jared
c682e6547d
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$units_conversion. Every result carries an attr(., "provenance") list matching inst/schemas/provenance-v1.json. cog_explain() prints the structured form via cli or returns the raw list for MCP/JSON consumers. Also: .fetch_or_cache_manifest() now handles local fixture paths so tests can point USCOGDATA_FIXTURE_URL at the pipeline publish_cache/ without a working HTTP server. Tests: 80 pass / 0 fail. devtools::check() 0E/0W/2N (both notes pre-existing / environmental).
83 lines
3.3 KiB
R
83 lines
3.3 KiB
R
test_that("cog_spending returns expected shape for Broward Corrections 2020", {
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skip_if_no_corpus()
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r <- cog_spending("101006006", years = 2020L, category = "Corrections")
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expect_s3_class(r, "tbl_df")
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expected_cols <- c("year", "canonical_govid", "gov_name", "spend_subtype",
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"category", "amt_nominal", "codes_included",
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"aggregate_fallback", "notes")
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expect_true(all(expected_cols %in% names(r)))
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expect_equal(unique(r$canonical_govid), "101006006")
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expect_equal(unique(r$year), 2020L)
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expect_equal(unique(r$category), "Corrections")
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expect_true(all(r$spend_subtype %in% c("operations", "capital")))
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expect_true(all(r$amt_nominal > 0))
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})
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test_that("cog_spending vectorised years + categories", {
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skip_if_no_corpus()
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r <- cog_spending("101006006", 2019:2020,
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category = c("Corrections", "Police"))
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expect_true(all(r$year %in% 2019:2020))
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expect_true(all(r$category %in% c("Corrections", "Police")))
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expect_gte(nrow(r), 4L)
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})
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test_that("cog_spending with per_capita adds per-capita nominal column", {
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skip_if_no_corpus()
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r <- cog_spending("101006006", 2020L, "Corrections", per_capita = TRUE)
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expect_true("amt_per_capita_nominal" %in% names(r))
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expect_false("amt_real" %in% names(r))
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expect_false("amt_per_capita_real" %in% names(r))
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expect_true(all(is.finite(r$amt_per_capita_nominal)))
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expect_true(all(r$amt_per_capita_nominal < r$amt_nominal))
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})
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test_that("cog_spending with adjust_to_year adds real column", {
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skip_if_no_corpus()
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r <- cog_spending("101006006", 2015:2020, "Corrections",
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adjust_to_year = 2022L)
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expect_true("amt_real" %in% names(r))
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r2015 <- dplyr::filter(r, year == 2015L)
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expect_true(any(r2015$amt_nominal != r2015$amt_real))
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})
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test_that("cog_spending with per_capita + adjust_to_year adds all columns", {
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skip_if_no_corpus()
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r <- cog_spending("101006006", 2020L, "Corrections",
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per_capita = TRUE, adjust_to_year = 2022L)
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expect_true(all(c("amt_nominal", "amt_real",
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"amt_per_capita_nominal", "amt_per_capita_real") %in%
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names(r)))
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})
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test_that("cog_spending for unknown govid returns empty tibble", {
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skip_if_no_corpus()
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r <- cog_spending("XXXINVALID", 2020L, "Corrections")
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expect_s3_class(r, "tbl_df")
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expect_equal(nrow(r), 0L)
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expect_true("notes" %in% names(r))
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# provenance still attached
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expect_false(is.null(attr(r, "provenance")))
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})
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test_that("cog_spending result has provenance attribute matching schema", {
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skip_if_no_corpus()
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r <- cog_spending("101006006", 2020L, "Corrections")
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prov <- attr(r, "provenance")
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expect_type(prov, "list")
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expect_equal(prov$verb, "cog_spending")
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required <- c("verb", "target", "years", "scope", "manifest", "sql_query")
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expect_true(all(required %in% names(prov)))
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expect_equal(prov$years, 2020L)
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expect_equal(prov$category, "Corrections")
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expect_type(prov$sql_query, "character")
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expect_true(grepl("spending_annotated", prov$sql_query))
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expect_type(prov$codes_summed$observed, "character")
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expect_true(all(c("E04") %in% prov$codes_summed$observed))
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})
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test_that("cog_spending rejects invalid inputs", {
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expect_error(cog_spending(123, 2020L), "character")
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expect_error(cog_spending("101006006", "2020"), "years")
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})
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