R FAQ

# R FAQ

## Frequently Asked Questions on R

## Version 1.1-0, 2000/06/12

# Table of Contents

- 1 Introduction
  - 1.1 Legalese
  - 1.2 Obtaining this document
  - 1.3 Citing this document
  - 1.4 Notation
  - 1.5 Feedback
- 2 R Basics
  - 2.1 What is R?
  - 2.2 What machines does R run on?
  - 2.3 What is the current version of R?
  - 2.4 How can R be obtained?
  - 2.5 How can R be installed?
    - 2.5.1 How can R be installed (Unix)
    - 2.5.2 How can R be installed (Windows)
    - 2.5.3 How can R be installed (Macintosh)
  - 2.6 Are there Unix binaries for R?
  - 2.7 What documentation exists for R?
  - 2.8 Citing R
  - 2.9 What mailing lists exist for R?
  - 2.10 What is CRAN?
- 3 R and S
  - 3.1 What is S?
  - 3.2 What is S-PLUS?
  - 3.3 What are the differences between R and S?
    - 3.3.1 Lexical scoping
    - 3.3.2 Models
    - 3.3.3 Others
  - 3.4 Is there anything R can do that S-PLUS cannot?
- 4 R Web Interfaces
- 5 R Add-On Packages
  - 5.1 Which add-on packages exist for R?
  - 5.2 How can add-on packages be installed?
  - 5.3 How can add-on packages be used?
  - 5.4 How can add-on packages be removed?
  - 5.5 How can I create an R package?
  - 5.6 How can I contribute to R?
- 6 R and Emacs
  - 6.1 Is there Emacs support for R?
  - 6.2 Should I run R from within Emacs?
  - 6.3 Debugging R from within Emacs
- 7 R Miscellania
  - 7.1 Why does R run out of memory?
  - 7.2 Why does sourcing a correct file fail?
  - 7.3 How can I set components of a list to NULL?
  - 7.4 How can I save my workspace?
  - 7.5 How can I clean up my workspace?
  - 7.6 How can I get eval() and D() to work?
  - 7.7 Why do my matrices lose dimensions?
  - 7.8 How does autoloading work?
  - 7.9 How should I set options?
  - 7.10 How do file names work in Windows?
  - 7.11 Why does plotting give a color allocation error?
  - 7.12 Is R Y2K-compliant?
  - 7.13 How do I convert factors to numeric?
  - 7.14 Are Trellis displays implemented in R?
  - 7.15 What are the enclosing and parent environments?
- 8 R Programming
  - 8.1 How should I write summary methods?
  - 8.2 How can I debug dynamically loaded code?
  - 8.3 How can I inspect R objects when debugging?
- 9 R Bugs
  - 9.1 What is a bug?
  - 9.2 How to report a bug
- 10 Acknowledgments

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

- Introduction:
- R Basics:
- R and S:
- R Web Interfaces:
- R Add-On Packages:
- R and Emacs:
- R Miscellania:
- R Programming:
- R Bugs:
- Acknowledgments:

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

# 1 Introduction

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

- Legalese:
- Obtaining this document:
- Citing this document:
- Notation:
- Feedback:

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

## 1.1 Legalese

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

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

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

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

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

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

## 1.2 Obtaining this document

The latest version of this document is always available from

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

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

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

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

## 1.3 Citing this document

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

Node:Notation, Next:Feedback, 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 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.

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

# 2 R Basics

- What is R?:
- What machines does R run on?:
- What is the current version of R?:
- How can R be obtained?:
- How can R be installed?:
- Are there Unix binaries for R?:
- What documentation exists for R?:
- Citing R:
- What mailing lists exist for R?:
- What is CRAN?:

Node:What is R?, Next:What machines does R run on?, 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 a discussion of the differences between R and S.

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

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

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

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

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

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

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

## 2.3 What is the current version of R?

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

Node:How can R be obtained?, Next:How can R be installed?, 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` (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/.

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

## 2.5 How can R be installed?

- How can R be installed (Unix):
- How can R be installed (Windows):
- How can R be installed (Macintosh):

Node:How can R be installed (Unix), Next:How can R be installed (Windows), 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 instructions.

