[jira] [Updated] (ARROW-12960) [C++][R] Option for is_nan(null) to evaluate to false or true

["Ian Cook (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-12960?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Ian Cook updated ARROW-12960:
-----------------------------
    Fix Version/s:     (was: 6.0.0)
                   7.0.0

> [C++][R] Option for is_nan(null) to evaluate to false or true
> -------------------------------------------------------------
>
>                 Key: ARROW-12960
>                 URL: https://issues.apache.org/jira/browse/ARROW-12960
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: C++, R
>            Reporter: Ian Cook
>            Assignee: Christian Cordova
>            Priority: Major
>              Labels: kernel
>             Fix For: 7.0.0
>
>
> (This is the flip side of ARROW-12959.)
> Currently the Arrow compute kernel {{is_nan}} always treats {{null}} as a missing value, returning {{null}} at positions of the input datum with {{null}} (missing) values.
> It would be helpful to be able to control this behavior with an option. The option could be named {{value_for_null}} or something similar and it would take a nullable boolean scalar.  It would default to {{null}}, consistent with current behavior. When set to {{false}} or {{true}}, it would return {{false}} or {{true}} at positions of the input datum with {{null}} values.
> Among other things, this would enable the {{arrow}} R package to evaluate {{is.nan()}} consistently with the way base R does. In base R, {{is.nan()}} returns {{FALSE}} on {{NA}}. But in the {{arrow}} R package, it returns {{NA}}:
> {code:r}
> > is.nan(c(3.14, NA, NaN))
> ##[1] FALSE FALSE  TRUE
> as.vector(is.nan(Array$create(c(3.14, NA, NaN))))
> ##[1] FALSE    NA  TRUE{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 would want the ability to return all non-missing values in the output from {{is_nan}} without needing to call another kernel to fill the missing values in. However, it would also be possible to solve this just in the R package, by changing the mapping of {{is.nan}} in the R package. If we choose to go that route, we should change this Jira issue summary to "[R] Make is.nan(NA) consistent with base R".



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