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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).

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Version

Install

install.packages('purrr')

Monthly Downloads

1,018,235

Version

0.2.5

License

GPL-3 | file LICENSE

Issues

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Stars

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Maintainer

Last Published

May 29th, 2018

Functions in purrr (0.2.5)

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