.notes_column now joins multiple per-row notes with '; '. Adds the
'No population denominator available for this gov type' note when
pop_source is 'unavailable'. Gracefully handles absent pop_source
(per_capita = FALSE). Two new tests: one corpus-level (census_f33
branch) and one synthetic unit test covering multi-note concatenation.
Adds a RED test asserting that cog_spending(per_capita = TRUE) divides
by the per-year F-33 population (Broward 2019: 1,935,878; 2020: 1,952,778)
rather than the static ACS value (1,940,907). Uses absolute-tolerance
expect_true(abs(...) < 1) instead of expect_equal(tolerance=1) because
testthat 3 treats the tolerance argument as relative.
Cross-task review found two edge cases that violated the basket-mode
soft-fail contract:
- Per-row excluded type (e.g. type = c(NA, "special_district")) hit
.coerce_type()'s abort inside the per-row resolver, killing the
whole basket call. Now treated as no_match in the sidecar.
- Malformed regex in the substring fallback (e.g. name = "San(Diego")
propagated DuckDB engine errors. .escape_regex() now backslash-
escapes meta characters before the regexp_matches call. Utility-
mode regex behavior is unchanged.
Plus a new public-surface test for the all-no-match case.
Sidecar accessors for basket-mode results. cog_basket_resolution()
returns the full resolution tibble (drops candidates list-col by
default for readable printing). cog_basket_unresolved() filters to
ambiguous/no_match rows for iterative refinement.
Vector name + state + type arguments dispatch to a per-row resolver
that produces a basket tibble with a 'resolution' sidecar attribute.
Utility mode (length-1 name) is unchanged.
Multi-row matches within a single govs_type pick the largest-population
row (status=largest_pop). Multi-row matches spanning >=2 types return no
basket row (status=ambiguous) with all candidates preserved for the
sidecar.
.resolve_basket_row() now falls back to case-insensitive substring
match when no exact match is found, and short-circuits empty/whitespace
input to no_match. Disambiguation stub raises pending Task 5.
Adds inst/extdata/fixture_corpus/ — a 3.6 MB two-year (2019/2020) slice
of the published corpus (OH+VT+WY fixture from cog_pipeline test profile
plus all 50 states). Includes canonical_fips_xwalk.parquet,
summary_categories.parquet, docs/, and a trimmed manifest.json.
setup.R now points USCOGDATA_URL at the bundled fixture automatically,
bypassing HTTP / Nextcloud entirely. DuckDB reads local parquet via the
existing .is_local_path() fast-path in manifest.R; no httpfs required.
session is reset between test files via withr::defer(cog_close()).
helper-fixture.R gains fixture_corpus_path(), a richer skip_if_no_corpus()
that checks the bundled fixture first, and with_fixture_corpus() for
tests that need explicit session isolation.
test-spending.R: adjust the inflate-column test to use 2019 (fixture year)
instead of 2015 (absent from fixture).
Result: 181 PASS / 0 FAIL / 0 SKIP — all tests run against real parquet
data with real DuckDB queries and no network dependency.
Discovery verb over the summary_categories view, grouped one row per
(category, subtype). Parallels cog_gov_search: analysts use it to
find the valid `category` values to pass into cog_spending(),
cog_revenue(), cog_geographic_rollup().
Columns: category, category_type, subtype, n_codes, item_codes
(comma-separated, alphabetical). Optional filters:
type = NULL | "spending" | "revenue"
pattern = regex matched case-insensitively on category
The user-facing "spending" alias is translated internally to the
corpus-native "expenditure" so callers don't have to learn Census
vocabulary, while the returned category_type column preserves the
native value for auditability.
Also: fix @noRd placement in session.R so devtools::document() stops
warning.
Tests: +16 new / 181 total pass. check 0E/0W/0N.
Two UX fixes surfaced by first real-user use:
1. cog_spending / cog_revenue / cog_geographic_rollup now accept either
a character vector OR a data.frame with a canonical_govid column
(e.g. output of cog_gov_search() or cog_find_peers()). Shared
.coerce_govid_input() helper in session.R. This lets the natural
pipe work:
cog_gov_search('MIAMI', state='FL', type='city') |>
cog_spending(years=2022, category='Police')
cog_peer_compare already accepted a data.frame for the peer arg;
behavior there is unchanged.
2. .check_govids_in_scope() message reworded. The old text led with
'v0.1 covers gov_types 0-3' which falsely implied the missing govids
were scope-excluded types when the more common real cause is a typo
or a guessed value. New message leads with typo + pre-2017 PID,
mentions scope exclusion as one possibility, and points at
cog_gov_search() as the recovery path.
