You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Would be nice with something similar as you have for the standard cor-function where users can specify how they would like to deal with NA’s. I do agree with the comment "Missing values and cleaning data are critical to getting great correlations" but a function like this is very convinient when having a few NAs in some columns.
The text was updated successfully, but these errors were encountered:
For sure, in binarize(), just having an option to convert NAs into a separate bin would minimize this issue and is very easy to implement.
Otherwise, in the case of a numeric variable for example, you would have to drop the missing observations or convert numeric NAs into an arbitrary value (e.g. zero), which would both distort (statistical bias) the analysis in unpredictable ways.
Having the option to break numeric variables in bins by a specified criterion (frequency or interval length for e.g.) instead of number of bins would be useful too.
Despite that, overall another great package, so thanks @mdancho84
Would be nice with something similar as you have for the standard cor-function where users can specify how they would like to deal with NA’s. I do agree with the comment "Missing values and cleaning data are critical to getting great correlations" but a function like this is very convinient when having a few NAs in some columns.
The text was updated successfully, but these errors were encountered: