Writing R Extensions

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

The current version of this document is 2.4.0- (2006-10-03).

ISBN 3-900051-11-9



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

Creating R packages

Packages provide a mechanism for loading optional code and attached documentation as needed. The R distribution provides several packages.

In the following, we assume that you know the `library()' command, including its `lib.loc' argument, and we also assume basic knowledge of the INSTALL utility. Otherwise, please look at R's help pages


before reading on.

Once a source package is created, it must be installed by the command R CMD INSTALL. See Add-on-packages, for further details.

Other types of extensions are supported: See Package types.

Package structure

A package consists of a subdirectory containing a file DESCRIPTION and the subdirectories R, data, demo, exec, inst, man, po, src, and tests (some of which can be missing). The package subdirectory may also contain files INDEX, NAMESPACE, configure, cleanup, and COPYING. Other files such as README, NEWS or ChangeLog will be ignored by R, but may be useful to end-users.

The DESCRIPTION and INDEX files are described in the sections below. The NAMESPACE file is described in Package name spaces.

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

The optional file COPYING contains a copy of the license to the package, e.g. a copy of the GNU public license. Whereas you should feel free to include a licence file in your source distribution, please do not arrange to install yet another copy of the GNU COPYING or COPYING.LIB files but refer to the copies in the R distribution (e.g., in directory share/licenses in your own COPYING file).

The package subdirectory should be given the same name as the package. Because some file systems (e.g., those on Windows) are not case-sensitive, to maintain portability it is strongly recommended that case distinctions not be used to distinguish different packages. For example, if you have a package named foo, do not also create a package named Foo.

To ensure that file names are valid across file systems and supported operating system platforms, the ASCII control characters as well as the characters `"', `*', `:', `/', `<', `>', `?', `\', and `|' are not allowed in file names. In addition, files with names `con', `prn', `aux', `clock$', `nul', `com1' to `com4', and `lpt1' to `lpt3' after conversion to lower case and stripping possible “extensions”, are disallowed. Also, file names in the same directory must not differ only by case (see the previous paragraph). In addition, the names of `.Rd' files will be used in URLs and so must be ASCII and not contain %.

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


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

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

Continuation lines (for example, for descriptions longer than one line) start with a space or tab. The `Package', `Version', `License', `Description', `Title', `Author', and `Maintainer' fields are mandatory, the remaining fields (`Date', `Depends', `URL', ...) are optional.

The DESCRIPTION file should be written entirely in ASCII for maximal portability.

The `Package' and `Version' fields give the name and the version of the package, respectively. The name should consist of letters, numbers, and the dot character and start with a letter. The version is a sequence of at least two (and usually three) non-negative integers separated by single `.' or `-' characters. The canonical form is as shown in the example, and a version such as `0.01' or `0.01.0' will be handled as if it were `0.1-0'. (Translation packages are allowed names of the form `Translation-ll'.)

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

The `Title' field should give a short description of the package. Some package listings may truncate the title to 65 characters in order to keep the overall size of the listing limited. It should be capitalized, not use any markup, not have any continuation lines, and not end in a period. Older versions of R used a separate file TITLE for giving this information; this is now defunct, and the `Title' field in DESCRIPTION is required.

The `Author' field describes who wrote the package. It is a plain text field intended for human readers, but not for automatic processing (such as extracting the email addresses of all listed contributors).

The `Maintainer' field should give a single name with a valid email address in angle brackets (for sending bug reports etc.). It should not end in a period or comma.

The optional `Date' field gives the release date of the current version of the package. It is strongly recommended to use the yyyy-mm-dd format conforming to the ISO standard.

The optional `Depends' field gives a comma-separated list of package names which this package depends on. The package name may be optionally followed by a comparison operator (currently only `>=' and `<=' are supported), whitespace and a valid version number in parentheses. (List package names even if they are part of a bundle.) You can also use the special package name `R' if your package depends on a certain version of R. E.g., if the package works only with R version 2.4.0 or newer, include `R (>= 2.4.0)' in the `Depends' field. Both library and the R package checking facilities use this field, hence it is an error to use improper syntax or misuse the `Depends' field for comments on other software that might be needed. Other dependencies (external to the R system) should be listed in the `SystemRequirements' field or a separate README file. The R INSTALL facilities check if the version of R used is recent enough for the package being installed, and the list of packages which is specified will be attached (after checking version dependencies) before the current package, both when library is called and when saving an image of the package's code or preparing for lazy-loading.

The optional `Imports' field lists packages whose name spaces are imported from but which do not need to be attached. Name spaces accessed by the `::' and `:::' operators must be listed here, or in `Suggests' or `Enhances' (see below). Ideally this field will include all the standard packages, and it is important to include S4-using packages (as their class definitions can change and the DESCRIPTION file is used to decide which packages to re-install when this happens).

The optional `Suggests' field uses the same syntax as `Depends' and lists packages that are not necessarily needed. This includes packages used only in examples or vignettes (see Writing package vignettes), and packages loaded in the body of functions. E.g., suppose an example from package foo uses a dataset from package bar. Then it is not necessary to have bar for routine use of foo, unless one wants to execute the examples: it is nice to have bar, but not necessary.

Finally, the optional `Enhances' field lists packages “enhanced” by the package at hand, e.g., by providing methods for classes from these packages.

The general rules are

  • Packages whose name space only is needed to load the package using library(pkgname) must be listed in the `Imports' field.
  • Packages that need to be attached to successfully load the package using library(pkgname) must be listed in the `Depends' field.
  • All packages that are needed to successfully run R CMD check on the package must be listed in one of `Depends' or `Suggests' or `Imports'.

In particular, large packages providing “only” data for examples or vignettes should be listed in `Suggests' rather than `Depends' in order to make lean installations possible.

The optional `URL' field may give a list of URLs separated by commas or whitespace, for example the homepage of the author or a page where additional material describing the software can be found. These URLs are converted to active hyperlinks on CRAN.

Base and recommended packages (i.e., packages contained in the R source distribution or available from CRAN and recommended to be included in every binary distribution of R) have a `Priority' field with value `base' or `recommended', respectively. These priorities must not be used by “other” packages.

An optional `Collate' field (or OS-specific variants `Collate.OStype', such as e.g. `Collate.windows') can be used for controlling the collation order for the R code files in a package when these are concatenated into a single file upon installation from source. The default is to try collating according to the `C' locale. If present, the collate specification must list all R code files in the package (taking possible OS-specific subdirectories into account, see Package subdirectories) as a whitespace separated list of file paths relative to the R subdirectory. Paths containing white space or quotes need to be quoted. Applicable OS-specific collate specifications take precedence.

The optional `LazyLoad' and `LazyData' fields control whether the R objects and the datasets (respectively) use lazy-loading: set the field's value to `yes' or `true' for lazy-loading and `no' or `false' for no lazy-loading. (Capitalized values are also accepted.)

The optional `SaveImage' field controls whether the R objects are stored in a saved image. (It takes the same values as the field `LazyLoad'.)

If the package you are writing uses the methods package, specify (preferably) `LazyLoad: yes' or `SaveImage: yes'.

The optional `ZipData' field controls whether the automatic Windows build will zip up the data directory or no: set this to `no' if your package will not work with a zipped data directory.

If the DESCRIPTION file is not entirely in ASCII it should contain an `Encoding' field specifying an encoding. This is currently used as the encoding of the DESCRIPTION file itself, and may in the future be taken as the encoding for other documentation in the package. Only encoding names latin1, latin2 and UTF-8 are known to be portable.

The optional `Type' field specifies the type of the package: see Package types.

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

The INDEX file

The optional file INDEX contains a line for each sufficiently interesting object in the package, giving its name and a description (functions such as print methods not usually called explicitly might not be included). Normally this file is missing, and the corresponding information is automatically generated from the documentation sources (using Rdindex() from package tools) when installing from source and when using the package builder (see Checking and building packages).

Rather than editing this file, it is preferable to put customized information about the package into an overview man page (see Documenting packages) and/or a vignette (see Writing package vignettes).

Package subdirectories

The R subdirectory contains R code files, only. The code files to be installed must start with a (lower or upper case) letter or digit and have one of the extensions .R, .S, .q, .r, or .s. We recommend using .R, as this extension seems to be not used by any other software. It should be possible to read in the files using source(), so R objects must be created by assignments. Note that there need be no connection between the name of the file and the R objects created by it. The R code files should only create R objects and not call functions with side effects such as require and options.

Two exceptions are allowed: if the R subdirectory contains a file sysdata.rda (a saved image of R objects) this will be lazy-loaded into the name space/package environment – this is intended for system datasets that are not intended to be user-accessible via data. Also, files ending in `.in' will be allowed in the R directory to allow a configure script to generate suitable files,

Only ASCII characters (and the control characters tab, formfeed, LF and CR) should be used in code files. Other characters are accepted in comments, but then the comments may not be readable in e.g. a UTF-8 locale. Non-ASCII characters in object names will normally1 fail when the package is installed. Any byte will be allowed2 in a quoted character string (but \uxxxx escapes should not be used), but non-ASCII character strings may not be usable in some locales and may display incorrectly in others.

Various R functions in a package can be used to initialize and clean up. For packages without a name space, these are .First.lib and .Last.lib. (See Load hooks, for packages with a name space.) It is conventional to define these functions in a file called zzz.R. If .First.lib is defined in a package, it is called with arguments libname and pkgname after the package is loaded and attached. (If a package is installed with version information, the package name includes the version information, e.g. `ash_1.0.9'.) A common use is to call library.dynam inside .First.lib to load compiled code: another use is to call those functions with side effects. If .Last.lib exists in a package it is called (with argument the full path to the installed package) just before the package is detached. It is uncommon to detach packages and rare to have a .Last.lib function: one use is to call library.dynam.unload to unload compiled code.

The man subdirectory should contain (only) documentation files for the objects in the package in R documentation (Rd) format. The documentation files to be installed must start with a (lower or upper case ASCII) letter or digit and have the extension .Rd (the default) or .rd. Further, the names must be valid in `file://' URLs, which means3 they must be entirely ASCII and not contain `%'. See Writing R documentation files, for more information. Note that all user-level objects in a package should be documented; if a package pkg contains user-level objects which are for “internal” use only, it should provide a file pkg-internal.Rd which documents all such objects, and clearly states that these are not meant to be called by the user. See e.g. the sources for package grid in the R distribution for an example. Note that packages which use internal objects extensively should hide those objects in a name space, when they do not need to be documented (see Package name spaces).

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

The C, C++, or FORTRAN4 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 object for loading into R. There are default variables and rules for this (determined when R is configured and recorded in R_HOME/etc/Makeconf). These rules can be tweaked by setting macros in a file src/Makevars (see Using Makevars). Note that this mechanism should be general enough to eliminate the need for a package-specific Makefile. If such a file is to be distributed, considerable care is needed to make it general enough to work on all R platforms. In addition, it should have a target `clean' which removes all files generated by Make. If necessary, platform-specific files can be used, for example Makevars.win or Makefile.win on Windows take precedence over Makevars or Makefile.

The data subdirectory is for additional data files the package makes available for loading using data(). Currently, data files can have one of three types as indicated by their extension: plain R code (.R or .r), tables (.tab, .txt, or .csv), or save() images (.RData or .rda). (All ports of R use the same binary (XDR) format and can read compressed images. Use images saved with save(, compress = TRUE), the default, to save space.) Note that R code should be “self-sufficient” and not make use of extra functionality provided by the package, so that the data file can also be used without having to load the package. It is no longer necessary to provide a 00Index file in the data directory—the corresponding information is generated automatically from the documentation sources when installing from source, or when using the package builder (see Checking and building packages). If your data files are enormous you can speed up installation by providing a file datalist in the data subdirectory. This should have one line per topic that data() will find, in the format `foo' if data(foo) provides `foo', or `foo: bar bah' if data(foo) provides `bar' and `bah'.

The demo subdirectory is for R scripts (for running via demo()) that demonstrate some of the functionality of the package. Demos may be interactive and are not checked automatically, so if testing is desired use code in the tests directory. The script files must start with a (lower or upper case) letter and have one of the extensions .R or .r. If present, the demo subdirectory should also have a 00Index file with one line for each demo, giving its name and a description separated by white space. (Note that it is not possible to generate this index file automatically.)

The contents of the inst subdirectory will be copied recursively to the installation directory. Subdirectories of inst should not interfere with those used by R (currently, R, data, demo, exec, libs, man, help, html, latex, R-ex, chtml, and Meta). The copying of the inst happens after src is built so its Makefile can create files to be installed. Note that with the exceptions of INDEX and COPYING, information files at the top level of the package will not be installed and so not be known to users of Windows and MacOS X compiled packages (and not seen by those who use R CMD INSTALL or install.packages on the tarball). So any information files you wish an end user to see should be included in inst. One thing you might like to add to inst is a CITATION file for use by the citation function.

Subdirectory tests is for additional package-specific test code, similar to the specific tests that come with the R distribution. Test code can either be provided directly in a .R file, or via a .Rin file containing code which in turn creates the corresponding .R file (e.g., by collecting all function objects in the package and then calling them with the strangest arguments). The results of running a .R file are written to a .Rout file. If there is a corresponding .Rout.save file, these two are compared, with differences being reported but not causing an error. The whose tests is copied to the check area, and the tests are run with the copy as the working directory and with R_LIBS set to ensure that the copy of the package installed during testing will be found by library(pkg_name).

Subdirectory exec could contain additional executables the package needs, typically scripts for interpreters such as the shell, Perl, or Tcl. This mechanism is currently used only by a very few packages, and still experimental.

Subdirectory po is used for files related to localization: see Localization.

Package bundles

Sometimes it is convenient to distribute several packages as a bundle. (An example is VR which contains four packages.) The installation procedures on both Unix-alikes and Windows can handle package bundles.

The DESCRIPTION file of a bundle has a `Bundle' field and no `Package' field, as in

             Bundle: VR
             Priority: recommended
             Contains: MASS class nnet spatial
             Version: 7.2-12
             Date: 2005-01-31
             Depends: R (>= 2.0.0), graphics, stats
             Suggests: lattice, nlme, survival
             Author: S original by Venables & Ripley.
               R port by Brian Ripley <ripley@stats.ox.ac.uk>, following earlier
               work by Kurt Hornik and Albrecht Gebhardt.
             Maintainer: Brian Ripley <ripley@stats.ox.ac.uk>
             BundleDescription: Functions and datasets to support Venables and
               Ripley, `Modern Applied Statistics with S' (4th edition).
             License: GPL (version 2 or later)  See file LICENCE.
             URL: http://www.stats.ox.ac.uk/pub/MASS4/

The `Contains' field lists the packages (space separated), which should be contained in separate subdirectories with the names given. During building and installation, packages will be installed in the order specified. Be sure to order this list so that dependencies are met appropriately.

The packages contained in a bundle are standard packages in all respects except that the DESCRIPTION file is replaced by a DESCRIPTION.in file which just contains fields additional to the DESCRIPTION file of the bundle, for example

             Package: spatial
             Description: Functions for kriging and point pattern analysis.
             Title: Functions for Kriging and Point Pattern Analysis

Any files in the package bundle except the DESCRIPTION file and the named packages will be ignored.

The `Depends' field in the bundle's DESCRIPTION file should list the dependencies of all the constituent packages (and similarly for `Imports' and `Suggests'), and then DESCRIPTION.in files should not contain these fields.

Configure and cleanup

Note that most of this section is Unix-specific: see the comments later on about the Windows port of R.

If your package needs some system-dependent configuration before installation you can include a (Bourne shell) script configure in your package which (if present) is executed by R CMD INSTALL before any other action is performed. This can be a script created by the Autoconf mechanism, but may also be a script written by yourself. Use this to detect if any nonstandard libraries are present such that corresponding code in the package can be disabled at install time rather than giving error messages when the package is compiled or used. To summarize, the full power of Autoconf is available for your extension package (including variable substitution, searching for libraries, etc.).

The (Bourne shell) script cleanup is executed as last thing by R CMD INSTALL if present and option --clean was given, and by R CMD build when preparing the package for building from its source. It can be used to clean up the package source tree. In particular, it should remove all files created by configure.

As an example consider we want to use functionality provided by a (C or FORTRAN) library foo. Using Autoconf, we can create a configure script which checks for the library, sets variable HAVE_FOO to TRUE if it was found and with FALSE otherwise, and then substitutes this value into output files (by replacing instances of `@HAVE_FOO@' in input files with the value of HAVE_FOO). For example, if a function named bar is to be made available by linking against library foo (i.e., using -lfoo), one could use


in configure.ac (assuming Autoconf 2.50 or better).

The definition of the respective R function in foo.R.in could be

    foo <- function(x) {
          stop("Sorry, library 'foo' is not available"))

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

    foo <- function(x) {
          stop("Sorry, library 'foo' is not available"))

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

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

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

    : ${R_HOME=`R RHOME`}
    if test -z "${R_HOME}"; then
      echo "could not determine R_HOME"
      exit 1
    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. You can use R CMD config for getting the value of the basic configuration variables, or the header and library flags necessary for linking against R, see R CMD config --help for details.

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

    F77=`"${R_HOME}/bin/R" CMD config F77`
    FLIBS=`"${R_HOME}/bin/R" CMD config FLIBS`
    ACX_BLAS([], AC_MSG_ERROR([could not find your BLAS library], 1))

Note that FLIBS as determined by R must be used to ensure that FORTRAN 77 code works on all R platforms. Calls to the Autoconf macro AC_F77_LIBRARY_LDFLAGS, which would overwrite FLIBS, must not be used (and hence e.g. removed from ACX_BLAS). (Recent versions of Autoconf in fact allow an already set FLIBS to override the test for the FORTRAN linker flags. Also, recent versions of R can detect external BLAS and LAPACK libraries.)

You should bear in mind that the configure script may well not work on Windows systems (this seems normally to be the case for those generated by Autoconf, although simple shell scripts do work). If your package is to be made publicly available, please give enough information for a user on a non-Unix platform to configure it manually, or provide a configure.win script to be used on that platform.

In some rare circumstances, the configuration and cleanup scripts need to know the location into which the package is being installed. An example of this is a package that uses C code and creates two shared object/DLLs. Usually, the object that is dynamically loaded by R is linked against the second, dependent, object. On some systems, we can add the location of this dependent object to the object that is dynamically loaded by R. This means that each user does not have to set the value of the LD_LIBRARY_PATH (or equivalent) environment variable, but that the secondary object is automatically resolved. Another example is when a package installs support files that are required at run time, and their location is substituted into an R data structure at installation time. (This happens with the Java Archive files in the SJava package.) The names of the top-level library directory (i.e., specifiable via the `-l' argument) and the directory of the package itself are made available to the installation scripts via the two shell/environment variables R_LIBRARY_DIR and R_PACKAGE_DIR. Additionally, the name of the package (e.g., `survival' or `MASS') being installed is available from the shell variable R_PACKAGE_NAME.

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 Makevars.in.

The most common use of a Makevars file is to set additional preprocessor (for example include paths) flags via PKG_CPPFLAGS, and additional compiler flags by setting PKG_CFLAGS, PKG_CXXFLAGS and PKG_FFLAGS, for C, C++, or FORTRAN respectively (see Creating shared objects).

Also, Makevars can be used to set flags for the linker, for example `-L' and `-l' options.

When writing a Makevars file for a package you intend to distribute, take care to ensure that it is not specific to your compiler: flags such as -O2 -Wall -pedantic are all specific to GCC.

There are some macros which are built whilst configuring the building of R itself, are stored on Unix-alikes in R_HOME/etc/Makeconf and can be used in Makevars. These include

FLIBS A macro containing the set of libraries need to link FORTRAN code. This may need to be included in PKG_LIBS.

BLAS_LIBS A macro containing the BLAS libraries used when building R. This may need to be included in PKG_LIBS. Beware that if it is empty then the R executable will contain all the double-precision and double-complex BLAS routines, but no single-precision or complex routines. If BLAS_LIBS is included, then FLIBS also needs to be5, as most BLAS libraries are written in FORTRAN.

LAPACK_LIBS A macro containing the LAPACK libraries (and paths where appropriate) used when building R. This may need to be included in PKG_LIBS. This may point to a dynamic library libRlapack which contains all the double-precision LAPACK routines as well as those double-complex LAPACK and BLAS routines needed to build R, or it may point to an external LAPACK library, or may be empty if an external BLAS library also contains LAPACK.

  • [There is no guarantee that the LAPACK library will provide more than all the double-precision and double-complex routines, and some do not provide all the auxiliary routines.]
  • The macros BLAS_LIBS and FLIBS should always be included after LAPACK_LIBS.

SAFE_FFLAGS A macro containing flags which are needed to circumvent over-optimization of Fortran code: it is typically `-g -O2 -ffloat-store' on `ix86' platforms using g77 or gfortran.

Note that Makevars should not normally contain targets, as it is included before the default Makefile and make is called without an explicit target. To circumvent that, use a suitable phony target before any actual targets: for example fastICA has

    all: $(SHLIB)     
    slamc.o: slamc.f
            $(F77) $(SLAMC_FFLAGS) -c -o slamc.o slamc.f

to ensure that the LAPACK routines find some constants without infinite looping.

Configure example

It may be helpful to give an extended example of using a configure script to create a src/Makevars file: this is based on that in the RODBC package.

The configure.ac file follows: configure is created from this by running autoconf in the top-level package directory (containing configure.ac).

             AC_INIT([RODBC], 1.1.5) dnl package name, version
             dnl Select an optional include path, from a configure option
             dnl or from an environment variable.
                                        [the location of ODBC header files]),
             if test [ -n "$odbc_include_path" ] ; then
                AC_SUBST([CPPFLAGS],["-I${odbc_include_path} ${CPPFLAGS}"])
               if test [ -n "${ODBC_INCLUDE}" ] ; then
                  AC_SUBST([CPPFLAGS],["-I${ODBC_INCLUDE} ${CPPFLAGS}"])
             dnl ditto for a library path
                                        [the location of ODBC libraries]),
             if test [ -n "$odbc_lib_path" ] ; then
                AC_SUBST([LIBS],[" -L${odbc_lib_path} ${LIBS}"])
               if test [ -n "${ODBC_LIBS}" ] ; then
                  AC_SUBST([LIBS],["-L${ODBC_LIBS} ${LIBS}"])
             dnl Another user-specifiable option
                              [specify the ODBC manager, e.g. odbc or iodbc]),
             dnl Now find the compiler and compiler flags to use
             : ${R_HOME=`R RHOME`}
             if test -z "${R_HOME}"; then
               echo "could not determine R_HOME"
               exit 1
             CC=`"${R_HOME}/bin/R" CMD config CC`
             CFLAGS=`"${R_HOME}/bin/R" CMD config CFLAGS`
             dnl Check the headers can be found
             AC_CHECK_HEADERS(sql.h sqlext.h)
             if test "${ac_cv_header_sql_h}" = no ||
                test "${ac_cv_header_sqlext_h}" = no; then
                AC_MSG_ERROR("ODBC headers sql.h and sqlext.h not found")
             dnl search for a library containing an ODBC function
             if test [ -n "${odbc_mgr}" ] ; then
               AC_SEARCH_LIBS(SQLTables, ${odbc_mgr}, ,
                   AC_MSG_ERROR("ODBC driver manager ${odbc_mgr} not found"))
               AC_SEARCH_LIBS(SQLTables, odbc iodbc, ,
                   AC_MSG_ERROR("no ODBC driver manager found"))
             dnl substitute CPPFLAGS and LIBS
             dnl and do subsitution in the src/Makevars.in

where src/Makevars.in would be simply

             PKG_CPPFLAGS = @CPPFLAGS@
             PKG_LIBS = @LIBS@

A user can then be advised to specify the location of the ODBC driver manager files by options like (lines broken for easier reading)

      --with-odbc-lib=/opt/local/lib --with-odbc-manager=iodbc'

or by setting the environment variables ODBC_INCLUDE and ODBC_LIBS.

