shadow_shift: Shift missing values to facilitate missing data...

In njtierney/naniar: Data Structures, Summaries, and Visualisations for Missing Data

Shift missing values to facilitate missing data exploration/visualisation

Description

shadow_shift transforms missing values to facilitate visualisation, and has different behaviour for different types of variables. For numeric variables, the values are shifted to 10% below the minimum value for a given variable plus some jittered noise, to separate repeated values, so that missing values can be visualised along with the rest of the data.

Usage

shadow_shift(...)

Arguments

...	arguments to impute_below().

Details

See Also

add_shadow_shift() cast_shadow_shift() cast_shadow_shift_label()

Examples

airquality$Ozone
shadow_shift(airquality$Ozone)
## Not run: 
library(dplyr)
airquality %>%
    mutate(Ozone_shift = shadow_shift(Ozone))

## End(Not run)

njtierney/naniar documentation built on March 19, 2024, 9:48 p.m.