Merge pull request 'feat(coverage): n_units_collected separates sampling from real zeros (#36)' (#71) from fix/coverage-collected-36 into main

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>

.Rbuildignore

@@ -3,17 +3,18 @@
 ^\.Rproj\.user$
 ^_pkgdown\.yml$
 ^docs$
+^pm$
+^Meta$
+^doc$
 ^pkgdown$
 ^\.github$
 ^LICENSE\.md$
 ^\.git$
 ^\.gitignore$
 \.gitkeep$
-^vignettes$
 ^specs$
 ^plans$
-^doc$
-^Meta$
 ^\.gitea$
 ^CLAUDE\.md$
 ^\.superpowers$
+^CONTRIBUTING\.md$

.gitea/workflows/ci.yml

@@ -11,6 +11,21 @@ jobs:
     steps:
       - name: Install system libraries and Node.js (required by actions/checkout)
         run: |
+          # Switch apt to HTTPS mirrors. Measured from this runner on
+          # 2026-08-04: the SAME index file takes 20.1s over http:// and 3.1s
+          # over https://. apt fetches many indexes serially, so http:// does
+          # not read as "slow" -- it reads as a hang (zero bytes in
+          # /var/cache/apt/archives after 3+ minutes, apt's http workers parked
+          # in S state). rocker/r-ver:4.4 already ships ca-certificates and
+          # apt 2.8.3 has the https method built in, so nothing needs to be
+          # installed over http first to bootstrap this.
+          # `|| true` because the step runs under `sh -e`: on an image whose
+          # sources live in the other location, the missing-file sed must not
+          # kill the job.
+          sed -i -E 's#http://(archive|security)\.ubuntu\.com#https://\1.ubuntu.com#g' \
+            /etc/apt/sources.list.d/ubuntu.sources 2>/dev/null || true
+          sed -i -E 's#http://(archive|security)\.ubuntu\.com#https://\1.ubuntu.com#g' \
+            /etc/apt/sources.list 2>/dev/null || true
           apt-get update -qq
           apt-get install -y --no-install-recommends \
             nodejs git \

.gitea/workflows/mirror-github.yml

@@ -0,0 +1,55 @@
+# Mirror the canonical Gitea repo to the public GitHub mirror.
+#
+# Deliberately a plain `git push`, NOT Gitea's built-in push mirror. A push
+# mirror force-updates the refs it owns: if anyone ever clicks Merge on a
+# GitHub PR, the next sync silently overwrites main, the PR still displays
+# "Merged", the commit becomes unreachable, and nothing anywhere says so.
+# A non-force push is REJECTED as non-fast-forward the moment that happens,
+# turning a silent data-loss trap into a red CI run in a place we already look.
+#
+# Do NOT add --force here, and do NOT add GitHub branch protection to the
+# mirror: protection rules block the mirror's legitimate pushes too, breaking
+# normal syncing to catch an abnormal case.
+#
+# PAT_GH is a GitHub personal access token (repo + workflow scope; workflow is
+# required because this pushes .github/workflows/). It is stored as a Gitea
+# Actions secret. The name cannot begin with GITHUB_ or GITEA_ -- Gitea
+# reserves both prefixes for its own injected variables and rejects the secret.
+name: Mirror to GitHub
+
+on:
+  push:
+    branches: [main]
+    tags: ['v*']
+
+jobs:
+  mirror:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Push main and tags to the GitHub mirror
+        env:
+          PAT_GH: ${{ secrets.PAT_GH }}
+        run: |
+          set -eu
+          if [ -z "${PAT_GH:-}" ]; then
+            echo "PAT_GH is unset -- add it under Settings > Actions > Secrets." >&2
+            exit 1
+          fi
+          # On a tag-triggered run, checkout materializes refs/tags/<tag> as a
+          # LIGHTWEIGHT tag at the commit SHA -- the annotated tag object Gitea
+          # holds is never fetched. Mirroring that strips the annotation, and the
+          # NEXT run on main (which does fetch the real object) is then rejected
+          # with "already exists" trying to correct it, because git will not
+          # clobber an existing tag. That is why v0.4.0 failed to mirror.
+          #
+          # Re-fetch canonical tag objects from Gitea first. --force here rewrites
+          # LOCAL tag refs only; it is not a force push and does not weaken the
+          # non-force guarantee on main documented above.
+          git fetch --tags --force origin
+
+          git push "https://x-access-token:${PAT_GH}@github.com/civilytics/uscogdata.git" \
+            HEAD:refs/heads/main --tags

