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 1.5.0 (2002-04-29). ISBN 3-901167-54-4
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, such as eda, mva, and stepfun.
In the following, we assume that you know the library() command,
including its lib.loc argument, and we also assume basic
knowledge of R CMD INSTALL. Otherwise, please look at R's
help pages
?library ?INSTALL
before reading on.
A package consists of a subdirectory containing the files
DESCRIPTION and INDEX, and the subdirectories R,
data, demo, exec, inst, man,
src, and tests (some of which can be missing).
Optionally the package can also contain (Bourne shell) script files
configure and cleanup which are executed before and
(provided that option --clean was given) after installation on
Unix, See Configure and cleanup.
DESCRIPTION fileThe DESCRIPTION file contains basic information about the package
in the following format:
Package: pkgname Version: 0.5-1 Date: 2000-01-04 Title: My first collection of functions Author: Friedrich Leisch <F.Leisch@ci.tuwien.ac.at>, with contributions from A. User <A.User@whereever.net>. Maintainer: Friedrich Leisch <F.Leisch@ci.tuwien.ac.at> Depends: R (>= 0.99), nlme Description: A short (one paragraph) description of what the package does and why it may be useful. License: GPL version 2 or newer 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 License field should contain an explicit statement or a
well-known abbreviation (such as GPL, LGPL, BSD, or
Artistic), perhaps followed by a reference to the actual license
file. 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 Title field should give a short description of the package
and not have any continuation lines. Older versions of R used a
separate file TITLE for giving this information; this is now
deprecated in favor of using the Title field in file
DESCRIPTION file.
The Maintainer field should give a single name with email
address in angle brackets (for sending bug reports etc.). It should not
end in a period or comma.
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.
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) and a version number in parentheses. 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 0.90 or newer, include R (>= 0.90) in the Depends
field. Future versions of R will use this field to autoload required
packages, 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 Description field or a separate README file.
The R INSTALL facilities already check if the version of R
used is recent enough for the package being installed.
INDEX fileThe 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).
Note that you can automatically create this file using something like
R CMD Rdindex man > INDEX, provided that Perl (5.005 or later) is
available on your system, or use the package builder (see Checking and building packages) to do so.
The R subdirectory contains R code files. The code files to
be installed must start with a (lower or upper case) letter 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. If necessary, one of these
files (historically zzz.R) should use library.dynam()
inside .First.lib() to load compiled code.
The man subdirectory should contain documentation files for the
objects in the package in R documentation (Rd) format. The
documentation files to be installed must also start with a (lower or
upper case) letter and have the extension .Rd (the default) or
.rd. See Writing R help 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
modreg in the R distribution for an example.
The R and man subdirectories may contain OS-specific
subdirectories named unix, windows or mac.
The C, C++, or FORTRAN1 source files
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 library for loading into R. There are default
variables and rules for this (determined when R is configured and
recorded in R_HOME/etc/Makeconf). These 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 Makefile. If such a
file is to be distributed, considerable care is needed to make it
general enough to work on all R platforms. In addition, it should
have a target clean which removes all files generated by Make.
If necessary, platform-specific files can be used, for example
Makevars.win or Makefile.win on Windows take precedence
over Makevars or Makefile.
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).
(As from R 1.5.0 one can assume that all ports of R have the same
binary (XDR) format and can read compressed images. For portability to
earlier versions use images saved with save(, ascii = TRUE,
version = 1).) 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. The
data subdirectory should also contain a 00Index file that
describes the datasets available. Ideally this should have a one-line
description of each dataset, with full documentation in the man
directory. Note that you can automatically create this file using
something like R CMD Rdindex --data man > data/00Index, provided
that Perl (5.005 or later) is available on your system, or use the
package builder (see Checking and building packages) to do so.
The demo subdirectory is for R scripts (for running via
demo()) which demonstrate some of the functionality of the
package. 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, see above. (Note that it is currently not possible to generate
this index file automatically.)
The contents of the inst subdirectory will be copied recursively
to the installation directory.
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.
Finally, 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.
Sometimes it is convenient to distribute several packages as a bundle. (The main current example is VR which contains four packages.) The installation procedures on both Unix and Windows can handle package bundles.
The DESCRIPTION file of a bundle has an extra Bundle
field, as in
Bundle: VR Contains: MASS class nnet spatial Version: 6.1-6 Date: 1999/11/26 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. BundleDescription: Various functions from the libraries of Venables and Ripley, `Modern Applied Statistics with S-PLUS' (3rd edition). License: GPL (version 2 or later)
The Contains field lists the packages, which should be contained
in separate subdirectories with the names given. These 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.
Note that most of this section is Unix-specific: see the comments later on about the Windows and classic MacOS ports 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 write 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`
(using ${R_HOME}/bin/R rather than just R is necessary
in order to use the `right' version of R when running the script as
part of R CMD INSTALL.
Note that earlier versions of this document recommended obtaining the
configure information by direct extraction (using grep and sed) from
R_HOME/etc/Makeconf, which only works for variables
recorded there as literals. R 1.5.0 has added R CMD config for
getting the value of the basic configuration variables, or the header
and library flags necessary for linking against R, see R CMD
config --help for more information.
