purrr v0.2.2.2

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Functional Programming Tools

Make your pure functions purr with the 'purrr' package. This package completes R's functional programming tools with missing features present in other programming languages.

purrr

Purrr makes your pure functions purr by completing R's functional programming tools with important features from other languages, in the style of the JS packages underscore.js, lodash and lazy.js.

Installation

Get the released version from CRAN:

install.packages("purrr")


Or the development version from github with:

# install.packages("devtools")


Examples

The following example uses purrr to solve a fairly realistic problem: split a data frame into pieces, fit a model to each piece, summarise and extract R^2.

library(purrr)

mtcars %>%
split(.$cyl) %>% map(~ lm(mpg ~ wt, data = .)) %>% map(summary) %>% map_dbl("r.squared")  Note the three types of input to map(): a function, a formula (converted to an anonymous function), or a string (used to extract named components). The following more complicated example shows how you might generate 100 random test-training splits, fit a model to each training split then evaluate based on the test split: library(dplyr) random_group <- function(n, probs) { probs <- probs / sum(probs) g <- findInterval(seq(0, 1, length = n), c(0, cumsum(probs)), rightmost.closed = TRUE) names(probs)[sample(g)] } partition <- function(df, n, probs) { replicate(n, split(df, random_group(nrow(df), probs)), FALSE) %>% transpose() %>% as_data_frame() } msd <- function(x, y) sqrt(mean((x - y) ^ 2)) # Generate 100 random test-training splits boot <- partition(mtcars, 100, c(training = 0.8, test = 0.2)) boot boot <- boot %>% mutate( # Fit the models models = map(training, ~ lm(mpg ~ wt, data = .)), # Make predictions on test data preds = map2(models, test, predict), diffs = map2(preds, test %>% map("mpg"), msd) ) # Evaluate mean-squared difference between predicted and actual mean(unlist(boot$diffs))


API

Transformation

• Apply a function to each element: map() returns a list; map_lgl()/map_int()/map_dbl()/map_chr() return a vector; walk() invisibly returns original list, calling the function for its side effects; map2() and pmap() vectorise over multiple inputs; at_depth() maps a function at a specified level of nested lists.

• Apply a function conditionally with map_if() (where a predicate returns TRUE) and map_at() (at specific locations).

• Apply a function to slices of a data frame with by_slice(), or to each row with by_row() or map_rows().

• Apply a function to list-elements of a list with lmap(), lmap_if() and lmap_at(). Compared to traditional mapping, the function is applied to x[i] instead of x[[i]], preserving the surrounding list and attributes.

• Reduce a list to a single value by iteratively applying a binary function: reduce() and reduce_right().

• Figure out if a list contains an object: contains().

• Order, sort and split a list based on its components with split_by(), order_by() and sort_by().

List manipulation and creation

• Transpose a list with transpose().

• Create the cartesian product of the elements of several lists with cross_n() and cross_d().

• Flatten a list with flatten().

• Splice lists and other objects with splice().

Predicate functions

(A predicate function is a function that either returns TRUE or FALSE)

• keep() or discard() elements that satisfy the predicate..

• Does every() element or some() elements satisfy the predicate?

• Find the value (detect()) and index (detect_index()) of the first element that satisfies the predicate.

• Find the head/tail that satisfies a predicate: head_while(), tail_while().

Lists of functions

• invoke() every function in a list with given arguments and returns a list, invoke_lgl()/invoke_int()/invoke_dbl()/invoke_chr() returns vectors.

Function operators

• Fill in function arguments with partial().

• Change the way your function takes input with lift() and the lift_xy() family of composition helpers.

• Compose multiple functions into a single function with compose().

• Negate a predicate funtion with negate().

Objects coercion

• Convert an array or matrix to a list with array_tree() and array_branch().

• Convert a list to a vector with as_vector().

Philosophy

The goal is not to try and simulate Haskell in R: purrr does not implement currying or destructuring binds or pattern matching. The goal is to give you similar expressiveness to an FP language, while allowing you to write code that looks and works like R.

• Instead of point free style, use the pipe, %>%, to write code that can be read from left to right.

• Instead of currying, we use ... to pass in extra arguments.

• Anonymous functions are verbose in R, so we provide two convenient shorthands. For unary functions, ~ .x + 1 is equivalent to function(.x) .x + 1. For chains of transformations functions, . %>% f() %>% g() is equivalent to function(.) . %>% f() %>% g().

• R is weakly typed, we need variants map_int(), map_dbl(), etc since we don't know what .f will return.

• R has named arguments, so instead of providing different functions for minor variations (e.g. detect() and detectLast()) I use a named argument, .first. Type-stable functions are easy to reason about so additional arguments will never change the type of the output.

• rlist, another R package to support working with lists. Similar goals but somewhat different philosophy.

• List operations defined in the Haskell prelude

• Scala's list methods.

Functions in purrr

 Name Description conditional-map Modify elements conditionally contains Does a list contain an object? array-coercion Coerce array to list as_function Convert an object into a function. head_while Find head/tail that all satisfies a predicate. invoke Invoke functions. %>% Pipe operator prepend Prepend a vector update_list Modify a list when Match/validate a set of conditions for an object and continue with the action accumulate Accumulate recursive folds across a list along Helper to create vectors with matching length. flatten Flatten a list of lists into a simple vector. get-attr Infix attribute accessor purrr-package purrr: Functional Programming Tools rbernoulli Generate random samples from a Bernoulli distribution every Do every or some elements of a list satisfy a predicate? flatmap Map a function and flatten the result by one-level keep Keep or discard elements using a predicate function. lift Lift the domain of a function splice Splice objects and lists of objects into a list split_by Split, order and sort lists by their components. bare-type-predicates Bare type predicates compose Compose multiple functions cross_n Produce all combinations of list elements detect Find the value or position of the first match. map2 Map over multiple inputs simultaneously. lmap Apply a function to list-elements of a list map Apply a function to each element of a vector scalar-type-predicates Scalar type predicates set_names Set names in a vector negate Negate a predicate function. rerun Re-run expressions multiple times. safely Capture side effects. transpose Transpose a list. as_vector Coerce a list to a vector at_depth Map a function over lower levels of a nested list is_empty Is a vector/list empty? is_formula Is a formula? null-default Default value for partial Partial apply a function, filling in some arguments. rdunif Generate random samples from a discrete uniform distribution reduce Reduce a list to a single value by iteratively applying a binary function. type-predicates Type predicates No Results!