use .datatable.aware=TRUE

_posts/2024-06-09-ave-for-the-average/ave-for-the-average.Rmd

@@ -178,11 +178,16 @@ It's the perfect mashup of base R + tidyverse. Base R takes care of the problem
 
 tidyverse 🤝 base R
 
-## Aside: `ave()` for the average {data.table} user
+## Aside: data.table 🤝 collapse
 
 Since I wrote this blog post, I discovered that `{data.table}` recently added in support for using `names(.SD)` in the LHS of the walrus `:=`. I'm so excited for this to hit the next release (v1.6.0)!
 
-I'm also trying to be more mindful of showcasing `{data.table}` where I talk about `{dplyr}`, so here's a solution to compare with the `dplyr::across()` solution above.
+I've trying to be more mindful of showcasing `{data.table}` whenever I talk about `{dplyr}`, so here's a solution to compare with the `dplyr::across()` solution above.
+
+```{r, echo=FALSE}
+.datatable.aware = TRUE
+```
+
 
 ```{r, message=FALSE}
 # data.table::update_dev_pkg()
@@ -192,26 +197,28 @@ input_dt
 ```
 
 ```{r}
-input_dt[ , paste0("freq_", names(.SD)) := lapply(.SD, \(x) ave(freq, x, FUN = sum)), .SDcols = a:c]
+input_dt[, paste0("freq_", names(.SD)) := lapply(.SD, \(x) ave(freq, x, FUN = sum)), .SDcols = a:c]
 input_dt
 ```
 
-In practice, I often pair `{data.table}` with `{collapse}`, where the latter provides a rich and performant set of split-apply-combine vector operations, to the likes ot `ave()`. In `{collapse}`, `ave(..., FUN = sum)` can be expressed with `fsum(..., TRA = "replace")`:
+In practice, I often pair `{data.table}` with `{collapse}`, where the latter provides a rich and performant set of split-apply-combine vector operations, to the likes of `ave()`. In `{collapse}`, `ave(..., FUN = sum)` can be expressed as `fsum(..., TRA = "replace")`:
 
 ```{r}
 library(collapse)
 ave(input_dt$freq, input_dt$a, FUN = sum)
-fsum(input_dt$freq, input_dt$a, TRA = "replace")
+fsum(input_dt$freq, input_dt$a, TRA = "replace") # Also, TRA = 2
 ```
 
-So the `{data.table}` 🤝 `{collapse}` version of this would be:^[I couldn't show this here with this particular example, but another nice feature of `{collapse}` 🤝 `{data.table}` is the fact that they do not shy away from consuming/producing matrices: see `scale()[,1]` vs. `fscale()` for a good example of this.]
+So the version of the solution integrating `fsum()` would be:^[I couldn't show this here with this particular example, but another nice feature of `{collapse}` 🤝 `{data.table}` is the fact that they do not shy away from consuming/producing matrices: see `scale()[,1]` vs. `fscale()` for a good example of this.]
 
 ```{r}
 input_dt[, names(.SD) := NULL, .SDcols = patterns("^freq_")]
-input_dt[ , paste0("freq_", names(.SD)) := lapply(.SD, \(x) fsum(freq, x, TRA = "replace")), .SDcols = a:c]
+input_dt[, paste0("freq_", names(.SD)) := lapply(.SD, \(x) fsum(freq, x, TRA = 2)), .SDcols = a:c]
 input_dt
 ```
 
+data.table 🤝 collapse
+
 ## sessionInfo()
 
 ```{r}