If R was configured to use the FORTRAN-to-C converter, configure
variable F77 is set to a shell script wrapper to compile/link
FORTRAN 77 code based on f2c which for the purpose of Autoconf qualifies
as a FORTRAN 77 compiler. E.g., to check for an exteral BLAS library
using the ACX_BLAS macro from the Official Autoconf Macro
Archive, one can simply do
F77=`${R_HOME}/bin/R CMD config F77`
AC_PROG_F77
ACX_BLAS([], AC_MSG_ERROR([could not find your BLAS library], 1))
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.
It is essential to provide manual configuration information if the package is to be usable on classic MacOS.
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
libraries/DLLs. Usually, the library that is dynamically loaded by R
is linked against the second, dependent, library. On some systems, we
can add the location of this dependent library to the library 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 library 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 Java 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.
MakevarsSometimes writing your own configure script can be avoided by
supplying a file Makevars: also one of the commonest uses of a
configure script is to make Makevars from
Makevars.in.
The most common use of a Makevars file is to set additional
compiler flags (for example include paths) by setting PKG_CFLAGS,
PKG_CXXFLAGS and PKG_FFLAGS, for C, C++, or FORTRAN
respectively (see Creating shared libraries).
Also, Makevars can be used to set flags for the linker, for
example -L and -l options.
There are some macros which are built whilst configuring the building of
R itself, are stored in R_HOME/etc/Makeconf and can be
used in Makevars. These include
FLIBS
PKG_LIBS.
BLAS_LIBS
PKG_LIBS. Beware that if it is
empty then the R executable will contain all the double-precision
BLAS routines, but no single-precision, complex nor double-complex
routines.
Using R CMD check, the R package checker, one can test whether
source R packages work correctly. (Under Windows the
equivalent command is Rcmd check.) This runs a series of checks.
DESCRIPTION file is checked for completeness, and some of its
entries for correctness.
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 assignment functions is called value.
\name, \alias,
\title, \description and \keyword) fields, and for
unbalanced braces (which indicate Rd syntax errors). The keywords found
are compared to the standard ones.
\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.
tests directory then the tests
specified in that directory are run. (Typically they will consist of a
set of .R source files and target output files
.Rout.save.)
latex program is available, the .dvi
version of the package's manual is created (to check that the Rd files
can be converted successfully).
Use R CMD check --help (Rcmd check --help on Windows) 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). The
Windows equivalent is Rcmd build.
Prior to actually building the package in the common gzipped tar file
format, a variety of diagnostic checks and cleanups are performed. In
particular, it is tested whether the DESCRIPTION file contains
the required entries, whether object and data indices exist (it will
build them if they do not) and can be assumed to be up-to-date.
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, files called CVS or GNUMakefile,
or with base names starting with .#, or starting and ending with
#, or ending in ~ or .swp, are excluded by default.
Note: file exclusion does not work correctly with GNU
tar 1.13 but does work with later versions (e.g., version
1.13.17).
Use R CMD build --help (Rcmd build --help on Windows) to obtain more information about the usage of the R package builder.
R CMD build can also build pre-compiled version of packages for
binary distributions.
Note to Windows users:Rcmd checkandRcmd buildwork well under Windows NT4/2000 but may not work correctly on Windows 95/98/ME because of problems with some versions of Perl on those limited OSes. Experiences vary.
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.
A special case are documents in Sweave format, which we call
package vignettes. Sweave allows to integrate LaTeX documents
and R code and is contained in package tools 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 they contain to ensure consistency between code and documentation.
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.
Only the R code inside the vignettes is part of the checking
procedure, typesetting manuals is not part of the package QA.
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.
The directory inst/doc should contain an index named
00Index.dcf of all documentation files, the format is similar to
the format of the DESCRIPTION file:
foo-summary.pdf: A short introduction to package foo. foo-manual.pdf: A comprehensive manual for package foo. someTips.pdf: Tips that might be useful.
If no index file is present, then R CMD build looks for
\VignetteIndexEntry statements in all Sweave files and
auto-generates the index. The \VignetteIndexEntry statement is
best placed in LaTeX comment, such that no definition of the command
is necessary.
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.
R CMD build to run a few further checks and 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.
When all the testing is done, upload the .tar.gz file to
<ftp://ftp.ci.tuwien.ac.at/incoming>
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. E.g., R CMD INSTALL will not work on
package.tgz files. Of course, R CMD build
automatically creates valid file names.
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 900 such files which can be
found in the src/library/pkg/man directories of the R
source tree, where pkg stands for package base where all the
standard objects are, and for the standard packages such as eda
and mva 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.
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{dnorm}
\alias{pnorm}
\alias{qnorm}
\alias{rnorm}
Note that the \name is not necessarily a topic documented.
\title{Title}
\description{...}
\usage{fun(arg1, arg2, ...)}
The usage information specified should in general match the function
definition exactly (such that automatic checking for consistency
between code and documentation is possible). Otherwise, include a
\synopsis
section with the actual definition.
