Open njtierney opened 1 year ago
Workshopping this.
> naniar::miss_summary(airquality)
153 observations of 6 variables
44 (4.8%) of 918 values are
* NA
* -99
* ""
2 of 6 (33.3%) variables have missing values
252 of 918 (27.5%) observations have missing values
For a 5-number summary, you could add a min/max/median amount of missing values for variables and for cases, but imho a compact print method is preferable. Many of us still work on our laptop, where screen real estate is a premium.
2 of 6 (33.3%) variables have missing values
Missing values are distributed throughout variables:
min median max
2 5 5
As discussed with @dicook, The aim with this is provide a ricer overall summary of missingness, providing information on:
This could also be used iteratively so you can run this summary after making changes to missings and see what the impact is.
I think the way to do this is to write a print method for
naniar::miss_summary(airquality)
, which is a function that does nearly every missingness summary. So you still get the underlying data when you do the summary, but it is presented in a really nice way.