.github/workflows/R-CMD-check.yaml

@@ -0,0 +1,61 @@
+# Multi-platform R CMD check, running on the GitHub mirror.
+#
+# This exists because the canonical Gitea runner is Linux-only, and this
+# package hard-depends on duckdb and httr2 -- both compiled, both with real
+# platform variance -- while having never been checked on Windows or macOS.
+# A large share of the audience is on Windows.
+#
+# Gitea reads .gitea/workflows and GitHub reads .github/workflows, so this
+# file is inert on the canonical repo and coexists with the Gitea CI that
+# remains authoritative for deploys.
+#
+# The suite needs NO credentials: tests/testthat/setup.R points USCOGDATA_URL
+# at the bundled fixture corpus. That is exactly why inst/extdata/fixture_corpus
+# must never be added to .Rbuildignore.
+on:
+  push:
+    branches: [main]
+  pull_request:
+
+name: R-CMD-check
+
+permissions: read-all
+
+jobs:
+  R-CMD-check:
+    runs-on: ${{ matrix.config.os }}
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - {os: macos-latest,   r: 'release'}
+          - {os: windows-latest, r: 'release'}
+          - {os: ubuntu-latest,  r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,  r: 'release'}
+
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: ${{ matrix.config.r }}
+          http-user-agent: ${{ matrix.config.http-user-agent }}
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::rcmdcheck
+          needs: check
+
+      - uses: r-lib/actions/check-r-package@v2
+        with:
+          upload-snapshots: true
+          build_args: 'c("--no-manual")'

.github/workflows/pr-welcome.yaml

@@ -0,0 +1,64 @@
+# Explain the mirror contribution flow on every incoming pull request.
+#
+# This repository is a MIRROR. A PR opened here is landed on the canonical Gitea
+# repository and syncs back; because the merge preserves the contributor's
+# commits at their original SHAs, GitHub marks the PR "Merged" on its own as
+# soon as the mirror syncs -- with nobody visibly clicking Merge.
+#
+# Without this comment, that reads as a rejection: the contributor sees their PR
+# close with no review, no merge button pressed, and no explanation. It is
+# actually the successful outcome. Say so up front, before it happens.
+#
+# WHY pull_request_target AND NOT pull_request:
+# a `pull_request` run from a fork gets a read-only token, so it cannot post a
+# comment -- which is exactly the case this workflow exists to serve.
+# `pull_request_target` runs in the context of the BASE repo and gets a writable
+# token. That is only safe because this job never checks out or executes the
+# contributor's code; it posts a fixed string. Do not add a checkout of
+# `github.event.pull_request.head.sha` here -- that combination is the standard
+# pull_request_target privilege-escalation hole.
+name: Explain the mirror flow
+
+on:
+  pull_request_target:
+    types: [opened]
+
+permissions:
+  pull-requests: write
+
+jobs:
+  comment:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Post the contribution-flow explainer
+        uses: actions/github-script@v7
+        with:
+          script: |
+            const body = [
+              "Thanks for this — and one thing worth knowing before it happens.",
+              "",
+              "**This repository is a mirror.** Development happens on Gitea at",
+              "`gitea.civilytics.org/Civilytics/uscogdata`. Your pull request will be fetched",
+              "from here, landed there, and synced back.",
+              "",
+              "Because that merge preserves your commits at their original SHAs, **GitHub will",
+              "mark this pull request \"Merged\" on its own** — without anyone visibly clicking",
+              "the Merge button, and possibly without a review comment on this page first.",
+              "",
+              "> If your pull request closes as \"Merged\" and nobody appears to have merged it,",
+              "> that is the normal, successful outcome — not a rejection.",
+              "",
+              "If it is *not* going to be merged, you will get an actual reply saying so.",
+              "",
+              "Substantial contributions get a `ctb` entry in `DESCRIPTION`, which surfaces in",
+              "`citation(\"uscogdata\")`. There is no CLA and no DCO sign-off.",
+              "",
+              "Full details: [CONTRIBUTING.md](https://github.com/civilytics/uscogdata/blob/main/CONTRIBUTING.md).",
+            ].join("\n");
+
+            await github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: context.payload.pull_request.number,
+              body,
+            });

.gitignore

@@ -4,6 +4,10 @@
 .Ruserdata
 *.Rproj
 inst/doc
+# pkgdown output. Compass used to keep its files in docs/pm/ and
+# docs/decisions/, which forced this to be written as children with two
+# re-includes -- git cannot re-include anything beneath an excluded directory.
+# Compass lives in pm/ now, so the whole directory can be excluded again.
 docs/
 /doc/
 /Meta/
@@ -12,3 +16,6 @@ docs/
 
