If you’re asking for R help, reporting a bug, or requesting a new feature, you’re more likely to succeed if you include a good reprex.

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.

  • Many of the functions and packages you already use offer a way to create a small data frame “inline”:

    • read.table() and friends have a text argument. Example:

      read.csv(text = "a,b\n1,2\n3,4")
      #>   a b
      #> 1 1 2
      #> 2 3 4
    • tibble::tribble() lets you use a natural and readable layout. Example:

      tibble::tribble(
        ~ a, ~ b,
          1,   2,
          3,   4
      )
      #> # A tibble: 2 x 2
      #>       a     b
      #>   <dbl> <dbl>
      #> 1     1     2
      #> 2     3     4
  • 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.

  • The datapasta package can generate code for data.frame(), tibble::tribble(), or data.table::data.table() based on an existing R data frame. For example, a call to tribble_format(head(ChickWeight, 3)) leaves this on the clipboard, ready to paste into your reprex:

    tibble::tribble(
     ~weight, ~Time, ~Chick, ~Diet,
          42,     0,    "1",   "1",
          51,     2,    "1",   "1",
          59,     4,    "1",   "1"
    )
  • dput() is a decent last resort, i.e. if you simply cannot make do with built-in or simulated data or inline data creation in a more readable format. But dput() output is not very human-readable. Avoid if at all possible.

  • 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(..., session_info = TRUE) for this.

Whitespace rationing is not in effect.

  • Use good coding style.
  • Use reprex(..., style = TRUE) to request automated styling of your code.

Pack it in, pack it out, and don’t take liberties with other people’s computers. You are asking people to run this code!

  • Don’t start with rm(list = ls()). It is anti-social to clobber other people’s workspaces.

  • 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 mask built-in functions, i.e. don’t define a new function named c or mean.

  • 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.

  • Take advantage of R’s built-in ability to create temporary files and directories. Read up on tempfile() and tempdir().

This seems like a lot of work!

Yes, creating a great reprex requires work. You are asking other people to do work too. It’s a partnership.

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.

Further reading:

How to make a great R reproducible example? thread on StackOverflow

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, if and only if they so choose. Don’t let inclusion of output break executability.

Accomplished like so:

  • Use rmarkdown::render() to run the code and capture output that you would normally see on your screen. This is done in a separate R process, via callr, to guarantee it is self-contained.

  • Use chunk option comment = "#>" to include the output while retaining executability.