# 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.

## Readme

# 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")
devtools::install_github("hadley/purrr")
```

## 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.

## Related work

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 associated with the first valid match. | |

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 NULL. | |

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 | |

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## Last month downloads

## Details

License | GPL-3 | file LICENSE |

LazyData | true |

URL | https://github.com/hadley/purrr |

BugReports | https://github.com/hadley/purrr/issues |

RoxygenNote | 6.0.1 |

NeedsCompilation | yes |

Packaged | 2017-05-10 14:14:02 UTC; lionel |

Repository | CRAN |

Date/Publication | 2017-05-11 18:22:22 UTC |

suggests | covr , dplyr (>= 0.4.3) , testthat |

imports | lazyeval (>= 0.2.0) , magrittr (>= 1.5) , Rcpp , tibble |

Contributors | RStudio, Hadley Wickham |

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