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

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

    $ ./configure
    $ make

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

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

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

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

`${prefix}/bin`
  the front-end shell script
`${prefix}/man/man1`
  the man page
`${prefix}/lib/R`
  all the rest (libraries, on-line help system, ...). This is the "R Home Directory" (`R_HOME`) of the installed system.

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

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

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

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

Node:How can R be installed (Windows), Next:How can R be installed (Macintosh), 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/windows-NT` directory of a CRAN site contains the latest binary distributions of R for 32 bit versions of MS Windows (i.e., 95, 98 or NT), as well as binary distributions for a large number of add-on packages from CRAN. The Windows version of R was created by Robert Gentleman, and is now being developed and maintained by Guido Masarotto 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.

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

Node:Are there Unix binaries for R?, Next:What documentation exists for R?, 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 2.1/2.2/2.3 packages for the i386 platform (now part of the Debian distribution and maintained by Doug Bates), Red Hat 6.x packages for the alpha, i386 and sparc platforms (maintained by Naoki Takebayashi, Martyn Plummer, and Vin Everett, respectively), SuSE 5.3/6.4 i386 packages by Albrecht Gebhardt, and Linuxppc 5.0 RPMs by Alex Buerkle.

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

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

Node:What documentation exists for R?, Next:Citing R, 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.

- "An Introduction to R" (`R-intro`) includes information on data types, programming elements, statistical modeling and graphics. This document is based on the "Notes on S-PLUS" by Bill Venables and David Smith.
- "Writing R Extensions" (`R-exts`) currently describes the process of creating R add-on packages, writing R documentation, R's system and foreign language interfaces, and the R API.

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

In addition to material written specifically for R, documentation for S/S-PLUS (see 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/.

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

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

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

## 2.8 Citing R

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

Node:What mailing lists exist for R?, Next:What is CRAN?, 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.

`r-announce`
  This list is for announcements about the development of R and the availability of new code.
`r-devel`
  This list is for discussions about the future of R and pre-testing of new versions. It is meant for those who maintain an active position in the development of R.
`r-help`
  The `main' R mailing list, for announcements about the development of R and the availability of new code, questions and answers about problems and solutions using R, enhancements and patches to the source code and documentation of R, comparison and compatibility with S and S-PLUS, and for the posting of nice examples and benchmarks.

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

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

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

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

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

Archives of the above three mailing lists are made available on the net in a monthly schedule via the `doc/mail/mail.html` file in CRAN. An HTML archive of the lists are available via http://www.ens.gu.edu.au/robertk/R/.

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

Node:What is CRAN?, 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 can be found at the URL

> http://cran.r-project.org/ | (Austria)

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

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

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

From CRAN, you can obtain the latest official release of R, daily snapshots of R (copies of the current CVS trees), as gzipped and bzipped tar files or as two gzipped tar files (ready for 1.4M floppies), a wealth of additional contributed code, as well as prebuilt binaries for various operating systems (Linux, Digital Unix, and MS Windows). CRAN also provides access to documentation on R, existing mailing lists and the R Bug Tracking system.

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

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

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

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

# 3 R and S

- What is S?:
- What is S-PLUS?:
- What are the differences between R and S?:
- Is there anything R can do that S-PLUS cannot?:

Node:What is S?, Next:What is S-PLUS?, 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. S was written by Richard A. Becker, John M. Chambers, and Allan R. Wilks of AT&T Bell Laboratories Statistics Research Department.

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

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

  This book is often called the "Blue Book".

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

  This is also called the "White Book".

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

In 1998, the Association for Computing Machinery (ACM) presented its Software System Award to John Chambers for the design of the S system. The ACM citation stated that "S has forever altered the way people analyze, visualize, and manipulate data .... S is an elegant, widely accepted, and enduring software system, with conceptual integrity, thanks to the insight, taste, and effort of John Chambers." See http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html for "Stages in the Evolution of S".

There is a huge amount of user-contributed code for S, available at the S Repository at CMU.

