purrr <-> base R

Introduction

This vignette compares purrr’s functionals to their base R equivalents, focusing primarily on the map family and related functions. This helps those familiar with base R understand better what purrr does, and shows purrr users how you might express the same ideas in base R code. We’ll start with a rough overview of the major differences, give a rough translation guide, and then show a few examples.

library(purrr)
library(tibble)

Key differences

There are two primary differences between the base apply family and the purrr map family: purrr functions are named more consistently, and more fully explore the space of input and output variants.

purrr functions consistently use . as prefix to avoid inadvertently matching arguments of the purrr function, instead of the function that you’re trying to call. Base functions use a variety of techniques including upper case (e.g. lapply(X, FUN, ...)) or require anonymous functions (e.g. Map()).
All map functions are type stable: you can predict the type of the output using little information about the inputs. In contrast, the base functions sapply() and mapply() automatically simplify making the return value hard to predict.
The map functions all start with the data, followed by the function, then any additional constant argument. Most base apply functions also follow this pattern, but mapply() starts with the function, and Map() has no way to supply additional constant arguments.
purrr functions provide all combinations of input and output variants, and include variants specifically for the common two argument case.

Direct translations

The following sections give a high-level translation between base R commands and their purrr equivalents. See function documentation for the details.

`Map` functions

Here x denotes a vector and f denotes a function

Output	Input	Base R	purrr
List	1 vector	`lapply()`	`map()`
List	2 vectors	`mapply()`, `Map()`	`map2()`
List	>2 vectors	`mapply()`, `Map()`	`pmap()`
Atomic vector of desired type	1 vector	`vapply()`	`map_lgl()` (logical), `map_int()` (integer), `map_dbl()` (double), `map_chr()` (character), `map_raw()` (raw)
Atomic vector of desired type	2 vectors	`mapply()`, `Map()`, then `is.*()` to check type	`map2_lgl()` (logical), `map2_int()` (integer), `map2_dbl()` (double), `map2_chr()` (character), `map2_raw()` (raw)
Atomic vector of desired type	>2 vectors	`mapply()`, `Map()`, then `is.*()` to check type	`pmap_lgl()` (logical), `pmap_int()` (integer), `pmap_dbl()` (double), `pmap_chr()` (character), `pmap_raw()` (raw)
Side effect only	1 vector	loops	`walk()`
Side effect only	2 vectors	loops	`walk2()`
Side effect only	>2 vectors	loops	`pwalk()`
Data frame (`rbind` outputs)	1 vector	`lapply()` then `rbind()`	`map_dfr()`
Data frame (`rbind` outputs)	2 vectors	`mapply()`/`Map()` then `rbind()`	`map2_dfr()`
Data frame (`rbind` outputs)	>2 vectors	`mapply()`/`Map()` then `rbind()`	`pmap_dfr()`
Data frame (`cbind` outputs)	1 vector	`lapply()` then `cbind()`	`map_dfc()`
Data frame (`cbind` outputs)	2 vectors	`mapply()`/`Map()` then `cbind()`	`map2_dfc()`
Data frame (`cbind` outputs)	>2 vectors	`mapply()`/`Map()` then `cbind()`	`pmap_dfc()`
Any	Vector and its names	`l/s/vapply(X, function(x) f(x, names(x)))` or `mapply/Map(f, x, names(x))`	`imap()`, `imap_*()` (`lgl`, `dbl`, `dfr`, and etc. just like for `map()`, `map2()`, and `pmap()`)
Any	Selected elements of the vector	`l/s/vapply(X[index], FUN, ...)`	`map_if()`, `map_at()`
List	Recursively apply to list within list	`rapply()`	`map_depth()`
List	List only	`lapply()`	`lmap()`, `lmap_at()`, `lmap_if()`

Extractor shorthands

Since a common use case for map functions is list extracting components, purrr provides a handful of shortcut functions for various uses of [[.

Input	base R	purrr
Extract by name	lapply(x, `[[`, "a")	`map(x, "a")`
Extract by position	lapply(x, `[[`, 3)	`map(x, 3)`
Extract deeply	`lapply(x, \(y) y[[1]][["x"]][[3]])`	`map(x, list(1, "x", 3))`
Extract with default value	`lapply(x, function(y) tryCatch(y[[3]], error = function(e) NA))`	`map(x, 3, .default = NA)`

Predicates

Here p, a predicate, denotes a function that returns TRUE or FALSE indicating whether an object fulfills a criterion, e.g. is.character().

Description	base R	purrr
Find a matching element	`Find(p, x)`	`detect(x, p)`,
Find position of matching element	`Position(p, x)`	`detect_index(x, p)`
Do all elements of a vector satisfy a predicate?	`all(sapply(x, p))`	`every(x, p)`
Does any elements of a vector satisfy a predicate?	`any(sapply(x, p))`	`some(x, p)`
Does a list contain an object?	`any(sapply(x, identical, obj))`	`has_element(x, obj)`
Keep elements that satisfy a predicate	`x[sapply(x, p)]`	`keep(x, p)`
Discard elements that satisfy a predicate	`x[!sapply(x, p)]`	`discard(x, p)`
Negate a predicate function	`function(x) !p(x)`	`negate(p)`

Other vector transforms

Description	base R	purrr
Accumulate intermediate results of a vector reduction	`Reduce(f, x, accumulate = TRUE)`	`accumulate(x, f)`
Recursively combine two lists	`c(X, Y)`, but more complicated to merge recursively	`list_merge()`, `list_modify()`
Reduce a list to a single value by iteratively applying a binary function	`Reduce(f, x)`	`reduce(x, f)`

Examples

Varying inputs

One input

Suppose we would like to generate a list of samples of 5 from normal distributions with different means:

means <- 1:4

There’s little difference when generating the samples:

