nealrichardson commented on a change in pull request #10034:
URL: https://github.com/apache/arrow/pull/10034#discussion_r614279844
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File path: r/NEWS.md
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@@ -37,6 +37,7 @@ Over 100 functions can now be called on Arrow objects inside
a `dplyr` verb:
* `cast(x, type)` and `dictionary_encode()` allow changing the type of columns
in Arrow objects; `as.numeric()`, `as.character()`, etc. are exposed as similar
type-altering conveniences
* `dplyr::between()`; the Arrow version also allows the `left` and `right`
arguments to be columns in the data and not just scalars
* Additionally, any Arrow C++ compute function can be called inside a `dplyr`
verb. This enables you to access Arrow functions that don't have a direct R
mapping. See `list_compute_functions()` for all available functions, which are
available in `dplyr` prefixed by `arrow_`.
+* Arrow C++ compute functions enforce stricter type matches when comparing
Arrays with Scalars. This makes comparisons and computation safer, however note
that some comparisons that worked in prior versions will result in a
type-mismatch (e.g. `dplyr::filter(arrow_dataset, string_column == 3)` will
error with a message about the type mismatch between the numeric `3` and the
string tyep of `string_column`).
Review comment:
```suggestion
* Arrow C++ compute functions now do more systematic type promotion when
called on data with different types (e.g. int32 and float64). Previously,
Scalars in an expressions were always cast to match the type of the
corresponding Array, so this new type promotion enables, among other things,
operations on two columns (Arrays) in a dataset. As a side effect, some
comparisons that worked in prior versions are no longer supported: for example,
`dplyr::filter(arrow_dataset, string_column == 3)` will error with a message
about the type mismatch between the numeric `3` and the string type of
`string_column`.
```
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