For example, abline is a function for adding a straight line to a
plot which can be used in several different ways, depending on the named
arguments specified. Hence, abline.Rd contains
\synopsis{
abline(a = NULL, b = NULL, h = NULL, v = NULL, reg = NULL,
coef = NULL, untf = FALSE, col = par("col"),
lty = par("lty"), lwd = NULL, ...)
}
\usage{
abline(a, b, \dots)
abline(h=, \dots)
abline(v=, \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 tools always have access to the full name.
For example, print.ts.Rd contains
\usage{
\method{print}{ts}(x, calendar, \dots)
}
\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, piechart.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{...}
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
\testonly{}
for extra commands for testing that should not be shown to users.
For example,
x <- runif(10) # Shown and run.
\dontrun{plot(x)} # Only shown.
\testonly{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).
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 loadable via data() (see ?data for more
info).
\keyword{key}
\keyword entry should specify one of the standard keywords
(as listed in the file R_HOME/doc/KEYWORDS.db). There must
be at least one \keyword entry, but can be more that 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/base/man/rivers.Rd
which documents the standard R data set rivers.
\name{rivers}
\alias{rivers}
\title{Lengths of Major North American Rivers}
\description{
This data set gives the lengths (in miles) of 141 ``major''
rivers in North America, as compiled by the US Geological
Survey.
}
\usage{data(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.
\format{...}
\source{...}
\references could give secondary sources and
usages.
Note also that when documenting data set bar,
\usage entry is always data(bar).
\keyword entry is always datasets.
If bar is a data frame, documenting it as a data set can again be
initiated via prompt(bar).
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 the additional named sections are always inserted at fixed
positions in the output (before \note, \seealso and the
examples), no matter where they appear in the input.
The following logical markup commands are available for indicating specific kinds of text.
\bold{word}set word in bold font if possible \emph{word}emphasize word using italic font if possible \code{word}for pieces of code, using typewriterfont if possible\file{word}for file names \email{word}for email addresses \url{word}for URLs
The first two, \bold and \emph, should be used
in plain text for emphasis.
Fragments of R code, including the names of R objects, should be
marked using \code. Only backslashes and percent signs need to
be escaped (by a backslash) inside \code.
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
centering.) 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
page for object foo. One main usage of \link is in the
\seealso section of the help page, see Rd format. (This only
affects the creation of hyperlinks, for example in the HTML pages
used by help.start() and the PDF version of the reference
manual.)
There are optional arguments specified as
\link[pkg]{foo} and
\link[pkg:bar]{foo} to link to the package
pkg with topic (file?) foo and bar respectively.
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" formula (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" and "control" characters % and \
always need to be escaped. Inside the verbatim-like commands
(\code and \examples), no other2
characters are special. Note that \file is not a
verbatim-like command.
In "regular" text (no verbatim, no \eqn, ...), you
currently must escape most LaTeX special characters, i.e., besides
%, {, and }, the four 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.
Sometimes the documentation needs to differ by platform. Currently
three OS-specific options are available, unix, windows and
mac, 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, windows or mac subdirectory.
Under UNIX versions of R there are several commands to process Rd files. Windows equivalents are described at the end of the section. 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.
Using R CMD Rdindex one can produce nicely formatted index files
displaying names and titles of the Rd files specified as arguments.
This can be used to create the INDEX of an add-on package and, if
it also contains data, the 00Index data index in the data
directory. Note that the R package builder R CMD build can be
used to automatically create these indices when building a package.
Finally, 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.
The exact usage and a detailed list of available options for each 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.
All of these have Windows equivalents. For most just replace R
CMD by Rcmd, with the exception that it is Rcmd Rd2dvi.sh
(and that needs the tools to build packages from source to be
installed). (You will need the files in the R binary Windows
distribution for installing source packages to be installed.)
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, with consistent indentation,
spaces around operators and consistent use of the preferred assignment
operator <-. This tidied version is much easier to read, not
least by other users who are used to the standard format.
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 (Unix) or Rterm --vanilla < tidy.R (Windows)
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 most Unix-like versions of R,
R has to be built to enable this, by supplying the option
--enable-R-profiling, profiling being enabled in a default
build. Profiling is also available on Windows, but not on the Macintosh.
The command Rprof is used to control profiling, and its help
page can be consulted for full details. Profiling works by recording at
fixed intervals (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 utility R CMD Rprof
Rprof.out can be used to summarize the activity. (Use Rcmd
Rprof Rprof.out on Windows.)
As an example, consider the following code (from Venables & Ripley, 1999).
library(MASS); library(boot); library(nls)
data(stormer)
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.
Two warnings: profiling does impose a small performance penalty, and the output files can be very large if long runs are profiled.
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).
.C and .FortranThese 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 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. (The mapping to the symbol name in the load table is given by the
functions symbol.C and symbol.For;
that the symbol is loaded can be tested by, for example,
is.loaded(symbol.C("loglin")).)
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*255
C type Rcomplex is a structure with double members
r and i defined in the header file Complex.h
included by R.h. 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.
