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# purrr [](https://cran.r-project.org/package=purrr) [](https://app.codecov.io/gh/tidyverse/purrr?branch=master) [](https://github.com/tidyverse/purrr/actions/workflows/R-CMD-check.yaml) ## 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](https://r4ds.had.co.nz/iteration.html) in R for data science. ## Installation ``` r # 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. ``` r 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).

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