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Hi! Thanks so much for the package; it's such an important tool that the R ecosystem really needed!
I've been using recode_shadow() to handle some special missings, and while that works to change the shadow columns / update the factor levels, when I try to use functions to summarize characteristics of missingness in the dataframe, eg, add_any_miss() or miss_var_table(), it doesn't recognize these recoded special missings AS missing; it marks the first row of the dataframe as complete despite the -99 value for wind being a special missing. It might be nice if there were an option to choose whether NA aggregations distinguish between "true" / plain NA and special NAs, but if not, I think this omission could easily mislead someone about the completeness of their data.
I'm glad to hear that you are using the special missings feature, and this is a great point that there should be some way to support/account for them in the missingness summaries.
When I'm next able to get some time to do a sprint on naniar and visdat I will revisit this and touch base, hopefully that will be sooner (0-3 months) rather than later!
Hi! Thanks so much for the package; it's such an important tool that the R ecosystem really needed!
I've been using recode_shadow() to handle some special missings, and while that works to change the shadow columns / update the factor levels, when I try to use functions to summarize characteristics of missingness in the dataframe, eg, add_any_miss() or miss_var_table(), it doesn't recognize these recoded special missings AS missing; it marks the first row of the dataframe as complete despite the -99 value for wind being a special missing. It might be nice if there were an option to choose whether NA aggregations distinguish between "true" / plain NA and special NAs, but if not, I think this omission could easily mislead someone about the completeness of their data.
Created on 2024-02-02 with reprex v2.1.0```
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