The "Frequently Asked Questions about S" contains further information about S, but is not up-to-date.

Node:What is S-PLUS?, Next:What are the differences between R and S?, 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 Statistical Sciences, Inc. (now a division of Mathsoft, Inc.). Based on the S language, S-PLUS provides functionality in a wide variety of areas, including robust regression, modern non-parametric regression, time series, survival analysis, multivariate analysis, classical statistical tests, quality control, and graphics drivers. Add-on modules add additional capabilities for wavelet analysis, spatial statistics, GARCH models, and design of experiments.

See the MathSoft S-PLUS page for further information.

Node:What are the differences between R and S?, Next:Is there anything R can do that S-PLUS cannot?, Previous:What is S-PLUS?, Up:R and S

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

- Lexical scoping:
- Models:
- Others:

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

### 3.3.1 Lexical scoping

Whereas the developers of R have tried to stick to the S language as defined in "The New S Language" (Blue Book, see What is S?), they have adopted the evaluation model of Scheme.

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

Consider the following function:

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

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

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

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

    R> cube(2)
    [1] 8

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

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

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

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

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

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

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

However, in R there is a much easier solution:

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

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

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

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

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

For R, `MC()` simply is

    MC <- function(f, env) f

(lexical scope!), a version for S is

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

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

Lexical scoping allows using function closures and maintaining local state. A simple example (taken from Abelson and Sussman) is obtained by typing `demo(scoping)` at the R prompt. Further information is provided in the standard R reference "R: A Language for Data Analysis and Graphics" (see What documentation exists for R?) and a paper on "Lexical Scope and Statistical Computing" by Robert Gentleman and Ross Ihaka which can be obtained from the `doc/misc` directory of a CRAN site and will appear in the Journal of Computational and Graphical Statistics around the beginning of 2000.

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

The down side is that if R crashes you will lose all the work for the current session. Saving and restoring the memory "images" (the functions and data stored in R's internal memory at any time) can be a bit slow, especially if they are big. In S this does not happen, because everything is saved in disk files and if you crash nothing is likely to happen to them. (In fact, one might conjecture that the S developers felt that the price of changing their approach to persistent storage just to accommodate lexical scope was far too expensive.) Hence, when doing important work, you might consider saving often (see 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:Others, 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

- Whereas in S, you would use `lm(y ~ x^3)` to regress `y` on `x^3`, in R, you have to insulate powers of numeric vectors (using `I()`), i.e., you have to use `lm(y ~ I(x^3))`.
- The glm family objects are implemented differently in R and S. The same functionality is available but the components have different names.
- Option `na.action` is set to `"na.omit"` by default in R, but not set in S.
- Terms objects are stored differently. In S a terms object is an expression with attributes, in R it is a formula with attributes. The attributes have the same names but are mostly stored differently. The major difference in functionality is that a terms object is subscriptable in S but not in R. If you can't imagine why this would matter then you don't need to know.
- Finally, in R `y~x+0` is an alternative to `y~x-1` for specifying a model with no intercept. Models with no parameters at all can be specified by `y~0`.

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 Book as much as possible, and hence really is an "implementation" of S. There are some intentional differences where the behavior of S is considered "not clean". In general, the rationale is that R should help you detect programming errors, while at the same time being as compatible as possible with S.

Some known differences are the following.

- In R, if `x` is a list, then `x[i] <- NULL` and `x[[i]] <- NULL` remove the specified elements from `x`. The first of these is incompatible with S, where it is a no-op. (Note that you can set elements to `NULL` using `x[i] <- list(NULL)`.)
- In S, the functions named `.First` and `.Last` in the `.Data` directory can be used for customizing, as they are executed at the very beginning and end of a session, respectively.

