     [file R-defs.texi not included]
     [file version.texi not included]

This is a guide to extending R.

Copyright 1999, 2000 R Development Core Team

permission

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.

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.

Package structure
=================

A package consists of a subdirectory containing the files DESCRIPTION and INDEX, and the subdirectories R, data, exec, inst, man, src, and tests (some of which can be missing).  Optionally the package can also contain script files configure and cleanup which are executed before and after installation, see Configure and cleanup.

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>.
          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, Author, and Description fields are mandatory, the remaining fields (Date, Depends, Address, URL, ...) are optional.  The Author field should contain an email address in angle brackets (for sending bug reports etc.).

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.  This information has been contained in a separate TITLE file in older versions of R, please use the title field in the DESCRIPTION file from now on.

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 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 please do not misuse the Depends field for comments on other software that might be needed or not using proper syntax.  Other dependencies should be listed in the Description field or a separate README file.  The R INSTALL facility already checks if the running version of R is good enough for a package.

Other files
-----------

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 is available on your system.

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 documentation, for more information.

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

The C or FORTRAN source files for the compiled code, and optionally files Makevars and Makefile, are in src.  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).  If a package needs to specify additional directories for searching header files (-I options) or additional libraries for linking (-l and -L options), it should do this by setting the variables PKG_CPPFLAGS and PKG_LIBS in src/Makevars. (Additional flags to be passed to the C, C++, or Fortran compilers can be specified by the variables PKG_CFLAGS, PKG_CXXFLAGS, and PKG_FFLAGS, respectively.)  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.

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). 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.

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 shell or Perl scripts.  This mechanism is currently not used by any Unix package, and still experimental.

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.

Configure and cleanup
=====================

If your package needs some system-dependent configuration before installation you can include a 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 script cleanup is executed as last thing by R CMD INSTALL if present and can be used to clean up the package source tree, especially remove 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_OUTPUT(foo.R)

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 may wish to 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.

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 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.

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. It is tried to install the package.  This will warn about missing cross-references and duplicate aliases in help files.

 2. The DESCRIPTION file is checked for completeness.

 3. The Rd files are checked for \name, \alias and \keyword fields.

 4. A check is made for undocumented user-level objects in the package.

 5. The examples provided by the package's documentation are run (see Writing R documentation, for information on using \examples to create executable example code.)

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

 6. 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.)

 7. If a working latex program is available, the .dvi version of the package's manual is created (to check that the Rd files can be converted successfully).

Use R CMD check --help to obtain more information about the usage of the R package checker.  By adding flags, a subset of the checking steps can be selected.

Note that some packages will require more than the default workspace size to run their examples: this needs to be specified by setting the environment variables R_NSIZE and R_VSIZE or the command-line options --nsize and --vsize.

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.

Use R CMD build --help 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.

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 WWWadmin@ci.tuwien.ac.at about it.  The CRAN maintainers will run these tests before putting a submission in the main archive.

Writing R documentation
***********************

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 700 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/rle.Rd which documents the R function rle.

          \name{rle}
          \alias{rle}
          \title{Run Length Encoding}
          \description{
            Compute the lengths and values of runs of equal values in
            a vector.
          }
          \usage{
          rle(x)
          }
          \arguments{
            \item{x}{a (numerical, logical or character) vector.}
          }
          \value{
            A list with components
            \item{lengths}{a vector containing the length of each run.}
            \item{values}{a vector of the same length as \code{lengths}
              with the corresponding values.}
          }
          \examples{
          x <- rev(rep(6:10, 1:5))
          rle(x)
          ## $lengths
          ## [1] 5 4 3 2 1
          ## $values
          ## [1] 10  9  8  7  6
          z <- c(TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE,TRUE)
          rle(z)
          rle(as.character(z))
          }
          \keyword{manip}

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 ``Guidelines for Rd files'' (http://developer.r-project.org/Rds.html) 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{FILE}
     FILE is the basename of the file.

