Add aggregation to pipelines — add

add_group() allows for the injection of aggregation into the transformation pipeline. Should you need to apply a transformation under aggregation (e.g. add_shuffle) this helper creates a grouped data.frame as would be done with dplyr::group_by(). The function add_ungroup() is supplied to perform the inverse operation.

add_group(object, ...)

add_ungroup(object, ...)

Arguments

object: Either a data.frame, tibble, or existing DeidentList pipeline.
...: Variables on which data is to be grouped.

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

Examples

pipe.grouped <- add_group(ShiftsWorked, Date, Shift)
pipe.grouped_shuffle <- add_shuffle(pipe.grouped, `Daily Pay`)
add_ungroup(pipe.grouped_shuffle, `Daily Pay`)
#> DeidentList
#>    3 step(s) implemented 
#>    Step 1 : 'Grouper(group_on = [Date, Shift])' on variable(s)  
#>    Step 2 : 'Shuffler' on variable(s) Daily Pay 
#>    Step 3 : 'Ungrouper' on variable(s) Daily Pay 
#> For data:
#>    columns: Record ID, Employee, Date, Shift, Shift Start, Shift End, Daily Pay