1 Getting Started and Getting Help | R Cookbook, 2nd Edition (2024)

Problem

You want to install R on your computer.

Solution

Windows and macOS users can download R from CRAN, the Comprehensive RArchive Network. Linux and Unix users can install R packages using theirpackage management tool:

Discussion

Installing R on Windows or macOS is straightforward because there areprebuilt binaries (compiled programs) for those platforms. You need only follow thepreceding instructions. The CRAN web pages also contain links toinstallation-related resources, such as frequently asked questions(FAQs) and tips for special situations (“Does R run under Windows Vista/7/8/Server 2008?”), that you may find useful.

The best way to install R on Linux or Unix is by using your Linux distribution package manager to install R as a package.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 undersudo to have the necessary privileges:

$ sudo apt-get install r-base

On Red Hat or Fedora, use yum:

$ sudo yum install R.i386

Most Linux platforms also have graphical package managers, which you mightfind more convenient.

Beyond the base packages, we recommend installing the documentationpackages, too. We like to installr-base-html (because we like browsing the hyperlinked documentation) aswell 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 packagesavailable on CRAN. We don’t use them because we’d rather get softwaredirectly from CRAN itself, which usually has the freshest versions.

In rare cases, you may need to build R from scratch. You might have anobscure, unsupported version of Unix, or you might have specialconsiderations regarding performance or configuration. The buildprocedure on Linux or Unix is quite standard. Download the tarball fromthe home page of your CRAN mirror; it’s called something like R-3.5.1.tar.gz, except the “3.5.1” will be replaced by the latestversion. Unpack the tarball, look for a file called INSTALL, andfollow the directions.

See Also

R in a Nutshell (O’Reilly)contains more details of downloading and installing R, includinginstructions for building the Windows and macOS versions. Perhaps theultimate guide is the one entitled “R Installation andAdministration”,available on CRAN, which describes building and installing R on avariety of platforms.

This recipe is about installing the base package. See Recipe 3.10,Installing Packages from CRAN, for installing add-on packages from CRAN.

Problem

You want a more comprehensive integrated development environment (IDE) than the Rdefault. In other words, you want to install RStudio Desktop.

Solution

Over the past few years RStudio has become the most widely used IDE for R. We are ofthe opinion that most all R work should be done in the RStudio Desktop IDE unlessthere is a compelling reason to do otherwise. RStudio makes multiple products including RStudio Desktop,RStudio Server, and RStudio Shiny Server, just to name a few. For this book we will usethe term RStudio to mean RStudio Desktop, though most concepts apply to RStudio Server as well.

To install RStudio, download the latest installer for your platform from the [RStudiowebsite]https://www.rstudio.com/products/rstudio/download/.

The RStudio Desktop Open Source License version is free to download and use.

Discussion

This book was written and built using RStudio version 1.2.x and R versions 3.5.x.New versions of RStudio arereleased every few months, so be sure to update regularly. Note that RStudio works with whicheverversion of R you have installed. So updating to the latest version of RStudio does not upgradeyour version of R. R must be upgraded separately.

Interacting with R is slightly different in RStudio than in the built-in R user interface.For this book, we’ve elected to use RStudio for all examples.

1.3.1 Problem

You want to run RStudio on your computer.

1.3.2 Solution

A common mistake made by new users of R and RStudio is to accidentally start R when they intended to start RStudio. The easiest way to ensure you’re actually starting RStudio is to search for RStudio on your desktop OS. Then use whatever method your OS provides for pinning the icon somewhere easy to find later.

Windows

Click on the Start Screen menu in the lower-left corner of the screen. In the search box, type **RStudio**.

macOS

Look in your launchpad for the RStudio app or press Command-space and type **Rstudio** to search using Spotlight Search.

Ubuntu

Press Alt-F1 and type **RStudio** to search for RStudio.

1.3.3 Discussion

It’s easy to get confused between R and RStudio because, as you can see in Figure 1.1, the icons look similar.

If you click on the R icon, you’ll be greeted by something like Figure 1.2, which is the Base R interface on a Mac, but certainly not RStudio.

When you start RStudio, by default it will reopen the last project you were working on in RStudio.

Problem

You’ve started RStudio. Now what?

Solution

When you start RStudio, the main window on the left is an R session. From there you can entercommands interactively directly to R.

