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Writing R Extensions

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

The current version of this document is 1.5.0 (2002-04-29). ISBN 3-901167-54-4

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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.

Node:Creating R packages, Next:, Previous:Acknowledgements, Up:Top

Creating R packages

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


before reading on.

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Package structure

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.

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The 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 <>, with
  contributions from A. User <>.
Maintainer: Friedrich Leisch <>
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.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.

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The INDEX file

The 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.

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Package subdirectories

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 or 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 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.

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Package bundles

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 <>, 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 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.

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Configure and cleanup

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


in (assuming Autoconf 2.50 or better).

The definition of the respective R function 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

if test -z "${R_HOME}"; then
  echo "could not determine R_HOME"
  exit 1
CC=`${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`
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 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.

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Using Makevars

Sometimes 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

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

A macro containing the set of libraries need to link FORTRAN code. This may need to be included in PKG_LIBS.
A macro containing the BLAS libraries used when building R. This may need to be included in PKG_LIBS. Beware that if it is empty then the R executable will contain all the double-precision BLAS routines, but no single-precision, complex nor double-complex routines.

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Checking and building packages

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.

  1. The package is installed. This will warn about missing cross-references and duplicate aliases in help files.
  2. The files and directories are checked for sufficient permissions (Unix only).
  3. The DESCRIPTION file is checked for completeness, and some of its entries for correctness.
  4. The R files are checked for syntax errors.
  5. The R files are checked for correct calls to 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.
  6. The Rd files are checked for the mandatory (\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.
  7. A check is made for undocumented user-level objects in the package.
  8. The functions and their documentation are compared, and any differences in the call sequences reported.
  9. It is checked whether all function arguments given in \usage sections of Rd files are documented in the corresponding \arguments section.
  10. C source and header files are tested for correct (CR-only) line endings.
  11. The examples provided by the package's documentation are run. (see Writing R help files, for information on using \examples to create executable example code.)

    Of course, released packages should be able to run at least their own examples.

  12. If the package sources contain a tests directory then the tests specified in that directory are run. (Typically they will consist of a set of .R source files and target output files
  13. The code in package vignettes (see Writing package vignettes) is executed.
  14. If a working 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 check and Rcmd build work 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.

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Writing package vignettes

In addition to the help files in Rd format, R packages allow the inclusion of documents in arbitrary other formats. The standard location for these is subdirectory inst/doc of a source package, the contents will be copied to subdirectory doc when the package is installed. Pointers from package help indices to the installed documents are automatically created. Documents in inst/doc can be in arbitrary format, however we strongly recommend 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.

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Submitting a package to CRAN

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.)

  1. Run 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.
  2. Run 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


and send a message to <> 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


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.,


are all equivalent and indicate an R source package, whereas


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.

Node:Writing R help files, Next:, Previous:Creating R packages, Up:Top

Writing R help files

Node:Rd format, Next:, Previous:Writing R help files, Up:Writing R help files

Rd format

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.

\title{Reload Saved Datasets}
  Reload the datasets written to a file with the function
load(file, envir = parent.frame())
  \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
## save all data
save(list = ls(), file= "all.Rdata")

## restore the saved values to the current environment

## restore the saved values to the workspace
load("all.Rdata", .GlobalEnv)

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.

Documenting functions

The basic markup commands used for documenting R objects (in particular, functions) are given in this subsection.

name typically is the basename of the Rd file containing the documentation. (It is the "name" of the Rd object represented by the file, and has to be unique in a package.)
The \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


Note that the \name is not necessarily a topic documented.

Title information for the Rd file. This should be capitalized, not end in a period, and not use any markup (which would cause problems for hypertext search).
A short description of what the function(s) do(es) (one paragraph, a few lines only). (If a description is "too long" and cannot easily be shortened, the file probably tries to document too much at once.)
\usage{fun(arg1, arg2, ...)}
One or more lines showing the synopsis of the function(s) and variables documented in the file. These are set verbatim in typewriter font.

