Write your own R functions, part 1

Skateboard >> perfectly formed rear-view mirror

This image widely attributed to the Spotify development team conveys an important point.

Build that skateboard before you build the car or some fancy car part. A limited-but-functioning thing is very useful. It also keeps the spirits high.

This is related to the valuable Telescope Rule:

It is faster to make a four-inch mirror then a six-inch mirror than to make a six-inch mirror.

Test on new inputs

Pick some new artificial inputs where you know (at least approximately) what your function should return.

max_minus_min(1:10)
## [1] 9
max_minus_min(runif(1000))
## [1] 0.997364

I know that 10 minus 1 is 9. I know that random uniform [0, 1] variates will be between 0 and 1. Therefore max - min should be less than 1. If I take LOTS of them, max - min should be pretty close to 1.

It is intentional that I tested on integer input as well as floating point. Likewise, I like to use valid-but-random data for this sort of check.

Test on real data but different real data

Back to the real world now. Two other quantitative variables are lying around: gdpPercap and pop. Let’s have a go.

max_minus_min(gapminder$gdpPercap)
## [1] 113282
max_minus_min(gapminder$pop)
## [1] 1318623085

Either check these results “by hand” or apply the “does that even make sense?” test.

Test on weird stuff

Now we try to break our function. Don’t get truly diabolical (yet). Just make the kind of mistakes you can imagine making at 2am when, 3 years from now, you rediscover this useful function you wrote. Give your function inputs it’s not expecting.

max_minus_min(gapminder) ## hey sometimes things "just work" on data.frames!
## Error in FUN(X[[i]], ...): only defined on a data frame with all numeric variables
max_minus_min(gapminder$country) ## factors are kind of like integer vectors, no?
## Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, : 'max' not meaningful for factors
max_minus_min("eggplants are purple") ## i have no excuse for this one
## Error in max(x) - min(x): non-numeric argument to binary operator

How happy are you with those error messages? You must imagine that some entire script has failed and that you were hoping to just source() it without re-reading it. If a colleague or future you encountered these errors, do you run screaming from the room? How hard is it to pinpoint the usage problem?

I will scare you now

Here are some great examples STAT545 students devised during class where the function should break but it does not.

max_minus_min(gapminder[c('lifeExp', 'gdpPercap', 'pop')])
## [1] 1318683072
max_minus_min(c(TRUE, TRUE, FALSE, TRUE, TRUE))
## [1] 1

In both cases, R’s eagerness to make sense of our requests is unfortunately successful. In the first case, a data.frame containing just the quantitative variables is eventually coerced into numeric vector. We can compute max minus min, even though it makes absolutely no sense at all. In the second case, a logical vector is converted to zeroes and ones, which might merit an error or at least a warning.

stopifnot

stopifnot() is the entry level solution. I use it here to make sure the input x is a numeric vector.

mmm <- function(x) {
  stopifnot(is.numeric(x))
  max(x) - min(x)
}
mmm(gapminder)
## Error in mmm(gapminder): is.numeric(x) is not TRUE
mmm(gapminder$country)
## Error in mmm(gapminder$country): is.numeric(x) is not TRUE
mmm("eggplants are purple")
## Error in mmm("eggplants are purple"): is.numeric(x) is not TRUE
mmm(gapminder[c('lifeExp', 'gdpPercap', 'pop')])
## Error in mmm(gapminder[c("lifeExp", "gdpPercap", "pop")]): is.numeric(x) is not TRUE
mmm(c(TRUE, TRUE, FALSE, TRUE, TRUE))
## Error in mmm(c(TRUE, TRUE, FALSE, TRUE, TRUE)): is.numeric(x) is not TRUE

And we see that it catches all of the self-inflicted damage we would like to avoid.

if then stop

stopifnot() doesn’t provide a very good error message. The next approach is very widely used. Put your validity check inside an if() statement and call stop() yourself, with a custom error message, in the body.

mmm2 <- function(x) {
  if(!is.numeric(x)) {
    stop('I am so sorry, but this function only works for numeric input!\n',
         'You have provided an object of class: ', class(x)[1])
  }
  max(x) - min(x)
}
mmm2(gapminder)
## Error in mmm2(gapminder): I am so sorry, but this function only works for numeric input!
## You have provided an object of class: tbl_df

In addition to a gratuitous apology, the error raised also contains two more pieces of helpful info:

• Which function threw the error.
• Hints on how to fix things: expected class of input vs actual class.

If it is easy to do so, I highly recommend this template: “you gave me THIS, but I need THAT”.

Sidebar: non-programming uses for assertions

Another good use of this pattern is to leave checks behind in data analytical scripts. Consider our repetitive use of Gapminder in this course. Every time we load it, we inspect it, hoping to see the usual stuff. If we were loading from file (vs. a stable data package), we might want to formalize our expectations about the number of rows and columns, the names and flavors of the variables, etc. This would alert us if the data suddenly changed, which can be a useful wake-up call in scripts that you re-run ad nauseum on auto-pilot or non-interactively.