It is possible to pass numeric vectors of storage mode double to
C as float * or FORTRAN as REAL by setting the attribute
Csingle, most conveniently by using the R functions
as.single, single or storage.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.
Finally, argument PACKAGE confines the search for the symbol name
to a specific shared library (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.
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 library (Unix, see Creating shared libraries for more information) or DLL (Windows).
The library/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 library. Programmers should not assume a specific file
extension for the library (such as .so) but use a construction
like
file.path(path1, path2, paste("mylib", .Platform$dynlib.ext, sep=""))
for platform independence. On Unix 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 library/DLL name with the extension
omitted.
Under some Unix systems there is a choice of how the symbols are
resolved when the library 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.
If a library/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.
Under Unix, shared libraries 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 C, C++, or FORTRAN
sources (with extensions .c, .cc or .cpp or
.C, and .f, respectively). See R CMD SHLIB --help,
or the on-line 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, and PKG_FFLAGS (for the
C, C++, and FORTRAN compilers, respectively) in the file Makevars
in the compilation directory, or write a Makefile in the
compilation directory containing the rules required (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 library.
If an add-on package pkg contains C, C++, or FORTRAN code in its
src subdirectory, R CMD INSTALL creates a shared library
(for loading into R in the .First.lib function of the package)
either automatically using the above R CMD SHLIB mechanism, or
using make if directory src contains a Makefile.
In both cases, if file Makevars exists it is read first when
invoking make. If a Makefile is really needed or
provided, it needs to ensure that the shared library created is linked
against all FORTRAN 77 intrinsic and run-time libraries that R was
linked against; Make variable FLIBS contains this information.
The Windows equivalent is the command Rcmd SHLIB; files
Makevars.win or Makefile.win are used in preference to
Makevars or Makefile if they exist. (This does need the
files in the R binary Windows distribution for installing source
packages to be installed.)
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.h>
#include "X.hh"
static Y y;
X::X() { cout << "constructor X" << endl; }
X::~X() { cout << "destructor X" << endl; }
Y::Y() { cout << "constructor Y" << endl; }
Y::~Y() { cout << "destructor Y" << 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 Unix system
(support for C++ was added in R version 1.1), one can simply use
R CMD SHLIB X.cc X_main.cc
to create the shared library, 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.
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
rather 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 list 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 higher-level 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 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 public subset of these is defined in
the header file Rinternals.h in the directory
R_HOME/include that should be available on any R
installation.
A more recent 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 certainly to be preferred if the code might be
shared with S at any stage.
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 SEXP3, 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, 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. (Prior to R 1.2, objects could be moved, too.)
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. 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.
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 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);
and then those 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);
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).
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 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: this is more or less completely hidden if
the Rdefines.h macros 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 ...objectNILSXPNULL SYMSXPname/symbol CLOSXPfunction or function closure ENVSXPenvironment
Among the important internal SEXPTYPEs are LANGSXP,
CHARSXP etc.
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 re-used 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.
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) .Call("out", as.double(x), as.double(y))
where we expect x and y to be numeric vectors, 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;
SEXP ans;
nx = length(x); ny = length(y);
PROTECT(ans = allocMatrix(REALSXP, nx, ny));
for(i = 0; i < nx; i++) {
tmp = REAL(x)[i];
for(j = 0; j < ny; j++)
REAL(ans)[i + nx*j] = tmp * REAL(y)[j];
}
UNPROTECT(1);
return(ans);
}
but 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)
{
int i, j, nx, ny;
double tmp;
SEXP ans, dim, dimnames;
nx = length(x); ny = length(y);
PROTECT(ans = allocVector(REALSXP, nx*ny));
for(i = 0; i < nx; i++) {
tmp = REAL(x)[i];
for(j = 0; j < ny; j++)
REAL(ans)[i + nx*j] = tmp * REAL(y)[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
names<-, dim<- and dimnames<-, 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) = 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 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 has moved 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 nls) is very useful:
/* get the list element named str, or return NULL */
SEXP getListElement(SEXP list, 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];
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.4 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).
.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.
.CallLet 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)
{
int 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.
.ExternalWe 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;
char *name;
if((nargs = length(args) - 1) > 0) {
for(i = 0; i < nargs; i++) {
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
showArgs <- function(...) .Call("showArgs1", list(...))
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
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)
{
int 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)
{
int 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 `list' of two elements, but
this will only be clear to those with a knowledge of a LISP-like
language.
In this section we re-work the example of call_S in Becker,
Chambers & Wilks (1988) on finding a zero of a univariate function,
which used to be used as an example for call_R in the now defunct
demo(dynload). 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);
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)));
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++)
REAL(gradient)[j + start] =
(REAL(ans1)[j] - REAL(ans)[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 representation 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++)
REAL(gradient)[j + start] =
(REAL(ans1)[j] - REAL(ans)[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.
Sooner or later programmers will be faced with the need to debug compiled code loaded into R. 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 use
dyn.load or library to load your
library.
Under Windows the R engine is itself in a DLL, and the procedure is
WinMain.
gdb .../bin/Rgui.exe (gdb) break WinMain (gdb) run [ stops with R.dll loaded ] (gdb) break R_ReadConsole (gdb) continue [ stops with console running ] (gdb) continue
dyn.load or library to load your library.