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

abline(a = NULL, b = NULL, h = NULL, v = NULL, reg = NULL,
       coef = NULL, untf = FALSE, col = par("col"),
       lty = par("lty"), lwd = NULL, ...)
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

\method{print}{ts}(x, calendar, \dots)

Description of the function's arguments, using an entry of the form
\item{arg_i}{Description of arg_i.}

for each element of the argument list. There may be optional text before and after these entries.

A detailed if possible precise description of the functionality provided, extending the basic information in the \description slot.
Description of the function's return 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).

A section with references to the literature. Use \url{} for web pointers.
Use this for a special note you want to have pointed out.

For example, piechart.Rd contains

  Pie charts are a very bad way of displaying information.
  The eye is good at judging linear measures and bad at
  judging relative areas.

Information about the author(s) of the Rd file. Use \email{} without extra delimiters (( ) or < >) to specify email addresses, or \url{} for web pointers.
Pointers to related R objects, using \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 of how to use the function. These are set verbatim in typewriter font.

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).

Each \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.

Documenting datasets

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.

\title{Lengths of Major North American Rivers}
  This data set gives the lengths (in miles) of 141 ``major''
  rivers in North America, as compiled by the US Geological
\format{A vector containing 141 observations.}
\source{World Almanac and Book of Facts, 1975, page 406.}
  McNeil, D. R. (1977) \emph{Interactive Data Analysis}.
  New York: Wiley.

This uses the following additional markup commands.

A description of the format of the dataset (as a vector, matrix, data frame, time series, ...). For matrices and data frames this should give a description of each column, preferably as a list or table. See Lists and tables, for more information.
Details of the original source (a reference or URL). In addition, section \references could give secondary sources and usages.

Note also that when documenting data set bar,

If bar is a data frame, documenting it as a data set can again be initiated via prompt(bar).

Node:Sectioning, Next:, Previous:Rd format, Up:Writing R help files


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.

Node:Marking text, Next:, Previous:Sectioning, Up:Writing R help files

Marking text

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 typewriter font 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.

Node:Lists and tables, Next:, Previous:Marking text, Up:Writing R help files

Lists and tables

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:

    [,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).

Node:Cross-references, Next:, Previous:Lists and tables, Up:Writing R help files


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.

Node:Mathematics, Next:, Previous:Cross-references, Up:Writing R help files


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, ....

Node:Insertions, Next:, Previous:Mathematics, Up:Writing R help files


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.

Node:Platform-specific sections, Next:, Previous:Insertions, Up:Writing R help files

Platform-specific documentation

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


#ifndef OS

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.

Node:Processing Rd format, Previous:Platform-specific sections, Up:Writing R help files

Processing Rd format

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 (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.)

Node:Tidying and profiling R code, Next:, Previous:Writing R help files, Up:Top

Tidying and profiling R code

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.

Node:Tidying R code, Next:, Previous:Tidying and profiling R code, Up:Tidying and profiling R code

Tidying R code

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.

  1. The option keep.source can be set to FALSE before the code is loaded into R.
  2. The stored source code can be removed by removing the 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)
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.

Node:Profiling R code, Previous:Tidying R code, Up:Tidying and profiling R code

Profiling R code

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) <- nls(Time ~ b*Viscosity/(Wt - c), stormer,
                start = c(b=29.401, c=2.2183))
st <- cbind(stormer, fit=fitted( <- function(rs, i) {
    st$Time <-  st$fit + rs[i]
    tmp <- nls(Time ~ (b * Viscosity)/(Wt - c), st,
               start = coef(
rs <- scale(resid(, scale = FALSE) # remove the mean
storm.boot <- boot(rs,, R = 4999) # pretty slow

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.

Node:System and foreign language interfaces, Next:, Previous:Tidying and profiling R code, Up:Top

System and foreign language interfaces

Node:Operating system access, Next:, Previous:System and foreign language interfaces, Up:System and foreign language interfaces

Operating system access

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).

Node:Interface functions .C and .Fortran, Next:, Previous:Operating system access, Up:System and foreign language interfaces

Interface functions .C and .Fortran

These two functions provide a standard interface to compiled code that has been linked into R, either at build time or via dyn.load (see dyn.load and dyn.unload). They are primarily intended for compiled C and FORTRAN 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
logical int * INTEGER
integer int * INTEGER
double double * DOUBLE PRECISION
complex Rcomplex * DOUBLE COMPLEX
character char ** 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)
     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.

Node:dyn.load and dyn.unload, Next:, Previous:Interface functions .C and .Fortran, Up:System and foreign language interfaces

dyn.load and dyn.unload

Compiled 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.

Node:Creating shared libraries, Next:, Previous:dyn.load and dyn.unload, Up:System and foreign language interfaces

Creating shared libraries

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 or 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.)

Node:Interfacing C++ code, Next:, Previous:Creating shared libraries, Up:System and foreign language interfaces

Interfacing C++ code

Suppose we have the following hypothetical C++ library, consisting of the two files X.hh and, 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 ();

#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,


#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


to create the shared library, typically (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
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.

Node:Handling R objects in C, Next:, Previous:Interfacing C++ code, Up:System and foreign language interfaces

Handling R objects in C

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.

Node:Garbage Collection, Next:, Previous:Handling R objects in C, Up:Handling R objects in C

Handling the effects of garbage collection

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;
  NUMERIC_POINTER(ab)[0] = 123.45;
  NUMERIC_POINTER(ab)[1] = 67.89;

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;

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).

Node:Allocating storage, Next:, Previous:Garbage Collection, Up:Handling R objects in C

Allocating storage

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.

Node:Details of R types, Next:, Previous:Allocating storage, Up:Handling R objects in C

Details of R types

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
REALSXP numeric with storage mode double
INTSXP integer
CPLXSXP complex
LGLSXP logical
STRSXP character
VECSXP list (generic vector)
LISTSXP "dotted-pair" list
DOTSXP a ... object
SYMSXP name/symbol
CLOSXP function or function closure
ENVSXP environment

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));


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.

Node:Attributes, Next:, Previous:Details of R types, Up:Handling R objects in C


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];

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);


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);

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.

Node:Classes, Next:, Previous:Attributes, Up:Handling R objects in C


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);

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.

Node:Handling lists, Next:, Previous:Classes, Up:Handling R objects in C

Handling lists

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);
  return elmt;

and enables us to say

  double g;
  g = REAL(getListElement(a, "g"))[0];

Node:Finding and setting variables, Previous:Handling lists, Up:Handling R objects in C

Finding and setting variables

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");
    error("rho should be an environment");
  ans = findVar(install(CHAR(STRING_ELT(name, 0))), rho);
  printf("first value is %f\n", REAL(ans)[0]);

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).

Node:Interface functions .Call and .External, Next:, Previous:Handling R objects in C, Up:System and foreign language interfaces

Interface functions .Call and .External

In 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.

Node:Calling .Call, Next:, Previous:Interface functions .Call and .External, Up:Interface functions .Call and .External

Calling .Call

Let us convert our finite convolution example to use .Call, first using the Rdefines.h macros. The calling function in R is

conv <- function(a, b) .Call("convolve2", a, b)

which could hardly be simpler, but as we shall see all the type checking must be transferred to the C code, which is

#include <R.h>
#include <Rdefines.h>

SEXP convolve2(SEXP a, SEXP b)
  int i, j, na, nb, nab;
  double *xa, *xb, *xab;
  SEXP ab;

  na = LENGTH(a); nb = LENGTH(b); nab = na + nb - 1;
  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];

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];

This is called in exactly the same way.

Node:Calling .External, Next:, Previous:Calling .Call, Up:Interface functions .Call and .External

Calling .External

We can use the same example to illustrate .External. The R code changes only by replacing .Call by .External

conv <- function(a, b) .External("convolveE", a, b)

but the main change is how the arguments are passed to the C code, this time as a single SEXP. The only change to the C code is how we handle the arguments.

