Return data used to create vis_miss plot
Create a tidy dataframe of missing data suitable for plotting
Usage
data_vis_miss(x, ...)
# S3 method for default
data_vis_miss(x, ...)
# S3 method for data.frame
data_vis_miss(x, cluster = FALSE, ...)
# S3 method for grouped_df
data_vis_miss(x, ...)
Arguments
- x
data.frame
- ...
extra arguments (currently unused)
- cluster
logical - whether to cluster missingness. Default is FALSE.
Examples
data_vis_miss(airquality)
#> # A tibble: 918 × 4
#> rows variable valueType value
#> <int> <chr> <chr> <chr>
#> 1 1 Day FALSE FALSE
#> 2 1 Month FALSE FALSE
#> 3 1 Ozone FALSE FALSE
#> 4 1 Solar.R FALSE FALSE
#> 5 1 Temp FALSE FALSE
#> 6 1 Wind FALSE FALSE
#> 7 2 Day FALSE FALSE
#> 8 2 Month FALSE FALSE
#> 9 2 Ozone FALSE FALSE
#> 10 2 Solar.R FALSE FALSE
#> # … with 908 more rows
if (FALSE) {
#return vis_dat data for each group
library(dplyr)
airquality %>%
group_by(Month) %>%
data_vis_miss()
}
data_vis_miss(airquality)
#> # A tibble: 918 × 4
#> rows variable valueType value
#> <int> <chr> <chr> <chr>
#> 1 1 Day FALSE FALSE
#> 2 1 Month FALSE FALSE
#> 3 1 Ozone FALSE FALSE
#> 4 1 Solar.R FALSE FALSE
#> 5 1 Temp FALSE FALSE
#> 6 1 Wind FALSE FALSE
#> 7 2 Day FALSE FALSE
#> 8 2 Month FALSE FALSE
#> 9 2 Ozone FALSE FALSE
#> 10 2 Solar.R FALSE FALSE
#> # … with 908 more rows