R FAQ
Frequently Asked Questions on R
Version 1.1-0, 2000/06/12
Kurt Hornik


Table of Contents
*****************


1 Introduction
  1.1 Legalese
  1.2 Obtaining this document
  1.3 Citing this document
  1.4 Notation
  1.5 Feedback

2 R Basics
  2.1 What is R?
  2.2 What machines does R run on?
  2.3 What is the current version of R?
  2.4 How can R be obtained?
  2.5 How can R be installed?
    2.5.1 How can R be installed (Unix)
    2.5.2 How can R be installed (Windows)
    2.5.3 How can R be installed (Macintosh)
  2.6 Are there Unix binaries for R?
  2.7 What documentation exists for R?
  2.8 Citing R
  2.9 What mailing lists exist for R?
  2.10 What is CRAN?

3 R and S
  3.1 What is S?
  3.2 What is S-PLUS?
  3.3 What are the differences between R and S?
    3.3.1 Lexical scoping
    3.3.2 Models
    3.3.3 Others
  3.4 Is there anything R can do that S-PLUS cannot?

4 R Web Interfaces

5 R Add-On Packages
  5.1 Which add-on packages exist for R?
  5.2 How can add-on packages be installed?
  5.3 How can add-on packages be used?
  5.4 How can add-on packages be removed?
  5.5 How can I create an R package?
  5.6 How can I contribute to R?

6 R and Emacs
  6.1 Is there Emacs support for R?
  6.2 Should I run R from within Emacs?
  6.3 Debugging R from within Emacs

7 R Miscellania
  7.1 Why does R run out of memory?
  7.2 Why does sourcing a correct file fail?
  7.3 How can I set components of a list to NULL?
  7.4 How can I save my workspace?
  7.5 How can I clean up my workspace?
  7.6 How can I get eval() and D() to work?
  7.7 Why do my matrices lose dimensions?
  7.8 How does autoloading work?
  7.9 How should I set options?
  7.10 How do file names work in Windows?
  7.11 Why does plotting give a color allocation error?
  7.12 Is R Y2K-compliant?
  7.13 How do I convert factors to numeric?
  7.14 Are Trellis displays implemented in R?
  7.15 What are the enclosing and parent environments?

8 R Programming
  8.1 How should I write summary methods?
  8.2 How can I debug dynamically loaded code?
  8.3 How can I inspect R objects when debugging?

9 R Bugs
  9.1 What is a bug?
  9.2 How to report a bug

10 Acknowledgments




1 Introduction
**************

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

1.1 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.

1.2 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 (*Note What is CRAN?::).

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

   In publications, please refer to this FAQ as Hornik (2000), "The R FAQ"
and give the above, _official_ URL.

1.4 Notation
============

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

1.5 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 port of R are described in the "Frequently
Asked Questions for R for Windows"
(http://www.stats.ox.ac.uk/pub/R/rw-FAQ.html).  If you have information on
Windows or Mac systems that you think should be added to this document,
please let me know.

2 R Basics
**********

2.1 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 (*note 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.  *Note What are the
differences between R and S?::, for a discussion of the differences between
R and S.

   R was initially written by Ross Ihaka <Ross.Ihaka@r-project.org> and
Robert Gentleman <Robert.Gentleman@r-project.org>, who are Senior Lecturers
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, 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").

2.2 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, ppc-linux,
mips-sgi-irix, alpha-linux, alpha-dec-osf4, rs6000-ibm-aix, hppa-hp-hpux,
sparc-linux, and sparc-sun-solaris.

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

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

   The current stable Unix/Windows version is 1.1.0, the unstable one is
1.2.0.  Typically, new features are introduced in the development versions;
updates of stable versions are for bug fixes mostly.  The version for the
Mac is pre-alpha.

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

   Sources, binaries and documentation for R can be obtained via CRAN, the
"Comprehensive R Archive Network" (see *Note 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 three
existing flavors of the R sources, and can be one of `r-release' (latest
released version), `r-release-patched' (latest released version with
patches applied), and `r-devel' (current development version).  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/'.

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

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

   If binaries are available for your platform (see *Note 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 *Note What
machines does R run on?::).  The file `INSTALL' that comes with the R
distribution contains instructions.

   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 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.

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

   The `bin/windows/windows-NT' directory of a CRAN site contains the
latest binary distributions of R for 32 bit versions of MS Windows (i.e.,
95, 98 or NT), as well as binary distributions for a large number of add-on
packages from CRAN.  The Windows version of R was created by Robert
Gentleman, and is now being developed and maintained by Guido Masarotto
<Guido.Masarotto@r-project.org> and Brian D. Ripley
<Brian.Ripley@r-project.org>.

