Using, Viewing and editing snippets

1. In RStudio, we can browse and define snippets under Tools -> Global Options... -> Code -> Edit Snippets window
2. When typing code, the snippet will appear as an auto-complete option (similar to function names) if we type the first few letters of its name
3. Use Shift+Tab to insert the snippet immediately or pick the snippet from the auto-complete list (by clicking or scrolling on it and pressing Tab)

Note that as there is no auto-completion when editing R Markdown documents, we need to use the Shift+Tab method exclusively in that case.

Sharing and exporting Snippets

Once we customize some snippets they are automatically saved to our ~/.R/snippets directory, one file per language. We can use these files to share our snippets with others, but also to edit them directly in the respecive file, without the need to click through the RStudio menus. RStudio will automatically load the Snippets from the .snippets files first, for languages that have the file present.

1. Automatically insert boilerplate or template-style code

The first and probably most frequent use of the Code Snippets feature is to quickly insert predefined pieces of code that require a lot of typing with little alternation, a.k.a. boilerplate code. A good illustration is a snippet covering a tryCatch block:

snippet tryc
    ${1:variable} <- tryCatch({
        ${2}
    }, warning = function(w) {
        message(sprintf("Warning in %s: %s", deparse(w[["call"]]), w[["message"]]))
        ${3}
    }, error = function(e) {
        message(sprintf("Error in %s: %s", deparse(e[["call"]]), e[["message"]]))
        ${4}
    }, finally = {
        ${5}
    })

Note that the snippet definition is intended using <tab> instead of spaces.

After defining this Snippet and running it we will automatically get a good template for the block and we can focus on writing the important parts:

The numbered sections prefixed with $ such as ${2} let us define sections to which the cursor will jump after pressing Tab. We can also use ${1:predefinedvalue} to predefine a value for the sections.

Another example of this type of use may be a testthat block that quickly prepares a unit-testing file:

snippet tt
    context("${1}")

    # ${2} ----------
    test_that(
      "${2}",
      expect_${3}(${4})
    )

2. Pre-fill code to be ran quickly

The second use case scenario where the Code Snippets come in really handy is to use them in the console when we want to run a block of code that we execute often in some scenarios. One such example is to attach the packages we use in a particular context. For example, when developing an R package, the following may be handy:

snippet dd
    "library('devtools'); library('testthat'); library('pryr')"

With this snippet, after pressing dd and then Shift+Tab in the console, the library statements will appear and we can just press enter to run them and attach the mentioned packages. We can of course make separate snippets for example for attaching packages we use for interactive data analysis and plotting. This is one way to keep our .Rprofile clean and still have packages easily available when needed.

Another example for this scenario is to quickly run a benchmark comparing two or more pieces of code and visualize the results with a boxplot to get an overview:

snippet mm
    bench <- microbenchmark::microbenchmark(
        times = ${1:1:100},
        ${2:one} = ${3},
        ${4:two} = ${5}
        )
    if (requireNamespace("highcharter")) {
      highcharter::hcboxplot(bench[["time"]], bench[["expr"]], outliers = FALSE)
    } else {
      boxplot(bench, outline = FALSE)
    }

3. Execute code combined with rstudioapi

The one scenario where RStudio really shines is combining multiple features it offers. We can neatly combine the use of snippets, rstudioapi and the Terminal feature that we discussed previously for an amazing variety of productivity boosts.

Just one practical example convenient when writing a blogdown site is to instantly serve a preview of the blog in a separate session via the Terminal and use the RStudio Viewer in one go to view the site. This is handy especially in the RStudio Server setting, where the site serving in the same session can make the IDE behave slow:

snippet ss
    `r eval({
      nocon <- function(link = 'http://127.0.0.1:9999') {
        inherits(suppressWarnings(try({
            con <- url(link, open = 'rb')
            close(con)
        }, silent = TRUE)), 'try-error')
      }
      if (nocon()) {
        termId <- rstudioapi::terminalExecute(
          'R -q -e \"blogdown::serve_site(port = 9999,  browser = FALSE)\"',
          show = FALSE
        )
        while (nocon() && !identical(rstudioapi::terminalExitCode(termId), 1L)) {
            Sys.sleep(0.25)
            cat(".")
        }
      }
      if (identical(rstudioapi::terminalExitCode(termId), 1L)) {
        cat(rstudioapi::terminalBuffer(termId), sep = "\n")
      } else {
        rstudioapi::viewer('http://127.0.0.1:9999')
      }
    })`

After pressing ss and Shift+Tab, the site will be served in a separate R Session and previewed in the viewer.

Using eval(expression) like above lets us execute R code in snippets. This gives a lot of flexibility, even more extensive when combined with eval(parse(text = "code as character string"))

4. Execute code and paste result at cursor

The fourth option is to inject text following the cursor using $$. An example simple but potentially powerful use of this feature is to pass commands to be executed via base R’s system and getting the results directly at our cursor:

snippet $$
    `r eval(parse(text = "system('$$', intern = TRUE)"))`

With the above, when typing $$ls into the editor and pressing Shift+Tab, we will see the list of files present in our working directory placed at our cursor.

Another handy use of this feature is to be able to quickly get a reproducible object definition by deparsing it:

snippet $$
    `r paste("$$ <-", deparse(eval(parse(text="$$")), width.cutoff = 500L))`