#include <R.h>
#include <Rinternals.h>

SEXP convolveE(SEXP args)
  int i, j, na, nb, nab;
  double *xa, *xb, *xab;
  SEXP a, b, ab;

  PROTECT(a = coerceVector(CADR(args), REALSXP));
  PROTECT(b = coerceVector(CADDR(args), REALSXP));

Once again we do not need to protect the arguments, as in the R side of the interface they are objects that are already in use. The macros

  first = CADR(args);
  second = CADDR(args);
  third = CADDDR(args);
  fourth = CAD4R(args);

provide convenient ways to access the first four arguments. More generally we can use the CDR and CAR macros as in

  args = CDR(args); a = CAR(args);
  args = CDR(args); b = CAR(args);

which clearly allows us to extract an unlimited number of arguments (whereas .Call has a limit, albeit at 65 not a small one).

More usefully, the .External interface provides an easy way to handle calls with a variable number of arguments, as length(args) will give the number of arguments supplied (of which the first is ignored). We may need to know the names (`tags') given to the actual arguments, which we can by using the TAG macro and using something like the following example, that prints the names and the first value of its arguments if they are vector types.

#include <R_ext/PrtUtil.h>

SEXP showArgs(SEXP args)
  int i, nargs;
  Rcomplex cpl;
  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]);
      case LGLSXP:
      case INTSXP:
        Rprintf("[%d] '%s' %d\n", i+1, name, INTEGER(CAR(args))[0]);
      case CPLXSXP:
        cpl = COMPLEX(CAR(args))[0];
        Rprintf("[%d] '%s' %f + %fi\n", i+1, name, cpl.r, cpl.i);
      case STRSXP:
        Rprintf("[%d] '%s' %s\n", i+1, name,
               CHAR(STRING_ELT(CAR(args), 0)));
        Rprintf("[%d] '%s' R type\n", i+1, name);

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(...))

Node:Missing and special values, Previous:Calling .External, Up:Interface functions .Call and .External

Missing and special values

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;
          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.

Node:Evaluating R expressions from C, Next:, Previous:Interface functions .Call and .External, Up:System and foreign language interfaces

Evaluating R expressions from C

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));

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));

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")
  .Call("zero", body(f.check), as.double(guesses), as.double(tol),

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;
    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.

Calculating numerical derivatives

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
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;

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);
  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.

Node:Debugging, Previous:Evaluating R expressions from C, Up:System and foreign language interfaces

Debugging compiled code

Sooner or later programmers will be faced with the need to debug compiled code loaded into R. Some "tricks" are worth knowing.

Node:Finding entry points, Next:, Previous:Debugging, Up:Debugging

Finding entry points in dynamically loaded code

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

Under Windows the R engine is itself in a DLL, and the procedure is

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.

Node:Inspecting R objects, Previous:Finding entry points, Up:Debugging

Inspecting R objects when debugging

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)
[1] "a" "b"

[1] "1" "2" "3"

[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

Node:The R API, Next:, Previous:System and foreign language interfaces, Up:Top

The R API: entry points for C code

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.

Node:Memory allocation, Next:, Previous:The R API, Up:The R API

Memory allocation

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).

Node:Transient, Next:, Previous:Memory allocation, Up:Memory allocation

Transient storage allocation

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.

Node:User-controlled, Previous:Transient, Up:Memory allocation

User-controlled memory

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.)

Node:Error handling, Next:, Previous:Memory allocation, Up:The R API

Error handling

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


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.

Error handling from FORTRAN

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.

Node:Random numbers, Next:, Previous:Error handling, Up:The R API

Random number generation

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


and after all the required variates have been generated, call


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.

Node:Missing and IEEE values, Next:, Previous:Random numbers, Up:The R API

Missing and IEEE special values

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's NA only
ISNAN(x)       True for R's NA and IEEE NaN
R_FINITE(x)    False for Inf, -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.

Node:Printing, Next:, Previous:Missing and IEEE values, Up:The R API


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.

