Compare missing observations for 'many' data
Arguments
- datacube
A datacube from one of the many packages.
- dataset
A dataset in a datacube from one of the many packages. NULL by default. That is, all datasets in the datacube are used. To select two or more datasets, please declare them as a vector.
- variable
Would you like to focus on one, or more, specific variables present in one or more datasets in the 'many' datacube? By default "all". For multiple variables, please declare variable names as a vector.
Value
compare_missing() returns a tibble with information about each dataset
including the number of observations, the number of variables,
the earliest date, and the latest date in all observations.
Details
compare_missing() compares the missing observations for variables
in each dataset in a 'many' datacube.
See also
Other compare_:
compare_categories(),
compare_dimensions(),
compare_overlap()
Examples
# \donttest{
compare_missing(emperors)
#> # A tibble: 24 × 6
#> Variable Dataset Class Count Missing `Percent Missing`
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Begin Wikipedia mdate 69 0 0
#> 2 Begin UNRV mdate 98 0 0
#> 3 Begin Britannica mdate 87 0 0
#> 4 Birth Wikipedia mdate 69 6 8.7
#> 5 Birth UNRV mdate 98 24 24.5
#> 6 Cause Wikipedia character 69 1 1.45
#> 7 CityBirth Wikipedia character 69 18 26.1
#> 8 Dataset Wikipedia character 69 0 0
#> 9 Dataset UNRV character 98 0 0
#> 10 Dataset Britannica character 87 0 0
#> # ℹ 14 more rows
plot(compare_missing(emperors))
# }