(gdb) clear R_ReadConsole (gdb) continue
to continue running with the breakpoints set.
Windows has little support for signals, so the usual idea of running a program under a debugger and sending it a signal to interrupt it and drop control back to the debugger only works with some debuggers.
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 to 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
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_HOME/include/R_ext, and there are other header files
there that can be included too, but many of the features they contain
should be regarded as undocumented and unstable.
An alternative is to include the header file S.h, which may be
useful when porting code from S. This includes rather less than
R.h, and has some compatibility definitions (for example the
S_complex type from S).
Most of these header files, including all those included by R.h,
can be used from C++ code.
Note: Because R re-maps many of its external names to avoid clashes with user code, it is essential to include the appropriate header files when using these entry points.
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(long n, int size)
which allocates n units of size bytes each. A typical usage (from package mva) is
x = (int *) R_alloc(nrows(merge)+2, sizeof(int));
There is a similar call, S_alloc, for compatibility with S,
which differs only in zeroing the memory allocated, and
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.
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.
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 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.)
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.)
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.
It is possible to compile R on a platform without IEC 559
(more commonly known as IEEE 754)-compatible arithmetic, so
users should not assume that it is available. Rather a set of functions
is provided to test for NA, Inf, -Inf (which exists
on all platforms) 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 function R_IsNaN is true for NaN but not NA.
Do use these rather than isnan or finite; the latter in
particular is often mendacious.
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.
Beware that these special values may be represented by extreme values which could occur in ordinary computations which run out of control, so you may need to test that they have not been generated inadvertently.
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 the
analogues using the vprintf interface.
In theory 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 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.
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 modreg, 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 file R_ext/Linpack.h contains details of the BLAS,
LINPACK and 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 nf(*)n1,n2,ncpgamma gammashape,scalegeometric geomphypergeometric hyperNR,NB,nlogistic logislocation,scalelognormal lnormlogmean,logsdnegative binomial nbinomn,pnormal normmu,sigmaPoisson poislambdaStudent's t tnnon-central t nt(*)df,deltaStudentized range tukey(*)rr,cc,dfuniform unifa,bWeibull weibullshape,scaleWilcoxon rank sum wilcoxm,nWilcoxon signed rank signrankn
Entries marked only have p and q functions available.
The argument names are not all quite the same as the R ones.
| double gammafn (double x) | Function |
| double lgammafn (double x) | Function |
| double digamma (double x) | Function |
| double trigamma (double x) | Function |
| double tetragamma (double x) | Function |
| double pentagamma (double x) | Function |
| The Gamma function, its natural logarithm and first four derivatives. |
| double beta (double a, double b) | Function |
| double lbeta (double a, double b) | Function |
| The (complete) Beta function and its natural logarithm. |
| double choose (double n, double k) | Function |
| double lchoose (double n, double k) | Function |
| The number of combinations of k items chosen from from n and its natural logarithm. n and k are rounded to the nearest integer. |
| double bessel_i (double x, double nu, double expo) | Function |
| double bessel_j (double x, double nu) | Function |
| double bessel_k (double x, double nu, double expo) | Function |
| double bessel_y (double x, double nu) | Function |
Bessel functions of types I, J, K and Y with index nu. For
bessel_i and bessel_k there is the option to return
exp(-x) I(x; nu) or exp(x) K(x; nu) if expo is 2. (Use expo == 1 for unscaled
values.)
|
There are a few other numerical utility functions available as entry points.
| double R_pow (double x, double y) | Function |
| double R_pow_di (double x, int i) | Function |
R_pow(x, y) and R_pow_di(x, i)
compute x^y and x^i, respectively
using R_FINITE checks and returning the proper result (the same
as R) for the cases where x, y or i are 0 or
missing or infinite or NaN.
|
| double pythag (double a, double b) | Function |
pythag(a, b) computes sqrt(a^2 +
b^2) without overflow or destructive underflow: for example it
still works when both a and b are between 1e200 and
1e300 (in IEEE double precision).
|
| double log1p (x) | Function |
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 |
| double expm1 (x) | Function |
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 |
| int imax2 (int x, int y) | Function |
| int imin2 (int x, int y) | Function |
| double fmax2 (double x, double y) | Function |
| double fmin2 (double x, double y) | Function |
Return the larger (max) or smaller (min) of two integer or
double numbers, respectively.
|
| double sign (double x) | Function |
| Compute the signum function, where sign(x) is 1, 0, or -1, when x is positive, 0, or negative, respectively. |
| double fsign (double x, double y) | Function |
| Performs "transfer of sign" and is defined as |x| * sign(x). |
| double fprec (double x, double digits) | Function |
|
Returns the value of x rounded to digits decimal digits
(after the decimal point).
This is the function used by R's |
| double fround (double x, double digits) | Function |
|
Returns the value of x rounded to digits significant
decimal digits.
This is the function used by R's |
| double ftrunc (double x) | Function |
| 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) defined in
the included header R_ext/Constants.h, the latter two 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 double 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 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 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 = 1
inf = -1
inf = 2
f 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 include the following.
| void R_isort (int* x, int n) | Function |
| void R_rsort (double* x, int n) | Function |
| void R_csort (Rcomplex* x, int n) | Function |
| void rsort_with_index (double* x, int* index, int n) | Function |
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.
|
| void revsort (double* x, int* index, int n) | Function |
Is similar to rsort_with_index but sorts into decreasing order,
and NAs are not handled.
|
| void iPsort (int* x, int n, int k) | Function |
| void rPsort (double* x, int n, int k) | Function |
| void cPsort (Rcomplex* x, int n, int k) | Function |
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.
|
| void R_qsort (double *v, int i, int j) | Function |
| void R_qsort_I (double *v, int *I, int i, int j) | Function |
| void R_qsort_int (int *iv, int i, int j) | Function |
| void R_qsort_int_I (int *iv, int *I, int i, int j) | Function |
|
These routines sort Note that |
| subroutine qsort4 (double precision v, integer indx, integerii, integer jj) | Function |
| subroutine qsort3 (double precision v, integer ii, integerjj) | Function |
|
The FORTRAN interface routines for sorting double precision vectors are
|
| void R_max_col (double* matrix, int* nr, int* nc, int* maxes) | Function |
Given the nr by ny matrix matrix in row ("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's max.col() function).
|
| int findInterval (double* xt, int n, double x, Rboolean rightmost_closed, Rboolean all_inside, int ilo, int* mflag) | Function |
Given the ordered vector xt of length n, return the interval
or index of x in xt[], typically max(i; 1 <= i <= n & xt[i] <=
x) where we use 1-indexing as in R and FORTRAN (but not C). If
rightmost_closed is true, also returns n-1 if x
equals xt[n]. If all_inside is not 0, the
result is coerced to lie in 1:(n-1) even when x is
outside the xt[] range. On return, *mflag 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 There is also an |
There is also the internal function use to expand file names in several
R functions, and called directly by path.expand.
| char * R_ExpandFileName (char* fn) | Function |
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.
|
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 us 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 (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(0, 99, 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 99.0).
It is possible to build Mathlib, the R set of mathematical
functions documented in Rmath.h, as a standalone library
libRmath under 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. 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. 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 shared library or DLL you will have to use the one supplied, which is the Marsaglia-multicarry with an entry point
set_seed(unsigned int, unsigned int)
to set its seeds).
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 formal methods of package methods of R 1.4.0 and later.
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.)
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 (in R 1.4.0) 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")? We are not going to give
you the answers because it is unreasonable that a user should be
expected to anticipate such behaviour. This leads to the
recommendation:
A method should use the same defaults as the generic.
Here there might be justifiable exceptions, which will need careful documentation.
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 <- get("foo", pos = NULL, mode = "function")
(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(... = )).)
Note that this cannot be used for functions in another package, as the order of packages on the search path cannot be controlled, except that all precede the base package.
.Internal and .PrimitiveC code compiled into R at build time can be called "directly" or
via the .Internal interface, which is very similar to the
.External interface except in syntax. More precisely, R
maintains a table of R function names and corresponding C functions to
call, which by convention all start with do_ and return a SEXP.
Via this table (R_FunTab in file src/main/names.c) one can
also specify how many arguments to a function are required or allowed,
whether the arguments are to be evaluated before calling or not, and
whether the function is "internal" in the sense that it must be
accessed via the .Internal interface, or directly accessible in
which case it is printed in R as .Primitive.
R's functionality can also be extended by providing corresponding C code and adding to this function table.
In general, all such functions use .Internal() as this is safer
and in particular allows for transparent handling of named and default
arguments. For example, axis is defined as
axis <- function(side, at = NULL, labels = NULL, ...)
.Internal(axis(side, at, labels, ...))
However, for reasons of convenience and also efficiency (as there is
some overhead in using the .Internal interface), there are
exceptions which can be accessed directly. Note that these functions
make no use of R code, and hence are very different from the usual
interpreted functions. In particular, args and body
return NULL for such objects.
The list of these "primitive" functions is subject to change: currently, it includes the following.
{ ( if for while repeat break next
return function quote on.exit
foo(a, b, ...)) for subsetting, assignment, arithmetic and logic.
These are the following 1-, 2-, and N-argument functions:
[ [[ $ <- <<- [<- [[<- $<- + - * / ^ %% %*% %/% < <= == != >= > | || & && !
sign abs floor ceiling trunc sqrt exp cos sin tan acos asin atan cosh sinh tanh acosh asinh atanh cumsum cumprod cummax cummin Im Re Arg Conj Mod
Note however that the R function log has an optional
named argument base, and therefore is defined as
log <- function(x, base = exp(1)) {
if(missing(base))
.Internal(log(x))
else
.Internal(log(x, base))
}
in order to ensure that log(x = pi, base = 2) is
identical to log(base = 2, x = pi).
nargs missing interactive is.xxx .Primitive .Internal .External symbol.C symbol.For globalenv pos.to.env unclass
(where xxx stands for almost 30 different notions, such as
function, vector, numeric, and so forth).
debug undebug trace untrace browser proc.time
length length<-
class class<-
attr attr<-
attributes attributes<-
dim dim<-
dimnames dimnames<-
environment<-
: ~ c list unlist call as.call expression substitute UseMethod invisible .C .Fortran .Call
When you (as R developer) add new functions to the R base (all the
packages distributed with R), be careful to check if make
test-Specific or particularly, cd tests; make no-segfault.Rout
still works (without interactive user intervention, and on a standalone
computer). If the new function, for example, accesses the Internet, or
requires GUI interaction, please add its name to the "stop
list" in tests/no-segfault.Rin.
