of formats in
which the text should be rendered. The alternate will be
rendered if the format does not match. Both text and
alternate may be any sequence of text and markup.
Currently the following formats are recognized: example,
html, latex and text. These select output for
the corresponding targets. (Note that example refers to
extracted example code rather than the displayed example in some other
format.) Also accepted are TRUE (matching all formats) and
FALSE (matching no formats). These could be the output
of the \Sexpr macro (see Dynamic pages).
The \out{literal} macro would usually be used within
the text part of \if{format}{text}. It
causes the renderer to output the literal text exactly, with no
attempt to escape special characters. For example, use
the following to output the markup necessary to display the Greek letter in
LaTeX or HTML, and the text string alpha in other formats:
\if{latex}{\out{\alpha}}\ifelse{html}{\out{α}}{alpha}
Two macros supporting dynamically generated man pages are \Sexpr
and \RdOpts. These are modelled after Sweave, and are intended
to contain executable R expressions in the Rd file.
The main argument to \Sexpr must be valid R code that can be
executed. It may also take options in square brackets before the main
argument. Depending on the options, the code may be executed at
package build time, package install time, or man page rendering time.
The options follow the same format as in Sweave, but different options are supported. Currently the allowed options and their defaults are:
eval=TRUE
Whether the R code should be evaluated.
echo=FALSE
Whether the R code should be echoed. If TRUE, a display will
be given in a preformatted block. For example,
\Sexpr[echo=TRUE]{ x <- 1 } will be displayed as
> x <- 1
keep.source=TRUE
Whether to keep the author's formatting when displaying the
code, or throw it away and use a deparsed version.
results=text
How should the results be displayed? The possibilities
are:
results=text
Apply as.character() to the result of the code, and insert it
as a text element.
results=verbatim
Print the results of the code just as if it was executed at the console,
and include the printed results verbatim. (Invisible results will not print.)
results=rd
The result is assumed to be a character vector containing markup to be
passed to parse_Rd(), with the result inserted in place. This
could be used to insert computed aliases, for instance.
parse_Rd() is called first with fragment = FALSE to allow
a single Rd section macro to be inserted. If that fails, it is called
again with fragment = TRUE, the older behavior.
results=hide
Insert no output.
strip.white=TRUE
Remove leading and trailing white space from each line of
output if strip.white=TRUE. With
strip.white=all, also remove blank lines.
stage=install
Control when this macro is run. Possible values are
stage=build
The macro is run when building a source tarball.
stage=install
The macro is run when installing from source.
stage=render
The macro is run when displaying the help page.
Conditionals such as #ifdef
(see Platform-specific sections) are applied after the
build macros but before the install macros. In some
situations (e.g. installing directly from a source directory without a
tarball, or building a binary package) the above description is not
literally accurate, but authors can rely on the sequence being
build, #ifdef, install, render, with all
stages executed.
Code is only run once in each stage, so a \Sexpr[results=rd]
macro can output an \Sexpr macro designed for a later stage,
but not for the current one or any earlier stage.
width, height, fig
These options are currently allowed but ignored.
The \RdOpts macro is used to set new defaults for options to apply
to following uses of \Sexpr.
For more details, see the online document “Parsing Rd files”.
The \newcommand and \renewcommand macros allow new macros
to be defined within an Rd file. These are similar but not identical to
the same-named LaTeX macros.
They each take two arguments which are parsed verbatim. The first is
the name of the new macro including the initial backslash, and the second
is the macro definition. As in LaTeX, \newcommand requires that the
new macro not have been previously defined, whereas \renewcommand
allows existing macros (including all built-in ones) to be replaced.
Also as in LaTeX, the new macro may be defined to take arguments,
and numeric placeholders such as #1 are used in the macro
definition. However, unlike LaTeX, the number of arguments is
determined automatically from the highest placeholder number seen in
the macro definition. For example, a macro definition containing
#1 and #3 (but no other placeholders) will define a
three argument macro (whose second argument will be ignored). As in
LaTeX, at most 9 arguments may be defined. If the #
character is followed by a non-digit it will have no special
significance. All arguments to user-defined macros will be parsed as
verbatim text, and simple text-substitution will be used to replace
the place-holders, after which the replacement text will be parsed.
For example, the NEWS.Rd file currently uses the definition
\newcommand{\PR}{\Sexpr[results=rd]{tools:::Rd_expr_PR(#1)}}
which defines \PR to be a single argument macro; then code like
\PR{1234}
will expand to
\Sexpr[results=rd]{tools:::Rd_expr_PR(1234)}
when parsed.
Rd files are text files and so it is impossible to deduce the encoding
they are written in unless ASCII: files with 8-bit characters
could be UTF-8, Latin-1, Latin-9, KOI8-R, EUC-JP, etc. So an
\encoding{} section must be used to specify the encoding if it
is not ASCII. (The \encoding{} section must be on a
line by itself, and in particular one containing no non-ASCII
characters. The encoding declared in the DESCRIPTION file will
be used if none is declared in the file.) The Rd files are
converted to UTF-8 before parsing and so the preferred encoding for the
files themselves is now UTF-8.
Wherever possible, avoid non-ASCII chars in Rd files, and
even symbols such as ‘<’, ‘>’, ‘$’, ‘^’, ‘&’,
‘|’, ‘@’, ‘~’, and ‘*’ outside verbatim
environments (since they may disappear in fonts designed to render
text). (Function showNonASCIIfile in package tools can help
in finding non-ASCII bytes in the files.)
For convenience, encoding names ‘latin1’ and ‘latin2’ are
always recognized: these and ‘UTF-8’ are likely to work fairly
widely. However, this does not mean that all characters in UTF-8 will
be recognized, and the coverage of non-Latin characters63 is fairly low. Using LaTeX
inputenx (see ?Rd2pdf in R) will give greater coverage
of UTF-8.
The \enc command (see Insertions) can be used to provide
transliterations which will be used in conversions that do not support
the declared encoding.
The LaTeX conversion converts the file to UTF-8 from the declared encoding, and includes a
\inputencoding{utf8}
command, and this needs to be matched by a suitable invocation of the \usepackage{inputenc} command. The R utility R CMD Rd2pdf looks at the converted code and includes the encodings used: it might for example use
\usepackage[utf8]{inputenc}
(Use of utf8 as an encoding requires LaTeX dated 2003/12/01 or
later. Also, the use of Cyrillic characters in ‘UTF-8’ appears to
also need ‘\usepackage[T2A]{fontenc}’, and R CMD Rd2pdf
includes this conditionally on the file t2aenc.def being present
and environment variable _R_CYRILLIC_TEX_ being set.)
Note that this mechanism works best with Latin letters: the coverage of UTF-8 in LaTeX is quite low.
There are several commands to process Rd files from the system command line.
Using R CMD Rdconv one can convert R documentation format to
other formats, or extract the executable examples for run-time testing.
The currently supported conversions are to plain text, HTML and
LaTeX as well as extraction of the examples.
R CMD Rd2pdf generates PDF output from documentation in Rd
files, which can be specified either explicitly or by the path to a
directory with the sources of a package. In the latter case, a
reference manual for all documented objects in the package is created,
including the information in the DESCRIPTION files.
R CMD Sweave and R CMD Stangle process vignette-like
documentation files (e.g. Sweave vignettes with extension
‘.Snw’ or ‘.Rnw’, or other non-Sweave vignettes).
R CMD Stangle is used to extract the R code fragments.
The exact usage and a detailed list of available options for all of
these commands can be obtained by running R CMD command
--help, e.g., R CMD Rdconv --help. All available commands can be
listed using R --help (or Rcmd --help under Windows).
All of these work under Windows. You may need to have installed the the tools to build packages from source as described in the “R Installation and Administration” manual, although typically all that is needed is a LaTeX installation.
It can be very helpful to prepare .Rd files using a editor which knows about their syntax and will highlight commands, indent to show the structure and detect mis-matched braces, and so on.
The system most commonly used for this is some version of Emacs (including XEmacs) with the ESS package (http://ess.r-project.org/: it is often is installed with Emacs but may need to be loaded, or even installed, separately).
Another is the Eclipse IDE with the Stat-ET plugin (http://www.walware.de/goto/statet), and (on Windows only) Tinn-R (http://sourceforge.net/projects/tinn-r/).
People have also used LaTeX mode in a editor, as .Rd files are rather similar to LaTeX files.
Some R front-ends provide editing support for .Rd files, for example RStudio (http://rstudio.org/).
R code which is worth preserving in a package and perhaps making available for others to use is worth documenting, tidying up and perhaps optimizing. The last two of these activities are the subject of this chapter.
R treats function code loaded from packages and code entered by users differently. By default code entered by users has the source code stored internally, and when the function is listed, the original source is reproduced. Loading code from a package (by default) discards the source code, and the function listing is re-created from the parse tree of the function.
Normally keeping the source code is a good idea, and in particular it avoids comments being removed from the source. However, we can make use of the ability to re-create a function listing from its parse tree to produce a tidy version of the function, for example with consistent indentation and spaces around operators. If the original source does not follow the standard format this tidied version can be much easier to read.
We can subvert the keeping of source in two ways.
keep.source can be set to FALSE before the code
is loaded into R.
removeSource()
function, for example by
myfun <- removeSource(myfun)
In each case if we then list the function we will get the standard layout.
Suppose we have a file of functions myfuns.R that we want to tidy up. Create a file tidy.R containing
source("myfuns.R", keep.source = FALSE)
dump(ls(all = TRUE), file = "new.myfuns.R")
and run R with this as the source file, for example by R --vanilla < tidy.R or by pasting into an R session. Then the file new.myfuns.R will contain the functions in alphabetical order in the standard layout. Warning: comments in your functions will be lost.
The standard format provides a good starting point for further tidying. Although the deparsing cannot do so, we recommend the consistent use of the preferred assignment operator ‘<-’ (rather than ‘=’) for assignment. Many package authors use a version of Emacs (on a Unix-alike or Windows) to edit R code, using the ESS[S] mode of the ESS Emacs package. See R coding standards for style options within the ESS[S] mode recommended for the source code of R itself.
It is possible to profile R code on Windows and most64 Unix-alike versions of R.
The command Rprof is used to control profiling, and its help
page can be consulted for full details. Profiling works by recording
at fixed intervals65 (by default every 20 msecs)
which line in which R function is being used, and recording the
results in a file (default Rprof.out in the working directory).
Then the function summaryRprof or the command-line utility
R CMD Rprof Rprof.out can be used to summarize the
activity.
As an example, consider the following code (from Venables & Ripley, 2002, pp. 225–6).
library(MASS); library(boot)
storm.fm <- nls(Time ~ b*Viscosity/(Wt - c), stormer,
start = c(b=30.401, c=2.2183))
st <- cbind(stormer, fit=fitted(storm.fm))
storm.bf <- function(rs, i) {
st$Time <- st$fit + rs[i]
tmp <- nls(Time ~ (b * Viscosity)/(Wt - c), st,
start = coef(storm.fm))
tmp$m$getAllPars()
}
rs <- scale(resid(storm.fm), scale = FALSE) # remove the mean
Rprof("boot.out")
storm.boot <- boot(rs, storm.bf, R = 4999) # slow enough to profile
Rprof(NULL)
Having run this we can summarize the results by
R CMD Rprof boot.out
Each sample represents 0.02 seconds.
Total run time: 22.52 seconds.
Total seconds: time spent in function and callees.
Self seconds: time spent in function alone.
% total % self
total seconds self seconds name
100.0 25.22 0.2 0.04 "boot"
99.8 25.18 0.6 0.16 "statistic"
96.3 24.30 4.0 1.02 "nls"
33.9 8.56 2.2 0.56 "<Anonymous>"
32.4 8.18 1.4 0.36 "eval"
31.8 8.02 1.4 0.34 ".Call"
28.6 7.22 0.0 0.00 "eval.parent"
28.5 7.18 0.3 0.08 "model.frame"
28.1 7.10 3.5 0.88 "model.frame.default"
17.4 4.38 0.7 0.18 "sapply"
15.0 3.78 3.2 0.80 "nlsModel"
12.5 3.16 1.8 0.46 "lapply"
12.3 3.10 2.7 0.68 "assign"
...
% self % total
self seconds total seconds name
5.7 1.44 7.5 1.88 "inherits"
4.0 1.02 96.3 24.30 "nls"
3.6 0.92 3.6 0.92 "$"
3.5 0.88 28.1 7.10 "model.frame.default"
3.2 0.80 15.0 3.78 "nlsModel"
2.8 0.70 9.8 2.46 "qr.coef"
2.7 0.68 12.3 3.10 "assign"
2.5 0.64 2.5 0.64 ".Fortran"
2.5 0.62 7.1 1.80 "qr.default"
2.2 0.56 33.9 8.56 "<Anonymous>"
2.1 0.54 5.9 1.48 "unlist"
2.1 0.52 7.9 2.00 "FUN"
...
This often produces surprising results and can be used to identify bottlenecks or pieces of R code that could benefit from being replaced by compiled code.
Two warnings: profiling does impose a small performance penalty, and the output files can be very large if long runs are profiled at the default sampling interval.
Profiling short runs can sometimes give misleading results. R from
time to time performs garbage collection to reclaim unused
memory, and this takes an appreciable amount of time which profiling
will charge to whichever function happens to provoke it. It may be
useful to compare profiling code immediately after a call to gc()
with a profiling run without a preceding call to gc.
More detailed analysis of the output can be achieved by the tools in the CRAN packages proftools and profr: in particular these allow call graphs to be studied.
Measuring memory use in R code is useful either when the code takes more memory than is conveniently available or when memory allocation and copying of objects is responsible for slow code. There are three ways to profile memory use over time in R code. All three require R to have been compiled with --enable-memory-profiling, which is not the default, but is currently used for the OS X and Windows binary distributions. All can be misleading, for different reasons.
In understanding the memory profiles it is useful to know a little more
about R's memory allocation. Looking at the results of gc()
shows a division of memory into Vcells used to store the contents
of vectors and Ncells used to store everything else, including
all the administrative overhead for vectors such as type and length
information. In fact the vector contents are divided into two
pools. Memory for small vectors (by default 128 bytes or less) is
obtained in large chunks and then parcelled out by R; memory for
larger vectors is obtained directly from the operating system.
Some memory allocation is obvious in interpreted code, for example,
y <- x + 1
allocates memory for a new vector y. Other memory allocation is
less obvious and occurs because R is forced to make good on its
promise of ‘call-by-value’ argument passing. When an argument is
passed to a function it is not immediately copied. Copying occurs (if
necessary) only when the argument is modified. This can lead to
surprising memory use. For example, in the ‘survey’ package we have
print.svycoxph <- function (x, ...)
{
print(x$survey.design, varnames = FALSE, design.summaries = FALSE,
...)
x$call <- x$printcall
NextMethod()
}
It may not be obvious that the assignment to x$call will cause
the entire object x to be copied. This copying to preserve the
call-by-value illusion is usually done by the internal C function
duplicate.
The main reason that memory-use profiling is difficult is garbage collection. Memory is allocated at well-defined times in an R program, but is freed whenever the garbage collector happens to run.
Rprof
The sampling profiler Rprof described in the previous section can
be given the option memory.profiling=TRUE. It then writes out the
total R memory allocation in small vectors, large vectors, and cons
cells or nodes at each sampling interval. It also writes out the number
of calls to the internal function duplicate, which is called to
copy R objects. summaryRprof provides summaries of this
information. The main reason that this can be misleading is that the
memory use is attributed to the function running at the end of the
sampling interval. A second reason is that garbage collection can make
the amount of memory in use decrease, so a function appears to use
little memory. Running under gctorture helps with both problems:
it slows down the code to effectively increase the sampling frequency
and it makes each garbage collection release a smaller amount of memory.
Changing the memory limits with mem.limits() may also be useful,
to see how the code would run under different memory conditions.
The second method of memory profiling uses a memory-allocation
profiler, Rprofmem(), which writes out a stack trace to an
output file every time a large vector is allocated (with a
user-specified threshold for ‘large’) or a new page of memory is
allocated for the R heap. Summary functions for this output are still
being designed.
Running the example from the previous section with
> Rprofmem("boot.memprof",threshold=1000)
> storm.boot <- boot(rs, storm.bf, R = 4999)
> Rprofmem(NULL)
shows that apart from some initial and final work in boot there
are no vector allocations over 1000 bytes.
The third method of memory profiling involves tracing copies made of a
specific (presumably large) R object. Calling tracemem on an
object marks it so that a message is printed to standard output when
the object is copied via duplicate or coercion to another type,
or when a new object of the same size is created in arithmetic
operations. The main reason that this can be misleading is that
copying of subsets or components of an object is not tracked. It may
be helpful to use tracemem on these components.
In the example above we can run tracemem on the data frame
st
> tracemem(st)
[1] "<0x9abd5e0>"
> storm.boot <- boot(rs, storm.bf, R = 4)
memtrace[0x9abd5e0->0x92a6d08]: statistic boot
memtrace[0x92a6d08->0x92a6d80]: $<-.data.frame $<- statistic boot
memtrace[0x92a6d80->0x92a6df8]: $<-.data.frame $<- statistic boot
memtrace[0x9abd5e0->0x9271318]: statistic boot
memtrace[0x9271318->0x9271390]: $<-.data.frame $<- statistic boot
memtrace[0x9271390->0x9271408]: $<-.data.frame $<- statistic boot
memtrace[0x9abd5e0->0x914f558]: statistic boot
memtrace[0x914f558->0x914f5f8]: $<-.data.frame $<- statistic boot
memtrace[0x914f5f8->0x914f670]: $<-.data.frame $<- statistic boot
memtrace[0x9abd5e0->0x972cbf0]: statistic boot
memtrace[0x972cbf0->0x972cc68]: $<-.data.frame $<- statistic boot
memtrace[0x972cc68->0x972cd08]: $<-.data.frame $<- statistic boot
memtrace[0x9abd5e0->0x98ead98]: statistic boot
memtrace[0x98ead98->0x98eae10]: $<-.data.frame $<- statistic boot
memtrace[0x98eae10->0x98eae88]: $<-.data.frame $<- statistic boot
The object is duplicated fifteen times, three times for each of the
R+1 calls to storm.bf. This is surprising, since none of the duplications happen inside nls. Stepping through storm.bf in the debugger shows that all three happen in the line
st$Time <- st$fit + rs[i]
Data frames are slower than matrices and this is an example of why.
Using tracemem(st$Viscosity) does not reveal any additional
copying.
Profiling compiled code is highly system-specific, but this section contains some hints gleaned from various R users. Some methods need to be different for a compiled executable and for dynamic/shared libraries/objects as used by R packages. We know of no good way to profile DLLs on Windows.
Options include using sprof for a shared object, and oprofile (see http://oprofile.sourceforge.net/) and perf (see https://perf.wiki.kernel.org/index.php/Tutorial) for any executable or shared object.
You can select shared objects to be profiled with sprof by setting the environment variable LD_PROFILE. For example
% setenv LD_PROFILE /path/to/R_HOME/library/stats/libs/stats.so
R
... run the boot example
% sprof /path/to/R_HOME/library/stats/libs/stats.so \
/var/tmp/path/to/R_HOME/library/stats/libs/stats.so.profile
Flat profile:
Each sample counts as 0.01 seconds.
% cumulative self self total
time seconds seconds calls us/call us/call name
76.19 0.32 0.32 0 0.00 numeric_deriv
16.67 0.39 0.07 0 0.00 nls_iter
7.14 0.42 0.03 0 0.00 getListElement
rm /var/tmp/path/to/R_HOME/library/stats/libs/stats.so.profile
... to clean up ...
It is possible that root access is needed to create the directories used for the profile data.
The oprofile project has two modes of operation. In what is now called ‘legacy’ mode, it is uses a daemon to collect information on a process (see below). Since version 0.9.8 (August 2012), the preferred mode is to use operf, so we discuss that first. The modes differ in how the profiling data is collected: it is analysed by tools such as opreport and oppannote in both.
Here is an example on x86_64 Linux using R 3.0.2. File
pvec.R contains the part of the examples from pvec in
package parallel:
library(parallel)
N <- 1e6
dates <- sprintf('%04d-%02d-%02d', as.integer(2000+rnorm(N)),
as.integer(runif(N, 1, 12)), as.integer(runif(N, 1, 28)))
system.time(a <- as.POSIXct(dates, format = "%Y-%m-%d"))
with timings from the final step
user system elapsed
0.371 0.237 0.612
R-level profiling by Rprof shows
self.time self.pct total.time total.pct
"strptime" 1.70 41.06 1.70 41.06
"as.POSIXct.POSIXlt" 1.40 33.82 1.42 34.30
"sprintf" 0.74 17.87 0.98 23.67
...
so the conversion from character to POSIXlt takes most of the
time.
This can be run under operf and analysed by
operf R -f pvec.R
opreport
opreport -l /path/to/R_HOME/bin/exec/R
opannotate --source /path/to/R_HOME/bin/exec/R
## And for the system time
opreport -l /lib64/libc.so.6
The first report shows where (which library etc) the time was spent:
CPU_CLK_UNHALT...|
samples| %|
------------------
166761 99.9161 Rdev
CPU_CLK_UNHALT...|
samples| %|
------------------
70586 42.3276 no-vmlinux
56963 34.1585 libc-2.16.so
36922 22.1407 R
1584 0.9499 stats.so
624 0.3742 libm-2.16.so
...
The rest of the output is voluminous, and only extracts are shown below.
Most of the time within R is spent in
samples % image name symbol name
10397 28.5123 R R_gc_internal
5683 15.5848 R do_sprintf
3036 8.3258 R do_asPOSIXct
2427 6.6557 R do_strptime
2421 6.6392 R Rf_mkCharLenCE
1480 4.0587 R w_strptime_internal
1202 3.2963 R Rf_qnorm5
1165 3.1948 R unif_rand
675 1.8511 R mktime0
617 1.6920 R makelt
617 1.6920 R validate_tm
584 1.6015 R day_of_the_week
...
opannotate shows that 31% of the time in R is spent in
memory.c, 21% in datetime.c and 7% in Rstrptime.h.
The analysis for libc showed that calls to wcsftime
dominated, so those calls were cached for R 3.0.3: the time spent in
no-vmlinux (the kernel) was reduced dramatically.
On platforms which support it, callgraphs can be produced by opcontrol --callgraph if collected via operf --callgraph.
The profiling data is by default stored in sub-directory oprofile_data of the current directory, which can be removed at the end of the session.
Another example, from sm version 2.2-5.4. The example for
sm.variogram took a long time:
system.time(example(sm.variogram))
...
user system elapsed
5.543 3.202 8.785
including a lot of system time. Profiling just the slow part, the second plot, showed
samples| %|
------------------
381845 99.9885 R
CPU_CLK_UNHALT...|
samples| %|
------------------
187484 49.0995 sm.so
169627 44.4230 no-vmlinux
12636 3.3092 libgfortran.so.3.0.0
6455 1.6905 R
so the system time was almost all in the Linux kernel. It is possible to dig deeper if you have a matching uncompressed kernel with debug symbols to specify via --vmlinux: we did not.
In ‘legacy’ mode oprofile works by running a daemon which
collects information. The daemon must be started as root, e.g.
% su
% opcontrol --no-vmlinux
% (optional, some platforms) opcontrol --callgraph=5
% opcontrol --start
% exit
Then as a user
% R
... run the boot example
% opcontrol --dump
% opreport -l /path/to/R_HOME/library/stats/libs/stats.so
...
samples % symbol name
1623 75.5939 anonymous symbol from section .plt
349 16.2552 numeric_deriv
113 5.2632 nls_iter
62 2.8878 getListElement
% opreport -l /path/to/R_HOME/bin/exec/R
...
samples % symbol name
76052 11.9912 Rf_eval
54670 8.6198 Rf_findVarInFrame3
37814 5.9622 Rf_allocVector
31489 4.9649 Rf_duplicate
28221 4.4496 Rf_protect
26485 4.1759 Rf_cons
23650 3.7289 Rf_matchArgs
21088 3.3250 Rf_findFun
19995 3.1526 findVarLocInFrame
14871 2.3447 Rf_evalList
13794 2.1749 R_Newhashpjw
13522 2.1320 R_gc_internal
...
Shutting down the profiler and clearing the records needs to be done as root.
On 64-bit (only) Solaris, the standard profiling tool gprof collects information from shared objects compiled with -pg.
Developers have recommended sample (or Sampler.app,
which is a GUI version), Shark (in version of Xcode
up to those for Snow Leopard), and Instruments (part of
Xcode, see
https://developer.apple.com/library/mac/#documentation/DeveloperTools/Conceptual/InstrumentsUserGuide/Introduction/Introduction.html).
This chapter covers the debugging of R extensions, starting with the ways to get useful error information and moving on to how to deal with errors that crash R. For those who prefer other styles there are contributed packages such as debug on CRAN (described in an article in R-News 3/3). (There are notes from 2002 provided by Roger Peng at http://www.biostat.jhsph.edu/~rpeng/docs/R-debug-tools.pdf which provide complementary examples to those given here.)
Most of the R-level debugging facilities are based around the
built-in browser. This can be used directly by inserting a call to
browser() into the code of a function (for example, using
fix(my_function) ). When code execution reaches that point in
the function, control returns to the R console with a special prompt.
For example
> fix(summary.data.frame) ## insert browser() call after for() loop
> summary(women)
Called from: summary.data.frame(women)
Browse[1]> ls()
[1] "digits" "i" "lbs" "lw" "maxsum" "nm" "nr" "nv"
[9] "object" "sms" "z"
Browse[1]> maxsum
[1] 7
Browse[1]>
height weight
Min. :58.0 Min. :115.0
1st Qu.:61.5 1st Qu.:124.5
Median :65.0 Median :135.0
Mean :65.0 Mean :136.7
3rd Qu.:68.5 3rd Qu.:148.0
Max. :72.0 Max. :164.0
> rm(summary.data.frame)
At the browser prompt one can enter any R expression, so for example
ls() lists the objects in the current frame, and entering the
name of an object will66 print it. The following commands are
also accepted
n
Enter ‘step-through’ mode. In this mode, hitting return executes the
next line of code (more precisely one line and any continuation lines).
Typing c will continue to the end of the current context, e.g.
to the end of the current loop or function.
c
In normal mode, this quits the browser and continues execution, and just
return works in the same way. cont is a synonym.
where
This prints the call stack. For example
> summary(women)
Called from: summary.data.frame(women)
Browse[1]> where
where 1: summary.data.frame(women)
where 2: summary(women)
Browse[1]>
Q
Quit both the browser and the current expression, and return to the top-level prompt.
Errors in code executed at the browser prompt will normally return
control to the browser prompt. Objects can be altered by assignment,
and will keep their changed values when the browser is exited. If
really necessary, objects can be assigned to the workspace from the
browser prompt (by using <<- if the name is not already in
scope).
