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.
The current version of this document is 2.6.1 (2007-11-26).
ISBN 3-900051-11-9
The contributions of Saikat DebRoy (who wrote the first draft of a guide
to using .Call and .External) and of Adrian Trapletti (who
provided information on the C++ interface) are gratefully acknowledged.
Packages provide a mechanism for loading optional code and attached documentation as needed. The R distribution provides several 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 INSTALL utility. Otherwise, please look at R's
help pages
?library
?INSTALL
before reading on.
A computing environment including a number of tools is assumed; the “R Installation and Administration” manual describes what is needed. Under a Unix-alike most of the tools are likely to be present by default, but Microsoft Windows and MacOS X will require careful setup.
Once a source package is created, it must be installed by
the command R CMD INSTALL.
See Add-on-packages, for further details.
Other types of extensions are supported: See Package types.
A package consists of a subdirectory containing a file DESCRIPTION and the subdirectories R, data, demo, exec, inst, man, po, src, and tests (some of which can be missing). The package subdirectory may also contain files INDEX, NAMESPACE, configure, cleanup, LICENSE, LICENCE, and COPYING. Other files such as README, NEWS or ChangeLog will be ignored by R, but may be useful to end-users.
The DESCRIPTION and INDEX files are described in the sections below. The NAMESPACE file is described in Package name spaces.
The optional files configure and cleanup are (Bourne shell) script files which are executed before and (provided that option --clean was given) after installation on Unix-alikes, see Configure and cleanup.
The optional file LICENSE/LICENCE or COPYING (where the former names are preferred) contains a copy of the license to the package, e.g. a copy of the GNU public license. 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).
The package subdirectory should be given the same name as the package. Because some file systems (e.g., those on Windows) 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 system platforms, 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 names of `.Rd' files
will be used in URLs and so must be ASCII and not contain
%.
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 Author: Joe Developer <Joe.Developer@some.domain.net>, with contributions from A. User <A.User@whereever.net>. 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
Continuation lines (for example, for descriptions longer than one line) start with a space or tab. The `Package', `Version', `License', `Description', `Title', `Author', and `Maintainer' fields are mandatory, the remaining fields (`Date', `Depends', `URL', ...) are optional.
The DESCRIPTION file should be written entirely in ASCII for maximal portability.
The `Package' and `Version' fields give the name and the version of the package, respectively. The name should consist of letters, numbers, and the dot character and start with a letter. The version 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'. (Translation packages are allowed names of the form `Translation-ll'.)
The `License' field should specify the license of the package in the following 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 Artistic-1.0 Artistic-2.0
as made available via http://www.r-project.org/Licenses/ and contained in subdirectory share/licenses of the R source or home directory.
License: GPL-2
License: GPL (>= 2) | BSD
License: LGPL (>= 2.0, < 3) | Mozilla Public License
License: GPL-2 | file LICENCE
Please note in particular that “Public domain” is not a valid license. It is very important that you include this information! Otherwise, it may not even be legally correct for others to distribute copies of the package.
The `Description' field should give a comprehensive description of what the package does. One can use several (complete) sentences, but only one paragraph.
The `Title' field should give a short description of the package. Some package listings may truncate the title to 65 characters in order to keep the overall size of the listing limited. It should be capitalized, not use any markup, not have any continuation lines, and not end in a period. Older versions of R used a separate file TITLE for giving this information; this is now defunct, and the `Title' field in DESCRIPTION is required.
The `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).
The `Maintainer' field should give a single name with a valid email address in angle brackets (for sending bug reports etc.). It should not end in a period or comma.
The optional `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 standard.
The optional `Depends' field gives a comma-separated list of
package names which this package depends on. The package name may be
optionally followed by a comparison operator (currently only `>='
and `<=' are supported), whitespace and a valid version number in
parentheses. (List package names even if they are part of a bundle.)
You can also use the special package name `R' if your package
depends on a certain version of R. E.g., if the package works only with
R version 2.4.0 or newer, include `R (>= 2.4.0)' in the
`Depends' field. 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. Other dependencies (external to the R system)
should be listed in the `SystemRequirements' field or a separate
README file. 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 dependencies) before the current package, both when
library is called and when saving an image of the package's code
or preparing for lazy-loading.
The optional `Imports' field lists packages whose name spaces are imported from but which do not need to be attached. Name spaces 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, 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).
The optional `Suggests' field uses the same syntax as `Depends' and lists packages that are not necessarily needed. This includes packages used only in examples 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 for routine use of foo, unless one wants to execute the examples: it is nice to have bar, but not necessary.
Finally, the optional `Enhances' field lists packages “enhanced” by the package at hand, e.g., by providing methods for classes from these packages.
The general rules are
library(pkgname) must be listed in the `Imports'
field.
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'.
In particular, large packages providing “only” data for examples or vignettes should be listed in `Suggests' rather than `Depends' in order to make lean installations possible.
The optional `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 on CRAN.
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.
An optional `Collate' field (or OS-specific variants `Collate.OStype', such as e.g. `Collate.windows') can be used for controlling the collation order for the R code files in a package when these are concatenated into a single file upon installation from source. The default is to try collating 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 applicable OS-specific collation field (`Collate.unix' or `Collate.windows') will be used instead of `Collate'.
The optional `LazyLoad' and `LazyData' fields control whether the R objects and the datasets (respectively) use lazy-loading: set the field's value to `yes' or `true' for lazy-loading and `no' or `false' for no lazy-loading. (Capitalized values are also accepted.)
If the package you are writing uses the methods package, specify `LazyLoad: yes'.
The optional `ZipData' field controls whether the automatic Windows build will zip up the data directory or no: set this to `no' if your package will not work with a zipped data directory.
If the DESCRIPTION file is not entirely in ASCII it
should contain an `Encoding' field specifying an encoding. This
is currently used as the encoding of the DESCRIPTION file
itself and of the R and NAMESPACE files, and is taken as
the default encoding of .Rd files as from R 2.6.0. 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.)
The optional `Type' field specifies the type of the package: see Package types.
Note: There should be no `Built' or `Packaged' fields, as these are added by the package management tools.
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 Rdindex() from package tools) when installing from
source and when using the package builder (see Checking and building packages).
Rather than editing this file, it is preferable to put customized information about the package into an overview man 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 extensions .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.
The R code files should only create R objects and not call
functions with side effects such as require and options.
Two exceptions are allowed: if the R subdirectory contains a file
sysdata.rda (a saved image of R objects) this will be
lazy-loaded into the name space/package 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 normally1 fail when the package is installed. Any byte will be
allowed2 in a quoted character string (but \uxxxx
escapes should not be used), but 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. For packages without a name space, these are .First.lib and
.Last.lib. (See Load hooks, for packages with a name space.)
It is conventional to define these functions in a file called
zzz.R. If .First.lib is defined in a package, it is
called with arguments libname and pkgname after the
package is loaded and attached. (If a package is installed with version
information, the package name includes the version information, e.g.
`ash_1.0.9'.) A common use is to call library.dynam
inside .First.lib to load compiled code: another use is to call
those functions with side effects. If .Last.lib exists in a
package it is called (with argument the full path to the installed
package) just before the package is detached. It is uncommon to detach
packages and rare to have a .Last.lib function: one use is to
call library.dynam.unload to unload compiled code.
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 means3 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 for an example. Note that packages which use internal objects extensively should hide those objects in a name space, when they do not need to be documented (see Package name spaces).
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 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 variables and rules for this (determined when R is
configured and recorded in R_HOME/etc/Makeconf), providing
support for C, C++, FORTRAN 77, Fortran 9x4, Objective
C and Objective C++ with associated extensions .c, .cc or
.cpp or .C, .f, .f90 or .f95,
.m, and .mm or .M, respectively. We recommend
using .h for headers, also for C++5 or Fortran
9x include files. The default rules can be tweaked by setting macros 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. It should have an appropriate first target
(conventionally called `all') and a (possibly empty) target
`clean' which removes all files generated by 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.
The data subdirectory is for additional data files the package
makes available for loading using data(). Currently, data files
can have one of three types as indicated by their extension: plain R
code (.R or .r), tables (.tab, .txt, or
.csv), or save() images (.RData or .rda).
(All ports of R use the same binary (XDR) format and can read
compressed images. Use images saved with save(, compress =
TRUE), the default, to save space.) 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. It is no longer necessary to provide a 00Index
file in the data directory—the corresponding information is
generated automatically from the documentation sources when installing
from source, or when using the package builder (see Checking and building packages). If your data files are enormous 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'.
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. 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.)
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, latex, R-ex, chtml, and Meta).
The copying of the inst happens after src is built so its
Makefile can create files to be installed. Note that with the
exceptions of INDEX, LICENSE/LICENCE and
COPYING, information files at the top level of the package will
not be installed and so not be known to users of Windows and
MacOS 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. One thing you might like to add to inst is a
CITATION file for use by the citation function.
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 corresponding .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).
Subdirectory exec could contain additional executables 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, and still experimental.
Subdirectory po is used for files related to localization: see Internationalization.
Sometimes it is convenient to distribute several packages as a bundle. (An example is VR which contains four packages.) The installation procedures on both Unix-alikes and Windows can handle package bundles.
