jared
d95c9032c5
The Census of Governments is a complete census only in years ending in 2 and
7. Every other year is a sample, and the sample varies enormously. Neither
cog_geographic_rollup() nor cog_peer_compare()/cog_find_peers() had any
concept of "the universe": each summed or labelled whichever govids happened
to have rows and returned that with nothing distinguishing "every government
reported" from "a fifth of them did".
On the bundled fixture, Wisconsin's 608-city universe rolls up 597
governments in FY2012 and 112 in FY2019. The peer side is worse exposure, not
better: a Madison-scale cohort looks stable because Madison is large, while
governments matched to a small target sit in exactly the population band the
sample cycle hits hardest. Chilton's 15-peer cohort reports 15 of 15 in
FY2012 and 3 of 15 in FY2019.
Implements the owner's settled design: coverage = c("all", "census",
"consistent") on all three verbs, defaulting to "all" so nothing currently
calling them changes, PLUS always-on provenance$coverage carrying per-year
n_units_reporting / n_units_expected / is_census_year and
provenance$coverage_mode. cog_explain() prints a "Reporting coverage"
section. The default mode can no longer mislead silently, which is the point
-- using these verbs correctly must not require knowing the survey calendar.
Decisions worth stating:
- n_units_expected is the universe the CALLER named, not the national one.
That is what makes the ratio mean something: "597 of the 608 Wisconsin
cities you asked about". For peers it is the cohort size, counted over
peer rows only -- including the target would inflate every count by one
and make a cohort that has entirely stopped reporting look non-empty.
- The coverage table is built from the REQUESTED years, not the years
present in the result, so a year in which nothing reported still appears
with n_units_reporting = 0. A year that vanishes silently is precisely
the disclosure failure at issue.
- "census" filters years BEFORE the query, and aborts when the range holds
no census year rather than returning an empty result for a query the
caller believes they made.
- "consistent" exempts the peer-comparison target: it is the subject of the
comparison, not a member of the cohort being balanced, and dropping it
would leave nothing to compare. The summary_* quantiles are computed
AFTER the filter so they describe the cohort actually returned.
- is_census_year is documented as a statement about the survey CALENDAR,
never a claim of completeness -- FY1967 is a census year in which only 97
of Wisconsin's 608 cities report (DoD 3). n_units_reporting is the number
that tells the truth.
On cog_find_peers(), where there is no year range, coverage governs the
cohort VINTAGE: "census" snaps to the most recent census year with an
observed population, so a cohort is not built from a sample year in which
most of the candidate universe is absent. "consistent" is a comparison-time
concept and selects like "all" there, carried on the result for
cog_peer_compare().
One fix to the committed test, which was internally inconsistent. It pinned
n_units_reporting == 597 for FY2012 AND asserted that number equals a raw
cross-check that answers 595. Both numbers are right for different questions:
VERNON VILLAGE and WAUKESHA VILLAGE carry type = 3 in `long` (their
as-of-year identity, as townships) while the xwalk lists them as govs_type =
2 (their present identity, as villages) -- schema v6 made the long table's
geography present-harmonized but `type` still reads as-of-year. The rollup
counts against the requested govid set, so 597 answers "how many of the
governments I asked about reported". The cross-check now scopes to that same
universe instead of to long.type/long.fips_state; it still reads raw parquet
rather than going through the verb under test.
Suite: 670 pass / 0 fail / 2 skip (was 658/0/3). rcmdcheck clean.
The two remaining skips are #11 and #12.
uscogdata
Curated R reader for the Civilytics US Census of Governments finance corpus.
Provides unit-level financial profiles, geographic rollups, and peer comparisons with auditable provenance and built-in cross-vintage correctness. Reads the published corpus (Hive-partitioned parquet + manifest.json) directly from Nextcloud via DuckDB httpfs — no local bulk downloads required.
Status
Under active development (Phase 2 of the cog_pipeline project). See
../cog_pipeline/docs/reader-specification.md for the reader contract this
package implements.
Installation
# pak::pkg_install("gitea.civilytics.org/Civilytics/uscogdata")
Amounts are in full US dollars
Every amount column this package returns — amt_nominal, amt_real,
amt_per_capita_nominal, amt_per_capita_real — is in full US dollars.
The raw Census source files report thousands of dollars, and the corpus's
own amt column preserves that. The verbs multiply by 1000 on the way out, so
you never have to. The conversion is recorded in every result:
r <- cog_spending("552025209777", 2020L)
attr(r, "provenance")$transformations$units_conversion
#> $applied TRUE $source_unit "$1,000s (raw Census)" $target_unit "$USD" $multiplier 1000
Do not multiply again. If you have read elsewhere that COG amounts are in
$1,000s — true of the raw corpus, and of cog_explorer's conventions doc —
that rule does not apply to anything a cog_*() verb hands you. Applying it
twice overstates every figure by 1000x, and the result looks plausible rather
than obviously wrong.
Configuration
USCOGDATA_URL— corpus root URL (public Nextcloud share, trailing slash)USCOGDATA_CACHE_DIR— optional override for the manifest cache directoryUSCOGDATA_MANIFEST_TTL_SECS— optional manifest re-fetch TTL (default 3600)
Direct vs Total spending
cog_spending(..., expenditure_concept = c("direct", "total")) controls
whose spending a result counts. "direct" (the default) is a government's
own current operations, capital outlay, and other direct spending. "total"
additionally adds in the intergovernmental legs — money it hands to other
governments to spend on its behalf — which is meaningful for describing one
government's own budget over time, but double-counts when summed across
governments (a state's payment to a county is the same dollar the county
reports as its own direct spending).
Rule of thumb: any figure that spans more than one government uses
direct. cog_geographic_rollup() and cog_peer_compare() enforce this
by refusing expenditure_concept = "total". See
vignette("total-spending", package = "uscogdata") for the full
explanation with worked examples.
Developer notes
Testing
The package ships a bundled fixture corpus at inst/extdata/fixture_corpus/ —
a 15 MB four-year slice (2011, 2012, 2019, 2020) of the full corpus covering
all 50 states. tests/testthat/setup.R automatically points USCOGDATA_URL
at this fixture, so the full test suite runs offline with no network
dependency:
devtools::test() # uses bundled fixture, no credentials required
Releasing against the live corpus
Before cutting a release, run the test suite against the published corpus to catch any drift between the fixture and the real data:
Sys.setenv(USCOGDATA_URL = "<published-corpus-url-with-trailing-slash>")
devtools::test()
When the live-corpus run is clean, strip the fixture from the built package by
adding this line to .Rbuildignore:
^inst/extdata/fixture_corpus$
The test suite is URL-agnostic — setup.R falls back to USCOGDATA_URL when
the bundled fixture is absent, so no test code changes are needed for the
release run or after stripping the fixture.