Using F95 code

R currently does not distinguish between FORTRAN 77 and Fortran 90/95 code, and assumes all FORTRAN comes in source files with extension .f. Commercial Unix systems tyically use a F95 compiler, but only since the release of gcc 4.0.0 in April 2005 have Linux and other non-commercial OSes had much support for F95. The compiler used for R on Windows is a F77 compiler.

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

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

There is a MinGW build of gfortran available from http://gcc.gnu.org/wiki/GFortranBinaries and a MinGW build6 of g95 from http://www.g95.org. Set F95 in MkRules to point to the installed compiler. Then R CMD SHLIB and R CMD INSTALL work for packages containing Fortran 90/95 source code.

Next: Writing package vignettes, Previous: Configure and cleanup, Up: Creating R packages 1.3 Checking and building packages

Before using these tools, please check that your package can be installed and loaded. R CMD check will inter alia do this, but you will get more informative error messages doing the checks directly.

  • Checking packages
  • Building packages
  • Customizing checking and building

Checking packages

Using R CMD check, the R package checker, one can test whether source R packages work correctly. It can be run on one or more directories, or gzipped package tar archives7 with extension `.tar.gz' or `.tgz'. This runs a series of checks, including

  • The package is installed. This will warn about missing cross-references and duplicate aliases in help files.
  • The file names are checked to be valid across file systems and supported operating system platforms.
  • The files and directories are checked for sufficient permissions (Unix only).
  • The DESCRIPTION file is checked for completeness, and some of its entries for correctness. Unless installation tests are skipped, checking is aborted if the package dependencies cannot be resolved at run time. One check is that the package name is not that of a standard package, nor of the defunct standard packages (`ctest', `eda', `lqs', `mle', `modreg', `mva', `nls', `stepfun' and `ts') which are handled specially by library. Another check is that all packages mentioned in library or requires or from which the NAMESPACE file imports or are called via :: or ::: are listed (in `Depends', `Imports', `Suggests' or `Contains'): this is not an exhaustive check of the actual imports.
  • Available index information (in particular, for demos and vignettes) is checked for completeness.
  • The package subdirectories are checked for suitable file names and for not being empty. The checks on file names are controlled by the option --check-subdirs=value. This defaults to `default', which runs the checks only if checking a tarball: the default can be overridden by specifying the value as `yes' or `no'. Further, the check on the src directory is only run if the package/bundle does not contain a configure script (which corresponds to the value `yes-maybe') and there is no src/Makefile or src/Makefile.in.
  • To allow a configure script to generate suitable files, files ending in `.in' will be allowed in the R directory.
  • The R files are checked for syntax errors. Bytes which are non-ASCII are reported as warnings, but these should be regarded as errors unless it is known that the package will always be used in the same locale.
  • It is checked that the package can be loaded, first with the usual default packages and then only with package base already loaded. If the package has a namespace, it is checked if this can be loaded in an empty session with only the base namespace loaded. (Namespaces and packages can be loaded very early in the session, before the default packages are available, so packages should work then.)
  • 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 replacement functions is called `value'. All foreign function calls (.C, .Fortran, .Call and .External calls) are tested to see if they have a PACKAGE argument, and if not, whether the appropriate DLL might be deduced from the name space of the package. Any other calls are reported. (The check is generous, and users may want to supplement this by examining the output of tools::checkFF("mypkg", verbose=TRUE), especially if the intention were to always use a PACKAGE argument)
  • The Rd files are checked for correct syntax and meta data, including the presence of the mandatory (\name, \alias, \title, \description and \keyword) fields. The Rd name and title are checked for being non-empty, and the keywords found are compared to the standard ones. There is a check for missing cross-references (links).
  • A check is made for missing documentation entries, such as undocumented user-level objects in the package.
  • Documentation for functions, data sets, and S4 classes is checked for consistency with the corresponding code.
  • It is checked whether all function arguments given in \usage sections of Rd files are documented in the corresponding \arguments section.
  • C, C++ and FORTRAN source and header files are tested for portable (LF-only) line endings. If there is a Makefile or Makefile.in or Makevars or Makevars.in in the src directory, it is checked for correct use of `$(BLAS_LIBS)'.
  • The examples provided by the package's documentation are run. (see Writing R documentation files, for information on using \examples to create executable example code.)
  • Of course, released packages should be able to run at least their own examples. Each example is run in a `clean' environment (so earlier examples cannot be assumed to have been run), and with the variables T and F redefined to generate an error unless they are set in the example: See Logical vectors.
  • If the package sources contain a tests directory then the tests specified in that directory are run. (Typically they will consist of a set of .R source files and target output files .Rout.save.)
  • The code in package vignettes (see Writing package vignettes) is executed.
  • 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 to obtain more information about the usage of the R package checker. A subset of the checking steps can be selected by adding flags.

Building packages

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

Prior to actually building the package in the common gzipped tar file format, a few diagnostic checks and cleanups are performed. In particular, it is tested whether object indices exist and can be assumed to be up-to-date, and C, C++ and FORTRAN source files are tested and converted to LF line-endings if necessary.

Run-time checks whether the package works correctly should be performed using R CMD check prior to invoking the build procedure.

To exclude files from being put into the package, one can specify a list of exclude patterns in file .Rbuildignore in the top-level source directory. These patterns should be Perl regexps, one per line, to be matched against the file names relative to the top-level source directory. In addition, directories called CVS or .svn or .arch-ids and files GNUMakefile or with base names starting with `.#', or starting and ending with `#', or ending in `~', `.bak' or `.swp', are excluded by default. In addition, those files in the R, demo and man directories which are flagged by R CMD check as having invalid names will be excluded.

Use R CMD build --help to obtain more information about the usage of the R package builder.

Unless R CMD build is invoked with the --no-vignettes option, it will attempt to rebuild the vignettes (see Writing package vignettes) in the package. To do so it installs the current package/bundle into a temporary library tree, but any dependent packages need to be installed in an available library tree (see the Note: below).

One of the checks that R CMD build runs is for empty source directories. These are in most cases unintentional, in which case they should be removed and the build re-run.

It can be useful to run R CMD check --check-subdirs=yes on the built tarball as a final check on the contents.

R CMD build can also build pre-compiled version of packages for binary distributions, but R CMD INSTALL --build is preferred (and is considerably more flexible). In particular, Windows users are recommended to use R CMD INSTALL --build and install into the main library tree (the default) so that HTML links are resolved.

  • Note: R CMD check and R CMD build run R with --vanilla, so none of the user's startup files are read. If you need R_LIBS set (to find packages in a non-standard library) you will need to set it in the environment.
  • Note to Windows users: R CMD check and R CMD build work well under Windows NT4/2000/XP/2003 but may not work correctly on Windows 95/98/ME because of problems with some versions of Perl on those limited OSes. Experiences vary. To use them you will need to have installed the files for building source packages (which is the default).

Customizing checking and building

In addition to the available command line options, R CMD check also allows customization by setting (Perl) configuration variables in a configuration file, the location of which can be specified via the --rcfile option and defaults to $HOME/.R/check.conf provided that the environment variable HOME is set.

The following configuration variables are currently available.

$R_check_use_install_log If true, record the output from installing a package as part of its check to a log file (00install.out by default), even when running interactively. Default: true.

$R_check_all_non_ISO_C If true, do not ignore compiler (typically GCC) warnings about non ISO C code in system headers. Default: false.

$R_check_weave_vignettes If true, weave package vignettes in the process of checking them. Default: true.

$R_check_subdirs_nocase If true, check the case of directories such as R and man. Default: false.

$R_check_subdirs_strict Initial setting for --check-subdirs. Default: `default' (which checks only tarballs, and checks in the src only if there is no configure file).

R_check_force_suggests If true, give an error if suggested packages are not available. Default: true.

Values `1' or a string with lower-cased version `"yes"' or `"true"' can be used for setting the variables to true; similarly, `0' or strings with lower-cased version `"no"' or `"false"' give false.

For example, a configuration file containing

    $R_check_use_install_log = "TRUE";
    $R_check_weave_vignettes = 0;

results in using install logs and turning off weaving.

Future versions of R will enhance this customization mechanism, and provide a similar scheme for R CMD build.

There are other internal settings that can be changed via environment variables _R_CHECK_*_: see the Perl source code. One that may be interesting is _R_CHECK_USE_CODETOOLS_ to make use of the codetools package available from http://www.stat.uiowa.edu/~luke/R/codetools.

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. To ensure that they can be accessed from a browser, the file names should start with an ASCII letter and be entirely in ASCII letters or digits or minus or underscore.

A special case are documents in Sweave format, which we call package vignettes. Sweave allows the integration of LaTeX documents and R code and is contained in package utils which is part of the base R distribution, see the Sweave help page for details on the document format. Package vignettes found in directory inst/doc are tested by R CMD check by executing all R code chunks they contain to ensure consistency between code and documentation. Code chunks with option eval=FALSE are not tested. The R working directory for all vignette tests in R CMD check is the installed version of the doc subdirectory. Make sure all files needed by the vignette (data sets, ...) are accessible by either placing them in the inst/doc hierarchy of the source package, or using calls to system.file().

R CMD build will automatically create PDF versions of the vignettes for distribution with the package sources. By including the PDF version in the package sources it is not necessary that the vignettes can be compiled at install time, i.e., the package author can use private LaTeX extensions which are only available on his machine. Only the R code inside the vignettes is part of the checking procedure, typesetting manuals is not part of the package quality control.

By default R CMD build will run Sweave on all files in Sweave format. If no Makefile is found in directory inst/doc, then texi2dvi --pdf is run on all vignettes. Whenever a Makefile is found, then R CMD build will try to run make after the Sweave step, such that PDF manuals can be created from arbitrary source formats (plain LaTeX files, ...). The Makefile should take care of both creation of PDF files and cleaning up afterwards, i.e., delete all files that shall not appear in the final package archive. Note that the make step is executed independently from the presence of any files in Sweave format.

It is no longer necessary to provide a 00Index.dcf file in the inst/doc directory—the corresponding information is generated automatically from the \VignetteIndexEntry statements in all Sweave files when installing from source, or when using the package builder (see Checking and building packages). The \VignetteIndexEntry statement is best placed in LaTeX comment, such that no definition of the command is necessary.

At install time an HTML index for all vignettes is automatically created from the \VignetteIndexEntry statements unless a file index.html exists in directory inst/doc. This index is linked into the HTML help system for each package.

Next: Package name spaces, Previous: Writing package vignettes, Up: Creating R packages 1.5 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.)

  • 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. If the package contains code that needs to be compiled, try to enable a reasonable amount of diagnostic messaging (“warnings”) when compiling, such as e.g. -Wall -pedantic for tools from GCC, the Gnu Compiler Collection. (If R was not configured accordingly, one can achieve this e.g. via PKG_CFLAGS and related variables.)
  • Run R CMD build to make the release .tar.gz file.

Please ensure that you can run through the complete procedure with only warnings that you understand and have reasons not to eliminate. In principle, packages must pass R CMD check without warnings to be admitted to the main CRAN package area.

When all the testing is done, upload the .tar.gz file, using `anonymous' as log-in name and your e-mail address as password, to

(note: use ftp and not sftp to connect to this server) and send a message to cran@r-project.org about it. The CRAN maintainers will run these tests before putting a submission in the main archive.

Note that the fully qualified name of the .tar.gz file must be of the form


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. Of course, R CMD build automatically creates valid file names.

Package name spaces

R has a name space management system for packages. This system allows the package writer to specify which variables in the package should be exported to make them available to package users, and which variables should be imported from other packages.

The current mechanism for specifying a name space for a package is to place a NAMESPACE file in the top level package directory. This file contains name space directives describing the imports and exports of the name space. Additional directives register any shared objects to be loaded and any S3-style methods that are provided. Note that although the file looks like R code (and often has R-style comments) it is not processed as R code. Only very simple conditional processing of if statements is implemented.

Like other packages, packages with name spaces are loaded and attached to the search path by calling library. Only the exported variables are placed in the attached frame. Loading a package that imports variables from other packages will cause these other packages to be loaded as well (unless they have already been loaded), but they will not be placed on the search path by these implicit loads.

Name spaces are sealed once they are loaded. Sealing means that imports and exports cannot be changed and that internal variable bindings cannot be changed. Sealing allows a simpler implementation strategy for the name space mechanism. Sealing also allows code analysis and compilation tools to accurately identify the definition corresponding to a global variable reference in a function body.

Note that adding a name space to a package changes the search strategy. The package name space comes first in the search, then the imports, then the base name space and then the normal search path.

  • Specifying imports and exports
  • Registering S3 methods
  • Load hooks
  • An example
  • Summary -- converting an existing package
  • Name spaces with formal classes and methods

Specifying imports and exports

Exports are specified using the export directive in the NAMESPACE file. A directive of the form

    export(f, g)

specifies that the variables f and g are to be exported. (Note that variable names may be quoted, and non-standard names such as [<-.fractions must be.)

For packages with many variables to export it may be more convenient to specify the names to export with a regular expression using exportPattern. The directive


exports all variables that do not start with a period.

A package with a name space implicitly imports the base name space. Variables exported from other packages with name spaces need to be imported explicitly using the directives import and importFrom. The import directive imports all exported variables from the specified package(s). Thus the directives

    import(foo, bar)

specifies that all exported variables in the packages foo and bar are to be imported. If only some of the exported variables from a package are needed, then they can be imported using importFrom. The directive

    importFrom(foo, f, g)

specifies that the exported variables f and g of the package foo are to be imported.

If a package only needs one (exported) object from another package it can use a fully qualified variable reference in the code instead of a formal import. A fully qualified reference to the function f in package foo is of the form foo::f. This is less efficient than a formal import and also loses the advantage of recording all dependencies in the NAMESPACE file, so this approach is usually not recommended. Evaluating foo::f will cause package foo to be loaded, but not attached, if it was not loaded already.

Registering S3 methods

The standard method for S3-style UseMethod dispatching might fail to locate methods defined in a package that is imported but not attached to the search path. To ensure that these methods are available the packages defining the methods should ensure that the generics are imported and register the methods using S3method directives. If a package defines a function print.foo intended to be used as a print method for class foo, then the directive

    S3method(print, foo)

ensures that the method is registered and available for UseMethod dispatch. The function print.foo does not need to be exported. Since the generic print is defined in base it does not need to be imported explicitly. This mechanism is intended for use with generics that are defined in a name space. Any methods for a generic defined in a package that does not use a name space should be exported, and the package defining and exporting the methods should be attached to the search path if the methods are to be found.

Load hooks

There are a number of hooks that apply to packages with name spaces. See help(".onLoad") for more details.

Packages with name spaces do not use the .First.lib function. Since loading and attaching are distinct operations when a name space is used, separate hooks are provided for each. These hook functions are called .onLoad and .onAttach. They take the same arguments as .First.lib; they should be defined in the name space but not exported.

However, packages with name spaces do use the .Last.lib function. There is also a hook .onUnload which is called when the name space is unloaded (via a call to unloadNamespace) with argument the full path to the directory in which the package was installed. .onUnload should be defined in the name space and not exported, but .Last.lib does need to be exported.

Packages are not likely to need .onAttach (except perhaps for a start-up banner); code to set options and load shared objects should be placed in a .onLoad function, or use made of the useDynLib directive described next.

There can be one or more useDynLib directives which allow shared objects that need to be loaded to be specified in the NAMESPACE file. The directive


registers the shared object foo for loading with library.dynam. Loading of registered object(s) occurs after the package code has been loaded and before running the load hook function. Packages that would only need a load hook function to load a shared object can use the useDynLib directive instead.

User-level hooks are also available: see the help on function setHook.

The useDynLib directive also accepts the names of the native routines that are to be used in R via the .C, .Call, .Fortran and .External interface functions. These are given as additional arguments to the directive, for example,

    useDynLib(foo, myRoutine, myOtherRoutine)

By specifying these names in the useDynLib directive, the native symbols are resolved when the package is loaded and R variables identifying these symbols are added to the package's name space with these names. These can be used in the .C, .Call, .Fortran and .External calls in place of the name of the routine and the PACKAGE argument. For instance, we can call the routine myRoutine from R with the code

     .Call(myRoutine, x, y)

rather than

     .Call("myRoutine", x, y, PACKAGE = "foo")

There are at least two benefits to this approach. Firstly, the symbol lookup is done just once for each symbol rather than each time it the routine is invoked. Secondly, this removes any ambiguity in resolving symbols that might be present in several compiled libraries. In particular, it allows for correctly resolving routines when different versions of the same package are loaded concurrently in the same R session.

In some circumstances, there will already be an R variable in the package with the same name as a native symbol. For example, we may have an R function in the package named myRoutine. In this case, it is necessary to map the native symbol to a different R variable name. This can be done in the useDynLib directive by using named arguments. For instance, to map the native symbol name myRoutine to the R variable myRoutine_sym, we would use

    useDynLib(foo, myRoutine_sym = myRoutine, myOtherRoutine)

We could then call that routine from R using the command

     .Call(myRoutine_sym, x, y)

Symbols without explicit names are assigned to the R variable with that name.

In some cases, it may be preferable not to create R variables in the package's name space that identify the native routines. It may be too costly to compute these for many routines when the package is loaded if many of these routines are not likely to be used. In this case, one can still perform the symbol resolution correctly using the DLL, but do this each time the routine is called. Given a reference to the DLL as an R variable, say dll, we can call the routine myRoutine using the expression

     .Call(dll$myRoutine, x, y)

The $ operator resolves the routine with the given name in the DLL using a call to getNativeSymbol. This is the same computation as above where we resolve the symbol when the package is loaded. The only difference is that this is done each time in the case of dll$myRoutine.

In order to use this dynamic approach (e.g., dll$myRoutine), one needs the reference to the DLL as an R variable in the package. The DLL can be assigned to a variable by using the variable = dllName format used above for mapping symbols to R variables. For example, if we wanted to assign the DLL reference for the DLL foo in the example above to the variable myDLL, we would use the following directive in the NAMESPACE file:

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

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

If the package has registration information (see Registering native routines), then we can use that directly rather than specifying the list of symbols again in the useDynLib directive in the NAMESPACE file. Each routine in the registration information is specified by giving a name by which the routine is to be specified along with the address of the routine and any information about the number and type of the parameters. Using the .registration argument of useDynLib, we can instruct the name space mechanism to create R variables for these symbols. For example, suppose we have the following registration information for a DLL named myDLL:

    R_CMethodDef cMethods[] = {
       {"foo", &foo, 4, {REALSXP, INTSXP, STRSXP, LGLSXP}},
       {"bar_sym", &bar, 0},
       {NULL, NULL, 0}
    R_CallMethodDef callMethods[] = {
       {"R_call_sym", &R_call, 4},
       {"R_version_sym", &R_version, 0},
       {NULL, NULL, 0}

Then, the directive in the NAMESPACE file

    useDynLib(myDLL, .registration = TRUE)

causes the DLL to be loaded and also for the R variables foo, bar_sym, R_call_sym and R_version_sym to be defined in the package's name space.

Note that the names for the R variables are taken from the entry in the registration information and do not need to be the same as the name of the native routine. This allows the creator of the registration information to map the native symbols to non-conflicting variable names in R, e.g. R_version to R_version_sym for use in an R function such as

    R_version <- function()

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

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

which makes the R variables corresponding to the Fortran symbols F_bkde and so on, and so avoid clashes with R code in the name space.

More information about this symbol lookup, along with some approaches for customizing it, is available from http://www.omegahat.org/examples/RDotCall.

An example

As an example consider two packages named foo and bar. The R code for package foo in file foo.R is

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

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

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

The second package bar has code file bar.R

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

and NAMESPACE file

             export(g, h)

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

    > g(6)
    [1]  1 13

This is consistent with the definitions of c in the two settings: in bar the function c is defined to be equivalent to sum, but in foo the variable c refers to the standard function c in base.

Summary – converting an existing package

To summarize, converting an existing package to use a name space involves several simple steps:

  • Identify the public definitions and place them in export directives.
  • Identify S3-style method definitions and write corresponding S3method declarations.
  • Identify dependencies and replace any require calls by import directives.
  • Replace .First.lib functions with .onLoad functions or useDynLib directives.

Some code analysis tools to aid in this process are currently under development.

Name spaces with formal classes and methods

Some additional steps are needed for packages which make use of formal (S4-style) classes and methods (unless these are purely used internally). There needs to be an .onLoad action to ensure that the methods package is loaded and attached:

    .onLoad <- function(lib, pkg) require(methods)

and any classes and methods which are to be exported need to be declared as such in the NAMESPACE file. For example, the now-defunct mle package had

    importFrom(graphics, plot)
    importFrom(stats, profile, confint)
    exportClasses("mle", "profile.mle", "summary.mle")
    exportMethods("confint", "plot", "profile", "summary", "show")

All formal classes need to be listed in an exportClasses directive. All generics for which formal methods are defined need to be declared in an exportMethods directive, and where the generics are formed by taking over existing functions, those functions need to be imported (explicitly unless they are defined in the base name space).

In addition, a package using classes and methods defined in another package needs to import them, with directives

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

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

             export(f1, ng1)


             importFrom(A, ng1)
             importClassesFrom(A, c1)
             importMethodsFrom(A, f1)
             export(f4, f5)
             exportMethods(f6, "[")
             exportClasses(c1, c2)


Writing portable packages

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

  • If your package has a configure script, provide a configure.win script to be used on Windows. The CRAN binary packages for Windows are built automatically, and if your package does not build without intervention it is unlikely to be easily available to a high proportion of R users.
  • Make use of the abilities of your compilers to check the standards-conformance of your code. For example, gcc can be used with options -Wall -pedantic to alert you to potential problems. Do not be tempted to assume that these are pure pedantry: for example R is regularly used on platforms where the C compiler does not accept C++ comments.
  • Do be very careful with passing arguments between R, C and FORTRAN code. In particular, long in C will be 32-bit on most R platforms (including those mostly used by the CRAN maintainers), but 64-bit on many modern Unix and Linux platforms. It is rather unlikely that the use of long in C code has been thought through: if you need a longer type than int you should use a configure test for a C99 type such as int_fast64_t (and failing that, long long) and typedef your own type to be long or long long, or use another suitable type (such as size_t). Note that integer in FORTRAN corresponds to int in C on all R platforms.
  • Errors in memory allocation and reading/writing outside arrays are very common causes of crashes (e.g., segfaults) on some machines. See Using valgrind for a tool which can be used to look for this.
  • The Mac OS X linker has some restrictions not found on other platforms. Try to ensure that C entry points shared between source files are declared as extern in all but one of the files.
  • Many platforms will allow unsatisfied entry points in compiled code, but will crash the application (here R) if they are ever used. Some (notably Windows) will not. Looking at the output of
             nm -pg mypkg.so  # or other extension such as `.sl' or `.dylib'
  • and checking if any of the symbols marked U is unexpected is a good way to avoid this.
  • Conflicts between symbols in DLLs are handled in very platform-specific ways. Good ways to avoid trouble are to make as many symbols as possible static (check with nm -pg), and to use unusual names, as well as ensuring you have used the PACKAGE argument that R CMD check checks for.

Diagnostic messages

Now that diagnostic messages can be made available for translation, it is important to write them in a consistent style. Using the tools described in the next section to extract all the messages can give a useful overview of your consistency (or lack of it).

Some guidelines follow.

