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 to import data from delimited files also 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").
tibble::tribble()lets you use a natural and readable layout. Example:
- 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, if you simply cannot make do with built-in or simulated data. 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
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(..., si = TRUE)for this.
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
If you change options, store original values at the start, do your thing, then restore them:
If you create files, delete them when you’re done:
- 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