The DESCRIPTION file of a bundle has a `Bundle' field and no `Package' field, as in
Bundle: VR Priority: recommended Contains: MASS class nnet spatial Version: 7.2-36 Date: 2007-08-29 Depends: R (>= 2.4.0), grDevices, graphics, stats, utils Suggests: lattice, nlme, survival Author: S original by Venables & Ripley. R port by Brian Ripley <ripley@stats.ox.ac.uk>, following earlier work by Kurt Hornik and Albrecht Gebhardt. Maintainer: Brian Ripley <ripley@stats.ox.ac.uk> BundleDescription: Functions and datasets to support Venables and Ripley, 'Modern Applied Statistics with S' (4th edition). License: GPL-2 | GPL-3 URL: http://www.stats.ox.ac.uk/pub/MASS4/
The `Contains' field lists the packages (space separated), which should be contained in separate subdirectories with the names given. During building and installation, packages will be installed in the order specified. Be sure to order this list so that dependencies are met appropriately.
The packages contained in a bundle are standard packages in all respects except that the DESCRIPTION file is replaced by a DESCRIPTION.in file which just contains fields additional to the DESCRIPTION file of the bundle, for example
Package: spatial Description: Functions for kriging and point pattern analysis. Title: Functions for Kriging and Point Pattern Analysis
Any files in the package bundle except the DESCRIPTION file and the named packages will be ignored.
The `Depends' field in the bundle's DESCRIPTION file should list the dependencies of all the constituent packages (and similarly for `Imports' and `Suggests'), and then DESCRIPTION.in files should not contain these fields.
Note that most of this section is Unix-specific: see the comments later on about the Windows port of R.
If your package needs some system-dependent configuration before
installation you can include a (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.).
The (Bourne shell) script cleanup is executed as last thing by
R CMD INSTALL if present and option --clean was given,
and by R CMD build when preparing the package for building from
its source. It can be used to clean up the package source tree. In
particular, it should remove all files created by configure.
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 with 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 better).
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 Unix, 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 `right' version of R when running the script as
part of R CMD INSTALL.)
Note that earlier versions of this document recommended obtaining the
configure information by direct extraction (using grep and sed) from
R_HOME/etc/Makeconf, which only works for variables
recorded there as literals. You can use R CMD config for getting
the value of the basic configuration variables, or the header and
library flags necessary for linking against R, see R CMD config
--help for details. (This works on Windows as from R 2.6.0.)
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. Also, recent versions
of R can detect external BLAS and LAPACK libraries.)
You should bear in mind that the configure script may well not work on Windows systems (this seems normally to be the case for those generated by Autoconf, although simple shell scripts do work). If your package is to be made publicly available, please give enough information for a user on a non-Unix 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 as well. Both should be shell scripts to be executed by ash, which is a minimal version of Bourne-style sh.)
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 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 shell variable R_PACKAGE_NAME.
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.
The most common use of a Makevars file is to set additional
preprocessor (for example include paths) flags via PKG_CPPFLAGS,
and additional compiler flags by setting PKG_CFLAGS,
PKG_CXXFLAGS and PKG_FFLAGS, for C, C++, or FORTRAN
respectively (see Creating shared objects).
Also, Makevars can be used to set flags for the linker, for example `-L' and `-l' options.
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 are all specific to GCC.
There are some macros which are built whilst configuring the building of R itself, are stored on Unix-alikes in R_HOME/etc/Makeconf and can be used in Makevars. These include
FLIBSPKG_LIBS.
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 or complex
routines. If BLAS_LIBS is included, then FLIBS also needs
to be6, as most BLAS libraries are written in FORTRAN.
LAPACK_LIBSPKG_LIBS. This may point to a dynamic library libRlapack
which contains all the double-precision LAPACK routines as well as those
double-complex LAPACK and BLAS 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.
[There is no guarantee that the LAPACK library will provide more than all the double-precision and double-complex routines, and some do not provide all the auxiliary routines.]
The macros BLAS_LIBS and FLIBS should always be included
after LAPACK_LIBS.
SAFE_FFLAGSPKG_FFLAGS, but a replacment 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.
Note that Makevars should not normally contain targets, as it is (except on Windows) included before the default makefile and make is called without an explicit target. To circumvent that, use a suitable phony target before any actual targets: for example fastICA has
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
to ensure that the LAPACK routines find some constants without infinite looping. The Windows equivalent is
slamc.o: slamc.f
$(F77) $(SAFE_FFLAGS) -c -o slamc.o slamc.f
More generally, on a Unix-alike one could have something like
.PHONY: all
all: before $(SHLIB) after
before:
Things that need to be done first like creating libraries
after:
Cleanup needed after 'before'
On Windows, one can add dependencies to the `all' target (which is what will get called), e.g. (based on package rcom)
all: ../inst/tst/bin/rcom_test.exe extraclean
../inst/tst/bin/rcom_test.exe: rcom_test.exe
$(MKDIR) -p ../inst/tst/bin
$(CP) $? $ rcom_test.exe: rcom_test.o
rcom_test-LIBS = -L. -lsupc++ -luuid -lole32 -loleaut32
extraclean:
$(RM) rcom_test.exe
The added dependencies will be built after the DLL: it is also possible (but not advisable) to have a target `all' with commands (rather than dependencies)
There are two another targets, `before' and `after', which by default have neither dependencies nor commands so can be overridden in a Makevars.win.
It may be helpful to give an extended example of using a configure script to create a src/Makevars file: this is based on that in the RODBC package.
The configure.ac file follows: configure is created from this by running autoconf in the top-level package directory (containing configure.ac).
AC_INIT([RODBC], 1.1.8) dnl package name, version
dnl A user-specifiable option
odbc_mgr=""
AC_ARG_WITH([odbc-manager],
AC_HELP_STRING([--with-odbc-manager=MGR],
[specify the ODBC manager, e.g. odbc or iodbc]),
[odbc_mgr=$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 currently does not distinguish between FORTRAN 77 and Fortran 90/95
code, and assumes all FORTRAN comes in source files with extension
.f. Commercial Unix systems typically use a F95 compiler, but
only since the release of gcc 4.0.0 in April 2005 have Linux and
other non-commercial OSes had much support for F95. The compiler used
for R on Windows is a F77 compiler.
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 code,
nor that a build of R will have support for such packages.
MinGW huilds of gcc 4.2.0 or later include a F95
compiler. For those using gcc 3.4.z, there is a MinGW
build of gfortran available from
http://gcc.gnu.org/wiki/GFortranBinaries and a MinGW
build7 of g95 from http://www.g95.org.
Set F95 in MkRules to point to the installed compiler.
Then R CMD SHLIB and R CMD INSTALL will work for
packages containing Fortran 90/95 source code.
Before using these tools, please check that your package can be
installed and loaded. R CMD check will inter alia do
this, but you will get more informative error messages doing the checks
directly.
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 gzipped package tar
archives8 with extension
.tar.gz or .tgz. This runs a series of checks, including
library. Another check is that all packages mentioned in
library or requires or from which the NAMESPACE
file imports or are called via :: or ::: are listed
(in `Depends', `Imports', `Suggests' or `Contains'):
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.
library.dynam (with
no extension). In addition, it is checked whether 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 name space 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,
\description and \keyword) fields. The Rd name and title
are checked for being non-empty, and the keywords found are compared to
the standard ones. There is a check for missing cross-references
(links).
\usage
sections of Rd files are documented in the corresponding
\arguments section.
\examples to create executable example code.)
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.
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 flags.
Using R CMD build, the R package builder, one can build R
packages from their sources (for example, for subsequent release).
Prior to actually building the package in the common 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 relvant make files 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 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 regexps, one per line, to be
matched against the file names relative to the top-level source
directory. In addition, directories called CVS or .svn or
.arch-ids and files GNUMakefile 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-vignettes option, it will attempt to rebuild the vignettes (see Writing package vignettes) in the package. To do so it installs the current package/bundle into a temporary library tree, but any dependent packages need to be installed in an available library tree (see the Note: below).
One of the checks that R CMD build runs is for empty source
directories. These are in most cases unintentional, in which case they
should be removed and the build re-run.
It can be useful to run R CMD check --check-subdirs=yes on the
built tarball as a final check on the contents.
R CMD build can also build pre-compiled version of packages for
binary distributions, but R CMD INSTALL --build is preferred (and
is considerably more flexible). In particular, Windows users are
recommended to use R CMD INSTALL --build and install into the
main library tree (the default) so that HTML links are resolved.
Note:R CMD checkandR CMD buildrun R with --vanilla, so none of the user's startup files are read. If you need R_LIBS set (to find packages in a non-standard library) you will need to set it in the environment.
Note to Windows users:R CMD checkandR CMD buildwork well under Windows NT4/2000/XP/2003 but may not work correctly on Windows 95/98/ME because of problems with some versions of Perl on those limited OSes. Experiences vary. To use them you will need to have installed the files for building source packages (which is the default).
In addition to the available command line options, R CMD check
also allows customization by setting (Perl) configuration variables in a
configuration file, the location of which can be specified via the
--rcfile option and defaults to $HOME/.R/check.conf
provided that the environment variable HOME is set.
The following configuration variables are currently available.
$R_check_use_install_log$R_check_all_non_ISO_C$R_check_weave_vignettes$R_check_latex_vignettes$R_check_weave_vignettes is also true), LaTeX
package vignettes in the process of checking them: this will show up
Sweave source errors, including missing source files.
Default: true.
$R_check_subdirs_nocase$R_check_subdirs_strict$R_check_force_suggests$R_check_use_codetools$R_check_Rd_style\method markup.
Default: true.
$R_check_Rd_xrefsValues `1' or a string with lower-cased version `"yes"' or `"true"' can be used for setting the variables to true; similarly, `0' or strings with lower-cased version `"no"' or `"false"' give false.
For example, a configuration file containing
$R_check_use_install_log = "TRUE";
$R_check_weave_vignettes = 0;
results in using install logs and turning off weaving.
Future versions of R may enhance this customization mechanism, and
provide a similar scheme for R CMD build.
There are other internal settings that can be changed via environment variables _R_CHECK_*_: see the Perl source code.
