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I think I wrongly assumes that miss_var_summary returns the missings in order because those just so happen to be the first two variables in airquality:
library(naniar)
library(dplyr)
#> #> Attaching package: 'dplyr'#> The following objects are masked from 'package:stats':#> #> filter, lag#> The following objects are masked from 'package:base':#> #> intersect, setdiff, setequal, union
miss_var_summary(airquality)
#> # A tibble: 6 x 4#> variable n_miss pct_miss n_miss_cumsum#> <chr> <int> <dbl> <int>#> 1 Ozone 37 24.2 37#> 2 Solar.R 7 4.58 44#> 3 Wind 0 0 44#> 4 Temp 0 0 44#> 5 Month 0 0 44#> 6 Day 0 0 44
I think I wrongly assumes that
miss_var_summary
returns the missings in order because those just so happen to be the first two variables in airquality:But as we can see - this is not the case:
Created on 2018-05-18 by the reprex package (v0.2.0).
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