jared
4de915b557
Adds cog_recipes() to list the curated harmonization_recipes catalog (24 recipes / schema_version >= 5), and a recipe= argument on cog_spending()/ cog_revenue() that runs a recipe's generic multi-code join instead of the category view: SUM(amt * weight) across whichever component codes are present for a (year, canonical_govid), scoped by gov_type_scope. The join deliberately does not filter is_aggregate -- the wide era (<= 2011) exposes these split families (corrections 04+05, IG *89/*47, U4- rents, etc.) ONLY as aggregate rows, with leaf codes first appearing in 2012, so excluding aggregates would zero out the wide-era half of every recipe. This is safe by corpus construction: wide-era rows are aggregate-only, modern rows are leaf-only, and every component is year-scoped, so there is no double-counting. recipe= is mutually exclusive with category=; the result's subtype column reads "recipe" and category reads the recipe's label. Adds recipe-component-driven signposting: when a basis="harmonized" + category query comes back with zero rows in a requested year, and a harmonization recipe covering that category would actually produce rows for this government in that year (via the same join .run_recipe() uses), the recipe is surfaced in provenance$suggestions plus one cli::cli_inform() message. This is deliberately keyed off recipe components rather than harmonization_map's suggested_recipe_id column (which is empty on every live row -- the wide era's split families are NA-by-construction via aggregate exclusion, not an NA ruling to hang a suggestion off of). Also populates the previously-always-empty provenance$series_break_refs (schema v5 only: series_breaks_pq rows whose fin_code is among the observed codes and whose break_year falls in the requested span), and extends cog_explain() with Basis/Harmonization/Recipe/Suggestions/Series breaks sections.
121 lines
3.7 KiB
R
121 lines
3.7 KiB
R
# R/provenance.R
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# Shared provenance construction. Matches inst/schemas/provenance-v1.json.
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#' @noRd
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.build_provenance <- function(verb, call, govid, years, category,
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per_capita, adjust_to_year, result, sql,
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subtype_col, basis = NA_character_,
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basis_note = NA_character_,
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harmonization = NULL, recipe = NULL,
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suggestions = list()) {
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manifest <- .uscogdata_env$manifest
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codes <- result[["codes_included"]]
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codes_observed <- if (length(codes) == 0L) {
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character(0)
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} else {
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sorted <- sort(unique(unlist(strsplit(codes, ",", fixed = TRUE))))
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sorted[nzchar(sorted)]
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}
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agg_flag <- result[["aggregate_fallback"]]
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agg_applied <- isTRUE(any(agg_flag, na.rm = TRUE))
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agg_years <- if (agg_applied) {
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unique(as.integer(result$year[which(agg_flag)]))
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} else {
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integer(0)
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}
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gov_names <- if (nrow(result) == 0L) {
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character(0)
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} else {
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unique(result$gov_name)
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}
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schema_version <- suppressWarnings(as.integer(manifest$schema_version %||% 0L))
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con <- .uscogdata_env$con
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break_refs <- if (!is.null(con) && DBI::dbIsValid(con)) {
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.build_series_break_refs(con, codes_observed, years, schema_version)
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} else {
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character(0)
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}
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list(
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verb = verb,
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call = paste(deparse(call), collapse = " "),
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target = list(
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canonical_govid = as.character(govid),
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gov_name = gov_names
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),
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years = as.integer(years),
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category = category,
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basis = basis,
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basis_note = basis_note,
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harmonization = harmonization %||% list(
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applied = FALSE, na_rows_excluded = 0L, na_amount_excluded = 0,
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note = NA_character_
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),
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recipe = recipe,
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suggestions = suggestions,
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scope = list(
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gov_types_included = as.integer(unlist(manifest$scope$gov_types_included)),
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gov_types_excluded = as.integer(unlist(manifest$scope$gov_types_excluded)),
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scope_note = manifest$scope$scope_note %||% ""
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),
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codes_summed = list(
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observed = codes_observed,
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subtype_column = subtype_col
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),
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aggregate_fallback = list(
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applied = agg_applied,
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years = agg_years
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),
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transformations = list(
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units_conversion = list(
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applied = TRUE,
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source_unit = "$1,000s (raw Census)",
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target_unit = "$USD",
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multiplier = 1000L
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),
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per_capita = list(
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applied = isTRUE(per_capita),
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denominator_source = if (isTRUE(per_capita)) {
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"Census F-33 population (per-year, from long.population)"
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} else {
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NA_character_
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},
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popyear_range = if (isTRUE(per_capita)) {
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attr(result, ".popyear_range") %||% integer(0)
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} else {
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integer(0)
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},
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pop_source_counts = if (isTRUE(per_capita)) {
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ps <- result[["pop_source"]]
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if (is.null(ps) || length(ps) == 0L) {
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list(census_f33 = 0L, unavailable = 0L)
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} else {
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list(
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census_f33 = sum(ps == "census_f33", na.rm = TRUE),
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unavailable = sum(ps == "unavailable", na.rm = TRUE)
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)
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}
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} else {
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NULL
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}
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),
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inflation = list(
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applied = !is.null(adjust_to_year),
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base_year = if (is.null(adjust_to_year)) NA_integer_ else as.integer(adjust_to_year),
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index = if (is.null(adjust_to_year)) NA_character_ else "CPI-U (BLS CPIAUCSL annual average, bundled)"
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)
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),
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series_break_refs = break_refs,
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manifest = list(
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schema_version = as.integer(manifest$schema_version),
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pipeline_commit = manifest$pipeline_commit %||% NA_character_,
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built_at = manifest$built_at %||% NA_character_
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),
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sql_query = sql
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)
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}
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