  • Messages are sentence fragments, and not viewed in isolation. So it is conventional not to capitalize the first word and not to end with a period (or other punctuation).
  • Try not to split up messages into small pieces. In C error messages use a single format string containing all English words in the messages.
  • In R error messages do not construct a message with paste (such messages will not be translated) but via multiple arguments to stop or warning, or via gettextf.
  • Do not use colloquialisms such as “can't” and “don't”.
  • If possible, make quotation marks part of your message as different languages have different conventions. In R messages this means not using sQuote or dQuote except where the argument is a variable.

Conventionally single quotation marks are used for quotations such as

             'ord' must be a positive integer, at most the number of knots

and double quotation marks when referring to an R character string such as

             'format' must be "normal" or "short" - using "normal"

Since ASCII does not contain directional quotation marks, it is best to use ` and let the translator (including automatic translation) use directional quotations where available. The range of quotation styles is immense: unfortunately we cannot reproduce them in a portable texinfo document. But as a taster, some languages use `up' and `down' (comma) quotes rather than left or right quotes, and some use guillemets (and some use what Adobe calls `guillemotleft' to start and others use it to end). Occasionally messages need to be singular or plural (and in other languages there may be no such concept or several plural forms – Slovenian has four). So avoid constructions such as was once used in library

             if((length(nopkgs) > 0) && !missing(lib.loc)) {
                 if(length(nopkgs) > 1)
                     warning("libraries ",
                             paste(sQuote(nopkgs), collapse = ", "),
                             " contain no packages")
                     warning("library ", paste(sQuote(nopkgs)),
                             " contains no package")

and was replaced by

             if((length(nopkgs) > 0) && !missing(lib.loc)) {
                 pkglist <- paste(sQuote(nopkgs), collapse = ", ")
                 msg <- sprintf(ngettext(length(nopkgs),
                                  "library %s contains no packages",
                                  "libraries %s contain no packages"),
                 warning(msg, domain=NA)

Note that it is much better to have complete clauses as here, since in another language one might need to say `There is no package in library %s' or `There are no packages in libraries %s'.


There are mechanisms to translate the R- and C-level error and warning messages. There are only available if R is compiled with NLS support (which is requested by configure option --enable-nls, the default).

The procedures make use of msgfmt and xgettext which are part of GNU gettext and this will need to be installed: Windows users can find pre-compiled binaries at the GNU archive mirrors and packaged with the poEdit package (http://poedit.sourceforge.net/download.php#win32).

   C-level messages
   R messages 

C-level messages

The process of enabling translations is

In a header file that will be included in all the C files containing messages that should be translated, declare

             #include <R.h>  /* to include Rconfig.h */
             #ifdef ENABLE_NLS
             #include <libintl.h>
             #define _(String) dgettext ("pkg", String)
             /* replace pkg as appropriate */
             #define _(String) (String)

For each message that should be translated, wrap it in _(...), for example

             error(_("'ord' must be a positive integer"));

In the package's src directory run

             xgettext --keyword=_ -o pkg.pot *.c

The file src/pkg.pot is the template file, and conventionally this is shipped as po/pkg.pot. A translator to another language makes a copy of this file and edits it (see the gettext manual) to produce say ll.po, where ll is the code for the language in which the translation is to be used. (This file would be shipped in the po directory.) Next run msgfmt on ll.po to produce ll.mo, and copy that to inst/po/ll/LC_MESSAGES/pkg.mo. Now when the package is loaded after installation it will look for translations of its messages in the po/lang/LC_MESSAGES/pkg.mo file for any language lang that matches the user's preferences (via the setting of the LANGUAGE environment variable or from the locale settings).

R messages

Mechanisms to support the automatic translation of R stop, warning and message messages are in place, provided the package has a name space. They make use of message catalogs in the same way as C-level messages, but using domain R-pkg rather than pkg. Translation of character strings inside stop, warning and message calls is automatically enabled, as well as other messages enclosed in calls to gettext or gettextf. (To suppress this, use argument domain=NA.)

Tools to prepare the R-pkg.pot file are provided in package tools: xgettext2pot will prepare a file from all strings occurring inside gettext/gettextf, stop, warning and message calls. Some of these are likely to be spurious and so the file is likely to need manual editing. xgettext extracts the actual calls and so is more useful when tidying up error messages.

Translation of messages which might be singular or plural can be very intricate: languages can have up to four different forms. The R function ngettext provides an interface to the C function of the same name, and will choose an appropriate singular or plural form for the selected language depending on the value of its first argument n.

Packages without name spaces will need to use domain="R-pkg" explicitly in calls to stop, warning, message, gettext/gettextf and ngettext.

Package types

The DESCRIPTION file has an optional field Type which if missing is assumed to be Package, the sort of extension discussed so far in this chapter. Currently two other types are recognized, both of which need write permission in the R installation tree.


This is a rather general mechanism, designed for adding new front-ends such as the gnomeGUI package. If a configure file is found in the top-level directory of the package it is executed, and then if a Makefile is found (often generated by configure), make is called. If R CMD INSTALL --clean is used make clean is called. No other action is taken.

R CMD build can package up this type of extension, but R CMD check will check the type and skip it.


Conventionally, a translation package for language ll is called Translation-ll and has Type: Translation. It needs to contain the directories share/locale/ll and library/pkgname/po/ll, or at least those for which translations are available. The files .mo are installed in the parallel places in the R installation tree.

For example, a package Translation-it might be prepared from an installed (and tested) version of R by

    mkdir Translation-it
    cd Translation-it
    (cd $R_HOME; tar cf - share/locale/it library/*/po/it) | tar xf -
    # the next step is not needed on Windows
    msgfmt -c -o share/locale/it/LC_MESSAGES/RGui.mo $R_SRC_HOME/po/RGui-it.gmo
    # create a DESCRIPTION file
    cd ..
    R CMD build Translation-it

It is probably appropriate to give the package a version number based on the version of R which has been translated. So the DESCRIPTION file might look like

    Package: Translation-it
    Type: Translation
    Version: 2.2.1-1
    Title: Italian Translations for R 2.2.1
    Description: Italian Translations for R 2.2.1
    Author: The translators
    Maintainer: Some Body <somebody@some.where.net>
    Licence: GPL Version 2 or later.

Writing R documentation 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 1200 such files which can be found in the src/library/pkg/man directories of the R source tree, where pkg stands for the standard packages which are included in the R distribution.

As an example, let us look at the file src/library/base/man/load.Rd which documents the R function load.

             \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
   Documenting data sets
   Documenting S4 classes and methods
   Documenting packages 

Documenting functions

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

\name{name} name typically8 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.

\alias{topic} 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


Also, it is often convenient to have several different ways to refer to an R object, and an \alias does not need to be the name of an object.

Note that the \name is not necessarily a topic documented, and if so desired it needs to have an explicit \alias entry (as in this example).

\title{Title} 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). Use of characters other than English text and punctuation (e.g., `<') may limit portability.

\description{...} 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 match the function definition exactly (such that automatic checking for consistency between code and documentation is possible).

It is no longer advisable to use \synopsis for the actual synopsis and show modified synopses in the \usage. Support for \synopsis will be removed eventually. To indicate that a function can be “used” in several different ways, depending on the named arguments specified, use section \details. E.g., abline.Rd contains

               Typical usages are
             abline(a, b, untf = FALSE, \dots)

Use \method{generic}{class} to indicate the name of an S3 method for the generic function generic for objects inheriting from class "class". In the printed versions, this will come out as generic (reflecting the understanding that methods should not be invoked directly but via method dispatch), but codoc() and other QC tools always have access to the full name.

For example, print.ts.Rd contains

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

which will print as

                  ## S3 method for class 'ts':
                  print(x, calendar, ...)

Usage for replacement functions should be given in the style of dim(x) <- value rather than explicitly indicating the name of the replacement function ("dim<-" in the above). Similarly, one can use \method{generic}{class}(arglist) <- value to indicate the usage of an S3 replacement method for the generic replacement function "generic<-" for objects inheriting from class "class". \arguments{...} 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.

\details{...} A detailed if possible precise description of the functionality provided, extending the basic information in the \description slot.

\value{...} 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).

\references{...} A section with references to the literature. Use \url{} for web pointers.

\note{...} Use this for a special note you want to have pointed out.

For example, pie.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.

\author{...} Information about the author(s) of the Rd file. Use \email{} without extra delimiters (`( )' or `< >') to specify email addresses, or \url{} for web pointers.

\seealso{...} 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{...} Examples of how to use the function. These are set as formatted in typewriter font: see Insertions for when characters need to be escaped. (Markup \link and \var will be interpreted, but no other.)

Examples are not only useful for documentation purposes, but also provide test code used for diagnostic checking of R. By default, text inside \examples{} will be displayed in the output of the help page and run by R CMD check. You can use \dontrun{} for commands that should only be shown, but not run, and \dontshow{} for extra commands for testing that should not be shown to users, but will be run by example(). (Previously this was called \testonly, and that is still accepted.)

For example,

             x <- runif(10)       # Shown and run.
             \dontrun{plot(x)}    # Only shown.
             \dontshow{log(x)}    # Only run.

Thus, example code not included in \dontrun must be executable! In addition, it should not use any system-specific features or require special facilities (such as Internet access or write permission to specific directories). Code included in \dontrun is indicated by comments in the processed help files.

Data needed for making the examples executable can be obtained by random number generation (for example, x <- rnorm(100)), or by using standard data sets listed by data() (see ?data for more info).

\keyword{key} Each \keyword entry should specify one of the standard keywords as listed in file KEYWORDS in the R documentation directory (default R_HOME/doc). Use e.g. file.show(file.path(R.home("doc"), "KEYWORDS")) to inspect the standard keywords from within R. There must be at least one \keyword entry, but can be more than one if the R object being documented falls into more than one category.

The special keyword `internal' marks a page of internal objects that are not part of the packages' API. If the help page for object foo has keyword `internal', then help(foo) gives this help page, but foo is excluded from several object indices, like the alphabetical list of objects in the HTML help system.

The R function prompt facilitates the construction of files documenting R objects. If foo is an R function, then prompt(foo) produces file foo.Rd which already contains the proper function and argument names of foo, and a structure which can be filled in with information.

Documenting data sets

The structure of Rd files which document R data sets is slightly different. Whereas sections such as \arguments and \value are not needed, the format and source of the data should be explained.

As an example, let us look at src/library/datasets/man/rivers.Rd which documents the standard R data set rivers.

             \title{Lengths of Major North American Rivers}
               This data set gives the lengths (in miles) of 141 \dQuote{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.

\docType{...} Indicates the “type” of the documentation object. Always `data' for data sets.

\format{...} A description of the format of the data set (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.

\source{...} 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,

  • The \usage entry is always bar or (for packages which do not use lazy-loading of data) data(bar). (In particular, only document a single data object per Rd file.)
  • The \keyword entry is always `datasets'.

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

Documenting S4 classes and methods

There are special ways to use the `?' operator, namely `class?topic' and `methods?topic', to access documentation for S4 classes and methods, respectively. This mechanism depends on conventions for the topic names used in \alias entries. The topic names for S4 classes and methods respectively are of the form


where signature_list contains the names of the classes in the signature of the method (without quotes) separated by `,' (without whitespace), with `ANY' used for arguments without an explicit specification. E.g., `genericFunction-class' is the topic name for documentation for the S4 class "genericFunction", and `coerce,ANY,NULL-method' is the topic name for documentation for the S4 method for coerce for signature c("ANY", "NULL").

Skeletons of documentation for S4 classes and methods can be generated by using the functions promptClass() and promptMethods() from package methods. If it is necessary or desired to provide an explicit function declaration (in a \usage section) for an S4 method (e.g., if it has “surprising arguments” to be mentioned explicitly), one can use the special markup


(e.g., `\S4method{coerce}{ANY,NULL}(from, to)').

To allow for making full use of the potential of the on-line documentation system, all user-visible S4 classes and methods in a package should at least have a suitable \alias entry in one of the package's Rd files. If a package has methods for a function defined originally somewhere else, and does not change the underlying default method for the function, the package is responsible for documenting the methods it creates, but not for the function itself or the default method.

See help("Documentation", package = "methods") for more information on using and creating on-line documentation for S4 classes and methods.

Documenting packages

Packages may have an overview man page with an \alias pkgname-package, e.g. `utils-package' for the utils package, when package?pkgname will open that help page. If a topic named pkgname does not exist in another Rd file, it is helpful to use this as an additional \alias.

Skeletons of documentation for a package can be generated using the function promptPackage(). If the final = TRUE argument is used, then the Rd file will be generated in final form, containing the information that would be produced by library(help = pkgname). Otherwise (the default) comments will be inserted giving suggestions for content.

The only requirement for this page is that it include a \docType{package} statement. All other content is optional. We suggest that it should be a short overview, to give a reader unfamiliar with the package enough information to get started. More extensive documentation is better placed into a package vignette (see Writing package vignettes) and referenced from this page, or into individual man pages for the functions, datasets, or classes.


To begin a new paragraph or leave a blank line in an example, just insert an empty line (as in (La)TeX). To break a line, use \cr. In addition to the predefined sections (such as \description{}, \value{}, etc.), you can “define” arbitrary ones by \section{section_title}{...}. For example

    \section{Warning}{You must not call this function unless ...}

For consistency with the pre-assigned sections, the section name (the first argument to \section) should be capitalized (but not all upper case).

Note that additional named sections are always inserted at a fixed position in the output (before \note, \seealso and the examples), no matter where they appear in the input (but in the same order as the input).

Marking text

The following logical markup commands are available for emphasizing or quoting text.


\strong{text} Emphasize text using italic and bold font if possible; \strong is stronger.

\bold{text} Set text in bold font if possible.

\sQuote{text} \dQuote{text} Portably single or double quote text (without hard-wiring the quotation marks).

The following logical markup commands are available for indicating specific kinds of text.

\code{text} Indicate text that is a literal example of a piece of a program, e.g., a fragment of R code or the name of an R object, using typewriter font if possible. Only percent signs and unpaired braces always need to be escaped (by a backslash) inside \code: backslash will need to be escaped if it precedes `%', `{' or `}'. The only markup interpreted inside \code is \link and \var.

\preformatted{text} Indicate text that is a literal example of a piece of a program, using typewriter font if possible. The same characters need to be escaped as for \code. All other formatting, e.g. line breaks, is preserved. The closing brace should be on a line by itself.

\kbd{keyboard-characters} Indicate keyboard input, using slanted typewriter font if possible, so users can distinguish the characters they are supposed to type from those the computer outputs.

\samp{text} Indicate text that is a literal example of a sequence of characters.

\pkg{package_name} Indicate the name of an R package.

\file{file_name} Indicate the name of a file. Note that special characters do need to be escaped.

\email{email_address} Indicate an electronic mail address.

\url{uniform_resource_locator} Indicate a uniform resource locator (URL) for the World Wide Web.

\var{metasyntactic_variable} Indicate a metasyntactic variable.

\env{environment_variable} Indicate an environment variable.

\option{option} Indicate a command-line option.

\command{command_name} Indicate the name of a command.

\dfn{term} Indicate the introductory or defining use of a term.

\cite{reference} Indicate a reference without a direct cross-reference via \link (see Cross-references), such as the name of a book.

\acronym{acronym} Indicate an acronym (an abbreviation written in all capital letters), such as GNU.

Note that unless explicitly stated otherwise, special characters (see Insertions) must be escaped inside the above markup commands.

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
lists in HTML and \description lists in LaTeX. The \tabular command takes two arguments. The first gives for each of the columns the required alignment (`l' for left-justification, `r' for right-justification or `c' for centring.) The second argument consists of an arbitrary number of lines separated by \cr, and with fields separated by \tab. For example: \tabular{rlll}{ [,1] \tab Ozone \tab numeric \tab Ozone (ppb)\cr [,2] \tab Solar.R \tab numeric \tab Solar R (lang)\cr [,3] \tab Wind \tab numeric \tab Wind (mph)\cr [,4] \tab Temp \tab numeric \tab Temperature (degrees F)\cr [,5] \tab Month \tab numeric \tab Month (1--12)\cr [,6] \tab Day \tab numeric \tab Day of month (1--31) } There must be the same number of fields on each line as there are alignments in the first argument, and they must be non-empty (but can contain only spaces).


The markup \link{foo} (usually in the combination \code{\link{foo}}) produces a hyperlink to the help for foo. Here foo is a topic, that is the argument of \alias markup in another Rd file (possibly in another package). Hyperlinks are supported in some of the formats to which Rd files are converted, for example HTML and PDF, but ignored in others, e.g. the text and S nroff formats.

One main usage of \link is in the \seealso section of the help page, see Rd format.

You can specify a link to a different topic than its name by \link[=dest]{name} which links to topic dest with name name. This can be used to refer to the documentation for S3/4 classes, for example \code{"\link[=abc-class]{abc}"} would be a way to refer to the documentation of an S4 class "abc" defined in your package, and \code{"\link[=terms.object]{terms}"} to the S3 "terms" class (in package stats). To make these easy to read, \code{"\linkS4class{abc}"} expands to the form given above.

There are two other forms of optional argument specified as \link[pkg]{foo} and \link[pkg:bar]{foo} to link to the package pkg, to files foo.html and bar.html respectively. These are rarely needed, perhaps to refer to not-yet-installed packages (but there the HTML help system will resolve the link at run time) or in the normally undesirable event that more than one package offers help on a topic9 (in which case the present package has precedence so this is only needed to refer to other packages). They are only in used in (C)HTML help (and not for hyperlinks in LaTeX nor S sgml conversions of help pages), and link to the file rather than the topic (since there is no way to know which topics are in which files in an uninstalled package).


Mathematical formulae should be set beautifully for printed documentation yet we still want something useful for text and HTML online help. To this end, the two commands \eqn{latex}{ascii} and \deqn{latex}{ascii} are used. Where \eqn is used for “inline” formulae (corresponding to TeX's $...$, \deqn gives “displayed equations” (as in LaTeX's displaymath environment, or TeX's $$...$$).

Both commands can also be used as \eqn{latexascii} (only one argument) which then is used for both latex and ascii.

The following example is from Poisson.Rd:

      \deqn{p(x) = \frac{\lambda^x e^{-\lambda}}{x!}}{%
            p(x) = lambda^x exp(-lambda)/x!}
      for \eqn{x = 0, 1, 2, \ldots}.

For HTML and text on-line help we get

                 p(x) = lambda^x exp(-lambda)/x!
             for x = 0, 1, 2, ....


Use \R for the R system itself (you don't need extra `{}' or `\'). Use \dots for the dots in function argument lists `...', and \ldots for ellipsis dots in ordinary text.

After a `%', you can put your own comments regarding the help text. The rest of the line will be completely disregarded, normally. Therefore, you can also use it to make part of the “help” invisible.

You can produce a backslash (`\') by escaping it by another backslash. (Note that \cr is used for generating line breaks.)

The “comment” character `%' and unpaired braces10 always need to be escaped by `\', and `\\' can be (but rarely needs to be) used for backslash. Inside the verbatim-like commands (\code, \preformatted and \examples), no other 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.

Text which might need to be represented differently in different encodings should be marked by \enc, e.g. \enc{Jöreskog}{Joreskog} where the first argument will be used where encodings are allowed and the second should be ASCII (and is used for e.g. the text conversion).


The \alias command (see Documenting functions) is used to specify the “topics” documented, which should include all R objects in a package such as functions and variables, data sets, and S4 classes and methods (see Documenting S4 classes and methods). The on-line help system searches the index data base consisting of all alias topics.

In addition, it is possible to provide “concept index entries” using \concept, which can be used for help.search() lookups. E.g., file cor.test.Rd in the standard package stats contains

    \concept{Kendall correlation coefficient}
    \concept{Pearson correlation coefficient}
    \concept{Spearman correlation coefficient}

so that e.g. help.search("Spearman") will succeed in finding the help page for the test for association between paired samples using Spearman's rho. (Note that concepts are not currently supported by the HTML search accessed via `help.start()'.)

(Note that help.search() only uses “sections” of documentation objects with no additional markup.)

If you want to cross reference such items from other help files via \link, you need to use \alias and not \concept.

Platform-specific documentation

Sometimes the documentation needs to differ by platform. Currently two OS-specific options are available, `unix' and `windows', and lines in the help source file can be enclosed in

    #ifdef OS


    #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 or windows subdirectory.


Rd files are text files and so it is impossible to deduce the encoding they are written in: ASCII, UTF-8, Latin-1, Latin-9 etc. So the \encoding{} directive must be used to specify the encoding if it is not ASCII. This is used when creating the header of the HTML conversion (if not present, for back-compatibility the processing to HTML assumes that the file is in Latin-1 (ISO-8859-1)) and to make a comment in the examples file. It is also used to indicate to LaTeX how to process the file (see below).

Wherever possible, avoid non-ASCII chars in Rd files, and even symbols such as `<', `>', `$', `^', `&', `|', `@', `~', and `*' outside verbatim environments (since they may disappear in fonts designed to render text).

For convenience, encoding names `latin1' and `latin2' are always recognized: these and `UTF-8' are likely to work fairly widely.

The \enc command (see Insertions) can be used to provide transliterations which will be used in conversions that do not support the declared encoding.

The LaTeX conversion converts an explicit encoding of the file to a


command, and this needs to be matched by a suitable invocation of the \usepackage{inputenc} command. The R utility R CMD Rd2dvi looks at the converted code and includes the encodings used: it might for example use


(Use of utf8 as an encoding requires LaTeX dated 2003/12/01 or later.)

Note that this mechanism works best with letters and for example the copyright symbol may be rendered as a subscript and the plus–minus symbol cannot be used in text.

Processing Rd format

There are several commands to process Rd files from the system command line. All of these need Perl to be installed.

Using R CMD Rdconv one can convert R documentation format to other formats, or extract the executable examples for run-time testing. Currently, conversions to plain text, HTML, LaTeX, and S version 3 or 4 documentation formats are supported.

In addition to this low-level conversion tool, the R distribution provides two user-level programs for processing Rd format. R CMD Rd2txt produces “pretty” plain text output from an Rd file, and is particularly useful as a previewer when writing Rd format documentation within Emacs. R CMD Rd2dvi generates DVI (or, if option --pdf is given, PDF) output from documentation in Rd files, which can be specified either explicitly or by the path to a directory with the sources of a package (or bundle). In the latter case, a reference manual for all documented objects in the package is created, including the information in the DESCRIPTION files.

R CMD Sd2Rd converts S version 3 documentation files (which use an extended Nroff format) and S version 4 documentation (which uses SGML markup) to Rd format. This is useful when porting a package originally written for the S system to R. S version 3 files usually have extension .d, whereas version 4 ones have extension .sgml or .sgm.

R CMD Sweave and R CMD Stangle process `Sweave' documentation files (usually with extension `.Snw' or `.Rnw'): R CMD Stangle is use to extract the R code fragments.

The exact usage and a detailed list of available options for all but the last two of the above commands can be obtained by running R CMD command --help, e.g., R CMD Rdconv --help. All available commands can be listed using R --help (or Rcmd --help under Windows).

All of these work under Windows. You will need to have installed the files in the R binary Windows distribution for installing source packages (this is true for a default installation), and for R CMD Rd2dvi also the tools to build packages from source as described in the “R Installation and Administration” manual.

Next: Debugging, Previous: Writing R documentation 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.

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, for example with consistent indentation and spaces around operators. This tidied version is much easier to read, not least by other users who are used to the standard format. Although the deparsing cannot do so, we recommend the consistent use of the preferred assignment operator `<-' (rather than `=') for assignment.

We can subvert the keeping of source in two ways.

   The option keep.source can be set to FALSE before the code is loaded into R.
   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 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.

