# Writing R Extensions

Node:Top, Next:Acknowledgements, Previous:(dir), Up:(dir)

## Writing R Extensions

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

The current version of this document is 1.5.1 (2002-06-17). ISBN 3-901167-54-4

- Acknowledgements:
- Creating R packages:
- Writing R help files:
- Tidying and profiling R code:
- System and foreign language interfaces:
- The R API:
- Generic functions and methods:
- R (internal) programming miscellanea:
- R coding standards:
- Function and variable index:
- Concept index:

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

## Acknowledgements

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

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

## Creating R packages

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

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

    ?library
    ?INSTALL

before reading on.

- Package structure:
- Configure and cleanup:
- Checking and building packages:
- Writing package vignettes:
- Submitting a package to CRAN:

Node:Package structure, Next:Configure and cleanup, Previous:Creating R packages, Up:Creating R packages

### Package structure

A package consists of a subdirectory containing the files `DESCRIPTION` and `INDEX`, and the subdirectories `R`, `data`, `demo`, `exec`, `inst`, `man`, `src`, and `tests` (some of which can be missing).

Optionally the package can also contain (Bourne shell) script files `configure` and `cleanup` which are executed before and (provided that option `--clean` was given) after installation on Unix, See Configure and cleanup.

- The DESCRIPTION file:
- The INDEX file:
- Package subdirectories:
- Package bundles:

Node:The DESCRIPTION file, Next:The INDEX file, Previous:Package structure, Up:Package structure

#### The `DESCRIPTION` file

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

>     Package: pkgname
>     Version: 0.5-1
>     Date: 2000-01-04
>     Title: My first collection of functions
>     Author: Friedrich Leisch <F.Leisch@ci.tuwien.ac.at>, with
>       contributions from A. User <A.User@whereever.net>.
>     Maintainer: Friedrich Leisch <F.Leisch@ci.tuwien.ac.at>
>     Depends: R (>= 0.99), nlme
>     Description: A short (one paragraph) description of what
>       the package does and why it may be useful.
>     License: GPL version 2 or newer
>     URL: http://www.r-project.org, http://www.another.url

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

The `License` field should contain an explicit statement or a well-known abbreviation (such as `GPL`, `LGPL`, `BSD`, or `Artistic`), perhaps followed by a reference to the actual license file. It is very important that you include this information! Otherwise, it may not even be legally correct for others to distribute copies of the package.

The `Title` field should give a short description of the package and not have any continuation lines. Older versions of R used a separate file `TITLE` for giving this information; this is now deprecated in favor of using the `Title` field in file `DESCRIPTION` file.

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

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

The optional `Depends` field gives a comma-separated list of package names which this package depends on. The package name may be optionally followed by a comparison operator (currently only `>=` and `<=` are supported) and a version number in parentheses. You can also use the special package name `R` if your package depends on a certain version of R. E.g., if the package works only with R version 0.90 or newer, include `R (>= 0.90)` in the `Depends` field. Future versions of R will use this field to autoload required packages, hence it is an error to use improper syntax or misuse the `Depends` field for comments on other software that might be needed. Other dependencies (external to the R system) should be listed in the `Description` field or a separate `README` file. The R `INSTALL` facilities already check if the version of R used is recent enough for the package being installed.

Node:The INDEX file, Next:Package subdirectories, Previous:The DESCRIPTION file, Up:Package structure

#### The `INDEX` file

The file `INDEX` contains a line for each sufficiently interesting object in the package, giving its name and a description (functions such as print methods not usually called explicitly might not be included). Note that you can automatically create this file using something like `R CMD Rdindex man > INDEX`, provided that Perl (5.005 or later) is available on your system, or use the package builder (see Checking and building packages) to do so.

Node:Package subdirectories, Next:Package bundles, Previous:The INDEX file, Up:Package structure

#### Package subdirectories

The `R` subdirectory contains R code files. The code files to be installed must start with a (lower or upper case) letter and have one of the extensions `.R`, `.S`, `.q`, `.r`, or `.s`. We recommend using `.R`, as this extension seems to be not used by any other software. It should be possible to read in the files using `source()`, so R objects must be created by assignments. Note that there need be no connection between the name of the file and the R objects created by it. If necessary, one of these files (historically `zzz.R`) should use `library.dynam()` inside `.First.lib()` to load compiled code.

The `man` subdirectory should contain documentation files for the objects in the package in R documentation (Rd) format. The documentation files to be installed must also start with a (lower or upper case) letter and have the extension `.Rd` (the default) or `.rd`. See Writing R help files, for more information. Note that all user-level objects in a package should be documented; if a package pkg contains user-level objects which are for "internal" use only, it should provide a file `pkg-internal.Rd` which documents all such objects, and clearly states that these are not meant to be called by the user. See e.g. the sources for package modreg in the R distribution for an example.

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

The C, C++, or FORTRAN1 source files for the compiled code are in `src`, plus optionally file `Makevars` or `Makefile`. When a package is installed using `R CMD INSTALL`, Make is used to control compilation and linking into a shared library for loading into R. There are default variables and rules for this (determined when R is configured and recorded in `R_HOME/etc/Makeconf`). These rules can be tweaked by setting macros in a file `src/Makevars` (see Using Makevars). Note that this mechanism should be general enough to eliminate the need for a package-specific `Makefile`. If such a file is to be distributed, considerable care is needed to make it general enough to work on all R platforms. In addition, it should have a target `clean` which removes all files generated by Make. If necessary, platform-specific files can be used, for example `Makevars.win` or `Makefile.win` on Windows take precedence over `Makevars` or `Makefile`.

The `data` subdirectory is for additional data files the package makes available for loading using `data()`. Currently, data files can have one of three types as indicated by their extension: plain R code (`.R` or `.r`), tables (`.tab`, `.txt`, or `.csv`), or `save()` images (`.RData` or `.rda`). (As from R 1.5.0 one can assume that all ports of R have the same binary (XDR) format and can read compressed images. For portability to earlier versions use images saved with `save(, ascii = TRUE, version = 1)`.) Note that R code should be "self-sufficient" and not make use of extra functionality provided by the package, so that the data file can also be used without having to load the package. The `data` subdirectory should also contain a `00Index` file that describes the datasets available. Ideally this should have a one-line description of each dataset, with full documentation in the `man` directory. Note that you can automatically create this file using something like `R CMD Rdindex --data man > data/00Index`, provided that Perl (5.005 or later) is available on your system, or use the package builder (see Checking and building packages) to do so.

The `demo` subdirectory is for R scripts (for running via `demo()`) which demonstrate some of the functionality of the package. The script files must start with a (lower or upper case) letter and have one of the extensions `.R` or `.r`. If present, the `demo` subdirectory should also have a `00Index` file, see above. (Note that it is currently not possible to generate this index file automatically.)

The contents of the `inst` subdirectory will be copied recursively to the installation directory.

Subdirectory `tests` is for additional package-specific test code, similar to the specific tests that come with the R distribution. Test code can either be provided directly in a `.R` file, or via a `.Rin` file containing code which in turn creates the corresponding `.R` file (e.g., by collecting all function objects in the package and then calling them with the strangest arguments). The results of running a `.R` file are written to a `.Rout` file. If there is a corresponding `.Rout.save` file, these two are compared, with differences being reported but not causing an error.

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

Node:Package bundles, Previous:Package subdirectories, Up:Package structure

#### Package bundles

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

The `DESCRIPTION` file of a bundle has an extra `Bundle` field, as in

>     Bundle: VR
>     Contains: MASS class nnet spatial
>     Version: 6.1-6
>     Date: 1999/11/26
>     Author: S original by Venables & Ripley.
>       R port by Brian Ripley <ripley@stats.ox.ac.uk>, following
>       earlier work by Kurt Hornik and Albrecht Gebhardt.
>     BundleDescription: Various functions from the libraries of
>       Venables and Ripley, `Modern Applied Statistics with S-PLUS'
>       (3rd edition).
>     License: GPL (version 2 or later)

The `Contains` field lists the packages, which should be contained in separate subdirectories with the names given. These are standard packages in all respects except that the `DESCRIPTION` file is replaced by a `DESCRIPTION.in` file which just contains fields additional to the `DESCRIPTION` file of the bundle, for example

>     Package: spatial
>     Description: Functions for kriging and point pattern analysis.

Node:Configure and cleanup, Next:Checking and building packages, Previous:Package structure, Up:Creating R packages

### Configure and cleanup

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

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

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

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

    AC_CHECK_LIB(foo, fun, [HAVE_FOO=TRUE], [HAVE_FOO=FALSE])
    AC_SUBST(HAVE_FOO)
    ......
    AC_CONFIG_FILES([foo.R])
    AC_OUTPUT

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

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

    foo <- function(x) {
        if(!@HAVE_FOO@) stop("Sorry, library `foo' is not available")
        ...

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

    foo <- function(x) {
        if(!FALSE) stop("Sorry, library `foo' is not available")
        ...

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

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

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

    : ${R_HOME=`R RHOME`}
    if test -z "${R_HOME}"; then
      echo "could not determine R_HOME"
      exit 1
    fi
    CC=`${R_HOME}/bin/R CMD config CC`
    CFLAGS=`${R_HOME}/bin/R CMD config CFLAGS`

(using `${R_HOME}/bin/R` rather than just `R` is necessary in order to use the `right' version of R when running the script as part of `R CMD INSTALL`.

Note that earlier versions of this document recommended obtaining the configure information by direct extraction (using grep and sed) from `R_HOME/etc/Makeconf`, which only works for variables recorded there as literals. R 1.5.0 has added `R CMD config` for getting the value of the basic configuration variables, or the header and library flags necessary for linking against R, see `R CMD config --help` for more information.

If R was configured to use the FORTRAN-to-C converter, configure variable `F77` is set to a shell script wrapper to compile/link FORTRAN 77 code based on f2c which for the purpose of Autoconf qualifies as a FORTRAN 77 compiler. E.g., to check for an exteral BLAS library using the `ACX_BLAS` macro from the Official Autoconf Macro Archive, one can simply do

    F77=`${R_HOME}/bin/R CMD config F77`
    AC_PROG_F77
    ACX_BLAS([], AC_MSG_ERROR([could not find your BLAS library], 1))

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

It is essential to provide manual configuration information if the package is to be usable on classic MacOS.

In some rare circumstances, the configuration and cleanup scripts need to know the location into which the package is being installed. An example of this is a package that uses C code and creates two shared libraries/DLLs. Usually, the library that is dynamically loaded by R is linked against the second, dependent, library. On some systems, we can add the location of this dependent library to the library that is dynamically loaded by R. This means that each user does not have to set the value of the `LD_LIBRARY_PATH` (or equivalent) environment variable, but that the secondary library is automatically resolved. Another example is when a package installs support files that are required at run time, and their location is substituted into an R data structure at installation time. (This happens with the Java Archive files in the Java package.)

The names of the top-level library directory (i.e., specifiable via the `-l` argument) and the directory of the package itself are made available to the installation scripts via the two shell/environment variables `R_LIBRARY_DIR` and `R_PACKAGE_DIR`. Additionally, the name of the package (e.g., `survival` or `MASS`) being installed is available from the shell variable `R_PACKAGE_NAME`.

- Using Makevars:

Node:Using Makevars, Previous:Configure and cleanup, Up:Configure and cleanup

#### 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 compiler flags (for example include paths) by setting `PKG_CFLAGS`, `PKG_CXXFLAGS` and `PKG_FFLAGS`, for C, C++, or FORTRAN respectively (see Creating shared libraries).

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

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

`FLIBS`
  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 BLAS routines, but no single-precision, complex nor double-complex routines.

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

### Checking and building packages

Using `R CMD check`, the R package checker, one can test whether source R packages work correctly. (Under Windows the equivalent command is `Rcmd check`.) This runs a series of checks.