Discussion

R prompts you with >. To get started, just treat R like a bigcalculator: enter an expression, and R will evaluate the expression andprint the result:

1 + 1#> [1] 2

The computer adds 1 and 1, and displays the result, 2.

The [1] before the 2 might be confusing. To R, the result is avector, 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 isnot surprising, since it’s the only element of the vector.

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

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

In contrast, max(1,3, is an incomplete expression, so R prompts youfor 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 andfrustrating. So R includes command-line editing to make life easier. Itdefines single keystrokes that let you easily recall, correct, andreexecute your commands. Our typical command-line interaction goeslike 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 tothe error.

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

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

7. I press Enter to reexecute the corrected command.

That’s just the basics. R supports the usual keystrokes for recallingand editing command lines, as listed in Table 1.2.

On most operating systems, you can also use the mouse to highlight commandsand then use the usual copy and paste commands to paste text into a newcommand line.

See Also

See Recipe 2.12, “Typing Less and Accomplishing More”. From the Windows main menu, follow Help → Console for a complete list of keystrokes useful for command-lineediting.

Problem

You want to exit from RStudio.

Solution

Windows and most Linux distributions

Select File → Quit Session from the main menu, or click on the X in theupper-right corner of the window frame.

macOS

Select File → Quit Session from the main menu; or press Command-q (⌘-q); or click on the red X in the upper-leftcorner of the window frame.

On all platforms, you can also use the q function (as in quit) toterminate R and RStudio:

q()

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

Discussion

Whenever you exit, R typically asks if you want to save your workspace. You havethree 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, R writes it to a file called .RDatain the current working directory. Saving the workspace saves any R objects you havecreated. The next time you start R in the same directory, the workspace will automatically load.Saving your workspace will overwrite the previouslysaved workspace, if any, so don’t save if you don’t like your changes (e.g., if you have accidentally erased critical data from your workspace).

We recommend never saving your workspace when you exit, and instead always explicitly saving your project, scripts, and data. We also recommend that you turn off the prompt to save and autorestore the workspace in RStudio using the global options found in the menu Tools → Global Options and shown in Figure 1.3. This way, when you exit R and RStudio, you won’t be prompted to save your workspace. But keep in mind that any objects created but not saved to disk will be lost.

See Also

See Recipe 3.1, “Getting and Setting the Working Directory”, for more about the current working directoryand Recipe 3.3, “Saving Your Workspace”, for more about saving your workspace. Also see Chapter 2 of R in a Nutshell.

Problem

You want to interrupt a long-running computation and return to thecommand prompt without exiting RStudio.

Solution

Press the Esc key on your keyboard, or click on the Session menu in RStudio and select Interrupt R. You may also click on the stop sign icon in the code console window.

Discussion

Interrupting R means telling R to stop running the current command but without deleting variables from memory or completely closing RStudio. That said, interrupting R can leave your variables in an indeterminate state, depending upon how far the computation had progressed, so check your workspace after interrupting.

See Also

See Recipe 1.5, “Exiting from R”.

Problem

You want to read the documentation supplied with R.

Solution

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

help.start()

From there, links are available to all the installed documentation. In RStudio the help willshow up in the help pane, which by default is on the righthand side of the screen.

In RStudio you can also click help → R Help to get a listing with help options for both R andRStudio.

Discussion

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

It is easy to browse this documentation via the help.start function,which opens on the top-level table of contents. Figure 1.4 shows how help.start appears inside the help pane in RStudio.

The two links in the Reference section are especially useful:

Packages

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

Search Engine & Keywords

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

The Base R documentation accessed via help.start is loaded on your computerwhen you install R. The RStudio help, which you access by using the menu optionhelp → R Help, presents a page with links to RStudio’s website. So, you will need internet access to access the RStudio help links.

See Also

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

Problem

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

Solution

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)

Discussion

We present many R functions in this book. Every R function has more bellsand whistles than we can possibly describe. If a function catches yourinterest, we 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 helpfunction like this:

help(mean)

This will open the help page for the mean function in the help pane in RStudio. Ashortcut for the help command is to simply type **?** followed by thefunction 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? For this case, use the args function:

args(mean)#> function (x, ...) #> NULL

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

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 ofnumbers. For sd, the synopsis shows the same vector, x, and anoptional argument called na.rm. (You can ignore the second line ofoutput, which is often just NULL.) In RStudio you will see the args output as afloating tool tip over your cursor when you type a function name, as shown in Figure 1.5.

