Return a list in dplyr mutate()

The idiomatic way to do this using data.table would be to use the := (assignment by reference) operator. Here's an illustration:

it[, c(paste0("V", 4:5)) := myfun(V2, V3)]

If you really want a list, why not:

as.list(it[, myfun(V2, V3)])

Alternatively, maybe this is what you want, but why don't you just use the data.table functionality:

it[, c(.SD, myfun(V2, V3))]#    V1 V2 V3 V4 V5# 1:  a  1  2  3 -1# 2:  a  2  3  5 -1# 3:  b  3  4  7 -1# 4:  b  4  2  6  2# 5:  c  5  2  7  3    

Note that if myfun were to name it's output, then the names would show up in the final result columns:

#    V1 V2 V3 new.1 new.2# 1:  a  1  2     3    -1# 2:  a  2  3     5    -1# 3:  b  3  4     7    -1# 4:  b  4  2     6     2# 5:  c  5  2     7     3    

Given the title to this question, I thought I'd post a tidyverse solution that uses dplyr::mutate. Note that myfun needs to output a data.frame to work.

library(tidyverse)it = data.frame(  v1 = c("a","a","b","b","c"),  v2 = c(1,2,3,4,5),   v3 = c(2,3,4,2,2))myfun = function(arg1,arg2) {  temp1 = arg1 + arg2  temp2 = arg1 - arg2  data.frame(temp1, temp2)}it %>%   nest(data = c(v2, v3)) %>%   mutate(out = map(data, ~myfun(.$v2, .$v3))) %>%   unnest(cols = c(data, out))#> # A tibble: 5 x 5#>   v1       v2    v3 temp1 temp2#>   <fct> <dbl> <dbl> <dbl> <dbl>#> 1 a         1     2     3    -1#> 2 a         2     3     5    -1#> 3 b         3     4     7    -1#> 4 b         4     2     6     2#> 5 c         5     2     7     3

^{Created on 2020-02-04 by the reprex package (v0.3.0)}

The mutate() function is designed to add new columns to the existing data frame. A data frame is a list of vectors of the same length. Thus, you cant add a list as a new column, because a list is not a vector.

You can rewrite your function as two functions, each of which return a vector. Then apply each of these separately using mutate() and it should work.