1. The package is installed. This will warn about missing cross-references and duplicate aliases in help files.
2. The files and directories are checked for sufficient permissions (Unix only).
3. The `DESCRIPTION` file is checked for completeness, and some of its entries for correctness.
4. The R files are checked for syntax errors.
5. The R files are checked for correct calls to `library.dynam` (with no extension). In addition, it is checked whether methods have all arguments of the corresponding generic, and whether the final argument of assignment functions is called `value`.
6. The Rd files are checked for the mandatory (`\name`, `\alias`, `\title`, `\description` and `\keyword`) fields, and for unbalanced braces (which indicate Rd syntax errors). The keywords found are compared to the standard ones.
7. A check is made for undocumented user-level objects in the package.
8. The functions and their documentation are compared, and any differences in the call sequences reported.
9. It is checked whether all function arguments given in `\usage` sections of Rd files are documented in the corresponding `\arguments` section.
10. C source and header files are tested for correct (CR-only) line endings.
11. The examples provided by the package's documentation are run. (see Writing R help files, for information on using `\examples` to create executable example code.)

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

12. If the package sources contain a `tests` directory then the tests specified in that directory are run. (Typically they will consist of a set of `.R` source files and target output files `.Rout.save`.)
13. The code in package vignettes (see Writing package vignettes) is executed.
14. If a working `latex` program is available, the `.dvi` version of the package's manual is created (to check that the Rd files can be converted successfully).

Use `R CMD check --help` (`Rcmd check --help` on Windows) to obtain more information about the usage of the R package checker. A subset of the checking steps can be selected by adding flags.

Using `R CMD build`, the R package builder, one can build R packages from their sources (for example, for subsequent release). The Windows equivalent is `Rcmd build`.

Prior to actually building the package in the common gzipped tar file format, a variety of diagnostic checks and cleanups are performed. In particular, it is tested whether the `DESCRIPTION` file contains the required entries, whether object and data indices exist (it will build them if they do not) and can be assumed to be up-to-date.

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

To exclude files from being put into the package, one can specify a list of exclude patterns in file `.Rbuildignore` in the top-level source directory. These patterns should be Perl regexps, one per line, to be matched against the file names relative to the top-level source directory. In addition, files called `CVS` or `GNUMakefile`, or with base names starting with `.#`, or starting and ending with `#`, or ending in `~` or `.swp`, are excluded by default.

> Note: file exclusion does not work correctly with GNU `tar` 1.13 but does work with later versions (e.g., version 1.13.17).

Use `R CMD build --help` (`Rcmd build --help` on Windows) to obtain more information about the usage of the R package builder.

`R CMD build` can also build pre-compiled version of packages for binary distributions.

> Note to Windows users: `Rcmd check` and `Rcmd build` work well under Windows NT4/2000 but may not work correctly on Windows 95/98/ME because of problems with some versions of Perl on those limited OSes. Experiences vary.

Node:Writing package vignettes, Next:Submitting a package to CRAN, Previous:Checking and building packages, Up:Creating R packages

### Writing package vignettes

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

A special case are documents in Sweave format, which we call package vignettes. Sweave allows to integrate LaTeX documents and R code and is contained in package tools which is part of the base R distribution, see the `Sweave` help page for details on the document format. Package vignettes found in directory `inst/doc` are tested by `R CMD check` by executing all R code they contain to ensure consistency between code and documentation.

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

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

The directory `inst/doc` should contain an index named `00Index.dcf` of all documentation files, the format is similar to the format of the `DESCRIPTION` file:

>     foo-summary.pdf:    A short introduction to package foo.
>     foo-manual.pdf:     A comprehensive manual for package foo.
>     someTips.pdf:       Tips that might be useful.

If no index file is present, then `R CMD build` looks for `\VignetteIndexEntry` statements in all Sweave files and auto-generates the index. The `\VignetteIndexEntry` statement is best placed in LaTeX comment, such that no definition of the command is necessary.

Node:Submitting a package to CRAN, Previous:Writing package vignettes, Up:Creating R packages

### Submitting a package to CRAN

CRAN is a network of WWW sites holding the R distributions and contributed code, especially R packages. Users of R are encouraged to join in the collaborative project and to submit their own packages to CRAN.

Before submitting a package mypkg, do run the following steps to test it is complete and will install properly. (Unix procedures only, run from the directory containing `mypkg` as a subdirectory.)

1. Run `R CMD check` to check that the package will install and will runs its examples, and that the documentation is complete and can be processed.
2. Run `R CMD build` to run a few further checks and to make the release `.tar.gz` file.

Please ensure that you can run through the complete procedure with only warnings that you understand and have reasons not to eliminate.

When all the testing is done, upload the `.tar.gz` file to

    <ftp://ftp.ci.tuwien.ac.at/incoming>

and send a message to <`cran@r-project.org`> about it. The CRAN maintainers will run these tests before putting a submission in the main archive.

Currently, packages containing compiled code should contain at most one dot in their name to work smoothly under Windows.

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

> `package_version[_engine[_type]]`,

where the `[ ]` indicates that the enclosed component is optional, package and version are the corresponding entries in file `DESCRIPTION`, engine gives the S engine the package is targeted for and defaults to `R`, and type indicated whether the file contains source or binaries for a certain platform, and defaults to `source`. I.e.,

    OOP_0.1-3.tar.gz
    OOP_0.1-3_R.tar.gz
    OOP_0.1-3_R_source.tar.gz

are all equivalent and indicate an R source package, whereas

    OOP_0.1-3_Splus6_sparc-sun-solaris.tar.gz

is a binary package for installation under Splus6 on the given platform.

This naming scheme has been adopted to ensure usability of code across S engines. R code and utilities operating on package `.tar.gz` files can only be assumed to work provided that this naming scheme is respected. E.g., `R CMD INSTALL` will not work on `package.tgz` files. Of course, `R CMD build` automatically creates valid file names.

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

## Writing R help files

- Rd format:
- Sectioning:
- Marking text:
- Lists and tables:
- Cross-references:
- Mathematics:
- Insertions:
- Platform-specific sections:
- Processing Rd format:

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

### Rd format

R objects are documented in files written in "R documentation" (Rd) format, a simple markup language closely resembling (La)TeX, which can be processed into a variety of formats, including LaTeX, HTML and plain text. The translation is carried out by the Perl script `Rdconv` in `R_HOME/bin` and by the installation scripts for packages.

The R distribution contains more than 900 such files which can be found in the `src/library/pkg/man` directories of the R source tree, where pkg stands for package base where all the standard objects are, and for the standard packages such as eda and mva which are included in the R distribution.

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

>     \name{load}
>     \alias{load}
>     \title{Reload Saved Datasets}
>     \description{
>       Reload the datasets written to a file with the function
>       \code{save}.
>     }
>     \usage{
>     load(file, envir = parent.frame())
>     }
>     \arguments{
>       \item{file}{a connection or a character string giving the
>         name of the file to load.}
>       \item{envir}{the environment where the data should be
>         loaded.}
>     }
>     \seealso{
>       \code{\link{save}}.
>     }
>     \examples{
>     ## save all data
>     save(list = ls(), file= "all.Rdata")

>     ## restore the saved values to the current environment
>     load("all.Rdata")

>     ## restore the saved values to the workspace
>     load("all.Rdata", .GlobalEnv)
>     }
>     \keyword{file}

An Rd file consists of three parts. The header gives basic information about the name of the file, the topics documented, a title, a short textual description and R usage information for the objects documented. The body gives further information (for example, on the function's arguments and return value, as in the above example). Finally, there is a footer with keyword information. The header and footer are mandatory.

See the "Guidelines for Rd files" for guidelines for writing documentation in Rd format which should be useful for package writers.

#### Documenting functions

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

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

      \name{Normal}
      \alias{dnorm}
      \alias{pnorm}
      \alias{qnorm}
      \alias{rnorm}

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

`\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).
`\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 in general match the function definition exactly (such that automatic checking for consistency between code and documentation is possible). Otherwise, include a `\synopsis` section with the actual definition.

  For example, `abline` is a function for adding a straight line to a plot which can be used in several different ways, depending on the named arguments specified. Hence, `abline.Rd` contains

      \synopsis{
      abline(a = NULL, b = NULL, h = NULL, v = NULL, reg = NULL,
             coef = NULL, untf = FALSE, col = par("col"),
             lty = par("lty"), lwd = NULL, ...)
      }
      \usage{
      abline(a, b, \dots)
      abline(h=, \dots)
      abline(v=, \dots)
      ...
      }

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

  For example, `print.ts.Rd` contains

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

`\arguments{...}`
  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, `piechart.Rd` contains

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

`\author{...}`
  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 verbatim in typewriter font.

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

  For example,

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

  Thus, example code not included in `\dontrun` must be executable! In addition, it should not use any system-specific features or require special facilities (such as Internet access or write permission to specific directories).

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

`\keyword{key}`
  Each `\keyword` entry should specify one of the standard keywords (as listed in the file `R_HOME/doc/KEYWORDS.db`). There must be at least one `\keyword` entry, but can be more that one if the R object being documented falls into more than one category.

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

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

#### Documenting datasets

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

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

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

This uses the following additional markup commands.

`\format{...}`
  A description of the format of the dataset (as a vector, matrix, data frame, time series, ...). For matrices and data frames this should give a description of each column, preferably as a list or table. See Lists and tables, for more information.
`\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 `data(bar)`.
- The `\keyword` entry is always `datasets`.

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

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

### Sectioning

To begin a new paragraph or leave a blank line in an example, just insert an empty line (as in (La)TeX). To break a line, use `\cr`.

In addition to the predefined sections (such as `\description{}`, `\value{}`, etc.), you can "define" arbitrary ones by `\section{section_title}{...}`. For example

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

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

Note that the additional named sections are always inserted at fixed positions in the output (before `\note`, `\seealso` and the examples), no matter where they appear in the input.

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

### Marking text

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

> `\bold{word}` | set word in bold font if possible
> `\emph{word}` | emphasize word using italic font if possible
> `\code{word}` | for pieces of code, using `typewriter` font if possible
> `\file{word}` | for file names
> `\email{word}` | for email addresses
> `\url{word}` | for URLs

The first two, `\bold` and `\emph`, should be used in plain text for emphasis.

Fragments of R code, including the names of R objects, should be marked using `\code`. Only backslashes and percent signs need to be escaped (by a backslash) inside `\code`.

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

### Lists and tables

The `\itemize` and `\enumerate` commands take a single argument, within which there may be one or more `\item` commands. The text following each `\item` is formatted as one or more paragraphs, suitably indented and with the first paragraph marked with a bullet point (`\itemize`) or a number (`\enumerate`).

`\itemize` and `\enumerate` commands may be nested.

The `\describe` command is similar to `\itemize` but allows initial labels to be specified. The `\item`s take two arguments, the label and the body of the item, in exactly the same way as argument and value `\item`s. `\describe` commands are mapped to `<DL>` lists in HTML and `\description` lists in LaTeX.

The `\tabular` command takes two arguments. The first gives for each of the columns the required alignment (`l` for left-justification, `r` for right-justification or `c` for centering.) The second argument consists of an arbitrary number of lines separated by `\cr`, and with fields separated by `\tab`. For example:

      \tabular{rlll}{
        [,1] \tab Ozone   \tab numeric \tab Ozone (ppb)\cr
        [,2] \tab Solar.R \tab numeric \tab Solar R (lang)\cr
        [,3] \tab Wind    \tab numeric \tab Wind (mph)\cr
        [,4] \tab Temp    \tab numeric \tab Temperature (degrees F)\cr
        [,5] \tab Month   \tab numeric \tab Month (1--12)\cr
        [,6] \tab Day     \tab numeric \tab Day of month (1--31)
      }

There must be the same number of fields on each line as there are alignments in the first argument, and they must be non-empty (but can contain only spaces).

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

### Cross-references

The markup `\link{foo}` (usually in the combination `\code{\link{foo}}`) produces a hyperlink to the help page for object foo. One main usage of `\link` is in the `\seealso` section of the help page, see Rd format. (This only affects the creation of hyperlinks, for example in the HTML pages used by `help.start()` and the PDF version of the reference manual.)

There are optional arguments specified as `\link[pkg]{foo}` and `\link[pkg:bar]{foo}` to link to the package pkg with topic (file?) foo and bar respectively.

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

### Mathematics

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

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

The following example is from `Poisson.Rd`:

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

For HTML and text on-line help we get

>         p(x) = lambda^x exp(-lambda)/x!

>     for x = 0, 1, 2, ....

