feat: cog_spending + cog_revenue + cog_explain
Three core query verbs over the spending_annotated / revenue_annotated DuckDB views. Each verb accepts vector govid, vector years, optional category filter, per_capita flag, and adjust_to_year for CPI-U real-dollar conversion (bundled index). Amounts are returned in full USD (SUM(amt) * 1000) so callers can freely rescale to millions/billions. The $1,000s -> $USD conversion is recorded in provenance$transformations$units_conversion. Every result carries an attr(., "provenance") list matching inst/schemas/provenance-v1.json. cog_explain() prints the structured form via cli or returns the raw list for MCP/JSON consumers. Also: .fetch_or_cache_manifest() now handles local fixture paths so tests can point USCOGDATA_FIXTURE_URL at the pipeline publish_cache/ without a working HTTP server. Tests: 80 pass / 0 fail. devtools::check() 0E/0W/2N (both notes pre-existing / environmental).
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# R/spending.R
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#' Summarized spending by category
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#'
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#' One row per `(year, canonical_govid, spend_subtype, category)`. Amounts are
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#' returned in **full U.S. dollars** (the raw corpus stores them in $1,000s;
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#' this verb multiplies by 1000 so downstream code can freely rescale to
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#' millions/billions). The conversion is recorded in the provenance attribute
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#' under `transformations$units_conversion`.
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#'
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#' @param govid Character vector of `canonical_govid` values.
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#' @param years Integer vector of years.
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#' @param category Character vector of category names (from
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#' `summary_categories.category`), or `NULL` for all categories.
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#' @param per_capita If `TRUE`, adds `amt_per_capita_nominal` (and
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#' `amt_per_capita_real` when `adjust_to_year` is set) using
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#' `population_acs` from the canonical xwalk.
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#' @param adjust_to_year Integer base year for CPI-U real-dollar conversion,
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#' or `NULL` for nominal only.
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#' @return Tibble with columns `year`, `canonical_govid`, `gov_name`,
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#' `spend_subtype`, `category`, `amt_nominal`, optional `amt_real`,
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#' optional `amt_per_capita_nominal`, optional `amt_per_capita_real`,
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#' `codes_included`, `aggregate_fallback`, `notes`. Carries a `provenance`
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#' attribute matching `inst/schemas/provenance-v1.json`.
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#' @export
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cog_spending <- function(govid, years, category = NULL,
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per_capita = FALSE, adjust_to_year = NULL) {
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.verb_spendrev(
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verb = "cog_spending",
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view = "spending_annotated",
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subtype_col = "spend_subtype",
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call = match.call(),
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govid = govid,
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years = years,
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category = category,
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per_capita = per_capita,
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adjust_to_year = adjust_to_year
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)
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}
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#' @noRd
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.verb_spendrev <- function(verb, view, subtype_col, call,
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govid, years, category,
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per_capita, adjust_to_year) {
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.validate_verb_inputs(govid, years, category, per_capita, adjust_to_year)
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govid <- as.character(govid)
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years <- as.integer(years)
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if (!is.null(adjust_to_year)) adjust_to_year <- as.integer(adjust_to_year)
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con <- .ensure_session()
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sql <- .build_verb_sql(view, subtype_col, govid, years, category)
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result <- tibble::as_tibble(DBI::dbGetQuery(con, sql))
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if (per_capita) result <- .attach_per_capita(result, con, govid)
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if (!is.null(adjust_to_year)) {
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result <- .attach_real_dollars(result, adjust_to_year, per_capita)
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}
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result$notes <- .notes_column(result)
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attr(result, "provenance") <- .build_provenance(
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verb = verb,
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call = call,
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govid = govid,
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years = years,
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category = category,
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per_capita = per_capita,
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adjust_to_year = adjust_to_year,
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result = result,
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sql = sql,
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subtype_col = subtype_col
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)
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result
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}
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#' @noRd
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.validate_verb_inputs <- function(govid, years, category,
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per_capita, adjust_to_year) {
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if (!is.character(govid) || length(govid) == 0L) {
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cli::cli_abort("`govid` must be a non-empty character vector.")
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}
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if (!(is.integer(years) || is.numeric(years)) || length(years) == 0L) {
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cli::cli_abort("`years` must be a non-empty integer vector.")
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}
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if (!is.null(category) && !is.character(category)) {
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cli::cli_abort("`category` must be character or NULL.")
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}
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if (!is.logical(per_capita) || length(per_capita) != 1L) {
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cli::cli_abort("`per_capita` must be a length-1 logical.")
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}
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if (!is.null(adjust_to_year)) {
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if (!(is.integer(adjust_to_year) || is.numeric(adjust_to_year)) ||
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length(adjust_to_year) != 1L) {
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cli::cli_abort("`adjust_to_year` must be NULL or a length-1 integer.")
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}
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}
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invisible(TRUE)
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}
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#' @noRd
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.sql_lit_chr <- function(x) {
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safe <- gsub("'", "''", x, fixed = TRUE)
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paste0("'", safe, "'", collapse = ",")
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}
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#' @noRd
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.build_verb_sql <- function(view, subtype_col, govid, years, category) {
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govid_lit <- .sql_lit_chr(govid)
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years_lit <- paste(as.integer(years), collapse = ",")
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category_pred <- if (is.null(category)) {
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""
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} else {
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sprintf("AND category IN (%s)", .sql_lit_chr(category))
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}
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sprintf(
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"SELECT
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year,
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canonical_govid,
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COALESCE(xwalk_gov_name, gov_name) AS gov_name,
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%1$s,
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category,
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SUM(amt) * 1000.0 AS amt_nominal,
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string_agg(DISTINCT item_code, ',' ORDER BY item_code) AS codes_included,
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bool_and(is_aggregate) AS aggregate_fallback
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FROM %2$s
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WHERE canonical_govid IN (%3$s)
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AND year IN (%4$s)
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%5$s
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GROUP BY year, canonical_govid, gov_name, xwalk_gov_name, %1$s, category
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ORDER BY year, canonical_govid, %1$s, category",
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subtype_col, view, govid_lit, years_lit, category_pred
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)
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}
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#' @noRd
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.attach_per_capita <- function(result, con, govid) {
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if (nrow(result) == 0L) {
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result$amt_per_capita_nominal <- numeric(0)
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return(result)
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}
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sql <- sprintf(
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"SELECT canonical_govid, population_acs
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FROM canonical_fips_xwalk
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WHERE canonical_govid IN (%s)",
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.sql_lit_chr(govid)
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)
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pops <- tibble::as_tibble(DBI::dbGetQuery(con, sql))
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result <- dplyr::left_join(result, pops, by = "canonical_govid")
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result$amt_per_capita_nominal <- result$amt_nominal / result$population_acs
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result$population_acs <- NULL
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result
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}
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#' @noRd
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.attach_real_dollars <- function(result, adjust_to_year, per_capita) {
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if (nrow(result) == 0L) {
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result$amt_real <- numeric(0)
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if (per_capita) result$amt_per_capita_real <- numeric(0)
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return(result)
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}
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result$amt_real <- .inflate(result$amt_nominal, result$year, adjust_to_year)
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if (per_capita && "amt_per_capita_nominal" %in% names(result)) {
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result$amt_per_capita_real <- .inflate(
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result$amt_per_capita_nominal, result$year, adjust_to_year
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)
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}
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result
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}
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#' @noRd
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.notes_column <- function(result) {
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if (nrow(result) == 0L) return(character(0))
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ifelse(
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isTRUE(result$aggregate_fallback) | result$aggregate_fallback %in% TRUE,
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"Aggregate fallback applied; see cog_explain()",
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""
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)
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}
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