Purrr Package in R Programming

Purrr package in R Programming , provides a set of functions that are designed to work with functional programming concepts, such as mapping, filtering and reducing. These functions are designed to work with lists, data frames and other objects, making it easier to work with complex data structures.

Functions Available in purrr Package

Here are some of the functions available in the purrr package.

Installation and Loading

To install the purrr package and load it into our R environment , we will use the install.packages() and library() function.

install.packages("purrr")
library(purr)

Simplifying Iterations

Iteration is another core concept in functional programming. It involves applying a function to a set of inputs, one at a time and returning a set of outputs. The purrr package provides a more efficient way to perform iteration using the map() and walk() functions. 

1. Iteration using map( ) function

The map() function is used to apply a function to each element of a list or a vector. It takes a function and a list or a vector as inputs and returns a new list or vector where the function has been applied to each element of the original list or vector. The output type will be the same as the input type. T

numbers <- c(1, 2, 3, 4, 5)

map(numbers, function(x) x^2)

Output:

> map(numbers, function(x) x^2)
[[1]]
[1] 1

[[2]]
[1] 4

[[3]]
[1] 9

[[4]]
[1] 16

[[5]]
[1] 25

list_of_dfs <- list(
  data.frame(a = c(1, 2, 3), b = c(4, 5, 6)),
  data.frame(a = c(7, 8, 9), b = c(10, 11, 12)),
  data.frame(a = c(13, 14, 15), b = c(16, 17, 18))
)

map(list_of_dfs, function(df) summary(df))

Output:

> map(list_of_dfs, function(df) summary(df))
[[1]]
a b
Min. :1.0 Min. :4.0
1st Qu.:1.5 1st Qu.:4.5
Median :2.0 Median:5.0
Mean :2.0 Mean :5.0
3rd Qu.:2.5 3rd Qu.:5.5
Max. :3.0 Max. :6.0

[[2]]
a b
Min. :7.0 Min. :10.0
1st Qu.:7.5 1st Qu.:10.5
Median :8.0 Median :11.0
Mean :8.0 Mean :11.0
3rd Qu.:8.5 3rd Qu.:11.5
Max. :9.0 Max. :12.0

[[3]]
a b
Min. :13.0 Min. :16.0
1st Qu.:13.5 1st Qu.:16.5
Median :14.0 Median :17.0
Mean :14.0 Mean :17.0
3rd Qu.:14.5 3rd Qu.:17.5
Max. :15.0 Max. :18.0

2. Iteration using walk( ) function

The walk() function is used to apply a function to each element of a list or a vector, but it does not return anything. Instead, it is used when we want to perform an operation on each element of a list or a vector, such as printing or saving to a file. 

library(purrr)

df <- data.frame(
  x = c(1, 2, 3, 4),
  y = c(5, 6, 7, 8)
)

walk(df$y, ~ print(.x * 3))

Output:

> walk(df$y, ~ print(.x * 3))
[1] 15
[1] 18
[1] 21
[1] 24

Functional Programming Tools

Purrr also provides a set of functional programming tools, such as reduce(), accumulate(), compose(), partial(), etc., that make it easier to work with functions.

1. reduce( ) function

The reduce() function is used to successively apply a binary function to the elements of a vector, list or data frame and returns a single value.

library(dplyr)
library(purrr)

data("iris")
long_sepals <- filter(iris, Sepal.Length > 5)
petal_sum <- reduce(long_sepals$Petal.Length, `+`)

petal_sum

Output:

> petal_sum
[1] 509.2

2. accumulate( ) function

The accumulate() function allows to apply a function iteratively to each element of a vector or list, accumulating the results at each step.

library(purrr)

iris_sum <- iris %>% 
  pull(Sepal.Length) %>% 
  accumulate(`+`)

print(iris_sum)

Output:

> print(iris_sum)
[1] 5.1 10.0 14.7 19.3 24.3 29.7 34.3 39.3 43.7 48.6 54.0 58.8 63.6
[14] 67.9 73.7 79.4 84.8 89.9 95.6 100.7 106.1 111.2 115.8 120.9 125.7 130.7
[27] 135.7 140.9 146.1 150.8 155.6 161.0 166.2 171.7 176.6 181.6 187.1 192.0 196.4
[40] 201.5 206.5 211.0 215.4 220.4 225.5 230.3 235.4 240.0 245.3 250.3 257.3 263.7
[53] 270.6 276.1 282.6 288.3 294.6 299.5 306.1 311.3 316.3 322.2 328.2 334.3 339.9
[66] 346.6 352.2 358.0 364.2 369.8 375.7 381.8 388.1 394.2 400.6 407.2 414.0 420.7
[79] 426.7 432.4 437.9 443.4 449.2 455.2 460.6 466.6 473.3 479.6 485.2 490.7 496.2
[92] 502.3 508.1 513.1 518.7 524.4 530.1 536.3 541.4 547.1 553.4 559.2 566.3 572.6
[105] 579.1 586.7 591.6 598.9 605.6 612.8 619.3 625.7 632.5 638.2 644.0 650.4 656.9
[118] 664.6 672.3 678.3 685.2 690.8 698.5 704.8 711.5 718.7 724.9 731.0 737.4 744.6
[131] 752.0 759.9 766.3 772.6 778.7 786.4 792.7 799.1 805.1 812.0 818.7 825.6 831.4
[144] 838.2 844.9 851.6 857.9 864.4 870.6 876.5

3. compose( ) function

compose() is a function allows to combine multiple functions into a single composite function. The resulting function applies each of the input functions in turn, with the output of one function being used as the input to the next function.

library(purrr)

my_func <- compose(sqrt, function(x) x^2)

map_dbl(mtcars$mpg, my_func)

Output:

> map_dbl(mtcars$mpg, my_func)
[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4
[17] 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 21.4

4. partial( ) function

The partial() function in the purrr package allows to create a new function by fixing one or more of the arguments of an existing function. The resulting function can then be used with the remaining unfixed arguments.

library(purrr)

my_cars <- data.frame(
  weight = c(1000, 2000, 3000),
  horsepower = c(100, 200, 300)
)

mpg_func <- partial(function(df, wt, hp) {
  df$mpg <- wt/hp
  df
}, wt = my_cars$weight)

map(my_cars, mpg_func, hp = 150)

Output:

> map(my_cars, mpg_func, hp = 150)
$weight
$weight[[1]]
[1] 1000

$weight[[2]]
[1] 2000

$weight[[3]]
[1] 3000

$weight$mpg
[1] 6.666667 13.333333 20.000000


$horsepower
$horsepower[[1]]
[1] 100

$horsepower[[2]]
[1] 200

$horsepower[[3]]
[1] 300

$horsepower$mpg
[1] 6.666667 13.333333 20.000000.

In this article, we explored the purrr package in R and covered key functions like map(), reduce() and accumulate(), which simplify data manipulation and improve code readability.

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