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

### Insertions

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

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

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

The "comment" and "control" characters `%` and `\` always need to be escaped. Inside the verbatim-like commands (`\code` and `\examples`), no other2 characters are special. Note that `\file` is not a verbatim-like command.

In "regular" text (no verbatim, no `\eqn`, ...), you currently must escape most LaTeX special characters, i.e., besides `%`, `{`, and `}`, the four specials `$`, `#`, and `_` are produced by preceding each with a `\`. (`&` can also be escaped, but need not be.) Further, enter `^` as `\eqn{\mbox{\textasciicircum}}{^}`, and `~` by `\eqn{\mbox{\textasciitilde}}{~}` or `\eqn{\sim}{~}` (for a short and long tilde respectively). Also, `<`, `>`, and `|` must only be used in math mode, i.e., within `\eqn` or `\deqn`.

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

### Platform-specific documentation

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

    #ifdef OS
       ...
    #endif

or

    #ifndef OS
       ...
    #endif

for OS-specific inclusion or exclusion.

If the differences between platforms are extensive or the R objects documented are only relevant to one platform, platform-specific Rd files can be put in a `unix`, `windows` or `mac` subdirectory.

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

### Processing Rd format

Under UNIX versions of R there are several commands to process Rd files. Windows equivalents are described at the end of the section. All of these need Perl to be installed.

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

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

Using `R CMD Rdindex` one can produce nicely formatted index files displaying names and titles of the Rd files specified as arguments. This can be used to create the `INDEX` of an add-on package and, if it also contains data, the `00Index` data index in the `data` directory. Note that the R package builder `R CMD build` can be used to automatically create these indices when building a package.

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

The exact usage and a detailed list of available options for each of the above commands can be obtained by running `R CMD command --help`, e.g., `R CMD Rdconv --help`. All available commands can be listed using `R --help`.

All of these have Windows equivalents. For most just replace `R CMD` by `Rcmd`, with the exception that it is `Rcmd Rd2dvi.sh` (and that needs the tools to build packages from source to be installed). (You will need the files in the R binary Windows distribution for installing source packages to be installed.)

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

## Tidying and profiling R code

- Tidying R code:
- Profiling R code:

R code which is worth preserving in a package and perhaps making available for others to use is worth documenting, tidying up and perhaps optimizing. The last two of these activities are the subject of this chapter.

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

### Tidying R code

R treats function code loaded from packages and code entered by users differently. Code entered by users has the source code stored in an attribute, and when the function is listed, the original source is reproduced. Loading code from a package (by default) discards the source code, and the function listing is re-created from the parse tree of the function.

Normally keeping the source code is a good idea, and in particular it avoids comments being moved around in the source. However, we can make use of the ability to re-create a function listing from its parse tree to produce a tidy version of the function, with consistent indentation, spaces around operators and consistent use of the preferred assignment operator `<-`. This tidied version is much easier to read, not least by other users who are used to the standard format.

We can subvert the keeping of source in two ways.

1. The option `keep.source` can be set to `FALSE` before the code is loaded into R.
2. The stored source code can be removed by removing the `source` attribute, for example by

       attr(myfun, "source") <- NULL

In each case if we then list the function we will get the standard layout.

Suppose we have a file of functions `myfuns.R` that we want to tidy up. Create a file `tidy.R` containing

    options(keep.source = FALSE)
    source("myfuns.R")
    dump(ls(all = TRUE), file = "new.myfuns.R")

and run R with this as the source file, for example by `R --vanilla < tidy.R` (Unix) or `Rterm --vanilla < tidy.R` (Windows) or by pasting into an R session. Then the file `new.myfuns.R` will contain the functions in alphabetical order in the standard layout. You may need to move comments to more appropriate places.

The standard format provides a good starting point for further tidying. Most package authors use a version of Emacs (on Unix or Windows) to edit R code, using the ESS[S] mode of the ESS Emacs package. See R coding standards for style options within the ESS[S] mode recommended for the source code of R itself.

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

### Profiling R code

It is possible to profile R code on most Unix-like versions of R, R has to be built to enable this, by supplying the option `--enable-R-profiling`, profiling being enabled in a default build. Profiling is also available on Windows, but not on the Macintosh.

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

As an example, consider the following code (from Venables & Ripley, 1999).

    library(MASS); library(boot); library(nls)
    data(stormer)
    storm.fm <- nls(Time ~ b*Viscosity/(Wt - c), stormer,
                    start = c(b=29.401, c=2.2183))
    st <- cbind(stormer, fit=fitted(storm.fm))
    storm.bf <- function(rs, i) {
        st$Time <-  st$fit + rs[i]
        tmp <- nls(Time ~ (b * Viscosity)/(Wt - c), st,
                   start = coef(storm.fm))
        tmp$m$getAllPars()
    }
    rs <- scale(resid(storm.fm), scale = FALSE) # remove the mean
    Rprof("boot.out")
    storm.boot <- boot(rs, storm.bf, R = 4999) # pretty slow
    Rprof(NULL)

Having run this we can summarize the results by

    R CMD Rprof boot.out

    Each sample represents 0.02 seconds.
    Total run time: 80.74 seconds.

    Total seconds: time spent in function and callees.
    Self seconds: time spent in function alone.

       %       total       %       self
     total    seconds     self    seconds    name
    100.00     80.74      0.22      0.18     "boot"
     99.65     80.46      1.19      0.96     "statistic"
     96.33     77.78      2.68      2.16     "nls"
     50.21     40.54      1.54      1.24     "<Anonymous>"
     47.11     38.04      1.83      1.48     ".Call"
     23.06     18.62      2.43      1.96     "eval"
     19.87     16.04      0.67      0.54     "as.list"
     18.97     15.32      0.64      0.52     "switch"
     17.88     14.44      0.47      0.38     "model.frame"
     17.41     14.06      1.73      1.40     "model.frame.default"
     17.41     14.06      2.80      2.26     "nlsModel"
     15.43     12.46      1.88      1.52     "qr.qty"
     13.40     10.82      3.07      2.48     "assign"
     12.73     10.28      2.33      1.88     "storage.mode<-"
     12.34      9.96      1.81      1.46     "qr.coef"
     10.13      8.18      5.42      4.38     "paste"
      ...

       %       self        %       total
     self     seconds    total    seconds    name
      5.42      4.38     10.13      8.18     "paste"
      3.37      2.72      6.71      5.42     "as.integer"
      3.29      2.66      5.00      4.04     "as.double"
      3.20      2.58      4.29      3.46     "seq.default"
      3.07      2.48     13.40     10.82     "assign"
      2.92      2.36      5.95      4.80     "names"
      2.80      2.26     17.41     14.06     "nlsModel"
      2.68      2.16     96.33     77.78     "nls"
      2.53      2.04      2.53      2.04     ".Fortran"
      2.43      1.96     23.06     18.62     "eval"
      2.33      1.88     12.73     10.28     "storage.mode<-"
      ...

This often produces surprising results and can be used to identify bottlenecks or pieces of R code that could benefit from being replaced by compiled code.

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

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

## System and foreign language interfaces

- Operating system access:
- Interface functions .C and .Fortran:
- dyn.load and dyn.unload:
- Creating shared libraries:
- Interfacing C++ code:
- Handling R objects in C:
- Interface functions .Call and .External:
- Evaluating R expressions from C:
- Debugging:

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

### Operating system access

Access to operating system functions is via the R function `system`. The details will differ by platform (see the on-line help), and about all that can safely be assumed is that the first argument will be a string `command` that will be passed for execution (not necessarily by a shell) and the second argument will be `internal` which if true will collect the output of the command into an R character vector.

The function `system.time` is available for timing (although the information available may be limited on non-Unix-like platforms).

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

### Interface functions `.C` and `.Fortran`

These two functions provide a standard interface to compiled code that has been linked into R, either at build time or via `dyn.load` (see dyn.load and dyn.unload). They are primarily intended for compiled C and FORTRAN code respectively, but the `.C` function can be used with other languages which can generate C interfaces, for example C++ (see Interfacing C++ code).

The first argument to each function is a character string given the symbol name as known to C or FORTRAN, that is the function or subroutine name. (The mapping to the symbol name in the load table is given by the functions `symbol.C` and `symbol.For`; that the symbol is loaded can be tested by, for example, `is.loaded(symbol.C("loglin"))`.)

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

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

> R storage mode | C type | FORTRAN type
> `logical` | `int *` | `INTEGER`
> `integer` | `int *` | `INTEGER`
> `double` | `double *` | `DOUBLE PRECISION`
> `complex` | `Rcomplex *` | `DOUBLE COMPLEX`
> `character` | `char **` | `CHARACTER*255`

C type `Rcomplex` is a structure with `double` members `r` and `i` defined in the header file `Complex.h` included by `R.h`. Only a single character string can be passed to or from FORTRAN, and the success of this is compiler-dependent. Other R objects can be passed to `.C`, but it is better to use one of the other interfaces. An exception is passing an R function for use with `call_R`, when the object can be handled as `void *` en route to `call_R`, but even there `.Call` is to be preferred.

It is possible to pass numeric vectors of storage mode `double` to C as `float *` or FORTRAN as `REAL` by setting the attribute `Csingle`, most conveniently by using the R functions `as.single`, `single` or `storage.mode`. This is intended only to be used to aid interfacing to existing C or FORTRAN code.

Unless formal argument `NAOK` is true, all the other arguments are checked for missing values `NA` and for the IEEE special values `NaN`, `Inf` and `-Inf`, and the presence of any of these generates an error. If it is true, these values are passed unchecked.

Argument `DUP` can be used to suppress copying. It is dangerous: see the on-line help for arguments against its use. It is not possible to pass numeric vectors as `float *` or `REAL` if `DUP=FALSE`.

Finally, argument `PACKAGE` confines the search for the symbol name to a specific shared library (or use `"base"` for code compiled into R). Its use is highly desirable, as there is no way to avoid two package writers using the same symbol name, and such name clashes are normally sufficient to cause R to crash.

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

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

    void convolve(double *a, int *na, double *b, int *nb, double *ab)
    {
      int i, j, nab = *na + *nb - 1;

      for(i = 0; i < nab; i++)
        ab[i] = 0.0;
      for(i = 0; i < *na; i++)
        for(j = 0; j < *nb; j++)
          ab[i + j] += a[i] * b[j];
    }

called from R by

    conv <- function(a, b)
      .C("convolve",
         as.double(a),
         as.integer(length(a)),
         as.double(b),
         as.integer(length(b)),
         ab = double(length(a) + length(b) - 1))$ab

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

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

### `dyn.load` and `dyn.unload`

Compiled code to be used with R is loaded as a shared library (Unix, see Creating shared libraries for more information) or DLL (Windows).

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

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

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

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

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

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

where `libname` is the library/DLL name with the extension omitted.

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

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

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

### Creating shared libraries

Under Unix, shared libraries for loading into R can be created using `R CMD SHLIB`. This accepts as arguments a list of files which must be object files (with extension `.o`) or C, C++, or FORTRAN sources (with extensions `.c`, `.cc` or `.cpp` or `.C`, and `.f`, respectively). See `R CMD SHLIB --help`, or the on-line help for `SHLIB`, for usage information. If compiling the source files does not work "out of the box", you can specify additional flags by setting some of the variables `PKG_CPPFLAGS` (for the C preprocessor, typically `-I` flags), `PKG_CFLAGS`, `PKG_CXXFLAGS`, and `PKG_FFLAGS` (for the C, C++, and FORTRAN compilers, respectively) in the file `Makevars` in the compilation directory, or write a `Makefile` in the compilation directory containing the rules required (or, of course, create the object files directly from the command line). Similarly, variable `PKG_LIBS` in `Makevars` can be used for additional `-l` and `-L` flags to be passed to the linker when building the shared library.

If an add-on package pkg contains C, C++, or FORTRAN code in its `src` subdirectory, `R CMD INSTALL` creates a shared library (for loading into R in the `.First.lib` function of the package) either automatically using the above `R CMD SHLIB` mechanism, or using `make` if directory `src` contains a `Makefile`. In both cases, if file `Makevars` exists it is read first when invoking `make`. If a `Makefile` is really needed or provided, it needs to ensure that the shared library created is linked against all FORTRAN 77 intrinsic and run-time libraries that R was linked against; Make variable `FLIBS` contains this information.

