R FAQ

Frequently Asked Questions on R

Version 1.4-0, 2001-12-17

Kurt Hornik

R FAQ
*****

Introduction
************

This document contains answers to some of the most frequently asked questions about R.

Legalese
========

This document is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2, or (at your option) any later version.

This document is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.

A copy of the GNU General Public License is available via WWW at

     http://www.gnu.org/copyleft/gpl.html.

You can also obtain it by writing to the Free Software Foundation, Inc., 59 Temple Place --- Suite 330, Boston, MA 02111-1307, USA.

Obtaining this document
=======================

The latest version of this document is always available from

     http://www.ci.tuwien.ac.at/~hornik/R/

From there, you can obtain versions converted to plain ASCII text, DVI, GNU info, HTML, PDF, PostScript as well as the Texinfo source used for creating all these formats using the GNU Texinfo system (http://texinfo.org/).

You can also obtain the R FAQ from the doc/FAQ subdirectory of a CRAN site (see What is CRAN?).

Citing this document
====================

In publications, please refer to this FAQ as Hornik (2001), ``The R FAQ'' and give the above, official URL.

Notation
========

Everything should be pretty standard.  R> is used for the R prompt, and a $ for the shell prompt (where applicable).

Feedback
========

Feedback is of course most welcome.

In particular, note that I do not have access to Windows or Mac systems. Features specific to the Windows and Mac OS ports of R are described in the ``R for Windows FAQ'' (http://www.stats.ox.ac.uk/pub/R/rw-FAQ.html) and the ``R for Macintosh FAQ/DOC'' (http://www.eco-dip.unimi.it/R/rmac-FAQ.html).  If you have information on Mac or Windows systems that you think should be added to this document, please let me know.

R Basics
********

What is R?
==========

R is a system for statistical computation and graphics.  It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files.

The design of R has been heavily influenced by two existing languages: Becker, Chambers & Wilks' S (see What is S?) and Sussman's Scheme (http://www.cs.indiana.edu/scheme-repository/home.html). Whereas the resulting language is very similar in appearance to S, the underlying implementation and semantics are derived from Scheme. See What are the differences between R and S?, for further details.

The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions.  Most of the user-visible functions in R are written in R.  It is possible for the user to interface to procedures written in the C, C++, or FORTRAN languages for efficiency.  The R distribution contains functionality for a large number of statistical procedures.  Among these are: linear and generalized linear models, nonlinear regression models, time series analysis, classical parametric and nonparametric tests, clustering and smoothing.  There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations.  Additional modules (``add-on packages'') are available for a variety of specific purposes (see R Add-On Packages).

R was initially written by Ross Ihaka and Robert Gentleman at the Department of Statistics of the University of Auckland in Auckland, New Zealand.  In addition, a large group of individuals has contributed to R by sending code and bug reports.

Since mid-1997 there has been a core group (the ``R Core Team'') who can modify the R source code CVS archive.  The group currently consists of Doug Bates, John Chambers, Peter Dalgaard, Robert Gentleman, Kurt Hornik, Stefano Iacus, Ross Ihaka, Friedrich Leisch, Thomas Lumley, Martin Maechler, Guido Masarotto, Paul Murrell, Brian Ripley, Duncan Temple Lang, and Luke Tierney.

R has a home page at http://www.r-project.org/.  It is free software distributed under a GNU-style copyleft, and an official part of the GNU project (``GNU S'').

What machines does R run on?
============================

R is being developed for the Unix, Windows and Mac families of operating systems.

The current version of R will configure and build under a number of common Unix platforms including i386-freebsd, i386-linux-gnu, i386-sun-solaris, powerpc-linux-gnu, powerpc-apple-darwin, mips-sgi-irix, alpha-linux-gnu, alpha-dec-osf4, rs6000-ibm-aix, hppa-hp-hpux, sparc-linux-gnu, and sparc-sun-solaris.

If you know about other platforms, please drop us a note.

What is the current version of R?
=================================

The current released version is 1.4.0.  Conforming to this `major.minor.patchlevel' numbering scheme, there are three development versions of R, working towards the next patch (`r-patched'), minor (`r-devel'), and major (`r-ng') releases of R, respectively.  Version r-patched is for bug fixes mostly.  New features are typically introduced in r-devel.  Version r-ng will eventually become the next generation of R.

How can R be obtained?
======================

Sources, binaries and documentation for R can be obtained via CRAN, the ``Comprehensive R Archive Network'' (see What is CRAN?).

Sources are also available via anonymous rsync.  Use

     rsync -rC rsync.r-project.org::MODULE R

to create a copy of the source tree specified by MODULE in the subdirectory R of the current directory, where MODULE specifies one of the four existing flavors of the R sources, and can be one of r-release (current released version), r-patched (patched released version), and r-devel (development version, less stable), and r-ng (next generation, unstable).  The rsync trees are created directly from the master CVS archive and are updated hourly.  The -C option in the rsync command is to cause it to skip the CVS directories.  Further information on rsync is available at http://rsync.samba.org/rsync/.

How can R be installed?
=======================

How can R be installed (Unix)
-----------------------------

If binaries are available for your platform (see Are there Unix binaries for R?), you can use these, following the instructions that come with them.

Otherwise, you can compile and install R yourself, which can be done very easily under a number of common Unix platforms (see What machines does R run on?).  The file INSTALL that comes with the R distribution contains a brief introduction, and the ``R Installation and Administration'' guide (see What documentation exists for R?) has full details.

Note that you need a FORTRAN compiler or f2c in addition to a C compiler to build R.  Also, you need Perl version 5 to build the R object documentations.  (If this is not available on your system, you can obtain a PDF version of the object reference manual via CRAN.)

In the simplest case, untar the R source code, change to the directory thus created, and issue the following commands (at the shell prompt):

     $ ./configure
     $ make

If these commands execute successfully, the R binary and a shell script font-end called R are created and copied to the bin directory.  You can copy the script to a place where users can invoke it, for example to /usr/local/bin.  In addition, plain text help pages as well as HTML and LaTeX versions of the documentation are built.

Use make dvi to create DVI versions of the R manuals, such as refman.dvi (an R object reference index) and R-exts.dvi, the ``R Extension Writers Guide'', in the doc/manual subdirectory.  These files can be previewed and printed using standard programs such as xdvi and dvips.  You can also use make pdf to build PDF (Portable Document Format) version of the manuals, and view these using e.g. Acrobat.  Manuals written in the GNU Texinfo system can also be converted to info files suitable for reading online with Emacs or stand-alone GNU Info; use make info to create these versions (note that this requires makeinfo version 4).

Finally, use make check to find out whether your R system works correctly.

You can also perform a ``system-wide'' installation using make install.  By default, this will install to the following directories:

${prefix}/bin
     the front-end shell script

${prefix}/man/man1
     the man page

${prefix}/lib/R
     all the rest (libraries, on-line help system, ...).  This is the ``R Home Directory'' (R_HOME) of the installed system.

In the above, prefix is determined during configuration (typically /usr/local) and can be set by running configure with the option

     $ ./configure --prefix=/where/you/want/R/to/go

(E.g., the R executable will then be installed into /where/you/want/R/to/go/bin.)

To install DVI, info and PDF versions of the manuals, use make install-dvi, make install-info and make install-pdf, respectively.

How can R be installed (Windows)
--------------------------------

The bin/windows directory of a CRAN site contains binaries for a base distribution and a large number of add-on packages from CRAN to run on Windows 95, 98, NT4, 2000 and ME (at least) on Intel and clones (but not on other platforms).  The Windows version of R was created by Robert Gentleman, and is now being developed and maintained by Guido Masarotto and Brian D. Ripley.

For most installations the installer SetupR.exe will be the easiest tool to use.

See the ``R for Windows FAQ'' (http://www.stats.ox.ac.uk/pub/R/rw-FAQ.html) for more details.

How can R be installed (Macintosh)
----------------------------------

The bin/macos directory of a CRAN site contains bin-hexed (hqx) and stuffit (sit) archives for a base distribution and a large number of add-on packages to run under MacOS 8.6 to MacOS 9.1 or MacOS X natively.  The Mac version of R and the Mac binaries are maintained by Stefano Iacus.

The ``R for Macintosh FAQ/DOC'' (http://www.eco-dip.unimi.it/R/rmac-FAQ.html) has more details.

Binaries of base distributions for MacOS X (Darwin) with X11 are made available by Jan de Leeuw in the bin/macosx directory of a CRAN site.

Are there Unix binaries for R?
==============================

The bin/linux directory of a CRAN site contains Debian potato/woody packages for the i386 platform (now part of the Debian distribution and maintained by Dirk Eddelbuettel), Red Hat 6.x i386, 7.x i386 and 7.x alpha packages (maintained by Stephen Eglen, Martyn Plummer and Naoki Takebayashi, respectively), SuSE 7.2 i386 packages by Albrecht Gebhardt, Mandrake 8.1 and 7.2 i386 packages by Michele Alzetta and Maximino Ameneiro Gomez, and LinuxPPC 5.0 RPMs by Alex Buerkle.

The Debian packages can be accessed through APT, the Debian package maintenance tool.  Simply add the line

     deb http://cran.r-project.org/bin/linux/debian DISTRIBUTION main

(where DISTRIBUTION is either stable or unstable; feel free to use a CRAN mirror instead of the master) to the file /etc/apt/sources.list.  Once you have added that line the programs apt-get, apt-cache, and dselect (using the apt access method) will automatically detect and install updates of the R packages.

The bin/osf directory of a CRAN site contains RPMs by Albrecht Gebhardt for alpha systems running Alpha Unix (OSF/Tru64).

No other binary distributions have thus far been made publically available.

What documentation exists for R?
================================

Online documentation for most of the functions and variables in R exists, and can be printed on-screen by typing help(NAME) (or ?NAME) at the R prompt, where NAME is the name of the topic help is sought for.  (In the case of unary and binary operators and control-flow special forms, the name may need to be be quoted.)

This documentation can also be made available as one reference manual for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX, see How can R be installed?.  An up-to-date HTML version is always available for web browsing at http://stat.ethz.ch/R/manual/.