Suppose your R program gives an error message. The first thing to
find out is what R was doing at the time of the error, and the most
useful tool is traceback(). We suggest that this is run whenever
the cause of the error is not immediately obvious. Daily, errors are
reported to the R mailing lists as being in some package when
traceback() would show that the error was being reported by some
other package or base R. Here is an example from the regression
suite.
> success <- c(13,12,11,14,14,11,13,11,12)
> failure <- c(0,0,0,0,0,0,0,2,2)
> resp <- cbind(success, failure)
> predictor <- c(0, 5^(0:7))
> glm(resp ~ 0+predictor, family = binomial(link="log"))
Error: no valid set of coefficients has been found: please supply starting values
> traceback()
3: stop("no valid set of coefficients has been found: please supply
starting values", call. = FALSE)
2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,
mustart = mustart, offset = offset, family = family, control = control,
intercept = attr(mt, "intercept") > 0)
1: glm(resp ~ 0 + predictor, family = binomial(link ="log"))
The calls to the active frames are given in reverse order (starting with
the innermost). So we see the error message comes from an explicit
check in glm.fit. (traceback() shows you all the lines of
the function calls, which can be limited by setting option
"deparse.max.lines".)
Sometimes the traceback will indicate that the error was detected inside
compiled code, for example (from ?nls)
Error in nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321), trace = TRUE) :
step factor 0.000488281 reduced below ‘minFactor’ of 0.000976563
> traceback()
2: .Call(R_nls_iter, m, ctrl, trace)
1: nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321), trace = TRUE)
This will be the case if the innermost call is to .C,
.Fortran, .Call, .External or .Internal, but
as it is also possible for such code to evaluate R expressions, this
need not be the innermost call, as in
> traceback()
9: gm(a, b, x)
8: .Call(R_numeric_deriv, expr, theta, rho, dir)
7: numericDeriv(form[[3]], names(ind), env)
6: getRHS()
5: assign("rhs", getRHS(), envir = thisEnv)
4: assign("resid", .swts * (lhs - assign("rhs", getRHS(), envir = thisEnv)),
envir = thisEnv)
3: function (newPars)
{
setPars(newPars)
assign("resid", .swts * (lhs - assign("rhs", getRHS(), envir = thisEnv)),
envir = thisEnv)
assign("dev", sum(resid^2), envir = thisEnv)
assign("QR", qr(.swts * attr(rhs, "gradient")), envir = thisEnv)
return(QR$rank < min(dim(QR$qr)))
}(c(-0.00760232418963883, 1.00119632515036))
2: .Call(R_nls_iter, m, ctrl, trace)
1: nls(yeps ~ gm(a, b, x), start = list(a = 0.12345, b = 0.54321))
Occasionally traceback() does not help, and this can be the case
if S4 method dispatch is involved. Consider the following example
> xyd <- new("xyloc", x=runif(20), y=runif(20))
Error in as.environment(pkg) : no item called "package:S4nswv"
on the search list
Error in initialize(value, ...) : S language method selection got
an error when called from internal dispatch for function ‘initialize’
> traceback()
2: initialize(value, ...)
1: new("xyloc", x = runif(20), y = runif(20))
which does not help much, as there is no call to as.environment
in initialize (and the note “called from internal dispatch”
tells us so). In this case we searched the R sources for the quoted
call, which occurred in only one place,
methods:::.asEnvironmentPackage. So now we knew where the
error was occurring. (This was an unusually opaque example.)
The error message
evaluation nested too deeply: infinite recursion / options(expressions=)?
can be hard to handle with the default value (5000). Unless you know that there actually is deep recursion going on, it can help to set something like
options(expressions=500)
and re-run the example showing the error.
Sometimes there is warning that clearly is the precursor to some later error, but it is not obvious where it is coming from. Setting options(warn = 2) (which turns warnings into errors) can help here.
Once we have located the error, we have some choices. One way to proceed is to find out more about what was happening at the time of the crash by looking a post-mortem dump. To do so, set options(error=dump.frames) and run the code again. Then invoke debugger() and explore the dump. Continuing our example:
> options(error = dump.frames)
> glm(resp ~ 0 + predictor, family = binomial(link ="log"))
Error: no valid set of coefficients has been found: please supply starting values
which is the same as before, but an object called last.dump has
appeared in the workspace. (Such objects can be large, so remove it
when it is no longer needed.) We can examine this at a later time by
calling the function debugger.
> debugger()
Message: Error: no valid set of coefficients has been found: please supply starting values
Available environments had calls:
1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))
2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, mus
3: stop("no valid set of coefficients has been found: please supply starting values
Enter an environment number, or 0 to exit Selection:
which gives the same sequence of calls as traceback, but in
outer-first order and with only the first line of the call, truncated to
the current width. However, we can now examine in more detail what was
happening at the time of the error. Selecting an environment opens the
browser in that frame. So we select the function call which spawned the
error message, and explore some of the variables (and execute two
function calls).
Enter an environment number, or 0 to exit Selection: 2
Browsing in the environment with call:
glm.fit(x = X, y = Y, weights = weights, start = start, etas
Called from: debugger.look(ind)
Browse[1]> ls()
[1] "aic" "boundary" "coefold" "control" "conv"
[6] "dev" "dev.resids" "devold" "EMPTY" "eta"
[11] "etastart" "family" "fit" "good" "intercept"
[16] "iter" "linkinv" "mu" "mu.eta" "mu.eta.val"
[21] "mustart" "n" "ngoodobs" "nobs" "nvars"
[26] "offset" "start" "valideta" "validmu" "variance"
[31] "varmu" "w" "weights" "x" "xnames"
[36] "y" "ynames" "z"
Browse[1]> eta
1 2 3 4 5
0.000000e+00 -2.235357e-06 -1.117679e-05 -5.588393e-05 -2.794197e-04
6 7 8 9
-1.397098e-03 -6.985492e-03 -3.492746e-02 -1.746373e-01
Browse[1]> valideta(eta)
[1] TRUE
Browse[1]> mu
1 2 3 4 5 6 7 8
1.0000000 0.9999978 0.9999888 0.9999441 0.9997206 0.9986039 0.9930389 0.9656755
9
0.8397616
Browse[1]> validmu(mu)
[1] FALSE
Browse[1]> c
Available environments had calls:
1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))
2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart
3: stop("no valid set of coefficients has been found: please supply starting v
Enter an environment number, or 0 to exit Selection: 0
> rm(last.dump)
Because last.dump can be looked at later or even in another R
session, post-mortem debugging is possible even for batch usage of R.
We do need to arrange for the dump to be saved: this can be done either
using the command-line flag --save to save the workspace at the
end of the run, or via a setting such as
> options(error = quote({dump.frames(to.file=TRUE); q()}))
See the help on dump.frames for further options and a worked
example.
An alternative error action is to use the function recover():
> options(error = recover)
> glm(resp ~ 0 + predictor, family = binomial(link = "log"))
Error: no valid set of coefficients has been found: please supply starting values
Enter a frame number, or 0 to exit
1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))
2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart
Selection:
which is very similar to dump.frames. However, we can examine
the state of the program directly, without dumping and re-loading the
dump. As its help page says, recover can be routinely used as
the error action in place of dump.calls and dump.frames,
since it behaves like dump.frames in non-interactive use.
Post-mortem debugging is good for finding out exactly what went wrong, but not necessarily why. An alternative approach is to take a closer look at what was happening just before the error, and a good way to do that is to use debug. This inserts a call to the browser at the beginning of the function, starting in step-through mode. So in our example we could use
> debug(glm.fit)
> glm(resp ~ 0 + predictor, family = binomial(link ="log"))
debugging in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,
mustart = mustart, offset = offset, family = family, control = control,
intercept = attr(mt, "intercept") > 0)
debug: {
## lists the whole function
Browse[1]>
debug: x <- as.matrix(x)
...
Browse[1]> start
[1] -2.235357e-06
debug: eta <- drop(x %*% start)
Browse[1]> eta
1 2 3 4 5
0.000000e+00 -2.235357e-06 -1.117679e-05 -5.588393e-05 -2.794197e-04
6 7 8 9
-1.397098e-03 -6.985492e-03 -3.492746e-02 -1.746373e-01
Browse[1]>
debug: mu <- linkinv(eta <- eta + offset)
Browse[1]> mu
1 2 3 4 5 6 7 8
1.0000000 0.9999978 0.9999888 0.9999441 0.9997206 0.9986039 0.9930389 0.9656755
9
0.8397616
(The prompt Browse[1]> indicates that this is the first level of
browsing: it is possible to step into another function that is itself
being debugged or contains a call to browser().)
debug can be used for hidden functions and S3 methods by
e.g. debug(stats:::predict.Arima). (It cannot be used for S4
methods, but an alternative is given on the help page for debug.)
Sometimes you want to debug a function defined inside another function,
e.g. the function arimafn defined inside arima. To do so,
set debug on the outer function (here arima) and
step through it until the inner function has been defined. Then
call debug on the inner function (and use c to get out of
step-through mode in the outer function).
To remove debugging of a function, call undebug with the argument
previously given to debug; debugging otherwise lasts for the rest
of the R session (or until the function is edited or otherwise
replaced).
trace can be used to temporarily insert debugging code into a
function, for example to insert a call to browser() just before
the point of the error. To return to our running example
## first get a numbered listing of the expressions of the function
> page(as.list(body(glm.fit)), method="print")
> trace(glm.fit, browser, at=22)
Tracing function "glm.fit" in package "stats"
[1] "glm.fit"
> glm(resp ~ 0 + predictor, family = binomial(link ="log"))
Tracing glm.fit(x = X, y = Y, weights = weights, start = start,
etastart = etastart, .... step 22
Called from: eval(expr, envir, enclos)
Browse[1]> n
## and single-step from here.
> untrace(glm.fit)
For your own functions, it may be as easy to use fix to insert
temporary code, but trace can help with functions in a namespace
(as can fixInNamespace). Alternatively, use
trace(,edit=TRUE) to insert code visually.
Errors in memory allocation and reading/writing outside arrays are very common causes of crashes (e.g., segfaults) on some machines. Often the crash appears long after the invalid memory access: in particular damage to the structures which R itself has allocated may only become apparent at the next garbage collection (or even at later garbage collections after objects have been deleted).
Note that memory access errors may be seen with LAPACK, BLAS and Java-using packages: some at least of these seem to be intentional, and some are related to passing characters to Fortran.
We can help to detect memory problems in R objects earlier by running
garbage collection as often as possible. This is achieved by
gctorture(TRUE), which as described on its help page
Provokes garbage collection on (nearly) every memory allocation. Intended to ferret out memory protection bugs. Also makes R run very slowly, unfortunately.
The reference to ‘memory protection’ is to missing C-level calls to
PROTECT/UNPROTECT (see Garbage Collection) which if
missing allow R objects to be garbage-collected when they are still
in use. But it can also help with other memory-related errors.
Normally running under gctorture(TRUE) will just produce a crash
earlier in the R program, hopefully close to the actual cause. See
the next section for how to decipher such crashes.
It is possible to run all the examples, tests and vignettes covered by
R CMD check under gctorture(TRUE) by using the option
--use-gct.
The function gctorture2 provides more refined control over the GC
torture process. Its arguments step, wait and
inhibit_release are documented on its help page. Environment
variables can also be used at the start of the R session to turn on
GC torture: R_GCTORTURE corresponds to the step argument to
gctorture2, R_GCTORTURE_WAIT to wait, and
R_GCTORTURE_INHIBIT_RELEASE to inhibit_release.
If R is configured with --enable-strict-barrier then a variety of tests for the integrity of the write barrier are enabled. In addition tests to help detect protect issues are enabled:
NEWSXP on creation.
NEWSXP are marked
as type FREESXP and their previous type is recorded.
SEXP inputs and
SEXP outputs and signal an error if a FREESXP is found.
The address of the node and the old type are included in the error
message.
R CMD check --use-gct can be set to use
gctorture2(n) rather than gctorture(TRUE) by setting
environment variable _R_CHECK_GCT_N_ to a positive integer value
to be used as n.
Used with a debugger and with gctorture or gctorture2 this
mechanism can be helpful in isolating memory protect problems.
If you have access to Linux on a common CPU type or supported versions
of OS X67 you can use
valgrind (http://www.valgrind.org/, pronounced to rhyme
with ‘tinned’) to check for possible problems. To run some examples
under valgrind use something like
R -d valgrind --vanilla < mypkg-Ex.R
R -d "valgrind --tool=memcheck --leak-check=full" --vanilla < mypkg-Ex.R
where mypkg-Ex.R is a set of examples, e.g. the file created in
mypkg.Rcheck by R CMD check. Occasionally this reports
memory reads of ‘uninitialised values’ that are the result of compiler
optimization, so can be worth checking under an unoptimized compile: for
maximal information use a build with debugging symbols. We know there
will be some small memory leaks from readline and R itself —
these are memory areas that are in use right up to the end of the R
session. Expect this to run around 20x slower than without
valgrind, and in some cases even slower than that. Several
versions of valgrind were not happy with some optimized BLASes
that use CPU-specific instructions so you may need to build a
version of R specifically to use with valgrind.
On platforms supported by valgrind you can build a version of
R with extra instrumentation to help valgrind detect errors in
the use of memory allocated from the R heap. The configure option is
--with-valgrind-instrumentation=level, where level
is 0, 1, or 2. Level 0 is the default and does not add any anything.
Level 1 will detect use of uninitialised memory and has little impact on
speed. Level 2 will detect many other memory-use bugs but makes R
much slower when running under valgrind. Using this in
conjunction with gctorture can be even more effective (and even
slower).
An example of valgrind output is
==12539== Invalid read of size 4
==12539== at 0x1CDF6CBE: csc_compTr (Mutils.c:273)
==12539== by 0x1CE07E1E: tsc_transpose (dtCMatrix.c:25)
==12539== by 0x80A67A7: do_dotcall (dotcode.c:858)
==12539== by 0x80CACE2: Rf_eval (eval.c:400)
==12539== by 0x80CB5AF: R_execClosure (eval.c:658)
==12539== by 0x80CB98E: R_execMethod (eval.c:760)
==12539== by 0x1B93DEFA: R_standardGeneric (methods_list_dispatch.c:624)
==12539== by 0x810262E: do_standardGeneric (objects.c:1012)
==12539== by 0x80CAD23: Rf_eval (eval.c:403)
==12539== by 0x80CB2F0: Rf_applyClosure (eval.c:573)
==12539== by 0x80CADCC: Rf_eval (eval.c:414)
==12539== by 0x80CAA03: Rf_eval (eval.c:362)
==12539== Address 0x1C0D2EA8 is 280 bytes inside a block of size 1996 alloc'd
==12539== at 0x1B9008D1: malloc (vg_replace_malloc.c:149)
==12539== by 0x80F1B34: GetNewPage (memory.c:610)
==12539== by 0x80F7515: Rf_allocVector (memory.c:1915)
...
This example is from an instrumented version of R, while tracking
down a bug in the Matrix package in 2006. The first line
indicates that R has tried to read 4 bytes from a memory address that
it does not have access to. This is followed by a C stack trace showing
where the error occurred. Next is a description of the memory that was
accessed. It is inside a block allocated by malloc, called from
GetNewPage, that is, in the internal R heap. Since this
memory all belongs to R, valgrind would not (and did not)
detect the problem in an uninstrumented build of R. In this example
the stack trace was enough to isolate and fix the bug, which was in
tsc_transpose, and in this example running under
gctorture() did not provide any additional information. When the
stack trace is not sufficiently informative the option
--db-attach=yes to valgrind may be helpful. This starts
a post-mortem debugger (by default gdb) so that variables in the
C code can be inspected (see Inspecting R objects).
valgrind is good at spotting the use of uninitialized values: use option --track-origins=yes to show where these originated from. What it cannot detect is the misuse of arrays allocated on the stack: this includes C automatic variables and some68 Fortran arrays.
It is possible to run all the examples, tests and vignettes covered by
R CMD check under valgrind by using the option
--use-valgrind. If you do this you will need to select the
valgrind options some other way, for example by having a
~/.valgrindrc file containing
--leak-check=full
--track-origins=yes
or setting the environment variable VALGRIND_OPTS.
On OS X you may need to ensure that debugging symbols are made available (so valgrind reports line numbers in files). This can usually be done with the valgrind option --dsymutil=yes to ask for the symbols to be dumped when the .so file is loaded. This will not work where packages are installed into a system area (such as the R.framework) and can be slow. Installing packages with R CMD INSTALL --dsym installs the dumped symbols. (This can also be done by setting environment variable PKG_MAKE_DSYM to a non-empty value before the INSTALL.)
AddressSanitizer (‘ASan’) is a tool with similar aims to the memory checker in valgrind. It is available with suitable builds69 of gcc 4.8.0 or clang 3.1 and later on common Linux and OS X platforms. See http://clang.llvm.org/docs/UsersManual.html#controlling-code-generation, http://clang.llvm.org/docs/AddressSanitizer.html and https://code.google.com/p/address-sanitizer/.
It requires code to have been compiled and linked with
-fsanitize=address70, and compiling with -fno-omit-frame-pointer
will give more legible reports. It has a runtime penalty of 2–3x,
extended compilation times and uses substantially more memory, often
1–2GB, at run time. On 64-bit platforms it reserves (but does not
allocate) 16–20TB of virtual memory: restrictive shell settings can
cause problems.
By comparison with valgrind, ASan can detect misuse of stack and global variables but not the use of uninitialized memory.
gcc as from version 4.9.0 returns symbolic addresses for the location of the error, but most other versions do not. For the latter, one possibility is to use an external symbolizer. Depending on the version, this can be done via an environment variable, e.g.
ASAN_SYMBOLIZER_PATH=/path/to/llvm-symbolizer
or by piping the output through asan_symbolize.py71 and perhaps then (for compiled C++ code) c++filt.
The simplest way to make use of this is to build a version of R with something like
CC="gcc-4.9 -std=gnu99 -fsanitize=address"
CFLAGS="-fno-omit-frame-pointer -g -O2 -Wall -pedantic -mtune=native"
which will ensure that the libasan run-time library is compiled
into the R executable. However this check can be enabled on a
per-package basis by using a ~/.R/Makevars file like
CC = gcc-4.9 -std=gnu99 -fsanitize=address -fno-omit-frame-pointer
CXX = g++-4.9 -fsanitize=address -fno-omit-frame-pointer
F77 = gfortran-4.9 -fsanitize=address
FC = gfortran-4.9 -fsanitize=address
(Note that -fsanitize=address has to be part of the compiler
specification to ensure it is used for linking. These settings will not
be honoured by packages which ignore ~/.R/Makevars.) It will
be necessary to build R with
MAIN_LDFLAGS = -fsanitize=address
to link the runtime libraries into the R executable if it was not specified as part of ‘CC’ when R was built.
For options available via the environment variable
ASAN_OPTIONS see
https://code.google.com/p/address-sanitizer/wiki/Flags#Run-time_flags.
With gcc additional control is available via the
--params flag: see its man page. For some builds on
x86_64 Linux this includes enabling the leak sanitizer
(https://code.google.com/p/address-sanitizer/wiki/LeakSanitizer),
which might even be enabled by default: this means any leaks give the
process a failure error status (by default 23). To disable this
and some strict checking use
setenv ASAN_OPTIONS ‘alloc_dealloc_mismatch=0:detect_leaks=0’
For more detailed information R can be run under a debugger with a breakpoint set before the address sanitizer report is produced: for gdb or lldb you could use
break __asan_report_error
(See http://code.google.com/p/address-sanitizer/wiki/AddressSanitizer#gdb.)
‘UBSanitizer’ is a tool for C/C++ source code selected by -fsanitize=undefined in suitable builds of clang, and GCC as from 4.9.0.
‘Undefined behaviour’ is where the language standard does not require
particular behaviour from the compiler. Examples include division by
zero (where for doubles R requires the
ISO/IEC 60559 behaviour but C/C++ do not), use
of zero-length arrays, shifts too far for signed types (e.g. int
x, y; y = x << 31;), out-of-range coercion, invalid C++ casts and
mis-alignment. Not uncommon examples of out-of-range coercion in R
are attempts to coerce a NaN to type int or
NA_INTEGER to an unsigned type such as size_t. Also
common is y[x - 1] forgetting that x might be
NA_INTEGER.
This sanitizer can be combined with the Address Sanitizer by -fsanitize=undefined,address. Its runtime library is linked into each package's DLL, so it is less often needed to be included in MAIN_LDFLAGS.
Finer control of what is checked can be achieved by other options: for clang see http://clang.llvm.org/docs/UsersManual.html#controlling-code-generation. The current set for clang is (on a single line):
-fsanitize=alignment,array-bounds,bool,enum,float-cast-overflow,
float-divide-by-zero,function,integer-divide-by-zero,null,object-size,
return,shift,signed-integer-overflow,unreachable,vla-bound,vptr
a subset of which could be combined with address, or use something
like
-fsanitize=undefined -fno-sanitize=float-divide-by-zero
In addition,
-fsanitize=unsigned-integer-overflow
is available as a separate option in some versions of clang (not enabled by -fsanitize=undefined).
A smaller selection is available for GCC, currently defaulting to
-fsanitize=integer-divide-by-zero,null,return,shift,signed-integer-overflow,unreachable,vla-bound
with
-fsanitize=float-divide-by-zero
as a separate option not enabled by -fsanitize=undefined.
Other useful flags include
-no-fsanitize=recover
which causes the first report to be fatal (it always is for the
unreachable and return suboptions).
There are also the compiler flags -fcatch-undefined-behavior and -ftrapv, said to be more reliable in clang than gcc.
For more details on the topic see http://blog.regehr.org/archives/213 and http://blog.llvm.org/2011/05/what-every-c-programmer-should-know.html (which has 3 parts).
This option does not currently compile OpenMP code.
clang has a ‘Static Analyser’ run on the source files during compilation: see http://clang-analyzer.llvm.org/.
Some versions of clang have a (currently experimental) memory sanitizer invoked by -fsanitize=memory which detects uses of uninitialized memory. See http://clang.llvm.org/docs/MemorySanitizer.html.
For x86_64 Linux there is a leak sanitizer: see
https://code.google.com/p/address-sanitizer/wiki/LeakSanitizer:
one way to invoke this from an ASAN-enabled build is by the environment
variable
ASAN_OPTIONS='detect_leaks=1'
‘Dr. Memory’ from http://www.drmemory.org/ is a memory checker for (currently) 32-bit Windows and Linux with similar aims to valgrind. It works with unmodified executables72 and detects memory access errors, uninitialized reads and memory leaks.
Most of the Fortran compilers used with R allow code to be compiled with checking of array bounds: for example gfortran has option -fbounds-check and Solaris Studio has -C. This will give an error when the upper or lower bound is exceeded, e.g.
At line 97 of file .../src/appl/dqrdc2.f
Fortran runtime error: Index ‘1’ of dimension 1 of array ‘x’ above upper bound of 0
One does need to be aware that lazy programmers often specify Fortran
dimensions as 1 rather than * or a real bound and these
will be reported.
It is easy to arrange to use this check on just the code in your package: add to ~/.R/Makevars something like (for gfortran)
FCFLAGS = -g -O2 -mtune=native -fbounds-check
FFLAGS = -g -O2 -mtune=native -fbounds-check
when you run R CMD check.
This may report incorrectly errors with the way that Fortran character variables are passed, particularly when Fortran subroutines are called from C code. This may include73 the use of BLAS and LAPACK subroutines in R, so it is not advisable to build R itself with bounds checking (and may not even be possible as these subroutines are called during the R build).
Sooner or later programmers will be faced with the need to debug
compiled code loaded into R. This section is geared to platforms
using gdb with code compiled by gcc, but similar things
are possible with other debuggers such as lldb
(http://lldb.llvm.org/, used on OS X) and Sun's dbx:
some debuggers have graphical front-ends available.
Consider first ‘crashes’, that is when R terminated unexpectedly with an illegal memory access (a ‘segfault’ or ‘bus error’), illegal instruction or similar. Unix-alike versions of R use a signal handler which aims to give some basic information. For example
*** caught segfault ***
address 0x20000028, cause ‘memory not mapped’
Traceback:
1: .identC(class1[[1]], class2)
2: possibleExtends(class(sloti), classi, ClassDef2 = getClassDef(classi,
where = where))
3: validObject(t(cu))
4: stopifnot(validObject(cu <- as(tu, "dtCMatrix")), validObject(t(cu)),
validObject(t(tu)))
Possible actions:
1: abort (with core dump)
2: normal R exit
3: exit R without saving workspace
4: exit R saving workspace
Selection: 3
Since the R process may be damaged, the only really safe options are the first or third. (Note that a core dump is only produced where enabled: a common default in a shell is to limit its size to 0, thereby disabling it.)
A fairly common cause of such crashes is a package which uses .C
or .Fortran and writes beyond (at either end) one of the
arguments it is passed. As from R 3.0.0 there is a good way to
detect this: using options(CBoundsCheck = TRUE) (which can be
selected via the environment variable R_C_BOUNDS_CHECK=yes)
changes the way .C and .Fortran work to check if the
compiled code writes in the 64 bytes at either end of an argument.
Another cause of a ‘crash’ is to overrun the C stack. R tries to track that in its own code, but it may happen in third-party compiled code. For modern POSIX-compliant OSes R can safely catch that and return to the top-level prompt, so one gets something like
> .C("aaa")
Error: segfault from C stack overflow
>
However, C stack overflows are fatal under Windows and normally defeat attempts at debugging on that platform. Further, the size of the stack is set when R is compiled, whereas on POSIX OSes it can be set in the shell from which R is launched.
If you have a crash which gives a core dump you can use something like
gdb /path/to/R/bin/exec/R core.12345
to examine the core dump. If core dumps are disabled or to catch errors that do not generate a dump one can run R directly under a debugger by for example
$ R -d gdb --vanilla
...
gdb> run
at which point R will run normally, and hopefully the debugger will catch the error and return to its prompt. This can also be used to catch infinite loops or interrupt very long-running code. For a simple example
> for(i in 1:1e7) x <- rnorm(100)
[hit Ctrl-C]
Program received signal SIGINT, Interrupt.
0x00397682 in _int_free () from /lib/tls/libc.so.6
(gdb) where
#0 0x00397682 in _int_free () from /lib/tls/libc.so.6
#1 0x00397eba in free () from /lib/tls/libc.so.6
#2 0xb7cf2551 in R_gc_internal (size_needed=313)
at /users/ripley/R/svn/R-devel/src/main/memory.c:743
#3 0xb7cf3617 in Rf_allocVector (type=13, length=626)
at /users/ripley/R/svn/R-devel/src/main/memory.c:1906
#4 0xb7c3f6d3 in PutRNGstate ()
at /users/ripley/R/svn/R-devel/src/main/RNG.c:351
#5 0xb7d6c0a5 in do_random2 (call=0x94bf7d4, op=0x92580e8, args=0x9698f98,
rho=0x9698f28) at /users/ripley/R/svn/R-devel/src/main/random.c:183
...