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 to provide them in PDF format, such that users on all platforms can easily read them. To ensure that they can be accessed from a browser, the file names should start with an ASCII letter and be comprised entirely of ASCII letters or digits or minus or underscore.
A special case are documents in Sweave format, which we call
package vignettes. Sweave allows the integration of LaTeX
documents and R code and is contained in package utils which is
part of the base R distribution, see the Sweave help page for
details on the document format. Package vignettes found in directory
inst/doc are tested by R CMD check by executing all R
code chunks they contain to ensure consistency between code and
documentation. Code chunks with option eval=FALSE are not
tested. The R working directory for all vignette tests in R CMD
check is the installed version of the doc
subdirectory. Make sure all files needed by the vignette (data sets,
...) are accessible by either placing them in the inst/doc
hierarchy of the source package, or using calls to system.file().
R CMD build will automatically create PDF versions of the
vignettes for distribution with the package sources. By including the
PDF version in the package sources it is not necessary that the
vignettes can be compiled at install time, i.e., the package author can
use private LaTeX extensions which are only available on his machine.
9
By default R CMD build will run Sweave on all files in
Sweave format. If no Makefile is found in directory
inst/doc, then texi2dvi --pdf is run on all vignettes.
Whenever a Makefile is found, then R CMD build will try to
run make after the Sweave step, such that PDF manuals
can be created from arbitrary source formats (plain LaTeX files,
...). The Makefile should take care of both creation of PDF
files and cleaning up afterwards, i.e., delete all files that shall not
appear in the final package archive. Note that the make step is
executed independently from the presence of any files in Sweave format.
It is no longer necessary to provide a 00Index.dcf file in the
inst/doc directory—the corresponding information is generated
automatically from the \VignetteIndexEntry statements in all
Sweave files when installing from source, or when using the package
builder (see Checking and building packages). The
\VignetteIndexEntry statement is best placed in LaTeX comment,
such that no definition of the command is necessary.
At install time an HTML index for all vignettes is automatically
created from the \VignetteIndexEntry statements unless a file
index.html exists in directory inst/doc. This index is
linked into the HTML help system for each package.
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.
Before submitting a package mypkg, do run the following steps to test it is complete and will install properly. (Unix procedures only, run from the directory containing mypkg as a subdirectory.)
R CMD check to check that the package will install and will
runs its examples, and that the documentation is complete and can be
processed. If the package contains code that needs to be compiled, try
to enable a reasonable amount of diagnostic messaging (“warnings”)
when compiling, such as e.g. -Wall -pedantic for tools from
GCC, the Gnu Compiler Collection. (If R was not configured
accordingly, one can achieve this e.g. via PKG_CFLAGS and
related variables.)
R CMD build to make the release .tar.gz file.
Please ensure that you can run through the complete procedure with only
warnings that you understand and have reasons not to eliminate. In
principle, packages must pass R CMD check without warnings to be
admitted to the main CRAN package area.
When all the testing is done, upload the .tar.gz file, using `anonymous' as log-in name and your e-mail address as password, to
ftp://cran.R-project.org/incoming/
(note: use ftp and not sftp to connect to this server) and
send a message to cran@r-project.org
about it. The CRAN maintainers will run these tests before
putting a submission in the main archive.
Note that the fully qualified name of the .tar.gz file must be of the form
package_version[_engine[_type]],
where the `[ ]' indicates that the enclosed component is optional, package and version are the corresponding entries in file DESCRIPTION, engine gives the S engine the package is targeted for and defaults to `R', and type indicated whether the file contains source or binaries for a certain platform, and defaults to `source'. I.e.,
OOP_0.1-3.tar.gz
OOP_0.1-3_R.tar.gz
OOP_0.1-3_R_source.tar.gz
are all equivalent and indicate an R source package, whereas
OOP_0.1-3_Splus6_sparc-sun-solaris.tar.gz
is a binary package for installation under Splus6 on the given platform.
This naming scheme has been adopted to ensure usability of code across S
engines. R code and utilities operating on package .tar.gz files
can only be assumed to work provided that this naming scheme is
respected. Of course, R CMD build automatically creates valid
file names.
Note that CRAN generally does not accept submissions of precompiled binaries due to security reasons.
R has a name space management system for 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 current mechanism for specifying a name space for a package is to
place a NAMESPACE file in the top level package directory. This
file contains name space directives describing the imports and
exports of the name space. 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.
Like other packages, packages with name spaces are loaded and attached
to the search path by calling library. 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.
Name spaces 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 name space mechanism. Sealing also allows code analysis and compilation tools to accurately identify the definition corresponding to a global variable reference in a function body.
Note that adding a name space to a package changes the search strategy. The package name space comes first in the search, then the imports, then the base name space and then the normal search path.
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.
A package with a name space implicitly imports the base name space.
Variables exported from other packages with name spaces 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.
It is possible to export variables from a name space that it has imported from other namespaces.
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 less efficient than a
formal import and also loses the advantage of recording all dependencies
in the NAMESPACE file, so this approach is usually not
recommended. Evaluating foo:::f will cause package foo to
be loaded, but not attached, if it was not loaded already—this can be
an advantage is delaying the loading of a rarely used package.
Using foo:::f allows access to unexported objects: to confine
references to exported objects use foo::f.
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. 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. This mechanism is intended for use with
generics that are defined in a name space. Any methods for a generic
defined in a package that does not use a name space should be exported,
and the package defining and exporting the methods should be attached to
the search path if the methods are to be found.
(Note that function and class names may be quoted, and reserved words
and non-standard names such as [<- and function must
be.)
There are a number of hooks that apply to packages with name spaces.
See help(".onLoad") for more details.
Packages with name spaces do not use the .First.lib function.
Since loading and attaching are distinct operations when a name space is
used, separate hooks are provided for each. These hook functions are
called .onLoad and .onAttach. They take the same
arguments as .First.lib; they should be defined in the name space
but not exported.
However, packages with name spaces do use the .Last.lib
function. There is also a hook .onUnload which is called when
the name space is unloaded (via a call to unloadNamespace) with
argument the full path to the directory in which the package was
installed. .onUnload should be defined in the name space 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.
There can be one or more useDynLib directives which allow shared
objects that need to be loaded to be specified in the NAMESPACE
file. The directive
useDynLib(foo)
registers the shared object foo 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.
User-level hooks are also available: see the help on function
setHook.
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 name space 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 it the routine is invoked. Secondly, this removes any ambiguity in resolving symbols that might be present in several compiled libraries. In particular, it allows for correctly resolving routines when different versions of the same package are loaded concurrently in the same R session.
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 name space 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 name space 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 name space 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", &foo, 4, {REALSXP, INTSXP, STRSXP, LGLSXP}},
{"bar_sym", &bar, 0},
{NULL, NULL, 0}
};
R_CallMethodDef callMethods[] = {
{"R_call_sym", &R_call, 4},
{"R_version_sym", &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 name space.
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 name
space.
More information about this symbol lookup, along with some approaches for customizing it, is available from http://www.omegahat.org/examples/RDotCall.
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.
To summarize, converting an existing package to use a name space involves several simple steps:
export directives.
S3method declarations.
require calls by
import directives (and make corresponding changes in the
.First.lib functions with .onLoad functions or
useDynLib directives.
Some code analysis tools to aid in this process are currently under development.
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 file and any classes and methods which are to be
exported need to be declared in the NAMESPACE file. For example,
the stats package has
export(mle)
importFrom(graphics, plot)
importFrom(stats, AIC, coef, confint, logLik, optim, profile,
qchisq, update, vcov)
exportClasses(mle, profile.mle, summary.mle)
exportMethods(AIC, BIC, coef, confint, logLik, plot, profile,
summary, show, update, vcov)
All formal classes need to be listed in an exportClasses
directive. Generics for which formal methods are defined need to be
declared in an exportMethods directive, and where the generics
are formed by taking over existing functions, those functions need to be
imported (explicitly unless they are defined in the base name
space).
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. Where a generic has been created in the package
solely to add S4 methods to it, it can be declared via either or
both of exports or exportMethods, but the latter seems
clearer (and is used in the stats4 example above).
Further, a package using classes and methods defined in another package needs to import them, 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.
If your package imports the whole of a name space, it will automatically import the classes from that namespace. It will also import methods, but it is best to do so explicitly, especially where there are methods being imported from more than one namespace.
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.
gcc can be used
with options -Wall -pedantic to alert you to potential
problems. Do not be tempted to assume that these are pure pedantry: for
example R is still used on platforms where the C compiler does not
accept C++/C99 comments (starting //).
If you use FORTRAN, ftnchek
(http://www.dsm.fordham.edu/~ftnchek/) provides thorough testing
of conformance to the standard.
long in C will be 32-bit
on most R platforms (including those mostly used by the
CRAN maintainers), but 64-bit on many 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) and typedef your own type to be
long or long long, or use another suitable type (such as
size_t). Note that integer in FORTRAN
corresponds to int in C on all R platforms.
extern in all but one of the files. (This is no
longer needed in recent versions of R, but is if your package is
not restricted to such versions.)
nm -pg mypkg.so # or other extension such as .sl or .dylib
and checking if any of the symbols marked U is unexpected is a
good way to avoid this.
nm -pg), and to use unusual names, as
well as ensuring you have used the PACKAGE argument that R
CMD check checks for.
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. What was not possible before R 2.5.0 was to mark the encoding used by character strings in R: if you want your package to work with earlier versions of R please consult the advice in the R 2.4.x version of this manual.
First, consider carefully if you really need non-ASCII text. Most 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. Other characters may not be rendered at all, rendered incorrectly, or cause your R code to give an error. For documentation, marking the encoding and including ASCII transliterations is likely to do a reasonable job.
