reprex

Prepare reproducible examples for posting to GitHub issues, StackOverflow, etc.

Given R code on the clipboard, as an expression (quoted or not), or in a file …

  • run it via rmarkdown::render(),
  • with deliberate choices re: arguments and setup chunk.

Get resulting runnable code + output as

  • Markdown, formatted for target venue, e.g. gh or so, or as
  • R code, augmented with commented output.

Result is returned invisibly, placed on the clipboard and written to file.

Preview an HTML version in RStudio viewer or default browser.

Install and load

devtools::install_github("jennybc/reprex")
library(reprex)

Quick start

Let’s say you copy this code onto your clipboard:

(y <- 1:4)
mean(y)

Then call reprex(), where the default target venue is GitHub:

A nicely rendered HTML preview will display in RStudio’s Viewer (if you’re in RStudio) or your default browser otherwise.

The relevant bit of GitHub-flavored Markdown is ready to be pasted from your clipboard:

``` r
(y <- 1:4)
#> [1] 1 2 3 4
mean(y)
#> [1] 2.5
```

Here’s what that Markdown would look like rendered in a GitHub issue:

(y <- 1:4)
#> [1] 1 2 3 4
mean(y)
#> [1] 2.5

Anyone else can copy, paste, and run this immediately.

But wait, there’s more!

More control

Examples of how to take greater control of your reprex.

knitr options

You can change the prefix used to comment the output with the comment argument.

reprex({y <- 1:4; mean(y)}, comment = "#;-)")

leads to this:

y <- 1:4
mean(y)
#;-) [1] 2.5

Supplement or override reprex defaults for any knitr chunk or package option via the arguments opts_chunk and opts_knit.

Embedded prose

Sometimes you want to mingle rendered code and prose. Put the embedded prose in as roxygen comments, i.e. comment lines that start with #'. This reprex code:

## a regular comment
x <- 1:100
#' Here is some embedded prose, as a roxygen comment.
mean(x)

renders to this this result:

## a regular comment
x <- 1:100

Here is some embedded prose, as a roxygen comment.

mean(x)
#> [1] 50.5

What is a reprex?

What is a reprex? It’s a {repr}oducible {ex}ample. Coined by Romain Francois on twitter.

Where and why are they used?

  • A StackOverflow question that includes a proper reprex is much more likely to get answered, by the most knowledgeable (and therefore busy!) people.
  • A GitHub issue that includes a proper reprex is more likely to achieve your goal: getting a bug fixed or getting a new feature, in a finite amount of time.

What are the main requirements?

  • Use the smallest, simplest, most built-in data possible.
    • Think: iris or mtcars. Bore me.
    • If you must make some objects, minimize their size and complexity.
    • Get just a bit of something with head() or by indexing with the result of sample(). If anything is random, consider using set.seed() to make it repeatable.
    • dput() is a good way to get the code to create an object you have lying around. Copy and paste the result of this into your reprex.
    • Look at official examples and try to write in that style. Consider adapting one.
  • Include commands on a strict “need to run” basis.
    • Ruthlessly strip out anything unrelated to the specific matter at hand.
    • Include every single command that is required, e.g. loading specific packages via library(foo).
  • Consider including so-called “session info”, i.e. your OS and versions of R and add-on packages, if it’s conceivable that it matters. Use reprex(..., si = TRUE) for this.
  • Whitespace rationing is not in effect. Use good coding style.
  • Pack it in, pack it out, and don’t take liberties with other people’s computers. You are asking people to run this code!
    • If you change options, store original values at the start, do your thing, then restore them: opar <- par(pch = 19) <blah blah blah> par(opar).
    • If you create files, delete them when you’re done: write(x, "foo.txt") <blah blah blah> file.remove("foo.txt").
    • Don’t delete files or objects that you didn’t create in the first place.
    • Don’t mask built-in functions, i.e. don’t define a new function named c.
    • Take advantage of R’s built-in ability to create temporary files and directories. Read up on tempfile() and tempdir().
    • Don’t start with setwd("C:\Users\jenny\path\that\only\I\have"), because it won’t work on anyone else’s computer.
    • Don’t start with rm(list = ls()), because it is anti-social to clobber other people’s workspaces.

But won’t that take time and effort?

  • Yes, yes it will!
  • 80% of the time you will solve your own problem in the course of writing an excellent reprex. YMMV.
  • The remaining 20% of the time, you will create a reprex that is more likely to elicit the desired behavior in others.

Get more concrete details here:

Package philosophy

The reprex code:

  • Must run and, therefore, should be run by the person posting. No faking it.
  • Should be easy for others to digest, so they don’t necessarily have to run it. You are encouraged to include selected bits of output. :scream:
  • Should be easy for others to copy + paste + run, iff they so choose. Don’t let inclusion of output break executability.

Accomplished like so:

  • use rmarkdown::render() or, under the hood, knitr::spin() to run the code and capture output that would display in R console
  • use chunk option comment = "#>" to include the output while retaining executability

Other work

If I had known about formatR::tidy_eval(), I probably would never had made reprex! But alas I did not. AFAICT here are the main differences:

  • reprex() accepts an expression as primary input, in addition to code on the clipboard, in a character vector, or in a file.
  • reprex() runs the reprex in a separate R process, via callr. tidy_eval() uses the existing R process and offers an envir argument.
  • reprex() writes the code to a .R file and calls rmarkdown::render(). tidy_eval() runs the code line-by-line via capture.output(eval(..., envir = envir)).
  • reprex() uploads figures to imgur and inserts the necessary link.