The R Project SVN – R – Diff – /trunk/FAQ

 R FAQ
 Frequently Asked Questions on R
-Version 0.99-4, 2000/02/20
+Version 1.0-0, 2000/02/27
 Kurt Hornik
 Table of Contents
 *****************
 .8 How does autoloading work?
 .9 How should I set options?
 .10 How do file names work in Windows?
 .11 Why does plotting give a color allocation error?
 .12 Is R Y2K-compliant?
+.13 How do I convert factors to numeric?
 R Programming
 .1 How should I write summary methods?
 .2 How can I debug dynamically loaded code?
 .3 How can I inspect R objects when debugging?
 site (*Note What is CRAN?::).
 .3 Citing this document
 ========================
-   In publications, please refer to this FAQ as Hornik (1999), "The R FAQ"
+   In publications, please refer to this FAQ as Hornik (2000), "The R FAQ"
 and give the above, _official_ URL.
 .4 Notation
 ============
 modify the R source code CVS archive.  The group currently consists of Doug
 Bates, 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://stat.auckland.ac.nz/r/r.html'.  It is free
+   R has a home page at `http://www.r-project.org/'.  It is free software
-software distributed under a GNU-style copyleft, and an official part of
+distributed under a GNU-style copyleft, and an official part of the GNU
-the GNU project ("GNU S").
+project ("GNU S").
 .2 What machines does R run on?
 ================================
    R is being developed for the Unix, Windows and Mac families of operating
    If you know about other platforms, please drop us a note.
 .3 What is the current version of R?
 =====================================
-   The current stable Unix/Windows version is 0.99.0, the unstable one is
+   The current stable Unix/Windows version is 1.0.0, the unstable one is
-.0.0.  Typically, new features are introduced in the development versions;
+.1.0.  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.
-   The release of version 1.0 is scheduled for February 29, 2000.
 .4 How can R be obtained?
 ==========================
    Sources, binaries and documentation for R can be obtained via CRAN, the
 "Comprehensive R Archive Network" (see *Note What is CRAN?::).
+   Sources are also available via anonymous rsync.  Use
+     rsync -rC rsync.r-project.org::MODULE R
+to create a copy of the source tree specified by MODULE in the subdirectory
+`R' of the current directory, where MODULE specifies one of the three
+existing flavors of the R sources, and can be one of `r-release' (latest
+released version), `r-release-patched' (latest released version with
+patches applied), and `r-devel' (current development version).  The rsync
+trees are created directly from the master CVS archive and are updated
+hourly.  The `-C' option in the rsync command is to cause it to skip the
+CVS directories.  Further information on rsync is available at
+`http://rsync.samba.org/rsync/'.
 .5 How can R be installed?
 ===========================
 .5.1 How can R be installed (Unix)
 -----------------------------------
    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, cd to the directory thus
+   In the simplest case, untar the R source code, change to the directory
-created, and issue the following commands (at the shell prompt):
+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
 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 standalone GNU Info; use `make
+suitable for reading online with Emacs or stand-alone GNU Info; use `make
 info' to create these versions (note that this requires `makeinfo' version
 ).
    Finally, use `make check' to find out whether your R system works
 correctly.
      (some) executables
 `${prefix}/man/man1'
      man pages
-`${prefix}/share/R'
+`${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
 distribution is available.  We hope that this will change soon.
 .6 Are there Unix binaries for R?
 ==================================
-   The `bin/linux' directory contains Debian 2.1 and 2.2 packages for the
+   The `bin/linux' directory of a CRAN site contains Debian 2.1 and 2.2
-i386 platform (now part of the Debian distribution and maintained by Doug
+packages for the i386 platform (now part of the Debian distribution and
-Bates), Red Hat 6.x packages for the alpha, i386 and sparc platforms
+maintained by Doug Bates), Red Hat 6.x packages for the alpha, i386 and
-(maintained by Naoki Takebayashi, Martyn Plummer, and Vin Everett,
+sparc platforms (maintained by Naoki Takebayashi, Martyn Plummer, and Vin
-respectively), SuSE 5.3/6.0/6.2/6.3 i386 packages by Albrecht Gebhardt, and
+Everett, respectively), SuSE 5.3/6.0/6.2/6.3 i386 packages by Albrecht
-Linuxppc 5.0 RPMs by Alex Buerkle.
+Gebhardt, and Linuxppc 5.0 RPMs by Alex Buerkle.
-   The `bin/osf' directory contains RPMs for alpha systems running Digital
+   The `bin/osf' directory of a CRAN site contains RPMs for alpha systems
-Unix 4.0 by Albrecht Gebhardt.
+running Digital Unix 4.0 by Albrecht Gebhardt.
    No other binary distributions have thus far been made publically
 available.
 .7 What documentation exists for R?
