Apply mock_spec post-processing rules

postprocess_mock_data() applies v0.4 mock_spec missing-code and garbage-value rules to an already generated baseline data frame. It records a mockdata_diagnostics attribute so downstream checks can distinguish values assigned by post-processing from values that were drawn naturally by the baseline generator.

Usage

postprocess_mock_data(data, spec, seed = NULL, diagnostics = TRUE)

Arguments

data

Data frame with one column for each variable in spec.

spec

A validated mock_spec object.

seed

Optional whole-number random seed. The previous R random state is restored after post-processing.

diagnostics

Logical. If TRUE, attach a mockdata_diagnostics attribute to the returned data frame.

Value

A data frame with post-processing applied.

Details

Missing-code diagnostics separate values that were naturally drawn as a declared missing code (preexisting_missing_code_indices) from values that were assigned by post-processing (assigned_missing_indices). Garbage rules are applied only to rows that are not missing-code diagnostics, preserving the audit trail for collision cases such as a valid category code that is also a declared missing code.

Garbage rules are applied in canonical order: low, then high, then any other named rules in caller order. Each garbage rule is a named list with a proportion field and a range field using MockData range notation, for example list(high = list(proportion = 0.05, range = "[150, 200]")).

Diagnostics are stored as a data-frame attribute. Base R subsetting and some downstream tools may drop attributes, so preserve the original post-processed object when diagnostics are part of the audit trail.

See also

generate_mock_data_native(), generate_mock_data_simstudy(), mock_spec()

Other mock generation APIs: create_mock_data(), generate_mock_data_native(), generate_mock_data_simstudy()

Examples

spec <- mock_categorical(
  "smoking",
  levels = c("never", "former", "current"),
  proportions = c(0.5, 0.3, 0.2),
  rtype = "character",
  missing_codes = "9",
  missing_proportions = 0.05
)
baseline <- generate_mock_data_native(spec, n = 20, seed = 1)
result <- postprocess_mock_data(baseline, spec, seed = 2)
attr(result, "mockdata_diagnostics")$variables$smoking
#> $n
#> [1] 20
#> 
#> $preexisting_missing_code_indices
#> integer(0)
#> 
#> $assigned_missing_indices
#> [1] 15
#> 
#> $assigned_missing_codes
#> [1] "9"
#> 
#> $assigned_garbage_indices
#> named list()
#> 
#> $assigned_garbage_values
#> named list()
#>