Printing from FORTRAN

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.

Node:Calling C from FORTRAN and vice versa, Next:, Previous:Printing, Up:The R API

Calling C from FORTRAN and vice versa

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.

to define a function in C to be called from FORTRAN
to declare a FORTRAN routine in C before use
to call a FORTRAN routine from C
to declare a FORTRAN common block in C
to access a FORTRAN common block from C

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()

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.

Node:Numerical analysis subroutines, Next:, Previous:Calling C from FORTRAN and vice versa, Up:The R API

Numerical analysis subroutines

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.

Node:Distribution functions, Next:, Previous:Numerical analysis subroutines, Up:Numerical analysis subroutines

Distribution functions

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 beta a, b
non-central beta nbeta a, b, lambda
binomial binom n, p
Cauchy cauchy location, scale
chi-squared chisq df
non-central chi-squared nchisq df, lambda
exponential exp scale
F f n1, n2
non-central F nf (*) n1, n2, ncp
gamma gamma shape, scale
geometric geom p
hypergeometric hyper NR, NB, n
logistic logis location, scale
lognormal lnorm logmean, logsd
negative binomial nbinom n, p
normal norm mu, sigma
Poisson pois lambda
Student's t t n
non-central t nt (*) df, delta
Studentized range tukey (*) rr, cc, df
uniform unif a, b
Weibull weibull shape, scale
Wilcoxon rank sum wilcox m, n
Wilcoxon signed rank signrank n

Entries marked only have p and q functions available.

The argument names are not all quite the same as the R ones.

Node:Mathematical functions, Next:, Previous:Distribution functions, Up:Numerical analysis subroutines

Mathematical functions

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(xnu) or exp(x) K(xnu) if expo is 2. (Use expo == 1 for unscaled values.)

Node:Utilities, Next:, Previous:Mathematical functions, Up:Numerical analysis subroutines


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 Rmath.h, but is (probably) in math.h. For backwards compatibility with R versions prior to 1.5.0, the entry point Rf_log1p is still provided.

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 Rmath.h, but is (probably) in math.h.

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 round().

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 signif().

double ftrunc (double x) Function
Returns the value of x truncated (to an integer value) towards zero.

Node:Mathematical constants, Previous:Utilities, Up:Numerical analysis subroutines

Mathematical constants

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_E e 2.7182818
M_LOG2E log2(e) 1.4426950
M_LOG10E log10(e) 0.4342945
M_LN2 ln(2) 0.6931472
M_LN10 ln(10) 2.3025851
M_PI pi 3.1415927
M_PI_2 pi/2 1.5707963
M_PI_4 pi/4 0.7853982
M_1_PI 1/pi 0.3183099
M_2_PI 2/pi 0.6366198
M_2_SQRTPI 2/sqrt(pi) 1.1283792
M_SQRT2 sqrt(2) 1.4142136
M_SQRT1_2 1/sqrt(2) 0.7071068
M_SQRT_3 sqrt(3) 1.7320508
M_SQRT_32 sqrt(32) 5.6568542
M_LOG10_2 log10(2) 0.3010300
M_2PI 2*pi 6.2831853
M_SQRT_PI sqrt(pi) 1.7724539
M_1_SQRT_2PI 1/sqrt(2*pi) 0.3989423
M_SQRT_2dPI sqrt(2/pi) 0.7978846
M_LN_SQRT_PI ln(sqrt(pi)) 0.5723649
M_LN_SQRT_2PI ln(sqrt(2*pi)) 0.9189385
M_LN_SQRT_PId2 ln(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.

Node:Optimization, Next:, Previous:Numerical analysis subroutines, Up:The R API


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

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.

Node:Integration, Next:, Previous:Optimization, Up:The R API


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.

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
corresponds to (bound, +Inf),
inf = -1
corresponds to (-Inf, bound),
inf = 2
corresponds to (-Inf, +Inf),

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).