   For most installations the installer `rwinst.exe' will be the easiest
tool to use.

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

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

   The Power Macintosh port is temporarily on hold, and currently no binary
distribution is available.  We hope that this will change soon.

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

   The `bin/linux' directory of a CRAN site contains Debian 2.1/2.2/2.3
packages for the i386 platform (now part of the Debian distribution and
maintained by Doug Bates), Red Hat 6.x packages for the alpha, i386 and
sparc platforms (maintained by Naoki Takebayashi, Martyn Plummer, and Vin
Everett, respectively), SuSE 5.3/6.4 i386 packages by Albrecht Gebhardt,
and Linuxppc 5.0 RPMs by Alex Buerkle.

   The `bin/osf' directory of a CRAN site contains RPMs for alpha systems
running Digital Unix 4.0 by Albrecht Gebhardt.

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

2.7 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
*Note 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.

Furthermore, the "R Language Definition" manual (`R-lang') is currently
being written, and will be available in R version 1.2.  This is the
"Kernighan & Ritchie of R", explaining evaluation, parsing, object oriented
programming, computing on the language, and so forth.

   In addition to material written specifically for R, documentation for
S/S-PLUS (see *Note R and S::) can be used in combination with this FAQ
(*note 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/'.

   Last, but not least, Ross' and Robert's experience in designing and
implementing R is described in:

     @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}
     }

2.8 Citing R
============

   To cite R in publications, use the above Ihaka & Gentleman (1996), "R: A
Language for Data Analysis and Graphics", _Journal of Computational and
Graphical Statistics_, *5*, 299-314.

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

   Thanks to Martin Maechler <Martin.Maechler@r-project.org>, 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.

   It is recommended that you send mail to r-help rather than only to the R
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 *Note 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/mail/mail.html' file in CRAN.  An HTML
archive of the lists are available via
`http://www.ens.gu.edu.au/robertk/R/'.

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

2.10 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 can be found at the URL

     `http://cran.r-project.org/'     (Austria)

(which is the same as `http://cran.at.r-project.org/') and is currently
being mirrored daily at

     `http://cran.dk.r-project.org/'  (Denmark)
     `http://cran.it.r-project.org/'  (Italy)
     `http://cran.ch.r-project.org/'  (Switzerland)
     `http://cran.uk.r-project.org/'  (United Kingdom)
     `http://cran.us.r-project.org/'  (USA/Wisconsin)

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 or as two gzipped tar files (ready for 1.4M floppies), 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.

3 R and S
*********

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

   S is a very high level language and an environment for data analysis and
graphics.  S was written by Richard A. Becker, John M. Chambers, and Allan
R. Wilks of AT&T Bell Laboratories Statistics Research Department.

   The primary references for S are two books by the creators of S.

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

     This book is often called the "_Blue Book_".

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

     This is also called the "_White Book_".

   Version 4 of S, a major revision of S designed by John Chambers to
improve its usefulness at every stage of the programming process, is
described in "Programming with Data"
(http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/) by John M. Chambers
(1998), Springer: New York, ISBN 0-387-98503-4.

   In 1998, the Association for Computing Machinery (ACM) presented its
Software System Award to John Chambers for the design of the S system.  The
ACM citation stated that "S 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."  See
`http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html' for "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.

   The "Frequently Asked Questions about S" (http://lib.stat.cmu.edu/S/faq)
contains further information about S, but is not up-to-date.

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

   S-PLUS is a value-added version of S sold by Statistical Sciences, Inc.
(now a division of Mathsoft, Inc.).  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 MathSoft S-PLUS page (http://www.mathsoft.com/splus/) for
further information.

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

3.3.1 Lexical scoping
---------------------

   Whereas the developers of R have tried to stick to the S language as
defined in "The New S Language" (Blue Book, see *Note What is S?::), they
have 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"
(*note What documentation exists for R?::) and a paper on "Lexical Scope
and Statistical Computing" by Robert Gentleman and Ross Ihaka which can be
obtained from the `doc/misc' directory of a CRAN site and will appear in the
_Journal of Computational and Graphical Statistics_ around the beginning of
2000.

   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 *Note 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 *Note 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.

3.3.2 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'.

3.3.3 Others
------------

   Apart from lexical scoping and its implications, R follows the S
language definition in the Blue Book 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'.  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.  It also loads a saved image 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 SHLIB'.

