feat(coverage): n_units_collected separates sampling from real zeros (#36)

provenance$coverage's n_units_reporting is category-conditional: it
counts governments with rows for the SPECIFIC requested category, which
conflates two different things -- a government never collected that
year (sampling), and one collected but genuinely spending nothing in
that category (a real zero). FY2012 Georgia Police is the motivating
case from the issue: a complete census year reads as a 69% "response
rate" because most of the gap is cities that contract policing to the
county sheriff, not non-response.

Adds a second counter, n_units_collected: how many of the caller's
expected cohort appear in the corpus that year for ANY category.
n_units_collected / n_units_expected is the true collection rate;
n_units_reporting / n_units_collected is category participation among
collected units. cog_geographic_rollup() and cog_peer_compare() both
carry it; cog_explain() prints it alongside n_units_reporting.

Two real bugs caught and fixed while finishing this (both against the
already-written, previously-uncommitted draft):

- .coverage_table()'s candidate list for the collection query was
  derived from the category-filtered result rows, not the caller's
  full expected cohort. A government with zero rows in the requested
  category across every requested year never appears in that result,
  so it was silently excluded from n_units_collected too -- collapsing
  the new counter back to the old, broken one for exactly the
  governments it exists to count. Fixed by threading an explicit
  `expected_ids` (all_govids / peer_govids) through instead.
- The collection query hardcoded long_view = "spending_long_harmonized",
  which does not exist on a corpus with schema_version < 5 (R/basis.R
  resolves basis = "raw" there; R/views.R only registers the
  harmonized views on v5+). cog_geographic_rollup()/cog_peer_compare()
  would hard-error on a corpus vintage the package otherwise explicitly
  supports. Fixed by deriving long_view from the basis cog_spending()
  actually resolved (prov$basis) via the existing .select_long_view()
  helper, matching how every other basis-aware query in the package
  already does this.

Also: cog_explain()'s general "complete census only in years ending in
2 or 7" footnote was gated on the OLD counter's absence, making it
permanently unreachable now that both callers always supply the new
one -- ungated it, since the explanation is orthogonal to which
counter set is present. Dropped a dead conditional branch, fixed two
stale roxygen blocks in R/peers.R/R/rollup.R still describing the old
two-counter model, fixed the same staleness in README.md, and switched
two `uscogdata:::` self-references to the package's own convention of
calling internal helpers unqualified.

1101 tests pass (2 skipped live-corpus), including new direct
regression tests for both bugs above (one exercising a government
collected-but-absent from a category result, one running the full
rollup/peer-compare path against a doctored schema_version 4 corpus).

Reviewed by an independent code-reviewer pass (1 HIGH, 1 MEDIUM, 3 LOW
-- all addressed above).

Closes #36.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

README.md

@@ -227,7 +227,8 @@ A statewide total resting on a fifth of the universe looks exactly like one
 resting on all of it, so every multi-government result now says which it is:
 
 ```r
-attr(rollup, "provenance")$coverage   # per-year n_units_reporting, is_census_year
+attr(rollup, "provenance")$coverage
+# per-year n_units_expected, n_units_collected, n_units_reporting, is_census_year
 ```
 
 `cog_geographic_rollup()`, `cog_peer_compare()` and `cog_find_peers()` take a
@@ -235,8 +236,14 @@ attr(rollup, "provenance")$coverage   # per-year n_units_reporting, is_census_ye
 `"consistent"` (only units reporting in every requested year, a balanced
 panel).
 
-`n_units_reporting` is **category-conditional**, and it is not a response rate. A government that was surveyed and genuinely spends
-nothing in the requested category is indistinguishable from one never surveyed.
+`n_units_reporting` is **category-conditional**: it counts governments with
+rows for the *specific* category you asked for, so a government that was
+surveyed and genuinely spends nothing in that category is indistinguishable
+from one never surveyed — it is not a response rate on its own.
+`n_units_collected` is the number that separates them: governments present in
+the corpus that year for *any* category. `n_units_collected / n_units_expected`
+is the true collection rate; `n_units_reporting / n_units_collected` is
+category participation among collected units.
 
 ### Absent cells mean two different things