Closed njtierney closed 1 year ago
For example:
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 library(naniar) airquality %>% group_by(Month) %>% miss_var_summary() #> # A tibble: 25 x 4 #> # Groups: Month [5] #> Month variable n_miss pct_miss #> <int> <chr> <int> <dbl> #> 1 5 Ozone 5 16.1 #> 2 5 Solar.R 4 12.9 #> 3 5 Wind 0 0 #> 4 5 Temp 0 0 #> 5 5 Day 0 0 #> 6 6 Ozone 21 70 #> 7 6 Solar.R 0 0 #> 8 6 Wind 0 0 #> 9 6 Temp 0 0 #> 10 6 Day 0 0 #> # … with 15 more rows
Created on 2020-05-13 by the reprex package (v0.3.0)
It should instead be:
# A tibble: 25 x 4 Month variables n_miss pct_miss <int> <chr> <int> <dbl> 1 5 Ozone 5 3.27 2 5 Solar.R 4 2.61 3 5 Wind 0 0 4 5 Temp 0 0 5 5 Day 0 0 6 6 Ozone 21 13.7 7 6 Solar.R 0 0 8 6 Wind 0 0 9 6 Temp 0 0 10 6 Day 0 0
The problem comes from pct_miss showing the percentage relative to the number of rows per group...not sure if this is a problem.
pct_miss
I think that this is fine
For example:
Created on 2020-05-13 by the reprex package (v0.3.0)
It should instead be: