Chapter 1. Getting Started and Getting Help


This chapter sets the groundwork for the other chapters. It explains how to download, install, and run R.

More importantly, it also explains how to get answers to your questions. The R community provides a wealth of documentation and help. You are not alone. Here are some common sources of help:

Local, installed documentation

When you install R on your computer, a mass of documentation is also installed. You can browse the local documentation (Recipe 1.6) and search it (Recipe 1.8). I am amazed how often I search the Web for an answer only to discover it was already available in the installed documentation.

Task views

A task view describes packages that are specific to one area of statistical work, such as econometrics, medical imaging, psychometrics, or spatial statistics. Each task view is written and maintained by an expert in the field. There are 28 such task views, so there is likely to be one or more for your areas of interest. I recommend that every beginner find and read at least one task view in order to gain a sense of R’s possibilities (Recipe 1.11).

Package documentation

Most packages include useful documentation. Many also include overviews and tutorials, called vignettes in the R community. The documentation is kept with the packages in package repositories, such as CRAN, and it is automatically installed on your machine when you install a package.

Mailing lists

Volunteers have generously donated many hours of time to answer beginners’ questions that are posted to the R mailing lists. The lists are archived, so you can search the archives for answers to your questions (Recipe 1.12).

Question and answer (Q&A) websites

On a Q&A site, anyone can post a question, and knowledgeable people can respond. Readers vote on the answers, so the best answers tend to emerge over time. All this information is tagged and archived for searching. These sites are a cross between a mailing list and a social network; the Stack Overflow site is a good example.

The Web

The Web is loaded with information about R, and there are R-specific tools for searching it (Recipe 1.10). The Web is a moving target, so be on the lookout for new, improved ways to organize and search information regarding R.

1.1. Downloading and Installing R


You want to install R on your computer.


Windows and OS X users can download R from CRAN, the Comprehensive R Archive Network. Linux and Unix users can install R packages using their package management tool:

  1. Open in your browser.

  2. Click on “CRAN”. You’ll see a list of mirror sites, organized by country.

  3. Select a site near you.

  4. Click on “Windows” under “Download and Install R”.

  5. Click on “base”.

  6. Click on the link for downloading the latest version of R (an .exe file).

  7. When the download completes, double-click on the .exe file and answer the usual questions.

  1. Open in your browser.

  2. Click on “CRAN”. You’ll see a list of mirror sites, organized by country.

  3. Select a site near you.

  4. Click on “MacOS X”.

  5. Click on the .pkg file for the latest version of R, under “Files:”, to download it.

  6. When the download completes, double-click on the .pkg file and answer the usual questions.

Linux or Unix

The major Linux distributions have packages for installing R. Here are some examples:

DistributionPackage name
Ubuntu or Debianr-base
Red Hat or FedoraR.i386

Use the system’s package manager to download and install the package. Normally, you will need the root password or sudo privileges; otherwise, ask a system administrator to perform the installation.


Installing R on Windows or OS X is straightforward because there are prebuilt binaries for those platforms. You need only follow the preceding instructions. The CRAN Web pages also contain links to installation-related resources, such as frequently asked questions (FAQs) and tips for special situations (“How do I install R when using Windows Vista?”) that you may find useful.

Theoretically, you can install R on Linux or Unix in one of two ways: by installing a distribution package or by building it from scratch. In practice, installing a package is the preferred route. The distribution packages greatly streamline both the initial installation and subsequent updates.

On Ubuntu or Debian, use apt-get to download and install R. Run under sudo to have the necessary privileges:

$ sudo apt-get install r-base

On Red Hat or Fedora, use yum:

$ sudo yum install R.i386

Most platforms also have graphical package managers, which you might find more convenient.

Beyond the base packages, I recommend installing the documentation packages, too. On my Ubuntu machine, for example, I installed r-base-html (because I like browsing the hyperlinked documentation) as well as r-doc-html, which installs the important R manuals locally:

$ sudo apt-get install r-base-html r-doc-html

Some Linux repositories also include prebuilt copies of R packages available on CRAN. I don’t use them because I’d rather get my software directly from CRAN itself, which usually has the freshest versions.

In rare cases, you may need to build R from scratch. You might have an obscure, unsupported version of Unix; or you might have special considerations regarding performance or configuration. The build procedure on Linux or Unix is quite standard. Download the tarball from the home page of your CRAN mirror; it’s called something like R-2.12.1.tar.gz, except the “2.12.1” will be replaced by the latest version. Unpack the tarball, look for a file called INSTALL, and follow the directions.

