4 Name details arguments

4.1 What are data and details arguments?

The arguments to a function typically fall into two broad sets: one set supplies the data to compute on, and the other supplies arguments that control the details of the computation. For example:

  • In log(), the data is x, and the detail is the base of the logarithm.

  • In mean(), the data is x, and the details are how much data to trim from the ends (trim) and how to handle missing values (na.rm).

  • In t.test(), the data are x and y, and the details of the test are specified by the alternative, mu, paired, var.equal, and conf.level arguments.

Typically, data arguments don’t have default values, and work with vectors or data frames, while details arguments have defaults, and take single values (like TRUE or FALSE, or a single string that specifies a method).

4.2 What’s the pattern?

When calling a function, data arguments come first, specified by position, followed by details arguments specified by name.

y <- c(1:10, NA)
# Good
mean(y, na.rm = TRUE)
#> [1] 5.5

# Bad
mean(x = y, , TRUE)
#> [1] 5.5
mean(, TRUE, x = y)
#> [1] 5.5

Never use partial matching, which allows you to refer to an argument by a unique prefix, e.g. mean(x, n = TRUE). Partial matching was useful in the early days of R because when you were doing a quick and dirty interactive analysis you could save a little time by shortening argument names. However, today, most R editing environments support autocomplete so partial matching only saves you a single keystroke, and it makes code substantially harder to read.

You can make R give you are warning that you’re using a partially named argument with a special option. Call usethis::use_partial_warnings() to make this the default for all R sessions.

options(warnPartialMatchArgs = TRUE)
mean(x = 1:10, n = FALSE)
#> Warning in mean.default(x = 1:10, n = FALSE): partial argument match of 'n' to
#> 'na.rm'
#> [1] 5.5

4.3 Why is this useful?

I think it’s reasonable to assume that the reader knows what a function does then they know what the data arguments are (and their order), and repeating their names just takes up space without aiding communication. This then leads naturally to %>% where you don’t specify the name of the first argument either (since it comes from the left-hand side of %>%.)

However, I don’t think it’s reasonable to expect that people will remember the order of the details arguments. For example, I don’t think that most people know that the second argument to mean() is trim, even though mean() is an extremely commonly used function. Spelling the names out in

4.4 What are the exceptions?

I think the main exception to this rule is when you are teaching a function for the first time. It makes sense to emphasis the names of the data arguments to help people understand exactly what’s going on. For example, in R for data science when we introduce ggplot2 we write code like:

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 

At the end of the chapter, we assume that the reader is familiar with the basic structure and so the rest of the book uses the style recommended here:

ggplot(mpg, aes(displ, hwy)) +