    This documentation can also be made available as one reference manual
 for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX, see
 *Note How can R be installed?::.  An up-to-date HTML version is always
 available for web browsing at `http://stat.ethz.ch/R/manual/'.
-   Currently, three further R manuals are being written:
+   The R distribution also comes with the following manuals.
    * "An Introduction to R" (`R-intro') includes information on data types,
-     programming elements, statistical modelling and graphics.  This
+     programming elements, statistical modeling and graphics.  This
      document is based on the "Notes on S-PLUS" by Bill Venables and David
      Smith.
-   * "R Language Definition" (`R-lang') is the "Kernighan & Ritchie of R",
-     explaining evaluation, parsing, object oriented programming, computing
-     on the language, and so forth.
-   * "Writing R Extensions" (`R-exts') currently desribes the process of
+   * "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
-The "R Language Definition" will be available for the 1.0 release, the
+being written, and will be available in R version 1.2.  This is the
+"Kernighan & Ritchie of R", explaining evaluation, parsing, object oriented
-other two already come with R.
+programming, computing on the language, and so forth.
    In addition to material written specifically for R, documentation for
 S/S-PLUS (see *Note R and S::) can be used in combination with this FAQ
 (*note What are the differences between R and S?::).  We recommend
      W. N. Venables and B. D. Ripley (1999), "Modern Applied Statistics with
      S-PLUS.  Third Edition".  Springer, ISBN 0-387-98825-4.
-which has a home page at `http://www.stats.ox.ac.uk/pub/MASS3/' providing
+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 provide 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 volume on "S Programming", due
 in about April 2000, will provide an in-depth guide to writing software in
 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
+`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.
 improve its usefulness at every stage of the programming process, is
 described in "Programming with Data"
 (http://cm.bell-labs.com/cm/ms/departments/sia/Sbook/) by John M. Chambers
 (1998), Springer: New York, ISBN 0-387-98503-4.
-   In 1998, the Association for Computing Machinery presented its Software
+   In 1998, the Association for Computing Machinery (ACM) presented its
-System Award to John Chambers for the design of the S system. The ACM
+Software System Award to John Chambers for the design of the S system.  The
-citation stated that "S has forever altered the way people analyze,
+ACM citation stated that "S has forever altered the way people analyze,
-visualize, and manipulate data .... S is an elegant, widely accepted, and
+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://netlib.bell-labs.com/cm/ms/departments/sia/S/index.html' for
+`http://cm.bell-labs.com/cm/ms/departments/sia/S/history.html' for "Stages
-"Stages in the Evolution of S".
+in the Evolution of S".
    There is a huge amount of user-contributed code for S, available at the
-S Repository (http://lib.stat.cmu.edu) at CMU.
+S Repository (http://lib.stat.cmu.edu/S/) at CMU.
    The "Frequently Asked Questions about S" (http://lib.stat.cmu.edu/S/faq)
 contains further information about S, but is not up-to-date.
 .2 What is S-PLUS?
 state.  A simple example (taken from Abelson and Sussman) is obtained by
 typing `demo(scoping)' at the R prompt.  Further information is provided in
 the standard R reference "R: A Language for Data Analysis and Graphics"
 (*note What documentation exists for R?::) and a paper on "Lexical Scope
 and Statistical Computing" by Robert Gentleman and Ross Ihaka which can be
-obtained from the `doc/misc' directory of a CRAN site and will appear in
+obtained from the `doc/misc' directory of a CRAN site and will appear in the
-the _Journal of Computational and Graphical Statistics_ around the
+_Journal of Computational and Graphical Statistics_ around the beginning of
-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
 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.)  R is still in a beta stage, and may
+lexical scope was far too expensive.)  Hence, when doing important work,
-crash from time to time.  Hence, for important work you should consider
+you might consider saving often (see *Note How can I save my workspace?::)
-saving often (see *Note How can I save my workspace?::).  Other
+to safeguard against possible crashes.  Other possibilities are logging
-possibilities are logging your sessions, or have your R commands stored in
+your sessions, or have your R commands stored in text files which can be
-text files which can be read in using `source()'.
+read in using `source()'.
      *Note:*  If you run R from within Emacs (see *Note R and Emacs::), you
      can save the contents of the interaction buffer to a file and
      conveniently manipulate it using `ess-transcript-mode', as well as
      save source copies of all functions and data used.
    * 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
      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
+     levels `"T"' or `"F"'.)  Hence, when writing code you should always
-     `TRUE' and `FALSE'.
+     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
    * 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
+     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.
 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.
+in plot texts, via input expressions reminiscent of TeX constructs.  See
-Unfortunately, this feature still is mostly undocumented.  The paper "An
+the help page for `plotmath', which features an impressive on-line example.