The most favourable circumstance is using UTF-8-encoded text in a
package that will only ever be used in a UTF-8 locale (and hence not on
Windows, for example). In that case it is likely that text will be
rendered correctly in the terminal/console used to run R, and files
written will be readable by other UTF-8-aware applications. However,
plotting will be problematic. On-screen plotting using the `X11()'
device will use a font that only covers a small proportion of UTF-8, and
different fonts will likely need to be selected for Polish, Russian and
Japanese (see help("X11")). 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.
Another fairly common scenario is where a package will only be used in
one language, e.g. French. It is not very safe to assume that all
such users would have their computers set to a French locale, but let us
assume so. The problem then is that there are several possible
encodings for French locales, the most common ones being `CP1252'
(Windows), `ISO 8859-1' (latin-1), `ISO 8859-15' (latin-9 which
includes the Euro), and `UTF-8'. For characters in the French
language the first three agree, but they do not agree with `UTF-8'.
Further, you (or different users) can run R in different locales in
different sessions, say `fr_CA.utf8' one day and
`fr_CH.iso88591' the next. As from R 2.5.0, declaring the
encoding as either `latin1' or `UTF-8' in the
DESCRIPTION file will enable this to work. If you have character
data in .rda files (for use by data or LazyData) these
need to have been prepared and saved in R 2.5.0 in an
appropriate locale (or marked via Encoding). For example (from
package FactoMineR version 1.02):
> library(FactoMineR)
> data(wine)
> Encoding(names(wine)) <- "latin1"
> Encoding(levels(wine$Terroir)) <- "latin1"
> save(wine, file="wine.rda")
was used to update a .rda file.
If you want to run R CMD check on a Unix-alike over a package that sets the encoding you may need to specify a suitable locale via an environment variable. The default is equivalent setting R_ENCODING_LOCALES to
"latin1=en_US:latin2=pl_PL:UTF-8=en_US.utf8:latin9=fr_FR.iso885915@euro"
which is appropriate for a system based on glibc, except if the
current locale is UTF-8 and `iconv' is available, when the
package code is translated to UTF-8 for syntax checking.
Now that diagnostic messages can be made available for translation, 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.
sQuote or dQuote except where the argument is a
variable.
Conventionally single quotation marks are used for quotations such as
'ord' must be a positive integer, at most the number of knots
and double quotation marks when referring to an R character string 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).
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"),
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 the GNU
archive mirrors and packaged with the poEdit package
(http://poedit.sourceforge.net/download.php#win32).
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"));
xgettext --keyword=_ -o pkg.pot *.c
The file src/pkg.pot is the template file, and
conventionally this is shipped as po/pkg.pot. A translator
to another language makes a copy of this file and edits it (see the
gettext manual) to produce say ll.po, where ll
is the code for the language in which the translation is to be used.
(This file would be shipped in the po directory.) Next run
msgfmt on ll.po to produce ll.mo, and
copy that to inst/po/ll/LC_MESSAGES/pkg.mo. Now when
the package is loaded after installation it will look for translations
of its messages in the po/lang/LC_MESSAGES/pkg.mo file
for any language lang that matches the user's preferences (via the
setting of the LANGUAGE environment variable or from the locale
settings).
Mechanisms to support the automatic translation of R stop,
warning and message messages are in place, provided the
package has a name space. 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.
Translation of messages which might be singular or plural can be very
intricate: languages can have up to four different forms. The R
function ngettext provides an interface to the C function of the
same name, and will choose an appropriate singular or plural form for
the selected language depending on the value of its first argument
n.
Packages without name spaces will need to use domain="R-pkg"
explicitly in calls to stop, warning, message,
gettext/gettextf and ngettext.
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 two other types are recognized, both
of which need write permission in the R installation tree.
This is a rather general mechanism, designed for adding new front-ends
such as the gnomeGUI package. 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.
Conventionally, a translation package for language ll is called
Translation-ll and has Type: Translation. It needs
to contain the directories share/locale/ll and
library/pkgname/po/ll, or at least those for
which translations are available. The files .mo are installed in
the parallel places in the R installation tree.
For example, a package Translation-it might be prepared from an installed (and tested) version of R by
mkdir Translation-it
cd Translation-it
(cd $R_HOME; tar cf - share/locale/it library/*/po/it) | tar xf -
# the next step is not needed on Windows
msgfmt -c -o share/locale/it/LC_MESSAGES/RGui.mo $R_SRC_HOME/po/RGui-it.gmo
# create a DESCRIPTION file
cd ..
R CMD build Translation-it
It is probably appropriate to give the package a version number based on the version of R which has been translated. So the DESCRIPTION file might look like
Package: Translation-it
Type: Translation
Version: 2.2.1-1
Title: Italian Translations for R 2.2.1
Description: Italian Translations for R 2.2.1
Author: The translators
Maintainer: Some Body <somebody@some.where.net>
License: GPL (>= 2)
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 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. 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 closely resembling (La)TeX, which can be processed into a variety of formats, including LaTeX, HTML and plain text. The translation is carried out by the Perl script Rdconv in R_HOME/bin and by the installation scripts for packages.
The R distribution contains more than 1200 such files which can be found in the src/library/pkg/man directories of the R source tree, where pkg stands for the standard packages which are included in the R distribution.
As an example, let us look at the file
src/library/base/man/load.Rd which documents the R function
load.
\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 a footer with keyword information. The header and footer are mandatory.
See the “Guidelines for Rd files” for guidelines for writing documentation in Rd format which should be useful for package writers.
The basic markup commands used for documenting R objects (in particular, functions) are given in this subsection.
\name{name}\alias{topic}\alias entries 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.
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}\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 eventually. 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, i)
\method{[}{factor}(x, \dots) <- value
}
which will print as
Usage:
## S3 method for class 'factor':
x[..., drop = FALSE]
## S3 method for class 'factor':
x[[i]]
## S3 replacement method for class 'factor':
x[...] <- value
\arguments{...} \item{arg_i}{Description of arg_i.}
for each element of the argument list. There may be optional text
before and after these entries.
\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 precede this
list (see the introductory example for rle).
\references{...}\url{} for
web pointers.
\note{...}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 (`( )' or `< >') to specify email
addresses, or \url{} 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{...}\link and \var will be interpreted, but
no other.)
Examples are not only useful for documentation purposes, but also
provide test code used for diagnostic checking of R. By default,
text inside \examples{} will be displayed in the output of the
help page and run by R CMD check. You can use
\dontrun{}
for commands 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.)
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). Code included in \dontrun is indicated by
comments in the processed help files.
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).
\keyword{key}\keyword entry should specify one of the standard keywords
as listed in file KEYWORDS in the R documentation directory
(default R_HOME/doc). Use e.g.
file.show(file.path(R.home("doc"), "KEYWORDS")) to inspect the
standard keywords from within R. There must be at least one
\keyword entry, but can be more than one if the R object being
documented falls into more than one category.
The special keyword `internal' marks a page of internal objects
that are not part of the packages' 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, like
the alphabetical list of objects in the HTML help system.
The R function prompt facilitates the construction of files
documenting R objects. If foo is an R function, then
prompt(foo) produces file foo.Rd which already contains
the proper function and argument names of foo, and a structure
which can be filled in with information.
The structure of Rd files which document R data sets is slightly
different. Whereas sections such as \arguments and \value
are not needed, 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{...}\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 is always `datasets'.
If bar is a data frame, documenting it as a data set can be initiated via prompt(bar).
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 allow for making 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.
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 man 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 by
library(help = pkgname). Otherwise (the default) comments
will be inserted giving suggestions for content.
The only requirement for this 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).
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 as the input).
The following logical markup commands are available for emphasizing or quoting text.
\emph{text}\strong{text}\strong is stronger.
\bold{text}\sQuote{text}\dQuote{text}The following logical markup commands are available for indicating specific kinds of text.
\code{text}typewriter font if possible. Some characters will need to be
escaped (see Insertions). The only markup interpreted inside
\code is \link and \var.
\preformatted{text}typewriter font if possible. The same characters need to be
escaped as for \code. All other formatting, e.g. line breaks,
is preserved. The closing brace should be on a line by itself.
\kbd{keyboard-characters}\samp{text}\pkg{package_name}\file{file_name}\email{email_address}\url{uniform_resource_locator}\var{metasyntactic_variable}\env{environment_variable}\option{option}\command{command_name}\dfn{term}\cite{reference}\link
(see Cross-references), such as the name of a book.
\acronym{acronym}Note that unless explicitly stated otherwise, special characters (see Insertions) must be escaped inside the above markup commands.
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).
\itemize and \enumerate commands may be nested.
The \describe command is similar to \itemize but allows
initial labels to be specified. The \items take two arguments,
the label and the body of the item, in exactly the same way as argument
and value \items. \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).
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 and S nroff formats.
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,
\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 topic12 (in which case
the present package has precedence so this is only needed to refer to
other packages). They are only in used in (C)HTML help (and not for
hyperlinks in LaTeX nor S sgml 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).
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. Where \eqn
is used for “inline” formulae (corresponding to TeX's
$...$, \deqn gives “displayed equations” (as in
LaTeX's displaymath environment, or TeX's
$$...$$).
Both commands can also be used as \eqn{latexascii} (only
one argument) which then is used for both latex and
ascii.
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 HTML and text on-line help we get
p(x) = lambda^x exp(-lambda)/x! for x = 0, 1, 2, ....
Use \R for the R system itself (you don't need extra
`{}' or `\'). Use \dots
for the dots in function argument lists `...', and
\ldots
for ellipsis dots in ordinary text.
