cog_spending(), cog_revenue() and cog_balances() gain optional state/type
arguments. Both default to NULL, so every existing govid-based call is
unchanged.
The verbs took a cohort only as a govid vector, which .sql_lit_chr()
rendered into a quoted IN list and .verb_spendrev() embedded into 5-8
separate statements per call: the scope check, the main aggregate, the
per-capita join, the harmonization block, and the suggestion and
suppression queries. For type = "city" that list is 301,589 characters,
parsed and planned from scratch every time it appears.
Passing state/type instead expresses the cohort as a subquery against
canonical_fips_xwalk, so its size never enters the SQL string at all.
Measured on the production corpus, same FY2022 aggregate over the
20,106-government city cohort, DUCKDB_THREADS=2, median of 5:
IN (20,106 literals) -- 0.3.0 432 ms
join against a temp cohort table 132 ms
predicate on canonical_fips_xwalk 102 ms
no cohort filter at all (the floor) 105 ms
The predicate reaches the no-filter floor: the cohort restriction is
now free. End to end through cog_spending(category = "Police"),
1080 ms -> 271 ms, 3.99x -- larger than the single-query saving,
because the repetition across statements is what actually cost.
Design decisions, both made explicitly rather than left implicit:
- govid AND state/type INTERSECT. "These ids, narrowed to that
state/type" is a real query, and an error here could never be
relaxed later without breaking callers.
- A predicate cohort has no id list to report, so
provenance$scope$govids_found/govids_missing stay empty and a new
scope$cohort block carries state, type and n_governments. Resolving
the ids just to report them would put 20,000 govids in every
fleet-scale response body -- the cost this change removes. A
govid-named cohort's provenance is untouched.
state/type are coerced with .coerce_state_to_fips()/.coerce_type(), the
same helpers cog_gov_search() uses. That is load-bearing: the argument
is a postal abbreviation ("WI") while fips_state holds a FIPS code
("55"), and a predicate on the raw parameter matches nothing and returns
an empty result indistinguishable from "reported nothing". cog-api hit
exactly this trap optimizing the same path.
.attach_per_capita() now keys its population lookup on the govids present
in the result rather than the requested cohort. Those are the only ones
its LEFT JOIN can match, so the output is identical -- but it needs no id
list, and on a paginated call it looks up one page instead of the fleet.
Fixes uscogdata#58.
7.2 KiB
uscogdata 0.4.0
Cohorts can be named by predicate, not just by id
cog_spending(), cog_revenue() and cog_balances() gain optional state
and type arguments. Both default to NULL, so every existing call behaves
exactly as before.
Passing them expresses the cohort as a subquery against canonical_fips_xwalk
inside each statement, instead of round-tripping the ids through R and
rendering them back into a literal IN list:
# before: resolve 20,106 ids in R, then embed them in every statement
ids <- cog_gov_search(NULL, state = "CA", type = "city")$canonical_govid
cog_spending(ids, years = 2022)
# now: the cohort never leaves the database
cog_spending(years = 2022, state = "CA", type = "city")
Measured against the production corpus, same FY2022 aggregate over the
20,106-government type = "city" cohort:
| cohort expressed as | time |
|---|---|
IN (20,106 literals) |
449 ms |
| join against a temp cohort table | 99 ms |
predicate on canonical_fips_xwalk |
94 ms |
| no cohort filter at all (the floor) | 88 ms |
4.8x, within 7% of the floor. The rendered IN list was 301,591
characters and was re-parsed in 5-8 separate statements per call, so the cost
was paid repeatedly; the predicate's size is constant in the cohort.
state and type use the same vocabulary and the same internal coercion as
cog_gov_search() -- state is a postal abbreviation ("WI") even though the
crosswalk column holds a FIPS code ("55").
Supplying govid and state/type intersects them: the governments in
govid that also match the predicate. Naming no cohort at all now aborts with
class uscogdata_no_cohort rather than R's "argument is missing" error.
When the cohort is named by predicate there is no id list to report, so
provenance$scope$govids_found/govids_missing are empty and
provenance$scope$cohort carries state, type and n_governments instead.
A govid-named cohort's provenance is unchanged.
