Binding a shadow matrix to a regular dataframe helps visualise and work with missing data.
Arguments
- data
a dataframe
- only_miss
logical - if FALSE (default) it will bind a dataframe with all of the variables duplicated with their shadow. Setting this to TRUE will bind variables only those variables that contain missing values. See the examples for more details.
- ...
extra options to pass to
recode_shadow()- a work in progress.
Examples
bind_shadow(airquality)
#> # A tibble: 153 × 12
#> Ozone Solar.R Wind Temp Month Day Ozone_NA Solar.R_NA Wind_NA Temp_NA
#> <int> <int> <dbl> <int> <int> <int> <fct> <fct> <fct> <fct>
#> 1 41 190 7.4 67 5 1 !NA !NA !NA !NA
#> 2 36 118 8 72 5 2 !NA !NA !NA !NA
#> 3 12 149 12.6 74 5 3 !NA !NA !NA !NA
#> 4 18 313 11.5 62 5 4 !NA !NA !NA !NA
#> 5 NA NA 14.3 56 5 5 NA NA !NA !NA
#> 6 28 NA 14.9 66 5 6 !NA NA !NA !NA
#> 7 23 299 8.6 65 5 7 !NA !NA !NA !NA
#> 8 19 99 13.8 59 5 8 !NA !NA !NA !NA
#> 9 8 19 20.1 61 5 9 !NA !NA !NA !NA
#> 10 NA 194 8.6 69 5 10 NA !NA !NA !NA
#> # ℹ 143 more rows
#> # ℹ 2 more variables: Month_NA <fct>, Day_NA <fct>
# bind only the variables that contain missing values
bind_shadow(airquality, only_miss = TRUE)
#> # A tibble: 153 × 8
#> Ozone Solar.R Wind Temp Month Day Ozone_NA Solar.R_NA
#> <int> <int> <dbl> <int> <int> <int> <fct> <fct>
#> 1 41 190 7.4 67 5 1 !NA !NA
#> 2 36 118 8 72 5 2 !NA !NA
#> 3 12 149 12.6 74 5 3 !NA !NA
#> 4 18 313 11.5 62 5 4 !NA !NA
#> 5 NA NA 14.3 56 5 5 NA NA
#> 6 28 NA 14.9 66 5 6 !NA NA
#> 7 23 299 8.6 65 5 7 !NA !NA
#> 8 19 99 13.8 59 5 8 !NA !NA
#> 9 8 19 20.1 61 5 9 !NA !NA
#> 10 NA 194 8.6 69 5 10 NA !NA
#> # ℹ 143 more rows
aq_shadow <- bind_shadow(airquality)
if (FALSE) {
# explore missing data visually
library(ggplot2)
# using the bounded shadow to visualise Ozone according to whether Solar
# Radiation is missing or not.
ggplot(data = aq_shadow,
aes(x = Ozone)) +
geom_histogram() +
facet_wrap(~Solar.R_NA,
ncol = 1)
}