After a `%', you can put your own comments regarding the help text. The rest of the line will be completely disregarded, normally. 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 braces13
always need to be escaped by `\', and `\\' can be used
for backslash and needs to be when there two or more adjacent
backslashes). Inside the verbatim-like commands (usage,
\code, \preformatted and \examples), no other
characters are special. Note that \file is not a
verbatim-like command.
In “regular” text (not verbatim-like, no \eqn, ...), you
currently must escape most LaTeX special characters, i.e., besides
`%', `{', and `}', the specials `$', `#', and
`_' are produced by preceding each with a `\'. (`&' can
also be escaped, but need not be.) Further, enter `^' as
\eqn{\mbox{\textasciicircum}}{^}, and `~' by
\eqn{\mbox{\textasciitilde}}{~} or \eqn{\sim}{~}
(for a short and long tilde respectively). Also, `<', `>',
and `|' must only be used in math mode, i.e., within \eqn or
\deqn.
Text which might need to be represented differently in different
encodings should be marked by \enc, e.g.
\enc{Jöreskog}{Joreskog} 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).
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. help.search("Spearman") will succeed in finding the help page for the test for association between paired samples using Spearman's rho. (Note that concepts are not currently supported by the HTML search accessed via `help.start()'.)
(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.
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.
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 the \encoding{} directive must be used to
specify the encoding if it is not ASCII. (The
\encoding{} directive must be on a line by itself, and in
particular one containing no non-ASCII characters. As from
R 2.6.0 the encoding declared in the DESCRIPTION file will
be used if none is declared in the file.) This is used when creating
the header of the HTML conversion (if not present, for
back-compatibility the processing to HTML assumes that the file is
in Latin-1 (ISO-8859-1)) and to add comments to the text and examples
conversions. It is also used to indicate to LaTeX how to process
the file (see below).
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).
For convenience, encoding names `latin1' and `latin2' are always recognized: these and `UTF-8' are likely to work fairly widely.
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 an explicit encoding of the file to a
\inputencoding{some_encoding}
command, and this needs to be matched by a suitable invocation of the \usepackage{inputenc} command. The R utility R CMD Rd2dvi looks at the converted code and includes the encodings used: it might for example use
\usepackage[latin1,latin9,utf8]{inputenc}
(Use of utf8 as an encoding requires LaTeX dated 2003/12/01 or
later.)
Note that this mechanism works best with letters and for example the copyright symbol may be rendered as a subscript and the plus–minus symbol cannot be used in text.
There are several commands to process Rd files from the system command line. All of these need Perl to be installed.
Using R CMD Rdconv one can convert R documentation format to
other formats, or extract the executable examples for run-time testing.
Currently, conversions to plain text, HTML, LaTeX, and S
version 3 or 4 documentation formats are supported.
In addition to this low-level conversion tool, the R distribution
provides two user-level programs for processing Rd format.
R CMD Rd2txt produces “pretty” plain text output from an Rd
file, and is particularly useful as a previewer when writing Rd format
documentation within Emacs. R CMD Rd2dvi generates DVI (or, if
option --pdf is given, 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 (or bundle). 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 Sd2Rd converts S version 3 documentation files
(which use an extended Nroff format) and S version 4 documentation
(which uses SGML markup) to Rd format. This is useful when porting a
package originally written for the S system to R. S version
3 files usually have extension .d, whereas version 4 ones have
extension .sgml or .sgm.
R CMD Sweave and R CMD Stangle process `Sweave'
documentation files (usually with extension `.Snw' or `.Rnw'):
R CMD Stangle is use to extract the R code fragments.
The exact usage and a detailed list of available options for all but the
last two of the above 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 will need to have installed the
files in the R binary Windows distribution for installing source
packages (this is true for a default installation), and for R CMD
Rd2dvi also the tools to build packages from source as described in the
“R Installation and Administration” manual.
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. Code entered by users has the source code stored in an attribute, 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 moved around in 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. This tidied version is much easier to read, not least by other users who are used to the standard format. Although the deparsing cannot do so, we recommend the consistent use of the preferred assignment operator `<-' (rather than `=') for assignment.
We can subvert the keeping of source in two ways.
keep.source can be set to FALSE before the code
is loaded into R.
source
attribute, for example by
attr(myfun, "source") <- NULL
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
options(keep.source = FALSE)
source("myfuns.R")
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. You may need to move comments to more appropriate places.
The standard format provides a good starting point for further tidying. Most package authors use a version of Emacs (on Unix 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 most14 Unix-like 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 intervals15 (by default every 20 msecs) 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).
library(MASS); library(boot)
storm.fm <- nls(Time ~ b*Viscosity/(Wt - c), stormer,
start = c(b=29.401, 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) # pretty slow
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: 80.74 seconds.
Total seconds: time spent in function and callees.
Self seconds: time spent in function alone.
% total % self
total seconds self seconds name
100.00 80.74 0.22 0.18 "boot"
99.65 80.46 1.19 0.96 "statistic"
96.33 77.78 2.68 2.16 "nls"
50.21 40.54 1.54 1.24 "<Anonymous>"
47.11 38.04 1.83 1.48 ".Call"
23.06 18.62 2.43 1.96 "eval"
19.87 16.04 0.67 0.54 "as.list"
18.97 15.32 0.64 0.52 "switch"
17.88 14.44 0.47 0.38 "model.frame"
17.41 14.06 1.73 1.40 "model.frame.default"
17.41 14.06 2.80 2.26 "nlsModel"
15.43 12.46 1.88 1.52 "qr.qty"
13.40 10.82 3.07 2.48 "assign"
12.73 10.28 2.33 1.88 "storage.mode<-"
12.34 9.96 1.81 1.46 "qr.coef"
10.13 8.18 5.42 4.38 "paste"
...
% self % total
self seconds total seconds name
5.42 4.38 10.13 8.18 "paste"
3.37 2.72 6.71 5.42 "as.integer"
3.29 2.66 5.00 4.04 "as.double"
3.20 2.58 4.29 3.46 "seq.default"
3.07 2.48 13.40 10.82 "assign"
2.92 2.36 5.95 4.80 "names"
2.80 2.26 17.41 14.06 "nlsModel"
2.68 2.16 96.33 77.78 "nls"
2.53 2.04 2.53 2.04 ".Fortran"
2.43 1.96 23.06 18.62 "eval"
2.33 1.88 12.73 10.28 "storage.mode<-"
...
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.
R CMD Rprof uses a Perl script that may be a little faster than
summaryRprof for large files. On the other hand
summaryRprof does not require Perl and provides the results as an
R object.
Two warnings: profiling does impose a small performance penalty, and the output files can be very large if long runs are profiled.
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 package proftools: in particular this allows 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. 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
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/) 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 /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.
oprofile works by running a daemon which collects information.
The daemon must be started as root, e.g.
% su
% opcontrol --no-vmlinux
% 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. You can use opannotate to annotate the source code with the times spent in each section, if the appropriate source code was compiled with debugging support.
On 64-bit (only) Solaris, the standard profiling tool gprof collects information from shared libraries compiled with -pg.
Developers have recommended sample (or Sampler.app, which is a GUI version) and Shark (see http://developer.apple.com/tools/sharkoptimize.html and http://developer.apple.com/tools/shark_optimize.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 will16 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 name space
(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).
We can help to detect memory problems 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.
If you have access to Linux on an ix86, x86_64 or
ppc32 platform 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. 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. Current
versions17 of valgrind are not happy with many optimized BLASes
that use cpu-specific instructions (3D now, SSE, SSE2, SSE3 and similar)
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
conjuction 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 January, 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).
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
--tool=memcheck
--memcheck:leak-check=full
or setting the environment variable VALGRIND_OPTS.
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 front-ends to gdb such as ddd and
insight, and other debuggers such as Sun's dbx.
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 option is the first.
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-compilant OSes we can safely catch that and return to the top-level prompt.
> .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.
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
...
Some “tricks” are worth knowing.
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
Access to operating system functions is via the R function
system.
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 will be internal which if
true will collect the output of the command into an R character
vector.
The function system.time
is available for timing (although the information available may be
limited on non-Unix-like platforms: these days only on the obsolete
Windows 9x/ME).
.C and .Fortran
These two functions provide a standard 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 given the
symbol name as known 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"): it is no longer necessary nor correct to use
symbol.For, which is defunct as from R 2.5.0.) (Note that the
underscore is not a valid character in a FORTRAN 77 subprogram name, and
on versions of R prior to 2.4.0 .Fortran may not correctly
translate names containing underscores.)
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 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 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). 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 which have it, this is
compatible withe C99 double complex type.) 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 better to use one of the other interfaces. An
exception is passing an R function for use with call_R,
when the object can be handled as void * en route to
call_R, but even there .Call is to be preferred.
Similarly, passing an R list as an argument to a C routine should be
done using the .Call interface. If one does use the .C
function to pass a list as an argument, it is visible to the routine as
an array in C of SEXP types (i.e., SEXP *). The elements
of the array correspond directly to the elements of the R list.
However, this array must be treated as read-only and one must not assign
values to its elements within the C routine — doing so bypasses R's
memory management facilities and will corrupt the object and the R
session.
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 to existing C or FORTRAN code.
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 DUP can be used to suppress copying. It is dangerous:
see the on-line help for arguments against its use. It is not possible
to pass numeric vectors as float * or REAL if
DUP=FALSE.
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 with a
name space, the shared object loaded by the first (if any)
useDynLib directive will be used.)