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

example(mean)#> #> mean> x <- c(0:10, 50)#> #> mean> xm <- mean(x)#> #> mean> c(xm, mean(x, trim = 0.10))#> [1] 8.75 5.50

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

See Also

See Recipe 1.9: “Searching the Supplied Documentation”, for searchingfor functions and Recipe 3.6: “Displaying Loaded Packages via the Search Path”, for more about the search path.

Problem

You want to know more about a function that is installed on yourmachine, but the help function reports that it cannot finddocumentation for any such function.

Alternatively, you want to search the installed documentation for akeyword.

Solution

Use help.search to search the R documentation on your computer:

help.search("pattern")

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

For your convenience, you can also invoke a search by using two questionmarks (in which case the quotes are not required). Note that searching for afunction by name uses one question mark, while searching for a text pattern usestwo:

> ??pattern

Discussion

You may occasionally request help on a function only to be told R knowsnothing 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 yourmachine. Here the problem is that the function’s package is notcurrently loaded, and you don’t know which package contains thefunction. It’s a kind of catch-22 (the error message indicates thepackage is not currently in your search path, so R cannot find the helpfile; see Recipe 3.6: “Displaying Loaded Packages via the Search Path”, for more details).

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

help.search("adf.test")

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

Help files with alias or concept or title matching 'adf.test' usingregular expression matching:tseries::adf.test Augmented Dickey-Fuller TestType '?PKG::FOO' to inspect entry 'PKG::FOO TITLE'.

The preceding output indicates that the tseries packagecontains the adf.test function. You can see its documentation byexplicitly telling help which package contains the function:

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

or you can use the double colon operator to tell R to look in a specific package:

?tseries::adf.test

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

help.search("dickey-fuller")

See Also

You can also access the local search engine through the documentationbrowser; see Recipe 1.7: “Viewing the Supplied Documentation”, for howthis is done. See Recipe 3.6: “Displaying Loaded Packages via the Search Path”, for more about the search pathand Recipe ??: “Getting Help on a Function”, for getting help on functions.

Problem

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

Solution

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

help(package = "packagename")

Discussion

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

This call to help would display the information for the tseriespackage, a standard package in the base distribution (try it!):

help(package = "tseries")

The information begins with a description and continues with an index offunctions and datasets. In RStudio, the HTML-formatted help page will open inthe help window of the IDE.

Some packages also include vignettes, which are additional documentssuch as introductions, tutorials, or reference cards. They are installedon your computer as part of the package documentation when you installthe package. The help page for a package includes a list of itsvignettes near the bottom.

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

vignette()

In RStudio this will open a new tab listing every package installed on yourcomputer that includes vignettes as well as the vignette names and descriptions.

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

vignette(package = "packagename")

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

vignette("vignettename")

See Also

See Recipe 1.8: “Getting Help on a Function”, for getting help on aparticular function in a package.

Problem

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

Solution

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

RSiteSearch("key phrase")

Inside your browser, try using these sites for searching:

RSeek

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

Stack Overflow

Stack Overflow is a searchable Q&A site from Stack Exchange that isoriented toward programming issues such as data structures, coding,and graphics. Stack Overflow is a great “first stop” for all your syntax questions.

Cross Validated

Cross Validated is a Stack Exchange site focused on statistics, machinelearning, and data analysis rather than programming. Cross Validated is agood place for questions about what statistical method to use.

RStudio Community

The RStudio Community site is a discussion forum hosted by RStudio. The topics include R, RStudio, and associated technology. Being an RStudio site, this forum is often visited by RStudio staff and those who use the software frequently. This is a good place for general questions and questions that possibly don’t fit as well into the Stack Overflow syntax-focused format.

Discussion

The RSiteSearch function will open a browser window and direct it tothe search engine on the R Project website.There you will see an initial search that you can refine. For example, thiscall 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 themailing list archives.

RSeek provides a wider search. Its virtue is that itharnesses the power of the Google search engine while focusing on sitesrelevant to R. That eliminates the extraneous results of a genericGoogle search. The beauty of RSeek is that it organizes theresults in a useful way.