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

- In R, `T` and `F` are just variables being set to `TRUE` and `FALSE`, respectively, but are not reserved words as in S and hence can be overwritten by the user. (This helps e.g. when you have factors with levels `"T"` or `"F"`.) Hence, when writing code you should always use `TRUE` and `FALSE`.
- In R, `dyn.load()` can only load shared libraries, as created for example by `R SHLIB`.
- In R, `attach()` currently only works for lists and data frames (not for directories). Also, you cannot attach at position 1.
- Categories do not exist in R, and never will as they are deprecated now in S. Use factors instead.
- In R, `For()` loops are not necessary and hence not supported.
- In R, `assign()` uses the argument `envir=` rather than `where=` as in S.
- The random number generators are different, and the seeds have different length.
- R passes integer objects to C as `int *` rather than `long *` as in S.
- R has no single precision storage mode. However, as of version 0.65.1, there is a single precision interface to C/FORTRAN subroutines.
- By default, `ls()` returns the names of the objects in the current (under R) and global (under S) environment, respectively. For example, given

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

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

- R allows for zero-extent matrices (and arrays, i.e., some elements of the `dim` attribute vector can be 0). This has been determined a useful feature as it helps reducing the need for special-case tests for empty subsets. For example, if `x` is a matrix, `x[, FALSE]` is not `NULL` but a "matrix" with 0 columns. Hence, such objects need to be tested for by checking whether their `length()` is zero (which works in both R and S), and not using `is.null()`.
- Named vectors are considered vectors in R but not in S (e.g., `is.vector(c(a = 1:3))` returns `FALSE` in S and `TRUE` in R).
- Data frames are not considered as matrices in R (i.e., if `DF` is a data frame, then `is.matrix(DF)` returns `FALSE` in R and `TRUE` in S).
- R by default uses treatment contrasts in the unordered case, whereas S uses the Helmert ones. This is a deliberate difference reflecting the opinion that treatment contrasts are more natural.
- In R, the last argument (which corresponds to the right hand side) of an assignment function must be named `value`. E.g., `fun(a) <- b` is evaluated as `(fun<-)(a, value = b)`.
- In S, `substitute()` searches for names for substitution in the given expression in three places: the actual and the default arguments of the matching call, and the local frame (in that order). R looks in the local frame only, with the special rule to use a "promise" if a variable is not evaluated. Since the local frame is initialized with the actual arguments or the default expressions, this is usually equivalent to S, until assignment takes place.
- In R, `eval(EXPR, sys.parent())` does not work. Instead, one should use `eval(EXPR, sys.frame(sys.parent())),` which also works in S.
- In S, the index variable in a `for()` loop is local to the inside of the loop. In R it is local to the environment where the `for()` statement is executed.
- In S, `tapply(simplify=TRUE)` returns a vector where R returns a one-dimensional array (which can have named dimnames).

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

Node:Is there anything R can do that S-PLUS cannot?, Previous:What are the differences between R and S?, Up:R and S

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

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

R offers several graphics features that S-PLUS does not, such as finer handling of line types, more convenient color handling (via palettes), gamma correction for color, and, most importantly, mathematical annotation in plot texts, via input expressions reminiscent of TeX constructs. See the help page for `plotmath`, which features an impressive on-line example. The paper "An Approach to Providing Mathematical Annotation in Plots" by Paul Murrell and Ross Ihaka, which will soon appear in the Journal of Computational and Graphical Statistics, has more details on this.

Node:R Web Interfaces, Next:R Add-On Packages, Previous:R and S, Up:Top

# 4 R Web Interfaces

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

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

Rweb is developed and maintained by Jeff Banfield. The Rweb Home Page 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/).

Node:R Add-On Packages, Next:R and Emacs, Previous:R Web Interfaces, Up:Top

# 5 R Add-On Packages

- Which add-on packages exist for R?:
- How can add-on packages be installed?:
- How can add-on packages be used?:
- How can add-on packages be removed?:
- How can I create an R package?:
- How can I contribute to R?:

Node:Which add-on packages exist for R?, Next:How can add-on packages be installed?, Previous:R Add-On Packages, Up:R Add-On Packages

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

The R distribution comes with the following extra packages:

ctest
  A collection of Classical TESTs, including the Bartlett, Fisher, Kruskal-Wallis, Kolmogorov-Smirnov, and Wilcoxon tests.
eda
  Exploratory Data Analysis. Currently only contains functions for robust line fitting, and median polish and smoothing.
lqs
  Resistant regression and covariance estimation.
modreg
  MODern REGression: smoothing and local methods.
mva
  MultiVariate Analysis. Currently contains code for principal components, canonical correlations, metric multidimensional scaling, and hierarchical and k-means clustering.
nls
  Nonlinear regression routines.
splines
  Regression spline functions and classes.
stepfun
  Code for dealing with STEP FUNctions, including empirical cumulative distribution functions.
tcltk
  Interface and language bindings to Tcl/Tk GUI elements.
ts
  Time Series.