For .C only you can specify an ENCODING argument: this
requests that (unless DUP = FALSE) character vectors be
re-encoded to the requested encoding before being passed in, and
re-encoded from the requested encoding when passed back. Note that
encoding names are not standardized, and not all R builds support
re-encoding. (The argument is ignored with a warning if re-encoding is
not supported at all: R code can test for this via
capabilities("iconv").) But this can be useful to allow code to
work in a UTF-8 locale by specifying ENCODING = "latin1".
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 i, j, nab = *na + *nb - 1;
for(i = 0; i < nab; i++)
ab[i] = 0.0;
for(i = 0; i < *na; i++)
for(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.
dyn.load and dyn.unloadCompiled code to be used with R is loaded as a shared object (Unix and MacOS 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, paste("mylib", .Platform$dynlib.ext, sep=""))
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 via a call to library.dynam
in the .First.lib function of a package. 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 (as it will be with a versioned install).
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.
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
libraries, the most recently loaded occurrence is used. The
PACKAGE argument provides a good way to avoid any ambiguity in
which occurrence is meant.
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. 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 NULL-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. 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. 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", &myCall, 3},
{NULL, NULL, 0}
};
along with any other routines for the .Call interface.
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", &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 allow 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, any registered routines are automatically removed. There is no (direct) 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).
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. An experimental interface to support this has been provided in R 2.4.0. The interface consists of two routines declared 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 insuring 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. For example
Depends: link2, link3
LinkingTo: link2, link3
It should also `Depend' on those packages for they have to be installed prior to this one, and loaded prior to this one (so the path to their compiled code can be found).
This then arranges that the include directories in the installed linked-to packages are added to the include paths for C and C++ code.
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 or .C, .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, and PKG_OBJCFLAGS (for the C, C++, FORTRAN
77, Fortran 9x, 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 override 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 etc/Makeconf on
Unix-alikes) 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. See Customizing package compilation under Unix, and also Customizing package compilation under Windows.
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 in the .First.lib or
.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 in place of the R CMD SHLIB
mechanism. make is called with makefiles
R_HOME/etc/Makeconf18, 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.
Note that whereas R CMD INSTALL makes use of a Makefile,
R CMD SHLIB does not. The file must be named Makefile,
not for example makefile nor GNUmakefile.
Under Windows19 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
details 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 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.
Suppose we have the following hypothetical C++ library, consisting of
the two files X.hh and X.cc, and implementing the two
classes X and Y which we want to use in R.
// X.hh class X { public: X (); ~X (); }; class Y { public: Y (); ~Y (); };
// X.cc #include <iostream> #include "X.hh" static Y y; X::X() { std::cout << "constructor X" << std::endl; } X::~X() { std::cout << "destructor X" << std::endl; } Y::Y() { std::cout << "constructor Y" << std::endl; } Y::~Y() { std::cout << "destructor Y" << std::endl; }
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.cc: #include "X.hh" 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.cc X_main.cc
to create the shared object, typically X.so (the file name extension may be different on your platform). Now starting R yields
R : Copyright 2000, The R Development Core Team
Version 1.1.0 Under development (unstable) (April 14, 2000)
...
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 various Windows compilers.
Using C++ iostreams, as in this example, 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.
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 the appropriate headers yourself.
We have already warned against the use of C++ iostreams not least
because output is not guaranteed to appear on the R console, and
this warning applies equally to Fortran output to units * and
6. See Printing from FORTRAN, which describes workarounds.
In the past most Fortran compilers implemented I/O on top of the C I/O
system and so the two interworked successfully. This was true of
g77, but it is less true of gfortran as used in
gcc 4.y.x. 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.
The gfortran I/O system assumes that it has exclusive access
to stdin/stdout/stderr and so should be avoided
if at all possible. Function La_Init in file
src/main/lapack.c shows how to mitigate the worst effects.
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.
One restriction of this interface is that the R objects can not be
handled directly in C. This becomes more troublesome when one wishes to
call R functions from within the C code. There is a C function
provided called call_R (also known as call_S for
compatibility with S) that can do that, but it is cumbersome to use, and
the mechanisms documented here are usually simpler to use, as well as
more powerful.
If a user really wants to write C code using internal R data
structures, then that can be done using the .Call and
.External function. 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 (which is
modelled on the interface of the same name in S version 4) is a
little 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 S4-compatibility macros 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 requiring no call to R, .C
suffices. The new interfaces are relatively recent additions to S
and R, and a great deal of useful code has been written using just
.C before they were available. The .Call and
.External 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.
There are two approaches that can be taken to handling R objects from
within C code. The first (historically) is to use the macros and
functions that have been used to implement the core parts of R
through .Internal calls. A public20 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.
Another approach is to use R versions of the macros and functions
defined for the S version 4 interface .Call, which are
defined in the header file Rdefines.h. This is a somewhat
simpler approach, and is to be preferred if the code is intended to be
shared with S. However, it is less well documented and even less
tested. Note too that some idiomatic S4 constructions with these macros
(such as assigning elements of character vectors or lists) are invalid
in R.
A substantial amount of R 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”: indeed many of the examples here were developed by examining closely R system functions for similar tasks. 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 SEXP21, 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.
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. First we use the macros in Rinternals.h:
#include <R.h>
#include <Rinternals.h>
SEXP ab;
....
PROTECT(ab = allocVector(REALSXP, 2));
REAL(ab)[0] = 123.45;
REAL(ab)[1] = 67.89;
UNPROTECT(1);
and then those in Rdefines.h:
#include <R.h>
#include <Rdefines.h>
SEXP ab;
....
PROTECT(ab = NEW_NUMERIC(2));
NUMERIC_POINTER(ab)[0] = 123.45;
NUMERIC_POINTER(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 have one example, 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;
....
PROTECT_WITH_INDEX(s = eval(OS->R_fcall, OS->R_env), &ipx);
REPROTECT(s = coerceVector(s, REALSXP), ipx);
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. These
allocate R objects of various types, and for the standard vector
types there are equivalent NEW_XXX macros defined in
Rdefines.h.
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: if the Rdefines.h macros are used then S4-compatible names are used.
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) LISTSXP“dotted-pair” list DOTSXPa `...' object NILSXPNULL SYMSXPname/symbol CLOSXPfunction or function closure ENVSXPenvironment
Among the important internal SEXPTYPEs are LANGSXP,
CHARSXP, PROMSXP, etc. (Note: 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. Once again there are two ways to do this,
and Rdefines.h has macros such as IS_NUMERIC.
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, equivalently,
PROTECT(newSexp = coerceVector(oldSexp, REALSXP));
or
PROTECT(newSexp = AS_NUMERIC(oldSexp));
Protection is needed as a new object is created; the object
formerly pointed to by the SEXP is still protected but now
unused.
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 i, j, nx, ny;
double tmp, *rx = REAL(x), *ry = REAL(y), *rans;
SEXP ans;
nx = length(x); ny = length(y);
PROTECT(ans = allocMatrix(REALSXP, nx, ny));
rans = REAL(ans);
for(i = 0; i < nx; i++) {
tmp = rx[i];
for(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.
Although allocMatrix provides a short cut, we will show how to
set the dim attribute directly.
#include <R.h>
#include <Rinternals.h>
SEXP out(SEXP x, SEXP y)
{
R_len_t i, j, nx, ny;
double tmp, *rx = REAL(x), *ry = REAL(y), *rans;
SEXP ans, dim, dimnames;
nx = length(x); ny = length(y);
PROTECT(ans = allocVector(REALSXP, nx*ny));
rans = REAL(ans);
for(i = 0; i < nx; i++) {
tmp = rx[i];
for(j = 0; j < ny; j++)
rans[i + nx*j] = tmp * ry[j];
}
PROTECT(dim = allocVector(INTSXP, 2));
INTEGER(dim)[0] = nx; INTEGER(dim)[1] = ny;
setAttrib(ans, R_DimSymbol, dim);
PROTECT(dimnames = 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;
PROTECT(version = 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.
In R the (S3) 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 <Rdefines.h>
....
SEXP ans, dim, dimnames, class;
....
PROTECT(class = 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);
int i;
for (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.
As of R 2.6.0, 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. As of R 2.6.0, 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.
Currently, it is still possible to create CHARSXPs using
allocVector or allocString; CHARSXPs created in
this way will not be captured by the global CHARSXP cache and
this should be avoided. In the future, all CHARSXPs will be
captured by the cache and this will allow further optimizations, for
example, replacing calls to strcmp with pointer comparisons. A
helper macro, CallocCharBuf, can be used to obtain a temporary
character buffer for in-place string manipulation: this memory must be
released using Free.
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);
printf("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.22 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. Suppose we wanted to pass a single logical
argument ignore_quotes: we could use
int ign;
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: is 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
list4 construct a pairlist from one to four items, and
lang1 to lang4 do the same for a language object (a
function to call plus zero to three arguments). Function 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.
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 assignment 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).)
Currently all arguments to a .Call call will have NAMED
set to 2, and so users must assume that they need to be duplicated
before alteration.
.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 .Internal. .External is more
complex but allows a variable number of arguments.
.Call
Let us convert our finite convolution example to use .Call, first
using the Rdefines.h macros. 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 checking must be transferred to the C code, which is
#include <R.h>
#include <Rdefines.h>
SEXP convolve2(SEXP a, SEXP b)
{
int i, j, na, nb, nab;
double *xa, *xb, *xab;
SEXP ab;
PROTECT(a = AS_NUMERIC(a));
PROTECT(b = AS_NUMERIC(b));
na = LENGTH(a); nb = LENGTH(b); nab = na + nb - 1;
PROTECT(ab = NEW_NUMERIC(nab));
xa = NUMERIC_POINTER(a); xb = NUMERIC_POINTER(b);
xab = NUMERIC_POINTER(ab);
for(i = 0; i < nab; i++) xab[i] = 0.0;
for(i = 0; i < na; i++)
for(j = 0; j < nb; j++) xab[i + j] += xa[i] * xb[j];
UNPROTECT(3);
return(ab);
}
Note that unlike the macros in S version 4, the R versions of these macros do check that coercion can be done and raise an error if it fails. They will raise warnings if missing values are introduced by coercion. Although we illustrate doing the coercion in the C code here, it often is simpler to do the necessary coercions in the R code.