-Approach to Providing Mathematical Annotation in Plots" by Paul Murrell and
+The paper "An Approach to Providing Mathematical Annotation in Plots" by
-Ross Ihaka, which will soon appear in the _Journal of Computational and
+Paul Murrell and Ross Ihaka, which will soon appear in the _Journal of
-Graphical Statistics_, has more details on this.
+Computational and Graphical Statistics_, has more details on this.
 R Web Interfaces
 ******************
-   *Rcgi* is a CGI WWW interface to R by Mark J Ray <h089@mth.uea.ac.uk>.
+   *Rcgi* is a CGI WWW interface to R by Mark J. Ray <h089@mth.uea.ac.uk>.
-Recent version have the ability to use "embedded code": you can mix user
+Recent versions have the ability to use "embedded code": you can mix user
 input and code, allowing the HTML author to do anything from load in data
 sets to enter most of the commands for users without writing CGI scripts.
 Graphical output is possible in PostScript or GIF formats and the executed
 code is presented to the user for revision.
    Demo and download are available from
 `http://www.mth.uea.ac.uk/~h089/Rcgi/'.
    *Rweb* is developed and maintained by Jeff Banfield
 <jeff@math.montana.edu>.  The Rweb Home Page
-(http://www.math.montana.edu/Rweb) provides access to all three versions of
+(http://www.math.montana.edu/Rweb/) provides access to all three versions
-Rweb--a simple text entry form that returns output and graphs, a more
+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.
-   A paper on Rweb, providing a detailed explanation of the different
+   The paper "Rweb: Web-based Statistical Analysis", providing a detailed
-versions of Rweb and an overview of how Rweb works, was published in the
+explanation of the different versions of Rweb and an overview of how Rweb
-Journal of Statistical Software
+works, was published in the Journal of Statistical Software
-(`http://www.stat.ucla.edu/journals/jss/v04/i01').
+(`http://www.stat.ucla.edu/journals/jss/v04/i01/').
 R Add-On Packages
 *******************
 .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,
+     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.
 *splines*
      Regression spline functions and classes.
 *stepfun*
-     Code for dealing with step functions, including empirical cumulative
+     Code for dealing with STEP FUNctions, including empirical cumulative
      distribution functions.
 *ts*
-     Time series.
+     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,
+     Engineering and the Sciences (5th ed)" by Jay L. Devore, 2000, Duxbury.
-     Duxbury.
 *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 library of Venables and Ripley,
+     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.
 *RmSQL*
      An interface between R and the mSQL database system.
 *Rnotes*
-     The data sets for the exercises in Rnotes (*note What documentation
+     The data sets for the exercises in "An Introduction to R" (*note What
-     exists for R?::).
+     documentation exists for R?::).
 *SASmixed*
-     Data sets and sample lme analyses corresponding to the examples in "SAS
+     Data sets and sample linear mixed effects analyses corresponding to the
-     System for Mixed Models" by Littel, Milliken, Stroup and Wolfinger,
+     examples in "SAS System for Mixed Models" by Littel, Milliken, Stroup
-, SAS Institute.
+     and Wolfinger, 1996, SAS Institute.
 *acepack*
      ace (Alternating Conditional Expectations) and avas (Additivity and
      VAriance Stabilization for regression) for selecting regression
      transformations.
 *stataread*
      Read and write Stata v6 `.dta' files.
 *survival5*
      Functions for survival analysis, version 5 (suggests *date*), the main
-     new feature being penalised (partial) likelihood.
+     new feature being penalized (partial) likelihood.
 *tree*
      Classification and regression trees.
 *tripack*
      $ 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 documentation
+packages from within R if you have access to CRAN.  See the help page for
-for `CRAN.packages()' for more information.
+`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 `$HOME/lib/R' and a public site-wide tree in
-`/usr/local/lib/R/site', put
+`/usr/local/lib/R-contrib', put
-     R_LIBS="$HOME/lib/R:/usr/local/lib/R/site"; export R_LIBS
+     R_LIBS="$HOME/lib/R:/usr/local/lib/R-contrib"; export R_LIBS
 into your (Bourne) shell profile or your `~/.Renviron' file.
 .3 How can add-on packages be used?
 ====================================
 `src' (some of which can be missing).  Optionally the package can also
 contain script files `configure' and `cleanup' which are executed before
 and after installation.
    See section "Creating R packages" in `Writing R Extensions', for details.
+This manual is included in the R distribution, *note What documentation
-This gives information on package structure, the configure and cleanup
+exists for R?::, and gives information on package structure, the configure
-mechanisms, and on automated package checking and building.
+and cleanup mechanisms, and on automated package checking and building.
    The web page `http://www.biostat.washington.edu/~thomas/Rlib.html'
 maintained by Thomas Lumley provides information on porting S packages to R.
    *Note What is CRAN?::, for information on uploading a package to CRAN.