   * In R, `attach()' currently only works for lists and data frames (not
     for directories).  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 `eval(EXPR, sys.frame(sys.parent())),' which also works 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).


   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.

3.4 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.
The paper "An Approach to Providing Mathematical Annotation in Plots" by
Paul Murrell and Ross Ihaka, which will soon appear in the _Journal of
Computational and Graphical Statistics_, has more details on this.

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

   *Rcgi* is a CGI WWW interface to R by Mark J. Ray <h089@mth.uea.ac.uk>.
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
<jeff@math.montana.edu>.  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/').

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

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

   The R distribution comes with the following extra packages:

*ctest*
     A collection of Classical TESTs, including the Bartlett, Fisher,
     Kruskal-Wallis, Kolmogorov-Smirnov, 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.

*modreg*
     MODern REGression: smoothing and local methods.

*mva*
     MultiVariate Analysis.  Currently contains code for principal
     components, canonical correlations, metric multidimensional scaling,
     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.

*ts*
     Time Series.

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

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

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

*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.

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

*RODBC*
     ODBC support and a back end database.

*RmSQL*
     An interface between R and the mSQL database system.

*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.

*acepack*
     ace (Alternating Conditional Expectations) and avas (Additivity and
     VAriance Stabilization for regression) for selecting regression
     transformations.

*akima*
     Linear or cubic spline interpolation for irregularly gridded data.

*ash*
     Programs for 1D and 2D density estimation.

*bindata*
     Generation of correlated artificial binary data.

*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.

*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.

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

*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.

*e1071*
     Miscellaneous functions used at the Department of Statistics at TU Wien
     (E1071), including moments, short-time Fourier transforms, Independent
     Component Analysis, and simulation of a Wiener process.

*fdim*
     Functions for calculating fractal dimension.

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

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

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

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

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

*integrate*
     Adaptive quadrature in up to 20 dimensions.

*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.

*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.

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

*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.

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

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

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

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

*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.

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

*pls*
     Univariate Partial Least Squares Regression.

*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.

*quadprog*
     For solving quadratic programming problems.

*quantreg*
     Compute regression quantiles and some related rank statistics.

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

*rpart*
     Recursive Partitioning.

*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.

*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.

*stataread*
     Read and write Stata v6 `.dta' files.

*survival5*
     Functions for survival analysis, version 5 (suggests *date*), the main
     new feature being penalized (partial) likelihood.

*tree*
     Classification and regression trees.

*tripack*
     A constrained two-dimensional Delaunay triangulation package.

*tseries*
     Additional code 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.

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

See CRAN `src/contrib/INDEX' 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

*HTML*
     Functions for exporting R objects as HTML tables.

*RMySQL*
     An interface between R and the MySQL database system.

*RPgSQL*
     An interface between R and the PostgreSQL database system.

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

*cxx*
     A small C++ test package.

*dopt*
     Finding D-optimal experimental designs.

*dse*
     Multivariate time series.

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

*funfits*
     An integrated set of functions for fitting curves and surfaces
     including thin plate splines, kriging and neural networks.

*hdf5*
     Interface to the NCSA HDF5 library.

*multidim*
     Code for correspondence analysis and other multidimensional descriptive
     statistics.

*syskern*
     Functions for writing code that is OS and R/S independent.

*tframe*
     Functions for writing code that is independent of the representation of
     time.

*timeslab*
     Time series routines.

   Jim Lindsey <jlindsey@luc.ac.be> 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.

5.2 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 INSTALL /path/to/PKG_VERSION.tar.gz

at the shell prompt to install to the default R directory tree (the
`library' subdirectory of `R_HOME').  To install to another tree (e.g.,
your private one), use

     $ R 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 your `~/.Renviron' file.

5.3 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':
     
     MASS         Main package of Venables and Ripley's MASS
     base         The R base package
     class        Functions for classification
     cluster      Functions for clustering
     ctest        Classical tests
     date         Functions for handling dates
     eda          Exploratory Data Analysis
     gee          Generalized Estimating Equation models
     locfit       Local regression, likelihood and density estimation
     lqs          Resistant regression and covariance estimation
     modreg       Modern regression: smoothing and local methods
     mva          Classical Multivariate Analysis
     nlme         Gaussian linear and nonlinear mixed-effects models
     nls          Nonlinear regression
     nnet         Software for feed-forward neural networks with a single
                  hidden layer and for multinomial log-linear models.
     splines      Regression spline functions and classes
     stepfun      Step functions, including empirical distributions
     survival5    Survival analysis
     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

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

   You can unload the loaded package PKG by

     detach("package:PKG")

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

   To remove the packages PKG_1, ..., PKG_N from the default library or the
library LIB, do

     $ R REMOVE PKG_1 ... PKG_N

or

     $ R REMOVE -l LIB PKG_1 ... PKG_N

respectively.

