This document contains answers to some of the most frequently asked questions about R.
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.
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.
You can also obtain the R FAQ from the doc/FAQ
subdirectory of a CRAN site (What is CRAN?).
In publications, please refer to this FAQ as Hornik (2001), "The R FAQ" and give the above, official URL.
Everything should be pretty standard. R> is used for the R
prompt, and a $ for the shell prompt (where applicable).
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". If you have information on Windows or Mac systems that you think should be added to this document, please let me know.
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. 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, 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").
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, i386-sun-solaris, 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.
The current stable version is 1.2.2, the unstable one is 1.3.0. Typically, new features are introduced in the development versions; updates of stable versions are for bug fixes mostly.
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 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/.
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 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 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
${prefix}/man/man1
${prefix}/lib/R
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.
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 rwinst.exe will be the
easiest tool to use.
See the R Windows FAQ for more details.
A binhexed self-extracting archive containing a binary distribution of R for the Mac (to run under MacOS 8.6 to MacOS 9.1 or MacOS X under Classic Environment) has been made available by Stefano Iacus at http://www.eco-dip.unimi.it/R/.
In the near future, Mac binaries will also be made available in the
bin/macos directory of a CRAN site.
The bin/linux directory of a CRAN site contains Debian
2.2/2.3 packages for the i386 platform (now part of the Debian
distribution and maintained by Doug Bates), Red Hat 6.x/7.x packages for
the alpha and i386 platforms and 6.x packages for the sparc platform
(maintained by Naoki Takebayashi, Martyn Plummer, and Vin Everett,
respectively), SuSE 6.4/7.0 i386 packages by Albrecht Gebhardt, Mandrake
7.2 i386 packages by Michele Alzetta, 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 Digital Unix 4.0.
No other binary distributions have thus far been made publically available.
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.
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.
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)
is a guide to importing and exporting data to and from R.
R-lang),
a first version of the "Kernighan & Ritchie of R", explains
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 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.
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}
}
Thanks to Martin Maechler, there are three mailing lists devoted to R.
r-announce
r-devel
r-help
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 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://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.
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.hu.r-project.org/ (Hungary) 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.
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.
This is also referred to as the "Brown Book", and of historical interest only.
This book is often called the "Blue Book", and introduced what is now known as S version 2.
This is also called the "White Book", and introduced S version 3, which added structures to facilitate statistical modeling in S.
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 at CMU.
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 for further information.
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.
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.
There are some differences in the modeling code, such as
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)).
na.action is set to "na.omit" by default in R,
but not set in S.
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.
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.
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).)
.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--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--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.
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.
dyn.load() can only load shared libraries, as
created for example by R CMD SHLIB.
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.
For() loops are not necessary and hence not supported.
assign() uses the argument envir= rather than
where= as in S.
int * rather than long *
as in S.
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.
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().
is.vector(c(a = 1:3)) returns FALSE in S and TRUE
in R).
DF is a
data frame, then is.matrix(DF) returns FALSE in R and
TRUE in S).
value. E.g., fun(a) <-
b is evaluated as (fun<-)(a, value = b).
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.
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.
for() loop is local to the inside
of the loop. In R it is local to the environment where the for()
statement is executed.
tapply(simplify=TRUE) returns a vector where R returns a
one-dimensional array (which can have named dimnames).
"aA" < "Bb" is
true or false). From version 1.2.0 the locale can be (re-)set in R by
the Sys.setlocale() function.
missing(arg) remains TRUE if arg is
subsequently modified; in R it doesn't.
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.
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.
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 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/).
The R distribution comes with the following extra packages:
The following packages are available from the CRAN
src/contrib area.
VR
bundle.
VR bundle.
8/30/53,
30Aug53, 30 August 1953, ..., August 30 53, or
any mixture of these.
VR bundle.
VR bundle.
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
Directory src/contrib/Omegahat contains yet unreleased packages
from the Omegahat Project for Statistical Computing. Currently, there are
More code has been posted to the r-help mailing list, and can be obtained from the mailing list archive.
(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 default R directory tree (the
library subdirectory of R_HOME). 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.)
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
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
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")
To remove the packages pkg_1, ..., pkg_n from the default library or the library lib, do
$ R CMD REMOVE pkg_1 ... pkg_n
or
$ R CMD REMOVE -l lib pkg_1 ... pkg_n
respectively.
A package consists of a subdirectory containing the files
DESCRIPTION and INDEX, and the subdirectories R,
data, exec, inst, man, src, and
tests (some of which can be missing). Optionally the package can
also contain script files configure and cleanup which are
executed before and after installation.
See 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.
See What is CRAN?, for information on uploading a package to CRAN.
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 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.
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.
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.
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
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.
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.
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.
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.
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.)
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.
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]
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.
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.
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").
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.
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.
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.
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.)
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.
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.
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:
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).
f(trim=.5)). Argument names are also usually syntactic names,
but again can be anything if they are quoted.
$ 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.)
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. The long-term plan is to have something
better than gam() for additive models, perhaps along the lines of
BRUTO.
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.
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?.
The most convenient way is to call R_PV from the symbolic
debugger.
See section "Inspecting R objects when debugging" in Writing R Extensions.
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/MakeconfMAKEFLAGS, as in
MAKEFLAGS="CFLAGS=-O3" R CMD SHLIB *.c
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.
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.
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 ...