Reprex do’s and don’ts

Main requirements

Use the smallest, simplest, most built-in data possible.

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.

Whitespace rationing is not in effect.

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: r opar <- par(pch = 19) <blah blah blah> par(opar)
If you create files, delete them when you’re done: r 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().

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.

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.