Profiling R code for speed

It is possible to profile R code on Windows and most11 Unix-like versions of R.

The command Rprof is used to control profiling, and its help page can be consulted for full details. Profiling works by recording at fixed intervals12 (by default every 20 msecs) which R function is being used, and recording the results in a file (default Rprof.out in the working directory). Then the function summaryRprof or the command-line utility R CMD Rprof Rprof.out can be used to summarize the activity.

As an example, consider the following code (from Venables & Ripley, 2002).

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

R CMD Rprof uses a Perl script that may be a little faster than summaryRprof for large files. On the other hand summaryRprof does not require Perl and provides the results as an R object.

Two warnings: profiling does impose a small performance penalty, and the output files can be very large if long runs are profiled.

Profiling short runs can sometimes give misleading results. R from time to time performs garbage collection to reclaim unused memory, and this takes an appreciable amount of time which profiling will charge to whichever function happens to provoke it. It may be useful to compare profiling code immediately after a call to gc() with a profiling run without a preceding call to gc.

Profiling R code for memory use

Measuring memory use in R code is useful either when the code takes more memory than is conveniently available or when memory allocation and copying of objects is responsible for slow code. There are three ways to profile memory use over time in R code. All three require R to have been compiled with --enable-memory-profiling, which is not the default. All can be misleading, for different reasons.

In understanding the memory profiles it is useful to know a little more about R's memory allocation. Looking at the results of gc() shows a division of memory into Vcells used to store the contents of vectors and Ncells used to store everything else, including all the administrative overhead for vectors such as type and length information. In fact the vector contents are divided into two pools. Memory for small vectors (by default 128 bytes or less) is obtained in large chunks and then parcelled out by R; memory for larger vectors is obtained directly from the operating system.

Some memory allocation is obvious in interpreted code, for example,

    y <- x + 1

allocates memory for a new vector y. Other memory allocation is less obvious and occurs because R is forced to make good on its promise of `call-by-value' argument passing. When an argument is passed to a function it is not immediately copied. Copying occurs (if necessary) only when the argument is modified. This can lead to surprising memory use. For example, in the `survey' package we have

    print.svycoxph <- function (x, ...)
        print(x$survey.design, varnames = FALSE, design.summaries = FALSE,
        x$call <- x$printcall

It may not be obvious that the assignment to x$call will cause the entire object x to be copied. This copying to preserve the call-by-value illusion is usually done by the internal C function duplicate.

The main reason that memory-use profiling is difficult is garbage collection. Memory is allocated at well-defined times in an R program, but is freed whenever the garbage collector happens to run.

   Memory statistics from Rprof
   Tracking memory allocations
   Tracing copies of an object 

Memory statistics from Rprof

The sampling profiler Rprof described in the previous section can be given the option memory.profiling=TRUE. It then writes the total R memory allocation in small vectors, large vectors, and cons cells or nodes at each sampling interval. It also writes out the number of calls to the internal function duplicate, which is called to copy R objects. summaryRprof provides summaries of this information. The main reason that this can be misleading is that the memory use is attributed to the function running at the end of the sampling interval. A second reason is that garbage collection can make the amount of memory in use decrease, so a function appears to use little memory. Running under gctorture helps with both problems: it slows down the code to effectively increase the sampling frequency and it makes each garbage collection release a smaller of memory. Changing the memory limits with mem.limits() may also be useful, to see how the code would run under different memory conditions.

Tracking memory allocations

The second method of memory profiling uses a memory-allocation profiler, Rprofmem(), which writes out a stack trace to an output file every time a large vector is allocated (with a user-specified threshold for `large') or a new page of memory is allocated for the R heap. Summary functions for this output are still being designed.

Running the example from the previous section with

    > Rprofmem("boot.memprof",threshold=1000)
    > storm.boot <- boot(rs, storm.bf, R = 4999)
    > Rprofmem(NULL)

shows that apart from some initial and final work in boot there are no vector allocations over 1000 bytes.

Tracing copies of an object

The third method of memory profiling involves tracing copies made of a specific (presumably large) R object. Calling tracemem on an object marks it so that a message is printed to standard output when the object is copied via duplicate or coercion to another type, or when a new object of the same size is created in arithmetic operations. The main reason that this can be misleading is that copying of subsets or components of an object is not tracked. It may be helpful to use tracemem on these components.

In the example above we can run tracemem on the data frame st

    > tracemem(st)
    [1] "<0x9abd5e0>"
    > storm.boot <- boot(rs, storm.bf, R = 4)
    memtrace[0x9abd5e0->0x92a6d08]: statistic boot
    memtrace[0x92a6d08->0x92a6d80]: $<-.data.frame $<- statistic boot
    memtrace[0x92a6d80->0x92a6df8]: $<-.data.frame $<- statistic boot
    memtrace[0x9abd5e0->0x9271318]: statistic boot
    memtrace[0x9271318->0x9271390]: $<-.data.frame $<- statistic boot
    memtrace[0x9271390->0x9271408]: $<-.data.frame $<- statistic boot
    memtrace[0x9abd5e0->0x914f558]: statistic boot
    memtrace[0x914f558->0x914f5f8]: $<-.data.frame $<- statistic boot
    memtrace[0x914f5f8->0x914f670]: $<-.data.frame $<- statistic boot
    memtrace[0x9abd5e0->0x972cbf0]: statistic boot
    memtrace[0x972cbf0->0x972cc68]: $<-.data.frame $<- statistic boot
    memtrace[0x972cc68->0x972cd08]: $<-.data.frame $<- statistic boot
    memtrace[0x9abd5e0->0x98ead98]: statistic boot
    memtrace[0x98ead98->0x98eae10]: $<-.data.frame $<- statistic boot
    memtrace[0x98eae10->0x98eae88]: $<-.data.frame $<- statistic boot

The object is duplicated fifteen times, three times for each of the R+1 calls to storm.bf. This is surprising, since none of the duplications happen inside nls. Stepping through storm.bf in the debugger shows that all three happen in the line

    st$Time <- st$fit + rs[i]

Data frames are slower than matrices and this is an example of why. Using tracemem(st$Viscosity) does not reveal any additional copying.

Profiling compiled code

Profiling compiled code is highly system-specific, but this section contains some hints gleaned from various R users. Some methods need to be different for a compiled executable and for dynamic/shared libraries/objects as used by R packages. We know of no good way to profile DLLs on Windows.


Options include using sprof for a shared object, and oprofile (see http://oprofile.sourceforge.net/) for any executable or shared object.


You can select shared objects to be profiled with sprof by setting the environment variable LD_PROFILE. For example

    % setenv LD_PROFILE /path/to/R_HOME/library/stats/libs/stats.so
    ... run the boot example
    % sprof /path/to/R_HOME/library/stats/libs/stats.so \
    Flat profile:
    Each sample counts as 0.01 seconds.
      %   cumulative   self              self     total
     time   seconds   seconds    calls  us/call  us/call  name
     76.19      0.32     0.32        0     0.00           numeric_deriv
     16.67      0.39     0.07        0     0.00           nls_iter
      7.14      0.42     0.03        0     0.00           getListElement
    rm /path/to/R_HOME/library/stats/libs/stats.so.profile
    ... to clean up ...

It is possible that root access is needed to create the directories used for the profile data.


oprofile works by running a daemon which collects information. The daemon must be started as root, e.g.

    % su
    % opcontrol --no-vmlinux
    % opcontrol --start
    % exit

Then as a user

    % R
    ... run the boot example
    % opcontrol --dump
    % opreport -l /path/to/R_HOME/library/stats/libs/stats.so
    samples  %        symbol name
    1623     75.5939  anonymous symbol from section .plt
    349      16.2552  numeric_deriv
    113       5.2632  nls_iter
    62        2.8878  getListElement
    % opreport -l /path/to/R_HOME/bin/exec/R
    samples  %        symbol name
    76052    11.9912  Rf_eval
    54670     8.6198  Rf_findVarInFrame3
    37814     5.9622  Rf_allocVector
    31489     4.9649  Rf_duplicate
    28221     4.4496  Rf_protect
    26485     4.1759  Rf_cons
    23650     3.7289  Rf_matchArgs
    21088     3.3250  Rf_findFun
    19995     3.1526  findVarLocInFrame
    14871     2.3447  Rf_evalList
    13794     2.1749  R_Newhashpjw
    13522     2.1320  R_gc_internal

Shutting down the profiler and clearing the records needs to be done as root. You can use opannotate to annotate the source code with the times spent in each section, if the appropriate source code was compiled with debugging support.


On 64-bit (only) Solaris, the standard profiling tool gprof collects information from shared libraries compiled with -pg.


Developers have recommended sample (or Sampler.app, which is a GUI version) and Shark (see http://developer.apple.com/tools/sharkoptimize.html and http://developer.apple.com/tools/shark_optimize.html).


This chapter covers the debugging of R extensions, starting with the ways to get useful error information and moving on to how to deal with errors that crash R. For those who prefer other styles there are contributed packages such as debug on CRAN (described in an article in R-News 3/3). (There are notes from 2002 provided by Roger Peng at http://www.biostat.jhsph.edu/~rpeng/docs/R-debug-tools.pdf which provide complementary examples to those given here.)


Most of the R-level debugging facilities are based around the built-in browser. This can be used directly by inserting a call to browser() into the code of a function (for example, using fix(my_function) ). When code execution reaches that point in the function, control returns to the R console with a special prompt. For example

    > fix(summary.data.frame) ## insert browser() call after for() loop
    > summary(women)
    Called from: summary.data.frame(women)
    Browse[1]> ls()
     [1] "digits" "i"      "lbs"    "lw"     "maxsum" "nm"     "nr"     "nv"
     [9] "object" "sms"    "z"
    Browse[1]> maxsum
    [1] 7
         height         weight
     Min.   :58.0   Min.   :115.0
     1st Qu.:61.5   1st Qu.:124.5
     Median :65.0   Median :135.0
     Mean   :65.0   Mean   :136.7
     3rd Qu.:68.5   3rd Qu.:148.0
     Max.   :72.0   Max.   :164.0
    > rm(summary.data.frame)

At the browser prompt one can enter any R expression, so for example ls() lists the objects in the current frame, and entering the name of an object will13 print it. The following commands are also accepted


Enter `step-through' mode. In this mode, hitting return executes the next line of code (more precisely one line and any continuation lines). Typing c will continue to the end of the current context, e.g. to the end of the current loop or function.


In normal mode, this quits the browser and continues execution, and just return works in the same way. cont is a synonym.


This prints the call stack. For example

             > summary(women)
             Called from: summary.data.frame(women)
             Browse[1]> where
             where 1: summary.data.frame(women)
             where 2: summary(women)

Quit both the browser and the current expression, and return to the top-level prompt.

Errors in code executed at the browser prompt will normally return control to the browser prompt. Objects can be altered by assignment, and will keep their changed values when the browser is exited. If really necessary, objects can be assigned to the workspace from the browser prompt (by using <<- if the name is not already in scope).

Debugging R code

Suppose your R program gives an error message. The first thing to find out is what R was doing at the time of the error, and the most useful tool is traceback(). We suggest that this is run whenever the cause of the error is not immediately obvious. Daily, errors are reported to the R mailing lists as being in some package when traceback() would show that the error was being reported by some other package or base R. Here is an example from the regression suite.

    > success <- c(13,12,11,14,14,11,13,11,12)
    > failure <- c(0,0,0,0,0,0,0,2,2)
    > resp <- cbind(success, failure)
    > predictor <- c(0, 5^(0:7))
    > glm(resp ~ 0+predictor, family = binomial(link="log"))
    Error: no valid set of coefficients has been found: please supply starting values
    > traceback()
    3: stop("no valid set of coefficients has been found: please supply
             starting values", call. = FALSE)
    2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,
           mustart = mustart, offset = offset, family = family, control = control,
           intercept = attr(mt, "intercept") > 0)
    1: glm(resp ~ 0 + predictor, family = binomial(link ="log"))

The calls to the active frames are given in reverse order (starting with the innermost). So we see the error message comes from an explicit check in glm.fit. (traceback() shows you all the lines of the function calls, which can be limited by setting option "deparse.max.lines".)

Sometimes the traceback will indicate that the error was detected inside compiled code, for example (from ?nls)

    Error in nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321), trace = TRUE) :
            step factor 0.000488281 reduced below 'minFactor' of 0.000976563
    >  traceback()
    2: .Call(R_nls_iter, m, ctrl, trace)
    1: nls(y ~ a + b * x, start = list(a = 0.12345, b = 0.54321), trace = TRUE)

This will be the case if the innermost call is to .C, .Fortran, .Call, .External or .Internal, but as it is also possible for such code to evaluate R expressions, this need not be the innermost call, as in

    > traceback()
    9: gm(a, b, x)
    8: .Call(R_numeric_deriv, expr, theta, rho, dir)
    7: numericDeriv(form3, names(ind), env)
    6: getRHS()
    5: assign("rhs", getRHS(), envir = thisEnv)
    4: assign("resid", .swts * (lhs - assign("rhs", getRHS(), envir = thisEnv)),
           envir = thisEnv)
    3: function (newPars)
           assign("resid", .swts * (lhs - assign("rhs", getRHS(), envir = thisEnv)),
               envir = thisEnv)
           assign("dev", sum(resid^2), envir = thisEnv)
           assign("QR", qr(.swts * attr(rhs, "gradient")), envir = thisEnv)
           return(QR$rank < min(dim(QR$qr)))
       }(c(-0.00760232418963883, 1.00119632515036))
    2: .Call(R_nls_iter, m, ctrl, trace)
    1: nls(yeps ~ gm(a, b, x), start = list(a = 0.12345, b = 0.54321))

Occasionally traceback() does not help, and this can be the case if S4 method dispatch is involved. Consider the following example

    > xyd <- new("xyloc", x=runif(20), y=runif(20))
    Error in as.environment(pkg) : no item called "package:S4nswv"
    on the search list
    Error in initialize(value, ...) : S language method selection got
    an error when called from internal dispatch for function 'initialize'
    > traceback()
    2: initialize(value, ...)
    1: new("xyloc", x = runif(20), y = runif(20))

which does not help much, as there is no call to as.environment in initialize (and the note “called from internal dispatch” tells us so). In this case we searched the R sources for the quoted call, which occurred in only one place, methods:::.asEnvironmentPackage. So now we knew where the error was occurring. (This was an unusually opaque example.)

The error message

    evaluation nested too deeply: infinite recursion / options(expressions=)?

can be hard to handle with the default value (5000). Unless you know that there actually is deep recursion going on, it can help to set something like


and re-run the example showing the error.

Sometimes there is warning that clearly is the precursor to some later error, but it is not obvious where it is coming from. Setting options(warn = 2) (which turns warnings into errors) can help here.

Once we have located the error, we have some choices. One way to proceed is to find out more about what was happening at the time of the crash by looking a post-mortem dump. To do so, set options(error=dump.frames) and run the code again. Then invoke debugger() and explore the dump. Continuing our example:

    > options(error = dump.frames)
    > glm(resp ~ 0 + predictor, family = binomial(link ="log"))
    Error: no valid set of coefficients has been found: please supply starting values

which is the same as before, but an object called last.dump has appeared in the workspace. (Such objects can be large, so remove it when it is no longer needed.) We can examine this at a later time by calling the function debugger.

    > debugger()
    Message:  Error: no valid set of coefficients has been found: please supply starting values
    Available environments had calls:
    1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))
    2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, mus
    3: stop("no valid set of coefficients has been found: please supply starting values
    Enter an environment number, or 0 to exit  Selection:

which gives the same sequence of calls as traceback, but in outer-first order and with only the first line of the call, truncated to the current width. However, we can now examine in more detail what was happening at the time of the error. Selecting an environment opens the browser in that frame. So we select the function call which spawned the error message, and explore some of the variables (and execute two function calls).

    Enter an environment number, or 0 to exit  Selection: 2
    Browsing in the environment with call:
       glm.fit(x = X, y = Y, weights = weights, start = start, etas
    Called from: debugger.look(ind)
    Browse[1]> ls()
     [1] "aic"        "boundary"   "coefold"    "control"    "conv"
     [6] "dev"        "dev.resids" "devold"     "EMPTY"      "eta"
    [11] "etastart"   "family"     "fit"        "good"       "intercept"
    [16] "iter"       "linkinv"    "mu"         "mu.eta"     "mu.eta.val"
    [21] "mustart"    "n"          "ngoodobs"   "nobs"       "nvars"
    [26] "offset"     "start"      "valideta"   "validmu"    "variance"
    [31] "varmu"      "w"          "weights"    "x"          "xnames"
    [36] "y"          "ynames"     "z"
    Browse[1]> eta
                1             2             3             4             5
     0.000000e+00 -2.235357e-06 -1.117679e-05 -5.588393e-05 -2.794197e-04
                6             7             8             9
    -1.397098e-03 -6.985492e-03 -3.492746e-02 -1.746373e-01
    Browse[1]> valideta(eta)
    [1] TRUE
    Browse[1]> mu
            1         2         3         4         5         6         7         8
    1.0000000 0.9999978 0.9999888 0.9999441 0.9997206 0.9986039 0.9930389 0.9656755
    Browse[1]> validmu(mu)
    [1] FALSE
    Browse[1]> c
    Available environments had calls:
    1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))
    2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart
    3: stop("no valid set of coefficients has been found: please supply starting v
    Enter an environment number, or 0 to exit  Selection: 0
    > rm(last.dump)

Because last.dump can be looked at later or even in another R session, post-mortem debugging is possible even for batch usage of R. We do need to arrange for the dump to be saved: this can be done either using the command-line flag --save to save the workspace at the end of the run, or via a setting such as

    > options(error = quote({dump.frames(to.file=TRUE); q()}))

See the help on dump.frames for further options and a worked example.

An alternative error action is to use the function recover():

    > options(error = recover)
    > glm(resp ~ 0 + predictor, family = binomial(link = "log"))
    Error: no valid set of coefficients has been found: please supply starting values
    Enter a frame number, or 0 to exit
    1: glm(resp ~ 0 + predictor, family = binomial(link = "log"))
    2: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart

which is very similar to dump.frames. However, we can examine the state of the program directly, without dumping and re-loading the dump. As its help page says, recover can be routinely used as the error action in place of dump.calls and dump.frames, since it behaves like dump.frames in non-interactive use.

Post-mortem debugging is good for finding out exactly what went wrong, but not necessarily why. An alternative approach is to take a closer look at what was happening just before the error, and a good way to do that is to use debug. This inserts a call to the browser at the beginning of the function, starting in step-through mode. So in our example we could use

    > debug(glm.fit)
    > glm(resp ~ 0 + predictor, family = binomial(link ="log"))
    debugging in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,
        mustart = mustart, offset = offset, family = family, control = control,
        intercept = attr(mt, "intercept") > 0)
    debug: {
    ## lists the whole function
    debug: x <- as.matrix(x)
    Browse[1]> start
    [1] -2.235357e-06
    debug: eta <- drop(x %*% start)
    Browse[1]> eta
                1             2             3             4             5
     0.000000e+00 -2.235357e-06 -1.117679e-05 -5.588393e-05 -2.794197e-04
                6             7             8             9
    -1.397098e-03 -6.985492e-03 -3.492746e-02 -1.746373e-01
    debug: mu <- linkinv(eta <- eta + offset)
    Browse[1]> mu
            1         2         3         4         5         6         7         8
    1.0000000 0.9999978 0.9999888 0.9999441 0.9997206 0.9986039 0.9930389 0.9656755

(The prompt Browse[1]> indicates that this is the first level of browsing: it is possible to step into another function that is itself being debugged or contains a call to browser().)

debug can be used for hidden functions and S3 methods by e.g. debug(stats:::predict.Arima). (It cannot be used for S4 methods, but an alternative is given on the help page for debug.) Sometimes you want to debug a function defined inside another function, e.g. the function arimafn defined inside arima. To do so, set debug on the outer function (here arima) and step through it until the inner function has been defined. Then call debug on the inner function (and use c to get out of step-through mode in the outer function).

To remove debugging of a function, call undebug with the argument previously given to debug; debugging otherwise lasts for the rest of the R session (or until the function is edited or otherwise replaced).

trace can be used to temporarily insert debugging code into a function, for example to insert a call to browser() just before the point of the error. To return to our running example

    ## first get a numbered listing of the expressions of the function
    > page(as.list(body(glm.fit)), method="print")
    > trace(glm.fit, browser, at=22)
    Tracing function "glm.fit" in package "stats"
    [1] "glm.fit"
    > glm(resp ~ 0 + predictor, family = binomial(link ="log"))
    Tracing glm.fit(x = X, y = Y, weights = weights, start = start,
       etastart = etastart,  .... step 22
    Called from: eval(expr, envir, enclos)
    Browse[1]> n
    ## and single-step from here.
    > untrace(glm.fit)

For your own functions, it may be as easy to use fix to insert temporary code, but trace can help with functions in a name space (as can fixInNamespace). Alternatively, use trace(,edit=TRUE) to insert code visually.

Using gctorture and valgrind

Errors in memory allocation and reading/writing outside arrays are very common causes of crashes (e.g., segfaults) on some machines. Often the crash appears long after the invalid memory access: in particular damage to the structures which R itself has allocated may only become apparent at the next garbage collection (or even at later garbage collections after objects have been deleted).

Using gctorture

We can help to detect memory problems earlier by running garbage collection as often as possible. This is achieved by gctorture(TRUE), which as described on its help page

Provokes garbage collection on (nearly) every memory allocation. Intended to ferret out memory protection bugs. Also makes R run very slowly, unfortunately.

The reference to `memory protection' is to missing C-level calls to PROTECT/UNPROTECT (see Garbage Collection) which if missing allow R objects to be garbage-collected when they are still in use. But it can also help with other memory-related errors.

Normally running under gctorture(TRUE) will just produce a crash earlier in the R program, hopefully close to the actual cause. See the next section for how to decipher such crashes.

It is possible to run all the examples, tests and vignettes covered by R CMD check under gctorture(TRUE) by using the option --use-gct.

Using valgrind

If you have access to Linux on an ix86, x86_64 or ppc32 platform you can use valgrind (http://www.valgrind.org/, pronounced to rhyme with `tinned') to check for possible problems. To run some examples under valgrind use something like

    R -d valgrind --vanilla < mypkg-Ex.R
    R -d "valgrind --tool=memcheck --leak-check=full" --vanilla < mypkg-Ex.R

where mypkg-Ex.R is a set of examples, e.g. the file created in mypkg.Rcheck by R CMD check. Occasionally this reports memory reads of `uninitialised values' that are the result of compiler optimization, so can be worth checking under an unoptimized compile. We know there will be some small memory leaks from readline and R itself — these are memory areas that are in use right up to the end of the R session. Expect this to run around 20x slower than without valgrind, and in some cases even slower than that. Current versions14 of valgrind are not happy with many optimized BLASes that use cpu-specific instructions (3D now, SSE, SSE2, SSE3 and similar) so you may need to build a version of R specifically to use with valgrind.

On platforms supported by valgrind you can build a version of R with extra instrumentation to help valgrind detect errors in the use of memory allocated from the R heap. The configure option is --with-valgrind-instrumentation=level, where level is 0, 1, or 2. Level 0 is the default and does not add any anything. Level 1 will detect use of uninitialised memory and has little impact on speed. Level 2 will detect many other memory use bugs but makes R much slower when running under valgrind. Using this in conjuction with gctorture can be even more effective (and even slower).