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

Devore5
  Data sets and sample analyses from "Probability and Statistics for Engineering and the Sciences (5th ed)" by Jay L. Devore, 2000, Duxbury.
GenKern
  Functions for generating and manipulating generalised binned kernel density estimates.
KernSmooth
  Functions for kernel smoothing (and density estimation) corresponding to the book "Kernel Smoothing" by M. P. Wand and M. C. Jones, 1995.
MASS
  Functions and datasets from the main package of Venables and Ripley, "Modern Applied Statistics with S-PLUS". Contained in the `VR` bundle.
NISTnls
  A set of test nonlinear least squares examples from NIST, the U.S. National Institute for Standards and Technology.
RODBC
  ODBC support and a back end database.
RmSQL
  An interface between R and the mSQL database system.
SASmixed
  Data sets and sample linear mixed effects analyses corresponding to the examples in "SAS System for Mixed Models" by R. C. Littell, G. A. Milliken, W. W. Stroup and R. D. Wolfinger, 1996, SAS Institute.
acepack
  ace (Alternating Conditional Expectations) and avas (Additivity and VAriance Stabilization for regression) for selecting regression transformations.
akima
  Linear or cubic spline interpolation for irregularly gridded data.
ash
  Programs for 1D and 2D density estimation.
bindata
  Generation of correlated artificial binary data.
boot
  Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Applications" by A. C. Davison and D. V. Hinkley, 1997, Cambridge University Press.
bootstrap
  Software (bootstrap, cross-validation, jackknife), data and errata for the book "An Introduction to the Bootstrap" by B. Efron and R. Tibshirani, 1993, Chapman and Hall.
cclust
  Convex clustering methods, including k-means algorithm, on-line update algorithm (Hard Competitive Learning) and Neural Gas algorithm (Soft Competitive Learning) and calculation of several indexes for finding the number of clusters in a data set.
cfa
  Analysis of configuration frequencies.
chron
  A package for working with chronological objects (times and dates).
class
  Functions for classification (k-nearest neighbor and LVQ). Contained in the `VR` bundle.
cluster
  Functions for cluster analysis.
coda
  Output analysis and diagnostics for Markov Chain Monte Carlo (MCMC) simulations.
date
  Functions for dealing with dates. The most useful of them accepts a vector of input dates in any of the forms `8/30/53`, `30Aug53`, `30 August 1953`, ..., `August 30 53`, or any mixture of these.
e1071
  Miscellaneous functions used at the Department of Statistics at TU Wien (E1071), including moments, short-time Fourier transforms, Independent Component Analysis, and simulation of a Wiener process.
fdim
  Functions for calculating fractal dimension.
fracdiff
  Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Applied Statistics, 1989).
gee
  An implementation of the Liang/Zeger generalized estimating equation approach to GLMs for dependent data.
gss
  A comprehensive package for structural multivariate function estimation using smoothing splines.
hpower
  A suite of functions to compute power and sample size for tests of the general linear hypothesis.
ineq
  Inequality, concentration and poverty measures, and Lorenz curves (empirical and theoretic).
integrate
  Adaptive quadrature in up to 20 dimensions.
leaps
  A package which performs an exhaustive search for the best subsets of a given set of potential regressors, using a branch-and-bound algorithm, and also performs searches using a number of less time-consuming techniques.
lmtest
  A collection of tests on the assumptions of linear regression models from the book "The linear regression model under test" by W. Kraemer and H. Sonnberger, 1986, Physica.
locfit
  Local Regression, likelihood and density estimation.
logspline
  Logspline density estimation.
maptree
  Functions with example data for graphing and mapping models from hierarchical clustering and classification and regression trees.
mclust
  Model-based cluster analysis.
mda
  Code for mixture discriminant analysis (MDA), flexible discriminant analysis (FDA), penalized discriminant analysis (PDA), multivariate additive regression splines (MARS), adaptive back-fitting splines (BRUTO), and penalized regression.
mlbench
  A collection of artificial and real-world machine learning benchmark problems, including the Boston housing data.
multilm
  A basic method for fitting and testing multivariate linear models, including stabilized test procedures by Laeuter et. al.
multiv
  Functions for hierarchical clustering, partitioning, bond energy algorithm, Sammon mapping, PCA and correspondence analysis.
nlme
  Fit and compare Gaussian linear and nonlinear mixed-effects models.
nnet
  Software for single hidden layer perceptrons ("feed-forward neural networks"), and for multinomial log-linear models. Contained in the `VR` bundle.
norm
  Analysis of multivariate normal datasets with missing values.
oz
  Functions for plotting Australia's coastline and state boundaries.
pls
  Univariate Partial Least Squares Regression.
polymars
  Polychotomous regression based on Multivariate Adaptive Regression Splines.
polynom
  A collection of functions to implement a class for univariate polynomial manipulations.
princurve
  Fits a principal curve to a matrix of points in arbitrary dimension.
pspline
  Smoothing splines with penalties on order m derivatives.
quadprog
  For solving quadratic programming problems.
quantreg
  Compute regression quantiles and some related rank statistics.
rmeta
  Functions for simple fixed and random effects meta-analysis for two-sample comparison of binary outcomes.
rpart
  Recursive Partitioning.
sgeostat
  An object-oriented framework for geostatistical modeling.
sm
  Software linked to the book "Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-PLUS Illustrations" by A. W. Bowman and A. Azzalini (1997), Oxford University Press.
spatial
  Functions for kriging and point pattern analysis from "Modern Applied Statistics with S-PLUS" by W. Venables and B. Ripley. Contained in the `VR` bundle.
stataread
  Read and write Stata v6 `.dta` files.
survival5
  Functions for survival analysis, version 5 (suggests date), the main new feature being penalized (partial) likelihood.
tree
  Classification and regression trees.
tripack
  A constrained two-dimensional Delaunay triangulation package.
tseries
  Additional code for time series analysis.
wavethresh
  Software to perform 1-d and 2-d wavelet statistics and transforms.
wle
  Robust statistical inference via a weighted likelihood approach.
xgobi
  Interface to the XGobi and XGvis programs for graphical data analysis.
zmatrix
  Matrices with numeric indices starting at zero rather than one.

