purrr enhances R’s functional programming (FP) toolkit by providing acomplete and consistent set of tools for working with functions andvectors. If you’ve never heard of FP before, the best place to start isthe family ofmap() functions which allow you to replacemany for loops with code that is both more succinct and easier to read.The best place to learn about themap() functions is theiteration chapter in R forData Science.
# 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")
The following example uses purrr to solve a fairly realistic problem:split a data frame into pieces, fit a model to each piece, compute thesummary, then extract the R2.
library(purrr)mtcars|>split(mtcars$cyl)|># from base Rmap(\(df)lm(mpg~ wt,data = df))|>map(summary)|>map_dbl("r.squared")#> 4 6 8#> 0.5086326 0.4645102 0.4229655This example illustrates some of the advantages of purrr functionsover the equivalents in base R:
The first argument is always the data, so purrr works naturallywith the pipe.
All purrr functions are type-stable. They always return theadvertised output type (map() returns lists;map_dbl() returns double vectors), or they throw anerror.
Allmap() functions accept functions (named,anonymous, and lambda), character vector (used to extract components byname), or numeric vectors (used to extract by position).
There are two less obvious advantages:
Allmap() functions have.progressargument so that you can easily track the progress of long runningjobs.
Allmap() functions work within_parallel() to easily spread computation across multiplecores on your computer, or multiple machines over the network.