The Windows equivalent is the command `Rcmd SHLIB`; files `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.)

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

### Interfacing C++ code

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

    // X.hh

    class X {
    public: X (); ~X ();
    };

    class Y {
    public: Y (); ~Y ();
    };

    // X.cc

    #include <iostream.h>
    #include "X.hh"

    static Y y;

    X::X()  { cout << "constructor X" << endl; }
    X::~X() { cout << "destructor X" << endl; }
    Y::Y()  { cout << "constructor Y" << endl; }
    Y::~Y() { cout << "destructor Y" << endl; }

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

    extern "C" {

    }

For example,

    // X_main.cc:

    #include "X.hh"

    extern "C" {

    void X_main () {
      X x;
    }

    } // extern "C"

Compiling and linking should be done with the C++ compiler-linker (rather than the C compiler-linker or the linker itself); otherwise, the C++ initialization code (and hence the constructor of the static variable `Y`) are not called. On a properly configured Unix system (support for C++ was added in R version 1.1), one can simply use

    R CMD SHLIB X.cc X_main.cc

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

    R : Copyright 2000, The R Development Core Team
    Version 1.1.0 Under development (unstable) (April 14, 2000)
    ...
    Type    "q()" to quit R.

    R> dyn.load(paste("X", .Platform$dynlib.ext, sep = ""))
    constructor Y
    R> .C("X_main")
    constructor X
    destructor X
    list()
    R> q()
    Save workspace image? [y/n/c]: y
    destructor Y

The R for Windows FAQ (`rw-FAQ`) contains details of how to compile this example under various Windows compilers.

Using C++ iostreams, as in this example, is best avoided. There is no guarantee that the output will appear in the R console, and indeed it will not on the R for Windows console. Use R code or the C entry points (see Printing) for all I/O if at all possible.

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

### Handling R objects in C

Using C code to speed up the execution of an R function is often very fruitful. Traditionally this has been done via the `.C` function in R. One restriction of this interface is that the R objects can not be handled directly in C. This becomes more troublesome when one wishes to call R functions from within the C code. There is a C function provided called `call_R` (also known as `call_S` for compatibility with S) that can do that, but it is cumbersome to use, and the mechanisms documented here are usually simpler to use, as well as more powerful.

If a user really wants to write C code using internal R data structures, then that can be done using the `.Call` and `.External` function. The syntax for the calling function in R in each case is similar to that of `.C`, but the two functions have rather different C interfaces. Generally the `.Call` interface (which is modelled on the interface of the same name in S version 4) is a little simpler to use, but `.External` is a little more general.

A call to `.Call` is very similar to `.C`, for example

    .Call("convolve2", a, b)

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

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

    SEXP convolve2(SEXP a, SEXP b)
     ...

A call to `.External` is almost identical

    .External("convolveE", a, b)

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

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

    SEXP convolveE(SEXP args)
     ...

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

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

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

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

A more recent approach is to use R versions of the macros and functions defined for the S version 4 interface `.Call`, which are defined in the header file `Rdefines.h`. This is a somewhat simpler approach, and is certainly to be preferred if the code might be shared with S at any stage.

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

It is necessary to know something about how R objects are handled in C code. All the R objects you will deal with will be handled with the type SEXP 3, which is a pointer to a structure with typedef `SEXPREC`. Think of this structure as a variant type that can handle all the usual types of R objects, that is vectors of various modes, functions, environments, language objects and so on. The details are given later in this section, but for most purposes the programmer does not need to know them. Think rather of a model such as that used by Visual Basic, in which R objects are handed around in C code (as they are in interpreted R code) as the variant type, and the appropriate part is extracted for, for example, numerical calculations, only when it is needed. As in interpreted R code, much use is made of coercion to force the variant object to the right type.

- Garbage Collection:
- Allocating storage:
- Details of R types:
- Attributes:
- Classes:
- Handling lists:
- Finding and setting variables:

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

#### Handling the effects of garbage collection

We need to know a little about the way R handles memory allocation. The memory allocated for R objects is not freed by the user; instead, the memory is from time to time garbage collected. That is, some or all of the allocated memory not being used is freed. (Prior to R 1.2, objects could be moved, too.)

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

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

Protecting an R object automatically protects all the R objects pointed to in the corresponding `SEXPREC`.

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

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

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

      SEXP ab;
        ....
      PROTECT(ab = NEW_NUMERIC(2));
      NUMERIC_POINTER(ab)[0] = 123.45;
      NUMERIC_POINTER(ab)[1] = 67.89;
      UNPROTECT(1);

and then those in `Rinternals.h`:

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

      SEXP ab;
        ....
      PROTECT(ab = allocVector(REALSXP, 2));
      REAL(ab)[0] = 123.45;
      REAL(ab)[1] = 67.89;
      UNPROTECT(1);

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

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

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

There is a less-used macro `UNPROTECT_PTR(s)` that unprotects the object pointed to by the `SEXP` s, even if it is not the top item on the pointer protection stack. This is rarely needed outside the parser (the R sources have one example, in `src/main/plot3d.c`).

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

#### Allocating storage

For many purposes it is sufficient to allocate R objects and manipulate those. There are quite a few `allocXxx` functions defined in `Rinternals.h`--you may want to explore them. These allocate R objects of various types, and for the standard vector types there are `NEW_XXX` macros defined in `Rdefines.h`.

If storage is required for C objects during the calculations this is best allocating by calling `R_alloc`; see Memory allocation. All of these memory allocation routines do their own error-checking, so the programmer may assume that they will raise an error and not return if the memory cannot be allocated.

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

#### Details of R types

Users of the `Rinternals.h` macros will need to know how the R types are known internally: this is more or less completely hidden if the `Rdefines.h` macros are used.

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

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

Among the important internal `SEXPTYPE`s are `LANGSXP`, `CHARSXP` etc.

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

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

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

or

    PROTECT(newSexp = AS_NUMERIC(oldSexp));

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

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

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

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

#### Attributes

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

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

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

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

C code to do the computations is

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

    SEXP out(SEXP x, SEXP y)
    {
      int i, j, nx, ny;
      double tmp;
      SEXP ans;

      nx = length(x); ny = length(y);
      PROTECT(ans = allocMatrix(REALSXP, nx, ny));
      for(i = 0; i < nx; i++) {
        tmp = REAL(x)[i];
        for(j = 0; j < ny; j++)
          REAL(ans)[i + nx*j] = tmp * REAL(y)[j];
      }
      UNPROTECT(1);
      return(ans);
    }

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

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

    SEXP out(SEXP x, SEXP y)
    {
      int i, j, nx, ny;
      double tmp;
      SEXP ans, dim, dimnames;

      nx = length(x); ny = length(y);
      PROTECT(ans = allocVector(REALSXP, nx*ny));
      for(i = 0; i < nx; i++) {
        tmp = REAL(x)[i];
        for(j = 0; j < ny; j++)
          REAL(ans)[i + nx*j] = tmp * REAL(y)[j];
      }

      PROTECT(dim = allocVector(INTSXP, 2));
      INTEGER(dim)[0] = nx; INTEGER(dim)[1] = ny;
      setAttrib(ans, R_DimSymbol, dim);

      PROTECT(dimnames = allocVector(VECSXP, 2));
      SET_VECTOR_ELT(dimnames, 0, getAttrib(x, R_NamesSymbol));
      SET_VECTOR_ELT(dimnames, 1, getAttrib(y, R_NamesSymbol));
      setAttrib(ans, R_DimNamesSymbol, dimnames);

      UNPROTECT(3);
      return(ans);
    }

This example introduces several new features. The `getAttrib` and `setAttrib` functions get and set individual attributes. Their second argument is a `SEXP` defining the name in the symbol table of the attribute we want; these and many such symbols are defined in the header file `Rinternals.h`.

There are shortcuts here too: the functions `namesgets`, `dimgets` and `dimnamesgets` are the internal versions of `names<-`, `dim<-` and `dimnames<-`, and there are functions such as `GetMatrixDimnames` and `GetArrayDimnames`.

What happens if we want to add an attribute that is not pre-defined? We need to add a symbol for it via a call to `install`. Suppose for illustration we wanted to add an attribute `"version"` with value `3.0`. We could use

      SEXP version;
      PROTECT(version = allocVector(REALSXP, 1));
      REAL(version) = 3.0;
      setAttrib(ans, install("version"), version);
      UNPROTECT(1);

Using `install` when it is not needed is harmless and provides a simple way to retrieve the symbol from the symbol table if it is already installed.

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

#### Classes

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

    #include <R.h>
    #include <Rdefines.h>
        ....
      SEXP ans, dim, dimnames, class;
        ....
      PROTECT(class = allocVector(STRSXP, 1));
      SET_STRING_ELT(class, 0, mkChar("mat"));
      classgets(ans, class);
      UNPROTECT(4);
      return(ans);
    }

As the value is a character vector, we have to know how to create that from a C character array, which we do using the function `mkChar`.

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

#### Handling lists

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

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

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

Then we can access `a$g` as `a[[2]]` by

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

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

    /* get the list element named str, or return NULL */

    SEXP getListElement(SEXP list, char *str)
    {
      SEXP elmt = R_NilValue, names = getAttrib(list, R_NamesSymbol);
      int i;

      for (i = 0; i < length(list); i++)
        if(strcmp(CHAR(STRING_ELT(names, i)), str) == 0) {
          elmt = VECTOR_ELT(list, i);
          break;
        }
      return elmt;
    }

and enables us to say

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

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

#### Finding and setting variables

It will be usual that all the R objects needed in our C computations are passed as arguments to `.Call` or `.External`, but it is possible to find the values of R objects from within the C given their names. The following code is the equivalent of `get(name, envir = rho)`.

    SEXP getvar(SEXP name, SEXP rho)
    {
      SEXP ans;

      if(!isString(name) || length(name) != 1)
        error("name is not a single string");
      if(!isEnvironment(rho))
        error("rho should be an environment");
      ans = findVar(install(CHAR(STRING_ELT(name, 0))), rho);
      printf("first value is %f\n", REAL(ans)[0]);
      return(R_NilValue);
    }

The main work is done by `findVar`, but to use it we need to install `name` as a name in the symbol table. As we wanted the value for internal use, we return `NULL`.

Similar functions with syntax

    void defineVar(SEXP symbol, SEXP value, SEXP rho)
    void setVar(SEXP symbol, SEXP value, SEXP rho)

can be used to assign values to R variables. `defineVar` creates a new binding or changes the value of an existing binding in the specified environment frame; it is the analogue of `assign(symbol, value, envir = rho, inherits = FALSE)`, but unlike `assign`, `defineVar` does not make a copy of the object `value`.4 `setVar` searches for an existing binding for `symbol` in `rho` or its enclosing environments. If a binding is found, its value is changed to `value`. Otherwise, a new binding with the specified value is created in the global environment. This corresponds to `assign(symbol, value, envir = rho, inherits = TRUE)`.

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

### Interface functions `.Call` and `.External`

In this section we consider the details of the R/C interfaces.

These two interfaces have almost the same functionality. `.Call` is based on the interface of the same name in S version 4, and `.External` is based on `.Internal`. `.External` is more complex but allows a variable number of arguments.

- Calling .Call:
- Calling .External:
- Missing and special values:

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

#### Calling `.Call`

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

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

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

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

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

      PROTECT(a = AS_NUMERIC(a));
      PROTECT(b = AS_NUMERIC(b));
      na = LENGTH(a); nb = LENGTH(b); nab = na + nb - 1;
      PROTECT(ab = NEW_NUMERIC(nab));
      xa = NUMERIC_POINTER(a); xb = NUMERIC_POINTER(b);
      xab = NUMERIC_POINTER(ab);
      for(i = 0; i < nab; i++) xab[i] = 0.0;
      for(i = 0; i < na; i++)
        for(j = 0; j < nb; j++) xab[i + j] += xa[i] * xb[j];
      UNPROTECT(3);
      return(ab);
    }

Note that unlike the macros in S version 4, the R versions of these macros do check that coercion can be done and raise an error if it fails. They will raise warnings if missing values are introduced by coercion. Although we illustrate doing the coercion in the C code here, it often is simpler to do the necessary coercions in the R code.