See CRAN `src/contrib/INDEX` for more information.

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

HTML
  Functions for exporting R objects as HTML tables.
RMySQL
  An interface between R and the MySQL database system.
RPgSQL
  An interface between R and the PostgreSQL database system.
cmprsk
  Estimation, testing and regression modeling of subdistribution functions in competing risks.
cxx
  A small C++ test package.
dopt
  Finding D-optimal experimental designs.
dse
  Multivariate time series.
foreign
  Functions for reading data stored by statistical software like Minitab, SAS, SPSS, etc.
funfits
  An integrated set of functions for fitting curves and surfaces including thin plate splines, kriging and neural networks.
hdf5
  Interface to the NCSA HDF5 library.
multidim
  Code for correspondence analysis and other multidimensional descriptive statistics.
syskern
  Functions for writing code that is OS and R/S independent.
tframe
  Functions for writing code that is independent of the representation of time.
timeslab
  Time series routines.

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

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

Node:How can add-on packages be installed?, Next:How can add-on packages be used?, Previous:Which add-on packages exist for R?, Up:R Add-On Packages

## 5.2 How can add-on packages be installed?

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

    $ R INSTALL /path/to/pkg_version.tar.gz

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

    $ R INSTALL -l lib /path/to/pkg_version.tar.gz

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

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

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

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

into your (Bourne) shell profile or your `~/.Renviron` file.