In many cases it is possible to attach a debugger to a running process: this is helpful if an alternative front-end is in use or to investigate a task that seems to be taking far too long. This is done by something like
gdb -p pid
where pid is the id of the R executable or front-end.
This stops the process so its state can be examined: use continue
to resume execution.
Some “tricks” worth knowing follow:
Under most compilation environments, compiled code dynamically loaded into R cannot have breakpoints set within it until it is loaded. To use a symbolic debugger on such dynamically loaded code under Unix-alikes use
dyn.load or library to load your
shared object.
Under Windows signals may not be able to be used, and if so the procedure is
more complicated. See the rw-FAQ and
www.stats.uwo.ca/faculty/murdoch/software/debuggingR/gdb.shtml.
The key to inspecting R objects from compiled code is the function
PrintValue(SEXP s) which uses the normal R printing
mechanisms to print the R object pointed to by s, or the safer
version R_PV(SEXP s) which will only print ‘objects’.
One way to make use of PrintValue is to insert suitable calls
into the code to be debugged.
Another way is to call R_PV from the symbolic debugger.
(PrintValue is hidden as Rf_PrintValue.) For example,
from gdb we can use
(gdb) p R_PV(ab)
using the object ab from the convolution example, if we have
placed a suitable breakpoint in the convolution C code.
To examine an arbitrary R object we need to work a little harder. For example, let
R> DF <- data.frame(a = 1:3, b = 4:6)
By setting a breakpoint at do_get and typing get("DF") at
the R prompt, one can find out the address in memory of DF, for
example
Value returned is $1 = (SEXPREC *) 0x40583e1c
(gdb) p *$1
$2 = {
sxpinfo = {type = 19, obj = 1, named = 1, gp = 0,
mark = 0, debug = 0, trace = 0, = 0},
attrib = 0x40583e80,
u = {
vecsxp = {
length = 2,
type = {c = 0x40634700 "0>X@D>X@0>X@", i = 0x40634700,
f = 0x40634700, z = 0x40634700, s = 0x40634700},
truelength = 1075851272,
},
primsxp = {offset = 2},
symsxp = {pname = 0x2, value = 0x40634700, internal = 0x40203008},
listsxp = {carval = 0x2, cdrval = 0x40634700, tagval = 0x40203008},
envsxp = {frame = 0x2, enclos = 0x40634700},
closxp = {formals = 0x2, body = 0x40634700, env = 0x40203008},
promsxp = {value = 0x2, expr = 0x40634700, env = 0x40203008}
}
}
(Debugger output reformatted for better legibility).
Using R_PV() one can “inspect” the values of the various
elements of the SEXP, for example,
(gdb) p R_PV($1->attrib)
$names
[1] "a" "b"
$row.names
[1] "1" "2" "3"
$class
[1] "data.frame"
$3 = void
To find out where exactly the corresponding information is stored, one needs to go “deeper”:
(gdb) set $a = $1->attrib
(gdb) p $a->u.listsxp.tagval->u.symsxp.pname->u.vecsxp.type.c
$4 = 0x405d40e8 "names"
(gdb) p $a->u.listsxp.carval->u.vecsxp.type.s[1]->u.vecsxp.type.c
$5 = 0x40634378 "b"
(gdb) p $1->u.vecsxp.type.s[0]->u.vecsxp.type.i[0]
$6 = 1
(gdb) p $1->u.vecsxp.type.s[1]->u.vecsxp.type.i[1]
$7 = 5
Another alternative is the R_inspect function which shows the
low-level structure of the objects recursively (addresses differ from
the above as this example is created on another machine):
(gdb) p R_inspect($1)
@100954d18 19 VECSXP g0c2 [OBJ,NAM(2),ATT] (len=2, tl=0)
@100954d50 13 INTSXP g0c2 [NAM(2)] (len=3, tl=0) 1,2,3
@100954d88 13 INTSXP g0c2 [NAM(2)] (len=3, tl=0) 4,5,6
ATTRIB:
@102a70140 02 LISTSXP g0c0 []
TAG: @10083c478 01 SYMSXP g0c0 [MARK,NAM(2),gp=0x4000] "names"
@100954dc0 16 STRSXP g0c2 [NAM(2)] (len=2, tl=0)
@10099df28 09 CHARSXP g0c1 [MARK,gp=0x21] "a"
@10095e518 09 CHARSXP g0c1 [MARK,gp=0x21] "b"
TAG: @100859e60 01 SYMSXP g0c0 [MARK,NAM(2),gp=0x4000] "row.names"
@102a6f868 13 INTSXP g0c1 [NAM(2)] (len=2, tl=1) -2147483648,-3
TAG: @10083c948 01 SYMSXP g0c0 [MARK,gp=0x4000] "class"
@102a6f838 16 STRSXP g0c1 [NAM(2)] (len=1, tl=1)
@1008c6d48 09 CHARSXP g0c2 [MARK,gp=0x21,ATT] "data.frame"
In general the representation of each object follows the format:
@<address> <type-nr> <type-name> <gc-info> [<flags>] ...
For a more fine-grained control over the the depth of the recursion
and the output of vectors R_inspect3 takes additional two character()
parameters: maximum depth and the maximal number of elements that will
be printed for scalar vectors. The defaults in R_inspect are
currently -1 (no limit) and 5 respectively.
Access to operating system functions is via the R functions
system and system2.
The details will differ by platform (see the on-line help), and about
all that can safely be assumed is that the first argument will be a
string command that will be passed for execution (not necessarily
by a shell) and the second argument to system will be
internal which if true will collect the output of the command
into an R character vector.
On POSIX-compliant OSes these commands pass a command-line to a shell:
Windows is not POSIX-compliant and there is a separate function
shell to do so.
The function system.time
is available for timing. Timing on child processes is only available on
Unix-alikes, and may not be reliable there.
.C and .Fortran
These two functions provide an interface to compiled code that has been
linked into R, either at build time or via dyn.load
(see dyn.load and dyn.unload). They are primarily intended for
compiled C and FORTRAN 77 code respectively, but the .C function
can be used with other languages which can generate C interfaces, for
example C++ (see Interfacing C++ code).
The first argument to each function is a character string specifying the
symbol name as known74 to C or
FORTRAN, that is the function or subroutine name. (That the symbol is
loaded can be tested by, for example, is.loaded("cg"). Use the
name you pass to .C or .Fortran rather than the translated
symbol name.)
There can be up to 65 further arguments giving R objects to be passed to compiled code. Normally these are copied before being passed in, and copied again to an R list object when the compiled code returns. If the arguments are given names, these are used as names for the components in the returned list object (but not passed to the compiled code).
The following table gives the mapping between the modes of R atomic vectors and the types of arguments to a C function or FORTRAN subroutine.
R storage mode C type FORTRAN type logicalint *INTEGERintegerint *INTEGERdoubledouble *DOUBLE PRECISIONcomplexRcomplex *DOUBLE COMPLEXcharacterchar **CHARACTER*255rawunsigned char *none
Do please note the first two. On the 64-bit Unix/Linux/OS X platforms,
long is 64-bit whereas int and INTEGER are 32-bit.
Code ported from S-PLUS (which uses long * for logical and
integer) will not work on all 64-bit platforms (although it may
appear to work on some, including Windows). Note also that if your
compiled code is a mixture of C functions and FORTRAN subprograms the
argument types must match as given in the table above.
C type Rcomplex is a structure with double members
r and i defined in the header file R_ext/Complex.h
included by R.h. (On most platforms this is stored in a way
compatible with the C99 double complex type: however, it may not
be possible to pass Rcomplex to a C99 function expecting a
double complex argument. Nor need it be compatible with a C++
complex type. Moreover, the compatibility can depends on the
optimization level set for the compiler.)
Only a single character string can be passed to or from FORTRAN, and the
success of this is compiler-dependent. Other R objects can be passed
to .C, but it is much better to use one of the other interfaces.
It is possible to pass numeric vectors of storage mode double to
C as float * or to FORTRAN as REAL by setting the
attribute Csingle, most conveniently by using the R functions
as.single, single or mode. This is intended only
to be used to aid interfacing existing C or FORTRAN code.
Logical values are sent as 0 (FALSE), 1
(TRUE) or INT_MIN = -2147483648 (NA, but only if
NAOK is true), and the compiled code should return one of these
three values. (Non-zero values other than INT_MIN are mapped to
TRUE.)
Unless formal argument NAOK is true, all the other arguments are
checked for missing values NA and for the IEEE special
values NaN, Inf and -Inf, and the presence of any
of these generates an error. If it is true, these values are passed
unchecked.
Argument PACKAGE confines the search for the symbol name to a
specific shared object (or use "base" for code compiled into
R). Its use is highly desirable, as there is no way to avoid two
package writers using the same symbol name, and such name clashes are
normally sufficient to cause R to crash. (If it is not present and
the call is from the body of a function defined in a package namespace,
the shared object loaded by the first (if any) useDynLib
directive will be used. However, prior to R 2.15.2 the detection of
the correct namespace is unreliable and you are strongly recommended to
use the PACKAGE argument for packages to be used with earlier
versions of R.
Note that the compiled code should not return anything except through
its arguments: C functions should be of type void and FORTRAN
subprograms should be subroutines.
To fix ideas, let us consider a very simple example which convolves two
finite sequences. (This is hard to do fast in interpreted R code, but
easy in C code.) We could do this using .C by
void convolve(double *a, int *na, double *b, int *nb, double *ab)
{
int nab = *na + *nb - 1;
for(int i = 0; i < nab; i++)
ab[i] = 0.0;
for(int i = 0; i < *na; i++)
for(int j = 0; j < *nb; j++)
ab[i + j] += a[i] * b[j];
}
called from R by
conv <- function(a, b)
.C("convolve",
as.double(a),
as.integer(length(a)),
as.double(b),
as.integer(length(b)),
ab = double(length(a) + length(b) - 1))$ab
Note that we take care to coerce all the arguments to the correct R
storage mode before calling .C; mistakes in matching the types
can lead to wrong results or hard-to-catch errors.
Special care is needed in handling character vector arguments in
C (or C++). On entry the contents of the elements are duplicated and
assigned to the elements of a char ** array, and on exit the
elements of the C array are copied to create new elements of a character
vector. This means that the contents of the character strings of the
char ** array can be changed, including to \0 to shorten
the string, but the strings cannot be lengthened. It is
possible75 to allocate a new string via
R_alloc and replace an entry in the char ** array by the
new string. However, when character vectors are used other than in a
read-only way, the .Call interface is much to be preferred.
Passing character strings to FORTRAN code needs even more care, and should be avoided where possible. Only the first element of the character vector is passed in, as a fixed-length (255) character array. Up to 255 characters are passed back to a length-one character vector. How well this works (or even if it works at all) depends on the C and FORTRAN compilers on each platform (including on their options). Often what is being passed to FORTRAN is one of a small set of possible values (a factor in R terms) which could alternatively be passed as an integer code: similarly FORTRAN code that wants to generate diagnostic messages can pass an integer code to a C or R wrapper which will convert it to a character string.
It is possible to pass some R objects other than atomic vectors via
.C, but this is only supported for historical compatibility: use
the .Call or .External interfaces for such objects. Any
C/C++ code that includes Rinternals.h should be called via
.Call or .External.
dyn.load and dyn.unloadCompiled code to be used with R is loaded as a shared object (Unix-alikes including OS X, see Creating shared objects for more information) or DLL (Windows).
The shared object/DLL is loaded by dyn.load and unloaded by
dyn.unload. Unloading is not normally necessary, but it is
needed to allow the DLL to be re-built on some platforms, including
Windows.
The first argument to both functions is a character string giving the path to the object. Programmers should not assume a specific file extension for the object/DLL (such as .so) but use a construction like
file.path(path1, path2, paste0("mylib", .Platform$dynlib.ext))
for platform independence. On Unix-alike systems the path supplied to
dyn.load can be an absolute path, one relative to the current
directory or, if it starts with ‘~’, relative to the user's home
directory.
Loading is most often done automatically based on the useDynLib()
declaration in the NAMESPACE file, but may be done
explicitly via a call to library.dynam.
This has the form
library.dynam("libname", package, lib.loc)
where libname is the object/DLL name with the extension
omitted. Note that the first argument, chname, should
not be package since this will not work if the package
is installed under another name.
Under some Unix-alike systems there is a choice of how the symbols are
resolved when the object is loaded, governed by the arguments
local and now. Only use these if really necessary: in
particular using now=FALSE and then calling an unresolved symbol
will terminate R unceremoniously.
R provides a way of executing some code automatically when a object/DLL
is either loaded or unloaded. This can be used, for example, to
register native routines with R's dynamic symbol mechanism, initialize
some data in the native code, or initialize a third party library. On
loading a DLL, R will look for a routine within that DLL named
R_init_lib where lib is the name of the DLL file with
the extension removed. For example, in the command
library.dynam("mylib", package, lib.loc)
R looks for the symbol named R_init_mylib. Similarly, when
unloading the object, R looks for a routine named
R_unload_lib, e.g., R_unload_mylib. In either case,
if the routine is present, R will invoke it and pass it a single
argument describing the DLL. This is a value of type DllInfo
which is defined in the Rdynload.h file in the R_ext
directory.
Note that there are some implicit restrictions on this mechanism as the
basename of the DLL needs to be both a valid file name and valid as part
of a C entry point (e.g. it cannot contain ‘.’): for portable
code it is best to confine DLL names to be ASCII alphanumeric
plus underscore. If entry point R_init_lib is not found it
is also looked for with ‘.’ replaced by ‘_’.
The following example shows templates for the initialization and
unload routines for the mylib DLL.
#include <R.h> #include <Rinternals.h> #include <R_ext/Rdynload.h> void R_init_mylib(DllInfo *info) { /* Register routines, allocate resources. */ } void R_unload_mylib(DllInfo *info) { /* Release resources. */ }
If a shared object/DLL is loaded more than once the most recent version
is used. More generally, if the same symbol name appears in several
shared objects, the most recently loaded occurrence is used. The
PACKAGE argument and registration (see the next section) provide
good ways to avoid any ambiguity in which occurrence is meant.
On Unix-alikes the paths used to resolve dynamically linked dependent
libraries are fixed (for security reasons) when the process is launched,
so dyn.load will only look for such libraries in the locations
set by the R shell script (via etc/ldpaths) and in
the OS-specific defaults.
Windows allows more control (and less security) over where dependent
DLLs are looked for. On all versions this includes the PATH
environment variable, but with lowest priority: note that it does not
include the directory from which the DLL was loaded. It is possible to
add a single path with quite high priority via the DLLpath
argument to dyn.load. This is (by default) used by
library.dynam to include the package's libs/i386 or
libs/x64 directory in the DLL search path.
By ‘native’ routine, we mean an entry point in compiled code.
In calls to .C, .Call, .Fortran and
.External, R must locate the specified native routine by
looking in the appropriate shared object/DLL. By default, R uses the
operating system-specific dynamic loader to lookup the symbol in all
loaded DLLs and elsewhere. Alternatively, the author of the DLL
can explicitly register routines with R and use a single,
platform-independent mechanism for finding the routines in the DLL. One
can use this registration mechanism to provide additional information
about a routine, including the number and type of the arguments, and
also make it available to R programmers under a different name. In
the future, registration may be used to implement a form of “secure”
or limited native access.
To register routines with R, one calls the C routine
R_registerRoutines. This is typically done when the DLL is first
loaded within the initialization routine R_init_dll name
described in dyn.load and dyn.unload. R_registerRoutines
takes 5 arguments. The first is the DllInfo object passed by
R to the initialization routine. This is where R stores the
information about the methods. The remaining 4 arguments are arrays
describing the routines for each of the 4 different interfaces:
.C, .Call, .Fortran and .External. Each
argument is a FIND-terminated array of the element types given in
the following table:
.CR_CMethodDef.CallR_CallMethodDef.FortranR_FortranMethodDef.ExternalR_ExternalMethodDef
Currently, the R_ExternalMethodDef is the same as
R_CallMethodDef type and contains fields for the name of the
routine by which it can be accessed in R, a pointer to the actual
native symbol (i.e., the routine itself), and the number of arguments
the routine expects to be passed from R. For example, if we had a
routine named myCall defined as
SEXP myCall(SEXP a, SEXP b, SEXP c);
we would describe this as
R_CallMethodDef callMethods[] = {
{"myCall", (DL_FUNC) &myCall, 3},
{NULL, NULL, 0}
};
along with any other routines for the .Call interface. For
routines with a variable number of arguments invoked via the
.External interface, one specifies -1 for the number of
arguments which tells R not to check the actual number passed. Note
that the number of arguments passed to .External were not
checked prior to R 3.0.0.
Routines for use with the .C and .Fortran interfaces are
described with similar data structures, but which have two additional
fields for describing the type and “style” of each argument. Each of
these can be omitted. However, if specified, each should be an array
with the same number of elements as the number of parameters for the
routine. The types array should contain the SEXP types
describing the expected type of the argument. (Technically, the elements
of the types array are of type R_NativePrimitiveArgType which is
just an unsigned integer.) The R types and corresponding type
identifiers are provided in the following table:
numericREALSXPintegerINTSXPlogicalLGLSXPsingleSINGLESXPcharacterSTRSXPlistVECSXP
Consider a C routine, myC, declared as
void myC(double *x, int *n, char **names, int *status);
We would register it as
R_CMethodDef cMethods[] = {
{"myC", (DL_FUNC) &myC, 4, {REALSXP, INTSXP, STRSXP, LGLSXP}},
{NULL, NULL, 0}
};
One can also specify whether each argument is used simply as input, or as output, or as both input and output. The style field in the description of a method is used for this. The purpose is to allow76 R to transfer values more efficiently across the R-C/FORTRAN interface by avoiding copying values when it is not necessary. Typically, one omits this information in the registration data.
Having created the arrays describing each routine, the last step is to
actually register them with R. We do this by calling
R_registerRoutines. For example, if we have the descriptions
above for the routines accessed by the .C and .Call
we would use the following code:
void
R_init_myLib(DllInfo *info)
{
R_registerRoutines(info, cMethods, callMethods, NULL, NULL);
}
This routine will be invoked when R loads the shared object/DLL named
myLib. The last two arguments in the call to
R_registerRoutines are for the routines accessed by
.Fortran and .External interfaces. In our example, these
are given as NULL since we have no routines of these types.
When R unloads a shared object/DLL, its registrations are automatically removed. There is no other facility for unregistering a symbol.
Examples of registering routines can be found in the different packages in the R source tree (e.g., stats). Also, there is a brief, high-level introduction in R News (volume 1/3, September 2001, pages 20–23, http://www.r-project.org/doc/Rnews/Rnews_2001-3.pdf).
Once routines are registered, they can be referred to as R objects if
they this is arranged in the useDynLib call in the package's
NAMESPACE file (see useDynLib). This avoids the overhead
of looking up an entry point each time it is used, and ensure that the
entry point in the package is the one used (without a PACKAGE =
"pkg" argument). So for example the stats package has
# Refer to all C/Fortran routines by their name prefixed by C_
useDynLib(stats, .registration = TRUE, .fixes = "C_")
in its NAMESPACE file, and then ansari.test's default
methods can contain
pansari <- function(q, m, n)
.C(C_pansari, as.integer(length(q)), p = as.double(q),
as.integer(m), as.integer(n))$p
Sometimes registering native routines or using a PACKAGE argument
can make a large difference. The results can depend quite markedly on
the OS (and even if it is 32- or 64-bit), on the version of R and
what else is loaded into R at the time.
To fix ideas, first consider x84_64 OS 10.7 and R 2.15.2. A
simple .Call function might be
foo <- function(x) .Call("foo", x)
with C code
SEXP foo(SEXP x)
{
return x;
}
If we compile with by R CMD SHLIB foo.c, load the code by
dyn.load("foo.so") and run foo(pi) it took around 22
microseconds (us). Specifying the DLL by
foo2 <- function(x) .Call("foo", x, PACKAGE = "foo")
reduced the time to 1.7 us.
Now consider making these functions part of a package whose
NAMESPACE file uses useDynlib(foo). This immediately
reduces the running time as "foo" will be preferentially looked
for foo.dll. Without specifying PACKAGE it took about 5
us (it needs to fathom out the appropriate DLL each time it is invoked
but it does not need to search all DLLs), and with the PACKAGE
argument it is again about 1.7 us.
Next suppose the package has registered the native routine foo.
Then foo() still has to find the appropriate DLL but can get to
the entry point in the DLL faster, in about 4.2 us. And foo2()
now takes about 1 us. If we register the symbols in the
NAMESPACE file and use
foo3 <- function(x) .Call(C_foo, x)
then the address for the native routine is looked up just once when the
package is loaded, and foo3(pi) takes about 0.8 us.
Versions using .C() rather than .Call() take about 0.2 us
longer.
These are all quite small differences, but C routines are not uncommonly invoked millions of times for run times of a few microseconds, and those doing such things may wish to be aware of the differences.
On Linux and Solaris there is a much smaller overhead in looking up
symbols so foo(pi) takes around 5 times as long as
foo3(pi).
Symbol lookup on Windows used to be far slower, so R maintains a small cache. If the cache is currently empty enough that the symbol can be stored in the cache then the performance is similar to Linux and Solaris: if not it may be slower. R's own code always uses registered symbols and so these never contribute to the cache: however many other packages do rely on symbol lookup.
In addition to registering C routines to be called by R, it can at times be useful for one package to make some of its C routines available to be called by C code in another package. The interface consists of two routines declared in header R_ext/Rdynload.h as
void R_RegisterCCallable(const char *package, const char *name,
DL_FUNC fptr);
DL_FUNC R_GetCCallable(const char *package, const char *name);
A package packA that wants to make a C routine myCfun
available to C code in other packages would include the call
R_RegisterCCallable("packA", "myCfun", myCfun);
in its initialization function R_init_packA. A package
packB that wants to use this routine would retrieve the function
pointer with a call of the form
p_myCfun = R_GetCCallable("packA", "myCfun");
The author of packB is responsible for ensuring that
p_myCfun has an appropriate declaration. In the future R may
provide some automated tools to simplify exporting larger numbers of
routines.
A package that wishes to make use of header files in other packages needs to declare them as a comma-separated list in the field ‘LinkingTo’ in the DESCRIPTION file. This then arranges that the include directories in the installed linked-to packages are added to the include paths for C and C++ code.
It must specify77 ‘Imports’ or ‘Depends’ of those packages, for they have to be loaded78 prior to this one (so the path to their compiled code has been registered).
A CRAN example of the use of this mechanism is package lme4, which links to Matrix.
Shared objects for loading into R can be created using R CMD
SHLIB. This accepts as arguments a list of files which must be object
files (with extension .o) or sources for C, C++, FORTRAN 77,
Fortran 9x, Objective C or Objective C++ (with extensions .c,
.cc or .cpp, .f, .f90 or .f95,
.m, and .mm or .M, respectively), or commands to be
passed to the linker. See R CMD SHLIB --help (or the R help
for SHLIB) for usage information.
If compiling the source files does not work “out of the box”, you can
specify additional flags by setting some of the variables
PKG_CPPFLAGS (for the C preprocessor, typically ‘-I’ flags),
PKG_CFLAGS, PKG_CXXFLAGS, PKG_FFLAGS,
PKG_FCFLAGS, PKG_OBJCFLAGS, and PKG_OBJCXXFLAGS
(for the C, C++, FORTRAN 77, Fortran 9x, Objective C, and Objective C++
compilers, respectively) in the file Makevars in the compilation
directory (or, of course, create the object files directly from the
command line).
Similarly, variable PKG_LIBS in Makevars can be used for
additional ‘-l’ and ‘-L’ flags to be passed to the linker when
building the shared object. (Supplying linker commands as arguments to
R CMD SHLIB will take precedence over PKG_LIBS in
Makevars.)
It is possible to arrange to include compiled code from other languages by setting the macro ‘OBJECTS’ in file Makevars, together with suitable rules to make the objects.
Flags which are already set (for example in file
etcR_ARCH/Makeconf) can be overridden by the environment
variable MAKEFLAGS (at least for systems using a POSIX-compliant
make), as in (Bourne shell syntax)
MAKEFLAGS="CFLAGS=-O3" R CMD SHLIB *.c
It is also possible to set such variables in personal Makevars files, which are read after the local Makevars and the system makefiles or in a site-wide Makevars.site file. See Customizing package compilation,
Note that as R CMD SHLIB uses Make, it will not remake a shared object just because the flags have changed, and if test.c and test.f both exist in the current directory
R CMD SHLIB test.f
will compile test.c!
If the src subdirectory of an add-on package contains source code
with one of the extensions listed above or a file Makevars but
not a file Makefile, R CMD INSTALL creates a
shared object (for loading into R through useDynlib in the
NAMESPACE, or in the .onLoad function of the package)
using the R CMD SHLIB mechanism. If file Makevars
exists it is read first, then the system makefile and then any personal
Makevars files.
If the src subdirectory of package contains a file
Makefile, this is used by R CMD INSTALL in place of the
R CMD SHLIB mechanism. make is called with makefiles
R_HOME/etcR_ARCH/Makeconf, src/Makefile and
any personal Makevars files (in that order). The first target
found in src/Makefile is used.
It is better to make use of a Makevars file rather than a Makefile: the latter should be needed only exceptionally.
Under Windows the same commands work, but Makevars.win will be
used in preference to Makevars, and only src/Makefile.win
will be used by R CMD INSTALL with src/Makefile being
ignored. For past experiences of building DLLs with a variety of
compilers, see file ‘README.packages’ and
http://www.stats.uwo.ca/faculty/murdoch/software/compilingDLLs/
. Under Windows you can supply an exports definitions file called
dllname-win.def: otherwise all entry points in objects (but
not libraries) supplied to R CMD SHLIB will be exported from the
DLL. An example is stats-win.def for the stats package: a
CRAN example in package fastICA.
If you feel tempted to read the source code and subvert these mechanisms, please resist. Far too much developer time has been wasted in chasing down errors caused by failures to follow this documentation, and even more by package authors demanding explanations as to why their packages no longer work. In particular, undocumented environment or make variables are not for use by package writers and are subject to change without notice.
Suppose we have the following hypothetical C++ library, consisting of
the two files X.h and X.cpp, and implementing the two
classes X and Y which we want to use in R.
