Ian Cook created ARROW-12959:
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Summary: [C++][R] Option for is_null(NaN) to evaluate to true
Key: ARROW-12959
URL: https://issues.apache.org/jira/browse/ARROW-12959
Project: Apache Arrow
Issue Type: Improvement
Components: C++, R
Reporter: Ian Cook
Currently the Arrow compute kernel {{is_null}} always treats {{NaN}} as a
non-missing value, returning {{false}} at positions of the input datum with
value {{NaN}}.
It would be helpful to be able to control this behavior with an option. The
option could be named {{nan_is_null}} or something similar. It would default
to {{false}}, consistent with current behavior. When set to {{true}}, it should
check if the input datum has a floating point data type, and if so, return
{{true}} at positions where the input is {{NaN}}. If the input datum has some
other type, the option should be silently ignored.
Among other things, this would enable the {{arrow}} R package to evaluate
{{is.na()}} consistently with the way base R does. In base R, {{is.na()}}
returns {{TRUE}} on {{NaN}}. But in the {{arrow}} R package, it returns
{{FALSE}}:
{code:r}
is.na(c(3.14, NA, NaN))
## [1] FALSE TRUE TRUE
as.vector(is.na(Array$create(c(3.14, NA, NaN))))
## [1] FALSE TRUE FALSE{code}
I think solving this with an option in the C++ kernel is the best solution,
because I suspect there are other cases in which users might want to treat
{{NaN}} as a missing value. However, it would also be possible to solve this
just in the R package, by defining a mapping of {{is.na}} in the R package that
checks if the input {{x}} has a floating point data type, and if so, evaluates
{{is.na(x) | is.nan(x)}}. If we choose to go that route, we should change this
Jira issue summary to "[R] Make is.na(NaN) consistent with base R".
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