\alias{TOPIC}
     The \alias entries specify all ``topics'' the file documents. This information (together with the file name) 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 of the file 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)
          ...
          }

\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 make check.  You can use \dontrun{} for commands that should only be shown, but not run, and \testonly{} for extra commands for testing R 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 info).

\keyword{KEY}
     Each \keyword entry should specify one of the standard keywords (as listed in the file $R_HOME/doc/KEYWORDS).  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 R function prompt facilitates the construction of files documenting R objects.  If foo is an R function, then prompt(foo) produces file prompt.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 always is data(BAR).

  * The \keyword entry always is datasets.

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

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.

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.

Lists and tables
================

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

\itemize and \enumerate commands may be nested.

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

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

       \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).

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.

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 the Poisson help page:

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

For the HTML and the ``direct'' (man-like) on-line help we get

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

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

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 other (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.) 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.

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.

Processing Rd format
====================

Under UNIX versions of R there are several commands to process Rd files, if Perl is 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 Sl 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.  In the latter case, a reference manual for all documented objects in the package is created. Future versions will also add the information in the DESCRIPTION files to the output.

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 Sl version 3 documentation files (which use an extended Nroff format) to Rd format.  This is useful when porting a package originally written for the Sl system to R.

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).

System and foreign language interfaces
**************************************

Operating system access
=======================

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

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

Interface functions .C and .Fortran
===================================

These two functions provide a standard interface to compiled code that has been linked into R, either at build time or via dyn.load (see dyn.load and dyn.unload).  They are primarily intended for compiled C and Fortran 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=TRUE.

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

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

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

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

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

called from R by

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

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

dyn.load and dyn.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.

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.

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;
     }

     }

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 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.

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 Sl 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.  Details on .Call and .External are given further below.

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 Sl 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.

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

Handling the effects of garbage collection
------------------------------------------

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

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

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

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

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

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

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

       SEXP ab;
         ....
       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).

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.

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)
     LISTXP     ``dotted-pair'' list
     DOTSXP     a ... object
     NILSXP     NULL
     SYMSXP     name/symbol
     CLOSXP     function or function closure
     ENVSXP     environment

Among the important internal SEXPTYPEs are LANGSXP, CHARSXP etc.

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

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

     PROTECT(NEWSEXP = coerceVector(OLDSEXP, REALSXP));

or

     PROTECT(NEWSEXP = AS_NUMERIC(OLDSEXP));

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

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

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

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.

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.

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];

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 objects, in the specified environment frame and to perform the equivalent of assign(x, value, envir = rho, inherits = TRUE) respectively.

Changes in R version 1.2
------------------------

R version 1.2.0 introduces a new ``generational'' garbage collector which means that strings and vectors (and language objects) are handled differently from the numerical atomic types.

Earlier code was written in a style like

       VECTOR(dimnames)[0] = getAttrib(x, R_NamesSymbol);

but that is no longer allowed.  The functions VECTOR_ELT and SET_VECTOR_ELT must now be used to access and set elements of a generic vector.  There are analogous functions STRING_ELT and SET_STRING_ELT for character vectors.

To convert existing code, use the following replacements.

     Original   Replacement

     foo = VECTOR(bar)[i]   foo = VECTOR_ELT(bar, i)
     VECTOR(foo)[j] = bar   SET_VECTOR_ELT(foo, j, bar)
     foo = STRING(bar)[i]   foo = STRING_ELT(bar, i)
     STRING(foo)[j] = bar   SET_STRING_ELT(foo, j, bar)

The new garbage collector requires that all assignments into fields containing SEXP values go through functions such as SET_VECTOR_ELT.  Packages compiled under older versions of R may contain binary code that modifies these fields directly.  These packages must be recompiled and re-installed.  Failure to do so may confuse the memory manager and result in heap corruption.