Now for the version in R-internal style. Only the C code changes.
#include <R.h>
#include <Rinternals.h>
SEXP convolve2(SEXP a, SEXP b)
{
R_len_t i, j, na, nb, nab;
double *xa, *xb, *xab;
SEXP ab;
PROTECT(a = coerceVector(a, REALSXP));
PROTECT(b = coerceVector(b, REALSXP));
na = length(a); nb = length(b); nab = na + nb - 1;
PROTECT(ab = allocVector(REALSXP, nab));
xa = REAL(a); xb = REAL(b);
xab = REAL(ab);
for(i = 0; i < nab; i++) xab[i] = 0.0;
for(i = 0; i < na; i++)
for(j = 0; j < nb; j++) xab[i + j] += xa[i] * xb[j];
UNPROTECT(3);
return(ab);
}
This is called in exactly the same way.
.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;
PROTECT(a = coerceVector(CADR(args), REALSXP));
PROTECT(b = 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.
#include <R_ext/PrtUtil.h>
SEXP showArgs(SEXP args)
{
int i, nargs;
Rcomplex cpl;
const char *name;
SEXP el;
args = CDR(args); /* skip 'name' */
for(i = 0; args != R_NilValue; i++, args = CDR(args)) {
args = CDR(args);
name = CHAR(PRINTNAME(TAG(args)));
switch(TYPEOF(CAR(args))) {
case REALSXP:
Rprintf("[%d] '%s' %f\n", i+1, name, REAL(CAR(args))[0]);
break;
case LGLSXP:
case INTSXP:
Rprintf("[%d] '%s' %d\n", i+1, name, INTEGER(CAR(args))[0]);
break;
case CPLXSXP:
cpl = COMPLEX(CAR(args))[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(CAR(args), 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(...) .External("showArgs", ...)
Note that this style of programming is convenient but not necessary, as an alternative style is
showArgs1 <- function(...) .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
IEEE 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(i = 0; i < na; i++)
for(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, R_NegInf and
R_NaReal can be used to set doubles to the special values.
We noted that the call_R interface could be used to evaluate R
expressions from C code, but the current interfaces are much more
convenient to use. The main function we will use is
SEXP eval(SEXP expr, SEXP rho);
the equivalent of the interpreted R code eval(expr, envir =
rho), 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)
{
R_len_t i, n = length(list);
SEXP ans;
if(!isNewList(list)) error("`list' must be a list");
if(!isEnvironment(rho)) error("`rho' should be an environment");
PROTECT(ans = allocVector(VECSXP, n));
for(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)
{
R_len_t i, 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");
PROTECT(R_fcall = lang2(fn, R_NilValue));
PROTECT(ans = allocVector(VECSXP, n));
for(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;
PROTECT(t = s = allocList(3));
SET_TYPEOF(s, LANGSXP);
CAR(t) = install("print"); t = CDR(t);
CAR(t) = CAR(a); t = CDR(t);
CAR(t) = allocVector(INTSXP, 1);
INTEGER(CAR(t))[0] = 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 call_S in Becker,
Chambers & Wilks (1988) 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;
PROTECT(ans = 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;
}
}
}
The C code is essentially unchanged from the call_R version, just
using a couple of functions to convert from double to SEXP
and to evaluate f.check.
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 * min(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.
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 start, i, j;
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");
PROTECT(ans = coerceVector(eval(expr, rho), REALSXP));
PROTECT(gradient = allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));
rgr = REAL(gradient); rans = REAL(ans);
for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {
PROTECT(par = 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;
PROTECT(ans1 = coerceVector(eval(expr, rho), REALSXP));
for(j = 0; j < LENGTH(ans); j++)
rgr[j + start] = (REAL(ans1)[j] - rans[j])/delta;
REAL(par)[0] = tt;
UNPROTECT(2); /* par, ans1 */
}
PROTECT(dimnames = 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
PROTECT(ans = coerceVector(eval(expr, rho), REALSXP));
We then allocate space for the calculated derivative by
PROTECT(gradient = 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.
for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {
PROTECT(par = 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
representation23 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;
PROTECT(ans1 = 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(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).
PROTECT(dimnames = 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.h24.
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;
int i;
ParseStatus status;
...
if(done == 1) {
PROTECT(cmdSexp = 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(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 srcfile object or the R NULL
object (as in the example above). In the former case 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 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
void R_RegisterFinalizer(SEXP s, SEXP fun);
void R_RegisterFinalizerEx(SEXP s, SEXP fun, Rboolean onexit);
typedef void (*R_CFinalizer_t)(SEXP);
void R_RegisterCFinalizer(SEXP s, R_CFinalizer_t fun);
void R_RegisterCFinalizerEx(SEXP s, R_CFinalizer_t fun, Rboolean onexit);
The R function indicated by fun should be a function of a
single argument, the object to be finalized. R does not perform a
garbage collection when shutting down, and the onexit argument of
the extended forms can be used to ask that the finalizer be run during a
normal shutdown of the R session. It is suggested that it is good
practice to clear the pointer on finalization.
The only R level function for interacting with external pointers is
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.
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.
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.)
As from R 2.4.0 the accessor functions check that they are being used
on an appropriate type of SEXP. By default a certain amount of
misuse is allowed where the internal representation is the same: for
example LOGICAL can be used on a INTSXP and
SET_VECTOR_ELT on a STRSXP. Strict checking can be
enabled by compiling R (specifically src/main/memory.c) with
`USE_TYPE_CHECKING_STRICT' defined (e.g. in as the configure
variable `DEFS' on a Unix-alike).
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 compiled without `USE_RINTERNALS' defined, as this provides a stricter test that the accessors have been used correctly.
As from R 2.5.0 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 currrent 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.
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 extra some 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 problems25, 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.
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.
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
clearing memory allocated subsequently by a call to vmaxset.
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 long, there are limits imposed by
R's internal allocation mechanism. These will only come into play on
64-bit systems, where the current limit for n is just under 16Gb.
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.
In addition, there is an interface (defined in header R_ext/Applic.h) to the generation of random 2-dimensional tables with given row and column totals using Patefield's algorithm.
Here, nrow and ncol give the numbers nr and nc of rows and columns and nrowt and ncolt the corresponding row and column totals, respectively, ntotal gives the sum of the row (or columns) totals, jwork is a workspace of length nc, and on output matrix a contains the nr * nc generated random counts in the usual column-major order.
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
few 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). It is wise to write
complete lines (including the "\n") before returning to R.
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.
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.
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/EISPACK 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) exactly the same as the corresponding R
functions of the same name, so the documentation of the R functions
can be used.
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,lambdabinomial binomn,pCauchy cauchylocation,scalechi-squared chisqdfnon-central chi-squared nchisqdf,lambdaexponential expscaleF fn1,n2non-central F nfn1,n2,ncpgamma gammashape,scalegeometric geomphypergeometric hyperNR,NB,nlogistic logislocation,scalelognormal lnormlogmean,logsdnegative binomial nbinomn,pnormal 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.
The argument names are not all quite the same as the R ones.
The Gamma function, its natural logarithm and first four derivatives and the n-th derivative of Psi, the digamma function.
The (complete) Beta function and its natural logarithm.
The number of combinations of k items chosen from from n and its natural logarithm. n and k are rounded to the nearest integer.
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.
pythag(a,b)computessqrt(a^2 +b^2)without overflow or destructive underflow: for example it still works when both a and b are between1e200and1e300(in IEEE double precision).
Computes
log(1 +x)(log 1 plus x), accurately even for small x, i.e., |x| << 1.This may 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. For backwards compatibility with R versions prior to 1.5.0, the entry point
Rf_log1pis still provided.
Computes
log(1 +x) -x (log 1 plus x minus x), accurately even for small x, i.e., |x| << 1.
Computes
exp(x) - 1(exp x minus 1), accurately even for small x, i.e., |x| << 1.This may 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.
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 overflows or throwing away too much accuracy.
Return the larger (
max) or smaller (min) of two integer or double numbers, respectively.
Compute the signum function, where sign(x) is 1, 0, or -1, when x is positive, 0, or negative, respectively.
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
round().
Returns the value of x rounded to digits significant decimal digits.
This is the function used by R's
signif().
Returns the value of x truncated (to an integer value) towards zero.
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 constants
TRUE and FALSE = 0 of type Rboolean in order to
provide a way of using “logical” variables in C consistently.
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. These 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.
The following two functions do numerical colorspace conversion from HSV to RGB and back. Note that all colours must be in [0,1].
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. A
NULLprefix is replaced by"".
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.
R has its own C-level interface to the encoding conversion
capabilities provided by iconv, for the following reasons
iconv was available at configure time.
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 should 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-endian systems
(e.g. sparc-sun-solaris2.6) and not on little-endian systems
(such as i686 under Linux or Windows). It can be useful when
manipulating binary files.
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 earlier 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 is recognized by some compilers used to
build R whereas others need __inline__ or do not support
inlining. Portable code 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. 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.
It is possible to build Mathlib, the R set of mathematical
functions documented in Rmath.h, as a standalone library
libRmath under both Unix 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 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 R_ext/RConvertors.h 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/RStartup.h for add-on front-ends 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/Random.h random number generation R_ext/RS.h definitions common to R.h and S.h, including F77_CALLetc.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 Rdevices.h (for writing graphics devices), Rgraphics.h and R_ext/Graphics{Base,Device,Engine}.h. Some entry points from the stats package are in R_ext/stats_package.h (currently related to the internals of `nls' and `nlminb').