Figure 1.6 shows the results ofvisiting RSeek and searching for “correlation.” Note that the tabs across the top allow for drilling in to different types of content:

• All results

• Packages

• Books

• Support

• Articles

• For Beginners

[Stack Overflow]http://stackoverflow.com/) is a Q&A site,which means that anyone can submit a question and experienced users willsupply 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. StackOverflow is strongly problem oriented, and the topics lean toward theprogramming 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 questionstagged for R and will avoid the Python and C++ questions.

Stack Overflow also includes a wiki about the R language that is an excellent community-curated list of online R resources.

Stack Exchange (parent company of Stack Overflow) has a Q&A areafor statistical analysis called Cross Validated. This area is more focused onstatistics than programming, so use this site when seeking answers that aremore concerned with statistics in general and less with R in particular.

RStudio hosts their own discussion board as well. This is a great place to ask general questions and more conceptual questions that may not work as well on Stack Overflow.

See Also

If your search reveals a useful package, use Recipe 3.10, “Installing Packages from CRAN”, to install it on your machine.

Problem

Of the 10,000+ packages for R, you have no idea which ones would beuseful to you.

Solution

• Visit CRAN’s list of task views.Find and read the taskview for your area, which will give you links to and descriptions ofrelevant packages. Or visit RSeek, search bykeyword,click on the Task Views tab, and select an applicable task view.

• Visit crantastic and search for packagesby keyword.

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

• To discover packages related to a certain field, exploreCRAN Task Views.

Discussion

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

As of this writing, there are more than 10,000 packages available forfree download from CRAN. Each package has a summary page with a shortdescription and links to the package documentation. Once you’ve locateda 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 thepackage, but you’ll rarely install the package that way; see Recipe 3.10,“Installing Packages from CRAN”.)

Sometimes you simply have a generic interest—such as Bayesian analysis,econometrics, optimization, or graphics. CRAN contains a set of taskview pages describing packages that may be useful. A task view is agreat place to start since you get an overview of what’s available. Youcan see the list of task view pages atCRAN Task Views or search for them as describedin the Solution. CRAN’s Task Views lists a number of broad fields and shows packages that are used in each field. For example, there are task views for high performancecomputing, genetics, time series, and social science, just to name a few.

Suppose you happen to know the name of a useful package—say, by seeingit mentioned online. A complete, alphabetical list of packages isavailable at CRAN with links tothe package summary pages.

See Also

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

Problem

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

Solution

Open Nabble in your browser. Search for a keyword orother search term from your question. This will show results from the support mailing lists.

Discussion

This recipe is really just an application of Recipe 1.11,“Searching the Web for Help”. But it’s an importantapplication because you should search the mailing list archives beforesubmitting a new question to the list. Your question has probably beenanswered before.

See Also

CRAN has a list of additional resources for searching the web; seeCRAN Search.

Problem

You have a question you can’t find the answer to online, so you want to submit a question to the R community.

Solution

The first step to asking a question online is to create a reproducible example. Havingexample code that someone can run and see your exact problem is the most critical part of asking for help online. A question with a good reproducible example has three components:

1. Example data - This can be simulated data or some real data that you provide

2. Example code - This code shows what you have tried or an error you are getting

3. Written description - This is where you explain what you have, what you’d like to have, and what you have tried that didn’t work.

The details of writing a reproducible example are covered in the Discussion. Once you havea reproducible example, you can post your question on Stack Overflow](https://stackoverflow.com/questions/ask). Be sure to include the r tag in the Tags section of the ask page.

If your question is more general or related to concepts instead of specific syntax, RStudio runs an RStudio Community discussion forum. Note that the site is broken into multiple topics, so pick the topic category that best fits your question.

Or you may submit your question to the R mailing lists (but don’t submit to multiple sites, the mailing lists, and Stack Overflow, as that’s considered rude cross-posting).

The mailing lists page containsgeneral 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 MailingList.

2. Write your question carefully and correctly and include your reproducible example.

3. Mail your question to r-help@r-project.org.

Discussion

The R mailing list, Stack Overflow, and the RStudio Community site are great resources, but please treat them as a lastresort. Read the help pages, read the documentation, search the helplist archives, and search the web. It is most likely that your questionhas already been answered. Don’t kid yourself: very few questions areunique. If you’ve exhausted all other options, maybe it’s time to create a good question.