See Also

R in a Nutshell (O’Reilly) contains more details of downloading and installing R, including instructions for building the Windows and OS X versions. Perhaps the ultimate guide is the one entitled R Installation and Administration, available on CRAN, which describes building and installing R on a variety of platforms.

This recipe is about installing the base package. See Recipe 3.9 for installing add-on packages from CRAN.

1.2. Starting R


You want to run R on your computer.



Click on Start All Programs R; or double-click on the R icon on your desktop (assuming the installer created an icon for you).


Either click on the icon in the Applications directory or put the R icon on the dock and click on the icon there. Alternatively, you can just type R on a Unix command line in a shell.

Linux or Unix

Start the R program from the shell prompt using the R command (uppercase R).


How you start R depends upon your platform.

Starting on Windows

When you start R, it opens a new window. The window includes a text pane, called the R Console, where you enter R expressions (see Figure 1-1).

R on Windows

Figure 1-1. R on Windows

There is an odd thing about the Windows Start menu for R. Every time you upgrade to a new version of R, the Start menu expands to contain the new version while keeping all the previously installed versions. So if you’ve upgraded, you may face several choices such as “R 2.8.1”, “R 2.9.1”, “R 2.10.1”, and so forth. Pick the newest one. (You might also consider uninstalling the older versions to reduce the clutter.)

Using the Start menu is cumbersome, so I suggest starting R in one of two other ways: by creating a desktop shortcut or by double-clicking on your .RData file.

The installer may have created a desktop icon. If not, creating a shortcut is easy: follow the Start menu to the R program, but instead of left-clicking to run R, press and hold your mouse’s right button on the program name, drag the program name to your desktop, and release the mouse button. Windows will ask if you want to Copy Here or Move Here. Select Copy Here, and the shortcut will appear on your desktop.

Another way to start R is by double-clicking on a .RData file in your working directory. This is the file that R creates to save your workspace. The first time you create a directory, start R and change to that directory. Save your workspace there, either by exiting or using the save.image function. That will create the .RData file. Thereafter, you can simply open the directory in Windows Explorer and then double-click on the .RData file to start R.

Perhaps the most baffling aspect of starting R on Windows is embodied in a simple question: When R starts, what is the working directory? The answer, of course, is that “it depends”:

  • If you start R from the Start menu, the working directory is normally either C:\Documents and Settings\<username>\My Documents (Windows XP) or C:\Users\<username>\Documents (Windows Vista, Windows 7). You can override this default by setting the R_USER environment variable to an alternative directory path.

  • If you start R from a desktop shortcut, you can specify an alternative startup directory that becomes the working directory when R is started. To specify the alternative directory, right-click on the shortcut, select Properties, enter the directory path in the box labeled “Start in”, and click OK.

  • Starting R by double-clicking on your .RData file is the most straightforward solution to this little problem. R will automatically change its working directory to be the file’s directory, which is usually what you want.

In any event, you can always use the getwd function to discover your current working directory (Recipe 3.1).

Just for the record, Windows also has a console version of R called Rterm.exe. You’ll find it in the bin subdirectory of your R installation. It is much less convenient than the graphic user interface (GUI) version, and I never use it. I recommend it only for batch (noninteractive) usage such as running jobs from the Windows scheduler. In this book, I assume you are running the GUI version of R, not the console version.

Starting on OS X

Run R by clicking the R icon in the Applications folder. (If you use R frequently, you can drag it from the folder to the dock.) That will run the GUI version, which is somewhat more convenient than the console version. The GUI version displays your working directory, which is initially your home directory.

OS X also lets you run the console version of R by typing R at the shell prompt.

Starting on Linux and Unix

Start the console version of R from the Unix shell prompt simply by typing R, the name of the program. Be careful to type an uppercase R, not a lowercase r.

The R program has a bewildering number of command line options. Use the --help option to see the complete list.

See Also

See Recipe 1.4 for exiting from R, Recipe 3.1 for more about the current working directory, Recipe 3.2 for more about saving your workspace, and Recipe 3.11 for suppressing the start-up message. See Chapter 2 of R in a Nutshell.

1.3. Entering Commands


You’ve started R, and you’ve got a command prompt. Now what?