The R distribution also comes with the following manuals.

  * ``An Introduction to R'' (R-intro) includes information on data types, programming elements, statistical modeling and graphics.  This document is based on the ``Notes on S-PLUS'' by Bill Venables and David Smith.

  * ``Writing R Extensions'' (R-exts) currently describes the process of creating R add-on packages, writing R documentation, R's system and foreign language interfaces, and the R API.

  * ``R Data Import/Export'' (R-data) is a guide to importing and exporting data to and from R.

  * ``The R Language Definition'' (R-lang), a first version of the ``Kernighan & Ritchie of R'', explains evaluation, parsing, object oriented programming, computing on the language, and so forth.

  * ``R Installation and Administration'' (R-admin).

In addition to material written specifically for R, documentation for S/S-PLUS (see R and S) can be used in combination with this FAQ (see What are the differences between R and S?).  We recommend

     W. N. Venables and B. D. Ripley (1999), ``Modern Applied Statistics with S-PLUS.  Third Edition''.  Springer, ISBN 0-387-98825-4.

This has a home page at http://www.stats.ox.ac.uk/pub/MASS3/ providing additional material, in particular ``R Complements'' which describe how to use the book with R.  These complements contain both descriptions of some of the differences between R and S-PLUS, and the modifications needed to run the examples in the book.  Its companion is

     W. N. Venables and B. D. Ripley (2000), ``S Programming''.  Springer, ISBN 0-387-98966-8.

This provides an in-depth guide to writing software in the S language which forms the basis of both the commercial S-PLUS and the Open Source R data analysis software systems.  See http://www.stats.ox.ac.uk/pub/MASS3/Sprog/ for more information.

More introductory books are

     P. Spector (1994), ``An introduction to S and S-PLUS'', Duxbury Press.

     A. Krause and M. Olsen (1997), ``The Basics of S and S-PLUS'', Springer.

The book

     J. C. Pinheiro and D. M. Bates (2000), ``Mixed-Effects Models in S and S-PLUS'', Springer, ISBN 0-387-98957-0

provides a comprehensive guide to the use of the nlme package for linear and nonlinear mixed-effects models.  This has a home page at http://nlme.stat.wisc.edu/MEMSS/.

As an example of how R can be used in teaching an advanced introductory statistics course, see

     D. Nolan and T. Speed (2000), ``Stat Labs: Mathematical Statistics Through Applications'', Springer Texts in Statistics, ISBN 0-387-98974-9

This integrates theory of statistics with the practice of statistics through a collection of case studies (``labs''), and uses R to analyze the data.  More information can be found at http://www.stat.Berkeley.EDU/users/statlabs/.

Last, but not least, Ross' and Robert's experience in designing and implementing R is described in Ihaka & Gentleman (1996), ``R: A Language for Data Analysis and Graphics'', Journal of Computational and Graphical Statistics, 5, 299--314. See Citing R.

An annotated bibliography (BibTeX format) of R-related publications which includes most of the above references can be found at

     http://www.r-project.org/doc/bib/R.bib

Citing R
========

To cite R in publications, use

     @article{,
       author =    {Ross Ihaka and Robert Gentleman},
       title =     {R: A Language for Data Analysis and Graphics},
       journal =   {Journal of Computational and Graphical Statistics},
       year =      1996,
       volume =    5,
       number =    3,
       pages =     {299--314}
     }

What mailing lists exist for R?
===============================

Thanks to Martin Maechler, there are three mailing lists devoted to R.

r-announce
     This list is for announcements about the development of R and the availability of new code.

r-devel
     This list is for discussions about the future of R and pre-testing of new versions.  It is meant for those who maintain an active position in the development of R.

r-help
     The `main' R mailing list, for announcements about the development of R and the availability of new code, questions and answers about problems and solutions using R, enhancements and patches to the source code and documentation of R, comparison and compatibility with S and S-PLUS, and for the posting of nice examples and benchmarks.

Note that the r-announce list is gatewayed into r-help, so you don't need to subscribe to both of them.

Send email to r-help@lists.r-project.org to reach everyone on the r-help mailing list.  To subscribe (or unsubscribe) to this list send subscribe (or unsubscribe) in the body of the message (not in the subject!) to r-help-request@lists.r-project.org.  Information about the list can be obtained by sending an email with info as its contents to r-help-request@lists.r-project.org.

Subscription and posting to the other lists is done analogously, with `r-help' replaced by `r-announce' and `r-devel', respectively.

Subscriptions to `r-help' and `r-devel' are also available in digest format, see the doc/html/mail.html file in CRAN for more information.

It is recommended that you send mail to r-help rather than only to the R Core developers (who are also subscribed to the list, of course).  This may save them precious time they can use for constantly improving R, and will typically also result in much quicker feedback for yourself.

Of course, in the case of bug reports it would be very helpful to have code which reliably reproduces the problem.  Also, make sure that you include information on the system and version of R being used.  See R Bugs for more details.

Archives of the above three mailing lists are made available on the net in a monthly schedule via the doc/html/mail.html file in CRAN.  Searchable archives of the lists are available via http://maths.newcastle.edu.au/~rking/R/.

The R Core Team can be reached at r-core@lists.r-project.org for comments and reports.

What is CRAN?
=============

The ``Comprehensive R Archive Network'' (CRAN) is a collection of sites which carry identical material, consisting of the R distribution(s), the contributed extensions, documentation for R, and binaries.

The CRAN master site at TU Wien, Austria, can be found at the URL

     http://cran.r-project.org/

and is currently being mirrored daily at

     http://cran.at.r-project.org/
     (TU Wien, Austria)
     http://cran.au.r-project.org/
     (PlanetMirror, Australia)
     http://cran.ch.r-project.org/
     (ETH Zürich, Switzerland)
     http://cran.de.r-project.org/
     (APP, Germany)
     http://cran.dk.r-project.org/
     (SunSITE, Denmark)
     http://cran.hu.r-project.org/
     (Semmelweis U, Hungary)
     http://cran.uk.r-project.org/
     (U of Bristol, United Kingdom)
     http://cran.us.r-project.org/
     (U of Wisconsin, USA)
     http://cran.za.r-project.org/
     (Rhodes U, South Africa)

Please use the CRAN site closest to you to reduce network load.

From CRAN, you can obtain the latest official release of R, daily snapshots of R (copies of the current CVS trees), as gzipped and bzipped tar files, a wealth of additional contributed code, as well as prebuilt binaries for various operating systems (Linux, Digital Unix, and MS Windows).  CRAN also provides access to documentation on R, existing mailing lists and the R Bug Tracking system.

To ``submit'' to CRAN, simply upload to ftp://cran.r-project.org/incoming/ and send an email to wwwadmin@cran.r-project.org.

     Note:  It is very important that you indicate the copyright (license) information (GPL, BSD, Artistic, ...) in your submission.

Please always use the URL of the master site when referring to CRAN.

Can I use R for commercial purposes?
====================================

R is released under the GNU General Public License (GPL).  If you have any questions regarding the legality of using R in any particular situation you should bring it up with your legal counsel.  We are in no position to offer legal advice.

It is the opinion of the R Core Team that one can use R for commercial purposes (e.g., in business or in consulting).  The GPL, like all Open Source licenses, permits all and any use of the package.  It only restricts distribution of R or of other programs containing code from R. This is made clear in clause 6 (``No Discrimination Against Fields of Endeavor'') of the Open Source Definition (http://www.opensource.org/docs/definition.html):

     The license must not restrict anyone from making use of the program in a specific field of endeavor.  For example, it may not restrict the program from being used in a business, or from being used for genetic research.

It is also explicitly stated in clause 0 of the GPL, which says in part

     Activities other than copying, distribution and modification are not covered by this License; they are outside its scope.  The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program.

Most add-on packages, including all recommended ones, also explicitly allow commercial use in this way.  A few packages are restricted to ``non-commercial use''; you should contact the author to clarify whether these may be used or seek the advice of your legal counsel.

None of the discussion in this section constitutes legal advice.  The R Core Team does not provide legal advice under any circumstances.

R and S
*******

What is S?
==========

S is a very high level language and an environment for data analysis and graphics.  In 1998, the Association for Computing Machinery (ACM) presented its Software System Award to John M. Chambers, the principal designer of S, for

     the S system, which has forever altered the way people analyze, visualize, and manipulate data ...

     S is an elegant, widely accepted, and enduring software system, with conceptual integrity, thanks to the insight, taste, and effort of John Chambers.

The evolution of the S language is characterized by four books by John Chambers and coauthors, which are also the primary references for S.

  * Richard A. Becker and John M. Chambers (1984), ``S.  An Interactive Environment for Data Analysis and Graphics,'' Monterey: Wadsworth and Brooks/Cole.

     This is also referred to as the ``Brown Book'', and of historical interest only.

  * Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), ``The New S Language,'' London: Chapman & Hall.

     This book is often called the ``Blue Book'', and introduced what is now known as S version 2.

  * John M. Chambers and Trevor J. Hastie (1992), ``Statistical Models in S,''  London: Chapman & Hall.

     This is also called the ``White Book'', and introduced S version 3, which added structures to facilitate statistical modeling in S.

  * John M. Chambers (1998), ``Programming with Data,'' New York: Springer, ISBN 0-387-98503-4 (http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/).

     This ``Green Book'' describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process.

See http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html for further information on ``Stages in the Evolution of S''.

There is a huge amount of user-contributed code for S, available at the S Repository (http://lib.stat.cmu.edu/S/) at CMU.

What is S-PLUS?
===============

S-PLUS is a value-added version of S sold by Insightful Corporation. Based on the S language, S-PLUS provides functionality in a wide variety of areas, including robust regression, modern non-parametric regression, time series, survival analysis, multivariate analysis, classical statistical tests, quality control, and graphics drivers. Add-on modules add additional capabilities for wavelet analysis, spatial statistics, GARCH models, and design of experiments.

See the Insightful S-PLUS page (http://www.insightful.com/products/splus/) for further information.