R is meant to run on a wide variety of platforms, including Linux and most variants of Unix as well as 32-bit Windows versions and on the Power Mac. Therefore, when extending R by either adding to the R base distribution or by providing an add-on package, one should not rely on features specific to only a few supported platforms, if this can be avoided. In particular, although most R developers use GNU tools, they should not employ the GNU extensions to standard tools. Whereas some other software packages explicitly rely on e.g. GNU make or the GNU C++ compiler, R does not. Nevertheless, R is a GNU project, and the spirit of the GNU Coding Standards should be followed if possible.
The following tools can "safely be assumed" for R extensions.
f2c, the FORTRAN-to-C converter.
make, considering the features of make in
4.2 BSD systems as a baseline.
GNU or other extensions, including pattern rules using
%, the automatic variable $^, the += syntax to
append to the value of a variable, the ("safe") inclusion of makefiles
with no error, conditional execution, and many more, must not be used
(see Chapter "Features" in the GNU Make Manual for
more information). On the other hand, building R in a separate
directory (not containing the sources) should work provided that
make supports the VPATH mechanism.
Windows-specific makefiles can assume GNU make 3.75
or later, as no other make is viable on that platform.
grep, sed, and awk.
There are POSIX standards for these tools, but these may not
fully be supported, and the precise standards are typically hard to
access. Baseline features could be determined from a book such as
The UNIX Programming Environment by Brian W. Kernighan & Rob
Pike. Note in particular that | in a regexp is an extended
regexp, and is not supported by all versions of grep or
sed.
Under Windows, these tools can be assumed because versions
(specifically, of basename, cat, comm,
cp, cut, diff, echo,
egrep, expr, find, gawk,
grep, ls, mkdir, mv,
rm, sed, sort, tar,
touch, unzip, wc and zip) are
provided at <http://www.stats.ox.ac.uk/pub/Rtools/tools.zip>. However,
redirection cannot be assumed to be available via system as
this does not use a standard shell (let alone a Bourne shell).
In addition, the following tools are needed for certain tasks.
check and
build (see Checking and building packages), require Perl.
The R Core Team has decided that Perl (version 5) can safely be assumed for building R from source, building and checking add-on packages, and for installing add-on packages from source. On the other hand, Perl cannot be assumed at all for installing binary (pre-built) versions of add-on packages, or at run time.
It is also important that code is written in a way that allows others to
understand it. This is particularly helpful for fixing problems, and
includes using self-descriptive variable names, commenting the code, and
also formatting it properly. The R Core Team recommends to use a
basic indentation of 4 for R and C (and most likely also Perl) code,
and 2 for documentation in Rd format. Emacs users can implement this
indentation style by putting the following in one of their startup
files. (For GNU Emacs 20: for GNU Emacs 21 use
customization to set the c-default-style to "bsd" and
c-basic-offset to 4.)
;;; C
(add-hook 'c-mode-hook
(lambda () (c-set-style "bsd")))
;;; ESS
(add-hook 'ess-mode-hook
(lambda ()
(ess-set-style 'C++)
;; Because
;; DEF GNU BSD K&R C++
;; ess-indent-level 2 2 8 5 4
;; ess-continued-statement-offset 2 2 8 5 4
;; ess-brace-offset 0 0 -8 -5 -4
;; ess-arg-function-offset 2 4 0 0 0
;; ess-expression-offset 4 2 8 5 4
;; ess-else-offset 0 0 0 0 0
;; ess-close-brace-offset 0 0 0 0 0
(add-hook 'local-write-file-hooks
(lambda ()
(nuke-trailing-whitespace)))))
;;; Perl
(add-hook 'perl-mode-hook
(lambda () (setq perl-indent-level 4)))
(The `GNU' styles for Emacs' C and R modes use a basic indentation of 2, which has been determined not to display the structure clearly enough when using narrow fonts.)