// X.h class X { public: X (); ~X (); }; class Y { public: Y (); ~Y (); };
// X.cpp #include <R.h> #include "X.h" static Y y; X::X() { REprintf("constructor X\n"); } X::~X() { REprintf("destructor X\n"); } Y::Y() { REprintf("constructor Y\n"); } Y::~Y() { REprintf("destructor Y\n"); }
To use with R, the only thing we have to do is writing a wrapper function and ensuring that the function is enclosed in
extern "C" {
}
For example,
// X_main.cpp: #include "X.h" extern "C" { void X_main () { X x; } } // extern "C"
Compiling and linking should be done with the C++ compiler-linker
(rather than the C compiler-linker or the linker itself); otherwise, the
C++ initialization code (and hence the constructor of the static
variable Y) are not called. On a properly configured system, one
can simply use
R CMD SHLIB X.cpp X_main.cpp
to create the shared object, typically X.so (the file name extension may be different on your platform). Now starting R yields
R version 2.14.1 Patched (2012-01-16 r58124)
Copyright (C) 2012 The R Foundation for Statistical Computing
...
Type "q()" to quit R.
R> dyn.load(paste("X", .Platform$dynlib.ext, sep = ""))
constructor Y
R> .C("X_main")
constructor X
destructor X
list()
R> q()
Save workspace image? [y/n/c]: y
destructor Y
The R for Windows FAQ (rw-FAQ) contains details of how to compile this example under Windows.
Earlier version of this example used C++ iostreams: this is best avoided. There is no guarantee that the output will appear in the R console, and indeed it will not on the R for Windows console. Use R code or the C entry points (see Printing) for all I/O if at all possible. Examples have been seen where merely loading a DLL that contained calls to C++ I/O upset R's own C I/O (for example by resetting buffers on open files).
Most R header files can be included within C++ programs, and they
should not be included within an extern "C" block (as
they include C++ system headers). It may not be possible to include
some R headers as they in turn include C header files that may cause
conflicts—if this happens, define ‘NO_C_HEADERS’ before including
the R headers, and include C++ versions (such as ‘cmath’) of the
appropriate headers yourself before the R headers.
We have already warned against the use of C++ iostreams not least
because output is not guaranteed to appear on the R console, and this
warning applies equally to Fortran (77 or 9x) output to units *
and 6. See Printing from FORTRAN, which describes workarounds.
In the past most Fortran compilers implemented I/O on top of the C I/O
system and so the two interworked successfully. This was true of
g77, but it is less true of gfortran as used in
gcc 4.y.z. In particular, any package that makes use of Fortran
I/O will when compiled on Windows interfere with C I/O: when the Fortran
I/O is initialized (typically when the package is loaded) the C
stdout and stderr are switched to LF line endings.
(Function init in file src/modules/lapack/init_win.c shows how to
mitigate this.)
It is not in general possible to link a DLL in package packA to a DLL provided by package packB (for the security reasons mentioned in dyn.load and dyn.unload, and also because some platforms distinguish between shared objects and dynamic libraries), but it is on Windows.
Note that there can be tricky versioning issues here, as package packB could be re-installed after package packA — it is desirable that the API provided by package packB remains backwards-compatible.
Shipping a static library in package packB for other packages to link to avoids most of the difficulties.
It is possible to link a shared object in package packA to a library provided by package packB under limited circumstances on a Unix-alike OS. There are severe portability issues, so this is not recommended for a distributed package.
This is easiest if packB provides a static library
packB/lib/libpackB.a. (Note using directory lib rather
than libs is conventional, and architecture-specific
sub-directories may be needed and are assumed in the sample code
below. The code in the static library will need to be compiled with
PIC flags on platforms where it matters.) Then as the code from
package packB is incorporated when package packA is
installed, we only need to find the static library at install time for
package packA. The only issue is to find package packB, and
for that we can ask R by something like (long lines broken for
display here)
PKGB_PATH=‘echo ’library(packB);
cat(system.file("lib", package="packB", mustWork=TRUE))' \
| "${R_HOME}/bin/R" --vanilla --slave`
PKG_LIBS="$(PKGB_PATH)$(R_ARCH)/libpackB.a"
For a dynamic library packB/lib/libpackB.so (packB/lib/libpackB.dylib on OS X: note that you cannot link to a shared object, .so, on that platform) we could use
PKGB_PATH=‘echo ’library(packB);
cat(system.file("lib", package="packB", mustWork=TRUE))' \
| "${R_HOME}/bin/R" --vanilla --slave`
PKG_LIBS=-L"$(PKGB_PATH)$(R_ARCH)" -lpackB
This will work for installation, but very likely not when package
packB is loaded, as the path to package packB's lib
directory is not in the ld.so79 search path. You can arrange to
put it there before R is launched by setting (on some
platforms) LD_RUN_PATH or LD_LIBRARY_PATH or adding to the
ld.so cache (see man ldconfig). On platforms that
support it, the path to the directory containing the dynamic library can
be hardcoded at install time (which assumes that the location of package
packB will not be changed nor the package updated to a changed
API). On systems with the gcc or clang and the
GNU linker (e.g. Linux) and some others (e.g. OS X) this
can be done by e.g.
PKGB_PATH=‘echo ’library(packB);
cat(system.file("lib", package="packB", mustWork=TRUE)))' \
| "${R_HOME}/bin/R" --vanilla --slave`
PKG_LIBS=-L"$(PKGB_PATH)$(R_ARCH)" -Wl,-rpath,"$(PKGB_PATH)$(R_ARCH)" -lpackB
Some other systems (e.g. Solaris with its native linker) use -Rdir rather than -rpath,dir (and this is accepted by the compiler as well as the linker).
It may be possible to figure out what is required semi-automatically from the result of R CMD libtool --config (look for ‘hardcode’), although that does not currently know the spell for OS X (as given in the example above, as -rpath is only supported for shared objects and not for executables).
Making headers provided by package packB available to the code to
be compiled in package packA can be done by the LinkingTo
mechanism (see Registering native routines).
Suppose package packA wants to make use of compiled code provided by packB in DLL packB/libs/exB.dll, possibly the package's DLL packB/libs/packB.dll. (This can be extended to linking to more than one package in a similar way.) There are three issues to be addressed:
This is done by the LinkingTo mechanism (see Registering native routines).
packA.dll to link to packB/libs/exB.dll.
This needs an entry in Makevars.win of the form
PKG_LIBS= -L<something> -lexB
and one possibility is that <something> is the path to the
installed pkgB/libs directory. To find that we need to ask R
where it is by something like
PKGB_PATH=‘echo ’library(packB);
cat(system.file("libs", package="packB", mustWork=TRUE))' \
| rterm --vanilla --slave`
PKG_LIBS= -L"$(PKGB_PATH)$(R_ARCH)" -lexB
Another possibility is to use an import library, shipping with package packA an exports file exB.def. Then Makevars.win could contain
PKG_LIBS= -L. -lexB
all: $(SHLIB) before
before: libexB.dll.a
libexB.dll.a: exB.def
and then installing package packA will make and use the import library for exB.dll. (One way to prepare the exports file is to use pexports.exe.)
If exB.dll was used by package packB (because it is in fact
packB.dll or packB.dll depends on it) and packB has
been loaded before packA, then nothing more needs to be done as
exB.dll will already be loaded into the R executable. (This
is the most common scenario.)
More generally, we can use the DLLpath argument to
library.dynam to ensure that exB.dll is found, for example
by setting
library.dynam("packA", pkg, lib,
DLLpath = system.file("libs", package="packB"))
Note that DLLpath can only set one path, and so for linking to
two or more packages you would need to resort to setting environment
variable PATH.
Using C code to speed up the execution of an R function is often very
fruitful. Traditionally this has been done via the .C
function in R. However, if a user wants to write C code using
internal R data structures, then that can be done using the
.Call and .External functions. The syntax for the calling
function in R in each case is similar to that of .C, but the
two functions have different C interfaces. Generally the .Call
interface is simpler to use, but .External is a little more
general.
A call to .Call is very similar to .C, for example
.Call("convolve2", a, b)
The first argument should be a character string giving a C symbol name of code that has already been loaded into R. Up to 65 R objects can passed as arguments. The C side of the interface is
#include <R.h>
#include <Rinternals.h>
SEXP convolve2(SEXP a, SEXP b)
...
A call to .External is almost identical
.External("convolveE", a, b)
but the C side of the interface is different, having only one argument
#include <R.h>
#include <Rinternals.h>
SEXP convolveE(SEXP args)
...
Here args is a LISTSXP, a Lisp-style pairlist from which
the arguments can be extracted.
In each case the R objects are available for manipulation via
a set of functions and macros defined in the header file
Rinternals.h or some S-compatibility macros80 defined
in Rdefines.h. See Interface functions .Call and .External
for details on .Call and .External.
Before you decide to use .Call or .External, you should
look at other alternatives. First, consider working in interpreted R
code; if this is fast enough, this is normally the best option. You
should also see if using .C is enough. If the task to be
performed in C is simple enough involving only atomic vectors and
requiring no call to R, .C suffices. A great deal of useful
code was written using just .C before .Call and
.External were available. These interfaces allow much more
control, but they also impose much greater responsibilities so need to
be used with care. Neither .Call nor .External copy their
arguments: you should treat arguments you receive through these
interfaces as read-only.
To handle R objects from within C code we use the macros and functions that have been used to implement the core parts of R. A public81 subset of these is defined in the header file Rinternals.h in the directory R_INCLUDE_DIR (default R_HOME/include) that should be available on any R installation.
A substantial amount of R, including the standard packages, is implemented using the functions and macros described here, so the R source code provides a rich source of examples and “how to do it”: do make use of the source code for inspirational examples.
It is necessary to know something about how R objects are handled in
C code. All the R objects you will deal with will be handled with
the type SEXP82, which is a
pointer to a structure with typedef SEXPREC. Think of this
structure as a variant type that can handle all the usual types
of R objects, that is vectors of various modes, functions,
environments, language objects and so on. The details are given later
in this section and in R Internal Structures, but for most
purposes the programmer does not need to know them. Think rather of a
model such as that used by Visual Basic, in which R objects are
handed around in C code (as they are in interpreted R code) as the
variant type, and the appropriate part is extracted for, for example,
numerical calculations, only when it is needed. As in interpreted R
code, much use is made of coercion to force the variant object to the
right type.
We need to know a little about the way R handles memory allocation. The memory allocated for R objects is not freed by the user; instead, the memory is from time to time garbage collected. That is, some or all of the allocated memory not being used is freed or marked as re-usable.
The R object types are represented by a C structure defined by a
typedef SEXPREC in Rinternals.h. It contains several
things among which are pointers to data blocks and to other
SEXPRECs. A SEXP is simply a pointer to a SEXPREC.
If you create an R object in your C code, you must tell R that you
are using the object by using the PROTECT macro on a pointer to
the object. This tells R that the object is in use so it is not
destroyed during garbage collection. Notice that it is the object which
is protected, not the pointer variable. It is a common mistake to
believe that if you invoked PROTECT(p) at some point then
p is protected from then on, but that is not true once a new
object is assigned to p.
Protecting an R object automatically protects all the R objects
pointed to in the corresponding SEXPREC, for example all elements
of a protected list are automatically protected.
The programmer is solely responsible for housekeeping the calls to
PROTECT. There is a corresponding macro UNPROTECT that
takes as argument an int giving the number of objects to
unprotect when they are no longer needed. The protection mechanism is
stack-based, so UNPROTECT(n) unprotects the last n
objects which were protected. The calls to PROTECT and
UNPROTECT must balance when the user's code returns. R will
warn about "stack imbalance in .Call" (or .External) if
the housekeeping is wrong.
Here is a small example of creating an R numeric vector in C code:
#include <R.h>
#include <Rinternals.h>
SEXP ab;
....
ab = PROTECT(allocVector(REALSXP, 2));
REAL(ab)[0] = 123.45;
REAL(ab)[1] = 67.89;
UNPROTECT(1);
Now, the reader may ask how the R object could possibly get removed
during those manipulations, as it is just our C code that is running.
As it happens, we can do without the protection in this example, but in
general we do not know (nor want to know) what is hiding behind the R
macros and functions we use, and any of them might cause memory to be
allocated, hence garbage collection and hence our object ab to be
removed. It is usually wise to err on the side of caution and assume
that any of the R macros and functions might remove the object.
In some cases it is necessary to keep better track of whether protection
is really needed. Be particularly aware of situations where a large
number of objects are generated. The pointer protection stack has a
fixed size (default 10,000) and can become full. It is not a good idea
then to just PROTECT everything in sight and UNPROTECT
several thousand objects at the end. It will almost invariably be
possible to either assign the objects as part of another object (which
automatically protects them) or unprotect them immediately after use.
Protection is not needed for objects which R already knows are in use. In particular, this applies to function arguments.
There is a less-used macro UNPROTECT_PTR(s) that unprotects
the object pointed to by the SEXP s, even if it is not the
top item on the pointer protection stack. This is rarely needed outside
the parser (the R sources currently have three examples, one in
src/main/plot3d.c).
Sometimes an object is changed (for example duplicated, coerced or
grown) yet the current value needs to be protected. For these cases
PROTECT_WITH_INDEX saves an index of the protection location that
can be used to replace the protected value using REPROTECT.
For example (from the internal code for optim)
PROTECT_INDEX ipx;
....
s = PROTECT_WITH_INDEX(eval(OS->R_fcall, OS->R_env), &ipx);
s = REPROTECT(coerceVector(s, REALSXP), ipx);
Note that it is dangerous to mix UNPROTECT_PTR with
PROTECT_WITH_INDEX, as the former changes the protection
locations of objects that were protected after the one being
unprotected.
There is another way to avoid the affects of garbage collection: a call
to R_PreserveObject adds an object to an internal list of objects
not to be collects, and a subsequent call to R_ReleaseObject
removes it from that list. This provides a way for objects which are
not returned as part of R objects to be protected across calls to
compiled code: on the other hand it becomes the user's responsibility to
release them when they are no longer needed (and this often requires the
use of a finalizer). It is less efficient that the normal protection
mechanism, and should be used sparingly.
For many purposes it is sufficient to allocate R objects and
manipulate those. There are quite a few allocXxx functions
defined in Rinternals.h—you may want to explore them.
One that is commonly used is allocVector, the C-level equivalent
of R-level vector() and its wrappers such as integer()
and character(). One distinction is that whereas the R
functions always initialize the elements of the vector,
allocVector only does so for lists, expressions and character
vectors (the cases where the elements are themselves R objects).
If storage is required for C objects during the calculations this is
best allocating by calling R_alloc; see Memory allocation.
All of these memory allocation routines do their own error-checking, so
the programmer may assume that they will raise an error and not return
if the memory cannot be allocated.
Users of the Rinternals.h macros will need to know how the R types are known internally. The different R data types are represented in C by SEXPTYPE. Some of these are familiar from R and some are internal data types. The usual R object modes are given in the table.
SEXPTYPE R equivalent REALSXPnumeric with storage mode doubleINTSXPinteger CPLXSXPcomplex LGLSXPlogical STRSXPcharacter VECSXPlist (generic vector) LISTSXPpairlist DOTSXPa ‘...’ object NILSXPNULL SYMSXPname/symbol CLOSXPfunction or function closure ENVSXPenvironment
Among the important internal SEXPTYPEs are LANGSXP,
CHARSXP, PROMSXP, etc. (N.B.: although it is
possible to return objects of internal types, it is unsafe to do so as
assumptions are made about how they are handled which may be violated at
user-level evaluation.) More details are given in R Internal Structures.
Unless you are very sure about the type of the arguments, the code
should check the data types. Sometimes it may also be necessary to
check data types of objects created by evaluating an R expression in
the C code. You can use functions like isReal, isInteger
and isString to do type checking. See the header file
Rinternals.h for definitions of other such functions. All of
these take a SEXP as argument and return 1 or 0 to indicate
TRUE or FALSE.
What happens if the SEXP is not of the correct type? Sometimes
you have no other option except to generate an error. You can use the
function error for this. It is usually better to coerce the
object to the correct type. For example, if you find that an
SEXP is of the type INTEGER, but you need a REAL
object, you can change the type by using
newSexp = PROTECT(coerceVector(oldSexp, REALSXP));
Protection is needed as a new object is created; the object
formerly pointed to by the SEXP is still protected but now
unused.83
All the coercion functions do their own error-checking, and generate
NAs with a warning or stop with an error as appropriate.
Note that these coercion functions are not the same as calling
as.numeric (and so on) in R code, as they do not dispatch on
the class of the object. Thus it is normally preferable to do the
coercion in the calling R code.
So far we have only seen how to create and coerce R objects from C code, and how to extract the numeric data from numeric R vectors. These can suffice to take us a long way in interfacing R objects to numerical algorithms, but we may need to know a little more to create useful return objects.
Many R objects have attributes: some of the most useful are classes
and the dim and dimnames that mark objects as matrices or
arrays. It can also be helpful to work with the names attribute
of vectors.
To illustrate this, let us write code to take the outer product of two
vectors (which outer and %o% already do). As usual the
R code is simple
out <- function(x, y)
{
storage.mode(x) <- storage.mode(y) <- "double"
.Call("out", x, y)
}
where we expect x and y to be numeric vectors (possibly
integer), possibly with names. This time we do the coercion in the
calling R code.
C code to do the computations is
#include <R.h>
#include <Rinternals.h>
SEXP out(SEXP x, SEXP y)
{
int nx = length(x), ny = length(y);
SEXP ans = PROTECT(allocMatrix(REALSXP, nx, ny));
double *rx = REAL(x), *ry = REAL(y), *rans = REAL(ans);
for(int i = 0; i < nx; i++) {
double tmp = rx[i];
for(int j = 0; j < ny; j++)
rans[i + nx*j] = tmp * ry[j];
}
UNPROTECT(1);
return ans;
}
Note the way REAL is used: as it is a function call it can be
considerably faster to store the result and index that.
However, we would like to set the dimnames of the result. We can use
#include <R.h>
#include <Rinternals.h>
SEXP out(SEXP x, SEXP y)
{
int nx = length(x), ny = length(y);
SEXP ans = PROTECT(allocMatrix(REALSXP, nx, ny));
double *rx = REAL(x), *ry = REAL(y), *rans = REAL(ans);
for(int i = 0; i < nx; i++) {
double tmp = rx[i];
for(int j = 0; j < ny; j++)
rans[i + nx*j] = tmp * ry[j];
}
SEXP dimnames = PROTECT(allocVector(VECSXP, 2));
SET_VECTOR_ELT(dimnames, 0, getAttrib(x, R_NamesSymbol));
SET_VECTOR_ELT(dimnames, 1, getAttrib(y, R_NamesSymbol));
setAttrib(ans, R_DimNamesSymbol, dimnames);
UNPROTECT(3);
return ans;
}
This example introduces several new features. The getAttrib and
setAttrib
functions get and set individual attributes. Their second argument is a
SEXP defining the name in the symbol table of the attribute we
want; these and many such symbols are defined in the header file
Rinternals.h.
There are shortcuts here too: the functions namesgets,
dimgets and dimnamesgets are the internal versions of the
default methods of names<-, dim<- and dimnames<-
(for vectors and arrays), and there are functions such as
GetMatrixDimnames and GetArrayDimnames.
What happens if we want to add an attribute that is not pre-defined? We
need to add a symbol for it via a call to
install. Suppose for illustration we wanted to add an attribute
"version" with value 3.0. We could use
SEXP version;
version = PROTECT(allocVector(REALSXP, 1));
REAL(version)[0] = 3.0;
setAttrib(ans, install("version"), version);
UNPROTECT(1);
Using install when it is not needed is harmless and provides a
simple way to retrieve the symbol from the symbol table if it is already
installed. However, the lookup takes a non-trivial amount of time, so
consider code such as
static SEXP VerSymbol = NULL;
...
if (VerSymbol == NULL) VerSymbol = install("version");
if it is to be done frequently.
This example can be simplified by another convenience function:
SEXP version = PROTECT(ScalarReal(3.0));
setAttrib(ans, install("version"), version);
UNPROTECT(1);
In R the class is just the attribute named "class" so it can
be handled as such, but there is a shortcut classgets. Suppose
we want to give the return value in our example the class "mat".
We can use
#include <R.h>
#include <Rinternals.h>
....
SEXP ans, dim, dimnames, class;
....
class = PROTECT(allocVector(STRSXP, 1));
SET_STRING_ELT(class, 0, mkChar("mat"));
classgets(ans, class);
UNPROTECT(4);
return ans;
}
As the value is a character vector, we have to know how to create that
from a C character array, which we do using the function
mkChar.
Some care is needed with lists, as R moved early on from using
LISP-like lists (now called “pairlists”) to S-like generic vectors.
As a result, the appropriate test for an object of mode list is
isNewList, and we need allocVector(VECSXP, n) and
not allocList(n).
List elements can be retrieved or set by direct access to the elements of the generic vector. Suppose we have a list object
a <- list(f = 1, g = 2, h = 3)
Then we can access a$g as a[[2]] by
double g;
....
g = REAL(VECTOR_ELT(a, 1))[0];
This can rapidly become tedious, and the following function (based on one in package stats) is very useful:
/* get the list element named str, or return NULL */
SEXP getListElement(SEXP list, const char *str)
{
SEXP elmt = R_NilValue, names = getAttrib(list, R_NamesSymbol);
for (int i = 0; i < length(list); i++)
if(strcmp(CHAR(STRING_ELT(names, i)), str) == 0) {
elmt = VECTOR_ELT(list, i);
break;
}
return elmt;
}
and enables us to say
double g;
g = REAL(getListElement(a, "g"))[0];
R character vectors are stored as STRSXPs, a vector type like
VECSXP where every element is of type CHARSXP. The
CHARSXP elements of STRSXPs are accessed using
STRING_ELT and SET_STRING_ELT.
CHARSXPs are read-only objects and must never be modified. In
particular, the C-style string contained in a CHARSXP should be
treated as read-only and for this reason the CHAR function used
to access the character data of a CHARSXP returns (const
char *) (this also allows compilers to issue warnings about improper
use). Since CHARSXPs are immutable, the same CHARSXP can
be shared by any STRSXP needing an element representing the same
string. R maintains a global cache of CHARSXPs so that there
is only ever one CHARSXP representing a given string in memory.
You can obtain a CHARSXP by calling mkChar and providing a
nul-terminated C-style string. This function will return a pre-existing
CHARSXP if one with a matching string already exists, otherwise
it will create a new one and add it to the cache before returning it to
you. The variant mkCharLen can be used to create a
CHARSXP from part of a buffer and will ensure null-termination.
Note that R character strings are restricted to 2^31 - 1
bytes, and hence so should the input to mkChar be (C allows
longer strings on 64-bit platforms).
It will be usual that all the R objects needed in our C computations
are passed as arguments to .Call or .External, but it is
possible to find the values of R objects from within the C given
their names. The following code is the equivalent of get(name,
envir = rho).
SEXP getvar(SEXP name, SEXP rho)
{
SEXP ans;
if(!isString(name) || length(name) != 1)
error("name is not a single string");
if(!isEnvironment(rho))
error("rho should be an environment");
ans = findVar(install(CHAR(STRING_ELT(name, 0))), rho);
Rprintf("first value is %f\n", REAL(ans)[0]);
return R_NilValue;
}
The main work is done by
findVar, but to use it we need to install name as a name
in the symbol table. As we wanted the value for internal use, we return
NULL.
Similar functions with syntax
void defineVar(SEXP symbol, SEXP value, SEXP rho)
void setVar(SEXP symbol, SEXP value, SEXP rho)
can be used to assign values to R variables. defineVar
creates a new binding or changes the value of an existing binding in the
specified environment frame; it is the analogue of assign(symbol,
value, envir = rho, inherits = FALSE), but unlike assign,
defineVar does not make a copy of the object
value.84 setVar searches for an existing
binding for symbol in rho or its enclosing environments.
If a binding is found, its value is changed to value. Otherwise,
a new binding with the specified value is created in the global
environment. This corresponds to assign(symbol, value, envir =
rho, inherits = TRUE).
Some operations are done so frequently that there are convenience functions to handle them. (All these are provided via the header file Rinternals.h.)
Suppose we wanted to pass a single logical argument
ignore_quotes: we could use
int ign = asLogical(ignore_quotes);
if(ign == NA_LOGICAL) error("'ignore_quotes' must be TRUE or FALSE");
which will do any coercion needed (at least from a vector argument), and
return NA_LOGICAL if the value passed was NA or coercion
failed. There are also asInteger, asReal and
asComplex. The function asChar returns a CHARSXP.
All of these functions ignore any elements of an input vector after the
first.
To return a length-one real vector we can use
double x;
...
return ScalarReal(x);
and there are versions of this for all the atomic vector types (those for
a length-one character vector being ScalarString with argument a
CHARSXP and mkString with argument const char *).
Some of the isXXXX functions differ from their apparent
R-level counterparts: for example isVector is true for any
atomic vector type (isVectorAtomic) and for lists and expressions
(isVectorList) (with no check on attributes). isMatrix is
a test of a length-2 "dim" attribute.
There are a series of small macros/functions to help construct pairlists
and language objects (whose internal structures just differ by
SEXPTYPE). Function CONS(u, v) is the basic building
block: it constructs a pairlist from u followed by v
(which is a pairlist or R_NilValue). LCONS is a variant
that constructs a language object. Functions list1 to
list5 construct a pairlist from one to five items, and
lang1 to lang6 do the same for a language object (a
function to call plus zero to five arguments). Functions elt and
lastElt find the ith element and the last element of a
pairlist, and nthcdr returns a pointer to the nth position
in the pairlist (whose CAR is the nth item).
Functions str2type and type2str map R
length-one character strings to and from SEXPTYPE numbers, and
type2char maps numbers to C character strings.
There is quite a collection of functions that may be used in your C code if you are willing to adapt to rare “API” changes. These typically contain “workhorses” of their R counterparts.
Functions any_duplicated and any_duplicated3 are fast
versions of R's any(duplicated(.)).
Function R_compute_identical corresponds to R's identical function.
When assignments are done in R such as
x <- 1:10
y <- x
the named object is not necessarily copied, so after those two
assignments y and x are bound to the same SEXPREC
(the structure a SEXP points to). This means that any code which
alters one of them has to make a copy before modifying the copy if the
usual R semantics are to apply. Note that whereas .C and
.Fortran do copy their arguments (unless the dangerous dup
= FALSE is used), .Call and .External do not. So
duplicate is commonly called on arguments to .Call before
modifying them.
However, at least some of this copying is unneeded. In the first
assignment shown, x <- 1:10, R first creates an object with
value 1:10 and then assigns it to x but if x is
modified no copy is necessary as the temporary object with value
1:10 cannot be referred to again. R distinguishes between
named and unnamed objects via a field in a SEXPREC that
can be accessed via the macros NAMED and SET_NAMED. This
can take values
012Note the past tenses: R does not do full reference counting and there may currently be fewer bindings.