What are the differences between R and S?
=========================================

We can regard S as a language with three current implementations or ``engines'', the ``old S engine'' (S version 3; S-PLUS 3.x and 4.x), the ``new S engine'' (S version 4; S-PLUS 5.x and above), and R. Given this understanding, asking for ``the differences between R and S'' really amounts to asking for the specifics of the R implementation of the S language, i.e., the difference between the R and S engines.

For the remainder of this section, ``S'' refers to the S engines and not the S language.

Lexical scoping
---------------

Contrary to other implementations of the S language, R has adopted the evaluation model of Scheme.

This difference becomes manifest when free variables occur in a function.  Free variables are those which are neither formal parameters (occurring in the argument list of the function) nor local variables (created by assigning to them in the body of the function).  Whereas S (like C) by default uses static scoping, R (like Scheme) has adopted lexical scoping.  This means the values of free variables are determined by a set of global variables in S, but in R by the bindings that were in effect at the time the function was created.

Consider the following function:

     cube <- function(n) {
       sq <- function() n * n
       n * sq()
     }

Under S, sq() does not ``know'' about the variable n unless it is defined globally:

     S> cube(2)
     Error in sq():  Object "n" not found
     Dumped
     S> n <- 3
     S> cube(2)
     [1] 18

In R, the ``environment'' created when cube() was invoked is also looked in:

     R> cube(2)
     [1] 8

As a more ``interesting'' real-world problem, suppose you want to write a function which returns the density function of the r-th order statistic from a sample of size n from a (continuous) distribution.  For simplicity, we shall use both the cdf and pdf of the distribution as explicit arguments.  (Example compiled from various postings by Luke Tierney.)

The S-PLUS documentation for call() basically suggests the following:

     dorder <- function(n, r, pfun, dfun) {
       f <- function(x) NULL
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       PF <- call(substitute(pfun), as.name("x"))
       DF <- call(substitute(dfun), as.name("x"))
       f[[length(f)]] <-
         call("*", con,
              call("*", call("^", PF, r - 1),
                   call("*", call("^", call("-", 1, PF), n - r),
                        DF)))
       f
     }

Rather tricky, isn't it?  The code uses the fact that in S, functions are just lists of special mode with the function body as the last argument, and hence does not work in R (one could make the idea work, though).

A version which makes heavy use of substitute() and seems to work under both S and R is

     dorder <- function(n, r, pfun, dfun) {
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       eval(substitute(function(x) K * PF(x)^a * (1 - PF(x))^b * DF(x),
                       list(PF = substitute(pfun), DF = substitute(dfun),
                            a = r - 1, b = n - r, K = con)))
     }

(the eval() is not needed in S).

However, in R there is a much easier solution:

     dorder <- function(n, r, pfun, dfun) {
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       function(x) {
         con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
       }
     }

This seems to be the ``natural'' implementation, and it works because the free variables in the returned function can be looked up in the defining environment (this is lexical scope).

Note that what you really need is the function closure, i.e., the body along with all variable bindings needed for evaluating it.  Since in the above version, the free variables in the value function are not modified, you can actually use it in S as well if you abstract out the closure operation into a function MC() (for ``make closure''):

     dorder <- function(n, r, pfun, dfun) {
       con <- round(exp(lgamma(n + 1) - lgamma(r) - lgamma(n - r + 1)))
       MC(function(x) {
            con * pfun(x)^(r - 1) * (1 - pfun(x))^(n - r) * dfun(x)
          },
          list(con = con, pfun = pfun, dfun = dfun, r = r, n = n))
     }

Given the appropriate definitions of the closure operator, this works in both R and S, and is much ``cleaner'' than a substitute/eval solution (or one which overrules the default scoping rules by using explicit access to evaluation frames, as is of course possible in both R and S).

For R, MC() simply is

     MC <- function(f, env) f

(lexical scope!), a version for S is

     MC <- function(f, env = NULL) {
       env <- as.list(env)
       if (mode(f) != "function")
         stop(paste("not a function:", f))
       if (length(env) > 0 && any(names(env) == ""))
         stop(paste("not all arguments are named:", env))
       fargs <- if(length(f) > 1) f[1:(length(f) - 1)] else NULL
       fargs <- c(fargs, env)
       if (any(duplicated(names(fargs))))
         stop(paste("duplicated arguments:", paste(names(fargs)),
              collapse = ", "))
       fbody <- f[length(f)]
       cf <- c(fargs, fbody)
       mode(cf) <- "function"
       return(cf)
     }

Similarly, most optimization (or zero-finding) routines need some arguments to be optimized over and have other parameters that depend on the data but are fixed with respect to optimization.  With R scoping rules, this is a trivial problem; simply make up the function with the required definitions in the same environment and scoping takes care of it.  With S, one solution is to add an extra parameter to the function and to the optimizer to pass in these extras, which however can only work if the optimizer supports this.

Lexical scoping allows using function closures and maintaining local state.  A simple example (taken from Abelson and Sussman) is obtained by typing demo(scoping) at the R prompt.  Further information is provided in the standard R reference ``R: A Language for Data Analysis and Graphics'' (see What documentation exists for R?) and in Robert Gentleman and Ross Ihaka (2000), ``Lexical Scope and Statistical Computing'', Journal of Computational and Graphical Statistics, 9, 491--508.

Lexical scoping also implies a further major difference.  Whereas S stores all objects as separate files in a directory somewhere (usually .Data under the current directory), R does not.  All objects in R are stored internally.  When R is started up it grabs a very large piece of memory and uses it to store the objects.  R performs its own memory management of this piece of memory.  Having everything in memory is necessary because it is not really possible to externally maintain all relevant ``environments'' of symbol/value pairs.  This difference also seems to make R faster than S.

The down side is that if R crashes you will lose all the work for the current session.  Saving and restoring the memory ``images'' (the functions and data stored in R's internal memory at any time) can be a bit slow, especially if they are big.  In S this does not happen, because everything is saved in disk files and if you crash nothing is likely to happen to them.  (In fact, one might conjecture that the S developers felt that the price of changing their approach to persistent storage just to accommodate lexical scope was far too expensive.) Hence, when doing important work, you might consider saving often (see How can I save my workspace?) to safeguard against possible crashes.  Other possibilities are logging your sessions, or have your R commands stored in text files which can be read in using source().

     Note:  If you run R from within Emacs (see R and Emacs), you can save the contents of the interaction buffer to a file and conveniently manipulate it using ess-transcript-mode, as well as save source copies of all functions and data used.

Models
------

There are some differences in the modeling code, such as

  * Whereas in S, you would use lm(y ~ x^3) to regress y on x^3, in R, you have to insulate powers of numeric vectors (using I()), i.e., you have to use lm(y ~ I(x^3)).

  * The glm family objects are implemented differently in R and S.  The same functionality is available but the components have different names.

  * Option na.action is set to "na.omit" by default in R, but not set in S.

  * Terms objects are stored differently.  In S a terms object is an expression with attributes, in R it is a formula with attributes.  The attributes have the same names but are mostly stored differently.  The major difference in functionality is that a terms object is subscriptable in S but not in R.  If you can't imagine why this would matter then you don't need to know.

  * Finally, in R y~x+0 is an alternative to y~x-1 for specifying a model with no intercept.  Models with no parameters at all can be specified by y~0.

Others
------

Apart from lexical scoping and its implications, R follows the S language definition in the Blue and White Books as much as possible, and hence really is an ``implementation'' of S.  There are some intentional differences where the behavior of S is considered ``not clean''.  In general, the rationale is that R should help you detect programming errors, while at the same time being as compatible as possible with S.

Some known differences are the following.

  * In R, if x is a list, then x[i] <- NULL and x[[i]] <- NULL remove the specified elements from x.  The first of these is incompatible with S, where it is a no-op.  (Note that you can set elements to NULL using x[i] <- list(NULL).)

  * In S, the functions named .First and .Last in the .Data directory can be used for customizing, as they are executed at the very beginning and end of a session, respectively.

     In R, the startup mechanism is as follows.  R first sources the system startup file $R_HOME/library/base/R/Rprofile.  Then, it searches for a site-wide startup profile unless the command line option --no-site-file was given.  The name of this file is taken from the value of the R_PROFILE environment variable.  If that variable is unset, the default is $R_HOME/etc/Rprofile.site ($R_HOME/etc/Rprofile in versions prior to 1.4.0).  This code is loaded in package base.  Then, unless --no-init-file was given, R searches for a file called .Rprofile in the current directory or in the user's home directory (in that order) and sources it into the user workspace.  It then loads a saved image of the user workspace from .RData in case there is one (unless --no-restore was specified).  If needed, the functions .First() and .Last() should be defined in the appropriate startup profiles.

  * In R, T and F are just variables being set to TRUE and FALSE, respectively, but are not reserved words as in S and hence can be overwritten by the user.  (This helps e.g. when you have factors with levels "T" or "F".)  Hence, when writing code you should always use TRUE and FALSE.

  * In R, dyn.load() can only load shared libraries, as created for example by R CMD SHLIB.

  * In R, attach() currently only works for lists and data frames, but not for directories.  (In fact, attach() also works for R data files created with save(), which is analogous to attaching directories in S.)  Also, you cannot attach at position 1.

  * Categories do not exist in R, and never will as they are deprecated now in S.  Use factors instead.

  * In R, For() loops are not necessary and hence not supported.

  * In R, assign() uses the argument envir= rather than where= as in S.

  * The random number generators are different, and the seeds have different length.

  * R passes integer objects to C as int * rather than long * as in S.

  * R has no single precision storage mode.  However, as of version 0.65.1, there is a single precision interface to C/FORTRAN subroutines.

  * By default, ls() returns the names of the objects in the current (under R) and global (under S) environment, respectively.  For example, given

          x <- 1; fun <- function() {y <- 1; ls()}

     then fun() returns "y" in R and "x" (together with the rest of the global environment) in S.

  * R allows for zero-extent matrices (and arrays, i.e., some elements of the dim attribute vector can be 0).  This has been determined a useful feature as it helps reducing the need for special-case tests for empty subsets.  For example, if x is a matrix, x[, FALSE] is not NULL but a ``matrix'' with 0 columns.  Hence, such objects need to be tested for by checking whether their length() is zero (which works in both R and S), and not using is.null().

