validate_schema: Validate a Data Frame Against a Provenance Schema

In rmoriebricklayer: Reproducible Data Capsules with Provenance and Fallback

Validate a Data Frame Against a Provenance Schema

Description

Checks a raw data frame against the schema block of a provenance object: required columns, row-count bounds, and allowed categorical value sets. Returns the issues found rather than raising, so the caller decides how to react.

Usage

validate_schema(df_raw, provenance)

Arguments

df_raw	The data frame to validate.
provenance	A provenance list as returned by load_provenance(). The schema block may contain expected_columns, structural_invariants (min_data_rows, max_data_rows), and expected_value_sets (a named list of allowed values per column).

Value

A named list of issues; each issue is a list with severity ("fatal" or "warning") and a human-readable message. A zero-length list means the data frame is clean.

Examples

prov <- list(schema = list(
  expected_columns      = c("id", "year"),
  structural_invariants = list(min_data_rows = 1),
  expected_value_sets   = list(year = 2020:2025)
))
df <- data.frame(id = 1:3, year = c(2020, 2021, 2030))
issues <- validate_schema(df, prov)
names(issues)  # flags the out-of-set year value

rmoriebricklayer documentation built on Aug. 5, 2026, 9:08 a.m.