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

   A package consists of a subdirectory containing the files `DESCRIPTION'
and `INDEX', and the subdirectories `R', `data', `exec', `inst', `man', and
`src' (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, *note What documentation
exists for R?::, and gives information on package structure, the configure
and cleanup mechanisms, and on automated package checking and building.

   *Note What is CRAN?::, for information on uploading a package to CRAN.

5.6 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 *Note What is S?::);
Generalized Additive Models (*gam*) 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 miscellania.

   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 <Kurt.Hornik@r-project.org>.

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

6.1 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, PSPP) 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.

6.2 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.  This prompts you for the arguments; in particular,
you can increase the memory size this way (*note Why does R run out of
memory?::).

6.3 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

7 R Miscellania
***************

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

   R (currently) uses a _static_ memory model.  This means that when it
starts up, it asks the operating system to reserve a fixed amount of memory
for it.  The size of this chunk cannot be changed subsequently.  Hence, it
can happen that not enough memory was allocated, e.g., when trying to read
large data sets into R.

   In these cases, you should restart R with more memory available, using
the command line options `--nsize' and `--vsize'.  To understand these
options, one needs to know that R maintains separate areas for fixed and
variable sized objects.  The first of these is allocated as an array of
"cons cells" (Lisp programmers will know what they are, others may think of
them as the building blocks of the language itself, parse trees, etc.), and
the second are thrown on a "heap".  The `--nsize' option can be used to
specify the number of cons cells which R is to use (the default is 250000),
and the `--vsize' option to specify the size of the vector heap in bytes
(the default is 6 MB).  Both options must either be integers or integers
ending with `M', `K', or `k' meaning `Mega' (2^20), (computer) `Kilo'
(2^10), or regular `kilo' (1000).

   E.g., to read in a table of 5000 observations on 40 numeric variables,
`R --vsize=6M' should do (which currently is the default).

   Note that the information on where to find vectors and strings on the
heap is stored using cons cells.  Thus, it may also be necessary to
allocate more space for cons cells in order to perform computations with
very "large" variable-size objects.

   You can find out the current memory consumption (the proportion of heap
and cons cells used) by typing `gc()' at the R prompt.  This may help you
in finding out whether to increase `--vsize' or `--nsize'.  Note that
following `gcinfo(TRUE)', automatic garbage collection always prints memory
use statistics.

   As of version 0.62.3, R will tell you whether you ran out of cons or
heap memory.

   The defaults for `--nsize' and `--vsize' can be changed by setting the
environment variables `R_NSIZE' and `R_VSIZE' respectively, perhaps most
conveniently on Unix in the R environment file (`~/.Renviron' by default).

   When using `read.table()', the memory requirements are in fact higher
than anticipated, because the file is first read in as one long string
which is then split again.  Use `scan()' if possible in case you run out of
memory when reading in a large table.

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

   R sometimes has problems parsing a file which does not end in a newline.
This can happen for example when Emacs is used for editing the file and
`next-line-add-newlines' is set to `nil'.  To avoid the problem, either set
`require-final-newline' to a non-`nil' value in one of your Emacs startup
files, or make sure R-mode (*note Is there Emacs support for R?::) is used
for editing R source files (which locally ensures this setting).

   Earlier R versions had a similar problem when reading in data files, but
this should have been taken care of now.

7.3 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.

7.4 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.

7.5 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.)

7.6 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.

7.7 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]

7.8 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.

7.9 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.

7.10 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"').

7.11 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.

7.12 Is R Y2K-compliant?
========================

   We expect R to be Y2K compliant when compiled and run on a Y2K compliant
system.  In particular R does not internally represent or manipulate dates
as two-digit quantities.  However, no guarantee of Y2K compliance is
provided for R.  R is free software and comes with _no warranty whatsoever_.

   R, like any other programming language, can be used to write programs
and manipulate data in ways that are not Y2K compliant.

7.13 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.

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

   Not yet.  Meanwhile, you could look at `coplot()' and `dotplot()' 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.

   (By the way, "Trellis" is a trademark which cannot be used in R;
instead, the term "lattice" has been proposed for the R equivalent.)

7.15 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.

8 R Programming
***************

8.1 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.

8.2 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,
*note What documentation exists for R?::.

8.3 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'.

9 R Bugs
********

9.1 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.

9.2 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@lists.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.

10 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, 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 ...

