In Section 3.5, we learned about the sapply function, which permitted us to apply the same function to each element of a vector. We constructed a function and used sapply to compute the sum of the first n integers for several values of n like this:

compute_s_n <- function(n){
  x <- 1:n
  sum(x)
}
n <- 1:25
s_n <- sapply(n, compute_s_n)

This type of operation, applying the same function or procedure to elements of an object, is quite common in data analysis. The purrr package includes functions similar to sapply but that better interact with other tidyverse functions. The main advantage is that we can better control the output type of functions. In contrast, sapply can return several different object types; for example, we might expect a numeric result from a line of code, but sapply might convert our result to character under some circumstances. purrr functions will never do this: they will return objects of a specified type or return an error if this is not possible.

The first purrr function we will learn is map, which works very similar to sapply but always, without exception, returns a list:

library(purrr)
s_n <- map(n, compute_s_n)
class(s_n)
#> [1] "list"

If we want a numeric vector, we can instead use map_dbl which always returns a vector of numeric values.

s_n <- map_dbl(n, compute_s_n)
class(s_n)
#> [1] "numeric"

This produces the same results as the sapply call shown above.

A particularly useful purrr function for interacting with the rest of the tidyverse is map_df, which always returns a tibble data frame. However, the function being called needs to return a vector or a list with names. For this reason, the following code would result in a Argument 1 must have names error:

s_n <- map_df(n, compute_s_n)

We need to change the function to make this work:

compute_s_n <- function(n){
  x <- 1:n
  tibble(sum = sum(x))
}
s_n <- map_df(n, compute_s_n)

The purrr package provides much more functionality not covered here. For more details you can consult this online resource¹.