tidyverse/purrr

purrr

Overview

purrr enhances R’s functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors. If you’ve never heard of FP before, the best place to start is the family of map() functions which allow you to replace many for loops with code that is both more succinct and easier to read. The best place to learn about the map() functions is the iteration chapter in R for Data Science.

Installation

# The easiest way to get purrr is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just purrr:
install.packages("purrr")

# Or the the development version from GitHub:
# install.packages("pak")
pak::pak("tidyverse/purrr")

Cheatsheet

Usage

The following example uses purrr to solve a fairly realistic problem: split a data frame into pieces, fit a model to each piece, compute the summary, then extract the R².

library(purrr)

mtcars |> 
  split(mtcars$cyl) |>  # from base R
  map(\(df) lm(mpg ~ wt, data = df)) |> 
  map(summary) |>
  map_dbl("r.squared")
#>         4         6         8 
#> 0.5086326 0.4645102 0.4229655

This example illustrates some of the advantages of purrr functions over the equivalents in base R:

• The first argument is always the data, so purrr works naturally with the pipe.

• All purrr functions are type-stable. They always return the advertised output type (map() returns lists; map_dbl() returns double vectors), or they throw an error.

• All map() functions accept functions (named, anonymous, and lambda), character vector (used to extract components by name), or numeric vectors (used to extract by position).

There are two less obvious advantages:

• All map() functions have .progress argument so that you can easily track the progress of long running jobs.

• All map() functions work with in_parallel() to easily spread computation across multiple cores on your computer, or multiple machines over the network.