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test: re-baseline canonical_govid literals to 12-char namespace

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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.
This commit is contained in:
jared
2026-07-11 09:32:02 -04:00
parent 570a9408a2
commit 92c9a7382e
9 changed files with 95 additions and 85 deletions
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tests/testthat/test-rollup.R
+15 -15
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@@ -2,9 +2,9 @@ test_that("cog_geographic_rollup aggregates state + county + city layers", {
skip_if_no_corpus()
r <- cog_geographic_rollup(
govids = list(
state = "100000000", # Florida state govt
county = "101006006", # Broward County
city = "102006004" # Fort Lauderdale City
state = "120000226351", # Florida state govt
county = "121011212191", # Broward County
city = "122011161585" # Fort Lauderdale City
),
category = "Police",
years = 2019:2020
@@ -23,7 +23,7 @@ test_that("cog_geographic_rollup aggregates state + county + city layers", {
test_that("cog_geographic_rollup respects per_capita + adjust_to_year", {
skip_if_no_corpus()
r <- cog_geographic_rollup(
govids = list(county = "101006006", city = "102006004"),
govids = list(county = "121011212191", city = "122011161585"),
category = "Police",
years = 2020L,
per_capita = TRUE,
@@ -43,8 +43,8 @@ test_that("cog_geographic_rollup respects per_capita + adjust_to_year", {
test_that("cog_geographic_rollup scope_notes describe each layer", {
skip_if_no_corpus()
r <- cog_geographic_rollup(
govids = list(state = "100000000", county = "101006006",
city = "102006004"),
govids = list(state = "120000226351", county = "121011212191",
city = "122011161585"),
category = "Police", years = 2020L
)
state_notes <- unique(r$scope_note[r$layer == "state"])
@@ -58,7 +58,7 @@ test_that("cog_geographic_rollup scope_notes describe each layer", {
test_that("cog_geographic_rollup single-layer call works", {
skip_if_no_corpus()
r <- cog_geographic_rollup(
govids = list(county = c("101006006")),
govids = list(county = c("121011212191")),
category = "Corrections",
years = 2020L
)
@@ -69,7 +69,7 @@ test_that("cog_geographic_rollup single-layer call works", {
test_that("cog_geographic_rollup provenance reports the outer verb", {
skip_if_no_corpus()
r <- cog_geographic_rollup(
govids = list(state = "100000000", county = "101006006"),
govids = list(state = "120000226351", county = "121011212191"),
category = "Police", years = 2020L
)
prov <- attr(r, "provenance")
@@ -80,7 +80,7 @@ test_that("cog_geographic_rollup provenance reports the outer verb", {
test_that("cog_geographic_rollup accepts data.frames per layer", {
skip_if_no_corpus()
fl_state <- cog_gov_search("^FLORIDA STATE GOVT$", type = "state")
fl_state <- cog_gov_search("^FLORIDA$", type = "state")
broward <- cog_gov_search("^BROWARD COUNTY$", state = "FL", type = "county")
r <- cog_geographic_rollup(
govids = list(state = fl_state, county = broward),
@@ -92,9 +92,9 @@ test_that("cog_geographic_rollup accepts data.frames per layer", {
test_that("cog_geographic_rollup rejects invalid inputs", {
expect_error(cog_geographic_rollup(list(), "Police", 2020L), "length")
expect_error(cog_geographic_rollup(c("101006006"), "Police", 2020L), "list")
expect_error(cog_geographic_rollup(c("121011212191"), "Police", 2020L), "list")
expect_error(
cog_geographic_rollup(list(planet = "100000000"), "Police", 2020L),
cog_geographic_rollup(list(planet = "120000226351"), "Police", 2020L),
"state|county|city"
)
})
@@ -103,8 +103,8 @@ test_that("cog_geographic_rollup per-capita uses summed per-year populations", {
skip_if_no_corpus()
with_fixture_corpus({
r <- cog_geographic_rollup(
govids = list(state = "010000000",
county = "101006006"),
govids = list(state = "010000226085",
county = "121011212191"),
category = "Police",
years = 2019:2020,
per_capita = TRUE
@@ -125,14 +125,14 @@ test_that("cog_geographic_rollup records included/excluded govids in provenance"
skip_if_no_corpus()
with_fixture_corpus({
r <- cog_geographic_rollup(
govids = list(county = "101006006"),
govids = list(county = "121011212191"),
category = "Police",
years = 2019:2020,
per_capita = TRUE
)
prov <- attr(r, "provenance")
expect_true("rollup" %in% names(prov))
expect_true("101006006" %in% prov$rollup$included_govids)
expect_true("121011212191" %in% prov$rollup$included_govids)
expect_true(is.character(prov$rollup$excluded_govids))
})
})