How to generalize a function to take more arguments? R - r

Question

Seems no one is able to tackle this, so I re-ask:
I would like to use an external function from sjstats package within my own function.
The external function takes 4 arguments as like: https://strengejacke.github.io/sjstats/reference/weighted_sd.html
weighted_mannwhitney(data, x, grp, weights, ...)
A simple call to that external function can be done like this.
weighted_mannwhitney(c12hour ~ c161sex + weight, efc)
I have built a simple function below, which currently only works when provided with data as input - adding arguments produces errors.
I would like to be able to pass on some arguments (data, x, grp, weights) to the internally called external functional, so I can change those arguments for different datasets.
The Question
My attempts at generalizing the function are not successful, can anyone show me how I can do this? I show how I would like to call the generalized function below.
library(sjstats) # has weighted Mann-Whitney function which I would like to automate within own function
library(tidyverse)
data(efc)
efc$weight <- abs(rnorm(nrow(efc), 1, .3))
# Own working function
own_funk <- function(data, ...) {
# Weighted MannWhitney Test p-value
res <- data %>%
summarise(
across(
c(c12hour,e17age),
~sjstats:::weighted_mannwhitney.formula(.x ~ c161sex + weight, data)$p.value[[1]]
)
) %>%
#round(., 3) %>%
tibble::rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname, values_from=value) %>%
rename("Outcome" = 1, "P-value" = 2) %>%
as.data.frame()
return(res)
}
Call to own working function
own_funk(efc)
How I would like to call the function
# NB: The x can take on many variables
own_funk(data = efc, x = c(c12hour,e17age), grp = c161sex, weights = weight)

Answer 1

Somewhat similar to #snaut's solution I'd use map to loop over your x columns/formulas. The difference mainly is that we probably don't want to hard code the variables within the function and rather pass these as an argument to the function.
library(sjstats) # has weighted Mann-Whitney function which I would like to automate within own function
library(tidyverse)
library(survey)
# Own working function
own_funk <- function(data, x, grp, weights) {
# Weighted MannWhitney Test p-value
formula <- paste0(x, " ~ ", grp, " + ", weights)
res <- map2(.x = formula,
.y = x,
.f = ~ data |>
summarize(!!sym(.y) := sjstats:::weighted_mannwhitney.formula(as.formula(.x), data = data)$p.value[[1]]) %>%
#round(., 3) %>%
tibble::rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname, values_from=value) %>%
rename("Outcome" = 1, "P-value" = 2) %>%
as.data.frame()) |>
bind_rows()
return(res)
}
own_funk(data = efc, x = c("c12hour", "e17age"), grp = "c161sex", weights = "weight")
Which gives:
Outcome P-value
1 c12hour 0.006806572
2 e17age 0.187765467

Answer 2

sjstats:::weighted_mannwhitney.formula is clearly meant for interactive use and not for programming. sjstats:::weighted_mannwhitney.default seems to be more suited for programming, but still does something with unqouting variable names that I did not quite figure out.
What I do most of the time when I have to use functions created to be used in an interactive way for programming is to create a temporary column with a fixed name and then rename the columns I want to iterate over to that column.
This is what I have done in my solution. I also don't use summarise and across but instead use map_dfr to iterate over the column names and output rows of a tibble.
I did not quite understand what you want to archive with the pivot_longer and pivot_wider but I'm sure you can format the output of my solution to your needs.
library(sjstats) # has weighted Mann-Whitney function which I would like to automate within own function
library(tidyverse)
data(efc)
efc$weight <- abs(rnorm(nrow(efc), 1, .3))
# Own working function
own_funk <- function(mydata, ...) {
# Weighted MannWhitney Test p-value
map_dfr(
c("c12hour", "e17age"),
function(column){
tmp_data <- mydata %>%
rename(tmp_col = {column})
tibble(
variable = column,
`P-value`=sjstats:::weighted_mannwhitney.formula(tmp_col ~ c161sex + weight, data=tmp_data)$p.value[[1]]
)
}
)
}
own_funk(efc)