data.table vs dplyr: apply function returning changing column names over groups

I want to apply a function (ratefunc()) to a grouped data frame which returns changing column names dependent on the result:

library(data.table)
library(dplyr)

dt <- data.table:::data.table(
  group=c(rep("A", 3), rep("B", 3)), 
  x=c(1:3, 6:8), 
  y=c(4:6, 9:11)
)

ratefunc <- function(data, x_col="x", y_col="y") {
  res <- sum(data[[x_col]] + data[[y_col]])
  if (res < 25) {
    return(
      data.frame(a=rep("a", nrow(data)))
    )
  } else {
    return(
      data.frame(b=rep("b", nrow(data)))
    )
  }
}

dplyr returns the desired result by

assigning a new column to each column name returned and
setting NA for the observation of the other groups

dt %>% 
  group_by(group) %>% 
  group_modify(
    ~ratefunc(data=.)
  )

Console output:

# A tibble: 6 × 3
# Groups:   group [2]
  group a     b    
  <chr> <chr> <chr>
1 A     a     NA   
2 A     a     NA   
3 A     a     NA   
4 B     NA    b    
5 B     NA    b    
6 B     NA    b

data.table instead coerces the results into one column ignoring the differently named columns:

dt[
  ,
  ratefunc(
    data=.SD
  )
  ,
  by=group
]

Console output:

    group      a
   <char> <char>
1:      A      a
2:      A      a
3:      A      a
4:      B      b
5:      B      b
6:      B      b

How can I get the identical result as in dplyr when using data.table?

And how is the approach of selecting the data frame columns by column name in ratefunc() (i.e. x_col and y_col being string inputs) to be evaluated in contrast to selecting them directly (i.e. x_col and y_col as vector/column inputs)?

Solution

Your ratefunc is not something I would create. I would do something like this. This could be wrapped in a function if necessary.

dt[, tmp := fifelse(sum(Reduce(`+`, .SD)) < 25, "a", "b"), 
   by = group, .SDcols = c("x", "y")]
dt[, ind := .I]
dcast(dt, group + ind ~ tmp, value.var = "tmp")
#Key: <group, ind>
#    group   ind      a      b
#   <char> <int> <char> <char>
#1:      A     1      a   <NA>
#2:      A     2      a   <NA>
#3:      A     3      a   <NA>
#4:      B     4   <NA>      b
#5:      B     5   <NA>      b
#6:      B     6   <NA>      b

Edit adressing additional requirements from comments:

dt[, colnames := fifelse(sum(Reduce(`+`, .SD)) < 25, "col1", "col2"), 
   by = group, .SDcols = c("x", "y")]

dt[data.table(colnames = c("col1", "col2"),
              colvalues = c("a", "b")), 
   colvalues := i.colvalues, on = .(colnames)]

dt[, ind := .I]
dcast(dt, group + ind ~ colnames, value.var = "colvalues")
# Key: <group, ind>
#     group   ind   col1   col2
#    <char> <int> <char> <char>
# 1:      A     1      a   <NA>
# 2:      A     2      a   <NA>
# 3:      A     3      a   <NA>
# 4:      B     4   <NA>      b
# 5:      B     5   <NA>      b
# 6:      B     6   <NA>      b