Node:How can add-on packages be used?, Next:How can add-on packages be removed?, Previous:How can add-on packages be installed?, Up:R Add-On Packages

## 5.3 How can add-on packages be used?

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

    library()

at the R prompt.

This produces something like

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

    mystuff      My own R functions, nicely packaged but not documented

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

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

You can "load" the installed package pkg by

    library(pkg)

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

    help(package = pkg)
    library(help = pkg)

You can unload the loaded package pkg by

    detach("package:pkg")

Node:How can add-on packages be removed?, Next:How can I create an R package?, Previous:How can add-on packages be used?, Up:R Add-On Packages

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

To remove the packages pkg_1, ..., pkg_n from the default library or the library lib, do

    $ R REMOVE pkg_1 ... pkg_n

or

    $ R REMOVE -l lib pkg_1 ... pkg_n

respectively.

Node:How can I create an R package?, Next:How can I contribute to R?, Previous:How can add-on packages be removed?, Up:R Add-On Packages

## 5.5 How can I create an R package?

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

See section "Creating R packages" in Writing R Extensions, for details. This manual is included in the R distribution, 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.

Node:How can I contribute to R?, Previous:How can I create an R package?, Up:R Add-On Packages

## 5.6 How can I contribute to R?

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

One place where functionality is still missing is the modeling software as described in "Statistical Models in S" (see What is S?); Generalized Additive Models (gam) 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 miscellania.

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

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

Node:R and Emacs, Next:R Miscellania, Previous:R Add-On Packages, Up:Top

# 6 R and Emacs

- Is there Emacs support for R?:
- Should I run R from within Emacs?:
- Debugging R from within Emacs:

Node:Is there Emacs support for R?, Next:Should I run R from within Emacs?, Previous:R and Emacs, Up:R and Emacs

## 6.1 Is there Emacs support for R?

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

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

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

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

ESS comes with detailed installation instructions.

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

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

Node:Should I run R from within Emacs?, Next:Debugging R from within Emacs, Previous:Is there Emacs support for R?, Up:R and Emacs

## 6.2 Should I run R from within Emacs?

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

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

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

To specify command line arguments for the inferior R process, use `C-u M-x R` for starting R. This prompts you for the arguments; in particular, you can increase the memory size this way (see Why does R run out of memory?).

Node:Debugging R from within Emacs, Previous:Should I run R from within Emacs?, Up:R and Emacs

## 6.3 Debugging R from within Emacs

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

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

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

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

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

Node:R Miscellania, Next:R Programming, Previous:R and Emacs, Up:Top

# 7 R Miscellania

- Why does R run out of memory?:
- Why does sourcing a correct file fail?:
- How can I set components of a list to NULL?:
- How can I save my workspace?:
- How can I clean up my workspace?:
- How can I get eval() and D() to work?:
- Why do my matrices lose dimensions?:
- How does autoloading work?:
- How should I set options?:
- How do file names work in Windows?:
- Why does plotting give a color allocation error?:
- Is R Y2K-compliant?:
- How do I convert factors to numeric?:
- Are Trellis displays implemented in R?:
- What are the enclosing and parent environments?:

Node:Why does R run out of memory?, Next:Why does sourcing a correct file fail?, Previous:R Miscellania, Up:R Miscellania

## 7.1 Why does R run out of memory?

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

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

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

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

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

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

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

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

Node:Why does sourcing a correct file fail?, Next:How can I set components of a list to NULL?, Previous:Why does R run out of memory?, Up:R Miscellania

## 7.2 Why does sourcing a correct file fail?

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

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

Node:How can I set components of a list to NULL?, Next:How can I save my workspace?, Previous:Why does sourcing a correct file fail?, Up:R Miscellania

## 7.3 How can I set components of a list to NULL?

You can use

    x[i] <- list(NULL)

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

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

Node:How can I save my workspace?, Next:How can I clean up my workspace?, Previous:How can I set components of a list to NULL?, Up:R Miscellania

## 7.4 How can I save my workspace?

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

Node:How can I clean up my workspace?, Next:How can I get eval() and D() to work?, Previous:How can I save my workspace?, Up:R Miscellania