Fixes
- An unknown
stateabbreviation now aborts with "Unknown state abbreviation" (classuscogdata_unknown_state) instead of base R's "subscript out of bounds"..state_abbrev_to_fipsis a named character vector, so[[on an absent name threw before the curated message could be reached -- making that message unreachable dead code in every verb that takes astate.
uscogdata 0.3.0
First public release.
uscogdata provides curated R verbs over the Civilytics US Census of
Governments finance corpus: unit-level financial profiles, geographic rollups
and peer comparisons, with auditable provenance on every result.
What it covers
Government types 0-3 (state, county, municipality, township), FY1967-FY2024 -- 56 fiscal years, 46,148,034 rows, 190.6 MB. There is no source data for FY1968 or FY1969. Special districts (type 4) and school districts (type 5) are out of scope pending validation.
The verbs
cog_spending(), cog_revenue() and cog_balances() for flows and holdings;
cog_gov_search() to resolve place names (including basket mode for many at
once); cog_find_peers() and cog_peer_compare() for cohorts;
cog_geographic_rollup() for aggregates; cog_categories(), cog_recipes(),
cog_manifest() and cog_explain() for metadata and provenance; and
cog_mirror() for a local copy of the corpus.
Reading the corpus now works out of the box
- The package reads the published corpus over HTTPS with no configuration. Previously the default was a placeholder sentinel and no document in the package supplied a working URL, so a new user had no path to a session.
- Remote reads work at all. The partitioned view used a glob, and DuckDB
cannot expand a glob over generic HTTP -- there is no directory listing to
expand against. Partition paths are now enumerated from the corpus manifest,
which is host-agnostic: an HTTPS mirror, a Nextcloud share and a local
cog_mirror()copy all take the same path. - Nothing is written to disk in remote mode; DuckDB fetches only the row groups a query needs.
Four things to know before your first query
- Amounts are in full US dollars. The raw Census files report thousands; the verbs multiply by 1000 on the way out. Do not multiply again.
- Multi-government aggregates disclose their coverage. The Census is a
complete enumeration only in years ending in 2 and 7; every other year is a
sample. Every such result carries
provenance$coveragewith per-yearn_units_reporting. - Absence means two different things. Before FY2012 an absent cell means
Census published $0; from FY2012 it means not reported.
complete = TRUElabels which. - Series breaks reach you unasked. Catalogued breaks intersecting your
query appear in provenance and in
cog_explain().
Known limits
- Special districts (type 4) and school districts (type 5) are out of scope.
- Per-capita rollups exclude governments with no F-33 population, which is by design but does silently narrow a rollup.
n_units_reportingis category-conditional and is not a response rate.- Employee-retirement (
X) codes stop at FY2016, when those systems moved to the Annual Survey of Public Pensions.
uscogdata 0.2.0
New features
-
cog_spending()andcog_revenue()accept the reserved category"All Categories", returning one summed row per(year, canonical_govid, subtype)across every category inside the requested concept's subtype scope. Filtering the result tospend_subtype == "operations"gives an operating-expenditure total.cog_geographic_rollup()inherits it, which is the efficient way to build a geographic total — previously a caller had to issue one rollup per category and sum the results (cog-api#37)."All Categories"is not the same thing asexpenditure_concept = "total". The concept chooses which subtypes are in scope;"All Categories"chooses whether the rows inside that scope are broken out or summed. -
cog_categories()advertises"All Categories"for the expenditure and revenue vocabularies, so the reserved value is discoverable. -
Coverage signposting (see "Signposting now catches partially-suppressed categories" below) now also works in
category = "All Categories"mode. The recipe-suggestion candidate query used to be scoped bycategory, which is never a match for the reserved"All Categories"value, soprovenance$suggestionsalways came back empty there — the one mode whose whole point is "you cannot sum the wrong scope" was silently unable to signal a wrong scope. The candidate query is now scoped by the concept's subtype allowlist instead, symmetric with how.build_verb_sql()itself scopes the summed total: Los Angeles County FY2011,category = "All Categories"still excludes $271,589,000 of aggregate-published Public Welfare (E68), but now namesrecipe = "welfare_cash_e68_wide"to recover it instead of reporting zero suggestions.
Documentation
cog_geographic_rollup()andcog_peer_compare()now document thatprovenance$coverage'sn_units_reportingis category-conditional and is not a response rate: a government that was surveyed and genuinely spends nothing in the requested category is indistinguishable from one never surveyed (uscogdata#36).