Davis Vaughan

We don't believe a data frame transpose mechanism is that useful because data frames can have multiple column types, but a transpose requires all column types to be forced into...

It looks like we may need `group_modify.rowwise_df()`. Possibly it can be the same as `group_modify.grouped_df()`, since that looks to be mostly generic. That said, `group_split.rowwise_df()` ignores `.keep`, so maybe `group_modify.rowwise_df()`...

Minimal reprex 1.0.10: ``` r # pak::pak("tidyverse/[email protected]") library(dplyr, warn.conflicts = FALSE) df x #> #> 1 3.5 # Works when wrapped fn(df, x, ~weighted.mean(.x, w = w)) #> # A...

Possible solution proposed by @lionel- is to allow `.fns = {{ fns }}` so that users can wrap with a pattern like: ``` fn

That probably can't and won't ever work because we can't "see" the expression that built the original object, we only see `FUNS`

This ultimately comes down to the fact that `between()` takes the common type of `x`, `left`, and `right` rather than doing a directional cast of `left` and `right` to the...

Another use case for `nest = "both"` here https://posit.co/blog/geospatial-distributed-processing-with-furrr/ Towards the end: ```r # Split Data by Province # Postal codes -- points can_pcodes_split % dplyr::filter(PROV != "Non Canadian") %>%...

Do you think we can delay this one for now and keep this behavior in the `collect.duckplyr_df()` method that you have here? https://github.com/duckdblabs/duckplyr/blob/main/R/collect.R With the goal of eventually moving it...

We can also lean into the ambiguity of `filter()` and think about adding an argument like `filter(.for = c(TRUE, FALSE, NA))` (which may also partly (completely?) handle #6560) Or maybe...

A key idea while thinking about this is realizing that both `keep_rows(df, a, b)` and `drop_rows(df, a, b)` would apply the exact same internal algorithm to combine `a, b` into...