  * Named vectors are considered vectors in R but not in S (e.g., is.vector(c(a = 1:3)) returns FALSE in S and TRUE in R).

  * Data frames are not considered as matrices in R (i.e., if DF is a data frame, then is.matrix(DF) returns FALSE in R and TRUE in S).

  * R by default uses treatment contrasts in the unordered case, whereas S uses the Helmert ones.  This is a deliberate difference reflecting the opinion that treatment contrasts are more natural.

  * In R, the last argument (which corresponds to the right hand side) of an assignment function must be named value.  E.g., fun(a) <- b is evaluated as (fun<-)(a, value = b).

  * In S, substitute() searches for names for substitution in the given expression in three places: the actual and the default arguments of the matching call, and the local frame (in that order).  R looks in the local frame only, with the special rule to use a ``promise'' if a variable is not evaluated.  Since the local frame is initialized with the actual arguments or the default expressions, this is usually equivalent to S, until assignment takes place.

  * In R, eval(EXPR, sys.parent()) does not work.  Instead, one should use either eval(EXPR, sys.frame(sys.parent())), which also works in S, or eval(EXPR, parent.frame()), which is more efficient but does not work in S.

  * In S, the index variable in a for() loop is local to the inside of the loop.  In R it is local to the environment where the for() statement is executed.

  * In S, tapply(simplify=TRUE) returns a vector where R returns a one-dimensional array (which can have named dimnames).

  * In S(-PLUS) the C locale is used, whereas in R the current operating system locale is used for determining which characters are alphanumeric and how they are sorted.  This affects the set of valid names for R objects (for example accented chars may be allowed in R) and ordering in sorts and comparisons (such as whether "aA" < "Bb" is true or false).  From version 1.2.0 the locale can be (re-)set in R by the Sys.setlocale() function.

  * In S, missing(ARG) remains TRUE if ARG is subsequently modified; in R it doesn't.

  * From R version 1.3.0, data.frame strips I() when creating (column) names.

There are also differences which are not intentional, and result from missing or incorrect code in R.  The developers would appreciate hearing about any deficiencies you may find (in a written report fully documenting the difference as you see it).  Of course, it would be useful if you were to implement the change yourself and make sure it works.

Is there anything R can do that S-PLUS cannot?
==============================================

Since almost anything you can do in R has source code that you could port to S-PLUS with little effort there will never be much you can do in R that you couldn't do in S-PLUS if you wanted to.  (Note that using lexical scoping may simplify matters considerably, though.)

R offers several graphics features that S-PLUS does not, such as finer handling of line types, more convenient color handling (via palettes), gamma correction for color, and, most importantly, mathematical annotation in plot texts, via input expressions reminiscent of TeX constructs.  See the help page for plotmath, which features an impressive on-line example.  More details can be found in Paul Murrell and Ross Ihaka (2000), ``An Approach to Providing Mathematical Annotation in Plots'', Journal of Computational and Graphical Statistics, 9, 582--599.

What is R-plus?
===============

There is no such thing.

R Web Interfaces
****************

Rcgi is a CGI WWW interface to R by Mark J. Ray.  Recent versions have the ability to use ``embedded code'': you can mix user input and code, allowing the HTML author to do anything from load in data sets to enter most of the commands for users without writing CGI scripts. Graphical output is possible in PostScript or GIF formats and the executed code is presented to the user for revision.

Demo and download are available from http://www.mth.uea.ac.uk/~h089/Rcgi/.

Rweb is developed and maintained by Jeff Banfield.  The Rweb Home Page (http://www.math.montana.edu/Rweb/) provides access to all three versions of Rweb---a simple text entry form that returns output and graphs, a more sophisticated Javascript version that provides a multiple window environment, and a set of point and click modules that are useful for introductory statistics courses and require no knowledge of the R language.  All of the Rweb versions can analyze Web accessible datasets if a URL is provided.

The paper ``Rweb: Web-based Statistical Analysis'', providing a detailed explanation of the different versions of Rweb and an overview of how Rweb works, was published in the Journal of Statistical Software (http://www.stat.ucla.edu/journals/jss/v04/i01/).

R Add-On Packages
*****************

Which add-on packages exist for R?
==================================

The R distribution comes with the following extra packages:

ctest
     A collection of Classical TESTs, including the Ansari-Bradley, Bartlett, chi-squared, Fisher, Kruskal-Wallis, Kolmogorov-Smirnov, t, and Wilcoxon tests.

eda
     Exploratory Data Analysis.  Currently only contains functions for robust line fitting, and median polish and smoothing.

lqs
     Resistant regression and covariance estimation.

methods
     Formally defined methods and classes for R objects, plus other programming tools, as described in the Green Book.

modreg
     MODern REGression: smoothing and local methods.

mva
     MultiVariate Analysis.  Currently contains code for principal components, canonical correlations, metric multidimensional scaling, factor analysis, and hierarchical and k-means clustering.

nls
     Nonlinear regression routines.

splines
     Regression spline functions and classes.

stepfun
     Code for dealing with STEP FUNctions, including empirical cumulative distribution functions.

tcltk
     Interface and language bindings to Tcl/Tk GUI elements.

tools
     Tools for package development and administration.

ts
     Time Series.

The following packages are available from the CRAN src/contrib area.

AnalyzeFMRI
     Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE format.

CoCoAn
     Constrained Correspondence Analysis.

Devore5
     Data sets and sample analyses from ``Probability and Statistics for Engineering and the Sciences (5th ed)'' by Jay L. Devore, 2000, Duxbury.

EMV
     Estimation of missing values in a matrix by a k-th nearest neighboors algorithm.

GLMMGibbs
     Generalised Linear Mixed Models by Gibbs sampling.

GenKern
     Functions for generating and manipulating generalised binned kernel density estimates.

GeneSOM
     Clustering genes using Self-Organizing Maps (SOMs).

KernSmooth
     Functions for kernel smoothing (and density estimation) corresponding to the book ``Kernel Smoothing'' by M. P. Wand and M. C. Jones, 1995.

MASS
     Functions and datasets from the main package of Venables and Ripley, ``Modern Applied Statistics with S-PLUS''.  Contained in the VR bundle.

Matrix
     A Matrix package.

NISTnls
     A set of test nonlinear least squares examples from NIST, the U.S. National Institute for Standards and Technology.

Oarray
     Arrays with arbitrary offsets.

PHYLOGR
     Manipulation and analysis of phylogenetically simulated data sets (as obtained from PDSIMUL in package PDAP) and phylogenetically-based analyses using GLS.

PTAk
     A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations.  Also includes some other multiway methods.

RArcInfo
     Functions to import Arc/Info V7.x coverages and data.

RMySQL
     An interface between R and the MySQL database system.

RODBC
     An ODBC database interface.

ROracle
     Oracle Database Interface driver for R.  Uses the ProC/C++ embedded SQL.

RPgSQL
     Provides methods for accessing data stored in PostgreSQL tables.

RSQLite
     Database Interface R driver for SQLite.  Embeds the SQLite database engine in R.

RandomFields
     Creating random fields using various methods.

RmSQL
     An interface between R and the mSQL database system.

Rwave
     An environment for the time-frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals), based on the book ``Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S'' by Rene Carmona, Wen L. Hwang and Bruno Torresani, 1998, Academic Press.

SASmixed
     Data sets and sample linear mixed effects analyses corresponding to the examples in ``SAS System for Mixed Models'' by R. C. Littell, G. A. Milliken, W. W. Stroup and R. D. Wolfinger, 1996, SAS Institute.

SuppDists
     Ten distributions supplementing those built into R (Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi squared, Spearman's rho, maximum F ratio, the Pearson product moment correlation coefficiant, Johnson distributions, normal scores and generalized hypergeometric distributions).

XML
     Facilities for reading XML documents and DTDs.

acepack
     ACE (Alternating Conditional Expectations) and AVAS (Additivity and VAriance Stabilization for regression) methods for selecting regression transformations.

adapt
     Adaptive quadrature in up to 20 dimensions.

akima
     Linear or cubic spline interpolation for irregularly gridded data.

ash
     David Scott's ASH routines for 1D and 2D density estimation.

bindata
     Generation of correlated artificial binary data.

blighty
     Function for drawing the coastline of the United Kingdom.

boot
     Functions and datasets for bootstrapping from the book ``Bootstrap Methods and Their Applications'' by A. C. Davison and D. V. Hinkley, 1997, Cambridge University Press.

bootstrap
     Software (bootstrap, cross-validation, jackknife), data and errata for the book ``An Introduction to the Bootstrap'' by B. Efron and R. Tibshirani, 1993, Chapman and Hall.

bqtl
     QTL mapping toolkit for inbred crosses and recombinant inbred lines. Includes maximum likelihood and Bayesian tools.

car
     Companion to Applied Regression, containing functions for applied regession, linear models, and generalized linear models, with an emphasis on regression diagnostics, particularly graphical diagnostic methods.

cclust
     Convex clustering methods, including k-means algorithm, on-line update algorithm (Hard Competitive Learning) and Neural Gas algorithm (Soft Competitive Learning) and calculation of several indexes for finding the number of clusters in a data set.

cfa
     Analysis of configuration frequencies.

chron
     A package for working with chronological objects (times and dates).

class
     Functions for classification (k-nearest neighbor and LVQ). Contained in the VR bundle.

cluster
     Functions for cluster analysis.

cmprsk
     Estimation, testing and regression modeling of subdistribution functions in competing risks.

coda
     Output analysis and diagnostics for Markov Chain Monte Carlo (MCMC) simulations.

conf.design
     A series of simple tools for constructing and manipulating confounded and fractional factorial designs.

cramer
     Routine for the multivariate nonparametric Cramer test.

date
     Functions for dealing with dates.  The most useful of them accepts a vector of input dates in any of the forms 8/30/53, 30Aug53, 30 August 1953, ..., August 30 53, or any mixture of these.