The reproducible example is the crux of a good help request. The first component is example data.A good way to get this is to simulate the data using a few R functions. The followingexample creates a data frame called example_df that has three columns, each of a differentdata type:

set.seed(42)n <- 4example_df <- data.frame( some_reals = rnorm(n), some_letters = sample(LETTERS, n, replace = TRUE), some_ints = sample(1:10, n, replace = TRUE))example_df#> some_reals some_letters some_ints#> 1 1.371 Q 4#> 2 -0.565 O 9#> 3 0.363 X 5#> 4 0.633 G 4

Note that this example uses the command set.seed at the beginning. This ensures thatevery time this code is run, the answers will be the same. The n value is the number of rowsof example data you would like to create. Make your example data as simple as possible toillustrate your question.

An alternative to creating simulated data is to use example data that comes with R. For example, the dataset mtcars contains a data frame with 32 records about different carmodels:

data(mtcars)head(mtcars)#> mpg cyl disp hp drat wt qsec vs am gear carb#> Mazda RX4 21.0 6 160 110 3.90 2.62 16.5 0 1 4 4#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.88 17.0 0 1 4 4#> Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1#> Hornet 4 Drive 21.4 6 258 110 3.08 3.21 19.4 1 0 3 1#> Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2#> Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1

If your example is reproducible only with your own data, you can use dputto put a bit of your own data in a string that you can use in your example. We’llillustrate that approach using two rows from the mtcars data:

dput(head(mtcars, 2))#> structure(list(mpg = c(21, 21), cyl = c(6, 6), disp = c(160, #> 160), hp = c(110, 110), drat = c(3.9, 3.9), wt = c(2.62, 2.875#> ), qsec = c(16.46, 17.02), vs = c(0, 0), am = c(1, 1), gear = c(4, #> 4), carb = c(4, 4)), row.names = c("Mazda RX4", "Mazda RX4 Wag"#> ), class = "data.frame")

You can put the resulting structure directly in your question:

example_df <- structure(list(mpg = c(21, 21), cyl = c(6, 6), disp = c(160, 160), hp = c(110, 110), drat = c(3.9, 3.9), wt = c(2.62, 2.875), qsec = c(16.46, 17.02), vs = c(0, 0), am = c(1, 1), gear = c(4, 4), carb = c(4, 4)), row.names = c("Mazda RX4", "Mazda RX4 Wag"), class = "data.frame")example_df#> mpg cyl disp hp drat wt qsec vs am gear carb#> Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4#> Mazda RX4 Wag 21 6 160 110 3.9 2.88 17.0 0 1 4 4

The second part of a good reproducible example is the example minimal code. The code exampleshould be as simple as possible and illustrate what you are trying to do or have alreadytried. It should not be a big block of code with many different things going on. Boil your example down to only the minimal amount of code needed. If you use any packages, be sure to include the library call at the beginning of your code. Also, don’t include anything in your question that is potentially harmful to someone running your code, such as rm(list=ls()), which would delete all R objects in memory. Have empathy for the person trying to help you, and realize that they are volunteering their time to help you out and may run your code on the same machine they use to do their own work.

To test your example, open a new R session and try running it. Once you’ve edited your code, it’s time to give just a bit more information to your potential respondents.In plain text, describe what you were trying to do, what you’ve tried, and your question. Be as concise as possible. As with the example code, your objective is to communicate as efficiently as possible with the person reading your question. You may find it helpful to include in your description which version of R you are running as well as which platform (Windows, Mac, Linux). You can get that information easily with the sessionInfo command.

If you are going to submit your question to the R mailing lists, you should know there are actually several mailing lists. R-help is the main list forgeneral 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 athttps://stat.ethz.ch/mailman/listinfo.If your question is specificto a domain, you’ll get a better answer by selecting theappropriate list. As with R-help, however, carefully search the SIG listarchives before submitting your question.

See Also

We suggest that you read Eric Raymond and Rick Moen’s excellent essay entitled “How to Ask Questions the SmartWay” before submitting any question. Seriously. Read it.

Stack Overflow has an excellent post that includes details about creating a reproducible example. You can find that at https://stackoverflow.com/q/5963269/37751.

Jenny Bryan has a great R package called reprex that helps in the creation of a good reproducible example and provides helper functions for writing the markdown text for sites like Stack Overflow. You can find that package on her GitHub page.