De-identification via categorical aggregation

add_blur() adds an bluring step to a transformation pipeline (NB: intended for categorical data). When ran as a transformation, values are recoded to a lower cardinality as defined by blur. #'

add_blur(object, ..., blur = c())

Arguments

object

Either a data.frame, tibble, or existing DeidentList pipeline.

...

variables to be transformed.

blur

a key-value pair such that 'key' is replaced by 'value' on transformation.

Value

A 'DeidentList' representing the untrained transformation pipeline. The object contains fields:

  • deident_methods a list of each step in the pipeline (consisting of variables and method)

and methods:

  • mutate apply the pipeline to a new data set

  • to_yaml serialize the pipeline to a '.yml' file

See also

category_blur() is provided to aid in defining the blur

Examples

.blur <- category_blur(ShiftsWorked$Shift, `Working` = "Day|Night")
pipe.blur <- add_blur(ShiftsWorked, `Shift`, blur = .blur)
#> Error in create_deident.R6ClassGenerator(deident, ...): object '.blur' not found
pipe.blur$mutate(ShiftsWorked)
#> Error: object 'pipe.blur' not found