Check whether synthetic data is code-compatible with the original

Evaluates whether code written against the synthetic development twin will run against the original data without errors. Checks column presence, R class compatibility, factor level coverage, all-NA columns, zero-variance columns, missingness spikes, and ID uniqueness. haven_labelled columns currently round-trip as character in synthetic data, so that class change is expected for now.

Usage

check_code_readiness(original, synthetic, roles = NULL)

Arguments

original: The original data frame.
synthetic: The synthetic data frame (from synthesize_data()).
roles: Optional; a dataganger_roles object from detect_roles(). Used for ID-uniqueness checks. Computed internally if NULL.

Value

An S3 object of class dataganger_code_readiness with components:

checks: A tibble with one row per check: check, scope, column, status ("pass"/"warn"/"fail"), message.
summary: List with n_pass, n_warn, n_fail, ready (TRUE when n_fail == 0).
meta: List with dimensions and generated_at.

Examples

orig <- data.frame(x = 1:10, y = factor(letters[1:10]))
spec <- synth_spec(purpose = "demo")
syn  <- synthesize_data(orig, spec)
#> Warning: Cannot guarantee level presence for columns: y (10 levels at k = 5 require
#> minimum n = 50). Largest minimum n = 50; output has n = 10. Restoring as many
#> levels as fit without removing another level's last copy.
check_code_readiness(orig, syn)
#> 
#> ── DataGangeR Code Readiness ───────────────────────────────────────────────────
#> 8 pass, 0 warn, 2 fail
#> ✖ Not ready: 2 blocking issues
#> 
#> 
#> ── Failures ──
#> 
#> ✖ [x] class mismatch: original 'integer' vs synthetic 'numeric'
#> ✖ [y] class mismatch: original 'factor' vs synthetic 'character'