purrr inside mutate [program]

(Builds on: purrr basics, Vector functions)
(Leads to: List-columns, purrr map with multiple inputs)

In the vector functions unit, you learned to add new variables to tibbles by giving mutate() a vector with an element for each row in the tibble. You saw that you can do any of the following to create this vector:

Give mutate() a single value, which is then repeated for each row in the tibble.
Explicitly give mutate() a vector with an element for each row in the tibble.
Create a vector using a vector function like + or case_when().

In this reading, you’ll learn about a fourth option: supplying mutate() with a vector created by a map function.

Using `map` functions inside `mutate()`

Here’s a simple scalar function that turns feelings into emoticons:

emoticons_1 <- function(x) {
  if (x == "happy") {
    ":)"
  } else if (x == "sad") {
    ":("
  } else {
    "Feeling too complex for emoticons"
  }
}

Recall that scalar functions fail if you try to give them vectors of values:

feelings <- c("happy", "befuddled")
emoticons_1(feelings)
#> Warning in if (x == "happy") {: the condition has length > 1 and only the
#> first element will be used
#> [1] ":)"

As you already know, we can turn emoticons() into a vector function by rewriting it using if_else(), case_when(), or recode(). Another option, however, is to leave emoticons() as a scalar function and apply it to the elements of feelings one-by-one using a map function:

map_chr(feelings, emoticons_1)
#> [1] ":)"                                "Feeling too complex for emoticons"

Here, we place each feeling in feelings on the conveyor belt. The worker picks each feeling up, finds the appropriate emoticon, and then places the emoticon on the output belt.

If we want to use emoticons() to add a variable to a tibble, we can place this map call inside mutate():

df <- 
  tibble(
    feeling = c("sad", "compunctious", "happy")
  ) 

df %>% 
  mutate(emoticon = map_chr(feeling, emoticons_1))
#> # A tibble: 3 x 2
#>   feeling      emoticon                         
#>   <chr>        <chr>                            
#> 1 sad          :(                               
#> 2 compunctious Feeling too complex for emoticons
#> 3 happy        :)

Remember that when you reference an existing column inside mutate(), you reference the entire vector of values. Therefore, the call to map_chr() works inside mutate() just as it does outside, applying emoticons() to each element of the feeling column and producing a vector with an emoticon for each feeling in the tibble.

This pattern will work with any scalar function, but remember to think about which map suffix to use. If the output type of the scalar function doesn’t match the map suffix, you’ll get an error about coercion:

df %>% 
  mutate(emoticon = map_int(feeling, emoticons_1))
#> Error in mutate_impl(.data, dots): Evaluation error: Can't coerce element 1 from a character to a integer.

Additional arguments and anonymous values

You can use the map functions inside mutate() as you would outside mutate(), and so you can still specify additional arguments by listing them after the function:

emoticons_2 <- function(x, default = "???") {
  if (x == "happy") {
    ":)"
  } else if (x == "sad") {
    ":("
  } else {
    default
  }
}

df %>% 
  mutate(emoticon = map_chr(feeling, emoticons_2, default = NA_character_))
#> # A tibble: 3 x 2
#>   feeling      emoticon
#>   <chr>        <chr>   
#> 1 sad          :(      
#> 2 compunctious <NA>    
#> 3 happy        :)

as well as create anonymous functions:

df %>% 
  mutate(is_happy = map_chr(feeling, ~ str_c("I feel ", emoticons_2(.))))
#> # A tibble: 3 x 2
#>   feeling      is_happy  
#>   <chr>        <chr>     
#> 1 sad          I feel :( 
#> 2 compunctious I feel ???
#> 3 happy        I feel :)

purrr inside mutate [program]

Using map functions inside mutate()

Additional arguments and anonymous values

Using `map` functions inside `mutate()`