I am looking for a function to append across function output to input data. Any lead
library(tidyverse)
gdf <-
tibble(g = c(1, 1, 2, 3), v1 = 10:13, v2 = 20:23) %>%
group_by(g)
gdf
#> # A tibble: 4 × 3
#> # Groups: g [3]
#> g v1 v2
#> <dbl> <int> <int>
#> 1 1 10 20
#> 2 1 11 21
#> 3 2 12 22
#> 4 3 13 23
set.seed(1)
# Outside: 1 normal variate
n <- rnorm(1)
gdf %>%
mutate(across(v1:v2, ~ .x + n))
#> # A tibble: 4 × 3
#> # Groups: g [3]
#> g v1 v2
#> <dbl> <dbl> <dbl>
#> 1 1 9.37 19.4
#> 2 1 10.4 20.4
#> 3 2 11.4 21.4
#> 4 3 12.4 22.4
To have across() append columns in mutate, you need to indicate the names of the new columns. You can do this either by putting the function(s) in a named list, which will default to using new names of the form {.col}_{.list_name}, or by using the .names argument:
gdf |> mutate(across(v1:v2, list(n = ~ .x + n)))
# # A tibble: 4 × 5
# # Groups: g [3]
# g v1 v2 v1_n v2_n
# <dbl> <int> <int> <dbl> <dbl>
# 1 1 10 20 9.37 19.4
# 2 1 11 21 10.4 20.4
# 3 2 12 22 11.4 21.4
# 4 3 13 23 12.4 22.4
gdf |> mutate(across(v1:v2, \(x) x + n, .names = "my_new_{.col}_n"))
# # A tibble: 4 × 5
# # Groups: g [3]
# g v1 v2 my_new_v1_n my_new_v2_n
# <dbl> <int> <int> <dbl> <dbl>
# 1 1 10 20 9.37 19.4
# 2 1 11 21 10.4 20.4
# 3 2 12 22 11.4 21.4
# 4 3 13 23 12.4 22.4