Simply enter expressions at the command prompt. R will evaluate them and print (display) the result. You can use command-line editing to facilitate typing.


R prompts you with “>”. To get started, just treat R like a big calculator: enter an expression, and R will evaluate the expression and print the result:

> 1+1
[1] 2

The computer adds one and one, giving two, and displays the result.

The [1] before the 2 might be confusing. To R, the result is a vector, even though it has only one element. R labels the value with [1] to signify that this is the first element of the vector...which is not surprising, since it’s the only element of the vector.

R will prompt you for input until you type a complete expression. The expression max(1,3,5) is a complete expression, so R stops reading input and evaluates what it’s got:

> max(1,3,5)
[1] 5

In contrast, “max(1,3,” is an incomplete expression, so R prompts you for more input. The prompt changes from greater-than (>) to plus (+), letting you know that R expects more:

> max(1,3,
+ 5)
[1] 5

It’s easy to mistype commands, and retyping them is tedious and frustrating. So R includes command-line editing to make life easier. It defines single keystrokes that let you easily recall, correct, and reexecute your commands. My own typical command-line interaction goes like this:

  1. I enter an R expression with a typo.

  2. R complains about my mistake.

  3. I press the up-arrow key to recall my mistaken line.

  4. I use the left and right arrow keys to move the cursor back to the error.

  5. I use the Delete key to delete the offending characters.

  6. I type the corrected characters, which inserts them into the command line.

  7. I press Enter to reexecute the corrected command.

That’s just the basics. R supports the usual keystrokes for recalling and editing command lines, as listed in Table 1-1.

Table 1-1. Keystrokes for command-line editing

Labeled keyCtrl-key combinationEffect
Up arrowCtrl-PRecall previous command by moving backward through the history of commands.
Down arrowCtrl-NMove forward through the history of commands.
BackspaceCtrl-HDelete the character to the left of cursor.
Delete (Del)Ctrl-DDelete the character to the right of cursor.
HomeCtrl-AMove cursor to the start of the line.
EndCtrl-EMove cursor to the end of the line.
Right arrowCtrl-FMove cursor right (forward) one character.
Left arrowCtrl-BMove cursor left (back) one character.
 Ctrl-KDelete everything from the cursor position to the end of the line.
 Ctrl-UClear the whole darn line and start over.
Tab Name completion (on some platforms).

On Windows and OS X, you can also use the mouse to highlight commands and then use the usual copy and paste commands to paste text into a new command line.

See Also

See Recipe 2.13. From the Windows main menu, follow Help Console for a complete list of keystrokes useful for command-line editing.

1.4. Exiting from R


You want to exit from R.



Select File Exit from the main menu; or click on the red X in the upper-right corner of the window frame.


Press CMD-q (apple-q); or click on the red X in the upper-left corner of the window frame.

Linux or Unix

At the command prompt, press Ctrl-D.

On all platforms, you can also use the q function (as in quit) to terminate the program.

> q()

Note the empty parentheses, which are necessary to call the function.


Whenever you exit, R asks if you want to save your workspace. You have three choices:

  • Save your workspace and exit.

  • Don’t save your workspace, but exit anyway.

  • Cancel, returning to the command prompt rather than exiting.

If you save your workspace, then R writes it to a file called .RData in the current working directory. This will overwrite the previously saved workspace, if any, so don’t save if you don’t like the changes to your workspace (e.g., if you have accidentally erased critical data).

See Also

See Recipe 3.1 for more about the current working directory and Recipe 3.2 for more about saving your workspace. See Chapter 2 of R in a Nutshell.

1.5. Interrupting R


You want to interrupt a long-running computation and return to the command prompt without exiting R.


Windows or OS X

Either press the Esc key or click on the Stop-sign icon.

Linux or Unix

Press Ctrl-C. This will interrupt R without terminating it.


Interrupting R can leave your variables in an indeterminate state, depending upon how far the computation had progressed. Check your workspace after interrupting.

See Also

See Recipe 1.4.

1.6. Viewing the Supplied Documentation


You want to read the documentation supplied with R.


Use the help.start function to see the documentation’s table of contents:

> help.start()

From there, links are available to all the installed documentation.


The base distribution of R includes a wealth of documentation—literally thousands of pages. When you install additional packages, those packages contain documentation that is also installed on your machine.