It is safe to modify the value of any SEXP for which
NAMED(foo) is zero, and if NAMED(foo) is two, the value
should be duplicated (via a call to duplicate) before any
modification. Note that it is the responsibility of the author of the
code making the modification to do the duplication, even if it is
x whose value is being modified after y <- x.
The case NAMED(foo) == 1 allows some optimization, but it can be
ignored (and duplication done whenever NAMED(foo) > 0). (This
optimization is not currently usable in user code.) It is intended
for use within replacement functions. Suppose we used
x <- 1:10
foo(x) <- 3
which is computed as
x <- 1:10
x <- "foo<-"(x, 3)
Then inside "foo<-" the object pointing to the current value of
x will have NAMED(foo) as one, and it would be safe to
modify it as the only symbol bound to it is x and that will be
rebound immediately. (Provided the remaining code in "foo<-"
make no reference to x, and no one is going to attempt a direct
call such as y <- "foo<-"(x).)
This mechanism is likely to be replaced in future versions of R.
.Call and .ExternalIn this section we consider the details of the R/C interfaces.
These two interfaces have almost the same functionality. .Call is
based on the interface of the same name in S version 4, and
.External is based on R's .Internal. .External
is more complex but allows a variable number of arguments.
.Call
Let us convert our finite convolution example to use .Call. The
calling function in R is
conv <- function(a, b) .Call("convolve2", a, b)
which could hardly be simpler, but as we shall see all the type coercion is transferred to the C code, which is
#include <R.h>
#include <Rinternals.h>
SEXP convolve2(SEXP a, SEXP b)
{
int na, nb, nab;
double *xa, *xb, *xab;
SEXP ab;
a = PROTECT(coerceVector(a, REALSXP));
b = PROTECT(coerceVector(b, REALSXP));
na = length(a); nb = length(b); nab = na + nb - 1;
ab = PROTECT(allocVector(REALSXP, nab));
xa = REAL(a); xb = REAL(b); xab = REAL(ab);
for(int i = 0; i < nab; i++) xab[i] = 0.0;
for(int i = 0; i < na; i++)
for(int j = 0; j < nb; j++) xab[i + j] += xa[i] * xb[j];
UNPROTECT(3);
return ab;
}
.External
We can use the same example to illustrate .External. The R
code changes only by replacing .Call by .External
conv <- function(a, b) .External("convolveE", a, b)
but the main change is how the arguments are passed to the C code, this time as a single SEXP. The only change to the C code is how we handle the arguments.
#include <R.h>
#include <Rinternals.h>
SEXP convolveE(SEXP args)
{
int i, j, na, nb, nab;
double *xa, *xb, *xab;
SEXP a, b, ab;
a = PROTECT(coerceVector(CADR(args), REALSXP));
b = PROTECT(coerceVector(CADDR(args), REALSXP));
...
}
Once again we do not need to protect the arguments, as in the R side of the interface they are objects that are already in use. The macros
first = CADR(args);
second = CADDR(args);
third = CADDDR(args);
fourth = CAD4R(args);
provide convenient ways to access the first four arguments. More
generally we can use the
CDR and CAR macros as in
args = CDR(args); a = CAR(args);
args = CDR(args); b = CAR(args);
which clearly allows us to extract an unlimited number of arguments
(whereas .Call has a limit, albeit at 65 not a small one).
More usefully, the .External interface provides an easy way to
handle calls with a variable number of arguments, as length(args)
will give the number of arguments supplied (of which the first is
ignored). We may need to know the names (‘tags’) given to the actual
arguments, which we can by using the TAG macro and using
something like the following example, that prints the names and the first
value of its arguments if they are vector types.
SEXP showArgs(SEXP args)
{
args = CDR(args); /* skip ‘name’ */
for(int i = 0; args != R_NilValue; i++, args = CDR(args)) {
const char *name =
isNull(TAG(args)) ? "" : CHAR(PRINTNAME(TAG(args)));
SEXP el = CAR(args);
if (length(el) == 0) {
Rprintf("[%d] ‘%s’ R type, length 0\n", i+1, name);
continue;
}
switch(TYPEOF(el)) {
case REALSXP:
Rprintf("[%d] ‘%s’ %f\n", i+1, name, REAL(el)[0]);
break;
case LGLSXP:
case INTSXP:
Rprintf("[%d] ‘%s’ %d\n", i+1, name, INTEGER(el)[0]);
break;
case CPLXSXP:
{
Rcomplex cpl = COMPLEX(el)[0];
Rprintf("[%d] ‘%s’ %f + %fi\n", i+1, name, cpl.r, cpl.i);
}
break;
case STRSXP:
Rprintf("[%d] ‘%s’ %s\n", i+1, name,
CHAR(STRING_ELT(el, 0)));
break;
default:
Rprintf("[%d] ‘%s’ R type\n", i+1, name);
}
}
return R_NilValue;
}
This can be called by the wrapper function
showArgs <- function(...) invisible(.External("showArgs", ...))
Note that this style of programming is convenient but not necessary, as an alternative style is
showArgs1 <- function(...) invisible(.Call("showArgs1", list(...)))
The (very similar) C code is in the scripts.
One piece of error-checking the .C call does (unless NAOK
is true) is to check for missing (NA) and IEEE special
values (Inf, -Inf and NaN) and give an error if any
are found. With the .Call interface these will be passed to our
code. In this example the special values are no problem, as
IEC60559 arithmetic will handle them correctly. In the current
implementation this is also true of NA as it is a type of
NaN, but it is unwise to rely on such details. Thus we will
re-write the code to handle NAs using macros defined in
R_exts/Arith.h included by R.h.
The code changes are the same in any of the versions of convolve2
or convolveE:
...
for(int i = 0; i < na; i++)
for(int j = 0; j < nb; j++)
if(ISNA(xa[i]) || ISNA(xb[j]) || ISNA(xab[i + j]))
xab[i + j] = NA_REAL;
else
xab[i + j] += xa[i] * xb[j];
...
Note that the ISNA macro, and the similar macros ISNAN
(which checks for NaN or NA) and R_FINITE (which is
false for NA and all the special values), only apply to numeric
values of type double. Missingness of integers, logicals and
character strings can be tested by equality to the constants
NA_INTEGER, NA_LOGICAL and NA_STRING. These and
NA_REAL can be used to set elements of R vectors to NA.
The constants R_NaN, R_PosInf and R_NegInf can be
used to set doubles to the special values.
The main function we will use is
SEXP eval(SEXP expr, SEXP rho);
the equivalent of the interpreted R code eval(expr, envir =
rho) (so rho must be an environment), although we can also make
use of findVar, defineVar and findFun (which
restricts the search to functions).
To see how this might be applied, here is a simplified internal version
of lapply for expressions, used as
a <- list(a = 1:5, b = rnorm(10), test = runif(100))
.Call("lapply", a, quote(sum(x)), new.env())
with C code
SEXP lapply(SEXP list, SEXP expr, SEXP rho)
{
int n = length(list);
SEXP ans;
if(!isNewList(list)) error("'list' must be a list");
if(!isEnvironment(rho)) error("'rho' should be an environment");
ans = PROTECT(allocVector(VECSXP, n));
for(int i = 0; i < n; i++) {
defineVar(install("x"), VECTOR_ELT(list, i), rho);
SET_VECTOR_ELT(ans, i, eval(expr, rho));
}
setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol));
UNPROTECT(1);
return ans;
}
It would be closer to lapply if we could pass in a function
rather than an expression. One way to do this is via interpreted
R code as in the next example, but it is possible (if somewhat
obscure) to do this in C code. The following is based on the code in
src/main/optimize.c.
SEXP lapply2(SEXP list, SEXP fn, SEXP rho)
{
int n = length(list);
SEXP R_fcall, ans;
if(!isNewList(list)) error("'list' must be a list");
if(!isFunction(fn)) error("'fn' must be a function");
if(!isEnvironment(rho)) error("'rho' should be an environment");
R_fcall = PROTECT(lang2(fn, R_NilValue));
ans = PROTECT(allocVector(VECSXP, n));
for(int i = 0; i < n; i++) {
SETCADR(R_fcall, VECTOR_ELT(list, i));
SET_VECTOR_ELT(ans, i, eval(R_fcall, rho));
}
setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol));
UNPROTECT(2);
return ans;
}
used by
.Call("lapply2", a, sum, new.env())
Function lang2 creates an executable pairlist of two elements, but
this will only be clear to those with a knowledge of a LISP-like
language.
As a more comprehensive example of constructing an R call in C code
and evaluating, consider the following fragment of
printAttributes in src/main/print.c.
/* Need to construct a call to
print(CAR(a), digits=digits)
based on the R_print structure, then eval(call, env).
See do_docall for the template for this sort of thing.
*/
SEXP s, t;
t = s = PROTECT(allocList(3));
SET_TYPEOF(s, LANGSXP);
SETCAR(t, install("print")); t = CDR(t);
SETCAR(t, CAR(a)); t = CDR(t);
SETCAR(t, ScalarInteger(digits));
SET_TAG(t, install("digits"));
eval(s, env);
UNPROTECT(1);
At this point CAR(a) is the R object to be printed, the
current attribute. There are three steps: the call is constructed as
a pairlist of length 3, the list is filled in, and the expression
represented by the pairlist is evaluated.
A pairlist is quite distinct from a generic vector list, the only
user-visible form of list in R. A pairlist is a linked list (with
CDR(t) computing the next entry), with items (accessed by
CAR(t)) and names or tags (set by SET_TAG). In this call
there are to be three items, a symbol (pointing to the function to be
called) and two argument values, the first unnamed and the second named.
Setting the type to LANGSXP makes this a call which can be evaluated.
In this section we re-work the example of Becker, Chambers & Wilks (1988, pp.~205–10) on finding a zero of a univariate function. The R code and an example are
zero <- function(f, guesses, tol = 1e-7) {
f.check <- function(x) {
x <- f(x)
if(!is.numeric(x)) stop("Need a numeric result")
as.double(x)
}
.Call("zero", body(f.check), as.double(guesses), as.double(tol),
new.env())
}
cube1 <- function(x) (x^2 + 1) * (x - 1.5)
zero(cube1, c(0, 5))
where this time we do the coercion and error-checking in the R code. The C code is
SEXP mkans(double x)
{
SEXP ans;
ans = PROTECT(allocVector(REALSXP, 1));
REAL(ans)[0] = x;
UNPROTECT(1);
return ans;
}
double feval(double x, SEXP f, SEXP rho)
{
defineVar(install("x"), mkans(x), rho);
return REAL(eval(f, rho))[0];
}
SEXP zero(SEXP f, SEXP guesses, SEXP stol, SEXP rho)
{
double x0 = REAL(guesses)[0], x1 = REAL(guesses)[1],
tol = REAL(stol)[0];
double f0, f1, fc, xc;
if(tol <= 0.0) error("non-positive tol value");
f0 = feval(x0, f, rho); f1 = feval(x1, f, rho);
if(f0 == 0.0) return mkans(x0);
if(f1 == 0.0) return mkans(x1);
if(f0*f1 > 0.0) error("x[0] and x[1] have the same sign");
for(;;) {
xc = 0.5*(x0+x1);
if(fabs(x0-x1) < tol) return mkans(xc);
fc = feval(xc, f, rho);
if(fc == 0) return mkans(xc);
if(f0*fc > 0.0) {
x0 = xc; f0 = fc;
} else {
x1 = xc; f1 = fc;
}
}
}
We will use a longer example (by Saikat DebRoy) to illustrate the use of
evaluation and .External. This calculates numerical derivatives,
something that could be done as effectively in interpreted R code but
may be needed as part of a larger C calculation.
An interpreted R version and an example are
numeric.deriv <- function(expr, theta, rho=sys.frame(sys.parent()))
{
eps <- sqrt(.Machine$double.eps)
ans <- eval(substitute(expr), rho)
grad <- matrix(, length(ans), length(theta),
dimnames=list(NULL, theta))
for (i in seq_along(theta)) {
old <- get(theta[i], envir=rho)
delta <- eps * max(1, abs(old))
assign(theta[i], old+delta, envir=rho)
ans1 <- eval(substitute(expr), rho)
assign(theta[i], old, envir=rho)
grad[, i] <- (ans1 - ans)/delta
}
attr(ans, "gradient") <- grad
ans
}
omega <- 1:5; x <- 1; y <- 2
numeric.deriv(sin(omega*x*y), c("x", "y"))
where expr is an expression, theta a character vector of
variable names and rho the environment to be used.
For the compiled version the call from R will be
.External("numeric_deriv", expr, theta, rho)
with example usage
.External("numeric_deriv", quote(sin(omega*x*y)),
c("x", "y"), .GlobalEnv)
Note the need to quote the expression to stop it being evaluated in the caller.
Here is the complete C code which we will explain section by section.
#include <R.h> /* for DOUBLE_EPS */
#include <Rinternals.h>
SEXP numeric_deriv(SEXP args)
{
SEXP theta, expr, rho, ans, ans1, gradient, par, dimnames;
double tt, xx, delta, eps = sqrt(DOUBLE_EPS), *rgr, *rans;
int i, start;
expr = CADR(args);
if(!isString(theta = CADDR(args)))
error("theta should be of type character");
if(!isEnvironment(rho = CADDDR(args)))
error("rho should be an environment");
ans = PROTECT(coerceVector(eval(expr, rho), REALSXP));
gradient = PROTECT(allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));
rgr = REAL(gradient); rans = REAL(ans);
for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {
par = PROTECT(findVar(install(CHAR(STRING_ELT(theta, i))), rho));
tt = REAL(par)[0];
xx = fabs(tt);
delta = (xx < 1) ? eps : xx*eps;
REAL(par)[0] += delta;
ans1 = PROTECT(coerceVector(eval(expr, rho), REALSXP));
for(int j = 0; j < LENGTH(ans); j++)
rgr[j + start] = (REAL(ans1)[j] - rans[j])/delta;
REAL(par)[0] = tt;
UNPROTECT(2); /* par, ans1 */
}
dimnames = PROTECT(allocVector(VECSXP, 2));
SET_VECTOR_ELT(dimnames, 1, theta);
dimnamesgets(gradient, dimnames);
setAttrib(ans, install("gradient"), gradient);
UNPROTECT(3); /* ans gradient dimnames */
return ans;
}
The code to handle the arguments is
expr = CADR(args);
if(!isString(theta = CADDR(args)))
error("theta should be of type character");
if(!isEnvironment(rho = CADDDR(args)))
error("rho should be an environment");
Note that we check for correct types of theta and rho but
do not check the type of expr. That is because eval can
handle many types of R objects other than EXPRSXP. There is
no useful coercion we can do, so we stop with an error message if the
arguments are not of the correct mode.
The first step in the code is to evaluate the expression in the
environment rho, by
ans = PROTECT(coerceVector(eval(expr, rho), REALSXP));
We then allocate space for the calculated derivative by
gradient = PROTECT(allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));
The first argument to allocMatrix gives the SEXPTYPE of
the matrix: here we want it to be REALSXP. The other two
arguments are the numbers of rows and columns. (Note that LENGTH
is intended to be used for vectors: length is more generally
applicable.)
for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {
par = PROTECT(findVar(install(CHAR(STRING_ELT(theta, i))), rho));
Here, we are entering a for loop. We loop through each of the
variables. In the for loop, we first create a symbol
corresponding to the i'th element of the STRSXP
theta. Here, STRING_ELT(theta, i) accesses the
i'th element of the STRSXP theta. Macro
CHAR() extracts the actual character
representation85 of it: it returns a pointer. We then
install the name and use findVar to find its value.
tt = REAL(par)[0];
xx = fabs(tt);
delta = (xx < 1) ? eps : xx*eps;
REAL(par)[0] += delta;
ans1 = PROTECT(coerceVector(eval(expr, rho), REALSXP));
We first extract the real value of the parameter, then calculate
delta, the increment to be used for approximating the numerical
derivative. Then we change the value stored in par (in
environment rho) by delta and evaluate expr in
environment rho again. Because we are directly dealing with
original R memory locations here, R does the evaluation for the
changed parameter value.
for(int j = 0; j < LENGTH(ans); j++)
rgr[j + start] = (REAL(ans1)[j] - rans[j])/delta;
REAL(par)[0] = tt;
UNPROTECT(2);
}
Now, we compute the i'th column of the gradient matrix. Note how
it is accessed: R stores matrices by column (like FORTRAN).
dimnames = PROTECT(allocVector(VECSXP, 2));
SET_VECTOR_ELT(dimnames, 1, theta);
dimnamesgets(gradient, dimnames);
setAttrib(ans, install("gradient"), gradient);
UNPROTECT(3);
return ans;
}
First we add column names to the gradient matrix. This is done by
allocating a list (a VECSXP) whose first element, the row names,
is NULL (the default) and the second element, the column names,
is set as theta. This list is then assigned as the attribute
having the symbol R_DimNamesSymbol. Finally we set the gradient
matrix as the gradient attribute of ans, unprotect the remaining
protected locations and return the answer ans.
Suppose an R extension want to accept an R expression from the user and evaluate it. The previous section covered evaluation, but the expression will be entered as text and needs to be parsed first. A small part of R's parse interface is declared in header file R_ext/Parse.h86.
An example of the usage can be found in the (example) Windows package windlgs included in the R source tree. The essential part is
#include <R.h>
#include <Rinternals.h>
#include <R_ext/Parse.h>
SEXP menu_ttest3()
{
char cmd[256];
SEXP cmdSexp, cmdexpr, ans = R_NilValue;
ParseStatus status;
...
if(done == 1) {
cmdSexp = PROTECT(allocVector(STRSXP, 1));
SET_STRING_ELT(cmdSexp, 0, mkChar(cmd));
cmdexpr = PROTECT(R_ParseVector(cmdSexp, -1, &status, R_NilValue));
if (status != PARSE_OK) {
UNPROTECT(2);
error("invalid call %s", cmd);
}
/* Loop is needed here as EXPSEXP will be of length > 1 */
for(int i = 0; i < length(cmdexpr); i++)
ans = eval(VECTOR_ELT(cmdexpr, i), R_GlobalEnv);
UNPROTECT(2);
}
return ans;
}
Note that a single line of text may give rise to more than one R expression.
R_ParseVector is essentially the code used to implement
parse(text=) at R level. The first argument is a character
vector (corresponding to text) and the second the maximal
number of expressions to parse (corresponding to n). The third
argument is a pointer to a variable of an enumeration type, and it is
normal (as parse does) to regard all values other than
PARSE_OK as an error. Other values which might be returned are
PARSE_INCOMPLETE (an incomplete expression was found) and
PARSE_ERROR (a syntax error), in both cases the value returned
being R_NilValue. The fourth argument is a length one character
vector to be used as a filename in error messages, a srcfile
object or the R NULL object (as in the example above). If a
srcfile object was used, a srcref attribute would be
attached to the result, containing a list of srcref objects of
the same length as the expression, to allow it to be echoed with its
original formatting.
The source references added by the parser are recorded by R's evaluator as it evaluates code. Two functions make these available to debuggers running C code:
SEXP R_GetCurrentSrcref(int skip);
This function checks R_Srcref and the current evaluation stack
for entries that contain source reference information. The
skip argument tells how many source references to skip before
returning the SEXP of the srcref object, counting from
the top of the stack. If skip < 0, abs(skip) locations
are counted up from the bottom of the stack. If too few or no source
references are found, NULL is returned.
SEXP R_GetSrcFilename(SEXP srcref);
This function extracts the filename from the source reference for
display, returning a length 1 character vector containing the
filename. If no name is found, "" is returned.
The SEXPTYPEs EXTPTRSXP and WEAKREFSXP can be
encountered at R level, but are created in C code.
External pointer SEXPs are intended to handle references to C
structures such as ‘handles’, and are used for this purpose in package
RODBC for example. They are unusual in their copying semantics in
that when an R object is copied, the external pointer object is not
duplicated. (For this reason external pointers should only be used as
part of an object with normal semantics, for example an attribute or an
element of a list.)
An external pointer is created by
SEXP R_MakeExternalPtr(void *p, SEXP tag, SEXP prot);
where p is the pointer (and hence this cannot portably be a
function pointer), and tag and prot are references to
ordinary R objects which will remain in existence (be protected from
garbage collection) for the lifetime of the external pointer object. A
useful convention is to use the tag field for some form of type
identification and the prot field for protecting the memory that
the external pointer represents, if that memory is allocated from the
R heap. Both tag and prot can be R_NilValue,
and often are.
The elements of an external pointer can be accessed and set via
void *R_ExternalPtrAddr(SEXP s);
SEXP R_ExternalPtrTag(SEXP s);
SEXP R_ExternalPtrProtected(SEXP s);
void R_ClearExternalPtr(SEXP s);
void R_SetExternalPtrAddr(SEXP s, void *p);
void R_SetExternalPtrTag(SEXP s, SEXP tag);
void R_SetExternalPtrProtected(SEXP s, SEXP p);
Clearing a pointer sets its value to the C NULL pointer.
An external pointer object can have a finalizer, a piece of code to be run when the object is garbage collected. This can be R code or C code, and the various interfaces are, respectively.
void R_RegisterFinalizerEx(SEXP s, SEXP fun, Rboolean onexit);
typedef void (*R_CFinalizer_t)(SEXP);
void R_RegisterCFinalizerEx(SEXP s, R_CFinalizer_t fun, Rboolean onexit);
The R function indicated by fun should be a function of a
single argument, the object to be finalized. R does not perform a
garbage collection when shutting down, and the onexit argument of
the extended forms can be used to ask that the finalizer be run during a
normal shutdown of the R session. It is suggested that it is good
practice to clear the pointer on finalization.
The only R level function for interacting with external pointers is
reg.finalizer which can be used to set a finalizer.
It is probably not a good idea to allow an external pointer to be
saved and then reloaded, but if this happens the pointer will be
set to the C NULL pointer.
Finalizers can be run at many places in the code base and much of it, including the R interpreter, is not re-entrant. So great care is needed in choosing the code to be run in a finalizer. As from R 3.0.3 finalizers are marked to be run at garbage collection but only run at a somewhat safe point thereafter.
Weak references are used to allow the programmer to maintain information on entities without preventing the garbage collection of the entities once they become unreachable.
A weak reference contains a key and a value. The value is reachable is
if it either reachable directly or via weak references with reachable
keys. Once a value is determined to be unreachable during garbage
collection, the key and value are set to R_NilValue and the
finalizer will be run later in the garbage collection.
Weak reference objects are created by one of
SEXP R_MakeWeakRef(SEXP key, SEXP val, SEXP fin, Rboolean onexit);
SEXP R_MakeWeakRefC(SEXP key, SEXP val, R_CFinalizer_t fin,
Rboolean onexit);
where the R or C finalizer are specified in exactly the same way as for an external pointer object (whose finalization interface is implemented via weak references).
The parts can be accessed via
SEXP R_WeakRefKey(SEXP w);
SEXP R_WeakRefValue(SEXP w);
void R_RunWeakRefFinalizer(SEXP w);
A toy example of the use of weak references can be found at
www.stat.uiowa.edu/~luke/R/references/weakfinex.html,
but that is used to add finalizers to external pointers which can now be
done more directly. At the time of writing no CRAN or
Bioconductor package uses weak references.
Package RODBC uses external pointers to maintain its
channels, connections to databases. There can be several
connections open at once, and the status information for each is stored
in a C structure (pointed to by this_handle) in the code extract
below) that is returned via an external pointer as part of the RODBC
‘channel’ (as the "handle_ptr" attribute). The external pointer
is created by
SEXP ans, ptr;
ans = PROTECT(allocVector(INTSXP, 1));
ptr = R_MakeExternalPtr(thisHandle, install("RODBC_channel"), R_NilValue);
PROTECT(ptr);
R_RegisterCFinalizerEx(ptr, chanFinalizer, TRUE);
...
/* return the channel no */
INTEGER(ans)[0] = nChannels;
/* and the connection string as an attribute */
setAttrib(ans, install("connection.string"), constr);
setAttrib(ans, install("handle_ptr"), ptr);
UNPROTECT(3);
return ans;
Note the symbol given to identify the usage of the external pointer, and
the use of the finalizer. Since the final argument when registering the
finalizer is TRUE, the finalizer will be run at the the of the
R session (unless it crashes). This is used to close and clean up
the connection to the database. The finalizer code is simply
static void chanFinalizer(SEXP ptr)
{
if(!R_ExternalPtrAddr(ptr)) return;
inRODBCClose(R_ExternalPtrAddr(ptr));
R_ClearExternalPtr(ptr); /* not really needed */
}
Clearing the pointer and checking for a NULL pointer avoids any
possibility of attempting to close an already-closed channel.
R's connections provide another example of using external pointers, in that case purely to be able to use a finalizer to close and destroy the connection if it is no longer is use.
The vector accessors like REAL and INTEGER and
VECTOR_ELT are functions when used in R extensions.
(For efficiency they are macros when used in the R source code, apart
from SET_STRING_ELT and SET_VECTOR_ELT which are always
functions.)
The accessor functions check that they are being used on an appropriate
type of SEXP.
If efficiency is essential, the macro versions of the accessors can be obtained by defining ‘USE_RINTERNALS’ before including Rinternals.h. If you find it necessary to do so, please do test that your code compiles without ‘USE_RINTERNALS’ defined, as this provides a stricter test that the accessors have been used correctly.
CHARSXPs can be marked as coming from a known encoding (Latin-1
or UTF-8). This is mainly intended for human-readable output, and most
packages can just treat such CHARSXPs as a whole. However, if
they need to be interpreted as characters or output at C level then it
would normally be correct to ensure that they are converted to the
encoding of the current locale: this can be done by accessing the data
in the CHARSXP by translateChar rather than by
CHAR. If re-encoding is needed this allocates memory with
R_alloc which thus persists to the end of the
.Call/.External call unless vmaxset is used
(see Transient storage allocation).
There is a similar function translateCharUTF8 which converts to
UTF-8: this has the advantage that a faithful translation is almost
always possible (whereas only a few languages can be represented in the
encoding of the current locale unless that is UTF-8).
There is a public interface to the encoding marked on CHARXSXPs
via
typedef enum {CE_NATIVE, CE_UTF8, CE_LATIN1, CE_SYMBOL, CE_ANY} cetype_t;
cetype_t getCharCE(SEXP);
SEXP mkCharCE(const char *, cetype_t);
Only CE_UTF8 and CE_LATIN1 are marked on CHARSXPs
(and so Rf_getCharCE will only return one of the first three),
and these should only be used on non-ASCII strings. Value
CE_SYMBOL is used internally to indicate Adobe Symbol encoding.
