jared
92c9a7382e
Swaps every hardcoded 9-char canonical_govid literal (Broward County,
Fort Lauderdale City, Florida/Alabama state govts, Bexar/Tarrant/Wayne
counties, San Diego/Oakland/Miami/Austin cities) for its 12-char Phase P
equivalent, resolved by name+type+state against the regenerated fixture
xwalk. Also updates two gov_name search patterns that no longer match
under Phase P canonical naming ("FLORIDA STATE GOVT" -> "FLORIDA"; the
"Miami" substring test now pins type = "city" since MIAMI-DADE COUNTY's
canonical name now also contains "Miami", which would otherwise make the
match ambiguous across govs_types instead of resolving via largest-pop).
Underlying per-year population figures for Broward County and Alabama
are unchanged, so no expected data-value literals needed recomputation.
Suite: 126 test blocks / 336 expectations, 0 FAIL / 0 WARN / 0 SKIP.
51 lines
1.8 KiB
R
51 lines
1.8 KiB
R
test_that("cog_explain prints verb header and target", {
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skip_if_no_corpus()
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r <- cog_spending("121011212191", 2020L, "Corrections")
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# cli writes to stderr; capture both stdout and message streams.
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txt <- paste(c(
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capture.output(cog_explain(r)),
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capture.output(cog_explain(r), type = "message")
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), collapse = "\n")
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expect_true(grepl("cog_spending", txt))
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expect_true(grepl("Corrections", txt))
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expect_true(grepl("121011212191", txt))
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})
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test_that("cog_explain format='list' returns structured provenance", {
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skip_if_no_corpus()
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r <- cog_spending("121011212191", 2020L, "Corrections")
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prov <- cog_explain(r, format = "list")
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expect_identical(prov, attr(r, "provenance"))
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})
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test_that("cog_explain returns result invisibly for chaining", {
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skip_if_no_corpus()
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r <- cog_spending("121011212191", 2020L, "Corrections")
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res <- withVisible(cog_explain(r))
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expect_false(res$visible)
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expect_identical(res$value, r)
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})
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test_that("cog_explain errors on non-verb input", {
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df <- tibble::tibble(a = 1)
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expect_error(cog_explain(df), "provenance")
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})
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test_that("cog_explain prints denominator + popyear_range + counts", {
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skip_if_no_corpus()
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with_fixture_corpus({
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r <- cog_spending("121011212191", years = 2019:2020,
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category = "Police", per_capita = TRUE)
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out <- paste(c(
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capture.output(cog_explain(r)),
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capture.output(cog_explain(r), type = "message")
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), collapse = "\n")
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expect_true(grepl("Census F-33", out))
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expect_true(grepl("popyear", out, ignore.case = TRUE))
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expect_true(grepl("census_f33", out))
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# popyear_range should render as 4-digit calendar years, not raw 2-digit
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expect_true(grepl("2019-2020", out))
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expect_false(grepl("popyear range: 19-20", out, fixed = TRUE))
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})
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})
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