I want to iterate through `data.table`, just as `purrr::map` does. While I was able to apply `data.table` functions by converting `data.frame` to `data.table` inside `purrr::map`, I want to know whether `data.table` has something built-in that would abnegate using `purrr::map`. I am asking this because I am unsure of `purrr::map`'s performance in terms of both speed and memory required. I have been disappointed with `dplyr`'s speed and memory utilization, as compared to `data.table` when dealing with large datasets.

I researched stackoverflow and found that accepted answer on [Iterate through data tables](https://stackoverflow.com/questions/34796713/iterate-through-data-tables) thread has used `for` loop. I am not a big fan of `for` loop for performance reasons.

Here's sample data file:

```
dput(Input_File)
structure(list(Zone = c("East", "East", "East", "East", "East", 
"East", "East", "West", "West", "West", "West", "West", "West", 
"West"), Fiscal.Year = c(2016, 2016, 2016, 2016, 2016, 2016, 
2017, 2016, 2016, 2016, 2017, 2017, 2018, 2018), Transaction.ID = c(132, 
133, 134, 135, 136, 137, 171, 171, 172, 173, 175, 176, 177, 178
), L.Rev = c(3, 0, 0, 1, 0, 0, 2, 1, 1, 2, 2, 1, 2, 1), L.Qty = c(3, 
0, 0, 1, 0, 0, 1, 1, 1, 2, 2, 1, 2, 1), A.Rev = c(0, 0, 0, 1, 
1, 1, 0, 0, 0, 0, 0, 1, 0, 0), A.Qty = c(0, 0, 0, 2, 2, 3, 0, 
0, 0, 0, 0, 3, 0, 0), I.Rev = c(4, 4, 4, 0, 1, 0, 3, 0, 0, 0, 
1, 0, 1, 1), I.Qty = c(2, 2, 2, 0, 1, 0, 3, 0, 0, 0, 1, 0, 1, 
1)), .Names = c("Zone", "Fiscal.Year", "Transaction.ID", "L.Rev", 
"L.Qty", "A.Rev", "A.Qty", "I.Rev", "I.Qty"), row.names = c(NA, 
14L), class = "data.frame")

```

Here's sample code with `purrr::map` and `data.table`

```
UZone <- unique(Input_File$Zone)
FYear <- unique(Input_File$Fiscal.Year)
a<-purrr::map(UZone, ~ dplyr::filter(Input_File, Zone == .)) %>%
   purrr::map(~ data.table::as.data.table(.)) %>%
   purrr::map(~ .[,.(sum = sum(L.Rev)),by=Fiscal.Year])

```

I am not too concerned about the output, but I want to know what alternatives are available to iterate through `data.table` based on a specific column. I'd appreciate any thoughts.