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


Frequently Asked Questions on R

Version 1.7-18, 2003-06-13

ISBN 3-901167-51-X

Kurt Hornik

Node:Introduction, Next:, Previous:Top, Up:Top

1 Introduction

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

Node:Legalese, Next:, Previous:Introduction, Up:Introduction

1.1 Legalese

This document is copyright © 1998-2003 by Kurt Hornik.

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


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

Node:Obtaining this document, Next:, Previous:Legalese, Up:Introduction

1.2 Obtaining this document

The latest version of this document is always available from


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 (see What is CRAN?).

Node:Citing this document, Next:, Previous:Obtaining this document, Up:Introduction

1.3 Citing this document

In publications, please refer to this FAQ as Hornik (2003), "The R FAQ", and give the above, official URL and the ISBN 3-901167-51-X.

Node:Notation, Next:, Previous:Citing this document, Up:Introduction

1.4 Notation

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

Node:Feedback, Previous:Notation, Up:Introduction

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 and Mac OS ports of R are described in the "R for Windows FAQ" and the "R for Macintosh FAQ/DOC". If you have information on Mac or Windows systems that you think should be added to this document, please let me know.

Node:R Basics, Next:, Previous:Introduction, Up:Top

2 R Basics

Node:What is R?, Next:, Previous:R Basics, Up: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 (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, Stefano Iacus, Ross Ihaka, Friedrich Leisch, Thomas Lumley, Martin Maechler, Guido Masarotto, Duncan Murdoch, Paul Murrell, Martyn Plummer, 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").

Node:What machines does R run on?, Next:, Previous:What is R?, Up:R Basics

2.2 What machines does R run on?

R is being developed for the Unix, Windows and Mac families of operating systems. Support for Mac OS Classic will end with the 1.7 series.

The current version of R will configure and build under a number of common Unix platforms including i386-freebsd, cpu-linux-gnu for the i386, alpha, arm, hppa, ia64, m68k, powerpc, and sparc CPUs (see e.g. http://buildd.debian.org/build.php?&pkg=r-base), i386-sun-solaris, powerpc-apple-darwin, mips-sgi-irix, alpha-dec-osf4, rs6000-ibm-aix, hppa-hp-hpux, and sparc-sun-solaris.

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

Node:What is the current version of R?, Next:, Previous:What machines does R run on?, Up:R Basics

2.3 What is the current version of R?

The current released version is 1.7.1. Based on this `major.minor.patchlevel' numbering scheme, there are two development versions of R, working towards the next patch (`r-patched') and minor or eventually major (`r-devel') releases of R, respectively. Version r-patched is for bug fixes mostly. New features are typically introduced in r-devel.

Node:How can R be obtained?, Next:, Previous:What is the current version of R?, Up:R Basics

2.4 How can R be obtained?

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

Sources are also available via anonymous rsync. Use

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

to create a copy of the source tree specified by module in the subdirectory R of the current directory, where module specifies one of the three existing flavors of the R sources, and can be one of r-release (current released version), r-patched (patched released version), and r-devel (development version). 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/.

Node:How can R be installed?, Next:, Previous:How can R be obtained?, Up:R Basics

2.5 How can R be installed?

Node:How can R be installed (Unix), Next:, Previous:How can R be installed?, Up:How can R be installed?

2.5.1 How can R be installed (Unix)

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

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

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

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

$ ./configure
$ make

If these commands execute successfully, the R binary and a shell script front-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:

the front-end shell script
the man page
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.

Node:How can R be installed (Windows), Next:, Previous:How can R be installed (Unix), Up:How can R be installed?

2.5.2 How can R be installed (Windows)

The bin/windows directory of a CRAN site contains binaries for a base distribution and a large number of add-on packages from CRAN to run on Windows 95, 98, ME, NT4, 2000, and XP (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 Duncan Murdoch and Brian D. Ripley.

For most installations the Windows installer program will be the easiest tool to use.

See the "R for Windows FAQ" for more details.

Node:How can R be installed (Macintosh), Previous:How can R be installed (Windows), Up:How can R be installed?

2.5.3 How can R be installed (Macintosh)

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

The "R for Macintosh FAQ/DOC" has more details.

