How do I pass multiple columns from a dataframe as individual arguments to a custom function in R-CodePudding

I have a dataframe with variable number of columns. I want to pass

for instance let's say I have the dataframe df and I want to pass columns a and b as individual arguments to my custom function; but the issue is that the list of column names of interest changes depending on the outcome of another operation and could be any lemgth etc.

df <- tibble(a = c(1:3), b = c(4:6), c=(7:9), d=c(10:12))
custom_function <- function(...){ do something }
custom_function(df$a, df$b)

I haven't found a clean way to achieve this. Any help would be great.

UPDATE

for better clarity I need to add that the challenge is the fact the list of columns of interest is retrieved from another variable. for instance col_names <- c("a","b")

CodePudding user response：

We can capture the ... as a list can then apply the function within do.call

custom_function <- function(fn, data = NULL, ...) {

   args <- list(...)
   if(length(args) == 1 && is.character(args[[1]]) && !is.null(data)) {
      args <- data[args[[1]]]
}
  
   
    do.call(fn, args)

}
custom_function(pmax, df$a, df$b)
[1] 4 5 6

and we can pass ridge

> custom_function(ridge, df$a, df$b)
   [,1] [,2]
[1,]    1    4
[2,]    2    5
[3,]    3    6
attr(,"class")
[1] "coxph.penalty"
...

> custom_function(ridge, data = df, c("a", "b"))
  a b
[1,] 1 4
[2,] 2 5
[3,] 3 6
attr(,"class")
[1] "coxph.penalty"
attr(,"pfun")
...

> custom_function(ridge, data = df, col_names)
     a b
[1,] 1 4
[2,] 2 5
[3,] 3 6
attr(,"class")
...

CodePudding user response：

If your outcome is a data.frame, a possibility is to use the the curly-curly tidyverse operator, using the command {{}}, the goal of this operator is to allow us to have an argument passed to our function refering to a column inside a dataframe.

Data

df <- tibble(a = c(1:3), b = c(4:6), c=(7:9), d=c(10:12))

Example

library(dplyr)

operations <- function(df,col1,col2){
  df %>% 
    summarise(
      n = n(),
      addition = {{col1}}   {{col2}},
      subtraction = {{col1}} - {{col2}},
      multiplication = {{col1}} * {{col2}},
      division = {{col1}} / {{col2}}
      
    ) 
}

Output

operations(df,a,b)

# A tibble: 3 x 5
      n addition subtraction multiplication division
  <int>    <int>       <int>          <int>    <dbl>
1     3        5          -3              4     0.25
2     3        7          -3             10     0.4 
3     3        9          -3             18     0.5