Create data functions for facilitating univariate/1D plots of missings

[njtierney]

No description provided.

[njtierney]

Current approaches are shown below, using the varname_NA pattern:

library(narnia)
library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, stats
#> lag():    dplyr, stats

airquality %>%
  bind_shadow() %>%
  ggplot(aes(x = Solar.R,
             colour = Ozone_NA)) + 
  geom_density()
#> Warning: Removed 7 rows containing non-finite values (stat_density).

airquality %>%
  bind_shadow() %>%
  ggplot(aes(x = Solar.R)) + 
  geom_histogram() +
  facet_wrap(~Ozone_NA,ncol = 1)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> Warning: Removed 7 rows containing non-finite values (stat_bin).

note

Renaming this issue to be more about the plots than about the shadow_shift

[njtierney]

Current behaviour / pattern for this is:

library(narnia)
library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, stats
#> lag():    dplyr, stats

aq_shadow <- bind_shadow(airquality)

ggplot(aq_shadow,
       aes(x = Temp,
           colour = Solar.R_NA)) + 
  geom_density()

ggplot(aq_shadow,
       aes(x = Temp)) + 
  geom_histogram() +
  facet_wrap(~Solar.R_NA,ncol = 1)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.