It is easy to browse this documentation via the help.start function, which opens a window on the top-level table of contents; see Figure 1-2.

Documentation table of contents

Figure 1-2. Documentation table of contents

The two links in the Reference section are especially useful:


Click here to see a list of all the installed packages, both in the base packages and the additional, installed packages. Click on a package name to see a list of its functions and datasets.

Search Engine & Keywords

Click here to access a simple search engine, which allows you to search the documentation by keyword or phrase. There is also a list of common keywords, organized by topic; click one to see the associated pages.

See Also

The local documentation is copied from the R Project website, which may have updated documents.

1.7. Getting Help on a Function


You want to know more about a function that is installed on your machine.


Use help to display the documentation for the function:

> help(functionname)

Use args for a quick reminder of the function arguments:

> args(functionname)

Use example to see examples of using the function:

> example(functionname)


I present many R functions in this book. Every R function has more bells and whistles than I can possibly describe. If a function catches your interest, I strongly suggest reading the help page for that function. One of its bells or whistles might be very useful to you.

Suppose you want to know more about the mean function. Use the help function like this:

> help(mean)

This will either open a window with function documentation or display the documentation on your console, depending upon your platform. A shortcut for the help command is to simply type ? followed by the function name:

> ?mean

Sometimes you just want a quick reminder of the arguments to a function: What are they, and in what order do they occur? Use the args function:

> args(mean)
function (x, ...) 
> args(sd)
function (x, na.rm = FALSE) 

The first line of output from args is a synopsis of the function call. For mean, the synopsis shows one argument, x, which is a vector of numbers. For sd, the synopsis shows the same vector, x, and an optional argument called na.rm. (You can ignore the second line of output, which is often just NULL.)

Most documentation for functions includes examples near the end. A cool feature of R is that you can request that it execute the examples, giving you a little demonstration of the function’s capabilities. The documentation for the mean function, for instance, contains examples, but you don’t need to type them yourself. Just use the example function to watch them run:

> example(mean)

mean> x <- c(0:10, 50)

mean> xm <- mean(x)

mean> c(xm, mean(x, trim = 0.1))
[1] 8.75 5.50

mean> mean(USArrests, trim = 0.2)
  Murder  Assault UrbanPop     Rape 
    7.42   167.60    66.20    20.16

The user typed example(mean). Everything else was produced by R, which executed the examples from the help page and displayed the results.

See Also

See Recipe 1.8 for searching for functions and Recipe 3.5 for more about the search path.

1.8. Searching the Supplied Documentation


You want to know more about a function that is installed on your machine, but the help function reports that it cannot find documentation for any such function.

Alternatively, you want to search the installed documentation for a keyword.


Use to search the R documentation on your computer:


A typical pattern is a function name or keyword. Notice that it must be enclosed in quotation marks.

For your convenience, you can also invoke a search by using two question marks (in which case the quotes are not required):

> ??pattern


You may occasionally request help on a function only to be told R knows nothing about it:

> help(adf.test)
No documentation for 'adf.test' in specified packages and libraries:
you could try '"adf.test")'

This can be frustrating if you know the function is installed on your machine. Here the problem is that the function’s package is not currently loaded, and you don’t know which package contains the function. It’s a kind of catch-22 (the error message indicates the package is not currently in your search path, so R cannot find the help file; see Recipe 3.5 for more details).

The solution is to search all your installed packages for the function. Just use the function, as suggested in the error message:


The search will produce a listing of all packages that contain the function:

Help files with alias or concept or title matching 'adf.test' using
regular expression matching:

tseries::adf.test       Augmented Dickey-Fuller Test

Type '?PKG::FOO' to inspect entry 'PKG::FOO TITLE'.

The following output, for example, indicates that the tseries package contains the adf.test function. You can see its documentation by explicitly telling help which package contains the function:

> help(adf.test, package="tseries")

Alternatively, you can insert the tseries package into your search list and repeat the original help command, which will then find the function and display the documentation.

You can broaden your search by using keywords. R will then find any installed documentation that contains the keywords. Suppose you want to find all functions that mention the Augmented Dickey–Fuller (ADF) test. You could search on a likely pattern:


On my machine, the result looks like this because I’ve installed two additional packages (fUnitRoots and urca) that implement the ADF test:

Help files with alias or concept or title matching 'dickey-fuller' using
fuzzy matching:

                         Dickey-Fuller p Values
tseries::adf.test        Augmented Dickey-Fuller Test
urca::ur.df              Augmented-Dickey-Fuller Unit Root Test

Type '?PKG::FOO' to inspect entry 'PKG::FOO TITLE'.