Now for the version in R-internal style. Only the C code changes.

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

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

      PROTECT(a = coerceVector(a, REALSXP));
      PROTECT(b = coerceVector(b, REALSXP));
      na = length(a); nb = length(b); nab = na + nb - 1;
      PROTECT(ab = allocVector(REALSXP, nab));
      xa = REAL(a); xb = REAL(b);
      xab = REAL(ab);
      for(i = 0; i < nab; i++) xab[i] = 0.0;
      for(i = 0; i < na; i++)
        for(j = 0; j < nb; j++) xab[i + j] += xa[i] * xb[j];
      UNPROTECT(3);
      return(ab);
    }

This is called in exactly the same way.

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

#### Calling `.External`

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

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

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

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

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

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

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

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

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

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

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

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

    #include <R_ext/PrtUtil.h>

    SEXP showArgs(SEXP args)
    {
      int i, nargs;
      Rcomplex cpl;
      char *name;

      if((nargs = length(args) - 1) > 0) {
        for(i = 0; i < nargs; i++) {
          args = CDR(args);
          name = CHAR(PRINTNAME(TAG(args)));
          switch(TYPEOF(CAR(args))) {
          case REALSXP:
            Rprintf("[%d] '%s' %f\n", i+1, name, REAL(CAR(args))[0]);
            break;
          case LGLSXP:
          case INTSXP:
            Rprintf("[%d] '%s' %d\n", i+1, name, INTEGER(CAR(args))[0]);
            break;
          case CPLXSXP:
            cpl = COMPLEX(CAR(args))[0];
            Rprintf("[%d] '%s' %f + %fi\n", i+1, name, cpl.r, cpl.i);
            break;
          case STRSXP:
            Rprintf("[%d] '%s' %s\n", i+1, name,
                   CHAR(STRING_ELT(CAR(args), 0)));
            break;
          default:
            Rprintf("[%d] '%s' R type\n", i+1, name);
          }
        }
      }
      return(R_NilValue);
    }

This can be called by the wrapper function

    showArgs <- function(...) .External("showArgs", ...)

Note that this style of programming is convenient but not necessary, as an alternative style is

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

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

#### Missing and special values

One piece of error-checking the `.C` call does (unless `NAOK` is true) is to check for missing (`NA`) and IEEE special values (`Inf`, `-Inf` and `NaN`) and give an error if any are found. With the `.Call` interface these will be passed to our code. In this example the special values are no problem, as IEEE arithmetic will handle them correctly. In the current implementation this is also true of `NA` as it is a type of `NaN`, but it is unwise to rely on such details. Thus we will re-write the code to handle `NA`s using macros defined in `Arith.h` included by `R.h`.

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

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

Note that the `ISNA` macro, and the similar macros `ISNAN` (which checks for `NaN` or `NA`) and `R_FINITE` (which is false for `NA` and all the special values), only apply to numeric values of type `double`. Missingness of integers, logicals and character strings can be tested by equality to the constants `NA_INTEGER`, `NA_LOGICAL` and `NA_STRING`. These and `NA_REAL` can be used to set elements of R vectors to `NA`.

The constants `R_NaN`, `R_PosInf`, `R_NegInf` and `R_NaReal` can be used to set `double`s to the special values.

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

### Evaluating R expressions from C

We noted that the `call_R` interface could be used to evaluate R expressions from C code, but the current interfaces are much more convenient to use. The main function we will use is

    SEXP eval(SEXP expr, SEXP rho);

the equivalent of the interpreted R code `eval(expr, envir = rho)`, although we can also make use of `findVar`, `defineVar` and `findFun` (which restricts the search to functions).

To see how this might be applied, here is a simplified internal version of `lapply` for expressions, used as

    a <- list(a = 1:5, b = rnorm(10), test = runif(100))
    .Call("lapply", a, quote(sum(x)), new.env())

with C code

    SEXP lapply(SEXP list, SEXP expr, SEXP rho)
    {
      int i, n = length(list);
      SEXP ans;

      if(!isNewList(list)) error("`list' must be a list");
      if(!isEnvironment(rho)) error("`rho' should be an environment");
      PROTECT(ans = allocVector(VECSXP, n));
      for(i = 0; i < n; i++) {
        defineVar(install("x"), VECTOR_ELT(list, i), rho);
        SET_VECTOR_ELT(ans, i, eval(expr, rho));
      }
      setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol));
      UNPROTECT(1);
      return(ans);
    }

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

    SEXP lapply2(SEXP list, SEXP fn, SEXP rho)
    {
      int i, n = length(list);
      SEXP R_fcall, ans;

      if(!isNewList(list)) error("`list' must be a list");
      if(!isFunction(fn)) error("`fn' must be a function");
      if(!isEnvironment(rho)) error("`rho' should be an environment");
      PROTECT(R_fcall = lang2(fn, R_NilValue));
      PROTECT(ans = allocVector(VECSXP, n));
      for(i = 0; i < n; i++) {
        SETCADR(R_fcall, VECTOR_ELT(list, i));
        SET_VECTOR_ELT(ans, i, eval(R_fcall, rho));
      }
      setAttrib(ans, R_NamesSymbol, getAttrib(list, R_NamesSymbol));
      UNPROTECT(2);
      return(ans);
    }

used by

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

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

#### Zero-finding

In this section we re-work the example of `call_S` in Becker, Chambers & Wilks (1988) on finding a zero of a univariate function, which used to be used as an example for `call_R` in the now defunct `demo(dynload)`. The R code and an example are

    zero <- function(f, guesses, tol = 1e-7) {
      f.check <- function(x) {
        x <- f(x)
        if(!is.numeric(x)) stop("Need a numeric result")
        as.double(x)
      }
      .Call("zero", body(f.check), as.double(guesses), as.double(tol),
            new.env())
    }

    cube1 <- function(x) (x^2 + 1) * (x - 1.5)
    zero(cube1, c(0, 5))

where this time we do the coercion and error-checking in the R code. The C code is

    SEXP mkans(double x)
    {
        SEXP ans;
        PROTECT(ans = allocVector(REALSXP, 1));
        REAL(ans)[0] = x;
        UNPROTECT(1);
        return ans;
    }

    double feval(double x, SEXP f, SEXP rho)
    {
        defineVar(install("x"), mkans(x), rho);
        return(REAL(eval(f, rho))[0]);
    }

    SEXP zero(SEXP f, SEXP guesses, SEXP stol, SEXP rho)
    {
        double x0 = REAL(guesses)[0], x1 = REAL(guesses)[1],
               tol = REAL(stol)[0];
        double f0, f1, fc, xc;

        if(tol <= 0.0) error("non-positive tol value");
        f0 = feval(x0, f, rho); f1 = feval(x1, f, rho);
        if(f0 == 0.0) return mkans(x0);
        if(f1 == 0.0) return mkans(x1);
        if(f0*f1 > 0.0) error("x[0] and x[1] have the same sign");

        for(;;) {
            xc = 0.5*(x0+x1);
            if(fabs(x0-x1) < tol) return  mkans(xc);
            fc = feval(xc, f, rho);
            if(fc == 0) return  mkans(xc);
            if(f0*fc > 0.0) {
                x0 = xc; f0 = fc;
            } else {
                x1 = xc; f1 = fc;
            }
        }
    }

The C code is essentially unchanged from the `call_R` version, just using a couple of functions to convert from `double` to `SEXP` and to evaluate `f.check`.

#### 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
      ans
    }
    omega <- 1:5; x <- 1; y <- 2
    numeric.deriv(sin(omega*x*y), c("x", "y"))

where `expr` is an expression, `theta` a character vector of variable names and `rho` the environment to be used.

For the compiled version the call from R will be

    .External("numeric_deriv", expr, theta, rho)

with example usage

    .External("numeric_deriv", quote(sin(omega*x*y)),
              c("x", "y"), .GlobalEnv)

Note the need to quote the expression to stop it being evaluated.

Here is the complete C code which we will explain section by section.

    #include <R.h> /* for DOUBLE_EPS */
    #include <Rinternals.h>

    SEXP numeric_deriv(SEXP args)
    {
      SEXP theta, expr, rho, ans, ans1, gradient, par, dimnames;
      double tt, xx, delta, eps = sqrt(DOUBLE_EPS);
      int start, i, j;

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

      PROTECT(ans = coerceVector(eval(expr, rho), REALSXP));
      PROTECT(gradient = allocMatrix(REALSXP, LENGTH(ans), LENGTH(theta)));

      for(i = 0, start = 0; i < LENGTH(theta); i++, start += LENGTH(ans)) {
        PROTECT(par = findVar(install(CHAR(STRING_ELT(theta, i))), rho));
        tt = REAL(par)[0];
        xx = fabs(tt);
        delta = (xx < 1) ? eps : xx*eps;
        REAL(par)[0] += delta;
        PROTECT(ans1 = coerceVector(eval(expr, rho), REALSXP));
        for(j = 0; j < LENGTH(ans); j++)
          REAL(gradient)[j + start] =
            (REAL(ans1)[j] - REAL(ans)[j])/delta;
        REAL(par)[0] = tt;
        UNPROTECT(2); /* par, ans1 */
      }

      PROTECT(dimnames = allocVector(VECSXP, 2));
      SET_VECTOR_ELT(dimnames, 1,  theta);
      dimnamesgets(gradient, dimnames);
      setAttrib(ans, install("gradient"), gradient);
      UNPROTECT(3); /* ans  gradient  dimnames */
      return ans;
    }

The code to handle the arguments is

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

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

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

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

We then allocate space for the calculated derivative by

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

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

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

Here, we are entering a for loop. We loop through each of the variables. In the `for` loop, we first create a symbol corresponding to the `i`'th element of the `STRSXP` `theta`. Here, `STRING_ELT(theta, i)` accesses the `i`'th element of the `STRSXP` `theta`. Macro `CHAR()` extracts the actual character representation of it: it returns a pointer. We then install the name and use `findVar` to find its value.

        tt = REAL(par)[0];
        xx = fabs(tt);
        delta = (xx < 1) ? eps : xx*eps;
        REAL(par)[0] += delta;
        PROTECT(ans1 = coerceVector(eval(expr, rho), REALSXP));

We first extract the real value of the parameter, then calculate `delta`, the increment to be used for approximating the numerical derivative. Then we change the value stored in `par` (in environment `rho`) by `delta` and evaluate `expr` in environment `rho` again. Because we are directly dealing with original R memory locations here, R does the evaluation for the changed parameter value.

        for(j = 0; j < LENGTH(ans); j++)
          REAL(gradient)[j + start] =
            (REAL(ans1)[j] - REAL(ans)[j])/delta;
        REAL(par)[0] = tt;
        UNPROTECT(2);
      }

Now, we compute the `i`'th column of the gradient matrix. Note how it is accessed: R stores matrices by column (like FORTRAN).

      PROTECT(dimnames = allocVector(VECSXP, 2));
      SET_VECTOR_ELT(dimnames, 1, theta);
      dimnamesgets(gradient, dimnames);
      setAttrib(ans, install("gradient"), gradient);
      UNPROTECT(3);
      return ans;
    }

First we add column names to the gradient matrix. This is done by allocating a list (a `VECSXP`) whose first element, the row names, is `NULL` (the default) and the second element, the column names, is set as `theta`. This list is then assigned as the attribute having the symbol `R_DimNamesSymbol`. Finally we set the gradient matrix as the gradient attribute of `ans`, unprotect the remaining protected locations and return the answer `ans`.

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

### Debugging compiled code

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

- Finding entry points:
- Inspecting R objects:

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

#### Finding entry points in dynamically loaded code

Under most compilation environments, compiled code dynamically loaded into R cannot have breakpoints set within it until it is loaded. To use a symbolic debugger on such dynamically loaded code under UNIX use

- 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 library.
- 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 the R engine is itself in a DLL, and the procedure is

- Start R under the debugger after setting a breakpoint for `WinMain`.

      gdb .../bin/Rgui.exe
      (gdb) break WinMain
      (gdb) run
      [ stops with R.dll loaded ]
      (gdb) break R_ReadConsole
      (gdb) continue
      [ stops with console running ]
      (gdb) continue

- At the R prompt, use `dyn.load` or `library` to load your library.
- Set the breakpoints in your code.
- Use

      (gdb) clear R_ReadConsole
      (gdb) continue

  to continue running with the breakpoints set.