diamonds
     Functions for illustrating aperture-4 diamond partitions in the plane, or on the surface of an octahedron or icosahedron, for use as analysis or sampling grids.

dse
     Dynamic System Estimation, a multivariate time series package.  Contains dse1 (DSE kernel plus ARMA and state space models), dse2 (DSE extensions), syskern (functions for writing code that is operating system and R/S independent), and tframe (functions for writing code that is independent of the representation of time).

e1071
     Miscellaneous functions used at the Department of Statistics at TU Wien (E1071), including moments, short-time Fourier transforms, Independent Component Analysis, Latent Class Analysis, support vector machines, and fuzzy clustering, shortest path computation, bagged clustering, and some more.

ellipse
     Package for drawing ellipses and ellipse-like confidence regions.

emplik
     Empirical likelihood ratio for means/quantiles/hazards from possibly right censored data.

event.chart
     Package for creating event charts.

exactRankTests
     Computes exact p-values and quantiles using an implementation of the Streitberg/Roehmel shift algorithm.

fastICA
     Implementation of FastICA algorithm to perform Independent Component Analysis (ICA) and Projection Pursuit.

fdim
     Functions for calculating fractal dimension.

fields
     A collection of programs for curve and function fitting with an emphasis on spatial data.  The major methods implemented include cubic and thin plate splines, universal Kriging and Kriging for large data sets.  The main feature is that any covariance function implemented in R can be used for spatial prediction.

foreign
     Functions for reading and writing data stored by statistical software like Minitab, SAS, SPSS, Stata, etc.

fracdiff
     Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Applied Statistics, 1989).

g.data
     Create and maintain delayed-data packages (DDP's).

gafit
     Genetic algorithm for curve fitting.

gee
     An implementation of the Liang/Zeger generalized estimating equation approach to GLMs for dependent data.

geoR
     Functions to perform geostatistical data analysis including model-based methods.

gld
     Basic functions for the generalised (Tukey) lambda distribution.

gregmisc
     Miscellaneous functions written/maintained by Gregory R. Warnes.

grid
     The Grid graphics package, a rewrite of the graphics layout capabilities, plus some support for interaction.

gss
     A comprehensive package for structural multivariate function estimation using smoothing splines.

ineq
     Inequality, concentration and poverty measures, and Lorenz curves (empirical and theoretic).

lattice
     Lattice graphics, an implementation of Trellis Graphics functions.

leaps
     A package which performs an exhaustive search for the best subsets of a given set of potential regressors, using a branch-and-bound algorithm, and also performs searches using a number of less time-consuming techniques.

lgtdl
     A set of methods for longitudinal data objects.

lmtest
     A collection of tests on the assumptions of linear regression models from the book ``The linear regression model under test'' by W. Kraemer and H. Sonnberger, 1986, Physica.

locfit
     Local Regression, likelihood and density estimation.

logspline
     Logspline density estimation.

lokern
     Kernel regression smoothing with adaptive local or global plug-in bandwidth selection.

lpridge
     Local polynomial (ridge) regression.

maptree
     Functions with example data for graphing and mapping models from hierarchical clustering and classification and regression trees.

maxstat
     Maximally selected rank and Gauss statistics with several p-value approximations.

mclust
     Model-based cluster analysis.

mda
     Code for mixture discriminant analysis (MDA), flexible discriminant analysis (FDA), penalized discriminant analysis (PDA), multivariate additive regression splines (MARS), adaptive back-fitting splines (BRUTO), and penalized regression.

meanscore
     Mean Score method for missing covariate data in logistic regression models.

mgcv
     Routines for GAMs and other genralized ridge regression problems with multiple smoothing parameter selection by GCV or UBRE.

mlbench
     A collection of artificial and real-world machine learning benchmark problems, including the Boston housing data.

moc
     Fits a variety of mixtures models for multivariate observations with user-difined distributions and curves.

muhaz
     Hazard function estimation in survival analysis.

multiv
     Functions for hierarchical clustering, partitioning, bond energy algorithm, Sammon mapping, PCA and correspondence analysis.

mvnmle
     ML estimation for multivariate normal data with missing values.

mvtnorm
     Multivariate normal and t distributions.

netCDF
     Read data from netCDF files.

nlme
     Fit and compare Gaussian linear and nonlinear mixed-effects models.

nlrq
     Nonlinear quantile regression.

nnet
     Software for single hidden layer perceptrons (``feed-forward neural networks''), and for multinomial log-linear models.  Contained in the VR bundle.

norm
     Analysis of multivariate normal datasets with missing values.

odesolve
     An interface for the Ordinary Differential Equation (ODE) solver lsoda. ODEs are expressed as R functions.

oz
     Functions for plotting Australia's coastline and state boundaries.

panel
     Functions and datasets for fitting models to Panel data.

pcurve
     Fits a principal curve to a numeric multivariate dataset in arbitrary dimensions.  Produces diagnostic plots.  Also calculates Bray-Curtis and other distance matrices and performs multi-dimensional scaling and principal component analyses.

permax
     Functions intended to facilitate certain basic analyses of DNA array data, especially with regard to comparing expression levels between two types of tissue.

pinktoe
     Converts S trees to HTML/Perl files for interactive tree traversal.

pixmap
     Functions for import, export, plotting and other manipulations of bitmapped images.

polymars
     Polychotomous regression based on Multivariate Adaptive Regression Splines.

polynom
     A collection of functions to implement a class for univariate polynomial manipulations.

princurve
     Fits a principal curve to a matrix of points in arbitrary dimension.

pspline
     Smoothing splines with penalties on order m derivatives.

qtl
     Analysis of experimental crosses to identify QTLs.

quadprog
     For solving quadratic programming problems.

quantreg
     Quantile  regression and related methods.

rmeta
     Functions for simple fixed and random effects meta-analysis for two-sample comparison of binary outcomes.

rpart
     Recursive PARTitioning and regression trees.

rpvm
     R interface to PVM (Parallel Virtual Machine).  Provides interface to PVM APIs, and examples and documentation for its use.

scatterplot3d
     Plots a three dimensional (3D) point cloud perspectively.

sem
     Functions for fitting general linear Structural Equation Models (with observed and unobserved variables) by the method of maximum likelihood using the RAM approach.

sgeostat
     An object-oriented framework for geostatistical modeling.

sm
     Software linked to the book ``Applied Smoothing Techniques for Data Analysis:  The Kernel Approach with S-PLUS Illustrations'' by A. W. Bowman and A. Azzalini (1997), Oxford University Press.

sma
     Functions for exploratory (statistical) microarray analysis.

sn
     Functions for manipulating skew-normal probability distributions and for fitting them to data, in the scalar and the multivariate case.

sna
     A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, p* modeling, and network visualization.

spatial
     Functions for kriging and point pattern analysis from ``Modern Applied Statistics with S-PLUS'' by W. Venables and B. Ripley.  Contained in the VR bundle.

splancs
     Spatial and space-time point pattern analysis functions.

sptests
     A collection of tests for spatial autocorrelation, including global Moran's I and Geary's C.

spweights
     A collection of functions to create spatial weights matrix objects from polygon contiguities, from point patterns by distance and tesselations, for summarising these objects, and for permitting their use in spatial data analysis.

strucchange
     Various tests on structural change in linear regression models.

survival
     Functions for survival analysis, including penalised likelihood.

tensor
     Tensor product of arrays.

tree
     Classification and regression trees.

tripack
     A constrained two-dimensional Delaunay triangulation package.

tseries
     Package for time series analysis with emphasis on non-linear modelling.

twostage
     Functions for optimal design of two-stage-studies using the Mean Score method.

vegan
     Various help functions for vegetation scientists and community ecologists.

waveslim
     Basic wavelet routines for time series analysis.

wavethresh
     Software to perform 1-d and 2-d wavelet statistics and transforms.

wle
     Robust statistical inference via a weighted likelihood approach.

xgobi
     Interface to the XGobi and XGvis programs for graphical data analysis.

xtable
     Export data to LaTeX and HTML tables.

zmatrix
     Matrices with numeric indices starting at zero rather than one.

See CRAN src/contrib/PACKAGES for more information.

There is also a CRAN src/contrib/Devel directory which contains packages still ``under development'' or depending on features only present in the current development versions of R.  Volunteers are invited to give these a try, of course.  This area of CRAN currently contains

DBI
     A common database interface (DBI) class and method definitions.  All classes in this package are virtual and need to be extended by the various DBMS implementations.

DBI.RODBC
     A simple DBI front-end to the RODBC package.

DBI.RPgSQL
     A simple DBI front-end to the RPgSQL package.

GRASS
     Interface between the GRASS geographical information system and R, based on starting R from within the GRASS environment and chosen LOCATION and MAPSET.  Wrapper and helper functions are provided for a range of R functions to match the interface metadata structures.

R2HTML
     Functions for exporting R objects & graphics in an HTML document.

StatDataML
     Read and write StatDataML.

cxx
     A small C++ test package.

dopt
     Finding D-optimal experimental designs.

dseplus
     Extensions to dse, the Dynamic Systems Estimation multivariate time series package.  Contains PADI, juice and monitoring extensions.

ensemble
     Ensembles of tree classifiers.

hdf5
     Interface to the NCSA HDF5 library.

hpower
     A suite of functions to compute power and sample size for tests of the general linear hypothesis.

multidim
     Code for correspondence analysis and other multidimensional descriptive statistics.

multilm
     A basic method for fitting and testing multivariate linear models, including stabilized test procedures by Laeuter et. al.

npConfRatio
     Nonparametric confidence intervals for the ratios of medians.

pls
     Univariate Partial Least Squares Regression.

regexp
     Simple regular expression interface.

write.snns
     Function for writing a SNNS pattern file from a data frame or matrix.

Directory src/contrib/Omegahat contains yet unreleased packages from the Omegahat Project for Statistical Computing (http://www.omegahat.org/).  Currently, there are

CORBA
     Dynamic CORBA client/server facilities for R.  Connects to other CORBA-aware applications developed in arbitrary languages, on different machines and allows R functionality to be exported in the same way to other applications.

