Return multiple rows per group in data.table

Is it possible to return multiple rows per group in a grouped command in data.table? In dplyr, this is done with reframe:

y <- c("a", "b", "d", "f")
df <- tibble(
  g = c(1, 1, 1, 2, 2, 2, 2),
  x = c("e", "a", "b", "e", "f", "c", "a")
)

library(dplyr)
df %>%
  reframe(x = setdiff(x, y), .by = g)
# g x    
# 1 e    
# 2 e    
# 2 c   

In data.table, this returns an error:

library(data.table)
dt <- setDT(df)
dt[, x := setdiff(x, y), g]

> Error in [.data.table(df, , :=(x, intersect(x, y)), g) :
> Supplied 2 items to be assigned to group 1 of size 3 in column 'x'.
> The RHS length must either be 1 (single values are ok) or match the
> LHS length exactly. If you wish to 'recycle' the RHS please use rep()
> explicitly to make this intent clear to readers of your code.

Anyway to get a data.table equivalent of reframe?

Wrap in .(...) and use = in place of := (because it's within .(..)).

as.data.table(df)[, .(x = setdiff(x, y)), by = g]
#        g      x
#    <num> <char>
# 1:     1      e
# 2:     2      e
# 3:     2      c

Note that under the hood, .(.) is really just list(.), so we could also use anything that returns list-like objects, including:

as.data.table(df)[, list(x = setdiff(x, y)), by = g]
as.data.table(df)[, data.table(x = setdiff(x, y)), by = g]
as.data.table(df)[, data.frame(x = setdiff(x, y)), by = g]