    One place where functionality is still missing is the modeling software
 as described in "Statistical Models in S" (see *Note What is S?::);
 Generalized Additive Models (*gam*) and some of the nonlinear modeling code
 are not there yet.
-   The R Developer Page (http://developer.r-project.org) acts as an
+   The R Developer Page (http://developer.r-project.org/) acts as an
 intermediate repository for more or less finalized ideas and plans for the
 R statistical system.  It contains (pointers to) TODO lists, RFCs, various
 other writeups, ideas lists, and CVS miscellania.
    Many (more) of the packages available at the Statlib S Repository might
 *************
 .1 Is there Emacs support for R?
 =================================
-   There is an Emacs package which provides a standard interface between
+   There is an Emacs package called ESS ("Emacs Speaks Statistics") which
-statistical programs and statistical processes called ESS ("Emacs Speaks
+provides a standard interface between statistical programs and statistical
-Statistics").  It is intended to provide assistance for interactive
+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 desire for bug fixes and extensions to S-mode 4.8
+   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
-a unifying interface and framework for the user interface, would benefit
+that a unifying interface and framework for the user interface would
-both the user and the developer, by helping both groups conform to standard
+benefit both the user and the developer, by helping both groups conform to
-Emacs usage.  The end result is an increase in efficiency for statistical
+standard Emacs usage.  The end result is an increase in efficiency for
-programming and data analysis, over the usual tools.
+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,
 the Emacs GUD (Grand Unified Debugger) library with the recommended
 debugger GDB, type `M-x gdb' and give the path to the R _binary_, typically
 `R.X11', 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').
+`run --vsize=12M').
-   If you have ESS, you can do `C-u M-x R<RET>-d gdb' to start an inferior
+   If you have ESS, you can do `C-u M-x R <RET> - d <SPC> g d b <RET>' to
-R process with arguments `-d gdb'.
+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 `$R_HOME/bin/R.X11' as the program to
+then start GUD (`M-x gdb') giving the R binary (using its full path name)
-debug.  Use the program `ps' to find the process number of the currently
+as the program to debug.  Use the program `ps' to find the process number
-running R process then use the `attach' command in gdb to attach it to that
+of the currently running R process then use the `attach' command in gdb to
-process.  One advantage of this method is that you have separate `*R*' and
+attach it to that process.  One advantage of this method is that you have
-`*gud-gdb*' windows.  Within the `*R*' window you have all the ESS
+separate `*R*' and `*gud-gdb*' windows.  Within the `*R*' window you have
-facilities, such as object-name completion, that we know and love.
+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
 "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).  Boths options must either be integers or integers
+(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).
 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
+`"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")
 * x
 or
      g <- function(y) eval(substitute(y), sys.frame(sys.parent(n = 2)))
      g(a * b)
-   See the help pages for more examples.
+   See the help page for `deriv()' for more examples.
 .7 Why do my matrices lose dimensions?
 =======================================
    When a matrix with a single row or column is created by a subscripting
 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,
+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
 ========================
    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 whatsover_.
+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.
+.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.
 R Programming
 ***************
 .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
+`"summary.foo"', _and_ you should write a method `print.summary.foo()'
-nicely prints the summary information and invisibly returns its object.
+which nicely prints the summary information and invisibly returns its
-This approach is preferred over having `summary.foo()' print summary
+object.  This approach is preferred over having `summary.foo()' print
-information and return something useful, as sometimes you need to grab
+summary information and return something useful, as sometimes you need to
-something computed by `summary()' inside a function or similar.  In such
+grab something computed by `summary()' inside a function or similar.  In
-cases you don't want anything printed.
+such cases you don't want anything printed.
 .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,
-`Writing R Extensions'.
+*note What documentation exists for R?::.
 .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'.
 R Bugs
 ********
 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
+where its usual definition ought to be reasonable, it is probably a bug.
-a command does the wrong thing, that is a bug.  But be sure you know for
+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.
    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.
+never returns.  Do not report that `data.frame()' fails for large data
-Perhaps it fails when a variable name is a day of the week.  If this is so
+sets.  Perhaps it fails when a variable name is a day of the week.  If this
-then when others got your report they would try out the `data.frame()'
+is so then when others got your report they would try out the
-command on a large data set, probably with no day of the week variable
+`data.frame()' command on a large data set, probably with no day of the
-name, and not see any problem.  There is no way in the world that others
+week variable name, and not see any problem.  There is no way in the world
-could guess that they should try a day of the week variable name.
+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
+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
+   Invoking R with the `--vanilla' option may help in isolating a bug.
-ensures that the site profile and saved data files are not read.
+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
+`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'.
+`http://bugs.r-project.org/'.
    Bug reports on contributed packages should perhaps be sent to the
 package maintainer rather than to r-bugs.
 Acknowledgments

The R Project SVN R

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