See Also

You can also access the local search engine through the documentation browser; see Recipe 1.6 for how this is done. See Recipe 3.5 for more about the search path and Recipe 4.4 for getting help on functions.

1.9. Getting Help on a Package


You want to learn more about a package installed on your computer.


Use the help function and specify a package name (without a function name):

> help(package="packagename")


Sometimes you want to know the contents of a package (the functions and datasets). This is especially true after you download and install a new package, for example. The help function can provide the contents plus other information once you specify the package name.

This call to help will display the information for the tseries package, a standard package in the base distribution:

> help(package="tseries")

The information begins with a description and continues with an index of functions and datasets. On my machine, the first few lines look like this:

                Information on package 'tseries'


Package:            tseries
Version:            0.10-22
Date:               2009-11-22
Title:              Time series analysis and computational finance
Author:             Compiled by Adrian Trapletti
Maintainer:         Kurt Hornik <>
Description:        Package for time series analysis and computational
Depends:            R (>= 2.4.0), quadprog, stats, zoo
Suggests:           its
Imports:            graphics, stats, utils
License:            GPL-2
Packaged:           2009-11-22 19:03:45 UTC; hornik
Repository:         CRAN
Date/Publication:   2009-11-22 19:06:50
Built:              R 2.10.0; i386-pc-mingw32; 2009-12-01 19:32:47 UTC;


NelPlo                  Nelson-Plosser Macroeconomic Time Series
USeconomic              U.S. Economic Variables
adf.test                Augmented Dickey-Fuller Test
arma                    Fit ARMA Models to Time Series

. (etc.)

Some packages also include vignettes, which are additional documents such as introductions, tutorials, or reference cards. They are installed on your computer as part of the package documentation when you install the package. The help page for a package includes a list of its vignettes near the bottom.

You can see a list of all vignettes on your computer by using the vignette function:

> vignette()

You can see the vignettes for a particular package by including its name:

> vignette(package="packagename")

Each vignette has a name, which you use to view the vignette:

> vignette("vignettename")

See Also

See Recipe 1.7 for getting help on a particular function in a package.

1.10. Searching the Web for Help


You want to search the Web for information and answers regarding R.


Inside R, use the RSiteSearch function to search by keyword or phrase:

> RSiteSearch("key phrase")

Inside your browser, try using these sites for searching:

This is a Google custom search that is focused on R-specific websites.

Stack Overflow is a searchable Q&A site oriented toward programming issues such as data structures, coding, and graphics.

The Statistical Analysis area on Stack Exchange is also a searchable Q&A site, but it is oriented more toward statistics than programming.


The RSiteSearch function will open a browser window and direct it to the search engine on the R Project website. There you will see an initial search that you can refine. For example, this call would start a search for “canonical correlation”:

> RSiteSearch("canonical correlation")

This is quite handy for doing quick web searches without leaving R. However, the search scope is limited to R documentation and the mailing-list archives.

The site provides a wider search. Its virtue is that it harnesses the power of the Google search engine while focusing on sites relevant to R. That eliminates the extraneous results of a generic Google search. The beauty of is that it organizes the results in a useful way.

Figure 1-3 shows the results of visiting and searching for “canonical correlation”. The left side of the page shows general results for search R sites. The right side is a tabbed display that organizes the search results into several categories:

  • Introductions

  • Task Views

  • Support Lists

  • Functions

  • Books

  • Blogs

  • Related Tools

Search results from

Figure 1-3. Search results from

If you click on the Introductions tab, for example, you’ll find tutorial material. The Task Views tab will show any Task View that mentions your search term. Likewise, clicking on Functions will show links to relevant R functions. This is a good way to zero in on search results.

Stack Overflow is a so-called Q&A site, which means that anyone can submit a question and experienced users will supply answers—often there are multiple answers to each question. Readers vote on the answers, so good answers tend to rise to the top. This creates a rich database of Q&A dialogs, which you can search. Stack Overflow is strongly problem oriented, and the topics lean toward the programming side of R.