An example of valgrind output is

    ==12539== Invalid read of size 4
    ==12539==    at 0x1CDF6CBE: csc_compTr (Mutils.c:273)
    ==12539==    by 0x1CE07E1E: tsc_transpose (dtCMatrix.c:25)
    ==12539==    by 0x80A67A7: do_dotcall (dotcode.c:858)
    ==12539==    by 0x80CACE2: Rf_eval (eval.c:400)
    ==12539==    by 0x80CB5AF: R_execClosure (eval.c:658)
    ==12539==    by 0x80CB98E: R_execMethod (eval.c:760)
    ==12539==    by 0x1B93DEFA: R_standardGeneric (methods_list_dispatch.c:624)
    ==12539==    by 0x810262E: do_standardGeneric (objects.c:1012)
    ==12539==    by 0x80CAD23: Rf_eval (eval.c:403)
    ==12539==    by 0x80CB2F0: Rf_applyClosure (eval.c:573)
    ==12539==    by 0x80CADCC: Rf_eval (eval.c:414)
    ==12539==    by 0x80CAA03: Rf_eval (eval.c:362)
    ==12539==  Address 0x1C0D2EA8 is 280 bytes inside a block of size 1996 alloc'd
    ==12539==    at 0x1B9008D1: malloc (vg_replace_malloc.c:149)
    ==12539==    by 0x80F1B34: GetNewPage (memory.c:610)
    ==12539==    by 0x80F7515: Rf_allocVector (memory.c:1915)

This example is from an instrumented version of R, while tracking down a bug in the Matrix package in January, 2006. The first line indicates that R has tried to read 4 bytes from a memory address that it does not have access to. This is followed by a C stack trace showing where the error occurred. Next is a description of the memory that was accessed. It is inside a block allocated by malloc, called from GetNewPage, that is, in the internal R heap. Since this memory all belongs to R, valgrind would not (and did not) detect the problem in an uninstrumented build of R. In this example the stack trace was enough to isolate and fix the bug, which was in tsc_transpose, and in this example running under gctorture() did not provide any additional information. When the stack trace is not sufficiently informative the option --db-attach=yes to valgrind may be helpful. This starts a post-mortem debugger (by default gdb) so that variables in the C code can be inspected (see Inspecting R objects).

It is possible to run all the examples, tests and vignettes covered by R CMD check under valgrind by using the option --use-valgrind. If you do this you will need to select the valgrind options some other way, for example by having a ~/.valgrindrc file containing


or setting the environment variable VALGRIND_OPTS.

Debugging compiled code

Sooner or later programmers will be faced with the need to debug compiled code loaded into R. This section is geared to platforms using gdb with code compiled by gcc, but similar things are possible with front-ends to gdb such as ddd and insight, and other debuggers such as Sun's dbx.

Consider first `crashes', that is when R terminated unexpectedly with an illegal memory access (a `segfault' or `bus error'), illegal instruction or similar. Unix-alike versions of R use a signal handler which aims to give some basic information. For example

     *** caught segfault ***
    address 0x20000028, cause 'memory not mapped'
     1: .identC(class11, class2)
     2: possibleExtends(class(sloti), classi, ClassDef2 = getClassDef(classi,
    where = where))
     3: validObject(t(cu))
     4: stopifnot(validObject(cu <- as(tu, "dtCMatrix")), validObject(t(cu)),
    Possible actions:
    1: abort (with core dump)
    2: normal R exit
    3: exit R without saving workspace
    4: exit R saving workspace
    Selection: 3

Since the R process may be damaged, the only really safe option is the first.

Another cause of a `crash' is to overrun the C stack. R tries to track that in its own code, but it may happen in third-party compiled code. For modern POSIX-compilant OSes we can safely catch that and return to the top-level prompt.

    > .C("aaa")
    Error: segfault from C stack overflow

However, C stack overflows are fatal under Windows and normally defeat attempts at debugging on that platform.

If you have a crash which gives a core dump you can use something like

    gdb /path/to/R/bin/exec/R core.12345

to examine the core dump. If core dumps are disabled or to catch errors that do not generate a dump one can run R directly under a debugger by for example

    $ R -d gdb --vanilla
    gdb> run

at which point R will run normally, and hopefully the debugger will catch the error and return to its prompt. This can also be used to catch infinite loops or interrupt very long-running code. For a simple example

    > for(i in 1:1e7) x <- rnorm(100)
    [hit Ctrl-C]
    Program received signal SIGINT, Interrupt.
    0x00397682 in _int_free () from /lib/tls/libc.so.6
    (gdb) where
    #0  0x00397682 in _int_free () from /lib/tls/libc.so.6
    #1  0x00397eba in free () from /lib/tls/libc.so.6
    #2  0xb7cf2551 in R_gc_internal (size_needed=313)
        at /users/ripley/R/svn/R-devel/src/main/memory.c:743
    #3  0xb7cf3617 in Rf_allocVector (type=13, length=626)
        at /users/ripley/R/svn/R-devel/src/main/memory.c:1906
    #4  0xb7c3f6d3 in PutRNGstate ()
        at /users/ripley/R/svn/R-devel/src/main/RNG.c:351
    #5  0xb7d6c0a5 in do_random2 (call=0x94bf7d4, op=0x92580e8, args=0x9698f98,
        rho=0x9698f28) at /users/ripley/R/svn/R-devel/src/main/random.c:183

Some “tricks” are worth knowing.

   Finding entry points
   Inspecting R objects 

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-alikes use

  • Call the debugger on the R executable, for example by R -d gdb.
  • Start R.
  • At the R prompt, use dyn.load or library to load your shared object.
  • Send an interrupt signal. This will put you back to the debugger prompt.
  • Set the breakpoints in your code.
  • Continue execution of R by typing signal 0<RET>.

Under Windows signals may not be able to be used, and if so the procedure is more complicated. See the rw-FAQ and www.stats.uwo.ca/faculty/murdoch/software/debuggingR/gdb.shtml.

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 of PrintValue is to insert suitable calls into the code to be debugged.

Another way is to call R_PV from the symbolic debugger. (PrintValue is hidden as Rf_PrintValue.) For example, from gdb we can use

    (gdb) p R_PV(ab)

using the object ab from the convolution example, if we have placed a suitable breakpoint in the convolution C code.

To examine an arbitrary R object we need to work a little harder. For example, let

    R> DF <- data.frame(a = 1:3, b = 4:6)

By setting a breakpoint at do_get and typing get("DF") at the R prompt, one can find out the address in memory of DF, for example

    Value returned is $1 = (SEXPREC *) 0x40583e1c
    (gdb) p *$1
    $2 = {
      sxpinfo = {type = 19, obj = 1, named = 1, gp = 0,
        mark = 0, debug = 0, trace = 0, = 0},
      attrib = 0x40583e80,
      u = {
        vecsxp = {
          length = 2,
          type = {c = 0x40634700 "0>X@D>X@0>X@", i = 0x40634700,
            f = 0x40634700, z = 0x40634700, s = 0x40634700},
          truelength = 1075851272,
        primsxp = {offset = 2},
        symsxp = {pname = 0x2, value = 0x40634700, internal = 0x40203008},
        listsxp = {carval = 0x2, cdrval = 0x40634700, tagval = 0x40203008},
        envsxp = {frame = 0x2, enclos = 0x40634700},
        closxp = {formals = 0x2, body = 0x40634700, env = 0x40203008},
        promsxp = {value = 0x2, expr = 0x40634700, env = 0x40203008}

(Debugger output reformatted for better legibility).

Using R_PV() one can “inspect” the values of the various elements of the SEXP, for example,

    (gdb) p R_PV($1->attrib)
    [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

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).

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 77 code respectively, but the .C function can be used with other languages which can generate C interfaces, for example C++ (see Interfacing C++ code).

The first argument to each function is a character string given the symbol name as known to C or FORTRAN, that is the function or subroutine name. (That the symbol is loaded can be tested by, for example, is.loaded("cg"): it is no longer necessary nor correct to use symbol.For, which is defunct as from R 2.5.0.) (Note that the underscore is not a valid character in a FORTRAN 77 subprogram name, and on versions of R prior to 2.4.0 .Fortran may not correctly translate names containing underscores.)

There can be up to 65 further arguments giving R objects to be passed to compiled code. Normally these are copied before being passed in, and copied again to an R list object when the compiled code returns. If the arguments are given names, these are used as names for the components in the returned list object (but not passed to the compiled code).

The following table gives the mapping between the modes of R vectors and the types of arguments to a C function or FORTRAN subroutine.

   R storage mode 	C type 	FORTRAN type
   logical 	int * 	INTEGER
   integer 	int * 	INTEGER
   double 	double * 	DOUBLE PRECISION
   complex 	Rcomplex * 	DOUBLE COMPLEX
   character 	char ** 	CHARACTER*255
   raw 	char * 	none

Do please note the first two. On the 64-bit Unix/Linux platforms, long is 64-bit whereas int and INTEGER are 32-bit. Code ported from S-PLUS (which uses long * for logical and integer) will not work on all 64-bit platforms (although it may appear to work on some). Note also that if your compiled code is a mixture of C functions and FORTRAN subprograms the argument types must match as given in the table above.

C type Rcomplex is a structure with double members r and i defined in the header file R_ext/Complex.h included by R.h. (On most platforms which have it, this is compatible withe C99 double complex type.) Only a single character string can be passed to or from FORTRAN, and the success of this is compiler-dependent. Other R objects can be passed to .C, but it is better to use one of the other interfaces. An exception is passing an R function for use with call_R, when the object can be handled as void * en route to call_R, but even there .Call is to be preferred. Similarly, passing an R list as an argument to a C routine should be done using the .Call interface. If one does use the .C function to pass a list as an argument, it is visible to the routine as an array in C of SEXP types (i.e., SEXP *). The elements of the array correspond directly to the elements of the R list. However, this array must be treated as read-only and one must not assign values to its elements within the C routine — doing so bypasses R's memory management facilities and will corrupt the object and the R session.

It is possible to pass numeric vectors of storage mode double to C as float * or to FORTRAN as REAL by setting the attribute Csingle, most conveniently by using the R functions as.single, single or 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.

Argument PACKAGE confines the search for the symbol name to a specific shared object (or use "base" for code compiled into R). Its use is highly desirable, as there is no way to avoid two package writers using the same symbol name, and such name clashes are normally sufficient to cause R to crash. (If it is not present and the call is from the body of a function defined in a package with a name space, the shared object loaded by the first (if any) useDynLib directive will be used.)

For .C only you can specify an ENCODING argument: this requests that (unless DUP = FALSE) character vectors be re-encoded to the requested encoding before being passed in, and re-encoded from the requested encoding when passed back. Note that encoding names are not standardized, and not all R builds support re-encoding. (The argument is ignored with a warning if re-encoding is not supported at all: R code can test for this via capabilities("iconv").) But this can be useful to allow code to work in a UTF-8 locale by specifiying ENCODING = "latin1".

Note that the compiled code should not return anything except through its arguments: C functions should be of type void and FORTRAN subprograms should be subroutines.

To fix ideas, let us consider a very simple example which convolves two finite sequences. (This is hard to do fast in interpreted R code, but easy in C code.) We could do this using .C by

    void convolve(double *a, int *na, double *b, int *nb, double *ab)
      int i, j, nab = *na + *nb - 1;
      for(i = 0; i < nab; i++)
        ab[i] = 0.0;
      for(i = 0; i < *na; i++)
        for(j = 0; j < *nb; j++)
          ab[i + j] += a[i] * b[j];

called from R by

    conv <- function(a, b)
         ab = double(length(a) + length(b) - 1))$ab

Note that we take care to coerce all the arguments to the correct R storage mode before calling .C; mistakes in matching the types can lead to wrong results or hard-to-catch errors.

dyn.load and dyn.unload

Compiled code to be used with R is loaded as a shared object (Unix and MacOS X, see Creating shared objects for more information) or DLL (Windows).

The shared object/DLL is loaded by dyn.load and unloaded by dyn.unload. Unloading is not normally necessary, but it is needed to allow the DLL to be re-built on some platforms, including Windows.

The first argument to both functions is a character string giving the path to the object. Programmers should not assume a specific file extension for the object/DLL (such as `.so') but use a construction like

    file.path(path1, path2, paste("mylib", .Platform$dynlib.ext, sep=""))

for platform independence. On Unix-alike systems the path supplied to dyn.load can be an absolute path, one relative to the current directory or, if it starts with `~', relative to the user's home directory.

Loading is most often done via a call to library.dynam in the .First.lib function of a package. This has the form

    library.dynam("libname", package, lib.loc)

where libname is the object/DLL name with the extension omitted. Note that the first argument, chname, should not be package since this will not work if the package is installed under another name (as it will be with a versioned install).

Under some Unix-alike systems there is a choice of how the symbols are resolved when the object is loaded, governed by the arguments local and now. Only use these if really necessary: in particular using now=FALSE and then calling an unresolved symbol will terminate R unceremoniously.

R provides a way of executing some code automatically when a object/DLL is either loaded or unloaded. This can be used, for example, to register native routines with R's dynamic symbol mechanism, initialize some data in the native code, or initialize a third party library. On loading a DLL, R will look for a routine within that DLL named R_init_lib where lib is the name of the DLL file with the extension removed. For example, in the command

    library.dynam("mylib", package, lib.loc)

R looks for the symbol named R_init_mylib. Similarly, when unloading the object, R looks for a routine named R_unload_lib, e.g., R_unload_mylib. In either case, if the routine is present, R will invoke it and pass it a single argument describing the DLL. This is a value of type DllInfo which is defined in the Rdynload.h file in the R_ext directory.

The following example shows templates for the initialization and unload routines for the mylib DLL.

             #include <R.h>
             #include <Rinternals.h>
             #include <R_ext/Rdynload.h>
             R_init_mylib(DllInfo *info)
               /* Register routines, allocate resources. */
             R_unload_mylib(DllInfo *info)
               /* Release resources. */

If a shared object/DLL is loaded more than once the most recent version is used. More generally, if the same symbol name appears in several libraries, the most recently loaded occurrence is used. The PACKAGE argument provides a good way to avoid any ambiguity in which occurrence is meant.

Registering native routines

By `native' routine, we mean an entry point in compiled code.

In calls to .C, .Call, .Fortran and .External, R must locate the specified native routine by looking in the appropriate shared object/DLL. By default, R uses the operating system-specific dynamic loader to lookup the symbol. Alternatively, the author of the DLL can explicitly register routines with R and use a single, platform-independent mechanism for finding the routines in the DLL. One can use this registration mechanism to provide additional information about a routine, including the number and type of the arguments, and also make it available to R programmers under a different name. In the future, registration may be used to implement a form of “secure” or limited native access.

To register routines with R, one calls the C routine R_registerRoutines. This is typically done when the DLL is first loaded within the initialization routine R_init_dll name described in dyn.load and dyn.unload. R_registerRoutines takes 5 arguments. The first is the DllInfo object passed by R to the initialization routine. This is where R stores the information about the methods. The remaining 4 arguments are arrays describing the routines for each of the 4 different interfaces: .C, .Call, .Fortran and .External. Each argument is a NULL-terminated array of the element types given in the following table:

   .C 	R_CMethodDef
   .Call 	R_CallMethodDef
   .Fortran 	R_FortranMethodDef
   .External 	R_ExternalMethodDef

Currently, the R_ExternalMethodDef is the same as R_CallMethodDef type and contains fields for the name of the routine by which it can be accessed in R, a pointer to the actual native symbol (i.e., the routine itself), and the number of arguments the routine expects. For routines with a variable number of arguments invoked via the .External interface, one specifies -1 for the number of arguments which tells R not to check the actual number passed. For example, if we had a routine named myCall defined as

    SEXP myCall(SEXP a, SEXP b, SEXP c);

we would describe this as

    R_CallMethodDef callMethods[]  = {
      {"myCall", &myCall, 3},
      {NULL, NULL, 0}

along with any other routines for the .Call interface.

Routines for use with the .C and .Fortran interfaces are described with similar data structures, but which have two additional fields for describing the type and “style” of each argument. Each of these can be omitted. However, if specified, each should be an array with the same number of elements as the number of parameters for the routine. The types array should contain the SEXP types describing the expected type of the argument. (Technically, the elements of the types array are of type R_NativePrimitiveArgType which is just an unsigned integer.) The R types and corresponding type identifiers are provided in the following table:

   numeric 	REALSXP
   integer 	INTSXP
   logical 	LGLSXP
   single 	SINGLESXP
   character 	STRSXP
   list 	VECSXP

Consider a C routine, myC, declared as

    void myC(double *x, int *n, char **names, int *status);

We would register it as

    R_CMethodDef cMethods[] = {
       {"myC", &myC, 4, {REALSXP, INTSXP, STRSXP, LGLSXP}},
       {NULL, NULL, 0}

One can also specify whether each argument is used simply as input, or as output, or as both input and output. The style field in the description of a method is used for this. The purpose is to allow R to transfer values more efficiently across the R-C/FORTRAN interface by avoiding copying values when it is not necessary. Typically, one omits this information in the registration data.

Having created the arrays describing each routine, the last step is to actually register them with R. We do this by calling R_registerRoutines. For example, if we have the descriptions above for the routines accessed by the .C and .Call we would use the following code:

    R_init_myLib(DllInfo *info)
       R_registerRoutines(info, cMethods, callMethods, NULL, NULL);

This routine will be invoked when R loads the shared object/DLL named myLib. The last two arguments in the call to R_registerRoutines are for the routines accessed by .Fortran and .External interfaces. In our example, these are given as NULL since we have no routines of these types.

When R unloads a shared object/DLL, any registered routines are automatically removed. There is no (direct) facility for unregistering a symbol.

Examples of registering routines can be found in the different packages in the R source tree (e.g., stats). Also, there is a brief, high-level introduction in R News (volume 1/3, September 2001, pages 20-23).

In addition to registering C routines to be called by R, it can at times be useful for one package to make some of its C routines available to be called by C code in another package. An experimental interface to support this has been provided in R 2.4.0. The interface consists of two routines declared as

    void R_RegisterCCallable(char *package, char *name, DL_FUNC fptr);
    DL_FUNC R_GetCCallable(char *package, char *name);

A package packA that wants to make a C routine myCfun available to C code in other packages would include the call

    R_RegisterCCallable("packA", "myCfun", myCfun);

in its initialization function R_init_packA. A package packB that wants to use this routine would retrieve the function pointer with a call of the form

    p_myCfun = R_GetCCallable("packA", "myCfun");

The author of packB is responsible for insuring that p_myCfun has an appropriate declaration. In the future R may provide some automated tools to simplify exporting larger numbers of routines.

A package that wishes to make use of header files in other packages needs to declare them as a comma-separated list in the field LinkingTo in the DESCRIPTION file. For example

    Depends: link2, link3
    LinkingTo: link2, link3

It should also `Depend' on those packages for they have to be installed prior to this one, and loaded prior to this one (so the path to their compiled code can be found).

This then arranges that the include directories in the installed linked-to packages are added to the include paths for C and C++ code.

Next: Interfacing C++ code, Previous: Registering native routines, Up: System and foreign language interfaces 5.5 Creating shared objects

Shared objects for loading into R can be created using R CMD SHLIB. This accepts as arguments a list of files which must be object files (with extension .o) or C, C++, or FORTRAN sources (with extensions .c, .cc or .cpp or !.C, and .f or .f90 or .f95, respectively) or commands to be passed to the linker. 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 object. (On most systems the PKG_* flags can be set in the environment instead of in Makevars, but they can also be set as part of the command line of R CMD SHLIB.)

Flags which are set in file etc/Makeconf can be overridden by the environment variable MAKEFLAGS (at least for systems using GNU make), as in (Bourne shell syntax)


or by using a Makefile.

Note that as R CMD SHLIB uses Make, it will not remake a shared object just because the flags have changed, and if test.c and test.f both exist in the current directory

    R CMD SHLIB test.f

will compile test.c!

If an add-on package pkg contains C, C++, or FORTRAN code in its src subdirectory, R CMD INSTALL creates a shared object (for loading into R in the .First.lib or .onLoad 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, if FORTRAN is involved it needs to ensure that the shared object created is linked against all FORTRAN 77 intrinsic and run-time libraries that R was linked against; Make variable FLIBS contains this information.

In Windows the same command works, but files Makevars.win or Makefile.win are used in preference to Makevars or Makefile if they exist. (This does need the files in the R binary Windows distribution for installing source packages to be installed.) For details of building DLLs with a variety of compilers, see `README.packages' and http://www.stats.uwo.ca/faculty/murdoch/software/compilingDLLs/ .

Interfacing C++ code

Suppose we have the following hypothetical C++ library, consisting of the two files X.hh and X.cc, and implementing the two classes X and Y which we want to use in R.

             // X.hh
             class X {
             public: X (); ~X ();
             class Y {
             public: Y (); ~Y ();
             // X.cc
             #include <iostream>
             #include "X.hh"
             static Y y;
             X::X()  { std::cout << "constructor X" << std::endl; }
             X::~X() { std::cout << "destructor X"  << std::endl; }
             Y::Y()  { std::cout << "constructor Y" << std::endl; }
             Y::~Y() { std::cout << "destructor Y"  << std::endl; }

To use with R, the only thing we have to do is writing a wrapper function and ensuring that the function is enclosed in

    extern "C" {

For example,

             // X_main.cc:
             #include "X.hh"
             extern "C" {
             void X_main () {
               X x;
             } // extern "C"

Compiling and linking should be done with the C++ compiler-linker (rather than the C compiler-linker or the linker itself); otherwise, the C++ initialization code (and hence the constructor of the static variable Y) are not called. On a properly configured system, one can simply use

    R CMD SHLIB X.cc X_main.cc

to create the shared object, typically X.so (the file name extension may be different on your platform). Now starting R yields

    R : Copyright 2000, The R Development Core Team
    Version 1.1.0 Under development (unstable) (April 14, 2000)
    Type    "q()" to quit R.
    R> dyn.load(paste("X", .Platform$dynlib.ext, sep = ""))
    constructor Y
    R> .C("X_main")
    constructor X
    destructor X
    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.

Some R header files are C and not C++ header files and should be included within an extern "C" block: for clarity this is advisable for all R header files.

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 different C interfaces. Generally the .Call interface (which is modelled on the interface of the same name in S version 4) is a little simpler to use, but .External is a little more general. A call to .Call is very similar to .C, for example

    .Call("convolve2", a, b)

The first argument should be a character string giving a C symbol name of code that has already been loaded into R. Up to 65 R objects can passed as arguments. The C side of the interface is

    #include <R.h>
    #include <Rinternals.h>
    SEXP convolve2(SEXP a, SEXP b)

A call to .External is almost identical

    .External("convolveE", a, b)

but the C side of the interface is different, having only one argument

    #include <R.h>
    #include <Rinternals.h>
    SEXP convolveE(SEXP args)

Here args is a LISTSXP, a Lisp-style pairlist from which the arguments can be extracted.

In each case the R objects are available for manipulation via a set of functions and macros defined in the header file Rinternals.h or some S4-compatibility macros defined in Rdefines.h. See Interface functions .Call and .External for details on .Call and .External.

Before you decide to use .Call or .External, you should look at other alternatives. First, consider working in interpreted R code; if this is fast enough, this is normally the best option. You should also see if using .C is enough. If the task to be performed in C is simple enough requiring no call to R, .C suffices. The new interfaces are relatively recent additions to S and R, and a great deal of useful code has been written using just .C before they were available. The .Call and .External interfaces allow much more control, but they also impose much greater responsibilities so need to be used with care. Neither .Call nor .External copy their arguments. You should treat arguments you receive through these interfaces as read-only.

There are two approaches that can be taken to handling R objects from within C code. The first (historically) is to use the macros and functions that have been used to implement the core parts of R through .Internal calls. A public subset of these is defined in the header file Rinternals.h in the directory R_INCLUDE_DIR (default R_HOME/include) that should be available on any R installation.

