# purrr v0.2.5

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## Functional Programming Tools

A complete and consistent functional programming toolkit for R.

# 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("devtools")
devtools::install_github("tidyverse/purrr")


## 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(.\$cyl) %>% # from base R
map(~ lm(mpg ~ wt, data = .)) %>%
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 errror.

• All map() functions either accept function, formulas (used for succinctly generating anonymous functions), a character vector (used to extract components by name), or a numeric vector (used to extract by position).

## Functions in purrr

 Name Description keep Keep or discard elements using a predicate function. map Apply a function to each element of a vector map2 Map over multiple inputs simultaneously. array-coercion Coerce array to list as_mapper Convert an object into a mapper function null-default Default value for NULL. every Do every or some elements of a list satisfy a predicate? partial Partial apply a function, filling in some arguments. detect Find the value or position of the first match. cross Produce all combinations of list elements set_names Set names in a vector get-attr Infix attribute accessor splice Splice objects and lists of objects into a list flatten Flatten a list of lists into a simple vector. invoke Invoke functions. has_element Does a list contain an object? transpose Transpose a list. when Match/validate a set of conditions for an object and continue with the action associated with the first valid match. is_numeric Test is an object is integer or double as_vector Coerce a list to a vector vec_depth Compute the depth of a vector accumulate Accumulate recursive folds across a list %>% Pipe operator compose Compose multiple functions along Helper to create vectors with matching length. head_while Find head/tail that all satisfies a predicate. lmap Apply a function to list-elements of a list list_modify Modify a list imap Apply a function to each element of a vector, and its index rerun Re-run expressions multiple times. pluck Pluck out a single an element from a vector or environment modify Modify elements selectively safely Capture side effects. prepend Prepend a vector rdunif Generate random sample from a discrete uniform distribution rbernoulli Generate random sample from a Bernoulli distribution purrr-package purrr: Functional Programming Tools negate Negate a predicate function. reduce Reduce a list to a single value by iteratively applying a binary function. reexports Objects exported from other packages lift Lift the domain of a function No Results!