eitsupi commented on issue #36458:
URL: https://github.com/apache/arrow/issues/36458#issuecomment-1624594686
We can use `dplyr::filter` with `dplyr::across` for this use case.
``` r
df <- data.frame(a = 1:3, b = c("a", NA, "c"))
df |>
arrow::as_arrow_table() |>
dplyr::filter(dplyr::across(everything(), ~ !is.na(.))) |>
dplyr::collect()
#> a b
#> 1 1 a
#> 2 3 c
```
<sup>Created on 2023-07-07 with [reprex
v2.0.2](https://reprex.tidyverse.org)</sup>
Note that this is why dbplyr does not implement drop_na either.
tidyverse/dbplyr#999
Related issue tidyverse/tidyr#939
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