jared
9ae46746c0
The plan-supplied predicate combined three redundant checks (is.null + any + %in% TRUE). Element-wise behavior was correct via scalar recycling, but the form was confusing — a code-quality reviewer misread it as a multi-row false-positive bug. Simplify to mirror the parts[[2]] structure: gate on column presence, then element-wise %in% TRUE check. Equivalent semantics, fewer ways to misread.
215 lines
7.2 KiB
R
215 lines
7.2 KiB
R
# 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 the per-year
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#' Census F-33 population from `gov_population_yearly`. Result also gains
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#' a `pop_source` column with values `"census_f33"` or `"unavailable"`
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#' (the latter for gov types 4/5 and any row whose population is missing
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#' in that year).
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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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#' optional `pop_source`, `codes_included`, `aggregate_fallback`, `notes`.
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#' Carries a `provenance` 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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govid <- .coerce_govid_input(govid, arg = "govid")
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.validate_verb_inputs(govid, years, category, per_capita, adjust_to_year)
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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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scope <- .check_govids_in_scope(govid)
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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$notes <- .notes_column(result)
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prov <- .build_provenance(
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verb = verb,
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call = call,
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govid = govid,
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years = years,
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category = category,
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per_capita = per_capita,
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adjust_to_year = adjust_to_year,
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result = result,
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sql = sql,
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subtype_col = subtype_col
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)
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prov$scope$govids_found <- scope$found
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prov$scope$govids_missing <- scope$missing
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attr(result, "provenance") <- prov
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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$amt_per_capita_nominal <- numeric(0)
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result$pop_source <- character(0)
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return(result)
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}
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years_lit <- paste(unique(as.integer(result$year)), collapse = ",")
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sql <- sprintf(
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"SELECT canonical_govid, year, population
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FROM gov_population_yearly
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WHERE canonical_govid IN (%s)
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AND year IN (%s)",
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.sql_lit_chr(govid), years_lit
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)
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pops <- tibble::as_tibble(DBI::dbGetQuery(con, sql))
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result <- dplyr::left_join(result, pops,
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by = c("canonical_govid", "year"))
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result$amt_per_capita_nominal <- result$amt_nominal / result$population
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result$pop_source <- ifelse(is.na(result$population),
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"unavailable", "census_f33")
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result$population <- NULL
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result
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}
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#' @noRd
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.attach_real_dollars <- function(result, adjust_to_year, per_capita) {
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if (nrow(result) == 0L) {
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result$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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n <- nrow(result)
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if (n == 0L) return(character(0))
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parts <- vector("list", 2L)
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agg <- result[["aggregate_fallback"]]
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parts[[1]] <- if (!is.null(agg)) {
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ifelse(agg %in% TRUE,
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"Aggregate fallback applied; see cog_explain()",
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NA_character_)
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} else {
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rep(NA_character_, n)
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}
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ps <- result[["pop_source"]]
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parts[[2]] <- if (!is.null(ps)) {
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ifelse(ps == "unavailable",
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"No population denominator available for this gov type",
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NA_character_)
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} else {
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rep(NA_character_, n)
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}
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out <- character(n)
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for (i in seq_len(n)) {
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pieces <- vapply(parts, `[[`, character(1), i)
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pieces <- pieces[!is.na(pieces)]
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out[i] <- if (length(pieces) == 0L) "" else paste(pieces, collapse = "; ")
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}
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out
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}
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