Non-equi join showing columns in a not so perfect style

Start with the non-equi join expression transactions[companies, on = .(company == id, year >= since)] and compare its output with the dplyr example. Determine whether the requested change concerns the displayed join columns, retaining both key columns, or both. Done means establishing the intended output and either identifying existing options or documenting the required behavior; no files or tests are named in the issue.

In the latest version of dplyr, non-equi joins could be realized by:

library(dplyr)

transactions <- tibble(
  company = c("A", "A", "B", "B"),
  year = c(2019, 2020, 2021, 2023),
  revenue = c(50, 4, 10, 12)
)
transactions
#> # A tibble: 4 × 3
#>   company  year revenue
#>   <chr>   <dbl>   <dbl>
#> 1 A        2019      50
#> 2 A        2020       4
#> 3 B        2021      10
#> 4 B        2023      12

companies <- tibble(
  id = c("A", "B", "B"),
  since = c(1973, 2009, 2022),
  name = c("Patagonia", "RStudio", "Posit")
)

companies
#> # A tibble: 3 × 3
#>   id    since name     
#>   <chr> <dbl> <chr>    
#> 1 A      1973 Patagonia
#> 2 B      2009 RStudio  
#> 3 B      2022 Posit


transactions |>
  inner_join(companies, join_by(company == id, year >= since))
#> # A tibble: 5 × 5
#>   company  year revenue since name     
#>   <chr>   <dbl>   <dbl> <dbl> <chr>    
#> 1 A        2019      50  1973 Patagonia
#> 2 A        2020       4  1973 Patagonia
#> 3 B        2021      10  2009 RStudio  
#> 4 B        2023      12  2009 RStudio  
#> 5 B        2023      12  2022 Posit

Using data.table, I yield:

library(data.table)
#> 
#> Attaching package: 'data.table'
#> The following objects are masked from 'package:dplyr':
#> 
#>     between, first, last
setDT(transactions)
setDT(companies)
transactions[companies, on = .(company == id, year >= since)]
#>    company year revenue      name
#> 1:       A 1973      50 Patagonia
#> 2:       A 1973       4 Patagonia
#> 3:       B 2009      10   RStudio
#> 4:       B 2009      12   RStudio
#> 5:       B 2022      12     Posit

The operation is the same, but the results is not desirable as that of dplyr's, and dplyr also supports to keep all the columns using argument keep = TRUE. How can I get something similar in data.table?

Thanks.