In order to achieve maximal source compatibility in the R 1.x series, add-on packages should either use conditionals of the form

     #if R_VERSION >= R_Version(1, 2, 0)
       NEW-STYLE-CODE
     #else
       OLD-STYLE-CODE
     #endif

or, maybe preferably in order to avoid unnecessarily duplicating code, use the new-style interface along with a private header file containing the following code:

     #if R_VERSION < R_Version(1, 2, 0)
     # define STRING_ELT(x,i)        (STRING(x)[i])
     # define VECTOR_ELT(x,i)        (VECTOR(x)[i])
     # define SET_STRING_ELT(x,i,v)  (STRING(x)[i] = (v))
     # define SET_VECTOR_ELT(x,i,v)  (VECTOR(x)[i] = (v))
     #endif

More extensive changes may be needed in code which manipulates language objects.

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 Sl version 4, and .External is based on .Internal.  .External is more complex but allows a variable number of arguments.

Calling .Call
-------------

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

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

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

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

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

       PROTECT(a = AS_NUMERIC(a));
       PROTECT(b = AS_NUMERIC(b));
       na = LENGTH(a); nb = LENGTH(b); nab = na + nb - 1;
       PROTECT(ab = NEW_NUMERIC(nab));
       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 Sl 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.

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(...))

Missing and special values
--------------------------

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

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

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

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

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

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 in demos/dynload of call_R (based on that for call_S in Becker, Chambers & Wilks (1988)) on finding a zero of a univariate function.  The R code and an example are

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

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

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

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

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

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

         if(tol <= 0.0) error("non-positive tol value");
         f0 = feval(x0, f, rho); f1 = feval(x1, f, rho);
         if(f0 == 0.0) return mkans(x0);
         if(f1 == 0.0) return mkans(x1);
         if(f0*f1 > 0.0) error("x[0] and x[1] have the same sign");
         for(;;) {
             xc = 0.5*(x0+x1);
             if(fabs(x0-x1) < tol) return  mkans(xc);
             fc = feval(xc, f, rho);
             if(fc == 0) return  mkans(xc);
             if(f0*fc > 0.0) {
                 x0 = xc; f0 = fc;
             } else {
                 x1 = xc; f1 = fc;
             }
         }
     }

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

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.

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 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 0RET.

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.

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

The R API: entry points for C code
**********************************

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

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

     #include <R.h>

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

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

     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.

Memory allocation
=================

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

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 Sl 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 (the region whose size is set by --vsize), 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.

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 Sl do so.)

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 Sl-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 Sl versions.  Here ...... is a set of arguments to printf, so can be a string or a format string followed by arguments separated by commas.

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 Sl-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 R_ext/Mathlib.h; see Distribution functions.  Those calls generate a single variate and should also be enclosed in calls to GetRNGstate and PutRNGstate.

Missing and IEEE special values
===============================

It is possible to compile R on a platform without 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.

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.

These routines are declared in R_ext/PrtUtil.h which is not included by R.h.

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.

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

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 LINPACK and EISPACK linear algebra functions include 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 R_ext/Mathlib.h lists other undocumented calls that are currently available, mainly special functions (such gamma, beta and Bessel functions).  These are subject to change.

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 1 (or LTRUE) for normal use, but can be 0 (or LFALSE) if the probability of the upper tail is desired or specified.

Note that you directly get the cumulative (or ``integrated'') hazard function, H(t) = - \log(1 - F(t)), H(t) = - log(1 - F(t)), by using

     - pDIST(t, ..., /*lower_tail = */ LFALSE, /* give_log = */ LTRUE)

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

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.

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-square   chisq   df
     non-central chi-square   nchisq   df, lambda
     exponential   exp   scale
     F   f   n1, n2
     non-central F   {p,q}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   {p,q}nt   df, delta
     Studentized range   {p,q}tukey   rr, cc, df
     uniform   unif   a, b
     Weibull   weibull   shape, scale
     Wilcoxon rank sum   wilcox   m, n
     Wilcoxon signed rank   signrank   n

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

Mathematical Utilities
======================

There are a few other numerical utilities available as entry points.

 -- Function: double R_pow (double X, double Y)
 -- Function: double R_pow_di (double X, int I)
     R_pow(X, Y) and R_pow_di(X, I) compute X^Y and X^I, respectively using R_FINITE checks and returning the proper result (the same as R) for the cases where X, Y or I are 0 or missing or infinite or NaN.