Value CE_ANY is used to indicate a character string that will not
need re-encoding – this is used for character strings known to be in
ASCII, and can also be used as an input parameter where the
intention is that the string is treated as a series of bytes. (See the
comments under mkChar about the length of input allowed.)
Function
const char *reEnc(const char *x, cetype_t ce_in, cetype_t ce_out,
int subst);
can be used to re-encode character strings: like translateChar it
returns a string allocated by R_alloc. This can translate from
CE_SYMBOL to CE_UTF8, but not conversely. Argument
subst controls what to do with untranslatable characters or
invalid input: this is done byte-by-byte with 1 indicates to
output hex of the form <a0>, and 2 to replace by .,
with any other value causing the byte to produce no output.
SEXP mkCharLenCE(const char *, size_t, cetype_t);
to create marked character strings of a given length.
There are a large number of entry points in the R executable/DLL that can be called from C code (and some that can be called from FORTRAN code). Only those documented here are stable enough that they will only be changed with considerable notice.
The recommended procedure to use these is to include the header file R.h in your C code by
#include <R.h>
This will include several other header files from the directory R_INCLUDE_DIR/R_ext, and there are other header files there that can be included too, but many of the features they contain should be regarded as undocumented and unstable.
An alternative is to include the header file S.h, which may be
useful when porting code from S. This includes rather less than
R.h, and has some extra compatibility definitions (for example
the S_complex type from S).
The defines used for compatibility with S sometimes causes
conflicts (notably with Windows headers), and the known
problematic defines can be removed by defining STRICT_R_HEADERS.
Most of these header files, including all those included by R.h,
can be used from C++ code. Some others need to be included within an
extern "C" declaration, and for clarity this is advisable for all
R header files.
Note: Because R re-maps many of its external names to avoid clashes with user code, it is essential to include the appropriate header files when using these entry points.
This remapping can cause problems87, and can be eliminated by defining R_NO_REMAP and
prepending ‘Rf_’ to all the function names used from
Rinternals.h and R_ext/Error.h. These problems can
usually be avoided by including other headers (such as system headers
and those for external software used by the package) before R.h.
We can classify the entry points as
There are two types of memory allocation available to the C programmer, one in which R manages the clean-up and the other in which user has full control (and responsibility).
Here R will reclaim the memory at the end of the call to .C,
.Call or .External. Use
char *R_alloc(size_t n, int size)
which allocates n units of size bytes each. A typical usage (from package stats) is
x = (int *) R_alloc(nrows(merge)+2, sizeof(int));
(size_t is defined in stddef.h which the header defining
R_alloc includes.)
There is a similar call, S_alloc (for compatibility with older
versions of S) which zeroes the memory allocated,
char *S_alloc(long n, int size)
and
char *S_realloc(char *p, long new, long old, int size)
which changes the allocation size from old to new units, and zeroes the additional units.
For compatibility with current versions of S, header S.h (only) defines wrapper macros equivalent to
type* Salloc(long n, int type)
type* Srealloc(char *p, long new, long old, int type)
This memory is taken from the heap, and released at the end of the
.C, .Call or .External call. Users can also manage
it, by noting the current position with a call to vmaxget and
subsequently clearing memory allocated by a call to vmaxset. An
example might be
void *vmax = vmaxget()
// a loop involving the use of R_alloc at each iteration
vmaxset(vmax)
This is only recommended for experts.
Note that this memory will be freed on error or user interrupt (if allowed: see Allowing interrupts).
Note that although n is size_t, there may be limits imposed
by R's internal allocation mechanism. These will only come into play
on 64-bit systems, where the limit for n prior to R 3.0.0 was
just under 16Gb.
These functions should only be used in code called by .C etc,
never from front-ends. They are not thread-safe.
The other form of memory allocation is an interface to malloc,
the interface providing R error handling. This memory lasts until
freed by the user and is additional to the memory allocated for the R
workspace.
The interface functions are
type* Calloc(size_t n, type)
type* Realloc(any *p, size_t n, type)
void Free(any *p)
providing analogues of calloc, realloc and free.
If there is an error during allocation it is handled by R, so if
these routines return the memory has been successfully allocated or
freed. Free will set the pointer p to NULL. (Some
but not all versions of S do so.)
Users should arrange to Free this memory when no longer needed,
including on error or user interrupt. This can often be done most
conveniently from an on.exit action in the calling R function
– see pwilcox for an example.
Do not assume that memory allocated by Calloc/Realloc
comes from the same pool as used by malloc: in particular do not
use free or strdup with it.
These entry points need to be prefixed by R_ if
STRICT_R_HEADERS has been defined.
The basic error handling routines are the equivalents of stop and
warning in R code, and use the same interface.
void error(const char * format, ...);
void warning(const char * format, ...);
These have the same call sequences as calls to printf, but in the
simplest case can be called with a single character string argument
giving the error message. (Don't do this if the string contains ‘%’
or might otherwise be interpreted as a format.)
If STRICT_R_HEADERS is not defined there is also an
S-compatibility interface which uses calls of the form
PROBLEM ...... ERROR
MESSAGE ...... WARN
PROBLEM ...... RECOVER(NULL_ENTRY)
MESSAGE ...... WARNING(NULL_ENTRY)
the last two being the forms available in all S versions. Here
‘......’ is a set of arguments to printf, so can be a string
or a format string followed by arguments separated by commas.
There are two interface function provided to call error and
warning from FORTRAN code, in each case with a simple character
string argument. They are defined as
subroutine rexit(message)
subroutine rwarn(message)
Messages of more than 255 characters are truncated, with a warning.
The interface to R's internal random number generation routines is
double unif_rand();
double norm_rand();
double exp_rand();
giving one uniform, normal or exponential pseudo-random variate. However, before these are used, the user must call
GetRNGstate();
and after all the required variates have been generated, call
PutRNGstate();
These essentially read in (or create) .Random.seed and write it
out after use.
File S.h defines seed_in and seed_out for
S-compatibility rather than GetRNGstate and
PutRNGstate. These take a long * argument which is
ignored.
The random number generator is private to R; there is no way to select the kind of RNG or set the seed except by evaluating calls to the R functions.
The C code behind R's rxxx functions can be accessed by
including the header file Rmath.h; See Distribution functions. Those calls generate a single variate and should also be
enclosed in calls to GetRNGstate and PutRNGstate.
A set of functions is provided to test for NA, Inf,
-Inf and NaN. These functions are accessed via macros:
ISNA(x) True for R'sNAonly ISNAN(x) True for R'sNAand IEEENaNR_FINITE(x) False forInf,-Inf,NA,NaN
and via function R_IsNaN which is true for NaN but not
NA.
Do use R_FINITE rather than isfinite or finite; the
latter is often mendacious and isfinite is only available on a
some platforms, on which R_FINITE is a macro expanding to
isfinite.
Currently in C code ISNAN is a macro calling isnan.
(Since this gives problems on some C++ systems, if the R headers is
called from C++ code a function call is used.)
You can check for Inf or -Inf by testing equality to
R_PosInf or R_NegInf, and set (but not test) an NA
as NA_REAL.
All of the above apply to double variables only. For integer
variables there is a variable accessed by the macro NA_INTEGER
which can used to set or test for missingness.
The most useful function for printing from a C routine compiled into
R is Rprintf. This is used in exactly the same way as
printf, but is guaranteed to write to R's output (which might
be a GUI console rather than a file, and can be re-directed by
sink). It is wise to write complete lines (including the
"\n") before returning to R. It is defined in
R_ext/Print.h.
The function REprintf is similar but writes on the error stream
(stderr) which may or may not be different from the standard
output stream.
Functions Rvprintf and REvprintf are analogues using the
vprintf interface. Because that is a C99 interface, they are
only defined by R_ext/Print.h in C++ code if the macro
R_USE_C99_IN_CXX is defined when it is included.
Another circumstance when it may be important to use these functions is when using parallel computation on a cluster of computational nodes, as their output will be re-directed/logged appropriately.
On many systems FORTRAN write and print statements can be
used, but the output may not interleave well with that of C, and will be
invisible on GUI interfaces. They are not portable and best
avoided.
Three subroutines are provided to ease the output of information from FORTRAN code.
subroutine dblepr(label, nchar, data, ndata)
subroutine realpr(label, nchar, data, ndata)
subroutine intpr (label, nchar, data, ndata)
Here label is a character label of up to 255 characters,
nchar is its length (which can be -1 if the whole label is
to be used), and data is an array of length at least ndata
of the appropriate type (double precision, real and
integer respectively). These routines print the label on one
line and then print data as if it were an R vector on
subsequent line(s). They work with zero ndata, and so can be used
to print a label alone.
Naming conventions for symbols generated by FORTRAN differ by platform: it is not safe to assume that FORTRAN names appear to C with a trailing underscore. To help cover up the platform-specific differences there is a set of macros that should be used.
F77_SUB(name)F77_NAME(name)F77_CALL(name)F77_COMDECL(name)F77_COM(name)On most current platforms these are all the same, but it is unwise to rely on this. Note that names with underscores are not legal in FORTRAN 77, and are not portably handled by the above macros. (Also, all FORTRAN names for use by R are lower case, but this is not enforced by the macros.)
For example, suppose we want to call R's normal random numbers from FORTRAN. We need a C wrapper along the lines of
#include <R.h>
void F77_SUB(rndstart)(void) { GetRNGstate(); }
void F77_SUB(rndend)(void) { PutRNGstate(); }
double F77_SUB(normrnd)(void) { return norm_rand(); }
to be called from FORTRAN as in
subroutine testit()
double precision normrnd, x
call rndstart()
x = normrnd()
call dblepr("X was", 5, x, 1)
call rndend()
end
Note that this is not guaranteed to be portable, for the return conventions might not be compatible between the C and FORTRAN compilers used. (Passing values via arguments is safer.)
The standard packages, for example stats, are a rich source of further examples.
Passing character strings from C to FORTRAN 77 or vice versa is
not portable (and to Fortran 90 or later is even less so). We have
found that it helps to ensure that a C string to be passed is followed
by several nuls (and not just the one needed as a C terminator).
But for maximal portability character strings in FORTRAN should be
avoided.
R contains a large number of mathematical functions for its own use, for example numerical linear algebra computations and special functions.
The header files R_ext/BLAS.h, R_ext/Lapack.h and R_ext/Linpack.h contains declarations of the BLAS, LAPACK and LINPACK linear algebra functions included in R. These are expressed as calls to FORTRAN subroutines, and they will also be usable from users' FORTRAN code. Although not part of the official API, this set of subroutines is unlikely to change (but might be supplemented).
The header file Rmath.h lists many other functions that are available and documented in the following subsections. Many of these are C interfaces to the code behind R functions, so the R function documentation may give further details.
The routines used to calculate densities, cumulative distribution functions and quantile functions for the standard statistical distributions are available as entry points.
The arguments for the entry points follow the pattern of those for the normal distribution:
double dnorm(double x, double mu, double sigma, int give_log);
double pnorm(double x, double mu, double sigma, int lower_tail,
int give_log);
double qnorm(double p, double mu, double sigma, int lower_tail,
int log_p);
double rnorm(double mu, double sigma);
That is, the first argument gives the position for the density and CDF
and probability for the quantile function, followed by the
distribution's parameters. Argument lower_tail should be
TRUE (or 1) for normal use, but can be FALSE (or
0) if the probability of the upper tail is desired or specified.
Finally, give_log should be non-zero if the result is required on log scale, and log_p should be non-zero if p has been specified on log scale.
Note that you directly get the cumulative (or “integrated”) hazard function, H(t) = - log(1 - F(t)), by using
- pdist(t, ..., /*lower_tail = */ FALSE, /* give_log = */ TRUE)
or shorter (and more cryptic) - pdist(t, ..., 0, 1).
The random-variate generation routine rnorm returns one normal
variate. See Random numbers, for the protocol in using the
random-variate routines.
Note that these argument sequences are (apart from the names and that
rnorm has no n) mainly the same as the corresponding R
functions of the same name, so the documentation of the R functions
can be used. Note that the exponential and gamma distributions are
parametrized by scale rather than rate.
For reference, the following table gives the basic name (to be prefixed by ‘d’, ‘p’, ‘q’ or ‘r’ apart from the exceptions noted) and distribution-specific arguments for the complete set of distributions.
beta betaa,bnon-central beta nbetaa,b,ncpbinomial binomn,pCauchy cauchylocation,scalechi-squared chisqdfnon-central chi-squared nchisqdf,ncpexponential expscale(and notrate)F fn1,n2non-central F nfn1,n2,ncpgamma gammashape,scalegeometric geomphypergeometric hyperNR,NB,nlogistic logislocation,scalelognormal lnormlogmean,logsdnegative binomial nbinomsize,probnormal normmu,sigmaPoisson poislambdaStudent's t tnnon-central t ntdf,deltaStudentized range tukey(*)rr,cc,dfuniform unifa,bWeibull weibullshape,scaleWilcoxon rank sum wilcoxm,nWilcoxon signed rank signrankn
Entries marked with an asterisk only have ‘p’ and ‘q’
functions available, and none of the non-central distributions have
‘r’ functions. After a call to dwilcox, pwilcox or
qwilcox the function wilcox_free() should be called, and
similarly for the signed rank functions.
(If remapping is suppressed, the Normal distribution names are
Rf_dnorm4, Rf_pnorm5 and Rf_qnorm5.)
The Gamma function, the natural logarithm of its absolute value and first four derivatives and the n-th derivative of Psi, the digamma function, which is the derivative of
lgammafn. In other words,digamma(x)is the same as(psigamma(x,0),trigamma(x) == psigamma(x,1), etc.
The (complete) Beta function and its natural logarithm.
The number of combinations of k items chosen from from n and the natural logarithm of its absolute value, generalized to arbitrary real n. k is rounded to the nearest integer (with a warning if needed).
Bessel functions of types I, J, K and Y with index nu. For
bessel_iandbessel_kthere is the option to return exp(-x) I(x; nu) or exp(x) K(x; nu) if expo is 2. (Use expo== 1for unscaled values.)
There are a few other numerical utility functions available as entry points.
R_pow(x,y)andR_pow_di(x,i)compute x^y and x^i, respectively usingR_FINITEchecks and returning the proper result (the same as R) for the cases where x, y or i are 0 or missing or infinite orNaN.
Computes
log(1 +x)(log 1 plus x), accurately even for small x, i.e., |x| << 1.This should be provided by your platform, in which case it is not included in Rmath.h, but is (probably) in math.h which Rmath.h includes.
Computes
log(1 +x) -x (log 1 plus x minus x), accurately even for small x, i.e., |x| << 1.
Computes
log(1 + exp(x))(log 1 plus exp), accurately, notably for large x, e.g., x > 720.
Computes
exp(x) - 1(exp x minus 1), accurately even for small x, i.e., |x| << 1.This should be provided by your platform, in which case it is not included in Rmath.h, but is (probably) in math.h which Rmath.h includes.
Computes
log(gamma(x+ 1))(log(gamma(1 plus x))), accurately even for small x, i.e., 0 < x < 0.5.
Computes
cos(pi * x)(wherepiis 3.14159...), accurately, notably for half integer x.This might be provided by your platform88, in which case it is not included in Rmath.h, but is in math.h which Rmath.h includes.
Computes
sin(pi * x)accurately, notably for (half) integer x.This might be provided by your platform, in which case it is not included in Rmath.h, but is in math.h which Rmath.h includes.
Computes
tan(pi * x)accurately, notably for (half) integer x.This might be provided by your platform, in which case it is not included in Rmath.h, but is in math.h which Rmath.h includes.
Compute the log of a sum or difference from logs of terms, i.e., “x + y” as
log (exp(logx) + exp(logy))and “x - y” aslog (exp(logx) - exp(logy)), without causing unnecessary overflows or throwing away too much accuracy.
Return the larger (
max) or smaller (min) of two integer or double numbers, respectively. Note thatfmax2andfmin2differ from C99'sfmaxandfminwhen one of the arguments is aNaN: these versions returnNaN.
Compute the signum function, where sign(x) is 1, 0, or -1, when x is positive, 0, or negative, respectively, and
NaNifxis aNaN.
Performs “transfer of sign” and is defined as |x| * sign(y).
Returns the value of x rounded to digits decimal digits (after the decimal point).
This is the function used by R's
signif().
Returns the value of x rounded to digits significant decimal digits.
This is the function used by R's
round().
Returns the value of x truncated (to an integer value) towards zero.
Note that this is no longer needed in C code, as C99 provide a
truncfunction. It is needed for portable C++98 code.
R has a set of commonly used mathematical constants encompassing
constants usually found math.h and contains further ones that are
used in statistical computations. All these are defined to (at least)
30 digits accuracy in Rmath.h. The following definitions
use ln(x) for the natural logarithm (log(x) in R).
Name Definition ( ln = log)round(value, 7) M_Ee 2.7182818 M_LOG2Elog2(e) 1.4426950 M_LOG10Elog10(e) 0.4342945 M_LN2ln(2) 0.6931472 M_LN10ln(10) 2.3025851 M_PIpi 3.1415927 M_PI_2pi/2 1.5707963 M_PI_4pi/4 0.7853982 M_1_PI1/pi 0.3183099 M_2_PI2/pi 0.6366198 M_2_SQRTPI2/sqrt(pi) 1.1283792 M_SQRT2sqrt(2) 1.4142136 M_SQRT1_21/sqrt(2) 0.7071068 M_SQRT_3sqrt(3) 1.7320508 M_SQRT_32sqrt(32) 5.6568542 M_LOG10_2log10(2) 0.3010300 M_2PI2*pi 6.2831853 M_SQRT_PIsqrt(pi) 1.7724539 M_1_SQRT_2PI1/sqrt(2*pi) 0.3989423 M_SQRT_2dPIsqrt(2/pi) 0.7978846 M_LN_SQRT_PIln(sqrt(pi)) 0.5723649 M_LN_SQRT_2PIln(sqrt(2*pi)) 0.9189385 M_LN_SQRT_PId2ln(sqrt(pi/2)) 0.2257914
There are a set of constants (PI, DOUBLE_EPS) (and so on)
defined (unless STRICT_R_HEADERS is defined) in the included
header R_ext/Constants.h, mainly for compatibility with S.
Further, the included header R_ext/Boolean.h has enumeration
constants TRUE and FALSE of type Rboolean in
order to provide a way of using “logical” variables in C consistently.
This can conflict with other software: for example it conflicts with the
headers in IJG's jpeg-9 (but not earlier versions).
The C code underlying optim can be accessed directly. The user
needs to supply a function to compute the function to be minimized, of
the type
typedef double optimfn(int n, double *par, void *ex);
where the first argument is the number of parameters in the second argument. The third argument is a pointer passed down from the calling routine, normally used to carry auxiliary information.
Some of the methods also require a gradient function
typedef void optimgr(int n, double *par, double *gr, void *ex);
which passes back the gradient in the gr argument. No function
is provided for finite-differencing, nor for approximating the Hessian
at the result.
The interfaces (defined in header R_ext/Applic.h) are
void nmmin(int n, double *xin, double *x, double *Fmin, optimfn fn,
int *fail, double abstol, double intol, void *ex,
double alpha, double beta, double gamma, int trace,
int *fncount, int maxit);
void vmmin(int n, double *x, double *Fmin,
optimfn fn, optimgr gr, int maxit, int trace,
int *mask, double abstol, double reltol, int nREPORT,
void *ex, int *fncount, int *grcount, int *fail);
void cgmin(int n, double *xin, double *x, double *Fmin,
optimfn fn, optimgr gr, int *fail, double abstol,
double intol, void *ex, int type, int trace,
int *fncount, int *grcount, int maxit);
void lbfgsb(int n, int lmm, double *x, double *lower,
double *upper, int *nbd, double *Fmin, optimfn fn,
optimgr gr, int *fail, void *ex, double factr,
double pgtol, int *fncount, int *grcount,
int maxit, char *msg, int trace, int nREPORT);
void samin(int n, double *x, double *Fmin, optimfn fn, int maxit,
int tmax, double temp, int trace, void *ex);
Many of the arguments are common to the various methods. n is
the number of parameters, x or xin is the starting
parameters on entry and x the final parameters on exit, with
final value returned in Fmin. Most of the other parameters can
be found from the help page for optim: see the source code
src/appl/lbfgsb.c for the values of nbd, which
specifies which bounds are to be used.
The C code underlying integrate can be accessed directly. The
user needs to supply a vectorizing C function to compute the
function to be integrated, of the type
typedef void integr_fn(double *x, int n, void *ex);
where x[] is both input and output and has length n, i.e.,
a C function, say fn, of type integr_fn must basically do
for(i in 1:n) x[i] := f(x[i], ex). The vectorization requirement
can be used to speed up the integrand instead of calling it n
times. Note that in the current implementation built on QUADPACK,
n will be either 15 or 21. The ex argument is a pointer
passed down from the calling routine, normally used to carry auxiliary
information.
There are interfaces (defined in header R_ext/Applic.h) for definite and for indefinite integrals. ‘Indefinite’ means that at least one of the integration boundaries is not finite.
void Rdqags(integr_fn f, void *ex, double *a, double *b,
double *epsabs, double *epsrel,
double *result, double *abserr, int *neval, int *ier,
int *limit, int *lenw, int *last,
int *iwork, double *work);
void Rdqagi(integr_fn f, void *ex, double *bound, int *inf,
double *epsabs, double *epsrel,
double *result, double *abserr, int *neval, int *ier,
int *limit, int *lenw, int *last,
int *iwork, double *work);
Only the 3rd and 4th argument differ for the two integrators; for the
definite integral, using Rdqags, a and b are the
integration interval bounds, whereas for an indefinite integral, using
Rdqagi, bound is the finite bound of the integration (if
the integral is not doubly-infinite) and inf is a code indicating
the kind of integration range,
inf = 1inf = -1inf = 2f and ex define the integrand function, see above;
epsabs and epsrel specify the absolute and relative
accuracy requested, result, abserr and last are the
output components value, abs.err and subdivisions
of the R function integrate, where neval gives the number of
integrand function evaluations, and the error code ier is
translated to R's integrate() $ message, look at that function
definition. limit corresponds to integrate(...,
subdivisions = *). It seems you should always define the two work
arrays and the length of the second one as
lenw = 4 * limit;
iwork = (int *) R_alloc(limit, sizeof(int));
work = (double *) R_alloc(lenw, sizeof(double));
The comments in the source code in src/appl/integrate.c give
more details, particularly about reasons for failure (ier >= 1).
R has a fairly comprehensive set of sort routines which are made available to users' C code. The following is declared in header file Rinternals.h.
This corresponds to R's
order(..., na.last, decreasing). More specifically,indx <- order(x, y, na.last, decreasing)corresponds toR_orderVector(indx, n, Rf_lang2(x, y), nalast, decreasing)and for three vectors,Rf_lang3(x,y,z)is used as arglist.Note that
R_orderVector()assumes the vectorindxto be allocated to length >= n. On return,indx[]contains a permutation of0:(n-1), i.e., 0-based C indices (and not 1-based R indices, as R'sorder()).
All other sort routines are declared in header file R_ext/Utils.h (included by R.h) and include the following.
The first three sort integer, real (double) and complex data respectively. (Complex numbers are sorted by the real part first then the imaginary part.)
NAs are sorted last.
rsort_with_indexsorts on x, and applies the same permutation to index.NAs are sorted last.
Is similar to
rsort_with_indexbut sorts into decreasing order, andNAs are not handled.
These all provide (very) partial sorting: they permute x so that x
[k]is in the correct place with smaller values to the left, larger ones to the right.
These routines sort v
[i:j]or iv[i:j](using 1-indexing, i.e., v[1]is the first element) calling the quicksort algorithm as used by R'ssort(v, method = "quick")and documented on the help page for the R functionsort. The..._I()versions also return thesort.index()vector inI. Note that the ordering is not stable, so tied values may be permuted.Note that
NAs are not handled (explicitly) and you should use different sorting functions ifNAs can be present.
The FORTRAN interface routines for sorting double precision vectors are
qsort3andqsort4, equivalent toR_qsortandR_qsort_I, respectively.
Given the nr by nc matrix
matrixin column-major (“FORTRAN”) order,R_max_col()returns in maxes[i-1]the column number of the maximal element in the i-th row (the same as R'smax.col()function). In the case of ties (multiple maxima),*ties_methis an integer code in1:3determining the method: 1 = “random”, 2 = “first” and 3 = “last”. See R's help page?max.col.
Given the ordered vector xt of length n, return the interval or index of x in xt
[], typically max(i; 1 <= i <= n & xt[i] <= x) where we use 1-indexing as in R and FORTRAN (but not C). If rightmost_closed is true, also returns n-1 if x equals xt[n]. If all_inside is not 0, the result is coerced to lie in1:(n-1)even when x is outside the xt[] range. On return,*mflag equals -1 if x < xt[1], +1 if x >= xt[n], and 0 otherwise.The algorithm is particularly fast when ilo is set to the last result of
findInterval()and x is a value of a sequence which is increasing or decreasing for subsequent calls.There is also an
F77_CALL(interv)()version offindInterval()with the same arguments, but all pointers.
A system-independent interface to produce the name of a temporary file is provided as
Return a pathname for a temporary file with name beginning with prefix and ending with fileext in directory tmpdir. A
NULLprefix or extension is replaced by"". Note that the return value ismalloced and should befreed when no longer needed (unlike the system calltmpnam).
There is also the internal function used to expand file names in several
R functions, and called directly by path.expand.
Expand a path name fn by replacing a leading tilde by the user's home directory (if defined). The precise meaning is platform-specific; it will usually be taken from the environment variable HOME if this is defined.
For historical reasons there are FORTRAN interfaces to functions
D1MACH and I1MACH. These can be called from C code as
e.g. F77_CALL(d1mach)(4). Note that these are emulations of
the original functions by Fox, Hall and Schryer on NetLib at
http://www.netlib.org/slatec/src/ for IEC 60559 arithmetic
(required by R).
R has its own C-level interface to the encoding conversion
capabilities provided by iconv because there are
incompatibilities between the declarations in different implementations
of iconv.
These are declared in header file R_ext/Riconv.h.
Set up a pointer to an encoding object to be used to convert between two encodings:"" indicates the current locale.
inbuf to outbuf. Initially
the int variables indicate the number of bytes available in the
buffers, and they are updated (and the char pointers are updated
to point to the next free byte in the buffer). The return value is the
number of characters converted, or (size_t)-1 (beware:
size_t is usually an unsigned type). It should be safe to assume
that an error condition sets errno to one of E2BIG (the
output buffer is full), EILSEQ (the input cannot be converted,
and might be invalid in the encoding specified) or EINVAL (the
input does not end with a complete multi-byte character).
Free the resources of an encoding object.
