Generate mock data with the native backend

generate_mock_data_native() consumes a validated mock_spec and generates baseline valid values using MockData's native R backend. This milestone does not yet apply missing-code injection, garbage values, diagnostics, formula evaluation, or optional simstudy features.

Usage

generate_mock_data_native(spec, n, seed = NULL)

Arguments

spec

A mock_spec object.

n

Non-negative whole number of rows to generate.

seed

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

Value

A data frame with one column per mock_spec variable and n rows.

Details

The native backend is the default MIT-licensed baseline engine. It currently supports uniform continuous variables, truncated-normal continuous variables, categorical variables, and uniform calendar dates. Missing codes, garbage values, and diagnostics are intentionally handled by postprocess_mock_data() so that all backends share the same audit trail.

If seed is supplied, the previous R random state is restored after generation. This gives reproducible output without advancing the caller's RNG stream. Formula variables are rejected loudly until the formula/dependency milestone promotes the spike evaluator into production.

See also

mock_spec(), mock_continuous(), mock_spec_from_recodeflow(), postprocess_mock_data(), generate_mock_data_simstudy()

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

Examples

spec <- mock_spec(
  mock_spec_continuous("age", range = c(18, 85), rtype = "integer"),
  mock_spec_categorical(
    "smoking",
    levels = c("never", "former", "current"),
    proportions = c(0.5, 0.3, 0.2)
  )
)
data <- generate_mock_data_native(spec, n = 10, seed = 1)
head(data)
#>   age smoking
#> 1  36   never
#> 2  43   never
#> 3  56  former
#> 4  79   never
#> 5  32  former
#> 6  78   never