Davis Vaughan

If you sample that up to 1 million rows (to actually get useful benchmarks) then it is pretty clear that repeated mutates are much faster than `if_else()` (which I expected)....

@markfairbanks do you think that this should apply the `.when` expression before or after groups are computed? ```r df %>% mutate(x = mean(y), .when = is.na(x), .by = g) ```...

Note (mainly to self): If we apply `.when` by group then we might end up with groups that don't have any rows left in them, i.e.: ``` x = [7,...

@bwiernik it doesn't work that way, `x = mean(x, na.rm = TRUE)` is only evaluated on the slice of `dat` where `.when = is.na(x)` is `TRUE`, so you'd be computing...

I think we are going to close this for now. We aren't entirely convinced that this will benefit a large part of the user base, as we have struggled to...

While you are thinking about this function, one thing I'm not sure what to do with is the fact that you can currently use expressions that return a scalar, and...

Would it be so bad to just use `tibble::add_column()` to add `.requirement` and `.row` columns to the front of the filtered data set without any name repair? That seems like...

Maybe think about removing some of the user friendly bits like `enforce_last()` and encourage people to use things like pointblank if they need more completely handling of these ideas

We have decided not to pursue this further for now. It feels like we either create an API that goes too far for what dplyr should do, or too simple...

I do wonder if we should hold off on this until dplyr 1.1.0. We have the same issue with `dplyr_row_slice()` ``` r library(dplyr) df