.C: Interface functions .C and .Fortran
.Call: Calling .Call, Handling R objects in C
.External: Calling .External, Handling R objects in C
.Fortran: Interface functions .C and .Fortran
.Internal: .Internal and .Primitive
.Primitive: .Internal and .Primitive
.Random.seed: Random numbers
\alias: Rd format
\arguments: Rd format
\author: Rd format
\bold: Marking text
\code: Marking text
\cr: Sectioning
\deqn: Mathematics
\describe: Lists and tables
\description: Rd format
\details: Rd format
\dontrun: Rd format
\dots: Insertions
\email: Marking text
\emph: Marking text
\enumerate: Lists and tables
\eqn: Mathematics
\examples: Rd format
\file: Marking text
\format: Rd format
\itemize: Lists and tables
\ldots: Insertions
\link: Rd format
\method: Rd format
\name: Rd format
\note: Rd format
\R: Insertions
\references: Rd format
\section: Sectioning
\seealso: Rd format
\source: Rd format
\synopsis: Rd format
\tabular: Lists and tables
\testonly: Rd format
\title: Rd format
\url: Marking text
\usage: Rd format
\value: Rd format
bessel_i: Mathematical functions
bessel_j: Mathematical functions
bessel_k: Mathematical functions
bessel_y: Mathematical functions
beta: Mathematical functions
BLAS_LIBS: Using Makevars
Calloc: User-controlled
CAR: Calling .External
CDR: Calling .External
cgmin: Optimization
choose: Mathematical functions
cPsort: Utility functions
defineVar: Finding and setting variables
digamma: Mathematical functions
dyn.load: dyn.load and dyn.unload
dyn.unload: dyn.load and dyn.unload
exp_rand: Random numbers
expm1: Utilities
FALSE: Mathematical constants
findInterval: Utility functions
findVar: Finding and setting variables
FLIBS: Using Makevars
fmax2: Utilities
fmin2: Utilities
fprec: Utilities
Free: User-controlled
fround: Utilities
fsign: Utilities
ftrunc: Utilities
gammafn: Mathematical functions
getAttrib: Attributes
GetRNGstate: Random numbers
imax2: Utilities
imin2: Utilities
install: Attributes
iPsort: Utility functions
ISNA: Missing and special values, Missing and IEEE values
ISNAN: Missing and IEEE values, Missing and special values
lbeta: Mathematical functions
lbfgsb: Optimization
lchoose: Mathematical functions
lgammafn: Mathematical functions
library.dynam: dyn.load and dyn.unload
log1p: Utilities
M_E: Mathematical constants
M_PI: Mathematical constants
NA_REAL: Missing and IEEE values
nmmin: Optimization
norm_rand: Random numbers
pentagamma: Mathematical functions
PKG_CFLAGS: Creating shared libraries
PKG_CPPFLAGS: Creating shared libraries
PKG_CXXFLAGS: Creating shared libraries
PKG_FFLAGS: Creating shared libraries
PKG_LIBS: Creating shared libraries
prompt: Rd format
PROTECT: Garbage Collection
PutRNGstate: Random numbers
pythag: Utilities
qsort3: Utility functions
qsort4: Utility functions
R CMD build: Checking and building packages
R CMD check: Checking and building packages
R CMD config: Configure and cleanup
R CMD Rd2dvi: Processing Rd format
R CMD Rd2txt: Processing Rd format
R CMD Rdconv: Processing Rd format
R CMD Rdindex: Processing Rd format
R CMD Sd2Rd: Processing Rd format
R CMD SHLIB: Creating shared libraries
R_alloc: Transient
R_csort: Utility functions
R_ExpandFileName: Utility functions
R_FINITE: Missing and IEEE values
R_IsNaN: Missing and IEEE values
R_isort: Utility functions
R_LIBRARY_DIR: Configure and cleanup
R_max_col: Utility functions
R_NegInf: Missing and IEEE values
R_PACKAGE_DIR: Configure and cleanup
R_PosInf: Missing and IEEE values
R_pow: Utilities
R_pow_di: Utilities
R_qsort: Utility functions
R_qsort_I: Utility functions
R_qsort_int: Utility functions
R_qsort_int_I: Utility functions
R_rsort: Utility functions
R_Version: Platform and version information
Rdqagi: Integration
Rdqags: Integration
Realloc: User-controlled
REprintf: Printing
REvprintf: Printing
revsort: Utility functions
Rprintf: Printing
Rprof: Profiling R code
rPsort: Utility functions
rsort_with_index: Utility functions
Rvprintf: Printing
S_alloc: Transient
S_realloc: Transient
samin: Optimization
seed_in: Random numbers
seed_out: Random numbers
setAttrib: Attributes
setVar: Finding and setting variables
sign: Utilities
symbol.C: Interface functions .C and .Fortran
symbol.For: Interface functions .C and .Fortran
system: Operating system access
system.time: Operating system access
tetragamma: Mathematical functions
trigamma: Mathematical functions
TRUE: Mathematical constants
unif_rand: Random numbers
UNPROTECT: Garbage Collection
UNPROTECT_PTR: Garbage Collection
vmaxget: Transient
vmaxset: Transient
vmmin: Optimization
Note that Ratfor is not supported. If you have Ratfor source code, you need to convert it to FORTRAN. On many Solaris systems mixing Ratfor and FORTRAN code will work.
This is not quite
true. Unpaired braces will give problems and should be escaped. See
the examples section in the file Paren.Rd for an example.
SEXP is an acronym for Simple EXPression, common in LISP-like language syntaxes.
You can assign a copy of the object in the
environment frame rho using defineVar(symbol,
duplicate(value), rho)).