Windows has little support for signals, so the usual idea of running a program under a debugger and sending it a signal to interrupt it and drop control back to the debugger only works with some debuggers.

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

#### Inspecting R objects when debugging

The key to inspecting R objects from compiled code is the function `PrintValue(SEXP s)` which uses the normal R printing mechanisms to print the R object pointed to by s, or the safer version `R_PV(SEXP s)` which will only print `objects'.

One way to make use to `PrintValue` is to insert suitable calls into the code to be debugged.

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

    (gdb) p R_PV(ab)

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

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

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

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

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

(Debugger output reformatted for better legibility).

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

    (gdb) p R_PV($1->attrib)
    $names
    [1] "a" "b"

    $row.names
    [1] "1" "2" "3"

    $class
    [1] "data.frame"

    $3 = void

To find out where exactly the corresponding information is stored, one needs to go "deeper":

    (gdb) set $a = $1->attrib
    (gdb) p $a->u.listsxp.tagval->u.symsxp.pname->u.vecsxp.type.c
    $4 = 0x405d40e8 "names"
    (gdb) p $a->u.listsxp.carval->u.vecsxp.type.s[1]->u.vecsxp.type.c
    $5 = 0x40634378 "b"
    (gdb) p $1->u.vecsxp.type.s[0]->u.vecsxp.type.i[0]
    $6 = 1
    (gdb) p $1->u.vecsxp.type.s[1]->u.vecsxp.type.i[1]
    $7 = 5

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

## The R API: entry points for C code

- Memory allocation:
- Error handling:
- Random numbers:
- Missing and IEEE values:
- Printing:
- Calling C from FORTRAN and vice versa:
- Numerical analysis subroutines:
- Optimization:
- Integration:
- Utility functions:
- Platform and version information:
- Standalone Mathlib:

There are a large number of entry points in the R executable/DLL that can be called from C code (and some that can be called from FORTRAN code). Only those documented here are stable enough that they will only be changed with considerable notice.

The recommended procedure to use these is to include the header file `R.h` in your C code by

    #include <R.h>

This will include several other header files from the directory `R_HOME/include/R_ext`, and there are other header files there that can be included too, but many of the features they contain should be regarded as undocumented and unstable.

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

Most of these header files, including all those included by `R.h`, can be used from C++ code.

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

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

### Memory allocation

- Transient:
- User-controlled:

There are two types of memory allocation available to the C programmer, one in which R manages the clean-up and the other in which user has full control (and responsibility).

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

#### Transient storage allocation

Here R will reclaim the memory at the end of the call to `.C`. Use

    char* R_alloc(long n, int size)

which allocates n units of size bytes each. A typical usage (from package mva) is

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

There is a similar call, `S_alloc`, for compatibility with S, which differs only in zeroing the memory allocated, and

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

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

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

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

#### User-controlled memory

The other form of memory allocation is an interface to `malloc`, the interface providing R error handling. This memory lasts until freed by the user and is additional to the memory allocated for the R workspace.

The interface functions are

    type* Calloc(size_t n, type)
    type* Realloc(any *p, size_t n, type)
    void Free(any *p)

providing analogues of `calloc`, `realloc` and `free`. If there is an error it is handled by R, so if these routines return the memory has been successfully allocated or freed. `Free` will set the pointer p to `NULL`. (Some but not all versions of S do so.)

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

### Error handling

The basic error handling routines are the equivalents of `stop` and `warning` in R code, and use the same interface.

    void error(const char * format, ...);
    void warning(const char * format, ...);

These have the same call sequences as calls to `printf`, but in the simplest case can be called with a single character string argument giving the error message. (Don't do this if the string contains `%` or might otherwise be interpreted as a format.)

There is also an S-compatibility interface which uses calls of the form

    PROBLEM ...... ERROR
    MESSAGE ...... WARN
    PROBLEM ...... RECOVER(NULL_ENTRY)
    MESSAGE ...... WARNING(NULL_ENTRY)

the last two being the forms available in all S versions. Here `......` is a set of arguments to `printf`, so can be a string or a format string followed by arguments separated by commas.

#### Error handling from FORTRAN

There are two interface function provided to call `error` and `warning` from FORTRAN code, in each case with a simple character string argument. They are defined as

    subroutine rexit(message)
    subroutine rwarn(message)

Messages of more than 255 characters are truncated, with a warning.

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

### Random number generation

The interface to R's internal random number generation routines is

    double unif_rand();
    double norm_rand();
    double exp_rand();

giving one uniform, normal or exponential pseudo-random variate. However, before these are used, the user must call

    GetRNGstate();

and after all the required variates have been generated, call

    PutRNGstate();

These essentially read in (or create) `.Random.seed` and write it out after use.

File `S.h` defines `seed_in` and `seed_out` for S-compatibility rather than `GetRNGstate` and `PutRNGstate`. These take a `long *` argument which is ignored.

The random number generator is private to R; there is no way to select the kind of RNG or set the seed except by evaluating calls to the R functions.

The C code behind R's `rxxx` functions can be accessed by including the header file `Rmath.h`; See Distribution functions. Those calls generate a single variate and should also be enclosed in calls to `GetRNGstate` and `PutRNGstate`.

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

### Missing and IEEE special values

It is possible to compile R on a platform without IEC 559 (more commonly known as IEEE 754)-compatible arithmetic, so users should not assume that it is available. Rather a set of functions is provided to test for `NA`, `Inf`, `-Inf` (which exists on all platforms) and `NaN`. These functions are accessed via macros:

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

and function `R_IsNaN` is true for `NaN` but not `NA`. Do use these rather than `isnan` or `finite`; the latter in particular is often mendacious.

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

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

Beware that these special values may be represented by extreme values which could occur in ordinary computations which run out of control, so you may need to test that they have not been generated inadvertently.

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

### Printing

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

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

#### Printing from FORTRAN

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

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

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

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

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

### Calling C from FORTRAN and vice versa

Naming conventions for symbols generated by FORTRAN differ by platform: it is not safe to assume that FORTRAN names appear to C with a trailing underscore. To help cover up the platform-specific differences there is a set of macros that should be used.

`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.

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

    #include <R.h>

    void F77_SUB(rndstart)(void) { GetRNGstate(); }
    void F77_SUB(rndend)(void) { PutRNGstate(); }
    double F77_SUB(normrnd)(void) { return norm_rand(); }

to be called from FORTRAN as in

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

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

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

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

### Numerical analysis subroutines

R contains a large number of mathematical functions for its own use, for example numerical linear algebra computations and special functions.

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

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

- Distribution functions:
- Mathematical functions:
- Utilities:
- Mathematical constants:

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

#### Distribution functions

The routines used to calculate densities, cumulative distribution functions and quantile functions for the standard statistical distributions are available as entry points.

The arguments for the entry points follow the pattern of those for the normal distribution:

    double dnorm(double x, double mu, double sigma, int give_log);
    double pnorm(double x, double mu, double sigma, int lower_tail,
                 int give_log);
    double qnorm(double p, double mu, double sigma, int lower_tail,
                 int log_p);
    double rnorm(double mu, double sigma);

That is, the first argument gives the position for the density and CDF and probability for the quantile function, followed by the distribution's parameters. Argument lower_tail should be `TRUE` (or `1`) for normal use, but can be `FALSE` (or `0`) if the probability of the upper tail is desired or specified.

Finally, give_log should be non-zero if the result is required on log scale, and log_p should be non-zero if p has been specified on log scale.

Note that you directly get the cumulative (or "integrated") hazard function, H(t) = - log(1 - F(t)), by using

    - pdist(t, ..., /*lower_tail = */ FALSE, /* give_log = */ TRUE)

or shorter (and more cryptic) `- pdist(t, ..., 0, 1)`.

The random-variate generation routine `rnorm` returns one normal variate. See Random numbers, for the protocol in using the random-variate routines.

Note that these argument sequences are (apart from the names and that `rnorm` has no n) exactly the same as the corresponding R functions of the same name, so the documentation of the R functions can be used.

For reference, the following table gives the basic name (to be prefixed by `d`, `p`, `q` or `r` apart from the exceptions noted) and distribution-specific arguments for the complete set of distributions.

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

Entries marked only have `p` and `q` functions available.

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

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

#### Mathematical functions

double gammafn (double x) | Function
double lgammafn (double x) | Function
double digamma (double x) | Function
double trigamma (double x) | Function
double tetragamma (double x) | Function
double pentagamma (double x) | Function

The Gamma function, its natural logarithm and first four derivatives.

double beta (double a, double b) | Function
double lbeta (double a, double b) | Function

The (complete) Beta function and its natural logarithm.

double choose (double n, double k) | Function
double lchoose (double n, double k) | Function

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

double bessel_i (double x, double nu, double expo) | Function
double bessel_j (double x, double nu) | Function
double bessel_k (double x, double nu, double expo) | Function
double bessel_y (double x, double nu) | Function

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

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

#### Utilities

There are a few other numerical utility functions available as entry points.

double R_pow (double x, double y) | Function
double R_pow_di (double x, int i) | Function

`R_pow(x, y)` and `R_pow_di(x, i)` compute `x^y` and `x^i`, respectively using `R_FINITE` checks and returning the proper result (the same as R) for the cases where x, y or i are 0 or missing or infinite or `NaN`.

double pythag (double a, double b) | Function

`pythag(a, b)` computes `sqrt(a^2 + b^2)` without overflow or destructive underflow: for example it still works when both a and b are between `1e200` and `1e300` (in IEEE double precision).

double log1p (x) | Function

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

double expm1 (x) | Function

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

int imax2 (int x, int y) | Function
int imin2 (int x, int y) | Function
double fmax2 (double x, double y) | Function
double fmin2 (double x, double y) | Function

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

double sign (double x) | Function

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

double fsign (double x, double y) | Function

Performs "transfer of sign" and is defined as |x| * sign(x).

double fprec (double x, double digits) | Function

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

double fround (double x, double digits) | Function

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

double ftrunc (double x) | Function

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

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

#### Mathematical constants

R has a set of commonly used mathematical constants encompassing constants usually found `math.h` and contains further ones that are used in statistical computations. All these are defined to (at least) 30 digits accuracy in `Rmath.h`. The following definitions use `ln(x)` for the natural logarithm (`log(x)` in R).

> Name | Definition (`ln = log`) | round(value, 7)
> `M_E` | e | 2.7182818
> `M_LOG2E` | log2(e) | 1.4426950
> `M_LOG10E` | log10(e) | 0.4342945
> `M_LN2` | ln(2) | 0.6931472
> `M_LN10` | ln(10) | 2.3025851
> `M_PI` | pi | 3.1415927
> `M_PI_2` | pi/2 | 1.5707963
> `M_PI_4` | pi/4 | 0.7853982
> `M_1_PI` | 1/pi | 0.3183099
> `M_2_PI` | 2/pi | 0.6366198
> `M_2_SQRTPI` | 2/sqrt(pi) | 1.1283792
> `M_SQRT2` | sqrt(2) | 1.4142136
> `M_SQRT1_2` | 1/sqrt(2) | 0.7071068
> `M_SQRT_3` | sqrt(3) | 1.7320508
> `M_SQRT_32` | sqrt(32) | 5.6568542
> `M_LOG10_2` | log10(2) | 0.3010300
> `M_2PI` | 2*pi | 6.2831853
> `M_SQRT_PI` | sqrt(pi) | 1.7724539
> `M_1_SQRT_2PI` | 1/sqrt(2*pi) | 0.3989423
> `M_SQRT_2dPI` | sqrt(2/pi) | 0.7978846
> `M_LN_SQRT_PI` | ln(sqrt(pi)) | 0.5723649
> `M_LN_SQRT_2PI` | ln(sqrt(2*pi)) | 0.9189385
> `M_LN_SQRT_PId2` | ln(sqrt(pi/2)) | 0.2257914

There are a set of constants (`PI`, `DOUBLE_EPS`) defined in the included header `R_ext/Constants.h`, the latter two mainly for compatibility with S.