Another approach is to use R versions of the macros and functions defined for the S version 4 interface .Call, which are defined in the header file Rdefines.h. This is a somewhat simpler approach, and is to be preferred if the code is intended to be shared with S. However, it is less well documented and even less tested. Note too that some idiomatic S4 constructions with these macros (such as assigning elements of character vectors or lists) are invalid in R.

A substantial amount of R is implemented using the functions and macros described here, so the R source code provides a rich source of examples and “how to do it”: indeed many of the examples here were developed by examining closely R system functions for similar tasks. Do make use of the source code for inspirational examples.

It is necessary to know something about how R objects are handled in C code. All the R objects you will deal with will be handled with the type SEXP15, which is a pointer to a structure with typedef SEXPREC. Think of this structure as a variant type that can handle all the usual types of R objects, that is vectors of various modes, functions, environments, language objects and so on. The details are given later in this section and in R Internal Structures, but for most purposes the programmer does not need to know them. Think rather of a model such as that used by Visual Basic, in which R objects are handed around in C code (as they are in interpreted R code) as the variant type, and the appropriate part is extracted for, for example, numerical calculations, only when it is needed. As in interpreted R code, much use is made of coercion to force the variant object to the right type.

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.

The R object types are represented by a C structure defined by a typedef SEXPREC in Rinternals.h. It contains several things among which are pointers to data blocks and to other SEXPRECs. A SEXP is simply a pointer to a SEXPREC.

If you create an R object in your C code, you must tell R that you are using the object by using the PROTECT macro on a pointer to the object. This tells R that the object is in use so it is not destroyed during garbage collection. Notice that it is the object which is protected, not the pointer variable. It is a common mistake to believe that if you invoked PROTECT(p) at some point then p is protected from then on, but that is not true once a new object is assigned to p.

Protecting an R object automatically protects all the R objects pointed to in the corresponding SEXPREC, for example all elements of a protected list are automatically protected.

The programmer is solely responsible for housekeeping the calls to PROTECT. There is a corresponding macro UNPROTECT that takes as argument an int giving the number of objects to unprotect when they are no longer needed. The protection mechanism is stack-based, so UNPROTECT(n) unprotects the last n objects which were protected. The calls to PROTECT and UNPROTECT must balance when the user's code returns. R will warn about "stack imbalance in .Call" (or .External) if the housekeeping is wrong.

Here is a small example of creating an R numeric vector in C code. First we use the macros in Rinternals.h:

    #include <R.h>
    #include <Rinternals.h>
      SEXP ab;
      PROTECT(ab = allocVector(REALSXP, 2));
      REAL(ab)[0] = 123.45;
      REAL(ab)[1] = 67.89;

and then those in Rdefines.h:

    #include <R.h>
    #include <Rdefines.h>
      SEXP ab;
      PROTECT(ab = NEW_NUMERIC(2));
      NUMERIC_POINTER(ab)[0] = 123.45;
      NUMERIC_POINTER(ab)[1] = 67.89;

Now, the reader may ask how the R object could possibly get removed during those manipulations, as it is just our C code that is running. As it happens, we can do without the protection in this example, but in general we do not know (nor want to know) what is hiding behind the R macros and functions we use, and any of them might cause memory to be allocated, hence garbage collection and hence our object ab to be removed. It is usually wise to err on the side of caution and assume that any of the R macros and functions might remove the object.

In some cases it is necessary to keep better track of whether protection is really needed. Be particularly aware of situations where a large number of objects are generated. The pointer protection stack has a fixed size (default 10,000) and can become full. It is not a good idea then to just PROTECT everything in sight and UNPROTECT several thousand objects at the end. It will almost invariably be possible to either assign the objects as part of another object (which automatically protects them) or unprotect them immediately after use.

Protection is not needed for objects which R already knows are in use. In particular, this applies to function arguments.

There is a less-used macro UNPROTECT_PTR(s) that unprotects the object pointed to by the SEXP s, even if it is not the top item on the pointer protection stack. This is rarely needed outside the parser (the R sources have one example, in src/main/plot3d.c). Sometimes an object is changed (for example duplicated, coerced or grown) yet the current value needs to be protected. For these cases PROTECT_WITH_INDEX saves an index of the protection location that can be used to replace the protected value using REPROTECT. For example (from the internal code for optim)

        PROTECT_INDEX ipx;
        PROTECT_WITH_INDEX(s = eval(OS->R_fcall, OS->R_env), &ipx);
        REPROTECT(s = coerceVector(s, REALSXP), ipx);

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 equlvalent 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.

Details of R types

Users of the Rinternals.h macros will need to know how the R types are known internally: if the Rdefines.h macros are used then S4-compatible names are used.

The different R data types are represented in C by SEXPTYPE. Some of these are familiar from R and some are internal data types. The usual R object modes are given in the table.

   SEXPTYPE 	R equivalent
   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, PROMSXP, etc. (Note: although it is possible to return objects of internal types, it is unsafe to do so as assumptions are made about how they are handled which may be violated at user-level evaluation.) More details are given in R Internal Structures.

Unless you are very sure about the type of the arguments, the code should check the data types. Sometimes it may also be necessary to check data types of objects created by evaluating an R expression in the C code. You can use functions like isReal, isInteger and isString to do type checking. See the header file Rinternals.h for definitions of other such functions. All of these take a SEXP as argument and return 1 or 0 to indicate TRUE or FALSE. Once again there are two ways to do this, and Rdefines.h has macros such as IS_NUMERIC.

What happens if the SEXP is not of the correct type? Sometimes you have no other option except to generate an error. You can use the function error for this. It is usually better to coerce the object to the correct type. For example, if you find that an SEXP is of the type INTEGER, but you need a REAL object, you can change the type by using, equivalently,

    PROTECT(newSexp = coerceVector(oldSexp, REALSXP));


    PROTECT(newSexp = AS_NUMERIC(oldSexp));

Protection is needed as a new object is created; the object formerly pointed to by the SEXP is still protected but now unused.

All the coercion functions do their own error-checking, and generate NAs with a warning or stop with an error as appropriate.

Note that these coercion functions are not the same as calling as.numeric (and so on) in R code, as they do not dispatch on the class of the object. Thus it is normally preferable to do the coercion in the calling R code.

So far we have only seen how to create and coerce R objects from C code, and how to extract the numeric data from numeric R vectors. These can suffice to take us a long way in interfacing R objects to numerical algorithms, but we may need to know a little more to create useful return objects.


Many R objects have attributes: some of the most useful are classes and the dim and dimnames that mark objects as matrices or arrays. It can also be helpful to work with the names attribute of vectors.

To illustrate this, let us write code to take the outer product of two vectors (which outer and %o% already do). As usual the R code is simple

    out <- function(x, y)
       storage.mode(x) <- storage.mode(y) <- "double"
       .Call("out", x, y)

where we expect x and y to be numeric vectors (possibly integer), possibly with names. This time we do the coercion in the calling R code.

C code to do the computations is

    #include <R.h>
    #include <Rinternals.h>
    SEXP out(SEXP x, SEXP y)
      int i, j, nx, ny;
      double tmp, *rx = REAL(x), *ry = REAL(y), *rans;
      SEXP ans;
      nx = length(x); ny = length(y);
      PROTECT(ans = allocMatrix(REALSXP, nx, ny));
      rans = REAL(ans);
      for(i = 0; i < nx; i++) {
        tmp = rx[i];
        for(j = 0; j < ny; j++)
          rans[i + nx*j] = tmp * ry[j];

Note the way REAL is used: as it is a function call it can be considerably faster to store the result and index that.

However, we would like to set the dimnames of the result. Although allocMatrix provides a short cut, we will show how to set the dim attribute directly.

    #include <R.h>
    #include <Rinternals.h>
    SEXP out(SEXP x, SEXP y)
      R_len_t i, j, nx, ny;
      double tmp, *rx = REAL(x), *ry = REAL(y), *rans;
      SEXP ans, dim, dimnames;
      nx = length(x); ny = length(y);
      PROTECT(ans = allocVector(REALSXP, nx*ny));
      rans = REAL(ans);
      for(i = 0; i < nx; i++) {
        tmp = rx[i];
        for(j = 0; j < ny; j++)
          rans[i + nx*j] = tmp * ry[j];
      PROTECT(dim = allocVector(INTSXP, 2));
      INTEGER(dim)[0] = nx; INTEGER(dim)[1] = ny;
      setAttrib(ans, R_DimSymbol, dim);
      PROTECT(dimnames = allocVector(VECSXP, 2));
      SET_VECTOR_ELT(dimnames, 0, getAttrib(x, R_NamesSymbol));
      SET_VECTOR_ELT(dimnames, 1, getAttrib(y, R_NamesSymbol));
      setAttrib(ans, R_DimNamesSymbol, dimnames);

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)[0] = 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.


In R the (S3) class is just the attribute named "class" so it can be handled as such, but there is a shortcut classgets. Suppose we want to give the return value in our example the class "mat". We can use

    #include <R.h>
    #include <Rdefines.h>
      SEXP ans, dim, dimnames, class;
      PROTECT(class = allocVector(STRSXP, 1));
      SET_STRING_ELT(class, 0, mkChar("mat"));
      classgets(ans, class);

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.

Handling lists

Some care is needed with lists, as R moved early on from using LISP-like lists (now called “pairlists”) to S-like generic vectors. As a result, the appropriate test for an object of mode list is isNewList, and we need allocVector(VECSXP, n) and not allocList(n).

List elements can be retrieved or set by direct access to the elements of the generic vector. Suppose we have a list object

    a <- list(f=1, g=2, h=3)

Then we can access a$g as a2 by

      double g;
      g = REAL(VECTOR_ELT(a, 1))[0];

This can rapidly become tedious, and the following function (based on one in package stats) is very useful:

    /* get the list element named str, or return NULL */
    SEXP getListElement(SEXP list, 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];

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.16 setVar searches for an existing binding for symbol in rho or its enclosing environments. If a binding is found, its value is changed to value. Otherwise, a new binding with the specified value is created in the global environment. This corresponds to assign(symbol, value, envir = rho, inherits = TRUE).

Some convenience functions

Some operations are done so frequently that there are convenience functions to handle them. Suppose we wanted to pass a single logical argument ignore_quotes: we could use

        int ign;
        ign = asLogical(ignore_quotes);
        if(ign == NA_LOGICAL) error("'ignore_quotes' must be TRUE or FALSE");

which will do any coercion needed, and return NA_LOGICAL if the value passed was NA or coercion failed. There are also asInteger, asReal and asComplex. The function asChar returns a CHARSXP. All of these functions ignore any elements of an imput vector after the first.

To return a length-one real vector we can use

        double x;
        return ScalarReal(x);

and there are versions of this for all the atomic vector types (those for a length-one character vector being ScalarString with argument a CHARSXP and mkString with argument char *).

Some of the isXXXX functions differ from their apparent R-level counterparts: for example isVector is true for any atomic vector type (isVectorAtomic) and for lists and expressions (isVectorList) (with no check on attributes). isMatrix is a test of a length-2 "dim" attribute.

There are a series of small macros/functions to help construct pairlists and language objects (whose internal structures just differ by SEXPTYPE. Function CONS(u, v) is the basic building block: is constructs a pairlist from u followed by v (which is a pairlist or R_NilValue). LCONS is a variant that constructs a language object. Functions list1 to list4 construct a pairlist from one to four items, and lang1 to lang4 do the same for a language object (a function to call plus zero to three arguments). Function elt and lastElt find the ith element and the last element of a pairlist, and nthcdr returns a pointer to the nth position in the pairlist (whose CAR is the nth item).

Named objects and copying

When assignments are done in R such as

    x <- 1:10
    y <- x

the named object is not necessarily copied, so after those two assignments y and x are bound to the same SEXPREC (the structure a SEXP points to). This means that any code which alters one of them has to make a copy before modifying the copy if the usual R semantics are to apply. Note that whereas .C and .Fortran do copy their arguments (unless the dangerous dup = FALSE is used), .Call and .External do not. So duplicate is commonly called on arguments to .Call before modifying them.

However, at least some of this copying is unneeded. In the first assignment shown, x <- 1:10, R first creates an object with value 1:10 and then assigns it to x but if x is modified no copy is necessary as the temporary object with value 1:10 cannot be referred to again. R distinguishes between named and unnamed objects via a field in a SEXPREC that can be accessed via the macros NAMED and SET_NAMED. This can take values

0 The object is not bound to any symbol

1 The object has been bound to exactly one symbol

2 The object has potentially been bound to two or more symbols, and one should act as if another variable is currently bound to this value.

Note the past tenses: R does not do full reference counting and there may currently be fewer bindings.

It is safe to modify the value of any SEXP for which NAMED(foo) is zero, and if NAMED(foo) is two, the value should be duplicated (via a call to duplicate) before any modification. Note that it is the responsibility of the author of the code making the modification to do the duplication, even if it is x whose value is being modified after y <- x.

The case NAMED(foo) == 1 allows some optimization, but it can be ignored (and duplication done whenever NAMED(foo) > 0). (This optimization is not currently usable in user code.) It is intended for use within assignment functions. Suppose we used

    x <- 1:10
    foo(x) <- 3

which is computed as

    x <- 1:10
    x <- "foo<-"(x, 3)

Then inside "foo<-" the object pointing to the current value of x will have NAMED(foo) as one, and it would be safe to modify it as the only symbol bound to it is x and that will be rebound immediately. (Provided the remaining code in "foo<-" make no reference to x, and no one is going to attempt a direct call such as y <- "foo<-"(x).)

Currently all arguments to a .Call call will have NAMED set to 2, and so users must assume that they need to be duplicated before alteration.

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.

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;
      PROTECT(a = AS_NUMERIC(a));
      PROTECT(b = AS_NUMERIC(b));
      na = LENGTH(a); nb = LENGTH(b); nab = na + nb - 1;
      PROTECT(ab = NEW_NUMERIC(nab));
      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)
      R_len_t i, j, na, nb, nab;
      double *xa, *xb, *xab;
      SEXP ab;
      PROTECT(a = coerceVector(a, REALSXP));
      PROTECT(b = coerceVector(b, REALSXP));
      na = length(a); nb = length(b); nab = na + nb - 1;
      PROTECT(ab = allocVector(REALSXP, nab));
      xa = REAL(a); xb = REAL(b);
      xab = REAL(ab);
      for(i = 0; i < nab; i++) xab[i] = 0.0;
      for(i = 0; i < na; i++)
        for(j = 0; j < nb; j++) xab[i + j] += xa[i] * xb[j];

This is called in exactly the same way.

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;
        SEXP el;
      args = CDR(args); /* skip 'name' */
      for(i = 0; args != R_NilValue; i++, args = CDR(args)) {
        args = CDR(args);
        name = CHAR(PRINTNAME(TAG(args)));
        switch(TYPEOF(CAR(args))) {
        case REALSXP:
          Rprintf("[%d] '%s' %f\n", i+1, name, REAL(CAR(args))[0]);
        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

    showArgs1 <- function(...) .Call("showArgs1", list(...))

The (very similar) C code is in the scripts.

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 R_exts/Arith.h included by R.h.

The code changes are the same in any of the versions of convolve2 or convolveE:

      for(i = 0; i < na; i++)
        for(j = 0; j < nb; j++)
            if(ISNA(xa[i]) || ISNA(xb[j]) || ISNA(xab[i + j]))
              xab[i + j] = NA_REAL;
              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.

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)
      R_len_t i, n = length(list);
      SEXP ans;
      if(!isNewList(list)) error("`list' must be a list");
      if(!isEnvironment(rho)) error("`rho' should be an environment");
      PROTECT(ans = allocVector(VECSXP, n));
      for(i = 0; i < n; i++) {
        defineVar(install("x"), VECTOR_ELT(list, i), rho);
        SET_VECTOR_ELT(ans, i, eval(expr, rho));
      setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol));

It would be closer to lapply if we could pass in a function rather than an expression. One way to do this is via interpreted R code as in the next example, but it is possible (if somewhat obscure) to do this in C code. The following is based on the code in src/main/optimize.c.

    SEXP lapply2(SEXP list, SEXP fn, SEXP rho)
      R_len_t i, n = length(list);
      SEXP R_fcall, ans;
      if(!isNewList(list)) error("`list' must be a list");
      if(!isFunction(fn)) error("`fn' must be a function");
      if(!isEnvironment(rho)) error("`rho' should be an environment");
      PROTECT(R_fcall = lang2(fn, R_NilValue));
      PROTECT(ans = allocVector(VECSXP, n));
      for(i = 0; i < n; i++) {
        SETCADR(R_fcall, VECTOR_ELT(list, i));
        SET_VECTOR_ELT(ans, i, eval(R_fcall, rho));
      setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol));

used by

    .Call("lapply2", a, sum, new.env())

Function lang2 creates an executable pairlist of two elements, but this will only be clear to those with a knowledge of a LISP-like language.

As a more comprehensive example of constructing an R call in C code and evaluating, consider the following fragment of printAttributes in src/main/print.c.

        /* Need to construct a call to
           print(CAR(a), digits=digits)
           based on the R_print structure, then eval(call, env).
           See do_docall for the template for this sort of thing.
        SEXP s, t;
        PROTECT(t = s = allocList(3));
        CAR(t) = install("print"); t = CDR(t);
        CAR(t) = CAR(a); t = CDR(t);
        CAR(t) = allocVector(INTSXP, 1);
        INTEGER(CAR(t))[0] = digits;
        SET_TAG(t, install("digits"));
        eval(s, env);

At this point CAR(a) is the R object to be printed, the current attribute. There are three steps: the call is constructed as a pairlist of length 3, the list is filled in, and the expression represented by the pairlist is evaluated.

A pairlist is quite distinct from a generic vector list, the only user-visible form of list in R. A pairlist is a linked list (with CDR(t) computing the next entry), with items (accessed by CAR(t)) and names or tags (set by SET_TAG). In this call there are to be three items, a symbol (pointing to the function to be called) and two argument values, the first unnamed and the second named. Setting the type to LANGSXP makes this a call which can be evaluated.

   Calculating numerical derivatives 


In this section we re-work the example of call_S in Becker, Chambers & Wilks (1988) on finding a zero of a univariate function, The R code and an example are

    zero <- function(f, guesses, tol = 1e-7) {
      f.check <- function(x) {
        x <- f(x)
        if(!is.numeric(x)) stop("Need a numeric result")
      .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), *rgr, *rans;
      int start, i, j;
      expr = CADR(args);
      if(!isString(theta = CADDR(args)))
        error("theta should be of type character");
      if(!isEnvironment(rho = CADDDR(args)))
        error("rho should be an environment");
      PROTECT(ans = coerceVector(eval(expr, rho), REALSXP));
      PROTECT(gradient = allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));
      rgr = REAL(gradient); rans = REAL(ans);
      for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {
        PROTECT(par = findVar(install(CHAR(STRING_ELT(theta, i))), rho));
        tt = REAL(par)[0];
        xx = fabs(tt);
        delta = (xx < 1) ? eps : xx*eps;
        REAL(par)[0] += delta;
        PROTECT(ans1 = coerceVector(eval(expr, rho), REALSXP));
        for(j = 0; j < LENGTH(ans); j++)
          rgr[j + start] = (REAL(ans1)[j] - rans[j])/delta;
        REAL(par)[0] = tt;
        UNPROTECT(2); /* par, ans1 */
      PROTECT(dimnames = allocVector(VECSXP, 2));
      SET_VECTOR_ELT(dimnames, 1,  theta);
      dimnamesgets(gradient, dimnames);
      setAttrib(ans, install("gradient"), gradient);
      UNPROTECT(3); /* ans  gradient  dimnames */
      return ans;

The code to handle the arguments is

      expr = CADR(args);
      if(!isString(theta = CADDR(args)))
        error("theta should be of type character");
      if(!isEnvironment(rho = CADDDR(args)))
        error("rho should be an environment");

Note that we check for correct types of theta and rho but do not check the type of expr. That is because eval can handle many types of R objects other than EXPRSXP. There is no useful coercion we can do, so we stop with an error message if the arguments are not of the correct mode.

The first step in the code is to evaluate the expression in the environment rho, by

      PROTECT(ans = coerceVector(eval(expr, rho), REALSXP));

We then allocate space for the calculated derivative by

      PROTECT(gradient = allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));

The first argument to allocMatrix gives the SEXPTYPE of the matrix: here we want it to be REALSXP. The other two arguments are the numbers of rows and columns.

      for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {
        PROTECT(par = findVar(install(CHAR(STRING_ELT(theta, i))), rho));

Here, we are entering a for loop. We loop through each of the variables. In the for loop, we first create a symbol corresponding to the i'th element of the STRSXP theta. Here, STRING_ELT(theta, i) accesses the i'th element of the STRSXP theta. Macro CHAR() extracts the actual character 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++)
          rgr[j + start] = (REAL(ans1)[j] - rans[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.

Parsing R code from C

Suppose an R extension want to accept an R expression from the user and evaluate it. The previous section covered evaluation, but the expression will be entered as text and needs to be parsed first. A small part of R's parse interface is declared in header file R_ext/Parse.h17.

An example of the usage can be found in the (example) Windows package windlgs included in the R source tree. The essential part is

    #include <R.h>
    #include <Rinternals.h>
    #include <R_ext/Parse.h>
    SEXP menu_ttest3()
        char cmd[256];
        SEXP cmdSexp, cmdexpr, ans = R_NilValue;
        int i;
        ParseStatus status;
        if(done == 1) {
            PROTECT(cmdSexp = allocVector(STRSXP, 1));
            SET_STRING_ELT(cmdSexp, 0, mkChar(cmd));
            cmdexpr = PROTECT(R_ParseVector(cmdSexp, -1, &status));
            if (status != PARSE_OK) {
                error("invalid call %s", cmd);
            /* Loop is needed here as EXPSEXP will be of length > 1 */
            for(i = 0; i < length(cmdexpr); i++)
                ans = eval(VECTOR_ELT(cmdexpr, i), R_GlobalEnv);
        return ans;

Note that a single line of text may give rise to more than one R expression.

R_ParseVector is essentially the code used to implement parse(text=) at R level. The first argument is a character vector (corresponding to text) and the second the maximal number of expressions to parse (corresponding to n). The third argument is a pointer to a variable of an enumeration type, and it is normal (as parse does) to regard all values other than PARSE_OK as an error. Other values which might be returned are PARSE_INCOMPLETE (an incomplete expression was found) and PARSE_ERROR (a syntax error), in both cases the value returned being R_NilValue.

External pointers and weak references

The SEXPTYPEs EXTPTRSXP and WEAKREFSXP can be encountered at R level, but are created in C code.

External pointer SEXPs are intended to handle references to C structures such as `handles', and are used for this purpose in package RODBC for example. They are unusual in their copying semantics in that when an R object is copied, the external pointer object is not duplicated. (For this reason external pointers should only be used as part of an object with normal semantics, for example an attribute or an element of a list.)

An external pointer is created by

    SEXP R_MakeExternalPtr(void *p, SEXP tag, SEXP prot);

where p is the pointer (and hence this cannot portably be a function pointer), tag is a symbol identifying the (class of) object, and prot is a reference to a `protecting' object: the external pointer object will remain in existence (be protected from garbage collection) for the lifetime of the `protecting' object. Both tag and prot can be R_NilValue, and often are.