No port of R can be interrupted whilst running long computations in compiled code, so programmers should make provision for the code to be interrupted at suitable points by calling from C
#include <R_ext/Utils.h>
void R_CheckUserInterrupt(void);
and from FORTRAN
subroutine rchkusr()
These check if the user has requested an interrupt, and if so branch to R's error handling functions.
Note that it is possible that the code behind one of the entry points defined here if called from your C or FORTRAN code could be interruptible or generate an error and so not return to your code.
The header files define USING_R, which can be used to test if
the code is indeed being used with R.
Header file Rconfig.h (included by R.h) is used to define
platform-specific macros that are mainly for use in other header files.
The macro WORDS_BIGENDIAN is defined on
big-endian89
systems (e.g. most OSes on Sparc and PowerPC hardware) and not on
little-endian systems (such as i686 and x86_64 on all
OSes, and Linux on Alpha and Itanium). It can be useful when
manipulating binary files. The macro SUPPORT_OPENMP is defined
on suitable systems and can be used in conjunction with the
SUPPORT_OPENMP_* macros in packages that want to make use of
OpenMP.
Header file Rversion.h (not included by R.h)
defines a macro R_VERSION giving the version number encoded as an
integer, plus a macro R_Version to do the encoding. This can be
used to test if the version of R is late enough, or to include
back-compatibility features. For protection against very old versions
of R which did not have this macro, use a construction such as
#if defined(R_VERSION) && R_VERSION >= R_Version(1, 9, 0)
...
#endif
More detailed information is available in the macros R_MAJOR,
R_MINOR, R_YEAR, R_MONTH and R_DAY: see the
header file Rversion.h for their format. Note that the minor
version includes the patchlevel (as in ‘9.0’).
The C99 keyword inline should be recognized by all compilers now
used to build R. Portable code which might be used with earlier
versions of R can be written using the macro R_INLINE (defined
in file Rconfig.h included by R.h), as for example from
package cluster
#include <R.h>
static R_INLINE int ind_2(int l, int j)
{
...
}
Be aware that using inlining with functions in more than one compilation
unit is almost impossible to do portably, see
http://www.greenend.org.uk/rjk/2003/03/inline.html, so this usage
is for static functions as in the example. All the R
configure code has checked is that R_INLINE can be used in a
single C file with the compiler used to build R. We recommend that
packages making extensive use of inlining include their own configure
code.
Header R_ext/Visibility has some definitions for controlling the visibility of entry points. These are only effective when ‘HAVE_VISIBILITY_ATTRIBUTE’ is defined – this is checked when R is configured and recorded in header Rconfig.h (included by R_ext/Visibility.h). It is generally defined on modern Unix-alikes with a recent compiler, but not supported on OS X nor Windows. Minimizing the visibility of symbols in a shared library will both speed up its loading (unlikely to be significant) and reduce the possibility of linking to the wrong entry points of the same name.
C/C++ entry points prefixed by attribute_hidden will not be
visible in the shared object. There is no comparable mechanism for
FORTRAN entry points, but there is a more comprehensive scheme used by,
for example package stats. Most compilers which allow control of
visibility will allow control of visibility for all symbols via a flag,
and where known the flag is encapsulated in the macros
‘C_VISIBILITY’ and F77_VISIBILITY for C and FORTRAN
compilers. These are defined in etc/Makeconf and so available
for normal compilation of package code. For example,
src/Makevars could include
PKG_CFLAGS=$(C_VISIBILITY)
PKG_FFLAGS=$(F77_VISIBILITY)
This would end up with no visible entry points, which would be
pointless. However, the effect of the flags can be overridden by using
the attribute_visible prefix. A shared object which registers
its entry points needs only for have one visible entry point, its
initializer, so for example package stats has
void attribute_visible R_init_stats(DllInfo *dll)
{
R_registerRoutines(dll, CEntries, CallEntries, FortEntries, NULL);
R_useDynamicSymbols(dll, FALSE);
...
}
The visibility mechanism is not available on Windows, but there is an equally effective way to control which entry points are visible, by supplying a definitions file pkgnme/src/pkgname-win.def: only entry points listed in that file will be visible. Again using stats as an example, it has
LIBRARY stats.dll
EXPORTS
R_init_stats
It is possible to build Mathlib, the R set of mathematical
functions documented in Rmath.h, as a standalone library
libRmath under both Unix-alikes and Windows. (This includes the
functions documented in Numerical analysis subroutines as from
that header file.)
The library is not built automatically when R is installed, but can be built in the directory src/nmath/standalone in the R sources: see the file README there. To use the code in your own C program include
#define MATHLIB_STANDALONE
#include <Rmath.h>
and link against ‘-lRmath’ (and perhaps ‘-lm’). There is an example file test.c.
A little care is needed to use the random-number routines. You will need to supply the uniform random number generator
double unif_rand(void)
or use the one supplied (and with a dynamic library or DLL you will have to use the one supplied, which is the Marsaglia-multicarry with an entry points
set_seed(unsigned int, unsigned int)
to set its seeds and
get_seed(unsigned int *, unsigned int *)
to read the seeds).
The header files which R installs are in directory R_INCLUDE_DIR (default R_HOME/include). This currently includes
R.h includes many other files S.h different version for code ported from S Rinternals.h definitions for using R's internal structures Rdefines.h macros for an S-like interface to the above (no longer maintained) Rmath.h standalone math library Rversion.h R version information Rinterface.h for add-on front-ends (Unix-alikes only) Rembedded.h for add-on front-ends R_ext/Applic.h optimization and integration R_ext/BLAS.h C definitions for BLAS routines R_ext/Callbacks.h C (and R function) top-level task handlers R_ext/GetX11Image.h X11Image interface used by package trkplot R_ext/Lapack.h C definitions for some LAPACK routines R_ext/Linpack.h C definitions for some LINPACK routines, not all of which are included in R R_ext/Parse.h a small part of R's parse interface: not part of the stable API. R_ext/RStartup.h for add-on front-ends R_ext/Rdynload.h needed to register compiled code in packages R_ext/R-ftp-http.h interface to internal method of download.fileR_ext/Riconv.h interface to iconvR_ext/Visibility.h definitions controlling visibility R_ext/eventloop.h for add-on front-ends and for packages that need to share in the R event loops (on all platforms)
The following headers are included by R.h:
Rconfig.h configuration info that is made available R_ext/Arith.h handling for NAs,NaNs,Inf/-InfR_ext/Boolean.h TRUE/FALSEtypeR_ext/Complex.h C typedefs for R's complexR_ext/Constants.h constants R_ext/Error.h error handling R_ext/Memory.h memory allocation R_ext/Print.h Rprintfand variations.R_ext/RS.h definitions common to R.h and S.h, including F77_CALLetc.R_ext/Random.h random number generation R_ext/Utils.h sorting and other utilities R_ext/libextern.h definitions for exports from R.dll on Windows.
The graphics systems are exposed in headers R_ext/GraphicsEngine.h, R_ext/GraphicsDevice.h (which it includes) and R_ext/QuartzDevice.h. Facilities for defining custom connection implementations are provided in R_ext/Connections.h, but make sure you consult the file before use.
Let us re-iterate the advice to include system headers before the R header files, especially Rinternals.h (included by Rdefines.h) and Rmath.h, which redefine names which may be used in system headers (fewer if ‘R_NO_REMAP’ is defined, or ‘R_NO_REMAP_RMATH’ for Rmath.h, as from R 3.1.0).
R programmers will often want to add methods for existing generic functions, and may want to add new generic functions or make existing functions generic. In this chapter we give guidelines for doing so, with examples of the problems caused by not adhering to them.
This chapter only covers the ‘informal’ class system copied from S3, and not with the S4 (formal) methods of package methods.
The key function for methods is NextMethod, which dispatches the
next method. It is quite typical for a method function to make a few
changes to its arguments, dispatch to the next method, receive the
results and modify them a little. An example is
t.data.frame <- function(x)
{
x <- as.matrix(x)
NextMethod("t")
}
Also consider predict.glm: it happens that in R for historical
reasons it calls predict.lm directly, but in principle (and in S
originally and currently) it could use NextMethod.
(NextMethod seems under-used in the R sources. Do be aware
that there are S/R differences in this area, and the example above works
because there is a next method, the default method, not that a
new method is selected when the class is changed.)
Any method a programmer writes may be invoked from another method
by NextMethod, with the arguments appropriate to the
previous method. Further, the programmer cannot predict which method
NextMethod will pick (it might be one not yet dreamt of), and the
end user calling the generic needs to be able to pass arguments to the
next method. For this to work
A method must have all the arguments of the generic, including
... if the generic does.
It is a grave misunderstanding to think that a method needs only to
accept the arguments it needs. The original S version of
predict.lm did not have a ... argument, although
predict did. It soon became clear that predict.glm needed
an argument dispersion to handle over-dispersion. As
predict.lm had neither a dispersion nor a ...
argument, NextMethod could no longer be used. (The legacy, two
direct calls to predict.lm, lives on in predict.glm in
R, which is based on the workaround for S3 written by Venables &
Ripley.)
Further, the user is entitled to use positional matching when calling
the generic, and the arguments to a method called by UseMethod
are those of the call to the generic. Thus
A method must have arguments in exactly the same order as the generic.
To see the scale of this problem, consider the generic function
scale, defined as
scale <- function (x, center = TRUE, scale = TRUE)
UseMethod("scale")
Suppose an unthinking package writer created methods such as
scale.foo <- function(x, scale = FALSE, ...) { }
Then for x of class "foo" the calls
scale(x, , TRUE)
scale(x, scale = TRUE)
would do most likely do different things, to the justifiable consternation of the end user.
To add a further twist, which default is used when a user calls
scale(x) in our example? What if
scale.bar <- function(x, center, scale = TRUE) NextMethod("scale")
and x has class c("bar", "foo")? It is the default
specified in the method that is used, but the default
specified in the generic may be the one the user sees.
This leads to the recommendation:
If the generic specifies defaults, all methods should use the same defaults.
An easy way to follow these recommendations is to always keep generics simple, e.g.
scale <- function(x, ...) UseMethod("scale")
Only add parameters and defaults to the generic if they make sense in all possible methods implementing it.
When creating a new generic function, bear in mind that its argument
list will be the maximal set of arguments for methods, including those
written elsewhere years later. So choosing a good set of arguments may
well be an important design issue, and there need to be good arguments
not to include a ... argument.
If a ... argument is supplied, some thought should be given
to its position in the argument sequence. Arguments which follow
... must be named in calls to the function, and they must be
named in full (partial matching is suppressed after ...).
Formal arguments before ... can be partially matched, and so
may ‘swallow’ actual arguments intended for .... Although it
is commonplace to make the ... argument the last one, that is
not always the right choice.
Sometimes package writers want to make generic a function in the base package, and request a change in R. This may be justifiable, but making a function generic with the old definition as the default method does have a small performance cost. It is never necessary, as a package can take over a function in the base package and make it generic by something like
foo <- function(object, ...) UseMethod("foo")
foo.default <- function(object, ...) base::foo(object)
Earlier versions of this manual suggested assigning foo.default <-
base::foo. This is not a good idea, as it captures the base
function at the time of installation and it might be changed as R is
patched or updated.
The same idea can be applied for functions in other packages with namespaces.
There are a number of ways to build front-ends to R: we take this to mean a GUI or other application that has the ability to submit commands to R and perhaps to receive results back (not necessarily in a text format). There are other routes besides those described here, for example the package Rserve (from CRAN, see also http://www.rforge.net/Rserve/) and connections to Java in ‘JRI’ (part of the rJava package on CRAN) and the Omegahat/Bioconductor package ‘SJava’.
Note that the APIs described in this chapter are only intended to be
used in an alternative front-end: they are not part of the API made
available for R packages and can be dangerous to use in a
conventional package (although packages may contain alternative
front-ends). Conversely some of the functions from the API (such as
R_alloc) should not be used in front-ends.
R can be built as a shared library90 if configured with --enable-R-shlib. This shared library can be used to run R from alternative front-end programs. We will assume this has been done for the rest of this section. Also, it can be built as a static library if configured with --enable-R-static-lib, and that can be used in a very similar way (at least on Linux: on other platforms one needs to ensure that all the symbols exported by libR.a and linked into the front-end).
The command-line R front-end, R_HOME/bin/exec/R, is one such example, and the former GNOME (see package gnomeGUI on CRAN's ‘Archive’ area) and OS X consoles are others. The source for R_HOME/bin/exec/R is in file src/main/Rmain.c and is very simple
int Rf_initialize_R(int ac, char **av); /* in ../unix/system.c */
void Rf_mainloop(); /* in main.c */
extern int R_running_as_main_program; /* in ../unix/system.c */
int main(int ac, char **av)
{
R_running_as_main_program = 1;
Rf_initialize_R(ac, av);
Rf_mainloop(); /* does not return */
return 0;
}
indeed, misleadingly simple. Remember that R_HOME/bin/exec/R is run from a shell script R_HOME/bin/R which sets up the environment for the executable, and this is used for
The first two of these can be achieved for your front-end by running it via R CMD. So, for example
R CMD /usr/local/lib/R/bin/exec/R
R CMD exec/R
will both work in a standard R installation. (R CMD looks first for executables in R_HOME/bin. These command-lines need modification if a sub-architecture is in use.) If you do not want to run your front-end in this way, you need to ensure that R_HOME is set and LD_LIBRARY_PATH is suitable. (The latter might well be, but modern Unix/Linux systems do not normally include /usr/local/lib (/usr/local/lib64 on some architectures), and R does look there for system components.)
The other senses in which this example is too simple are that all the
internal defaults are used and that control is handed over to the
R main loop. There are a number of small examples91 in the
tests/Embedding directory. These make use of
Rf_initEmbeddedR in src/main/Rembedded.c, and essentially
use
#include <Rembedded.h>
int main(int ac, char **av)
{
/* do some setup */
Rf_initEmbeddedR(argc, argv);
/* do some more setup */
/* submit some code to R, which is done interactively via
run_Rmainloop();
A possible substitute for a pseudo-console is
R_ReplDLLinit();
while(R_ReplDLLdo1() > 0) {
/* add user actions here if desired */
}
*/
Rf_endEmbeddedR(0);
/* final tidying up after R is shutdown */
return 0;
}
If you do not want to pass R arguments, you can fake an argv
array, for example by
char *argv[]= {"REmbeddedPostgres", "--silent"};
Rf_initEmbeddedR(sizeof(argv)/sizeof(argv[0]), argv);
However, to make a GUI we usually do want to run run_Rmainloop
after setting up various parts of R to talk to our GUI, and arranging
for our GUI callbacks to be called during the R mainloop.
One issue to watch is that on some platforms Rf_initEmbeddedR and
Rf_endEmbeddedR change the settings of the FPU (e.g. to allow
errors to be trapped and to make use of extended precision registers).
The standard code sets up a session temporary directory in the usual
way, unless R_TempDir is set to a non-NULL value before
Rf_initEmbeddedR is called. In that case the value is assumed to
contain an existing writable directory (no check is done), and it is not
cleaned up when R is shut down.
Rf_initEmbeddedR sets R to be in interactive mode: you can set
R_Interactive (defined in Rinterface.h) subsequently to
change this.
Note that R expects to be run with the locale category
‘LC_NUMERIC’ set to its default value of C, and so should
not be embedded into an application which changes that.
It is the user's responsibility to attempt to initialize only once. To
protect the R interpreter, Rf_initialize_R will exit the
process if re-initialization is attempted.
Suitable flags to compile and link against the R (shared or static) library can be found by
R CMD config --cppflags
R CMD config --ldflags
(These apply only to an uninstalled copy or a standard install.)
If R is installed, pkg-config is available and neither
sub-architectures nor an OS X framework have been used, alternatives for
a shared R library are
pkg-config --cflags libR
pkg-config --libs libR
and for a static R library
pkg-config --cflags libR
pkg-config --libs --static libR
(This may work for an installed OS framework if pkg-config is
taught where to look for libR.pc: it is installed inside the
framework.)
However, a more comprehensive way is to set up a Makefile to compile the front-end. Suppose file myfe.c is to be compiled to myfe. A suitable Makefile might be
include ${R_HOME}/etc${R_ARCH}/Makeconf
all: myfe
## The following is not needed, but avoids PIC flags.
myfe.o: myfe.c
$(CC) $(ALL_CPPFLAGS) $(CFLAGS) -c myfe.c -o $@
## replace $(LIBR) $(LIBS) by $(STATIC_LIBR) if R was build with a static libR
myfe: myfe.o
$(MAIN_LINK) -o $@ myfe.o $(LIBR) $(LIBS)
invoked as
R CMD make
R CMD myfe
Additional flags which $(MAIN_LINK) includes are, amongst others,
those to select OpenMP and --export-dynamic for the GNU linker
on some platforms. In principle $(LIBS) is not needed
when using a shared R library as libR is linked against
those libraries, but some platforms need the executable also linked
against them.
For Unix-alikes there is a public header file Rinterface.h that
makes it possible to change the standard callbacks used by R in a
documented way. This defines pointers (if R_INTERFACE_PTRS is
defined)
extern void (*ptr_R_Suicide)(const char *);
extern void (*ptr_R_ShowMessage)(const char *);
extern int (*ptr_R_ReadConsole)(const char *, unsigned char *, int, int);
extern void (*ptr_R_WriteConsole)(const char *, int);
extern void (*ptr_R_WriteConsoleEx)(const char *, int, int);
extern void (*ptr_R_ResetConsole)();
extern void (*ptr_R_FlushConsole)();
extern void (*ptr_R_ClearerrConsole)();
extern void (*ptr_R_Busy)(int);
extern void (*ptr_R_CleanUp)(SA_TYPE, int, int);
extern int (*ptr_R_ShowFiles)(int, const char **, const char **,
const char *, Rboolean, const char *);
extern int (*ptr_R_ChooseFile)(int, char *, int);
extern int (*ptr_R_EditFile)(const char *);
extern void (*ptr_R_loadhistory)(SEXP, SEXP, SEXP, SEXP);
extern void (*ptr_R_savehistory)(SEXP, SEXP, SEXP, SEXP);
extern void (*ptr_R_addhistory)(SEXP, SEXP, SEXP, SEXP);
// added in R 3.0.0
extern int (*ptr_R_EditFiles)(int, const char **, const char **, const char *);
extern SEXP (*ptr_do_selectlist)(SEXP, SEXP, SEXP, SEXP);
extern SEXP (*ptr_do_dataentry)(SEXP, SEXP, SEXP, SEXP);
extern SEXP (*ptr_do_dataviewer)(SEXP, SEXP, SEXP, SEXP);
extern void (*ptr_R_ProcessEvents)();
which allow standard R callbacks to be redirected to your GUI. What these do is generally documented in the file src/unix/system.txt.
This should display the message, which may have multiple lines: it should be brought to the user's attention immediately.
This function invokes actions (such as change of cursor) when R embarks on an extended computation (which
=1) and when such a state terminates (which=0).
These functions interact with a console.
R_ReadConsoleprints the given prompt at the console and then does afgets(3)–like operation, transferring up to buflen characters into the buffer buf. The last two bytes should be set to ‘"\n\0"’ to preserve sanity. If hist is non-zero, then the line should be added to any command history which is being maintained. The return value is 0 is no input is available and >0 otherwise.
R_WriteConsoleExwrites the given buffer to the console, otype specifies the output type (regular output or warning/error). Call toR_WriteConsole(buf, buflen)is equivalent toR_WriteConsoleEx(buf, buflen, 0). To ensure backward compatibility of the callbacks,ptr_R_WriteConsoleExis used only ifptr_R_WriteConsoleis set toNULL. To ensure thatstdout()andstderr()connections point to the console, set the corresponding files toNULLviaR_Outputfile = NULL; R_Consolefile = NULL;
R_ResetConsoleis called when the system is reset after an error.R_FlushConsoleis called to flush any pending output to the system console.R_ClearerrConsoleclears any errors associated with reading from the console.
This function is used to display the contents of files.
Choose a file and return its name in buf of length len. Return value is 0 for success, > 0 otherwise.
Send nfile files to an editor, with titles possibly to be used for the editor window(s).
.Internalfunctions forloadhistory,savehistoryandtimestamp.If the console has no history mechanism these can be as simple as
SEXP R_loadhistory (SEXP call, SEXP op, SEXP args, SEXP env) { errorcall(call, "loadhistory is not implemented"); return R_NilValue; } SEXP R_savehistory (SEXP call, SEXP op , SEXP args, SEXP env) { errorcall(call, "savehistory is not implemented"); return R_NilValue; } SEXP R_addhistory (SEXP call, SEXP op , SEXP args, SEXP env) { return R_NilValue; }The
R_addhistoryfunction should return silently if no history mechanism is present, as a user may be callingtimestamppurely to write the time stamp to the console.
This should abort R as rapidly as possible, displaying the message. A possible implementation is
void R_Suicide (const char *message) { char pp[1024]; snprintf(pp, 1024, "Fatal error: %s\n", s); R_ShowMessage(pp); R_CleanUp(SA_SUICIDE, 2, 0); }
This function invokes any actions which occur at system termination. It needs to be quite complex:
#include <Rinterface.h> #include <Rembedded.h> /* for Rf_KillAllDevices */ void R_CleanUp (SA_TYPE saveact, int status, int RunLast) { if(saveact == SA_DEFAULT) saveact = SaveAction; if(saveact == SA_SAVEASK) { /* ask what to do and set saveact */ } switch (saveact) { case SA_SAVE: if(runLast) R_dot_Last(); if(R_DirtyImage) R_SaveGlobalEnv(); /* save the console history in R_HistoryFile */ break; case SA_NOSAVE: if(runLast) R_dot_Last(); break; case SA_SUICIDE: default: break; } R_RunExitFinalizers(); /* clean up after the editor e.g. CleanEd() */ R_CleanTempDir(); /* close all the graphics devices */ if(saveact != SA_SUICIDE) Rf_KillAllDevices(); fpu_setup(FALSE); exit(status); }
These callbacks should never be changed in a running R session (and hence cannot be called from an extension package).
.Externalfunctions fordataentry(andediton matrices and data frames),Viewandselect.list. These can be changed if they are not currently in use.
An application embedding R needs a different way of registering
symbols because it is not a dynamic library loaded by R as would be
the case with a package. Therefore R reserves a special
DllInfo entry for the embedding application such that it can
register symbols to be used with .C, .Call etc. This
entry can be obtained by calling getEmbeddingDllInfo, so a
typical use is
DllInfo *info = R_getEmbeddingDllInfo();
R_registerRoutines(info, cMethods, callMethods, NULL, NULL);
The native routines defined by cMethods and callMethods
should be present in the embedding application. See Registering native routines for details on registering symbols in general.
One of the most difficult issues in interfacing R to a front-end is the handling of event loops, at least if a single thread is used. R uses events and timers for
locator()).
Sys.sleep().
Specifically, the Unix-alike command-line version of R runs separate event loops for
download.file() and for
direct socket access, in files
src/modules/internet/nanoftp.c,
src/modules/internet/nanohttp.c and
src/modules/internet/Rsock.c
There is a protocol for adding event handlers to the first two types of
event loops, using types and functions declared in the header
R_ext/eventloop.h and described in comments in file
src/unix/sys-std.c. It is possible to add (or remove) an input
handler for events on a particular file descriptor, or to set a polling
interval (via R_wait_usec) and a function to be called
periodically via R_PolledEvents: the polling mechanism is used by
the tcltk package.
It is not intended that these facilities are used by packages, but if they are needed exceptionally, the package should ensure that it cleans up and removes its handlers when its namespace is unloaded.
An alternative front-end needs both to make provision for other R events whilst waiting for input, and to ensure that it is not frozen out during events of the second type. This is not handled very well in the existing examples. The GNOME front-end ran a private handler for polled events by setting
extern int (*R_timeout_handler)();
extern long R_timeout_val;
if (R_timeout_handler && R_timeout_val)
gtk_timeout_add(R_timeout_val, R_timeout_handler, NULL);
gtk_main ();
whilst it is waiting for console input. This obviously handles events
for Gtk windows (such as the graphics device in the gtkDevice
package), but not X11 events (such as the X11() device) or for
other event handlers that might have been registered with R. It does
not attempt to keep itself alive whilst R is waiting on sockets. The
ability to add a polled handler as R_timeout_handler is used by
the tcltk package.
Embedded R is designed to be run in the main thread, and all the
testing is done in that context. There is a potential issue with the
stack-checking mechanism where threads are involved. This uses two
variables declared in Rinterface.h (if CSTACK_DEFNS is
defined) as
extern uintptr_t R_CStackLimit; /* C stack limit */
extern uintptr_t R_CStackStart; /* Initial stack address */
Note that uintptr_t is a C99 type for which a substitute is
defined in R, so your code needs to define HAVE_UINTPTR_T
appropriately.
These will be set92 when Rf_initialize_R is called, to values appropriate to the
main thread. Stack-checking can be disabled by setting
R_CStackLimit = (uintptr_t)-1, but it is better to if possible
set appropriate values. (What these are and how to determine them are
OS-specific, and the stack size limit may differ for secondary threads.
If you have a choice of stack size, at least 10Mb is recommended.)
You may also want to consider how signals are handled: R sets signal
handlers for several signals, including SIGINT, SIGSEGV,
SIGPIPE, SIGUSR1 and SIGUSR2, but these can all be
suppressed by setting the variable R_SignalHandlers (declared in
Rinterface.h) to 0.
Note that these variables must not be changed by an R package: a package should not calling R internals which makes use of the stack-checking mechanism on a secondary thread.
All Windows interfaces to R call entry points in the DLL R.dll, directly or indirectly. Simpler applications may find it easier to use the indirect route via (D)COM.
(D)COM is a standard Windows mechanism used for communication between Windows applications. One application (here R) is run as COM server which offers services to clients, here the front-end calling application. The services are described in a ‘Type Library’ and are (more or less) language-independent, so the calling application can be written in C or C++ or Visual Basic or Perl or Python and so on. The ‘D’ in (D)COM refers to ‘distributed’, as the client and server can be running on different machines.
The basic R distribution is not a (D)COM server, but two addons are currently available that interface directly with R and provide a (D)COM server:
StatConnector written by Thomas
Baier available via http://sunsite.univie.ac.at/rcom/,
which works with R packages to support transfer of data to and from
R and remote execution of R commands, as well as embedding of an
R graphics window.
Recent versions have usage restrictions.
RDCOMServer, is available from
http://www.omegahat.org/. Its philosophy is discussed in
http://www.omegahat.org/RDCOMServer/Docs/Paradigm.html and is
very different from the purpose of this section.
The R DLL is mainly written in C and has _cdecl entry
points. Calling it directly will be tricky except from C code (or C++
with a little care).