Tests: 165 pass / 0 fail. check 0E/0W/0N.
Three pieces:
1. cog_gov_search: name/state/type search over canonical_fips_xwalk
for resolving human-readable place names into canonical_govids.
Accepts USPS abbrev ('FL') or FIPS int (12) for state; integer
0-3 or name ('state','county','city','township') for type. Types
4/5 emit an explanatory cli message and return an empty tibble
(v0.1 corpus excludes them). USPS<->FIPS table hardcoded with
50 states + DC + territories; FIPS 66 = GU (not GA).
2. cog_mirror: downloads manifest-listed files to a local directory
with SHA-256 idempotency (files with matching hash return status
'cached'). Supports HTTP and local-path fixture URLs. Round-trip
test: mirror + re-open against the mirror + query Broward 2020
returns identical results.
3. Scope-aware verbs: .check_govids_in_scope() helper in session.R
queries canonical_fips_xwalk for the requested govids, emits a
cli_inform listing any missing ones, and records the found/missing
sets under provenance$scope. Wired into cog_spending (and
transitively into cog_revenue, cog_geographic_rollup,
cog_peer_compare via their cog_spending calls).
Also: dropped dbplyr from Imports (unused).
Tests: +29 (22 search + 12 mirror - 5 refactored) / 159 total pass.
devtools::check() now clean: 0E / 0W / 0N.
cog_find_peers selects peers from canonical_fips_xwalk by same-type,
same-state, and population-range (ratio or absolute) criteria,
ordered by |log(pop_ratio)| ascending.
cog_peer_compare accepts the find_peers result (or a plain character
vector of govids), pulls spending for target + peers via cog_spending,
and appends summary rows (summary_p25/p50/p75) so the whole result
can be faceted by `role` in a single ggplot call. Summary rows honor
per_capita + adjust_to_year by picking the right value column.
target_rank reports the target's rank among target+peers at max(years).
Provenance is rewritten with verb = cog_peer_compare and peer_count.
Also: globalVariables('.data') in zzz.R to silence R CMD check on
tidy-eval pronouns.
Tests: 21 new / 120 total pass. devtools::check() 0E/0W/2N.
Wraps cog_spending across a named list of state/county/city layers,
tagging each row with its `layer` and attaching a scope_note that
documents geographic-scope caveats (state totals are statewide, county
totals include areas outside a listed city, city proper excludes
special districts). Per-capita uses each layer's own population from
the canonical_fips_xwalk.
Provenance is inherited from cog_spending but rewritten to reflect
the outer verb (verb, call, layers).
Tests: 19 new / 99 total pass. devtools::check() 0E/0W/2N.
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).
Adds R/sysdata.rda with the annual-average CPIAUCSL index (1947-2026,
80 years, sourced from FRED) and an internal .inflate() helper that
converts nominal amounts between two years via the ratio of CPI values.
Bundling CPI in the package (rather than publishing a cpi_annual.parquet
in the corpus) matches the reader-specification intent: real-dollar
conversion is a verb-level option, not a corpus-level artifact, so the
target_year stays flexible at query time.
data-raw/cpi_annual.R carries the one-shot FRED refresh used to build
sysdata.rda. Re-run when the CPI series needs to roll forward.
Verified: CPI(2000)/CPI(2021) ≈ 0.635, matching the ~0.63 sanity
anchor in cog_explorer's existing inflation logic.
Tests: tests/testthat/test-adjust.R covers the known 2000→2021 ≈ 1.574x
anchor, vectorized from_year, error cases for out-of-range years, and
NA-amount preservation. 14 pass / 0 fail.
Seven DuckDB views register on session open: long (raw), spending_long
and revenue_long (prefix-filtered, NOT is_aggregate per reader-spec §4),
canonical_fips_xwalk and summary_categories (identity), spending_annotated
and revenue_annotated (LEFT JOIN xwalk + categories for verb composition).
File prefix `NN-` enforces creation order so *_annotated views resolve
their *_long dependencies.
Deviation from plan: series_breaks/cpi_annual/legacy_aggregate_map and
*_with_transforms views are deferred — their backing parquets are not
in the v0.1 corpus (manifest.files.metadata only lists canonical_fips_xwalk
and summary_categories). The verbs will compute CPI adjustment verb-side
against a bundled cpi table in a later task.
jared