The elements of an external pointer can be accessed and set via

    void *R_ExternalPtrAddr(SEXP s);
    SEXP R_ExternalPtrTag(SEXP s);
    SEXP R_ExternalPtrProtected(SEXP s);
    void R_ClearExternalPtr(SEXP s);
    void R_SetExternalPtrAddr(SEXP s, void *p);
    void R_SetExternalPtrTag(SEXP s, SEXP tag);
    void R_SetExternalPtrProtected(SEXP s, SEXP p);

Clearing a pointer sets its value to the C NULL pointer.

An external pointer object can have a finalizer, a piece of code to be run when the object is garbage collected. This can be R code or C code, and the various interfaces are

    void R_RegisterFinalizer(SEXP s, SEXP fun);
    void R_RegisterFinalizerEx(SEXP s, SEXP fun, Rboolean onexit);
    typedef void (*R_CFinalizer_t)(SEXP);
    void R_RegisterCFinalizer(SEXP s, R_CFinalizer_t fun);
    void R_RegisterCFinalizerEx(SEXP s, R_CFinalizer_t fun, Rboolean onexit);

The R function indicated by fun should be a function of a single argument, the object to be finalized. R does not perform a garbage collection when shutting down, and the onexit argument of the extended forms can be used to ask that the finalizer be run during a normal shutdown of the R session. It is suggested that it is good practice to clear the pointer on finalization.

The only R level function for interacting with external pointers is reg.finalizer which can be used to set a finalizer.

It is probably not a good idea to allow an external pointer to be saved and then reloaded, but it this happens the pointer will be set to the C NULL pointer.

Weak references are used to allow the programmer to maintain information on entities without preventing the garbage collection of the entities once they become unreachable.

A weak reference contains a key and a value. The value is reachable is if it either reachable directly or via weak references with reachable keys. Once a value is determined to be unreachable during garbage collection, the key and value are set to R_NilValue and the finalizer will be run later in the garbage collection.

Weak reference objects are created by one of

    SEXP R_MakeWeakRef(SEXP key, SEXP val, SEXP fin, Rboolean onexit);
    SEXP R_MakeWeakRefC(SEXP key, SEXP val, R_CFinalizer_t fin,
                        Rboolean onexit);

where the R or C finalizer are specified in exactly the same way as for an external pointer object (whose finalization interface is implemented via weak references).

The parts can be accessed via

    SEXP R_WeakRefKey(SEXP w);
    SEXP R_WeakRefValue(SEXP w);
    void R_RunWeakRefFinalizer(SEXP w);

A toy example of the use of weak references can be found at www.stat.uiowa.edu/~luke/R/references/weakfinex.html, but that is used to add finalizers to external pointers which can now be done more directly.

Previous: External pointers and weak references, Up: System and foreign language interfaces 5.12 Vector accessor functions

The vector accessors like REAL and INTEGER and VECTOR_ELT are functions when used in R extensions. (For efficiency they are macros when used in the R source code, apart from SET_STRING_ELT and SET_VECTOR_ELT which are always functions.)

As from R 2.4.0 the accessor functions check that they are being used on an appropriate type of SEXP. By default an certain amount of misuse is allowed where the internal representation is the same: for example LOGICAL can be used on a INTSXP and SET_VECTOR_ELT on a STRSXP. Strict checking can be enabled by compiling R (specifically src/main/memory.c) with `USE_TYPE_CHECKING_STRICT' defined (e.g. in as the configure variable `DEFS' on a Unix-alike).

If efficiency is essential, the macro versions of the accessors can be obtained by defining `USE_RINTERNALS' before including Rinternals.h. If you find it necessary to do so, please do test that your code compiled without `USE_RINTERNALS' defined as this provides a stricter test that the accessors have been used correctly.

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_INCLUDE_DIR/R_ext, and there are other header files there that can be included too, but many of the features they contain should be regarded as undocumented and unstable.

An alternative is to include the header file S.h, which may be useful when porting code from S. This includes rather less than R.h, and has extra some compatibility definitions (for example the S_complex type from S).

The defines used for compatibility with S sometimes causes conflicts (notably with Windows headers), and the known problematic defines can be removed by defining STRICT_R_HEADERS.

Most of these header files, including all those included by R.h, can be used from C++ code. Some others need to be included within an extern "C" declaration, and for clarity this is advisable for all R header files.

   Note: Because R re-maps many of its external names to avoid clashes with user code, it is essential to include the appropriate header files when using these entry points. 

This remapping can cause problems18, and can be eliminated by defining R_NO_REMAP and prepending Rf_ to all the function names used from Rinternals.h and R_ext/Error.h.

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).

6.1.1 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 stats) is

    x = (int *) R_alloc(nrows(merge)+2, sizeof(int));

There is a similar call, S_alloc (for compatibility with older versions of S) which zeroes the memory allocated,

    char* S_alloc(long n, int size)


    char* S_realloc(char *p, long new, long old, int size)

which changes the allocation size from old to new units, and zeroes the additional units.

For compatibility with current versions of S, header S.h (only) defines wrapper macros equivalent to

    type* Salloc(long n, int type)
    type* Srealloc(char *p, long new, long old, int type)

This memory is taken from the heap, and released at the end of the .C, .Call or .External call. Users can also manage it, by noting the current position with a call to vmaxget and clearing memory allocated subsequently by a call to vmaxset. This is only recommended for experts.

Note that this memory will be freed on error or user interrupt (if allowed: see Allowing interrupts).

Note that although n is long, there are limits imposed by R's internal allocation mechanism. These will only come into play on 64-bit systems, where the current limit for n is just under 16Gb.

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 during allocation it is handled by R, so if these routines return the memory has been successfully allocated or freed. Free will set the pointer p to NULL. (Some but not all versions of S do so.)

Users should arrange to Free this memory when no longer needed, including on error or user interrupt. This can often be done most conveniently from an on.exit action in the calling R function – see pwilcox for an example.

Do not assume that memory allocated by Calloc/Realloc comes from the same pool as used by malloc: in particular do not use free or strdup with it.

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.)

If STRICT_R_HEADERS is not defined there is also an S-compatibility interface which uses calls of the form

    PROBLEM ...... ERROR
    MESSAGE ...... WARN

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.

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.

In addition, there is an interface (defined in header R_ext/Applic.h) to the generation of random 2-dimensional tables with given row and column totals using Patefield's algorithm. — Function: void rcont2 (int* nrow, int* ncol, int* nrowt, int* ncolt, int* ntotal, double* fact, int* jwork, int* matrix)

   Here, nrow and ncol give the numbers nr and nc of rows and columns and nrowt and ncolt the corresponding row and column totals, respectively, ntotal gives the sum of the row (or columns) totals, jwork is a workspace of length nc, and on output matrix a contains the nr * nc generated random counts in the usual column-major order. 

Missing and IEEE special values

A set of functions is provided to test for NA, Inf, -Inf and NaN. These functions are accessed via macros:

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

and via function R_IsNaN which is true for NaN but not NA.

Do use R_FINITE rather than isfinite or finite; the latter is often mendacious and isfinite is only available on a few platforms, on which R_FINITE is a macro expanding to isfinite.

Currently in C code ISNAN is a macro calling isnan. (Since this gives problems on some C++ systems, if the R headers is called from C++ code a function call is used.)

You can check for Inf or -Inf by testing equality to R_PosInf or R_NegInf, and set (but not test) an NA as NA_REAL.

All of the above apply to double variables only. For integer variables there is a variable accessed by the macro NA_INTEGER which can used to set or test for missingness.


The most useful function for printing from a C routine compiled into R is Rprintf. This is used in exactly the same way as printf, but is guaranteed to write to R's output (which might be a GUI console rather than a file). It is wise to write complete lines (including the "\n") before returning to R.

The function REprintf is similar but writes on the error stream (stderr) which may or may not be different from the standard output stream. Functions Rvprintf and REvprintf are analogues using the vprintf interface.

Printing from FORTRAN

On many systems FORTRAN write and print statements can be used, but the output may not interleave well with that of C, and will be invisible on GUI interfaces. They are not portable and best avoided.

Three subroutines are provided to ease the output of information from FORTRAN code.

    subroutine dblepr(label, nchar, data, ndata)
    subroutine realpr(label, nchar, data, ndata)
    subroutine intpr (label, nchar, data, ndata)

Here label is a character label of up to 255 characters, nchar is its length (which can be -1 if the whole label is to be used), and data is an array of length at least ndata of the appropriate type (double precision, real and integer respectively). These routines print the label on one line and then print data as if it were an R vector on subsequent line(s). They work with zero ndata, and so can be used to print a label alone.

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.

F77_SUB(name) to define a function in C to be called from FORTRAN

F77_NAME(name) to declare a FORTRAN routine in C before use

F77_CALL(name) to call a FORTRAN routine from C

F77_COMDECL(name) to declare a FORTRAN common block in C

F77_COM(name) 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. Note that names with underscores are not legal in FORTRAN 77, and are not portably handled by the above macros. (Also, all FORTRAN names for use by R are lower case, but this is not enforced by the macros.)

For example, suppose we want to call R's normal random numbers from FORTRAN. We need a C wrapper along the lines of

    #include <R.h>
    void F77_SUB(rndstart)(void) { GetRNGstate(); }
    void F77_SUB(rndend)(void) { PutRNGstate(); }
    double F77_SUB(normrnd)(void) { return norm_rand(); }

to be called from FORTRAN as in

          subroutine testit()
          double precision normrnd, x
          call rndstart()
          x = normrnd()
          call dblepr("X was", 5, x, 1)
          call rndend()

Note that this is not guaranteed to be portable, for the return conventions might not be compatible between the C and FORTRAN compilers used. (Passing values via arguments is safer.)

The standard packages, for example stats, are a rich source of further examples.

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 files R_ext/BLAS.h, R_ext/Lapack.h and R_ext/Linpack.h contains declarations of the BLAS, LAPACK and LINPACK/EISPACK linear algebra functions included in R. These are expressed as calls to FORTRAN subroutines, and they will also be usable from users' FORTRAN code. Although not part of the official API, this set of subroutines is unlikely to change (but might be supplemented).

The header file Rmath.h lists many other functions that are available and documented in the following subsections. Many of these are C interfaces to the code behind R functions, so the R function documentation may give further details.

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 with an asterisk only have `p' and `q' functions available, and none of the non-central distributions have `r' functions. After a call to dwilcox, pwilcox or qwilcox the function wilcox_free() should be called, and similarly for the signed rank functions.

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

Mathematical functions

— Function: 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: double psigamma (double x, double deriv)

   The Gamma function, its natural logarithm and first four derivatives and the n-th derivative of Psi, the digamma function. 

— Function: double beta (double a, double b) — Function: double lbeta (double a, double b)

   The (complete) Beta function and its natural logarithm. 

— Function: double choose (double n, double k) — Function: double lchoose (double n, double k)

   The number of combinations of k items chosen from from n and its natural logarithm. n and k are rounded to the nearest integer. 

— Function: 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)

   Bessel functions of types I, J, K and Y with index nu. For bessel_i and bessel_k there is the option to return exp(-x) I(x; nu) or exp(x) K(x; nu) if expo is 2. (Use expo == 1 for unscaled values.) 

Numerical Utilities

There are a few other numerical utility functions available as entry points. — Function: double R_pow (double x, double y) — Function: double R_pow_di (double x, int i)

   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. 

— Function: double pythag (double a, double b)

   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). 

— Function: double log1p (double x)

   Computes log(1 + x) (log 1 plus x), accurately even for small x, i.e., |x| << 1.
   This may be provided by your platform, in which case it is not included in Rmath.h, but is (probably) in math.h which Rmath.h includes. For backwards compatibility with R versions prior to 1.5.0, the entry point Rf_log1p is still provided. 

— Function: double log1pmx (double x)

   Computes log(1 + x) - x (log 1 plus x minus x), accurately even for small x, i.e., |x| << 1. 

— Function: double expm1 (double x)

   Computes exp(x) - 1 (exp x minus 1), accurately even for small x, i.e., |x| << 1.
   This may be provided by your platform, in which case it is not included in Rmath.h, but is (probably) in math.h which Rmath.h includes. 

— Function: double lgamma1p (double x)

   Computes log(gamma(x + 1)) (log(gamma(1 plus x))), accurately even for small x, i.e., 0 < x < 0.5. 

— Function: double logspace_add (double logx, double logy) — Function: double logspace_sub (double logx, double logy)

   Compute the log of a sum or difference from logs of terms, i.e., “x + y” as log (exp(logx) + exp(logy)) and “x - y” as log (exp(logx) - exp(logy)), without causing overflows or throwing away too much accuracy. 

— Function: 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)

   Return the larger (max) or smaller (min) of two integer or double numbers, respectively. 

— Function: double sign (double x)

   Compute the signum function, where sign(x) is 1, 0, or -1, when x is positive, 0, or negative, respectively. 

— Function: double fsign (double x, double y)

   Performs “transfer of sign” and is defined as |x| * sign(y). 

— Function: double fprec (double x, double digits)

   Returns the value of x rounded to digits decimal digits (after the decimal point).
   This is the function used by R's round(). 

— Function: double fround (double x, double digits)

   Returns the value of x rounded to digits significant decimal digits.
   This is the function used by R's signif(). 

— Function: double ftrunc (double x)

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

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) (and so on) defined (unless STRICT_R_HEADERS is defined) in the included header R_ext/Constants.h, mainly for compatibility with S.

Further, the included header R_ext/Boolean.h has constants TRUE and FALSE = 0 of type Rboolean in order to provide a way of using “logical” variables in C consistently.


The C code underlying optim can be accessed directly. The user needs to supply a function to compute the function to be minimized, of the type

    typedef double optimfn(int n, double *par, void *ex);

where the first argument is the number of parameters in the second argument. The third argument is a pointer passed down from the calling routine, normally used to carry auxiliary information.

Some of the methods also require a gradient function

    typedef void optimgr(int n, double *par, double *gr, void *ex);

which passes back the gradient in the gr argument. No function is provided for finite-differencing, nor for approximating the Hessian at the result.

The interfaces (defined in header R_ext/Applic.h) are

   Nelder Mead:
             void nmmin(int n, double *xin, double *x, double *Fmin, optimfn fn,
                        int *fail, double abstol, double intol, void *ex,
                        double alpha, double beta, double gamma, int trace,
                        int *fncount, int maxit);
             void vmmin(int n, double *x, double *Fmin,
                        optimfn fn, optimgr gr, int maxit, int trace,
                        int *mask, double abstol, double reltol, int nREPORT,
                        void *ex, int *fncount, int *grcount, int *fail);
   Conjugate gradients:
             void cgmin(int n, double *xin, double *x, double *Fmin,
                        optimfn fn, optimgr gr, int *fail, double abstol,
                        double intol, void *ex, int type, int trace,
                        int *fncount, int *grcount, int maxit);
   Limited-memory BFGS with bounds:
             void lbfgsb(int n, int lmm, double *x, double *lower,
                         double *upper, int *nbd, double *Fmin, optimfn fn,
                         optimgr gr, int *fail, void *ex, double factr,
                         double pgtol, int *fncount, int *grcount,
                         int maxit, char *msg, int trace, int nREPORT);
   Simulated annealing:
             void samin(int n, double *x, double *Fmin, optimfn fn, int maxit,
                        int tmax, double temp, int trace, void *ex);

Many of the arguments are common to the various methods. n is the number of parameters, x or xin is the starting parameters on entry and x the final parameters on exit, with final value returned in Fmin. Most of the other parameters can be found from the help page for optim: see the source code src/appl/lbfgsb.c for the values of nbd, which specifies which bounds are to be used.


The C code underlying integrate can be accessed directly. The user needs to supply a vectorizing C function to compute the function to be integrated, of the type

    typedef void integr_fn(double *x, int n, void *ex);

where x[] is both input and output and has length n, i.e., a C function, say fn, of type integr_fn must basically do for(i in 1:n) x[i] := f(x[i], ex). The vectorization requirement can be used to speed up the integrand instead of calling it n times. Note that in the current implementation built on QUADPACK, n will be either 15 or 21. The ex argument is a pointer passed down from the calling routine, normally used to carry auxiliary information.

There are interfaces (defined in header R_ext/Applic.h) for definite and for indefinite integrals. `Indefinite' means that at least one of the integration boundaries is not finite.

             void Rdqags(integr_fn f, void *ex, double *a, double *b,
                         double *epsabs, double *epsrel,
                         double *result, double *abserr, int *neval, int *ier,
                         int *limit, int *lenw, int *last,
                         int *iwork, double *work);
             void Rdqagi(integr_fn f, void *ex, double *bound, int *inf,
                         double *epsabs, double *epsrel,
                         double *result, double *abserr, int *neval, int *ier,
                         int *limit, int *lenw, int *last,
                         int *iwork, double *work);

Only the 3rd and 4th argument differ for the two integrators; for the definite integral, using Rdqags, a and b are the integration interval bounds, whereas for an indefinite integral, using Rdqagi, bound is the finite bound of the integration (if the integral is not doubly-infinite) and inf is a code indicating the kind of integration range,

inf = 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).

Utility functions

R has a fairly comprehensive set of sort routines which are made available to users' C code. These are declared in header file R_ext/Utils.h (included by R.h) and include the following. — Function: 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)

   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. 

— Function: void revsort (double* x, int* index, int n)

   Is similar to rsort_with_index but sorts into decreasing order, and NAs are not handled. 

— Function: 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)

   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. 

— Function: 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)

   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. 

— Function: subroutine qsort4 (double precision v, integer indx, integer ii, integer jj) — Function: subroutine qsort3 (double precision v, integer ii, integer jj)

   The FORTRAN interface routines for sorting double precision vectors are qsort3 and qsort4, equivalent to R_qsort and R_qsort_I, respectively. 

— Function: void R_max_col (double* matrix, int* nr, int* nc, int* maxes, int* ties_meth)

   Given the nr by nc matrix matrix in column-major (“FORTRAN”) order, R_max_col() returns in maxes[i-1] the column number of the maximal element in the i-th row (the same as R's max.col() function). In the case of ties (multiple maxima), *ties_meth is an integer code in 1:3 determining the method: 1 = “random”, 2 = “first” and 3 = “last”. See R's help page ?max.col. 

— Function: int findInterval (double* xt, int n, double x, Rboolean rightmost_closed, Rboolean all_inside, int ilo, int* mflag)

   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. 

The following two functions do numerical colorspace conversion from HSV to RGB and back. Note that all colours must be in [0,1]. — Function: void hsv2rgb (double h, double s, double v, double *r, double *g, double *b) — Function: void rgb2hsv (double r, double g, double b, double *h, double *s, double *v)

A system-independent interface to produce the name of a temporary file is provided as — Function: char * R_tmpnam (const char* prefix)

   Return a pathname for a temporary file with name beginning with prefix. A NULL prefix is replaced by "". 

There is also the internal function used to expand file names in several R functions, and called directly by path.expand. — Function: char * R_ExpandFileName (char* fn)

   Expand a path name fn by replacing a leading tilde by the user's home directory (if defined). The precise meaning is platform-specific; it will usually be taken from the environment variable HOME if this is defined. 


R has its own C-level interface to the the encoding conversion capabilities provided by iconv, for the following reasons

  • These wrapper routines do error-handling when no usable implementation of iconv was available at configure time.
  • Under Windows they arrange to load the iconv.dll at first use.
  • There are incompatibilities between the declarations in different implementations of iconv.

These are declared in header file R_ext/Riconv.h. — Function: void *Riconv_open (char* to, char* from) Set up a pointer to an encoding object to be used to convert between two encodings: "" indicates the current locale. — Function: size_t Riconv (void *cd, char **inbuf, size_t *inbytesleft, char **outbuf, size_t *outbytesleft) Convert as much as possible of inbuf to outbuf. Initially the int variables indicate the number of bytes available in the buffers, and they are updated (and the char pointers are updated to point to the next free byte in the buffer). The return value is the number of characters converted, or (size_t)-1 (beware: size_t is usually an unsigned type). It should be safe to assume that an error condition sets errno to one of E2BIG (the output buffer is full), EILSEQ (the input cannot be converted, and might be invalid in the encoding specified) or EINVAL (the input does not end with a complete multi-byte character). — Function: int Riconv_close (void * cd) Free the resources of an encoding object.

Allowing interrupts

No port of R can be interrupted whilst running long computations in compiled code, so programmers should make provision for the code to be interrupted at suitable points by calling from C

    #include <R_ext/Utils.h>
    void R_CheckUserInterrupt(void);

and from FORTRAN

    subroutine rchkusr()

These check if the user has requested an interrupt, and if so branch to R's error handling functions.

Note that it is possible that the code behind one of the entry points defined here if called from your C or FORTRAN code could be interruptible or generate an error and so not return to your code.

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 use in other header files. The macro WORDS_BIGENDIAN is defined on big-endian systems (e.g. sparc-sun-solaris2.6) and not on little-endian systems (such as i686 under Linux or Windows). It can be useful when manipulating binary files.

Header file Rversion.h (not included by R.h) defines a macro R_VERSION giving the version number encoded as an integer, plus a macro R_Version to do the encoding. This can be used to test if the version of R is late enough, or to include back-compatibility features. For protection against earlier versions of R which did not have this macro, use a construction such as

    #if defined(R_VERSION) && R_VERSION >= R_Version(1, 9, 0)

More detailed information is available in the macros R_MAJOR, R_MINOR, R_YEAR, R_MONTH and R_DAY: see the header file Rversion.h for their format. Note that the minor version includes the patchlevel (as in `9.0').

Inlining C functions

The C99 keyword inline is recognized by some compilers used to build R whereas others need __inline__ or do not support inlining. Portable code can be written using the macro R_INLINE (defined in file Rconfig.h included by R.h), as for example from package cluster

    #include <R.h>
    static R_INLINE int ind_2(int l, int j)

Be aware that using inlining with functions in more than one compilation unit is almost impossible to do portably: see http://www.greenend.org.uk/rjk/2003/03/inline.html. All the R configure code has checked is that R_INLINE can be used in a single C file with the compiler used to build R. We recommend that packages making extensive use of inlining include their own configure code.

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 both Unix and Windows. (This includes the functions documented in Numerical analysis subroutines as from that header file.)

The library is not built automatically when R is installed, but can be built in the directory src/nmath/standalone in the R sources: see the file README there. To use the code in your own C program include

    #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 dynamic library or DLL you will have to use the one supplied, which is the Marsaglia-multicarry with an entry points

    set_seed(unsigned int, unsigned int)

to set its seeds and

    get_seed(unsigned int *, unsigned int *)

to read the seeds).

Organization of header files

The header files which R installs are in directory R_INCLUDE_DIR (default R_HOME/include). This currently contains

   R.h 	includes many other files
   S.h 	different version for code ported from S
   Rinternals.h 	definitions for using R's internal structures
   Rdefines.h 	macros for an S-like interface to the above
   Rmath.h 	standalone math library
   Rversion.h 	R version information
   Rinterface.h 	for add-on front-ends (Unix-alikes only)
   R_ext/RStartup.h 	ditto
   R_ext/eventloop.h 	for add-on front-ends and for packages that need to share in the R event loops (on all platforms)
   R_ext/Applic.h 	optimization and integration
   R_ext/BLAS.h 	C definitions for BLAS routines
   R_ext/Lapack.h 	C definitions for some LAPACK routines
   R_ext/Linpack.h 	C definitions for some LINPACK routines, not all of which are included in R
   R_ext/GetX11Image.h 	X11Image interface used by package trkplot
   R_ext/Parse.h 	a small part of R's parse interface
   R_ext/Callbacks.h 	C (and R function) top-level task handlers
   R_ext/Rdynload.h 	needed to register compiled code in packages
   R_ext/R-ftp-http.h 	interface to internal method of download.file
   R_ext/Riconv.h 	interface to iconv

The following headers are included by R.h:

   Rconfig.h 	configuration info that is made available
   R_ext/Arith.h 	handling for NAs, NaNs, Inf/-Inf
   R_ext/Boolean.h 	TRUE/FALSE type
   R_ext/Complex.h 	C typedefs for R's complex
   R_ext/Constants.h 	constants
   R_ext/Error.h 	error handling
   R_ext/Memory.h 	memory allocation
   R_ext/Print.h 	Rprintf and variations.
   R_ext/Random.h 	random number generation
   R_ext/RS.h 	definitions common to R.h and S.h, including F77_CALL etc.
   R_ext/Utils.h 	sorting and other utilities
   R_ext/libextern.h 	definitions for exports from R.dll on Windows.