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

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

### Optimization

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 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);

- BFGS:

      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.

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

### Integration

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

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

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

There are interfaces for definite and for indefinite integrals. `Indefinite' means that at least one of the integration boundaries is not finite.

- 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);

- Indefinite:

      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`).

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

### Utility functions

R has a fairly comprehensive set of sort routines which are made available to users' C code. These include the following.

void R_isort (int* x, int n) | Function
void R_rsort (double* x, int n) | Function
void R_csort (Rcomplex* x, int n) | Function
void rsort_with_index (double* x, int* index, int n) | Function

The first three sort integer, real (double) and complex data respectively. (Complex numbers are sorted by the real part first then the imaginary part.) `NA`s are sorted last. `rsort_with_index` sorts on x, and applies the same permutation to index. `NA`s are sorted last.

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

Is similar to `rsort_with_index` but sorts into decreasing order, and `NA`s are not handled.

void iPsort (int* x, int n, int k) | Function
void rPsort (double* x, int n, int k) | Function
void cPsort (Rcomplex* x, int n, int k) | Function

These all provide (very) partial sorting: they permute x so that `x[k]` is in the correct place with smaller values to the left, larger ones to the right.

void R_qsort (double *v, int i, int j) | Function
void R_qsort_I (double *v, int *I, int i, int j) | Function
void R_qsort_int (int *iv, int i, int j) | Function
void R_qsort_int_I (int *iv, int *I, int i, int j) | Function

These routines sort `v[i:j]` or `iv[i:j]` (using 1-indexing, i.e. `v[1]` is the first element) calling the quicksort algorithm as used by R's `sort(v, method = "quick")` and documented on the help page for the R function `sort`. The `..._I()` versions also return the `sort.index()` vector in `I`. Note that the ordering is not stable, so tied values may be permuted. Note that `NA`s are not handled (explicitly) and you should use different sorting functions if NAs can be present.

subroutine qsort4 (double precision v, integer indx, integerii, integer jj) | Function
subroutine qsort3 (double precision v, integer ii, integerjj) | Function

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

void R_max_col (double* matrix, int* nr, int* nc, int* maxes) | Function

Given the nr by ny matrix `matrix` in row ("FORTRAN") order, `R_max_col()` returns in `maxes[i-1]` the column number of the maximal element in the i-th row (the same as R's `max.col()` function).

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

Given the ordered vector xt of length n, return the interval or index of x in `xt[]`, typically max(i; 1 <= i <= n & xt[i] <= x) where we use 1-indexing as in R and FORTRAN (but not C). If rightmost_closed is true, also returns n-1 if x equals xt[n]. If all_inside is not 0, the result is coerced to lie in `1:(n-1)` even when x is outside the xt[] range. On return, `*mflag` equals -1 if x < xt[1], +1 if x >= xt[n], and 0 otherwise. The algorithm is particularly fast when ilo is set to the last result of `findInterval()` and x is a value of a sequence which is increasing or decreasing for subsequent calls. There is also an `F77_CALL(interv)()` version of `findInterval()` with the same arguments, but all pointers.

There is also the internal function use to expand file names in several R functions, and called directly by `path.expand`.

char * R_ExpandFileName (char* fn) | Function

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

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

### Platform and version information

The header files define `USING_R`, which should be used to test if the code is indeed being used with R.

Header file `Rconfig.h` (included by `R.h`) is used to define platform-specific macros that are mainly for us in other header files. The macro `WORDS_BIGENDIAN` is defined on big-endian systems (e.g. `sparc-sun-solaris2.6`) and not on little-endian systems (such as `i686` under Linux or Windows). It can be useful when manipulating binary files.

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

    #if defined(R_VERSION) && R_VERSION >= R_Version(0, 99, 0)
      ...
    #endif

More detailed information is available in the macros `R_MAJOR`, `R_MINOR`, `R_YEAR`, `R_MONTH` and `R_DAY`: see the header file `Rversion.h` for their format. Note that the minor version includes the patchlevel (as in `99.0`).

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

### Using these functions in your own C code

It is possible to build `Mathlib`, the R set of mathematical functions documented in `Rmath.h`, as a standalone library `libRmath` under Unix and Windows. (This includes the functions documented in Numerical analysis subroutines as from that header file.)

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

    #define MATHLIB_STANDALONE
    #include <Rmath.h>

and link against `-lRmath`. There is an example file `test.c`.

A little care is needed to use the random-number routines. You will need to supply the uniform random number generator

    double unif_rand(void)

or use the one supplied (and with a shared library or DLL you will have to use the one supplied, which is the Marsaglia-multicarry with an entry point

    set_seed(unsigned int, unsigned int)

to set its seeds).

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

## Generic functions and methods

R programmers will often want to add methods for existing generic functions, and may want to add new generic functions or make existing functions generic. In this chapter we give guidelines for doing so, with examples of the problems caused by not adhering to them.

This chapter only covers the `informal' class system copied from S3, and not with the formal methods of package methods of R 1.4.0 and later.

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

    t.data.frame <- function(x)
    {
        x <- as.matrix(x)
        NextMethod("t")
    }

Also consider `predict.glm`: it happens that in R for historical reasons it calls `predict.lm` directly, but in principle (and in S originally and currently) it could use `NextMethod`. (`NextMethod` seems under-used in the R sources.)

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

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

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

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

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

To see the scale of this problem, consider the generic function `scale`, defined (in R 1.4.0) as

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

Suppose an unthinking package writer created methods such as

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

Then for `x` of class `"foo"` the calls

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

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

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

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

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

> A method should use the same defaults as the generic.

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

- Adding new generics:

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

### Adding new generics

When creating a new generic function, bear in mind that its argument list will be the maximal set of arguments for methods, including those written elsewhere years later. So choosing a good set of arguments may well be an important design issue, and there need to be good arguments not to include a `...` argument.

If a `...` argument is supplied, some thought should be given to its position in the argument sequence. Arguments which follow `...` must be named in calls to the function, and they must be named in full (partial matching is suppressed after `...`). Formal arguments before `...` can be partially matched, and so may `swallow' actual arguments intended for `...`. Although it is commonplace to make the `...` argument the last one, that is not always the right choice.

Sometimes package writers want to make generic a function in the base package, and request a change in R. This may be justifiable, but making a function generic with the old definition as the default method does have a small performance cost. It is never necessary, as a package can take over a function in the base package and make it generic by

    foo <- function(object, ...) UseMethod("foo")
    foo.default <- get("foo", pos = NULL, mode = "function")

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

Note that this cannot be used for functions in another package, as the order of packages on the search path cannot be controlled, except that all precede the base package.

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

## R (internal) programming miscellanea

- .Internal and .Primitive:
- Testing R code:

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

### `.Internal` and `.Primitive`

C code compiled into R at build time can be called "directly" or via the `.Internal` interface, which is very similar to the `.External` interface except in syntax. More precisely, R maintains a table of R function names and corresponding C functions to call, which by convention all start with `do_` and return a SEXP. Via this table (`R_FunTab` in file `src/main/names.c`) one can also specify how many arguments to a function are required or allowed, whether the arguments are to be evaluated before calling or not, and whether the function is "internal" in the sense that it must be accessed via the `.Internal` interface, or directly accessible in which case it is printed in R as `.Primitive`.

R's functionality can also be extended by providing corresponding C code and adding to this function table.

In general, all such functions use `.Internal()` as this is safer and in particular allows for transparent handling of named and default arguments. For example, `axis` is defined as

    axis <- function(side, at = NULL, labels = NULL, ...)
        .Internal(axis(side, at, labels, ...))

However, for reasons of convenience and also efficiency (as there is some overhead in using the `.Internal` interface), there are exceptions which can be accessed directly. Note that these functions make no use of R code, and hence are very different from the usual interpreted functions. In particular, `args` and `body` return `NULL` for such objects.

The list of these "primitive" functions is subject to change: currently, it includes the following.

1. "Special functions" which really are language elements, however exist as "primitive" functions in R:

       {       (         if     for      while  repeat  break  next
       return  function  quote  on.exit

2. Basic operators (i.e., functions usually not called as `foo(a, b, ...)`) for subsetting, assignment, arithmetic and logic. These are the following 1-, 2-, and N-argument functions:

                 [    [[    $
       <-   <<-  [<-  [[<-  $<-

       +    -    *    /     ^    %%   %*%  %/%
       <    <=   ==   !=    >=   >
       |    ||   &    &&    !

3. "Low level" 0- and 1-argument functions which belong to one of the following groups of functions:
   1. Basic mathematical functions with a single argument, i.e.,

          sign    abs
          floor   ceiling  trunc

          sqrt    exp
          cos     sin      tan
          acos    asin     atan
          cosh    sinh     tanh
          acosh   asinh    atanh

          cumsum  cumprod
          cummax  cummin

          Im      Re
          Arg     Conj     Mod

      Note however that the R function `log` has an optional named argument `base`, and therefore is defined as

          log <- function(x, base = exp(1)) {
            if(missing(base))
              .Internal(log(x))
            else
              .Internal(log(x, base))
          }

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

   2. Functions rarely used outside of "programming" (i.e., mostly used inside other functions), such as

          nargs        missing
          interactive  is.xxx
          .Primitive   .Internal   .External
          symbol.C     symbol.For
          globalenv    pos.to.env  unclass

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

   3. The programming and session management utilities

          debug    undebug    trace  untrace
          browser  proc.time

4. The following basic assignment and extractor functions

       length      length<-
       class       class<-
       attr        attr<-
       attributes  attributes<-
       dim         dim<-
       dimnames    dimnames<-
                   environment<-

5. The following few N-argument functions are "primitive" for efficiency reasons. Care is taken in order to treat named arguments properly:

       :          ~          c           list        unlist
       call       as.call    expression  substitute
       UseMethod  invisible
       .C         .Fortran   .Call

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

### Testing R code

When you (as R developer) add new functions to the R base (all the packages distributed with R), be careful to check if `make test-Specific` or particularly, `cd tests; make no-segfault.Rout` still works (without interactive user intervention, and on a standalone computer). If the new function, for example, accesses the Internet, or requires GUI interaction, please add its name to the "stop list" in `tests/no-segfault.Rin`.

Node:R coding standards, Next:Function and variable index, Previous:R (internal) programming miscellanea, Up:Top

## R coding standards

R is meant to run on a wide variety of platforms, including Linux and most variants of Unix as well as 32-bit Windows versions and on the Power Mac. Therefore, when extending R by either adding to the R base distribution or by providing an add-on package, one should not rely on features specific to only a few supported platforms, if this can be avoided. In particular, although most R developers use GNU tools, they should not employ the GNU extensions to standard tools. Whereas some other software packages explicitly rely on e.g. GNU make or the GNU C++ compiler, R does not. Nevertheless, R is a GNU project, and the spirit of the GNU Coding Standards should be followed if possible.

The following tools can "safely be assumed" for R extensions.

- An ISO 9899, also known as ISO C89 or ANSI C compiler. If you do not have access to this C standard, refer to the 2nd edition of Brian W. Kernighan & Dennis M. Ritchie, The C Programming Language. Any extensions, such as POSIX or ISO C99, must be tested for, typically using Autoconf (see Configure and cleanup).
- A FORTRAN 77 compiler or `f2c`, the FORTRAN-to-C converter.
- A simple `make`, considering the features of `make` in 4.2 BSD systems as a baseline.

  GNU or other extensions, including pattern rules using `%`, the automatic variable `$^`, the `+=` syntax to append to the value of a variable, the ("safe") inclusion of makefiles with no error, conditional execution, and many more, must not be used (see Chapter "Features" in the GNU Make Manual for more information). On the other hand, building R in a separate directory (not containing the sources) should work provided that `make` supports the `VPATH` mechanism.

  Windows-specific makefiles can assume GNU `make` 3.75 or later, as no other `make` is viable on that platform.

- A Bourne shell and the "traditional" Unix programming tools, including `grep`, `sed`, and `awk`.

  There are POSIX standards for these tools, but these may not fully be supported, and the precise standards are typically hard to access. Baseline features could be determined from a book such as The UNIX Programming Environment by Brian W. Kernighan & Rob Pike. Note in particular that `|` in a regexp is an extended regexp, and is not supported by all versions of `grep` or `sed`.