Stack Overflow hosts questions for many programming languages; therefore, when entering a term into their search box, prefix it with “[r]” to focus the search on questions tagged for R. For example, searching via “[r] standard error” will select only the questions tagged for R and will avoid the Python and C++ questions.

Stack Exchange (not Overflow) has a Q&A area for Statistical Analysis. The area is more focused on statistics than programming, so use this site when seeking answers that are more concerned with statistics in general and less with R in particular.

See Also

If your search reveals a useful package, use Recipe 3.9 to install it on your machine.

1.11. Finding Relevant Functions and Packages


Of the 2,000+ packages for R, you have no idea which ones would be useful to you.


  • Visit the list of task views at Find and read the task view for your area, which will give you links to and descriptions of relevant packages. Or visit, search by keyword, click on the Task Views tab, and select an applicable task view.

  • Visit crantastic and search for packages by keyword.

  • To find relevant functions, visit, search by name or keyword, and click on the Functions tab.


This problem is especially vexing for beginners. You think R can solve your problems, but you have no idea which packages and functions would be useful. A common question on the mailing lists is: “Is there a package to solve problem X?” That is the silent scream of someone drowning in R.

As of this writing, there are more than 2,000 packages available for free download from CRAN. Each package has a summary page with a short description and links to the package documentation. Once you’ve located a potentially interesting package, you would typically click on the “Reference manual” link to view the PDF documentation with full details. (The summary page also contains download links for installing the package, but you’ll rarely install the package that way; see Recipe 3.9.)

Sometimes you simply have a generic interest—such as Bayesian analysis, econometrics, optimization, or graphics. CRAN contains a set of task view pages describing packages that may be useful. A task view is a great place to start since you get an overview of what’s available. You can see the list of task view pages at or search for them as described in the Solution.

Suppose you happen to know the name of a useful package—say, by seeing it mentioned online. A complete, alphabetical list of packages is available at with links to the package summary pages.

See Also

You can download and install an R package called sos that provides powerful other ways to search for packages; see the vignette at

1.12. Searching the Mailing Lists


You have a question, and you want to search the archives of the mailing lists to see whether your question was answered previously.


  • Open in your browser. Search for a keyword or other search term from your question. When the search results appear, click on the “Support Lists” tab.

  • You can perform a search within R itself. Use the RSiteSearch function to initiate a search:

    > RSiteSearch("keyphrase")

    The initial search results will appear in a browser. Under “Target”, select the R-help sources, clear the other sources, and resubmit your query.


This recipe is really just an application of Recipe 1.10. But it’s an important application because you should search the mailing list archives before submitting a new question to the list. Your question has probably been answered before.

See Also

CRAN has a list of additional resources for searching the Web; see

1.13. Submitting Questions to the Mailing Lists


You want to submit a question to the R community via the R-help mailing list.


The Mailing Lists page contains general information and instructions for using the R-help mailing list. Here is the general process:

  1. Subscribe to the R-help list at the Main R Mailing List.

  2. Read the Posting Guide for instructions on writing an effective submission.

  3. Write your question carefully and correctly. If appropriate, include a minimal self-reproducing example so that others can reproduce your error or problem.

  4. Mail your question to


The R mailing list is a powerful resource, but please treat it as a last resort. Read the help pages, read the documentation, search the help list archives, and search the Web. It is most likely that your question has already been answered. Don’t kid yourself: very few questions are unique.

After writing your question, submitting it is easy. Just mail it to You must be a list subscriber, however; otherwise your email submission may be rejected.

Your question might arise because your R code is causing an error or giving unexpected results. In that case, a critical element of your question is the minimal self-contained example:


Construct the smallest snippet of R code that displays your problem. Remove everything that is irrelevant.


Include the data necessary to exactly reproduce the error. If the list readers can’t reproduce it, they can’t diagnose it. For complicated data structures, use the dump function to create an ASCII representation of your data and include it in your message.

Including an example clarifies your question and greatly increases the probability of getting a useful answer.

There are actually several mailing lists. R-help is the main list for general questions. There are also many special interest group (SIG) mailing lists dedicated to particular domains such as genetics, finance, R development, and even R jobs. You can see the full list at If your question is specific to one such domain, you’ll get a better answer by selecting the appropriate list. As with R-help, however, carefully search the SIG list archives before submitting your question.

See Also

An excellent essay by Eric Raymond and Rick Moen is entitled “How to Ask Questions the Smart Way”. I suggest that you read it before submitting any question.

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