How to move R code into functions to generalise behaviour - r

Question

I have a huge messy piece of R code with loads of ugly repetition. There is an opportunity to massively reduce it. Starting with this piece of code:
table <-
risk_assigned %>%
group_by(rental_type, room_type) %>%
summarise_all(funs( sum(!is.na(.)) / length(.) ) ) %>%
select(-c(device_id, ts, room, hhi, temp)) %>%
adorn_pct_formatting()
I would like to generalise it into a function so it can be reused.
LayKable = function(kableDetails) {
table <-
risk_assigned %>%
group_by(kableDetails$group1 , kableDetails$group2) %>%
summarise_all(funs( sum(!is.na(.)) / length(.) ) ) #%>%
select(-c(device_id, ts, room, hhi, temp)) %>%
adorn_pct_formatting()
...
kable <- table
return(kable)
}
kableDetails <- list(
group1 = "rental_type",
group2 = "room_type"
)
newKable <- LayKable(kableDetails)
This rather half-hearted attempt serves to explain what I want to do. How can I pass stuff into this function inside a list (I'm a C programmer, pretending it's a struct).

Answer 1

When passing function arguments to a dplyr verb inside a function you have to use rlang terms. But should be simple to define a function you can pass a number of grouping terms to:
library(dplyr)
test_func <- function(..., data = mtcars) {
# Passing `data` as a default argument as it's nice to be flexible!
data %>%
group_by(!!!enquos(...)) %>%
summarise(across(.fns = sum), .groups = "drop")
}
test_func(cyl, gear)
#> # A tibble: 8 x 11
#> cyl gear mpg disp hp drat wt qsec vs am carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 4 3 21.5 120. 97 3.7 2.46 20.0 1 0 1
#> 2 4 4 215. 821 608 32.9 19.0 157. 8 6 12
#> 3 4 5 56.4 215. 204 8.2 3.65 33.6 1 2 4
#> 4 6 3 39.5 483 215 5.84 6.68 39.7 2 0 2
#> 5 6 4 79 655. 466 15.6 12.4 70.7 2 2 16
#> 6 6 5 19.7 145 175 3.62 2.77 15.5 0 1 6
#> 7 8 3 181. 4291. 2330 37.4 49.2 206. 0 0 37
#> 8 8 5 30.8 652 599 7.76 6.74 29.1 0 2 12
Update - adding a list
I see your ideal would be to write a list of arguments for each function call and pass these rather than write out the arguments in each call. You can do this using do.call to pass a list of named arguments to a function. Again, when using dplyr verbs you can quote variable names in constructing your list (so that R doesn't try to find them in the global environment when compiling the list) and !!enquo each one in the calls to then use them there:
library(dplyr)
test_func2 <- function(.summary_var, .group_var, data = mtcars) {
data %>%
group_by(!!enquo(.group_var)) %>%
summarise(mean = mean(!!enquo(.summary_var)))
}
# Test with bare arguments
test_func2(hp, cyl)
#> # A tibble: 3 x 2
#> cyl mean
#> <dbl> <dbl>
#> 1 4 82.6
#> 2 6 122.
#> 3 8 209.
# Construct and pass list
args <- list(.summary_var = quote(hp), .group_var = quote(cyl))
do.call(test_func2, args = args)
#> # A tibble: 3 x 2
#> cyl mean
#> <dbl> <dbl>
#> 1 4 82.6
#> 2 6 122.
#> 3 8 209.
A handy guide to tidy evaluation where most of these ideas are explained more clearly.
Created on 2021-12-21 by the reprex package (v2.0.1)