mutate across with vectorized function parameters-CodePudding

I know the "across" paradigm is "many columns, one function" so this might not be possible. The idea is that I want to apply the same function to several columns with a parameter varying based on the column.

I got this to work using cur_column() but it basically amounts to computing the parameters 1 by 1 rather than providing a vector of equal size to the number of columns containing the parameters.

This first block produces what I want but it I'm wondering if there's a cleaner way.

library(dplyr)

df = data.frame(column1 = 1:100, column2 = 1:100)

parameters = data.frame(
    column_names = c('column1','column2'),
    parameters = c(10,100))
    
custom_function = function(x,addend){
    x   addend
}

df2 = df %>% mutate(
    across(x$column_names,
           ~custom_function(.,addend = x %>%
                                        filter(column_names == cur_column()) %>% 
                                        pull(parameters))))

What I would like to do for the last line would look like

df2 = df %>% mutate(
    across(x$column_names,~custom_function(.,addend = x$parameters)))

CodePudding user response：

I think a mapping function operating on the parameters would easier than across on the data:

library(tidyverse)

with(parameters, as_tibble(map2(df[column_names], parameters, custom_function)))
#> # A tibble: 100 x 2
#>    column1 column2
#>      <dbl>   <dbl>
#>  1      11     101
#>  2      12     102
#>  3      13     103
#>  4      14     104
#>  5      15     105
#>  6      16     106
#>  7      17     107
#>  8      18     108
#>  9      19     109
#> 10      20     110
#> # ... with 90 more rows

^{Created on 2022-12-15 with reprex v2.0.2}

CodePudding user response：

You could also just add the column to your function like this:

library(dplyr)

df = data.frame(column1 = 1:100, column2 = 1:100)

parameters = data.frame(
  column_names = c('column1','column2'),
  parameters = c(10,100))

custom_function = function(x,column, parameters){
  addend = parameters %>%
    filter(column_names == column) %>% 
    pull(parameters)
  x   addend
}

df2 = df %>% mutate(
  across(parameters$column_names,custom_function,cur_column(),parameters))

CodePudding user response：

We can do this in base with mapply:

mapply(` `, df[,parameters$column_names], parameters$parameters)

##>      column1 column2
##> [1,]      11     101
##> [2,]      12     102
##> [3,]      13     103
##> [4,]      14     104
##> [5,]      15     105
##> ...