Pass list of arguments to mutate_at - r

Question

I'm sure there's a way to do this but I can't figure it out. I'd like to be able to pass a list of arguments to mutate_at() within a function without having to specify each argument
library(tidyverse)
fake_data <-
tibble(
id = letters[1:6],
ind_group_a = rep(0:1, times = 3),
ind_group_b = rep(1:0, each = 3)
)
# id ind_group_a ind_group_b
# a 0 1
# b 1 1
# c 0 1
# d 1 0
# e 0 0
# f 1 0
This function will then converts all 1's to "yes" and 0's to "no"
recode_indicator <- function(x, if_1 = "yes", if_0 = "no") {
ifelse(x == 1, if_1, if_0)
}
And I can use it fine like so:
fake_data %>%
mutate_at(
vars(starts_with("ind_")),
recode_indicator,
if_1 = "Has",
if_0 = "Missing"
)
# id ind_group_a ind_group_b
# chr> <chr> <chr>
# a Missing Has
# b Has Has
# c Missing Has
# d Has Missing
# e Missing Missing
# f Has Missing
This is a simplified example but what I'd like to do is make it available in a function without having to write out all of the arguments. Ideally something short like binary_values = list(...)but I can't figure out how to pass these items as the additional arguments of recode_indicator()
roll_up_indicators <- function(x,
#binary_values = list(if_1 = "yes", if_0 = "no"),
...) {
ind_cols <- grep("^ind_", names(x))
df <-
x %>%
rename_at(ind_cols, str_remove, "^ind_") %>%
mutate_at(
ind_cols,
recode_indicator # ,
# binary_values # <- here's the problem area
) %>%
group_by_at(ind_cols) %>%
count() %>%
ungroup()
knitr::kable(df, ...)
}
fake_data %>% roll_up_indicators()
# |group_a |group_b | n|
# |:-------|:-------|--:|
# |No |No | 1|
# |No |Yes | 2|
# |Yes |No | 2|
# |Yes |Yes | 1|
Update
In terms of not rewriting all of the arguments, the formals() function can be used:
roll_up_indicators <- function(x,
binary_values = formals(recode_indicator), # <--- formals
...) {
ind_cols <- grep("^ind_", names(x))
df <-
x %>%
rename_at(ind_cols, str_remove, "^ind_") %>%
mutate_at(
ind_cols,
partial(recode_indicator, !!!binary_values) # <--- the winning answer
) %>%
group_by_at(ind_cols) %>%
count() %>%
ungroup()
knitr::kable(df, ...)
}

Answer 1

One solution is to use purrr::partial to specify that if_1 and if_0 arguments should come from binary_values:
roll_up_indicators <- function(x,
binary_values = list(if_1 = "yes", if_0 = "no"),
...) {
ind_cols <- grep("^ind_", names(x))
df <-
x %>%
rename_at(ind_cols, str_remove, "^ind_") %>%
mutate_at(
ind_cols,
partial(recode_indicator, !!!binary_values) ## <--- partial() here
) %>%
group_by_at(ind_cols) %>%
count() %>%
ungroup()
knitr::kable(df, ...)
}
fake_data %>% roll_up_indicators()
# |group_a |group_b | n|
# |:-------|:-------|--:|
# |No |No | 1|
# |No |Yes | 2|
# |Yes |No | 2|
# |Yes |Yes | 1|

Answer 2

It's probably best to go with the pre-made functions, like recode, but I've also adapted your function if you wanted to add additional functionality. For that, I'm assuming that binary_values is appropriately named and will only ever include two values.
Option 1: Use recode
This requires you to put the starting and ending values within a list. You'll need to quote strings, obviously and either quote or use `` around numbers.
binary_values = list("1" = "yes", "0" = "no")
fake_data %>%
mutate_at(vars(starts_with("ind_")),
list(~recode(.,!!!binary_values)))
Option 2: Specify location or name in list within function
recode_value <- function(x,
binary_values = list(if_1 = "yes", if_0 = "no")
## You'll need to decide whether you'll name them as expected or always put them in this order; it's up to you
) {
if_1 = binary_values$if_1 # or binary_values[[1]]
if_0 = binary_values$if_0 # or binary_values[[1]]
ifelse(x == 1, if_1, if_0)
}
binary_values = list(if_1 = "yes", if_0 = "no")
fake_data %>%
mutate_at(
vars(starts_with("ind_")),
recode_value, ## fixed typo
binary_values
)