  Under Windows, these tools can be assumed because versions (specifically, of `basename`, `cat`, `comm`, `cp`, `cut`, `diff`, `echo`, `egrep`, `expr`, `find`, `gawk`, `grep`, `ls`, `mkdir`, `mv`, `rm`, `sed`, `sort`, `tar`, `touch`, `unzip`, `wc` and `zip`) are provided at <`http://www.stats.ox.ac.uk/pub/Rtools/tools.zip`>. However, redirection cannot be assumed to be available via `system` as this does not use a standard shell (let alone a Bourne shell).

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

- Perl version 5 is needed for converting documentation written in Rd format to plain text, HTML, LaTeX, and to extract the examples. In addition, several other tools, in particular `check` and `build` (see Checking and building packages), require Perl.

  The R Core Team has decided that Perl (version 5) can safely be assumed for building R from source, building and checking add-on packages, and for installing add-on packages from source. On the other hand, Perl cannot be assumed at all for installing binary (pre-built) versions of add-on packages, or at run time.

- Makeinfo version 4 is needed to build the Info files for the R manuals written in the GNU Texinfo system. (Future distributions of R will contain the Info files.)

It is also important that code is written in a way that allows others to understand it. This is particularly helpful for fixing problems, and includes using self-descriptive variable names, commenting the code, and also formatting it properly. The R Core Team recommends to use a basic indentation of 4 for R and C (and most likely also Perl) code, and 2 for documentation in Rd format. Emacs users can implement this indentation style by putting the following in one of their startup files. (For GNU Emacs 20: for GNU Emacs 21 use customization to set the `c-default-style` to `"bsd"` and `c-basic-offset` to `4`.)

    ;;; C
    (add-hook 'c-mode-hook
              (lambda () (c-set-style "bsd")))
    ;;; ESS
    (add-hook 'ess-mode-hook
              (lambda ()
                (ess-set-style 'C++)
                ;; Because
                ;;                                 DEF GNU BSD K&R C++
                ;; ess-indent-level                  2   2   8   5   4
                ;; ess-continued-statement-offset    2   2   8   5   4
                ;; ess-brace-offset                  0   0  -8  -5  -4
                ;; ess-arg-function-offset           2   4   0   0   0
                ;; ess-expression-offset             4   2   8   5   4
                ;; ess-else-offset                   0   0   0   0   0
                ;; ess-close-brace-offset            0   0   0   0   0
                (add-hook 'local-write-file-hooks
                          (lambda ()
                            (nuke-trailing-whitespace)))))
    ;;; Perl
    (add-hook 'perl-mode-hook
              (lambda () (setq perl-indent-level 4)))

(The `GNU' styles for Emacs' C and R modes use a basic indentation of 2, which has been determined not to display the structure clearly enough when using narrow fonts.)

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

## Function and variable index

- `.C`: Interface functions .C and .Fortran
- `.Call`: Handling R objects in C, Calling .Call
- `.External`: Handling R objects in C, Calling .External
- `.Fortran`: Interface functions .C and .Fortran
- `.Internal`: .Internal and .Primitive
- `.Primitive`: .Internal and .Primitive
- `.Random.seed`: Random numbers
- `\alias`: Rd format
- `\arguments`: Rd format
- `\author`: Rd format
- `\bold`: Marking text
- `\code`: Marking text
- `\cr`: Sectioning
- `\deqn`: Mathematics
- `\describe`: Lists and tables
- `\description`: Rd format
- `\details`: Rd format
- `\dontrun`: Rd format
- `\dots`: Insertions
- `\email`: Marking text
- `\emph`: Marking text
- `\enumerate`: Lists and tables
- `\eqn`: Mathematics
- `\examples`: Rd format
- `\file`: Marking text
- `\format`: Rd format
- `\itemize`: Lists and tables
- `\ldots`: Insertions
- `\link`: Rd format
- `\method`: Rd format
- `\name`: Rd format
- `\note`: Rd format
- `\R`: Insertions
- `\references`: Rd format
- `\section`: Sectioning
- `\seealso`: Rd format
- `\source`: Rd format
- `\synopsis`: Rd format
- `\tabular`: Lists and tables
- `\testonly`: Rd format
- `\title`: Rd format
- `\url`: Marking text
- `\usage`: Rd format
- `\value`: Rd format
- `bessel_i`: Mathematical functions
- `bessel_j`: Mathematical functions
- `bessel_k`: Mathematical functions
- `bessel_y`: Mathematical functions
- `beta`: Mathematical functions
- `BLAS_LIBS`: Using Makevars
- `Calloc`: User-controlled
- `CAR`: Calling .External
- `CDR`: Calling .External
- `cgmin`: Optimization
- `choose`: Mathematical functions
- `cPsort`: Utility functions
- `defineVar`: Finding and setting variables
- `digamma`: Mathematical functions
- `dyn.load`: dyn.load and dyn.unload
- `dyn.unload`: dyn.load and dyn.unload
- `exp_rand`: Random numbers
- `expm1`: Utilities
- `FALSE`: Mathematical constants
- `findInterval`: Utility functions
- `findVar`: Finding and setting variables
- `FLIBS`: Using Makevars
- `fmax2`: Utilities
- `fmin2`: Utilities
- `fprec`: Utilities
- `Free`: User-controlled
- `fround`: Utilities
- `fsign`: Utilities
- `ftrunc`: Utilities
- `gammafn`: Mathematical functions
- `getAttrib`: Attributes
- `GetRNGstate`: Random numbers
- `imax2`: Utilities
- `imin2`: Utilities
- `install`: Attributes
- `iPsort`: Utility functions
- `ISNA`: Missing and IEEE values, Missing and special values
- `ISNAN`: Missing and special values, Missing and IEEE values
- `lbeta`: Mathematical functions
- `lbfgsb`: Optimization
- `lchoose`: Mathematical functions
- `lgammafn`: Mathematical functions
- `library.dynam`: dyn.load and dyn.unload
- `log1p`: Utilities
- `M_E`: Mathematical constants
- `M_PI`: Mathematical constants
- `NA_REAL`: Missing and IEEE values
- `nmmin`: Optimization
- `norm_rand`: Random numbers
- `pentagamma`: Mathematical functions
- `PKG_CFLAGS`: Creating shared libraries
- `PKG_CPPFLAGS`: Creating shared libraries
- `PKG_CXXFLAGS`: Creating shared libraries
- `PKG_FFLAGS`: Creating shared libraries
- `PKG_LIBS`: Creating shared libraries
- `prompt`: Rd format
- `PROTECT`: Garbage Collection
- `PutRNGstate`: Random numbers
- `pythag`: Utilities
- `qsort3`: Utility functions
- `qsort4`: Utility functions
- `R CMD build`: Checking and building packages
- `R CMD check`: Checking and building packages
- `R CMD config`: Configure and cleanup
- `R CMD Rd2dvi`: Processing Rd format
- `R CMD Rd2txt`: Processing Rd format
- `R CMD Rdconv`: Processing Rd format
- `R CMD Rdindex`: Processing Rd format
- `R CMD Sd2Rd`: Processing Rd format
- `R CMD SHLIB`: Creating shared libraries
- `R_alloc`: Transient
- `R_csort`: Utility functions
- `R_ExpandFileName`: Utility functions
- `R_FINITE`: Missing and IEEE values
- `R_IsNaN`: Missing and IEEE values
- `R_isort`: Utility functions
- `R_LIBRARY_DIR`: Configure and cleanup
- `R_max_col`: Utility functions
- `R_NegInf`: Missing and IEEE values
- `R_PACKAGE_DIR`: Configure and cleanup
- `R_PosInf`: Missing and IEEE values
- `R_pow`: Utilities
- `R_pow_di`: Utilities
- `R_qsort`: Utility functions
- `R_qsort_I`: Utility functions
- `R_qsort_int`: Utility functions
- `R_qsort_int_I`: Utility functions
- `R_rsort`: Utility functions
- `R_Version`: Platform and version information
- `Rdqagi`: Integration
- `Rdqags`: Integration
- `Realloc`: User-controlled
- `REprintf`: Printing
- `REvprintf`: Printing
- `revsort`: Utility functions
- `Rprintf`: Printing
- `Rprof`: Profiling R code
- `rPsort`: Utility functions
- `rsort_with_index`: Utility functions
- `Rvprintf`: Printing
- `S_alloc`: Transient
- `S_realloc`: Transient
- `samin`: Optimization
- `seed_in`: Random numbers
- `seed_out`: Random numbers
- `setAttrib`: Attributes
- `setVar`: Finding and setting variables
- `sign`: Utilities
- `symbol.C`: Interface functions .C and .Fortran
- `symbol.For`: Interface functions .C and .Fortran
- `system`: Operating system access
- `system.time`: Operating system access
- `tetragamma`: Mathematical functions
- `trigamma`: Mathematical functions
- `TRUE`: Mathematical constants
- `unif_rand`: Random numbers
- `UNPROTECT`: Garbage Collection
- `UNPROTECT_PTR`: Garbage Collection
- `vmaxget`: Transient
- `vmaxset`: Transient
- `vmmin`: Optimization

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

## Concept index

- Allocating storage: Allocating storage
- Attributes: Attributes
- Bessel functions: Mathematical functions
- Beta function: Mathematical functions
- Building packages: Checking and building packages
- C++ code, interfacing: Interfacing C++ code
- Calling C from FORTRAN and vice versa: Calling C from FORTRAN and vice versa
- Checking packages: Checking and building packages
- Classes: Classes
- CRAN: Submitting a package to CRAN
- CRAN submission: Submitting a package to CRAN
- Creating packages: Creating R packages
- Creating shared libraries: Creating shared libraries
- Cross-references in documentation: Cross-references
- cumulative hazard: Distribution functions
- Debugging: Debugging
- DESCRIPTION file: The DESCRIPTION file
- Details of R types: Details of R types
- Distribution functions from C: Distribution functions
- Documentation, writing: Writing R help files
- Dynamic loading: dyn.load and dyn.unload
- Error handling from C: Error handling
- Error handling from FORTRAN: Error handling
- Evaluating R expressions from C: Evaluating R expressions from C
- Finding variables: Finding and setting variables
- Gamma function: Mathematical functions
- Garbage collection: Garbage Collection
- Generic functions: Generic functions and methods
- Handling lists: Handling lists
- Handling R objects in C: Handling R objects in C
- IEEE special values: Missing and IEEE values, Missing and special values
- Inspecting R objects when debugging: Inspecting R objects
- integration: Integration
- Interfaces to compiled code: Interface functions .C and .Fortran, Interface functions .Call and .External
- Interfacing C++ code: Interfacing C++ code
- Lists and tables in documentation: Lists and tables
- Marking text in documentation: Marking text
- Mathematics in documentation: Mathematics
- Memory allocation from C: Memory allocation
- Method functions: Generic functions and methods
- Missing values: Missing and IEEE values, Missing and special values
- Numerical analysis subroutines from C: Numerical analysis subroutines
- Numerical derivatives: Evaluating R expressions from C
- Operating system access: Operating system access
- optimization: Optimization
- Package builder: Checking and building packages
- Package bundles: Package bundles
- Package structure: Package structure
- Package subdirectories: Package subdirectories
- Packages: Creating R packages
- Platform-specific documentation: Platform-specific sections
- Printing from C: Printing
- Printing from FORTRAN: Printing
- Processing Rd format: Processing Rd format
- Profiling: Profiling R code
- Random numbers in C: Distribution functions, Random numbers
- Setting variables: Finding and setting variables
- Sort functions from C: Utility functions
- Submitting to CRAN: Submitting a package to CRAN
- Sweave: Writing package vignettes
- Tidying R code: Tidying R code
- Version information from C: Platform and version information
- vignettes: Writing package vignettes
- Zero-finding: Evaluating R expressions from C

#### Footnotes

1. Note that Ratfor is not supported. If you have Ratfor source code, you need to convert it to FORTRAN. On many Solaris systems mixing Ratfor and FORTRAN code will work.

2. This is not quite true. Unpaired braces will give problems and should be escaped. See the examples section in the file `Paren.Rd` for an example.

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

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