## 9.3 Purrr style

Before we go on to explore more map variants, let’s take a quick look at how you tend to use multiple purrr functions to solve a moderately realistic problem: fitting a model to each subgroup and extracting a coefficient of the model. For this toy example, I’m going to break the mtcars data set down into groups defined by the number of cylinders, using the base split function:

by_cyl <- split(mtcars, mtcars\$cyl)

This creates a list of three data frames: the cars with 4, 6, and 8 cylinders respectively.

Now imagine we want to fit a linear model, then extract the second coefficient (i.e. the slope). The following code shows how you might do that with purrr:

by_cyl %>%
map(~ lm(mpg ~ wt, data = .x)) %>%
map(coef) %>%
map_dbl(2)
#>     4     6     8
#> -5.65 -2.78 -2.19

(If you haven’t seen %>%, the pipe, before, it’s described in Section 6.3.)

I think this code is easy to read because each line encapsulates a single step, you can easily distinguish the functional from what it does, and the purrr helpers allow us to very concisely describe what to do in each step.

How would you attack this problem with base R? You certainly could replace each purrr function with the equivalent base function:

by_cyl %>%
lapply(function(data) lm(mpg ~ wt, data = data)) %>%
lapply(coef) %>%
vapply(function(x) x[[2]], double(1))
#>     4     6     8
#> -5.65 -2.78 -2.19

But this isn’t really base R since we’re using the pipe. To tackle purely in base I think you’d use an intermediate variable, and do more in each step:

models <- lapply(by_cyl, function(data) lm(mpg ~ wt, data = data))
vapply(models, function(x) coef(x)[[2]], double(1))
#>     4     6     8
#> -5.65 -2.78 -2.19

Or, of course, you could use a for loop:

intercepts <- double(length(by_cyl))
for (i in seq_along(by_cyl)) {
model <- lm(mpg ~ wt, data = by_cyl[[i]])
intercepts[[i]] <- coef(model)[[2]]
}
intercepts
#> [1] -5.65 -2.78 -2.19

It’s interesting to note that as you move from purrr to base apply functions to for loops you tend to do more and more in each iteration. In purrr we iterate 3 times (map(), map(), map_dbl()), with apply functions we iterate twice (lapply(), vapply()), and with a for loop we iterate once. I prefer more, but simpler, steps because I think it makes the code easier to understand and later modify.