# A Crazy Little Thing Called {purrr} - Part 4: mappers

I won’t talk about the Queen reference anymore.

I’ve been working lately on a new package called {attempt}, which is an attempt at making try catch and condition handling in R a little bit friendlier. If you have some time to spare, I’ll be glad if you can give me some feedbacks about it: positive, negative, PR, improvement suggestions https://github.com/ColinFay/attempt… Feel free!

This package relies a lot on {purrr} mappers (and on {rlang} but I won’t be talking about {rlang} today… maybe in another series of posts). Today I’m gonna talk briefly about this cool stuff in {purrr} called mappers. (Well, these mappers are forwarded to {rlang} so this is a cool stuff from {rlang}, yet the mappers constructor `as_mapper` is a {purrr} function).

## What on earth are mappers

You’ve been using mappers a lot if, like me, you’ve been using {purrr} a lot. These are the one sided formulas which are created as such :

``````library(purrr)
map_dbl(1:3, ~ .x * 10)
``````
``````## [1] 10 20 30
``````
``````# or
map_dbl(1:3, ~ . * 10)
``````
``````## [1] 10 20 30
``````
``````# or
map2_dbl(1:3, 3:5, ~ .x + .y)
``````
``````## [1] 4 6 8
``````

Here, you’re creating a lambda function on the fly: the first and second multiplying each element by 10, and the third adding each `x` and `y` together.

## What’s happening behind?

The first thing that `map` (and other map_*) do is turning a the ```~ .x \* 10``` into a mapper with `as_mapper`.

``````map
``````
``````## function(.x, .f, ...) {
##   .f <- as_mapper(.f, ...)
##   .Call(map_impl, environment(), ".x", ".f", "list")
## }
## <environment: namespace:purrr>
``````

In the background, mappers are created with {rlang} `as_function`: so, as you could have guessed, this turns formulas into functions.

``````addition <- as_mapper(~ .x + .y)
``````
``````## function (..., .x = ..1, .y = ..2, . = ..1)
## .x + .y
``````

As you can see, the function is created with the .x matching the first argument, the .y matching the second, and the . alone matching the first. It’s that simple, you now have a good old R function:

``````addition(1,3)
``````
``````## [1] 4
``````

## How is that useful?

### Don’t repeat yourself

This first comes handy when you have a lot of mapping to do:

``````map2_int(1:3, 4:6, addition)
``````
``````## [1] 5 7 9
``````
``````map2_int(1:10, 21:30, addition)
``````
``````##  [1] 22 24 26 28 30 32 34 36 38 40
``````
``````map2_int(2:3, 4:5, addition)
``````
``````## [1] 6 8
``````

### Flexible mappers

Mappers are what allow {purrr} flexibility with mapping, as it alows you to use a defaut R functions inside map:

``````as_mapper(is.numeric)
``````
``````## function (x)
## is.numeric(x = x)
## <environment: base>
``````

or characters and numbers that creates calls to `pluck`:

``````as_mapper(1)
``````
``````## function (x, ...)
## pluck(x, list(1), .default = NULL)
## <environment: 0x7fcae3543788>
``````
``````as_mapper("this")
``````
``````## function (x, ...)
## pluck(x, list("this"), .default = NULL)
## <environment: 0x7fcae3280190>
``````

or even another lambda function :

``````as_mapper(function(x, y) {x+y} )
``````
``````## function(x, y) {x+y}
``````

### mappers with more than two arguments

So, here’s the trick if you want to create mappers with more than two arguments: use `..1`, `..2`, `..3`, etc. Here’s an example with `pmap`.

``````l <- list(rnorm(10),
rnorm(100),
rnorm(1000))
pmap_dbl(list(l, 20, TRUE), ~ mean(..1, ..2, ..3))
``````
``````## [1] 0.48879956 0.15248405 0.01861908
``````

## Using mapper inside {attempt}

### try_catch

These are also what allows {attempt} to be flexible when building trycatch calls and condition handlers.

As you may know, `tryCatch` takes four arguments :

• The expression to evaluate (mandatory)
• A function to be run on error
• A function to be run on warning
• A function to be run on exit

So if you’ve followed along, you now know that I can use mappers here as arguments of a `tryCatch` call. This is exactly what `try_catch`, from {attempt}, does.

Here’s a shorten example (and slightly different from the one in {attempt} as `try_that` is built with {rlang} `lang`) :

``````try_catch <- function(expr, .e) {
tryCatch( expr,
error = as_mapper(.e)
)
}
try_catch(log("a"), .e = ~ paste("There was an error:", .x))
``````
``````## [1] "There was an error: Error in log(\"a\"): argument non numérique pour une fonction mathématique\n"
``````

### stop_if

As package developers, we need to keep in mind that everything is possible when it comes to functions being used in the wild (characters in place of number is just an example).

So the first lines of a function are usually used to verify the inputs. Like :

``````pimped_sum <- function(x, y){
if (! is.numeric(x) | ! is.numeric(y)) {
stop("x and y must be numeric")
}
x + y
}
pimped_sum("1", 2)
``````
``````## Error in pimped_sum("1", 2): x and y must be numeric
``````

But that’s a lot of duplicated code, a lot of if if there are lot of checks to do, and not that much functions stating clearly what they do.

So here comes {attempt}:

``````library(attempt)
``````
``````##
## Attaching package: 'attempt'

## The following object is masked _by_ '.GlobalEnv':
##
##     try_catch
``````
``````pimped_sum <- function(x, y){
stop_if_any( c(x, y),
~ ! is.numeric(.x),
msg = "your args should be numeric")
x + y
}
pimped_sum("1", 2)
``````
``````## Error: your args should be numeric
``````

You can have a look at what {attempt} does on the GitHub repo.

``````# Simpler example
small_by_ten <- function(x){
stop_if( x,
~ .x > 10,
msg = "x should be less than ten" )
x * 10
}
small_by_ten(11)
``````
``````## Error: x should be less than ten
``````

So here, you can see that we are using a formula inside `stop_if`. How does if work ? The second argument is turned into a mapper, and then used to test x. Here’s a simplified version of this function:

``````stop_if <- function(.x, .p){
.p <- as_mapper(.p)
if ( .p(.x) )
stop("Error!")
}
stop_if(10, ~ .x < 11)
``````
``````## Error in stop_if(10, ~.x < 11): Error!
``````

The flexibily of the mapper system allows you to pass base and classical function, because as we’ve seen, they are all turned to the same function template :

``````stop_if(10, is.numeric)
``````
``````## Error in stop_if(10, is.numeric): Error!
``````
``````stop_if(10, function(x) sqrt(x) < 100)
``````
``````## Error in stop_if(10, function(x) sqrt(x) < 100): Error!
``````

Aaaaaand that’s it for today :)

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