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

 -- Function: double log1p (X)
     Computes log(1 + X) (log 1 plus x), accurately even for small X, i.e. |x| \ll 1, |x| << 1.

 -- Function: int imax2 (int X, int Y)
 -- Function: int imin2 (int X, int Y)
 -- Function: double fmax2 (double X, double Y)
 -- Function: double fmin2 (double X, double Y)
     Return the larger (max) or smaller (min) of two integer or double numbers, respectively.

 -- Function: double sign (double X)
     Compute the signum function, where sign(X) is 1, 0, or -1, when X is positive, 0, or negative, respectively.

 -- Function: double fsign (double X, double Y)
     Performs ``transfer of sign'' and is defined as |x| *
     \hboxsign(y), |x| * sign(x).

 -- Function: double fprec (double X, double DIGITS)
     Returns the value of X rounded to DIGITS decimal digits (after the decimal point).

     This is the function used by R's round().

 -- Function: double fround (double X, double DIGITS)
     Returns the value of X rounded to DIGITS significant decimal digits.

     This is the function used by R's signif().

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 R_ext/Mathlib.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_32        = sqrt(32)          5.6568542
     M_LOG10_2        = log10(2)          0.3010300
     M_SQRT_PI        = sqrt(pi)          1.7724539
     M_1_SQRT_2PI     = 1/sqrt(2pi)       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 (for example PI, DOUBLE_EPS) defined in the included header R_ext/Constants.h, mainly for compatibility with Sl.

Utility functions
=================

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

 -- Function: void R_isort (int* X, int N)
 -- Function: void R_rsort (double* X, int N)
 -- Function: void R_csort (Rcomplex* X, int N)
 -- Function: void rsort_with_index (double* X, int* INDEX, int N)
     The first three sort integer, real (double) and complex data respectively.  (Complex numbers are sorted by the real part first then the imaginary part.)

     rsort_with_index sorts on X, and applies the same permutation to INDEX.

 -- Function: void revsort (double* X, int* INDEX, int N)
     Is similar to rsort_with_index but sorts into decreasing order.

 -- Function: void iPsort (int* X, int N, int K)
 -- Function: void rPsort (double* X, int N, int K)
 -- Function: void cPsort (Rcomplex* X, int N, int K)
     These all provide (very) partial sorting: they permute X so that X[K] is in the correct place with smaller values to the left, larger ones to the right.

 -- Function: void R_max_col (double* MATRIX, int* NR, int* NC, int* MAXES)
     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).

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 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).

Using these functions in your own C code
========================================

It is possible to build Mathlib, the R set of mathematical functions documented in R_ext/Mathlib.h, as a standalone library libRmath under Unix and Windows.  This includes the functions documented in Distribution functions and Mathematical Utilities and the constants in Mathematical Constants.

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 "R_ext/Mathlib.h"

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

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

     double unif_rand(void)

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

     set_seed(unsigned int, unsigned int)

to set its seeds).

R (internal) programming miscellania
************************************

.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.

These ``primitive'' functions are fully specified as follows.

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

          {       (         if       for  while  repeat  break  next
          return  function  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:

      a. Basic mathematical functions with a single argument, i.e.,

               sign    abs
               floor   ceiling
               ----------------------
               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).

      b. 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).

      c. The programming and session management utilities

               debug    undebug    trace  untrace
               browser  proc.time

 4. The following basic assignment and extractor functions

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

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

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

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/make-no-segfault.R.

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 eventually (again) 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 ANSI C compiler.  If you do not have access to the ANSI standard, refer to the 2nd edition of Brian W. Kernighan & Dennis M. Ritchie, The C Programming Language.  Any extensions, such as POSIX, 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 cat, cp, cut, diff, echo, egrep, expr, find,gawk, grep, ls, mkdir, mv, rm, sed and sort) are provided at CRAN.  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.

     ;;; 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.)