Base R uses lapply():

set.seed(2020)
samples <- lapply(means, rnorm, n = 5, sd = 1)
str(samples)
#> List of 4
#>  $ : num [1:5] 1.377 1.302 -0.098 -0.13 -1.797
#>  $ : num [1:5] 2.72 2.94 1.77 3.76 2.12
#>  $ : num [1:5] 2.15 3.91 4.2 2.63 2.88
#>  $ : num [1:5] 5.8 5.704 0.961 1.711 4.058

purrr uses map():

set.seed(2020)
samples <- map(means, rnorm, n = 5, sd = 1)
str(samples)
#> List of 4
#>  $ : num [1:5] 1.377 1.302 -0.098 -0.13 -1.797
#>  $ : num [1:5] 2.72 2.94 1.77 3.76 2.12
#>  $ : num [1:5] 2.15 3.91 4.2 2.63 2.88
#>  $ : num [1:5] 5.8 5.704 0.961 1.711 4.058

Two inputs

Lets make the example a little more complicated by also varying the standard deviations:

means <- 1:4
sds <- 1:4

This is relatively tricky in base R because we have to adjust a number of mapply()’s defaults.

set.seed(2020)
samples <- mapply(
  rnorm, 
  mean = means, 
  sd = sds, 
  MoreArgs = list(n = 5), 
  SIMPLIFY = FALSE
)
str(samples)
#> List of 4
#>  $ : num [1:5] 1.377 1.302 -0.098 -0.13 -1.797
#>  $ : num [1:5] 3.44 3.88 1.54 5.52 2.23
#>  $ : num [1:5] 0.441 5.728 6.589 1.885 2.63
#>  $ : num [1:5] 11.2 10.82 -8.16 -5.16 4.23

Alternatively, we could use Map() which doesn’t simply, but also doesn’t take any constant arguments, so we need to use an anonymous function:

samples <- Map(function(...) rnorm(..., n = 5), mean = means, sd = sds)

In R 4.1 and up, you could use the shorter anonymous function form:

samples <- Map(\(...) rnorm(..., n = 5), mean = means, sd = sds)

Working with a pair of vectors is a common situation so purrr provides the map2() family of functions:

set.seed(2020)
samples <- map2(means, sds, rnorm, n = 5)
str(samples)
#> List of 4
#>  $ : num [1:5] 1.377 1.302 -0.098 -0.13 -1.797
#>  $ : num [1:5] 3.44 3.88 1.54 5.52 2.23
#>  $ : num [1:5] 0.441 5.728 6.589 1.885 2.63
#>  $ : num [1:5] 11.2 10.82 -8.16 -5.16 4.23

Any number of inputs

We can make the challenge still more complex by also varying the number of samples:

ns <- 4:1

Using base R’s Map() becomes more straightforward because there are no constant arguments.

set.seed(2020)
samples <- Map(rnorm, mean = means, sd = sds, n = ns)
str(samples)
#> List of 4
#>  $ : num [1:4] 1.377 1.302 -0.098 -0.13
#>  $ : num [1:3] -3.59 3.44 3.88
#>  $ : num [1:2] 2.31 8.28
#>  $ : num 4.47

In purrr, we need to switch from map2() to pmap() which takes a list of any number of arguments.

set.seed(2020)
samples <- pmap(list(mean = means, sd = sds, n = ns), rnorm)
str(samples)
#> List of 4
#>  $ : num [1:4] 1.377 1.302 -0.098 -0.13
#>  $ : num [1:3] -3.59 3.44 3.88
#>  $ : num [1:2] 2.31 8.28
#>  $ : num 4.47

Outputs

Given the samples, imagine we want to compute their means. A mean is a single number, so we want the output to be a numeric vector rather than a list.

There are two options in base R: vapply() or sapply(). vapply() requires you to specific the output type (so is relatively verbose), but will always return a numeric vector. sapply() is concise, but if you supply an empty list you’ll get a list instead of a numeric vector.
```
# type stable
medians <- vapply(samples, median, FUN.VALUE = numeric(1L))
medians
#> [1] 0.6017626 3.4411470 5.2946304 4.4694671

# not type stable
medians <- sapply(samples, median)
```

purrr is little more compact because we can use map_dbl().

medians <- map_dbl(samples, median)
medians
#> [1] 0.6017626 3.4411470 5.2946304 4.4694671

What if we want just the side effect, such as a plot or a file output, but not the returned values?

In base R we can either use a for loop or hide the results of lapply.

# for loop
for (s in samples) {
  hist(s, xlab = "value", main = "")
}

# lapply
invisible(lapply(samples, function(s) {
  hist(s, xlab = "value", main = "")
}))

In purrr, we can use walk().

walk(samples, ~ hist(.x, xlab = "value", main = ""))

Pipes

You can join multiple steps together either using the magrittr pipe:

set.seed(2020)
means %>%
  map(rnorm, n = 5, sd = 1) %>%
  map_dbl(median)
#> [1] -0.09802317  2.72057350  2.87673977  4.05830349

Or the base pipe R:

set.seed(2020)
means |> 
  lapply(rnorm, n = 5, sd = 1) |> 
  sapply(median)
#> [1] -0.09802317  2.72057350  2.87673977  4.05830349

(And of course you can mix and match the piping style with either base R or purrr.)

The pipe is particularly compelling when working with longer transformations. For example, the following code splits mtcars up by cyl, fits a linear model, extracts the coefficients, and extracts the first one (the intercept).

mtcars |>
  split(mtcars$cyl) |> 
  map(\(df) lm(mpg ~ wt, data = df))|> 
  map(coef) |> 
  map_dbl(1)
#>        4        6        8 
#> 39.57120 28.40884 23.86803