OOP
     OOP style classes and methods for R and S-PLUS.  Object references and class-based method definition are supported in the style of languages such as Java and C++.

REmbeddedPostgres
     Allows R functions and objects to be used to implement SQL functions --- per-record, aggregate and trigger functions.

RGnumeric
     A plugin for the Gnumeric spreadsheet that allows R functions to be called from cells within the sheet, automatic recalculation, etc.

RJavaDevice
     A graphics device for R that uses Java components and graphics APIs.

RSMethods
     An implementation of S version 4 methods and classes for R, consistent with the basic material in ``Programming with data'' by John M. Chambers, 1998, Springer NY.

RSPerl
     An interface from R to an embedded, persistent Perl interpreter, allowing one to call arbitrary Perl subroutines, classes and methods.

RSPython
     Allows Python programs to invoke S functions, methods, etc., and S code to call Python functionality.

SASXML
     Example for reading XML files in SAS 8.2 manner.

SJava
     An interface from R to Java to create and call Java objects and methods.

SLanguage
     Functions and C support utilities to support S language programming that can work in both R and S-PLUS.

SNetscape
     Plugin for Netscape and JavaScript.

SXalan
     Process XML documents using XSL functions implemented in R and dynamically substituting output from R.

Slcc
     Parses C source code, allowing one to analyze and automatically generate interfaces from S to that code, including the table of S-accessible native symbols, parameter count and type information, S constructors from C objects, call graphs, etc.

Sxslt
     An extension module for libxslt, the XML-XSL document translator, that allows XSL functions to be implemented via R functions.

Jim Lindsey has written a collection of R packages for nonlinear regression and repeated measurements, consisting of event (event history procedures and models), gnlm (generalized nonlinear regression models), growth (multivariate normal and elliptically-contoured repeated measurements models), repeated (non-normal repeated measurements models), rmutil (utilities for nonlinear regression and repeated measurements), and stable (probability functions and generalized regression models for stable distributions).  All analyses in the new edition of his book ``Models for Repeated Measurements'' (1999, Oxford University Press) were carried out using these packages. Jim has also started dna, a package with procedures for the analysis of DNA sequences.  Jim's packages can be obtained from http://www.luc.ac.be/~jlindsey/rcode.html.

More code has been posted to the r-help mailing list, and can be obtained from the mailing list archive.

How can add-on packages be installed?
=====================================

(Unix only.)  The add-on packages on CRAN come as gzipped tar files named PKG_VERSION.tar.gz, which may in fact be ``bundles'' containing more than one package.  Provided that tar and gzip are available on your system, type

     $ R CMD INSTALL /path/to/PKG_VERSION.tar.gz

at the shell prompt to install to the library tree rooted at the first directory given in R_LIBS (see below) if this is set and non-null, and to the default library (the library subdirectory of R_HOME) otherwise.  (Versions of R prior to 1.3.0 installed to the default library by default.)

To install to another tree (e.g., your private one), use

     $ R CMD INSTALL -l LIB /path/to/PKG_VERSION.tar.gz

where LIB gives the path to the library tree to install to.

Even more conveniently, you can install and automatically update packages from within R if you have access to CRAN.  See the help page for CRAN.packages() for more information.

You can use several library trees of add-on packages.  The easiest way to tell R to use these is via the environment variable R_LIBS which should be a colon-separated list of directories at which R library trees are rooted.  You do not have to specify the default tree in R_LIBS.  E.g., to use a private tree in $HOME/lib/R and a public site-wide tree in /usr/local/lib/R-contrib, put

     R_LIBS="$HOME/lib/R:/usr/local/lib/R-contrib"; export R_LIBS

into your (Bourne) shell profile or even preferably, add the line

     R_LIBS="$HOME/lib/R:/usr/local/lib/R-contrib"

your ~/.Renviron file.  (Note that no export statement is needed or allowed in this file; see the on-line help for Startup for more information.)

How can add-on packages be used?
================================

To find out which additional packages are available on your system, type

     library()

at the R prompt.

This produces something like

     Packages in `/home/me/lib/R':

     mystuff       My own R functions, nicely packaged but not documented

     Packages in `/usr/local/lib/R/library':

     KernSmooth    Functions for kernel smoothing for Wand & Jones (1995)
     MASS          Main Library of Venables and Ripley's MASS
     base          The R base package
     boot          Bootstrap R (S-Plus) Functions (Canty)
     class         Functions for classification
     cluster       Functions for clustering (by Rousseeuw et al.)
     ctest         Classical Tests
     eda           Exploratory Data Analysis
     foreign       Read data stored by Minitab, S, SAS, SPSS, Stata, ...
     lqs           Resistant Regression and Covariance Estimation
     mgcv          Multiple smoothing parameter estimation and GAMs by GCV
     modreg        Modern Regression: Smoothing and Local Methods
     mva           Classical Multivariate Analysis
     nlme          Linear and nonlinear mixed effects models
     nls           Nonlinear regression
     nnet          Feed-forward neural networks and multinomial log-linear
                   models
     rpart         Recursive partitioning
     spatial       functions for kriging and point pattern analysis
     splines       Regression Spline Functions and Classes
     stepfun       Step Functions, including Empirical Distributions
     survival      Survival analysis, including penalised likelihood
     tcltk         Interface to Tcl/Tk
     ts            Time series functions

You can ``load'' the installed package PKG by

     library(PKG)

You can then find out which functions it provides by typing one of

     library(help = PKG)
     help(package = PKG)

You can unload the loaded package PKG by

     detach("package:PKG")

How can add-on packages be removed?
===================================

Use

     $ R CMD REMOVE PKG_1 ... PKG_N

to remove the packages PKG_1, ..., PKG_N from the library tree rooted at the first directory given in R_LIBS if this is set and non-null, and from the default library otherwise.  (Versions of R prior to 1.3.0 removed from the default library by default.)

To remove from library LIB, do

     $ R CMD REMOVE -l LIB PKG_1 ... PKG_N

How can I create an R package?
==============================

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

See section ``Creating R packages'' in Writing R Extensions, for details. This manual is included in the R distribution, see What documentation exists for R?, and gives information on package structure, the configure and cleanup mechanisms, and on automated package checking and building.

R version 1.3.0 has added the function package.skeleton() which will set up directories, save data and code, and create skeleton help files for a set of R functions and datasets.

See What is CRAN?, for information on uploading a package to CRAN.

How can I contribute to R?
==========================

R is in active development and there is always a risk of bugs creeping in.  Also, the developers do not have access to all possible machines capable of running R.  So, simply using it and communicating problems is certainly of great value.

One place where functionality is still missing is the modeling software as described in ``Statistical Models in S'' (see What is S?); Generalized Additive Models (see Are GAMs implemented in R?) and some of the nonlinear modeling code are not there yet.

The R Developer Page (http://developer.r-project.org/) acts as an intermediate repository for more or less finalized ideas and plans for the R statistical system.  It contains (pointers to) TODO lists, RFCs, various other writeups, ideas lists, and CVS miscellanea.

Many (more) of the packages available at the Statlib S Repository might be worth porting to R.

If you are interested in working on any of these projects, please notify Kurt Hornik.

R and Emacs
***********

Is there Emacs support for R?
=============================

There is an Emacs package called ESS (``Emacs Speaks Statistics'') which provides a standard interface between statistical programs and statistical processes.  It is intended to provide assistance for interactive statistical programming and data analysis. Languages supported include: S dialects (S 3/4, S-PLUS 3.x/4.x/5.x, and R), LispStat dialects (XLispStat, ViSta), SAS, Stata, SPSS dialects (SPSS, Fiasco) and SCA.

ESS grew out of the need for bug fixes and extensions to S-mode 4.8 (which was a GNU Emacs interface to S/S-PLUS version 3 only).  The current set of developers desired support for XEmacs, R, S4, and MS Windows.  In addition, with new modes being developed for R, Stata, and SAS, it was felt that a unifying interface and framework for the user interface would benefit both the user and the developer, by helping both groups conform to standard Emacs usage.  The end result is an increase in efficiency for statistical programming and data analysis, over the usual tools.

R support contains code for editing R source code (syntactic indentation and highlighting of source code, partial evaluations of code, loading and error-checking of code, and source code revision maintenance) and documentation (syntactic indentation and highlighting of source code, sending examples to running ESS process, and previewing), interacting with an inferior R process from within Emacs (command-line editing, searchable command history, command-line completion of R object and file names, quick access to object and search lists, transcript recording, and an interface to the help system), and transcript manipulation (recording and saving transcript files, manipulating and editing saved transcripts, and re-evaluating commands from transcript files).

The latest versions of ESS are available from http://ess.stat.wisc.edu/pub/ESS/ or ftp://ess.stat.wisc.edu/pub/ESS/, or via CRAN.  The HTML version of the documentation can be found at http://stat.ethz.ch/ESS/.

ESS comes with detailed installation instructions.

For help with ESS, send email to ESS-help@stat.ethz.ch.

Please send bug reports and suggestions on ESS to ESS-bugs@stat.math.ethz.ch.  The easiest way to do this from is within Emacs by typing M-x ess-submit-bug-report or using the [ESS] or [iESS] pulldown menus.

Should I run R from within Emacs?
=================================

Yes, definitely.  Inferior R mode provides a readline/history mechanism, object name completion, and syntax-based highlighting of the interaction buffer using Font Lock mode, as well as a very convenient interface to the R help system.

Of course, it also integrates nicely with the mechanisms for editing R source using Emacs.  One can write code in one Emacs buffer and send whole or parts of it for execution to R; this is helpful for both data analysis and programming.  One can also seamlessly integrate with a revision control system, in order to maintain a log of changes in your programs and data, as well as to allow for the retrieval of past versions of the code.

In addition, it allows you to keep a record of your session, which can also be used for error recovery through the use of the transcript mode.

To specify command line arguments for the inferior R process, use C-u M-x R for starting R.