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
foo <- function(object, ...) UseMethod("foo")
foo.default <- base::foo
(If the thus defined default method needs a `...' added to its
argument list, one can e.g. use formals(foo.default) <-
c(formals(foo.default), alist(... = )).)
The same idea can be applied for functions in other packages with name spaces.
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 `SJava' (see http://www.omegahat.org/RSJava/ and `JRI', part of the rJava package on CRAN).
R can be built as a shared library26 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.
The command-line R front-end, R_HOME/bin/exec/R is one such example, and the unbundled GNOME and MacOS 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.) 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 examples27 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 don't 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 set 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.
Suitable flags to compile and link against the R shared library can be found by
R CMD config --cppflags
R CMD config --ldflags
If R is installed, pkg-config is available and
sub-architectures have not be used, alternatives are
pkg-config --cflags libR
pkg-config --libs libR
For Unix-alkes 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)(char *);
extern void (*ptr_R_ShowMessage)(char *);
extern int (*ptr_R_ReadConsole)(char *, unsigned char *, int, int);
extern void (*ptr_R_WriteConsole)(char *, int);
extern void (*ptr_R_WriteConsoleEx)(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, char **, char **, char *,
Rboolean, char *);
extern int (*ptr_R_ChooseFile)(int, char *, int);
extern int (*ptr_R_EditFile)(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);
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 agets(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.
.Internalfunctions forloadhistory,savehistoryandtimestamp: these are called after checking the number of arguments.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 (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 <Rdevices.h> /* for 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) KillAllDevices(); fpu_setup(FALSE); exit(status); }
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 cMethod 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 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.
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 can run a own 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 set28 when R_initialize_R is called, to values appropriate to
the main thread. Stack-checking can be disabled by seting
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 8Mb 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.
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 on CRAN
(http://cran.r-project.org/other-software.html) which works
with Rproxy.dll (in the R distribution) and R.dll to
support transfer of data to and from R and remote execution of R
commands, as well as embedding of an R graphics window. The rcom
package on CRAN provides a (D)COM server in a running R session.
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 interface callng
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 Rproxy.dll used by
StatConnector and 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/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(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(char *buf, int len)
{
printf("%s", buf);
}
void myCallBack()
{
/* 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 and can be
set there by running the program R_HOME\bin\RSetReg.exe.
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 easier29 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.
*Riconv_open: Re-encoding.C: Interface functions .C and .Fortran.Call: Handling R objects in C.Call: Calling .Call.External: Handling R objects in C.External: Calling .External.First.lib: Package subdirectories.Fortran: Interface functions .C and .Fortran.Last.lib: Package subdirectories.Last.lib: Load hooks.onAttach: 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\file: Marking text\format: Documenting data sets\itemize: Lists and tables\kbd: Marking text\keyword: Documenting functions\ldots: Insertions\link: Cross-references\method: Documenting functions\name: Documenting functions\note: Documenting functions\option: Marking text\pkg: Marking text\preformatted: Marking text\R: Insertions\references: Documenting functions\samp: Marking text\section: Sectioning\seealso: Documenting functions\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 textbessel_i: Mathematical functionsbessel_j: Mathematical functionsbessel_k: Mathematical functionsbessel_y: Mathematical functionsbeta: Mathematical functionsBLAS_LIBS: Using Makevarsbrowser: BrowsingCalloc: User-controlledCAR: Calling .ExternalCDR: Calling .Externalcgmin: Optimizationchoose: Mathematical functionscPsort: 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: Name spaces with formal classes and methodsexportMethods: Name spaces with formal classes and methodsexportPattern: Specifying imports and exportsFALSE: Mathematical constantsfindInterval: Utility functionsfindVar: Finding and setting variablesFLIBS: Using Makevarsfmax2: Numerical Utilitiesfmin2: Numerical Utilitiesfprec: Numerical UtilitiesFree: User-controlledfround: Numerical Utilitiesfsign: Numerical Utilitiesftrunc: Numerical Utilitiesgammafn: Mathematical functionsgctorture: Using gctorturegetAttrib: AttributesGetRNGstate: Random numbershsv2rgb: Utility functionsimax2: Numerical Utilitiesimin2: Numerical Utilitiesimport: Specifying imports and exportsimportClassesFrom: Name spaces with formal classes and methodsimportFrom: Specifying imports and exportsimportMethodsFrom: Name spaces with formal 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 Utilitieslog1pmx: Numerical Utilitieslogspace_add: Numerical Utilitieslogspace_sub: Numerical UtilitiesM_E: Mathematical constantsM_PI: Mathematical constantsNA_REAL: Missing and IEEE valuesnmmin: Optimizationnorm_rand: Random numbersOBJECTS: Creating shared objectsOBJECTS: Using Makevarspentagamma: Mathematical functionsPKG_CFLAGS: Creating shared objectsPKG_CPPFLAGS: 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callbacksR_ExpandFileName: Utility functionsR_FINITE: Missing and IEEE valuesR_FlushConsole: Setting R callbacksR_GetCCallable: Registering native routinesR_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_PACKAGE_DIR: Configure and cleanupR_ParseVector: Parsing R code from CR_PosInf: Missing and IEEE valuesR_pow: Numerical UtilitiesR_pow_di: Numerical UtilitiesR_qsort: Utility functionsR_qsort_I: Utility functionsR_qsort_int: Utility functionsR_qsort_int_I: Utility functionsR_ReadConsole: Setting R callbacksR_RegisterCCallable: Registering native routinesR_ResetConsole: Setting R callbacksR_rsort: Utility functionsR_savehistory: Setting R callbacksR_ShowFiles: Setting R callbacksR_ShowMessage: Setting R callbacksR_Suicide: Setting R callbacksR_tmpnam: Utility functionsR_Version: Platform and version informationR_WriteConsole: Setting R callbacksR_WriteConsoleEx: Setting R callbacksrcont2: Random numbersRdqagi: IntegrationRdqags: IntegrationRealloc: User-controlledrecover: Debugging R codeREprintf: PrintingREPROTECT: Garbage CollectionREvprintf: Printingrevsort: Utility functionsrgb2hsv: Utility functionsRiconv: Re-encodingRiconv_close: Re-encodingRprintf: PrintingRprof: Profiling R code for speedRprof: Memory statistics from RprofRprofmem: Tracking memory allocationsrPsort: Utility functionsrsort_with_index: Utility functionsRvprintf: PrintingS3method: Registering S3 methodsS_alloc: TransientS_realloc: TransientSAFE_FFLAGS: Using Makevarssamin: Optimizationseed_in: Random numbersseed_out: Random numberssetAttrib: AttributessetVar: Finding and setting variablessign: Numerical UtilitiessummaryRprof: Memory statistics from Rprofsystem: Operating system accesssystem.time: Operating system accesstetragamma: Mathematical functionstrace: Debugging R codetraceback: Debugging R codetracemem: Tracing copies of an objecttranslateChar: Character encoding issuestrigamma: Mathematical functionsTRUE: Mathematical constantsundebug: Debugging R codeunif_rand: Random numbersUNPROTECT: Garbage CollectionUNPROTECT_PTR: Garbage Collectionuntracemem: Tracing copies of an objectuseDynLib: Load hooksvmaxget: Transientvmaxset: Transientvmmin: Optimization[1] This is true for OSes which implement the `C' locale, unless neither lazy-loading nor saving an image are used, in which case it would fail if loaded in a `C' locale. (Windows' idea of the `C' locale uses the WinAnsi charset.)
[2] It is good practice to encode them as octal or hex escape sequences.
[3] More precisely, they can contain the English alphanumeric characters and the symbols `$ - _ . + ! ' ( ) , ; = &'.
[4] 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 some 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.
[5] Using .hpp, although somewhat popular, is not guaranteed to be portable.
[6] on Unix-alikes: Windows resolves such dependencies at link time.
[7] Remember to set LIBRARY_PATH to point to your MinGW lib directory
[8] This may require GNU tar: the command used can be set with environment variable TAR.
[9] provided the conditions of the licence are met: many would see this as incompatible with an Open Source licence.
[10] 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-insensitve file system.
[11] Currently it is rendered differently only in HTML conversions, and latex conversion outside `\usage' and `\examples' environments.
[12] a common
example in CRAN packages is \link[mgcv]{gam}.
[13] See the examples section in the file Paren.Rd for an example.
[14] R has to be built to enable this, but the option --enable-R-profiling is the default.
[15] For Unix-alikes these are intervals of CPU time, and for Windows of elapsed time.
[16] With the exceptions of the commands
listed below: an object of such a name can be printed via an
explicit call to print.
[17] Although this is supposed to have been improved,
valgrind 3.2.0 still aborts using optimized BLASes on an
Opteron.
[18] or the version specific to a sub-architecture
[19] The files in the R binary Windows distribution for installing source packages need to be installed.
[20] see The R API: note that these are not all part of the API.
[21] SEXP is an acronym for Simple EXPression, common in LISP-like language syntaxes.
[22] You can assign a copy of the object in the
environment frame rho using defineVar(symbol,
duplicate(value), rho)).
[23] see Character encoding issues for why this might not be what is required.
[24] This is only guaranteed to show the current interface: it is liable to change.
[25] Known problems are redefining
error, length, vector and warning
[26] In the parlance of MacOS X this is a dynamic library, and is the normal way to build R on that platform.
[27] but these are not part of the automated test procedures and so little tested.
[28] 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
MacOS X.
[29] 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.