## 7.5 How can I clean up my workspace?

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

    rm(list = ls(all = TRUE))

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

Node:How can I get eval() and D() to work?, Next:Why do my matrices lose dimensions?, Previous:How can I clean up my workspace?, Up:R Miscellania

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

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

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

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

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

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

or

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

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

Node:Why do my matrices lose dimensions?, Next:How does autoloading work?, Previous:How can I get eval() and D() to work?, Up:R Miscellania

## 7.7 Why do my matrices lose dimensions?

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

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

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

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

    somerows <- mat[index, ]

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

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

Node:How does autoloading work?, Next:How should I set options?, Previous:Why do my matrices lose dimensions?, Up:R Miscellania

## 7.8 How does autoloading work?

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

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

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

Node:How should I set options?, Next:How do file names work in Windows?, Previous:How does autoloading work?, Up:R Miscellania

## 7.9 How should I set options?

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

For example, given

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

we obtain:

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

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

Node:How do file names work in Windows?, Next:Why does plotting give a color allocation error?, Previous:How should I set options?, Up:R Miscellania

## 7.10 How do file names work in Windows?

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

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

Node:Why does plotting give a color allocation error?, Next:Is R Y2K-compliant?, Previous:How do file names work in Windows?, Up:R Miscellania

## 7.11 Why does plotting give a color allocation error?

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

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

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

Node:Is R Y2K-compliant?, Next:How do I convert factors to numeric?, Previous:Why does plotting give a color allocation error?, Up:R Miscellania

## 7.12 Is R Y2K-compliant?

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

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

Node:How do I convert factors to numeric?, Next:Are Trellis displays implemented in R?, Previous:Is R Y2K-compliant?, Up:R Miscellania

## 7.13 How do I convert factors to numeric?

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

    as.numeric(as.character(f))

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

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

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

Node:Are Trellis displays implemented in R?, Next:What are the enclosing and parent environments?, Previous:How do I convert factors to numeric?, Up:R Miscellania

## 7.14 Are Trellis displays implemented in R?

Not yet. Meanwhile, you could look at `coplot()` and `dotplot()` which might do at least some of what you want. Note also that the R version of `pairs()` is fairly general and provides most of the functionality of `splom()`, and that R's default plot method has an argument `asp` allowing to specify (and fix against device resizing) the aspect ratio of the plot.

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

Node:What are the enclosing and parent environments?, Previous:Are Trellis displays implemented in R?, Up:R Miscellania

## 7.15 What are the enclosing and parent environments?

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

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

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

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

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

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

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

Node:R Programming, Next:R Bugs, Previous:R Miscellania, Up:Top

# 8 R Programming

- How should I write summary methods?:
- How can I debug dynamically loaded code?:
- How can I inspect R objects when debugging?:

Node:How should I write summary methods?, Next:How can I debug dynamically loaded code?, Previous:R Programming, Up:R Programming

## 8.1 How should I write summary methods?

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

Node:How can I debug dynamically loaded code?, Next:How can I inspect R objects when debugging?, Previous:How should I write summary methods?, Up:R Programming

## 8.2 How can I debug dynamically loaded code?

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

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

Node:How can I inspect R objects when debugging?, Previous:How can I debug dynamically loaded code?, Up:R Programming

## 8.3 How can I inspect R objects when debugging?

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

See section "Inspecting R objects when debugging" in Writing R Extensions.

Node:R Bugs, Next:Acknowledgments, Previous:R Programming, Up:Top

# 9 R Bugs

- What is a bug?:
- How to report a bug:

Node:What is a bug?, Next:How to report a bug, Previous:R Bugs, Up:R Bugs

## 9.1 What is a bug?

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

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

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

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

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

Node:How to report a bug, Previous:What is a bug?, Up:R Bugs

## 9.2 How to report a bug

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

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

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

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

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

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

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

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

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

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

Node:Acknowledgments, Previous:R Bugs, Up:Top

# 10 Acknowledgments

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

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

More to some soon ...