Debugging R from within Emacs
=============================

To debug R ``from within Emacs'', there are several possibilities.  To use the Emacs GUD (Grand Unified Debugger) library with the recommended debugger GDB, type M-x gdb and give the path to the R binary as argument.  At the gdb prompt, set R_HOME and other environment variables as needed (using e.g. set env R_HOME /path/to/R/, but see also below), and start the binary with the desired arguments (e.g., run --vsize=12M).

If you have ESS, you can do C-u M-x R RET - d SPC g d b RET to start an inferior R process with arguments -d gdb.

A third option is to start an inferior R process via ESS (M-x R) and then start GUD (M-x gdb) giving the R binary (using its full path name) as the program to debug.  Use the program ps to find the process number of the currently running R process then use the attach command in gdb to attach it to that process.  One advantage of this method is that you have separate *R* and *gud-gdb* windows.  Within the *R* window you have all the ESS facilities, such as object-name completion, that we know and love.

When using GUD mode for debugging from within Emacs, you may find it most convenient to use the directory with your code in it as the current working directory and then make a symbolic link from that directory to the R binary.  That way .gdbinit can stay in the directory with the code and be used to set up the environment and the search paths for the source, e.g. as follows:

     set env R_HOME /opt/R
     set env R_PAPERSIZE letter
     set env R_PRINTCMD lpr
     dir /opt/R/src/appl
     dir /opt/R/src/main
     dir /opt/R/src/nmath
     dir /opt/R/src/unix

R Miscellanea
*************

Why does R run out of memory?
=============================

Versions of R prior to 1.2.0 used a static memory model.  At startup, R asked the operating system to reserve a fixed amount of memory for it.  The size of this chunk could not be changed subsequently.  Hence, it could happen that not enough memory was allocated, e.g., when trying to read large data sets into R.  In such cases, it was necessary to restart R with more memory available, as controlled by the command line options --nsize and --vsize.

R version 1.2.0 introduces a new ``generational'' garbage collector, which will increase the memory available to R as needed.  Hence, user intervention is no longer necessary for ensuring that enough memory is available.

The new garbage collector does not move objects in memory, meaning that it is possible for the free memory to become fragmented so that large objects cannot be allocated even when there is apparently enough memory for them.

Why does sourcing a correct file fail?
======================================

Versions of R prior to 1.2.1 may have had problems parsing files not ending in a newline.  Earlier R versions had a similar problem when reading in data files.  This should no longer happen.

How can I set components of a list to NULL?
===========================================

You can use

     x[i] <- list(NULL)

to set component i of the list x to NULL, similarly for named components.  Do not set x[i] or x[[i]] to NULL, because this will remove the corresponding component from the list.

For dropping the row names of a matrix x, it may be easier to use rownames(x) <- NULL, similarly for column names.

How can I save my workspace?
============================

save.image() saves the objects in the user's .GlobalEnv to the file .RData in the R startup directory.  (This is also what happens after q("yes").)  Using save.image(FILE) one can save the image under a different name.

How can I clean up my workspace?
================================

To remove all objects in the currently active environment (typically .GlobalEnv), you can do

     rm(list = ls(all = TRUE))

(Without all = TRUE, only the objects with names not starting with a . are removed.)

How can I get eval() and D() to work?
=====================================

Strange things will happen if you use eval(print(x), envir = e) or D(x^2, "x").  The first one will either tell you that "x" is not found, or print the value of the wrong x. The other one will likely return zero if x exists, and an error otherwise.

This is because in both cases, the first argument is evaluated in the calling environment first.  The result (which should be an object of mode "expression" or "call") is then evaluated or differentiated.  What you (most likely) really want is obtained by ``quoting'' the first argument upon surrounding it with expression().  For example,

     R> D(expression(x^2), "x")
     2 * x

Although this behavior may initially seem to be rather strange, is perfectly logical.  The ``intuitive'' behavior could easily be implemented, but problems would arise whenever the expression is contained in a variable, passed as a parameter, or is the result of a function call.  Consider for instance the semantics in cases like

     D2 <- function(e, n) D(D(e, n), n)

or

     g <- function(y) eval(substitute(y), sys.frame(sys.parent(n = 2)))
     g(a * b)

See the help page for deriv() for more examples.

Why do my matrices lose dimensions?
===================================

When a matrix with a single row or column is created by a subscripting operation, e.g., row <- mat[2, ], it is by default turned into a vector.  In a similar way if an array with dimension, say, 2 x 3 x 1 x 4 is created by subscripting it will be coerced into a 2 x 3 x 4 array, losing the unnecessary dimension.  After much discussion this has been determined to be a feature.

To prevent this happening, add the option drop = FALSE to the subscripting.  For example,

     rowmatrix <- mat[2, , drop = FALSE]  # creates a row matrix
     colmatrix <- mat[, 2, drop = FALSE]  # creates a column matrix
     a <- b[1, 1, 1, drop = FALSE]        # creates a 1 x 1 x 1 array

The drop = FALSE option should be used defensively when programming.  For example, the statement

     somerows <- mat[index, ]

will return a vector rather than a matrix if index happens to have length 1, causing errors later in the code.  It should probably be rewritten as

     somerows <- mat[index, , drop = FALSE]

How does autoloading work?
==========================

R has a special environment called .AutoloadEnv.  Using autoload(NAME, PKG), where NAME and PKG are strings giving the names of an object and the package containing it, stores some information in this environment.  When R tries to evaluate NAME, it loads the corresponding package PKG and reevaluates NAME in the new package's environment.

Using this mechanism makes R behave as if the package was loaded, but does not occupy memory (yet).

See the help page for autoload() for a very nice example.

How should I set options?
=========================

The function options() allows setting and examining a variety of global ``options'' which affect the way in which R computes and displays its results.  The variable .Options holds the current values of these options, but should never directly be assigned to unless you want to drive yourself crazy---simply pretend that it is a ``read-only'' variable.

For example, given

     test1 <- function(x = pi, dig = 3) {
       oo <- options(digits = dig); on.exit(options(oo));
       cat(.Options$digits, x, "\n")
     }
     test2 <- function(x = pi, dig = 3) {
       .Options$digits <- dig
       cat(.Options$digits, x, "\n")
     }

we obtain:

     R> test1()
     3 3.14
     R> test2()
     3 3.141593

What is really used is the global value of .Options, and using options(OPT = VAL) correctly updates it.  Local copies of .Options, either in .GlobalEnv or in a function environment (frame), are just silently disregarded.

How do file names work in Windows?
==================================

As R uses C-style string handling, \ is treated as an escape character, so that for example one can enter a newline as \n. When you really need a \, you have to escape it with another \.

Thus, in filenames use something like "c:\\data\\money.dat".  You can also replace \ by / ("c:/data/money.dat").

Why does plotting give a color allocation error?
================================================

Sometimes plotting, e.g., when running demo(image), results in ``Error: color allocation error''.  This is an X problem, and only indirectly related to R.  It occurs when applications started prior to R have used all the available colors.  (How many colors are available depends on the X configuration; sometimes only 256 colors can be used.)

One application which is notorious for ``eating'' colors is Netscape. If the problem occurs when Netscape is running, try (re)starting it with either the -no-install (to use the default colormap) or the -install (to install a private colormap) option.

You could also set the colortype of X11() to "pseudo.cube" rather than the default "pseudo".  See the help page for X11() for more information.

How do I convert factors to numeric?
====================================

It may happen that when reading numeric data into R (usually, when reading in a file), they come in as factors.  If f is such a factor object, you can use

     as.numeric(as.character(f))

to get the numbers back.  More efficient, but harder to remember, is

     as.numeric(levels(f))[as.integer(f)]

In any case, do not call as.numeric() or their likes directly.

Are Trellis displays implemented in R?
======================================

An R package called lattice, which provides graphical functionality that is compatible with most Trellis commands, is under development.  The latest version of the package may be downloaded from the contrib/Devel area of CRAN.  lattice currently relies on another package called grid (also under development) so you will need to download that as well.  Because these packages are under active development, they might only work with the latest development version of R.

You could also look at coplot() and dotchart() which might do at least some of what you want.  Note also that the R version of pairs() is fairly general and provides most of the functionality of splom(), and that R's default plot method has an argument asp allowing to specify (and fix against device resizing) the aspect ratio of the plot.

(Because the word ``Trellis'' has been claimed as a trademark we do not use it in R.  The name ``lattice'' has been proposed for the R equivalent.)

What are the enclosing and parent environments?
===============================================

Inside a function you may want to access variables in two additional environments: the one that the function was defined in (``enclosing''), and the one it was invoked in (``parent'').

If you create a function at the command line or load it in a package its enclosing environment is the global workspace.  If you define a function f() inside another function g() its enclosing environment is the environment inside g().  The enclosing environment for a function is fixed when the function is created.  You can find out the enclosing environment for a function f() using environment(f).

The ``parent'' environment, on the other hand, is defined when you invoke a function.  If you invoke lm() at the command line its parent environment is the global workspace, if you invoke it inside a function f() then its parent environment is the environment inside f().  You can find out the parent environment for an invocation of a function by using parent.frame() or sys.frame(sys.parent()).

So for most user-visible functions the enclosing environment will be the global workspace, since that is where most functions are defined.  The parent environment will be wherever the function happens to be called from.  If a function f() is defined inside another function g() it will probably be used inside g() as well, so its parent environment and enclosing environment will probably be the same.

Parent environments are important because things like model formulas need to be evaluated in the environment the function was called from, since that's where all the variables will be available.  This relies on the parent environment being potentially different with each invocation.

Enclosing environments are important because a function can use variables in the enclosing environment to share information with other functions or with other invocations of itself (see the section on lexical scoping).  This relies on the enclosing environment being the same each time the function is invoked.

Scoping is hard.  Looking at examples helps.  It is particularly instructive to look at examples that work differently in R and S and try to see why they differ.  One way to describe the scoping differences between R and S is to say that in S the enclosing environment is always the global workspace, but in R the enclosing environment is wherever the function was created.

