Use the smallest, simplest, most built-in data possible.
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
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:
::tribble( tibble~ 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.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:
::tribble( tibble~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
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.
reprex(..., session_info = TRUE)for this.
Whitespace rationing is not in effect.
- Use good coding style.
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
- 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