Consider the examples below using SQL and the hacksaw package. Is there a more 'canonical' and 'tidy' way to get this result?

```
library(sqldf)
#> Loading required package: gsubfn
#> Loading required package: proto
#> Loading required package: RSQLite
library(hacksaw)
library(tidyverse)
# With SQL I can do this:
sqldf("select cyl,
avg(mpg) AS mean_mpg_total,
avg(mpg) filter (where hp > 110) AS mean_mpg_high_hp
from mtcars
group by cyl")
#> cyl mean_mpg_total mean_mpg_high_hp
#> 1 4 26.66364 30.4
#> 2 6 19.74286 18.9
#> 3 8 15.10000 15.1
# With hacksaw I can do this:
# (But if feels esoteric)
mtcars |>
group_by(cyl) |>
eval_split(
select(everything()),
filter(hp > 110)
) |>
map(~summarize(.x, mean_mpg = mean(mpg))) |>
reduce(left_join, by = "cyl", suffix = c("_total", "_high_hp"))
#> # A tibble: 3 × 3
#> cyl mean_mpg_total mean_mpg_high_hp
#> <dbl> <dbl> <dbl>
#> 1 4 26.7 30.4
#> 2 6 19.7 18.9
#> 3 8 15.1 15.1
# Is there a canonical 'tidy' way?
mtcars |>
group_by(cyl) |>
summarize(
mean_mpg_total = mean(mpg),
# what goes here??
)
#> # A tibble: 3 × 2
#> cyl mean_mpg_total
#> <dbl> <dbl>
#> 1 4 26.7
#> 2 6 19.7
#> 3 8 15.1
```

^{Created on 2023-05-18 with reprex v2.0.2}