The graphics systems are exposed in headers Rdevices.h (for writing graphics devices), Rgraphics.h and R_ext/Graphics{Base,Device,Engine}.h.

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 S4 (formal) methods of package methods.

The key function for methods is NextMethod, which dispatches the next method. It is quite typical for a method function to make a few changes to its arguments, dispatch to the next method, receive the results and modify them a little. An example is

    t.data.frame <- function(x)
        x <- as.matrix(x)

Also consider predict.glm: it happens that in R for historical reasons it calls predict.lm directly, but in principle (and in S originally and currently) it could use NextMethod. (NextMethod seems under-used in the R sources. Do be aware that there as S/R differences in this area, and the example above works because there is a next method, the default method, not that a new method is selected when the class is changed.)

Any method a programmer writes may be invoked from another method by NextMethod, with the arguments appropriate to the previous method. Further, the programmer cannot predict which method NextMethod will pick (it might be one not yet dreamt of), and the end user calling the generic needs to be able to pass arguments to the next method. For this to work

   A method must have all the arguments of the generic, including ... if the generic does. 

It is a grave misunderstanding to think that a method needs only to accept the arguments it needs. The original S version of predict.lm did not have a ... argument, although predict did. It soon became clear that predict.glm needed an argument dispersion to handle over-dispersion. As predict.lm had neither a dispersion nor a ... argument, NextMethod could no longer be used. (The legacy, two direct calls to predict.lm, lives on in predict.glm in R, which is based on the workaround for S3 written by Venables & Ripley.)

Further, the user is entitled to use positional matching when calling the generic, and the arguments to a method called by UseMethod are those of the call to the generic. Thus

   A method must have arguments in exactly the same order as the generic. 

To see the scale of this problem, consider the generic function scale, defined as

    scale <- function (x, center = TRUE, scale = TRUE)

Suppose an unthinking package writer created methods such as

    scale.foo <- function(x, scale = FALSE, ...) { }

Then for x of class "foo" the calls

    scale(x, , TRUE)
    scale(x, scale = TRUE)

would do most likely do different things, to the justifiable consternation of the end user.

To add a further twist, which default is used when a user calls scale(x) in our example? What if

    scale.bar <- function(x, center, scale = TRUE) NextMethod("scale")

and x has class c("bar", "foo")? We are not going to give you the answers because it is unreasonable that a user should be expected to anticipate such behaviour. This leads to the recommendation:

   A method should use the same defaults as the generic. 

Here there might be justifiable exceptions, which will need careful documentation.

   Adding new generics 

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 <- base::foo

(If the thus defined default method needs a `...' added to its argument list, one can e.g. use formals(foo.default) <- c(formals(foo.default), alist(... = )).)

The same idea can be applied for functions in other packages with name spaces.

Linking GUIs and other front-ends to R

There are a number of ways to build front-ends to R: we take this to mean a GUI or other application that has the ability to submit commands to R and perhaps to receive results back (not necessarily in a text format). There are other routes besides those described here, for example `Rserve' (see http://www.rosuda.org/Rserve) and connections to Java in `SJava' (see http://www.omegahat.org/RSJava/ and `JRI', part of the rJava package on CRAN).

   Embedding R under Unix-alikes
   Embedding R under Windows 

Embedding R under Unix-alikes

R can be built as a shared library19 if configured with --enable-R-shlib. This shared library can be used to run R from alternative front-end programs. We will assume this has been done for the rest of this section.

The command-line R front-end, R_HOME/bin/exec/R is one such example, and the unbundled GNOME and MacOS X consoles are others. The source for R_HOME/bin/exec/R is in file src/main/Rmain.c and is very simple

    int Rf_initialize_R(int ac, char **av); /* in ../unix/system.c */
    void Rf_mainloop();                     /* in main.c */
    extern int R_running_as_main_program;   /* in ../unix/system.c */
    int main(int ac, char **av)
        R_running_as_main_program = 1;
        Rf_initialize_R(ac, av);
        Rf_mainloop(); /* does not return */
        return 0;

indeed, misleadingly simple. Remember that R_HOME/bin/exec/R is run from a shell script R_HOME/bin/R which sets up the environment for the executable, and this is used for

  • Setting R_HOME and checking it is valid, as well as the path R_SHARE_DIR and R_DOC_DIR to the installed share and doc directory trees. Also setting R_ARCH if needed.
  • Setting LD_LIBRARY_PATH to include the directories used in linking R. This is recorded as the default setting of R_LD_LIBRARY_PATH in the shell script.
  • Processing some of the arguments, for example to run R under a debugger and to launch alternative front-ends to provide GUIs.

The first two of these can be achieved for your front-end by running it via R CMD. So, for example

  • R CMD /usr/local/lib/R/bin/exec/R
  • R CMD exec/R

will both work in a standard R installation. (R CMD looks first for executables in R_HOME/bin.) If you do not want to run your front-end in this way, you need to ensure that R_HOME is set and LD_LIBRARY_PATH is suitable. (The latter might well be, but modern Unix/Linux systems do not normally include /usr/local/lib, and R does look there for system components.)

The other senses in which this example is too simple are that all the internal defaults are used and that control is handed over to the R main loop. There are a number of small examples20 in the tests/Embedding directory. These make use of Rf_initEmbeddedR in src/main/Rembedded.c, and essentially use

    #include <Rembedded.h>
    int main(int ac, char **av)
        /* do some setup */
        Rf_initEmbeddedR(argc, argv);
        /* do some more setup */
        /* submit some code to R, which is done interactively via
            A possible substitute for a pseudo-console is
            while(R_ReplDLLdo1() > 0) {
            /* add user actions here if desired */
        /* final tidying up after R is shutdown */
        return 0;

If you don't want to pass R arguments, you can fake an argv array, for example by

        char *argv[]= {"REmbeddedPostgres", "--silent"};
        Rf_initEmbeddedR(sizeof(argv)/sizeof(argv[0]), argv);

However, to make a GUI we usually do want to run run_Rmainloop after setting up various parts of R to talk to our GUI, and arranging for our GUI callbacks to be called during the R mainloop.

One issue to watch is that on some platforms Rf_initEmbeddedR and Rf_endEmbeddedR change the settings of the FPU (e.g. to allow errors to be trapped and to set extended precision registers).

The standard code sets up a session temporary directory in the usual way, unless R_TempDir is set to a non-NULL value before Rf_initEmbeddedR is called. In that case the value is assumed to contain an existing writable directory (no check is done), and it is not cleaned up when R is shut down.

Setting R callbacks

For Unix-alkes there is a public header file Rinterface.h that makes it possible to change the standard callbacks used by R in a documented way. This defines pointers (if R_INTERFACE_PTRS is defined)

    extern void (*ptr_R_Suicide)(char *);
    extern void (*ptr_R_ShowMessage)(char *);
    extern int  (*ptr_R_ReadConsole)(char *, unsigned char *, int, int);
    extern void (*ptr_R_WriteConsole)(char *, int);
    extern void (*ptr_R_ResetConsole)();
    extern void (*ptr_R_FlushConsole)();
    extern void (*ptr_R_ClearerrConsole)();
    extern void (*ptr_R_Busy)(int);
    extern void (*ptr_R_CleanUp)(SA_TYPE, int, int);
    extern int  (*ptr_R_ShowFiles)(int, char **, char **, char *,
                                   Rboolean, char *);
    extern int  (*ptr_R_ChooseFile)(int, char *, int);
    extern int  (*ptr_R_EditFile)(char *);
    extern void (*ptr_R_loadhistory)(SEXP, SEXP, SEXP, SEXP);
    extern void (*ptr_R_savehistory)(SEXP, SEXP, SEXP, SEXP);
    extern void (*ptr_R_addhistory)(SEXP, SEXP, SEXP, SEXP);

which allow standard R callbacks to be redirected to your GUI. What these do is generally documented in the file src/unix/system.txt. — Function: void R_ShowMessage (char *message)

This should display the message, which may have multiple lines: it should be brought to the user's attention immediately.

— Function: void R_Busy (int which)

This function invokes actions (such as change of cursor) when R embarks on an extended computation (which=1) and when such a state terminates (which=0).

— Function: int R_ReadConsole (char *prompt, char *buf, int buflen, int hist) — Function: void R_WriteConsole (char *buf, int buflen) — Function: void R_ResetConsole () — Function: void R_FlushConsole () — Function: void R_ClearErrConsole ()

These functions interact with a console.

  • R_ReadConsole prints the given prompt at the console and then does a gets(3)–like operation, transferring up to buflen characters into the buffer buf. The last two characters should be set to `"\n\0"' to preserve sanity. If hist is non-zero, then the line should be added to any command history which is being maintained. The return value is 0 on success and >0 on failure.
  • R_WriteConsole writes the given buffer to the console.
  • R_ResetConsole is called when the system is reset after an error. R_FlushConsole is called to flush any pending output to the system console. R_ClearerrConsole clears any errors associated with reading from the console.

— Function: void R_ShowFiles (int nfile, char **file, char **headers, char *wtitle, Rboolean del, char *pager)

This function is used to display the contents of files.

— Function: int R_ChooseFile (int new, char *buf, int len)

Choose a file and return its name in buf of length len. Return value is 0 for success, > 0 otherwise.

— Function: int R_EditFile (char *buf)

Send a file to an editor window.

— Function: SEXP R_loadhistory (SEXP, SEXP, SEXP, SEXP); — Function: SEXP R_savehistory (SEXP, SEXP, SEXP, SEXP); — Function: SEXP R_addhistory (SEXP, SEXP, SEXP, SEXP);

.Internal functions for loadhistory, savehistory and timestamp: these are called after checking the number of arguments.

If the console has no history mechanism these can be as simple as

             SEXP R_loadhistory (SEXP call, SEXP op, SEXP args, SEXP env)
                 errorcall(call, "loadhistory is not implemented");
                 return R_NilValue;
             SEXP R_savehistory (SEXP call, SEXP op , SEXP args, SEXP env)
                 errorcall(call, "savehistory is not implemented");
                 return R_NilValue;
             SEXP R_addhistory (SEXP call, SEXP op , SEXP args, SEXP env)
                 return R_NilValue;

The R_addhistory function should return silently if no history mechanism is present, as a user may be calling timestamp purely to write the time stamp to the console.

— Function: void R_Suicide (char *message)

This should abort R as rapidly as possible, displaying the message. A possible implementation is

             void R_Suicide (char *message)
                 char  pp[1024];
                 snprintf(pp, 1024, "Fatal error: %s\n", s);
                 R_CleanUp(SA_SUICIDE, 2, 0);

— Function: void R_CleanUp (SA_TYPE saveact, int status, int RunLast)

This function invokes any actions which occur at system termination. It needs to be quite complex:

             #include <Rinterface.h>
             #include <Rdevices.h>    /* for KillAllDevices */
             void R_CleanUp (SA_TYPE saveact, int status, int RunLast)
                 char *tmpdir;
                 if(saveact == SA_DEFAULT) saveact = SaveAction;
                 if(saveact == SA_SAVEASK) {
                    /* ask what to do and set saveact */
                 switch (saveact) {
                 case SA_SAVE:
                     if(runLast) R_dot_Last();
                     if(R_DirtyImage) R_SaveGlobalEnv();
                     /* save the console history in R_HistoryFile */
                 case SA_NOSAVE:
                     if(runLast) R_dot_Last();
                 case SA_SUICIDE:
                 /* clean up after the editor e.g. CleanEd() */
                 /* close all the graphics devices */
                 if(saveact != SA_SUICIDE) KillAllDevices();

Meshing event loops

One of the most difficult issues in interfacing R to a front-end is the handling of event loops, at least if a single thread is used. R uses events and timers for

  • Running X11 windows such as the graphics device and data editor, and interacting with them (e.g., using locator()).
  • Supporting Tcl/Tk events for the tcltk package.
  • Preparing input.
  • Timing operations, for example for profiling R code and Sys.sleep().
  • Interrupts, where permitted.

Specifically, the Unix command-line version of R runs separate event loops for

  • Preparing input at the console command-line, in file src/unix/sys-unix.c.
  • Waiting for a response from a socket in the internal functions underlying FTP and HTTP transfers in download.file() and for direct socket access, in files src/modules/internet/nanoftp.c, src/modules/internet/nanohttp.c and src/modules/internet/Rsock.c
  • Mouse and window events when displaying the X11-based dataentry window, in file src/modules/X11/dataentry.c. This is regarded as modal, and no other events are serviced whilst it is active.

There is a protocol for adding event handlers to the first two types of event loops, using types and functions declared in the header R_ext/eventloop.h and described in comments in file src/unix/sys-std.c. It is possible to add (or remove) an input handler for events on a particular file descriptor, or to set a polling interval (via R_wait_usec) and a function to be called periodically via R_PolledEvents: the polling mechanism is used by the tcltk package.

An alternative front-end needs both to make provision for other R events whilst waiting for input, and to ensure that it is not frozen out during events of the second type. This is not handled very well in the existing examples. The GNOME front-end can run a own handler for polled events by setting

    extern int (*R_timeout_handler)();
    extern long R_timeout_val;
          if (R_timeout_handler && R_timeout_val)
              gtk_timeout_add(R_timeout_val, R_timeout_handler, NULL);
          gtk_main ();

whilst it is waiting for console input. This obviously handles events for Gtk windows (such as the graphics device in the gtkDevice package), but not X11 events (such as the X11() device) or for other event handlers that might have been registered with R. It does not attempt to keep itself alive whilst R is waiting on sockets. The ability to add a polled handler as R_timeout_handler is used by the tcltk package.

Threading issues

Enbedded R is designed to be run in the main thread, and all the testing is done in that context. There is a potential issue with the stack-checking mechanism where threads are involved. This uses two variables declared in Rinterface.h (if CSTACK_DEFNS is defined) as

    extern uintptr_t R_CStackLimit; /* C stack limit */
    extern uintptr_t R_CStackStart; /* Initial stack address */

Note that uintptr_t is a C99 type for which a substitute is defined in R, so your code needs to define HAVE_UINTPTR_T appropriately.

These will be set21 when R_initialize_R is called, to values appropriate to the main thread. Stack-checking can be disabled by seting R_CStackLimit = (uintptr_t)-1, but it is better to if possible set appropriate values. (What these are and how to determine them are OS-specific, and the stack size limit may differ for secondary threads. If you have a choice of stack size, at least 8Mb is recommended.)

You may also want to consider how signals are handled: R sets signal handlers for several signals, including SIGINT, SIGSEGV, SIGPIPE, SIGUSR1 and SIGUSR2, but these can all be suppressed by setting the variable R_SignalHandlers (declared in declared in Rinterface.h) to 0.

Embedding R under Windows

All Windows interfaces to R call entry points in the DLL R.dll, directly or indirectly. Simpler applications may find it easier to use the indirect route via (D)COM.

Using (D)COM

(D)COM is a standard Windows mechanism used for communication between Windows applications. One application (here R) is run as COM server which offers services to clients, here the front-end calling application. The services are described in a `Type Library' and are (more or less) language-independent, so the calling application can be written in C or C++ or Visual Basic or Perl or Python and so on. The `D' in (D)COM refers to `distributed', as the client and server can be running on different machines.

The basic R distribution is not a (D)COM server, but two addons are currently available that interface directly with R and provide a (D)COM server:

  • There is a (D)COM server called StatConnector written by Thomas Baier available on CRAN (http://cran.r-project.org/other-software.html) which works with Rproxy.dll (in the R distribution) and R.dll to support transfer of data to and from R and remote execution of R commands, as well as embedding of an R graphics window. The rcom package on CRAN provides a (D)COM server in a running R session.
  • Another (D)COM server, RDCOMServer, is available from http://www.omegahat.org/. Its philosophy is discussed in http://www.omegahat.org/RDCOMServer/Docs/Paradigm.html and is very different from the purpose of this section.

Calling R.dll directly

The R DLL is mainly written in C and has _cdecl entry points. Calling it directly will be tricky except from C code (or C++ with a little care).

There is a version of the Unix interface callng

    int Rf_initEmbeddedR(int ac, char **av);
    void Rf_endEmbeddedR(int fatal);

which is an entry point in R.dll. Examples of its use (and a suitable Makefile.win) can be found in the tests/Embedding directory of the sources. You may need to ensure that R_HOME/bin is in your PATH so the R DLLs are found.

Examples of calling R.dll directly are provided in the directory src/gnuwin32/front-ends, including Rproxy.dll used by StatConnector and a simple command-line front end rtest.c whose code is

    #define Win32
    #include <windows.h>
    #include <stdio.h>
    #include <Rversion.h>
    #define LibExtern __declspec(dllimport) extern
    #include <Rembedded.h>
    #include <R_ext/RStartup.h>
    /* for askok and askyesnocancel */
    #include <graphapp/graphapp.h>
    /* for signal-handling code */
    #include <psignal.h>
    /* simple input, simple output */
    /* This version blocks all events: a real one needs to call ProcessEvents
       frequently. See rterm.c and ../system.c for one approach using
       a separate thread for input.
    int myReadConsole(char *prompt, char *buf, int len, int addtohistory)
        fputs(prompt, stdout);
        if(fgets(buf, len, stdin)) return 1; else return 0;
    void myWriteConsole(char *buf, int len)
        printf("%s", buf);
    void myCallBack()
        /* called during i/o, eval, graphics in ProcessEvents */
    void myBusy(int which)
        /* set a busy cursor ... if which = 1, unset if which = 0 */
    static void my_onintr(int sig) { UserBreak = 1; }
    int main (int argc, char **argv)
        structRstart rp;
        Rstart Rp = &rp;
        char Rversion[25], *RHome;
        sprintf(Rversion, "%s.%s", R_MAJOR, R_MINOR);
        if(strcmp(getDLLVersion(), Rversion) != 0) {
            fprintf(stderr, "Error: R.DLL version does not match\n");
        if((RHome = get_R_HOME()) == NULL) {
             fprintf(stderr, "R_HOME must be set in the environment or Registry\n");
        Rp->rhome = RHome;
        Rp->home = getRUser();
        Rp->CharacterMode = LinkDLL;
        Rp->ReadConsole = myReadConsole;
        Rp->WriteConsole = myWriteConsole;
        Rp->CallBack = myCallBack;
        Rp->ShowMessage = askok;
        Rp->YesNoCancel = askyesnocancel;
        Rp->Busy = myBusy;
        Rp->R_Quiet = TRUE;        /* Default is FALSE */
        Rp->R_Interactive = FALSE; /* Default is TRUE */
        Rp->RestoreAction = SA_RESTORE;
        Rp->SaveAction = SA_NOSAVE;
        R_set_command_line_arguments(argc, argv);
        signal(SIGBREAK, my_onintr);
        GA_initapp(0, 0);
    #ifdef SIMPLE_CASE
        while(R_ReplDLLdo1() > 0) {
    /* add user actions here if desired */
    /* only get here on EOF (not q()) */
        return 0;

The ideas are

  • Check that the front-end and the linked R.dll match – other front-ends may allow a looser match.
  • Find and set the R home directory and the user's home directory. The former may be available from the Windows Registry: it will normally be in HKEY_LOCAL_MACHINE\Software\R-core\R\InstallPath and can be set there by running the program R_HOME\bin\RSetReg.exe.
  • Define startup conditions and callbacks via the Rstart structure. R_DefParams sets the defaults, and R_SetParams sets updated values.
  • Record the command-line arguments used by R_set_command_line_arguments for use by the R function commandArgs().
  • Set up the signal handler and the basic user interface.
  • Run the main R loop, possibly with our actions intermeshed.
  • Arrange to clean up.

An underlying theme is the need to keep the GUI `alive', and this has not been done in this example. The R callback R_ProcessEvents needs to be called frequently to ensure that Windows events in R windows are handled expeditiously. Conversely, R needs to allow the GUI code (which is running in the same process) to update itself as needed – two ways are provided to allow this:

R_ProcessEvents calls the callback registered by Rp->callback. A version of this is used to run package Tcl/Tk for tcltk under Windows, for the code is

             void R_ProcessEvents(void)
                 while (peekevent()) doevent(); /* Windows events for GraphApp */
                 if (UserBreak) { UserBreak = FALSE; onintr(); }
                 if(R_tcldo) R_tcldo();

The mainloop can be split up to allow the calling application to take some action after each line of input has been dealt with: see the alternative code below #ifdef SIMPLE_CASE.

It may be that no R GraphApp windows need to be considered, although these include pagers, the windows() graphics device, the R data and script editors and various popups such as choose.file() and select.list(). It would be possible to replace all of these, but it seems easier to allow GraphApp to handle most of them.

It is possible to run R in a GUI in a single thread (as RGui.exe shows) but it will normally be easier22 to use multiple threads.

Note that R's own front ends use a stack size of 10Mb, whereas MinGW executables default to 2Mb, and Visual C++ ones to 1Mb. The latter stack sizes are too small for a number of R applications, so general-purpose front-ends should use a larger stack size.


[1] This is true for OSes which implement the `C' locale, unless neither lazy-loading nor saving an image are used, in which case it would fail if loaded in a `C' locale. (Windows' idea of the `C' locale uses the WinAnsi charset.)

[2] It is good practice to encode them as octal or hex escape sequences.

[3] More precisely, they can contain the English alphanumeric characters and the symbols `$ - _ . + ! ' ( ) , ; = &'.

[4] Note that Ratfor is not supported. If you have Ratfor source code, you need to convert it to FORTRAN. Only FORTRAN-77 (which we write in upper case) is supported on all platforms, but some also support Fortran-95 (for which we use title case). If you want to ship Ratfor source files, please do so in a subdirectory of src and not in the main subdirectory.

[5] on Unix-alikes: Windows resolves such dependencies at link time.

[6] Remember to set LIBRARY_PATH to point to your MinGW lib directory

[7] This may require GNU tar: the command used can be set with environment variable TAR.

[8] There can be exceptions: for example Rd files are not allowed to start with a dot, and have to be uniquely named on a case-insensitve file system.

[9] a common example in CRAN packages is \link[mgcv]{gam}.

[10] See the examples section in the file Paren.Rd for an example.

[11] R has to be built to enable this, but the option --enable-R-profiling is the default.

[12] For Unix-alikes these are intervals of CPU time, and for WIndows of elapsed time.

[13] With the exceptions of the commands listed below: an object of such a name can be printed via an explicit call to print.

[14] Although this is supposed to have been improved, valgrind 3.2.0 still aborts using optimized BLASes on an Opteron.

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

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

[17] This is only guaranteed to show the current interface: it is liable to change.

[18] Known problems are redefining error, length, vector and warning

[19] In the parlance of MacOS X this is a dynamic library, and is the normal way to build R on that platform.

[20] but these are not part of the automated test procedures and so little tested.

[21] at least on platforms where the values are available, that is having getrlimit and on Linux or having sysctl supporting KERN_USRSTACK, including FreeBSD and MacOS X.

[22] An attempt to use only threads in the late 1990s failed to work correctly under Windows 95, the predominant version of Windows at that time.

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