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
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:
<- par(pch = 19) opar <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