How can I substitute into a plot label?
=======================================

Often, it is desired to use the value of an R object in a plot label, e.g., a title.  This is easily accomplished using paste() if the label is a simple character string, but not always obvious in case the label is an expression (for refined mathematical annotation).  In such a case, either use parse() on your pasted character string or use substitute() on an expression.  For example, if ahat is an estimator of your parameter a of interest, use

     title(substitute(hat(a) == ahat, list(ahat = ahat)))

(note that it is == and not =).  There are more worked examples in the mailing list achives.

What are valid names?
=====================

When creating data frames using data.frame() or read.table(), R by default ensures that the variable names are syntactically valid.  (The argument check.names to these functions controls whether variable names are checked and adjusted by make.names() if needed.)

To understand what names are ``valid'', one needs to take into account that the term ``name'' is used in several different (but related) ways in the language:

 1. A syntactic name is a string the parser interprets as this type of expression.  It consists of letters, numbers, and the dot character and starts with a letter or the dot.

 2. An object name is a string associated with an object that is assigned in an expression either by having the object name on the left of an assignment operation or as an argument to the assign() function.  It is usually a syntactic name as well, but can be any non-empty string if it is quoted (and it is always quoted in the call to assign()).

 3. An argument name is what appears to the left of the equals sign when supplying an argument in a function call (for example, f(trim=.5)).  Argument names are also usually syntactic names, but again can be anything if they are quoted.

 4. An element name is a string that identifies a piece of an object (a component of a list, for example.)  When it is used on the right of the $ operator, it must be a syntactic name, or quoted. Otherwise, element names can be any strings.  (When an object is used as a database, as in a call to eval() or attach(), the element names become object names.)

 5. Finally, a file name is a string identifying a file in the operating system for reading, writing, etc.  It really has nothing much to do with names in the language, but it is traditional to call these strings file ``names''.

Are GAMs implemented in R?
==========================

There is a gam() function for Generalized Additive Models in package mgcv, but it is not an exact clone of what is described in the White Book (no lo() for example).  Package gss can fit spline-based GAMs too.  And if you can accept regression splines you can use glm().  For gaussian GAMs you can use bruto() from package mda.

Why is the output not printed when I source() a file?
=====================================================

Most R commands do not generate any output. The command

     1+1

computes the value 2 and returns it; the command

     summary(glm(y~x+z, family=binomial))

fits a logistic regression model, computes some summary information and returns an object of class "summary.glm" (see How should I write summary methods?).

If you type 1+1 or summary(glm(y~x+z, family=binomial)) at the command line the returned value is automatically printed (unless it is invisible()), but in other circumstances, such as in a source()d file or inside a function it isn't printed unless you specifically print it.

To print the value use

     print(1+1)

or

     print(summary(glm(y~x+z, family=binomial)))

instead, or use source(FILE, echo=TRUE).

Why does outer() behave strangely with my function?
===================================================

As the help for outer() indicates, it does not work on arbitrary functions the way the apply() family does.  It requires functions that are vectorized to work elementwise on arrays.  As you can see by looking at the code, outer(x, y, FUN) creates two large vectors containing every possible combination of elements of x and y and then passes this to FUN all at once.  Your function probably cannot handle two large vectors as parameters.

If you have a function that cannot handle two vectors but can handle two scalars, then you can still use outer() but you will need to wrap your function up first, to simulate vectorized behavior.  Suppose your function is

     foo <- function(x, y, happy) {
       stopifnot(length(x) == 1, length(y) == 1) # scalars only!
       (x + y) * happy
     }

If you define the general function

     wrapper <- function(x, y, my.fun, ...) {
       sapply(seq(along=x), FUN = function(i) my.fun(x[i], y[i], ...))
     }

then you can use outer() by writing, e.g.,

     outer(1:4, 1:2, FUN = wrapper, my.fun = foo, happy = 10)

Why does the output from anova() depend on the order of factors in the model?
=============================================================================

In a model such as ~A+B+A:B, R will report the difference in sums of squares between the models ~1, ~A, ~A+B and ~A+B+A:B.  If the model were ~B+A+A:B, R would report differences between ~1, ~B, ~A+B, and ~A+B+A:B . In the first case the sum of squares for A is comparing ~1 and ~A, in the second case it is comparing ~B and ~B+A.  In a non-orthogonal design (i.e., most unbalanced designs) these comparisons are (conceptually and numerically) different.

Some packages report instead the sums of squares based on comparing the full model to the models with each factor removed one at a time (the famous `Type III sums of squares' from SAS, for example).  These do not depend on the order of factors in the model.  The question of which set of sums of squares is the Right Thing provokes low-level holy wars on r-help from time to time.

There is no need to be agitated about the particular sums of squares that R reports.  You can compute your favorite sums of squares quite easily.  Any two models can be compared with anova(model1, model2), and drop1(model1) will show the sums of squares resulting from dropping single terms.

R Programming
*************

How should I write summary methods?
===================================

Suppose you want to provide a summary method for class foo.  Then summary.foo() should not print anything, but return an object of class "summary.foo", and you should write a method print.summary.foo() which nicely prints the summary information and invisibly returns its object.  This approach is preferred over having summary.foo() print summary information and return something useful, as sometimes you need to grab something computed by summary() inside a function or similar.  In such cases you don't want anything printed.

How can I debug dynamically loaded code?
========================================

Roughly speaking, you need to start R inside the debugger, load the code, send an interrupt, and then set the required breakpoints.

See section ``Finding entry points in dynamically loaded code'' in Writing R Extensions. This manual is included in the R distribution, see What documentation exists for R?.

How can I inspect R objects when debugging?
===========================================

The most convenient way is to call R_PV from the symbolic debugger.

See section ``Inspecting R objects when debugging'' in Writing R Extensions.

How can I change compilation flags?
===================================

Suppose you have C code file for dynloading into R, but you want to use R CMD SHLIB with compilation flags other than the default ones (which were determined when R was built).  You could change the file R_HOME/etc/Makeconf to reflect your preferences.  If you are a Bourne shell user, you can also pass the desired flags to Make (which is used for controlling compilation) via the Make variable MAKEFLAGS, as in

     MAKEFLAGS="CFLAGS=-O3" R CMD SHLIB *.c

R Bugs
******

What is a bug?
==============

If R executes an illegal instruction, or dies with an operating system error message that indicates a problem in the program (as opposed to something like ``disk full''), then it is certainly a bug.  If you call .C(), .Fortran(), .External() or .Call() (or .Internal()) yourself (or in a function you wrote), you can always crash R by using wrong argument types (modes).  This is not a bug.

Taking forever to complete a command can be a bug, but you must make certain that it was really R's fault.  Some commands simply take a long time.  If the input was such that you know it should have been processed quickly, report a bug.  If you don't know whether the command should take a long time, find out by looking in the manual or by asking for assistance.

If a command you are familiar with causes an R error message in a case where its usual definition ought to be reasonable, it is probably a bug. If a command does the wrong thing, that is a bug.  But be sure you know for certain what it ought to have done.  If you aren't familiar with the command, or don't know for certain how the command is supposed to work, then it might actually be working right.  Rather than jumping to conclusions, show the problem to someone who knows for certain.

Finally, a command's intended definition may not be best for statistical analysis.  This is a very important sort of problem, but it is also a matter of judgment.  Also, it is easy to come to such a conclusion out of ignorance of some of the existing features.  It is probably best not to complain about such a problem until you have checked the documentation in the usual ways, feel confident that you understand it, and know for certain that what you want is not available.  If you are not sure what the command is supposed to do after a careful reading of the manual this indicates a bug in the manual.  The manual's job is to make everything clear.  It is just as important to report documentation bugs as program bugs.  However, we know that the introductory documentation is seriously inadequate, so you don't need to report this.

If the online argument list of a function disagrees with the manual, one of them must be wrong, so report the bug.

How to report a bug
===================

When you decide that there is a bug, it is important to report it and to report it in a way which is useful.  What is most useful is an exact description of what commands you type, starting with the shell command to run R, until the problem happens.  Always include the version of R, machine, and operating system that you are using; type version in R to print this.

The most important principle in reporting a bug is to report facts, not hypotheses or categorizations.  It is always easier to report the facts, but people seem to prefer to strain to posit explanations and report them instead.  If the explanations are based on guesses about how R is implemented, they will be useless; others will have to try to figure out what the facts must have been to lead to such speculations.  Sometimes this is impossible.  But in any case, it is unnecessary work for the ones trying to fix the problem.

For example, suppose that on a data set which you know to be quite large the command

     R> data.frame(x, y, z, monday, tuesday)

never returns.  Do not report that data.frame() fails for large data sets.  Perhaps it fails when a variable name is a day of the week. If this is so then when others got your report they would try out the data.frame() command on a large data set, probably with no day of the week variable name, and not see any problem.  There is no way in the world that others could guess that they should try a day of the week variable name.

Or perhaps the command fails because the last command you used was a method for "["() that had a bug causing R's internal data structures to be corrupted and making the data.frame() command fail from then on.  This is why others need to know what other commands you have typed (or read from your startup file).

It is very useful to try and find simple examples that produce apparently the same bug, and somewhat useful to find simple examples that might be expected to produce the bug but actually do not.  If you want to debug the problem and find exactly what caused it, that is wonderful.  You should still report the facts as well as any explanations or solutions.  Please include an example that reproduces the problem, preferably the simplest one you have found.

Invoking R with the --vanilla option may help in isolating a bug.  This ensures that the site profile and saved data files are not read.

On Unix systems a bug report can be generated using the function bug.report().  This automatically includes the version information and sends the bug to the correct address.  Alternatively the bug report can be emailed to r-bugs@r-project.org or submitted to the Web page at http://bugs.r-project.org/.

Bug reports on contributed packages should perhaps be sent to the package maintainer rather than to r-bugs.

Acknowledgments
***************

Of course, many many thanks to Robert and Ross for the R system, and to the package writers and porters for adding to it.

Special thanks go to Doug Bates, Peter Dalgaard, Paul Gilbert, Stefano Iacus, Fritz Leisch, Jim Lindsey, Thomas Lumley, Martin Maechler, Brian D. Ripley, Anthony Rossini, and Andreas Weingessel for their comments which helped me improve this FAQ.

More to some soon ...