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

Node:Are there Unix binaries for R?, Next:, Previous:How can R be installed?, Up:R Basics

2.6 Are there Unix binaries for R?

The bin/linux directory of a CRAN site contains Debian stable/testing packages for the i386 platform (now part of the Debian distribution and maintained by Dirk Eddelbuettel), Mandrake 8.0/8.1/8.2/9.0/9.1 i386 packages by Michele Alzetta, Red Hat 7.x/8.x/9 i386 and 7.x alpha packages (maintained by Martyn Plummer and Naoki Takebayashi, respectively), SuSE 7.3/8.0/8.1/8.2 i386 packages by Detlef Steuer, and VineLinux 2.6 i386 packages by Susunu Tanimura.

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 testing; 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.

No other binary distributions are currently publically available.

Node:What documentation exists for R?, Next:, Previous:Are there Unix binaries for R?, Up:R Basics

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 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.

Books on R include

Peter Dalgaard (2002), "Introductory Statistics with R", Springer: New York, ISBN 0-387-95475-9.

J. Fox (2002), "An R and S-PLUS Companion to Applied Regression", Sage Publications, ISBN 0-761-92280-6 (softcover) or 0-761-92279-2 (hardcover), http://www.socsci.mcmaster.ca/jfox/Books/Companion/.

The book

W. N. Venables and B. D. Ripley (2002), "Modern Applied Statistics with S. Fourth Edition". Springer, ISBN 0-387-95457-0

has a home page at http://www.stats.ox.ac.uk/pub/MASS4/ providing additional material. Its companion is

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

and 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.

In addition to material written specifically or explicitly 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?). Introductory books include

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

A. Krause and M. Olsen (2002), "The Basics of S-PLUS" (Third Edition). Springer, ISBN 0-387-95456-2

The book

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

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

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

D. Nolan and T. Speed (2000), "Stat Labs: Mathematical Statistics Through Applications", Springer Texts in Statistics, ISBN 0-387-98974-9

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

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

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


Node:Citing R, Next:, Previous:What documentation exists for R?, Up:R Basics

2.8 Citing R

To cite R in publications, use

  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}

Node:What mailing lists exist for R?, Next:, Previous:Citing R, Up:R Basics

2.9 What mailing lists exist for R?

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

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

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

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

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

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

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

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

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

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

Node:What is CRAN?, Next:, Previous:What mailing lists exist for R?, Up:R Basics

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 at TU Wien, Austria, can be found at the URL


and is currently being mirrored daily at

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

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

From CRAN, you can obtain the latest official release of R, daily snapshots of R (copies of the current CVS trees), as gzipped and bzipped tar files, a wealth of additional contributed code, as well as prebuilt binaries for various operating systems (Linux, MacOS Classic, MacOS X, 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 cran@r-project.org. Note that CRAN generally does not accept submissions of precompiled binaries due to security reasons.

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.

Node:Can I use R for commercial purposes?, Previous:What is CRAN?, Up:R Basics

2.11 Can I use R for commercial purposes?

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

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

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

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

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

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

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

Node:R and S, Next:, Previous:R Basics, Up:Top

3 R and S

Node:What is S?, Next:, Previous:R and S, Up:R and S

3.1 What is S?

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

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

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

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

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.

Node:What is S-PLUS?, Next:, Previous:What is S?, Up:R and S

3.2 What is S-PLUS?

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

See the Insightful S-PLUS page for further information.

Node:What are the differences between R and S?, Next:, Previous:What is S-PLUS?, Up:R and S

3.3 What are the differences between R and S?

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

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

Node:Lexical scoping, Next:, Previous:What are the differences between R and S?, Up:What are the differences between R and S?

3.3.1 Lexical scoping

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

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

Consider the following function:

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

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

S> cube(2)
Error in sq():  Object "n" not found
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),

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"

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.

Node:Models, Next:, Previous:Lexical scoping, Up:What are the differences between R and S?

3.3.2 Models

There are some differences in the modeling code, such as

Node:Others, Previous:Models, Up:What are the differences between R and S?

3.3.3 Others

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

Some known differences are the following.