139 lines
5.1 KiB
R
139 lines
5.1 KiB
R
test_that("cog_geographic_rollup aggregates state + county + city layers", {
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skip_if_no_corpus()
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r <- cog_geographic_rollup(
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govids = list(
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state = "100000000", # Florida state govt
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county = "101006006", # Broward County
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city = "102006004" # Fort Lauderdale City
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),
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category = "Police",
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years = 2019:2020
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)
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expect_s3_class(r, "tbl_df")
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expected_cols <- c("year", "layer", "canonical_govid", "gov_name",
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"spend_subtype", "category", "amt_nominal",
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"codes_included", "aggregate_fallback",
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"scope_note", "notes")
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expect_true(all(expected_cols %in% names(r)))
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expect_setequal(unique(r$layer), c("state", "county", "city"))
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expect_true(all(r$category == "Police"))
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expect_true(all(r$year %in% 2019:2020))
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})
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test_that("cog_geographic_rollup respects per_capita + adjust_to_year", {
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skip_if_no_corpus()
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r <- cog_geographic_rollup(
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govids = list(county = "101006006", city = "102006004"),
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category = "Police",
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years = 2020L,
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per_capita = TRUE,
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adjust_to_year = 2022L
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)
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expect_true(all(c("amt_nominal", "amt_real",
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"amt_per_capita_nominal", "amt_per_capita_real") %in%
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names(r)))
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# Each layer's per-capita uses its own population: city pop < county pop,
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# so per_capita_nominal for city rows should differ meaningfully from county.
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city_pc <- r$amt_per_capita_nominal[r$layer == "city"]
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cty_pc <- r$amt_per_capita_nominal[r$layer == "county"]
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expect_true(length(city_pc) > 0L)
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expect_true(length(cty_pc) > 0L)
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})
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test_that("cog_geographic_rollup scope_notes describe each layer", {
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skip_if_no_corpus()
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r <- cog_geographic_rollup(
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govids = list(state = "100000000", county = "101006006",
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city = "102006004"),
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category = "Police", years = 2020L
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)
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state_notes <- unique(r$scope_note[r$layer == "state"])
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expect_true(any(grepl("state total", state_notes)))
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county_notes <- unique(r$scope_note[r$layer == "county"])
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expect_true(any(grepl("county", county_notes)))
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city_notes <- unique(r$scope_note[r$layer == "city"])
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expect_true(any(grepl("city proper", city_notes)))
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})
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test_that("cog_geographic_rollup single-layer call works", {
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skip_if_no_corpus()
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r <- cog_geographic_rollup(
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govids = list(county = c("101006006")),
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category = "Corrections",
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years = 2020L
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)
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expect_true(all(r$layer == "county"))
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expect_gt(nrow(r), 0L)
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})
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test_that("cog_geographic_rollup provenance reports the outer verb", {
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skip_if_no_corpus()
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r <- cog_geographic_rollup(
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govids = list(state = "100000000", county = "101006006"),
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category = "Police", years = 2020L
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)
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prov <- attr(r, "provenance")
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expect_equal(prov$verb, "cog_geographic_rollup")
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expect_setequal(prov$layers, c("state", "county"))
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expect_true(grepl("cog_geographic_rollup", prov$call))
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})
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test_that("cog_geographic_rollup accepts data.frames per layer", {
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skip_if_no_corpus()
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fl_state <- cog_gov_search("^FLORIDA STATE GOVT$", type = "state")
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broward <- cog_gov_search("^BROWARD COUNTY$", state = "FL", type = "county")
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r <- cog_geographic_rollup(
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govids = list(state = fl_state, county = broward),
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category = "Police", years = 2020L
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)
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expect_setequal(unique(r$layer), c("state", "county"))
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expect_gt(nrow(r), 0L)
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})
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test_that("cog_geographic_rollup rejects invalid inputs", {
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expect_error(cog_geographic_rollup(list(), "Police", 2020L), "length")
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expect_error(cog_geographic_rollup(c("101006006"), "Police", 2020L), "list")
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expect_error(
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cog_geographic_rollup(list(planet = "100000000"), "Police", 2020L),
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"state|county|city"
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)
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})
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test_that("cog_geographic_rollup per-capita uses summed per-year populations", {
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skip_if_no_corpus()
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with_fixture_corpus({
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r <- cog_geographic_rollup(
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govids = list(state = "010000000",
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county = "101006006"),
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category = "Police",
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years = 2019:2020,
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per_capita = TRUE
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)
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state_ops <- r[r$layer == "state" & r$spend_subtype == "operations", ]
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county_ops <- r[r$layer == "county" & r$spend_subtype == "operations", ]
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state_implied <- state_ops$amt_nominal / state_ops$amt_per_capita_nominal
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county_implied <- county_ops$amt_nominal /
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county_ops$amt_per_capita_nominal
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# Per-year, per-layer denominator is the layer's own per-year population
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expect_equal(state_implied[state_ops$year == 2019], 4874747, tolerance = 1)
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expect_equal(state_implied[state_ops$year == 2020], 4903185, tolerance = 1)
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expect_equal(county_implied[county_ops$year == 2019], 1935878, tolerance = 1)
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})
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})
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test_that("cog_geographic_rollup records included/excluded govids in provenance", {
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skip_if_no_corpus()
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with_fixture_corpus({
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r <- cog_geographic_rollup(
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govids = list(county = "101006006"),
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category = "Police",
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years = 2019:2020,
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per_capita = TRUE
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)
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prov <- attr(r, "provenance")
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expect_true("rollup" %in% names(prov))
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expect_true("101006006" %in% prov$rollup$included_govids)
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expect_true(is.character(prov$rollup$excluded_govids))
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})
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})
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