Node:Utility functions, Next:, Previous:Integration, Up:The R API

Utility functions

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.

rsort_with_index sorts on x, and applies the same permutation to index. 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 v[i:j] or iv[i:j] (using 1-indexing, i.e. v[1] is the first element) calling the quicksort algorithm as used by R's sort(v, method = "quick") and documented on the help page for the R function sort. The ..._I() versions also return the sort.index() vector in I. Note that the ordering is not stable, so tied values may be permuted.

Note that NAs are not handled (explicitly) and you should use different sorting functions if NAs can be present.

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 qsort3 and qsort4, equivalent to R_qsort and R_qsort_I respectively.

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 findInterval() and x is a value of a sequence which is increasing or decreasing for subsequent calls.

There is also an F77_CALL(interv)() version of findInterval() with the same arguments, but all pointers.

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.

Node:Platform and version information, Next:, Previous:Utility functions, Up:The R API

Platform and version information

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)

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).

Node:Standalone Mathlib, Previous:Platform and version information, Up:The R API

Using these functions in your own C code

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

#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).

Node:Generic functions and methods, Next:, Previous:The R API, Up:Top

Generic functions and methods

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 <- function(x)
    x <- as.matrix(x)

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)

Suppose an unthinking package writer created methods such as <- 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 <- 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.

Node:Adding new generics, Previous:Generic functions and methods, Up:Generic functions and methods

Adding new generics

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.

Node:R (internal) programming miscellanea, Next:, Previous:Generic functions and methods, Up:Top

R (internal) programming miscellanea

Node:.Internal and .Primitive, Next:, Previous:R (internal) programming miscellanea, Up:R (internal) programming miscellanea

.Internal and .Primitive

C 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.

  1. "Special functions" which really are language elements, however exist as "primitive" functions in R:
    {       (         if     for      while  repeat  break  next
    return  function  quote  on.exit
  2. Basic operators (i.e., functions usually not called as foo(a, b, ...)) for subsetting, assignment, arithmetic and logic. These are the following 1-, 2-, and N-argument functions:
              [    [[    $
    <-   <<-  [<-  [[<-  $<-
    +    -    *    /     ^    %%   %*%  %/%
    <    <=   ==   !=    >=   >
    |    ||   &    &&    !
  3. "Low level" 0- and 1-argument functions which belong to one of the following groups of functions:
    1. Basic mathematical functions with a single argument, i.e.,
      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)) {
          .Internal(log(x, base))

      in order to ensure that log(x = pi, base = 2) is identical to log(base = 2, x = pi).

    2. Functions rarely used outside of "programming" (i.e., mostly used inside other functions), such as
      nargs        missing
      .Primitive   .Internal   .External
      symbol.C     symbol.For
      globalenv  unclass

      (where xxx stands for almost 30 different notions, such as function, vector, numeric, and so forth).

    3. The programming and session management utilities
      debug    undebug    trace  untrace
      browser  proc.time
  4. The following basic assignment and extractor functions
    length      length<-
    class       class<-
    attr        attr<-
    attributes  attributes<-
    dim         dim<-
    dimnames    dimnames<-
  5. The following few N-argument functions are "primitive" for efficiency reasons. Care is taken in order to treat named arguments properly:
    :          ~          c           list        unlist
    call    expression  substitute
    UseMethod  invisible
    .C         .Fortran   .Call

Node:Testing R code, Previous:.Internal and .Primitive, Up:R (internal) programming miscellanea

Testing R code

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.

Node:R coding standards, Next:, Previous:R (internal) programming miscellanea, Up:Top

R coding standards

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.

In addition, the following tools are needed for certain tasks.

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 ()
;;; 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.)

Node:Function and variable index, Next:, Previous:R coding standards, Up:Top

Function and variable index

Node:Concept index, Previous:Function and variable index, Up:Top

Concept index


  1. 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.

  2. 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.

  3. SEXP is an acronym for Simple EXPression, common in LISP-like language syntaxes.

  4. You can assign a copy of the object in the environment frame rho using defineVar(symbol, duplicate(value), rho)).