There is a version of the Unix-alike interface calling
int Rf_initEmbeddedR(int ac, char **av);
void Rf_endEmbeddedR(int fatal);
which is an entry point in R.dll. Examples of its use (and a suitable Makefile.win) can be found in the tests/Embedding directory of the sources. You may need to ensure that R_HOME/bin is in your PATH so the R DLLs are found.
Examples of calling R.dll directly are provided in the directory src/gnuwin32/front-ends, including a simple command-line front end rtest.c whose code is
#define Win32
#include <windows.h>
#include <stdio.h>
#include <Rversion.h>
#define LibExtern __declspec(dllimport) extern
#include <Rembedded.h>
#include <R_ext/RStartup.h>
/* for askok and askyesnocancel */
#include <graphapp.h>
/* for signal-handling code */
#include <psignal.h>
/* simple input, simple output */
/* This version blocks all events: a real one needs to call ProcessEvents
frequently. See rterm.c and ../system.c for one approach using
a separate thread for input.
*/
int myReadConsole(const char *prompt, char *buf, int len, int addtohistory)
{
fputs(prompt, stdout);
fflush(stdout);
if(fgets(buf, len, stdin)) return 1; else return 0;
}
void myWriteConsole(const char *buf, int len)
{
printf("%s", buf);
}
void myCallBack(void)
{
/* called during i/o, eval, graphics in ProcessEvents */
}
void myBusy(int which)
{
/* set a busy cursor ... if which = 1, unset if which = 0 */
}
static void my_onintr(int sig) { UserBreak = 1; }
int main (int argc, char **argv)
{
structRstart rp;
Rstart Rp = &rp;
char Rversion[25], *RHome;
sprintf(Rversion, "%s.%s", R_MAJOR, R_MINOR);
if(strcmp(getDLLVersion(), Rversion) != 0) {
fprintf(stderr, "Error: R.DLL version does not match\n");
exit(1);
}
R_setStartTime();
R_DefParams(Rp);
if((RHome = get_R_HOME()) == NULL) {
fprintf(stderr, "R_HOME must be set in the environment or Registry\n");
exit(1);
}
Rp->rhome = RHome;
Rp->home = getRUser();
Rp->CharacterMode = LinkDLL;
Rp->ReadConsole = myReadConsole;
Rp->WriteConsole = myWriteConsole;
Rp->CallBack = myCallBack;
Rp->ShowMessage = askok;
Rp->YesNoCancel = askyesnocancel;
Rp->Busy = myBusy;
Rp->R_Quiet = TRUE; /* Default is FALSE */
Rp->R_Interactive = FALSE; /* Default is TRUE */
Rp->RestoreAction = SA_RESTORE;
Rp->SaveAction = SA_NOSAVE;
R_SetParams(Rp);
R_set_command_line_arguments(argc, argv);
FlushConsoleInputBuffer(GetStdHandle(STD_INPUT_HANDLE));
signal(SIGBREAK, my_onintr);
GA_initapp(0, 0);
readconsolecfg();
setup_Rmainloop();
#ifdef SIMPLE_CASE
run_Rmainloop();
#else
R_ReplDLLinit();
while(R_ReplDLLdo1() > 0) {
/* add user actions here if desired */
}
/* only get here on EOF (not q()) */
#endif
Rf_endEmbeddedR(0);
return 0;
}
The ideas are
HKEY_LOCAL_MACHINE\Software\R-core\R\InstallPath from an
administrative install and
HKEY_CURRENT_USER\Software\R-core\R\InstallPath otherwise, if
selected during installation (as it is by default).
Rstart structure.
R_DefParams sets the defaults, and R_SetParams sets
updated values.
R_set_command_line_arguments for use by the R function
commandArgs().
An underlying theme is the need to keep the GUI ‘alive’, and this has
not been done in this example. The R callback R_ProcessEvents
needs to be called frequently to ensure that Windows events in R
windows are handled expeditiously. Conversely, R needs to allow the
GUI code (which is running in the same process) to update itself as
needed – two ways are provided to allow this:
R_ProcessEvents calls the callback registered by
Rp->callback. A version of this is used to run package Tcl/Tk
for tcltk under Windows, for the code is
void R_ProcessEvents(void)
{
while (peekevent()) doevent(); /* Windows events for GraphApp */
if (UserBreak) { UserBreak = FALSE; onintr(); }
R_CallBackHook();
if(R_tcldo) R_tcldo();
}
#ifdef SIMPLE_CASE.
It may be that no R GraphApp windows need to be considered, although
these include pagers, the windows() graphics device, the R
data and script editors and various popups such as choose.file()
and select.list(). It would be possible to replace all of these,
but it seems easier to allow GraphApp to handle most of them.
It is possible to run R in a GUI in a single thread (as RGui.exe shows) but it will normally be easier93 to use multiple threads.
Note that R's own front ends use a stack size of 10Mb, whereas MinGW executables default to 2Mb, and Visual C++ ones to 1Mb. The latter stack sizes are too small for a number of R applications, so general-purpose front-ends should use a larger stack size.
Both applications which embed R and those which use a system
call to invoke R (as Rscript.exe, Rterm.exe or
R.exe) need to be able to find the R bin directory.
The simplest way to do so is the ask the user to set an environment
variable R_HOME and use that, but naive users may be flummoxed as
to how to do so or what value to use.
The R for Windows installers have for a long time allowed the value
of R_HOME to be recorded in the Windows Registry: this is
optional but selected by default. Where it is recorded has
changed over the years to allow for multiple versions of R to be
installed at once, and to allow 32- and 64-bit versions of R to be
installed on the same machine.
The basic Registry location is Software\R-core\R. For an
administrative install this is under HKEY_LOCAL_MACHINE and on a
64-bit OS HKEY_LOCAL_MACHINE\Software\R-core\R is by default
redirected for a 32-bit application, so a 32-bit application will see
the information for the last 32-bit install, and a 64-bit application
that for the last 64-bit install. For a personal install, the
information is under HKEY_CURRENT_USER\Software\R-core\R which is
seen by both 32-bit and 64-bit applications and so records the last
install of either architecture. To circumvent this, there are locations
Software\R-core\R32 and Software\R-core\R64 which always
refer to one architecture.
When R is installed and recording is not disabled then two string
values are written at that location for keys InstallPath and
Current Version, and these keys are removed when R is
uninstalled. To allow information about other installed versions to be
retained, there is also a key named something like 3.0.0 or
3.0.0 patched or 3.1.0 Pre-release with a value for
InstallPath.
So a comprehensive algorithm to search for R_HOME is something
like
HKEY_CURRENT_USER\Software often gets reverted to
an earlier version. Do the following for one or both of
HKEY_CURRENT_USER and HKEY_LOCAL_MACHINE.
Software\R-core\R32
or Software\R-core\R64, and if that does not exist or the
architecture is immaterial, in Software\R-core\R.
InstallPath exists then this is R_HOME (recorded
using backslashes). If it does not, look for version-specific keys like
2.11.0 alpha, pick the latest (which is of itself a complicated
algorithm as 2.11.0 patched > 2.11.0 > 2.11.0 alpha > 2.8.1) and
use its value for InstallPath.
Prior to R 2.12.0 R.dll and the various front-end executables were in R_HOME\bin, but they are now in R_HOME\bin\i386 or R_HOME\bin\x64. So you may need to arrange to look first in the architecture-specific subdirectory and then in R_HOME\bin.
.C: Interface functions .C and .Fortran.Call: Calling .Call.Call: Handling R objects in C.External: Calling .External.External: Handling R objects in C.Fortran: Interface functions .C and .Fortran.Last.lib: Load hooks.onAttach: Load hooks.onDetach: Load hooks.onLoad: Load hooks.onUnload: Load hooks.Random.seed: Random numbers\acronym: Marking text\alias: Documenting functions\arguments: Documenting functions\author: Documenting functions\bold: Marking text\cite: Marking text\code: Marking text\command: Marking text\concept: Indices\cr: Sectioning\deqn: Mathematics\describe: Lists and tables\description: Documenting functions\details: Documenting functions\dfn: Marking text\dontrun: Documenting functions\dontshow: Documenting functions\dots: Insertions\dQuote: Marking text\email: Marking text\emph: Marking text\enc: Insertions\enumerate: Lists and tables\env: Marking text\eqn: Mathematics\examples: Documenting functions\figure: Figures\file: Marking text\format: Documenting data sets\href: Marking text\if: Conditional text\ifelse: Conditional text\itemize: Lists and tables\kbd: Marking text\keyword: Documenting functions\ldots: Insertions\link: Cross-references\method: Documenting functions\name: Documenting functions\newcommand: User-defined macros\note: Documenting functions\option: Marking text\out: Conditional text\pkg: Marking text\preformatted: Marking text\R: Insertions\RdOpts: Dynamic pages\references: Documenting functions\renewcommand: User-defined macros\S3method: Documenting functions\samp: Marking text\section: Sectioning\seealso: Documenting functions\Sexpr: Dynamic pages\source: Documenting data sets\sQuote: Marking text\strong: Marking text\tabular: Lists and tables\title: Documenting functions\url: Marking text\usage: Documenting functions\value: Documenting functions\var: Marking text\verb: Marking textallocVector: Allocating storageAUTHORS: Package subdirectoriesbessel_i: Mathematical functionsbessel_j: Mathematical functionsbessel_k: Mathematical functionsbessel_y: Mathematical functionsbeta: Mathematical functionsBLAS_LIBS: Using Makevarsbrowser: BrowsingCalloc: User-controlled memoryCAR: Calling .ExternalCDR: Calling .Externalcgmin: Optimizationchoose: Mathematical functionsCITATION: Preparing translationsCITATION: Package subdirectoriesCOPYRIGHTS: Package subdirectoriesCOPYRIGHTS: The DESCRIPTION filecospi: Numerical UtilitiescPsort: Utility functionsdebug: Debugging R codedebugger: Debugging R codedefineVar: Finding and setting variablesdigamma: Mathematical functionsdump.frames: Debugging R codeduplicate: Named objects and copyingdyn.load: dyn.load and dyn.unloaddyn.unload: dyn.load and dyn.unloadexp_rand: Random numbersexpm1: Numerical Utilitiesexport: Specifying imports and exportsexportClasses: Namespaces with S4 classes and methodsexportClassPattern: Namespaces with S4 classes and methodsexportMethods: Namespaces with S4 classes and methodsexportPattern: Namespaces with S4 classes and methodsexportPattern: Specifying imports and exportsFALSE: Mathematical constantsfindInterval: Utility functionsfindVar: Finding and setting variablesFLIBS: Using Makevarsfmax2: Numerical Utilitiesfmin2: Numerical Utilitiesfprec: Numerical UtilitiesFree: User-controlled memoryfround: Numerical Utilitiesfsign: Numerical Utilitiesftrunc: Numerical Utilitiesgammafn: Mathematical functionsgctorture: Using gctorturegetAttrib: AttributesgetCharCE: Character encoding issuesGetRNGstate: Random numbersimax2: Numerical Utilitiesimin2: Numerical Utilitiesimport: Specifying imports and exportsimportClassesFrom: Namespaces with S4 classes and methodsimportFrom: Specifying imports and exportsimportMethodsFrom: Namespaces with S4 classes and methodsinstall: AttributesiPsort: Utility functionsISNA: Missing and IEEE valuesISNA: Missing and special valuesISNAN: Missing and IEEE valuesISNAN: Missing and special valuesLAPACK_LIBS: Using Makevarslbeta: Mathematical functionslbfgsb: Optimizationlchoose: Mathematical functionslgamma1p: Numerical Utilitieslgammafn: Mathematical functionslibrary.dynam: dyn.load and dyn.unloadlibrary.dynam: Package subdirectorieslog1p: Numerical Utilitieslog1pexp: Numerical Utilitieslog1pmx: Numerical Utilitieslogspace_add: Numerical Utilitieslogspace_sub: Numerical UtilitiesM_E: Mathematical constantsM_PI: Mathematical constantsmkChar: Handling character datamkCharCE: Character encoding issuesmkCharLen: Handling character datamkCharLenCE: Character encoding issuesNA_REAL: Missing and IEEE valuesNEWS.Rd: Package subdirectoriesnmmin: Optimizationnorm_rand: Random numbersOBJECTS: Creating shared objectsOBJECTS: Using Makevarspentagamma: Mathematical functionsPKG_CFLAGS: Creating shared objectsPKG_CPPFLAGS: Creating shared objectsPKG_CXXFLAGS: Creating shared objectsPKG_FCFLAGS: Creating shared objectsPKG_FFLAGS: Creating shared objectsPKG_LIBS: Creating shared objectsPKG_OBJCFLAGS: Creating shared objectsPKG_OBJCXXFLAGS: Creating shared objectsprompt: Rd formatPROTECT: Garbage CollectionPROTECT_WITH_INDEX: Garbage Collectionpsigamma: Mathematical functionsPutRNGstate: Random numbersqsort3: Utility functionsqsort4: Utility functionsR CMD build: Building package tarballsR CMD check: Checking packagesR CMD config: Configure and cleanupR CMD Rd2pdf: Processing documentation filesR CMD Rdconv: Processing documentation filesR CMD SHLIB: Creating shared objectsR CMD Stangle: Processing documentation filesR CMD Sweave: Processing documentation filesR_addhistory: Setting R callbacksR_alloc: Transient storage allocationR_Busy: Setting R callbacksR_ChooseFile: Setting R callbacksR_CleanUp: Setting R callbacksR_ClearErrConsole: Setting R callbacksR_csort: Utility functionsR_dataentry: Setting R callbacksR_dataviewer: Setting R callbacksR_EditFile: Setting R callbacksR_EditFiles: Setting R callbacksR_ExpandFileName: Utility functionsR_FINITE: Missing and IEEE valuesR_FlushConsole: Setting R callbacksR_GetCCallable: Linking to native routines in other packagesR_GetCurrentSrcref: Accessing source referencesR_GetSrcFilename: Accessing source referencesR_INLINE: Inlining C functionsR_IsNaN: Missing and IEEE valuesR_isort: Utility functionsR_LIBRARY_DIR: Configure and cleanupR_loadhistory: Setting R callbacksR_max_col: Utility functionsR_NegInf: Missing and IEEE valuesR_orderVector: Utility functionsR_PACKAGE_DIR: Configure and cleanupR_PACKAGE_NAME: Configure and cleanupR_ParseVector: Parsing R code from CR_PosInf: Missing and IEEE valuesR_pow: Numerical UtilitiesR_pow_di: Numerical UtilitiesR_PreserveObject: Garbage CollectionR_qsort: Utility functionsR_qsort_I: Utility functionsR_qsort_int: Utility functionsR_qsort_int_I: Utility functionsR_ReadConsole: Setting R callbacksR_RegisterCCallable: Linking to native routines in other packagesR_registerRoutines: Registering native routinesR_ReleaseObject: Garbage CollectionR_ResetConsole: Setting R callbacksR_rsort: Utility functionsR_savehistory: Setting R callbacksR_selectlist: Setting R callbacksR_ShowFiles: Setting R callbacksR_ShowMessage: Setting R callbacksR_Srcref: Accessing source referencesR_Suicide: Setting R callbacksR_tmpnam: Utility functionsR_tmpnam2: Utility functionsR_Version: Platform and version informationR_WriteConsole: Setting R callbacksR_WriteConsoleEx: Setting R callbacksRdqagi: IntegrationRdqags: IntegrationRealloc: User-controlled memoryrecover: Debugging R codereEnc: Character encoding issuesREprintf: PrintingREPROTECT: Garbage CollectionREvprintf: Printingrevsort: Utility functionsRiconv: Re-encodingRiconv_close: Re-encodingRiconv_open: Re-encodingRprintf: PrintingRprof: Memory statistics from RprofRprof: Profiling R code for speedRprofmem: Tracking memory allocationsrPsort: Utility functionsrsort_with_index: Utility functionsRvprintf: PrintingS3method: Registering S3 methodsS_alloc: Transient storage allocationS_realloc: Transient storage allocationSAFE_FFLAGS: Using Makevarssamin: Optimizationseed_in: Random numbersseed_out: Random numberssetAttrib: AttributessetVar: Finding and setting variablessign: Numerical Utilitiessinpi: Numerical UtilitiessummaryRprof: Memory statistics from Rprofsystem: Operating system accesssystem.time: Operating system accesssystem2: Operating system accesstanpi: Numerical Utilitiestetragamma: Mathematical functionstrace: Debugging R codetraceback: Debugging R codetracemem: Tracing copies of an objecttranslateChar: Character encoding issuestranslateCharUTF8: Character encoding issuestrigamma: Mathematical functionsTRUE: Mathematical constantsundebug: Debugging R codeunif_rand: Random numbersUNPROTECT: Garbage CollectionUNPROTECT_PTR: Garbage Collectionuntracemem: Tracing copies of an objectuseDynLib: useDynLibvmaxget: Transient storage allocationvmaxset: Transient storage allocationvmmin: Optimization[1] although this is a persistent mis-usage. It seems to stem from S, whose analogues of R's packages were officially known as library sections and later as chapters, but almost always referred to as libraries.
[2] This seems to be commonly used for a file in ‘markdown’ format. Be aware that most users of R will not know that, nor know how to view such a file: platforms such as OS X and Windows do not have a default viewer set in their file associations. The CRAN package web pages render such files in HTML.
[3] currently, top-level files .Rbuildignore and .Rinstignore, and vignettes/.install_extras.
[4] false positives are possible, but only a handful have been seen so far.
[5] at least if this is done in a locale which matches the package encoding.
[6] But it is checked for Open Source packages by R CMD check --as-cran.
[7] This includes all packages
directly called by library and require calls, as well as
data obtained via data(theirdata, package = "somepkg")
calls: R CMD check will warn about all of these. But there
are subtler uses which it will not detect: e.g. if package A uses
package B and makes use of functionality in package B which uses package
C which package B suggests or enhances, then package C needs to be in
the ‘Suggests’ list for package A. Nor will undeclared uses in
included files be reported, nor unconditional uses of packages listed
under ‘Enhances’.
[8] Extensions .S and .s arise from code originally written for S(-PLUS), but are commonly used for assembler code. Extension .q was used for S, which at one time was tentatively called QPE.
[9] This is true for OSes which implement the ‘C’ locale: Windows' idea of the ‘C’ locale uses the WinAnsi charset.
[10] More precisely, they can contain the English alphanumeric characters and the symbols ‘$ - _ . + ! ' ( ) , ; = &’.
[11] Note that Ratfor is not supported. If you have Ratfor source code, you need to convert it to FORTRAN. Only FORTRAN 77 (which we write in upper case) is supported on all platforms, but most also support Fortran-95 (for which we use title case). If you want to ship Ratfor source files, please do so in a subdirectory of src and not in the main subdirectory.
[12] either or both of which may not be supported on particular platforms
[13] Using .hpp is not guaranteed to be portable.
[14] There is also ‘__APPLE_CC__’, but that indicates a compiler with Apple-specific features, not the OS. It is used in Rinlinedfuns.h.
[15] the POSIX terminology, called ‘make variables’ by GNU make.
[16] on all platforms from R 3.1.0
[17] The best way to generate such a file is to copy the .Rout from a successful run of R CMD check. If you want to generate it separately, do run R with options --vanilla --slave and with environment variable LANGUAGE=en set to get messages in English.
[18] e.g http://tools.ietf.org/html/rfc4180.
[19] in POSIX parlance: GNU make calls these ‘make variables’.
[20] at least on Unix-alikes: the Windows build currently resolves such dependencies to a static FORTRAN library when Rblas.dll is built.
[21] http://www.openmp.org/, http://en.wikipedia.org/wiki/OpenMP, https://computing.llnl.gov/tutorials/openMP/
[22] Which it was at the time of writing with GCC, Solaris Studio and Intel compilers.
[23] some Windows toolchains have the typo ‘_REENTRANCE’ instead.
[24] Cygwin used g77 up to 2011, and some pre-built versions of R for Unix OSes still do.
[25] For details of these and related macros, see file config.site in the R sources.
[26] OS X 10.7 and later have clang++ but for 10.7 and 10.8 it uses the g++ 4.2.x headers.
[27] On systems which use sub-architectures, architecture-specific versions such as ~/.R/check.Renviron.i386 take precedence.
[28] A suitable file.exe is part of the Windows toolset: it checks for gfile if a suitable file is not found: the latter is available in the OpenCSW collection for Solaris at http://www.opencsw.org. The source repository is ftp://ftp.astron.com/pub/file/.
[29] An exception is made for subdirectories with names starting ‘win’ or ‘Win’.
[30] on most other platforms such runtime libraries are dynamic, but static libraries are currently used on Windows because the toolchain is not a standard part of the OS.
[31] or if option --use-valgrind is used or environment variable _R_CHECK_ALWAYS_LOG_VIGNETTE_OUTPUT_ is set to a true value or if there are differences from a target output file
[32] Windows users behind proxies may want to set environment variable R_WIN_INTERNET2 to a non-empty value, e.g. in ~/.R/check_environ.
[33] For example, in early 2014 gdata declared ‘Imports: gtools’ and gtools declared ‘Imports: gdata’.
[34] loading, examples, tests, vignettes
[35] on all platforms from R 3.1.0.
[36] called CVS or .svn or .arch-ids or .bzr or .git (but not files called .git) or .hg.
[37] called .metadata.
[38] and to avoid problems with case-insensitive file systems, lower-case versions of all these extensions.
[39] unless inhibited by using ‘BuildVignettes: no’ in the DESCRIPTION file.
[40] provided the conditions of the package's license are met: many, including CRAN, see the omission of source components as incompatible with an Open Source license.
[41] R_HOME/bin is prepended to the
PATH so that references to R or Rscript in the
Makefile do make use of the currently running version of R.
[42] they will be called with two unnamed arguments, in that order.
[43] NB: this will only be read in all versions of R if the package contains R code in a R directory.
[44] Note that this is the basename of the shared object, and the appropriate extension (.so or .dll) will be added.
[45] This was necessary at least prior to R 3.0.2 as the methods package looked for its own R code on the search path.
[46] This defaults to the same
pattern as exportPattern: use something like
exportClassPattern("^$") to override this.
[47] if it does, there will be opaque warnings about replacing imports if the classes/methods are also imported.
[48] People use dev.new() to open a
device at a particular size: that is not portable but using
dev.new(noRStudioGD = TRUE) helps.
[49] GNU make, BSD make as in FreeBSD, AT&T make as implemented on Solaris.
[50] but
note that long long is not a standard C++ type, and C++ compilers
set up for strict checking will reject it.
[51] Not doing so is the default on Windows, overridden for the R executables. It is also the default on some Solaris compilers.
[52] Select ‘Save as’, and select ‘Reduce file size’ from the ‘Quartz filter’ menu': this can be accessed in other ways, for example by Automator.
[53] except perhaps some special characters such as backslash and hash which may be taken over for currency symbols.
[54] Typically on a Unix-alike this is done by telling fontconfig where to find suitable fonts to select glyphs from.
[55] e.g. \alias, \keyword and
\note sections.
[56] There can be exceptions: for example Rd files are not allowed to start with a dot, and have to be uniquely named on a case-insensitive file system.
[57] in the current locale, and with special treatment for LaTeX special characters and with any ‘pkgname-package’ topic moved to the top of the list.
[58] Text between or after list items is discouraged.
[59] Currently it is rendered differently only in HTML conversions, and LaTeX conversion outside ‘\usage’ and ‘\examples’ environments.
[60] a common
example in CRAN packages is \link[mgcv]{gam}.
[61] There is only a fine
distinction between \dots and \ldots. It is technically
incorrect to use \ldots in code blocks and tools::checkRd
will warn about this—on the other hand the current converters treat
them the same way in code blocks, and elsewhere apart from the small
distinction between the two in LaTeX.
[62] See the examples section in the file Paren.Rd for an example.
[63] R 2.9.0 added support for UTF-8 Cyrillic characters in LaTeX, but on some OSes this will need Cyrillic support added to LaTeX, so environment variable _R_CYRILLIC_TEX_ may need to be set to a non-empty value to enable this.
[64] R has to be built to enable this, but the option --enable-R-profiling is the default.
[65] For Unix-alikes these are intervals of CPU time, and for Windows of elapsed time.
[66] With the exceptions of the commands
listed below: an object of such a name can be printed via an
explicit call to print.
[67] at the time of writing mainly for 10.7 with some support for 10.8, none for the current 10.9.
[68] small fixed-size arrays by default in gfortran, for example.
[69] not including the versions distributed by Apple in
Xcode 4.6 or 5, nor of clang in Fedora prior to an
update in Fedora 19. On some platforms, e.g. Fedora, the runtime
library, libasan, needs to be installed separately. OS X users can
install a suitable clang from the sources or possibly
distributions such as MacPorts or Homebrew.
[70] -faddress-sanitizer in clang 3.1
[71] installed on some Linux systems as asan_symbolize, and obtainable from https://llvm.org/svn/llvm-project/compiler-rt/trunk/lib/asan/scripts/asan_symbolize.py: it makes use of llvm-symbolizer if available.
[72] but works better if inlining and frame pointer optimizations are disabled.
[73] depending on the compiler
version, with gfortran 4.8.x doing this less than
4.7.x.
[74] possibly after some platform-specific translation, e.g. adding leading or trailing underscores.
[75] Note that this is then not checked for over-runs by
option CBoundsCheck = TRUE.
[76] but this is not currently done.
[77] whether or not ‘LinkingTo’ is used.
[78] so there needs to be a corresponding import or
importFrom entry in the NAMESPACE file.
[79] dyld on OS X, and DYLD_LIBRARY_PATHS below.
[80] That is, similar to those defined in S version 4 from the 1990s: these are not kept up to date and are not recommended for new projects.
[81] see The R API: note that these are not all part of the API.
[82] SEXP is an acronym for Simple EXPression, common in LISP-like language syntaxes.
[83] If no coercion was required, coerceVector would
have passed the old object through unchanged.
[84] You can assign a copy of the object in the
environment frame rho using defineVar(symbol,
duplicate(value), rho)).
[85] see Character encoding issues for why this might not be what is required.
[86] This is only guaranteed to show the current interface: it is liable to change.
[87] Known problems are redefining
LENGTH, error, length, vector and
warning
[88] It is an optional C11 extension.
[89] http://en.wikipedia.org/wiki/Endianness.
[90] In the parlance of OS X this is a dynamic library, and is the normal way to build R on that platform.
[91] but these are not part of the automated test procedures and so little tested.
[92] at least on platforms where the values are
available, that is having getrlimit and on Linux or having
sysctl supporting KERN_USRSTACK, including FreeBSD and OS
X.
[93] An attempt to use only threads in the late 1990s failed to work correctly under Windows 95, the predominant version of Windows at that time.