 # SDD working artifacts (ledger, briefs, review packages) — plans/ stays tracked
 .superpowers/sdd/
+.compass-cache/
+# roborev snapshots
+/.roborev/

.roborev.toml

@@ -0,0 +1,60 @@
+# roborev configuration, initialised by compass.
+# Reviews are queued to a background daemon -- they never block a commit.
+
+post_commit_review = 'commit'
+excluded_commit_patterns = ['WIP', 'chore:', 'chore(', 'docs:', 'Merge ']
+
+review_guidelines = '''
+# --- compass:begin (generated -- edit the sources, not this) ---
+- Prefer returning new values to mutating arguments in place. A function that edits
+  its caller's object is a bug waiting for a second caller.
+- Validate at system boundaries -- user input, API responses, file contents, config.
+  Fail fast with a message naming the field and the file.
+- Never swallow an error. Handle it or let it propagate; a bare catch that continues
+  is worse than a crash.
+- No hardcoded secrets, tokens, or credentials, and no secrets in log output or error
+  messages.
+- Parameterise every query. String-built SQL is a defect even when the input looks safe.
+- Keep functions under roughly 50 lines and files under roughly 400. Flag nesting
+  deeper than four levels.
+- No magic numbers or hardcoded paths -- name them as constants or read them from config.
+- New behaviour needs a test. A bug fix needs a test that fails without the fix.
+- Prose a person reads -- an issue title or body, a journal entry, a decision record,
+  the narrative on the status board -- names the action or the thing, not the shape of
+  the machinery. Flag "gate", "seam", "surface area", "load-bearing", "first-class",
+  "primitive", "blast radius". A project's own defined vocabulary is not the target.
+- Use the native pipe `|>`, not magrittr `%>%`.
+- snake_case for objects and functions; UPPER_SNAKE for constants. Never use `.` as a
+  word separator in a function name -- it collides with S3 dispatch.
+- Validate arguments at the top of exported functions with `stopifnot()` or an explicit
+  check, and say which argument was wrong.
+- Never `setDT()`, `set()`, or otherwise modify by reference a data.table the caller
+  still owns. `as.data.table()` copies; use it.
+- Prefer `vapply()` to `sapply()` -- `sapply()` silently returns a list when the type
+  varies, which turns a type error into a downstream mystery.
+- Use `seq_len(n)` / `seq_along(x)`, never `1:n`, which iterates backwards when n is 0.
+- Compare strings with `==` only after checking for NA; use `identical()` for scalars
+  where NA would be wrong.
+- Do not call `library()` inside package or module files; attach packages in scripts and
+  test helpers only.
+- Namespace-qualify calls into other packages (`stats::sd`) in code that is sourced.
+- Every exported function needs roxygen with `@param` for each argument (type, meaning,
+  and why the default is what it is) and `@return`. Add `@examples` for exported API.
+- Declare dependencies in DESCRIPTION. Prefer base R or an existing dependency over
+  adding a new one; a package with zero hard deps is worth keeping that way.
+- Signal errors with `stop()` carrying a condition class, so callers can catch the kind
+  rather than matching on message text.
+- Keep internals internal. Export only what a user needs; an accidentally exported
+  helper becomes an API you have to keep.
+- Tests use testthat edition 3. Each test is self-sufficient -- no reliance on state
+  left by an earlier test or on a fixture built elsewhere in the file.
+- Prefer duplication in tests over a helper that hides what is being asserted.
+- Every verb calls .ensure_session() first, then queries via DBI::dbGetQuery().
+- A verb's return value is always a tbl_df carrying a provenance attribute.
+- govid inputs always go through .coerce_govid_input(); it accepts a character vector or a data frame.
+- SQL has two layers: view definitions are numbered .sql files in inst/sql/ registered by .register_views(); query construction is inline sprintf() in R. Add a view as a file; build a query in R.
+- No arrow dependency -- DuckDB reads parquet natively.
+- withr is Suggests-only and must appear in tests alone.
+- Tests must pass offline against the bundled fixture; tests/testthat/setup.R sets USCOGDATA_URL for that.
+# --- compass:end ---
+'''

CLAUDE.md

@@ -124,3 +124,10 @@ devtools::test()
   a numbered `.sql` file; build a query in R.
 - No arrow dependency — DuckDB reads parquet natively
 - `withr` is a Suggests-only dep; only used in tests
+
+## Domain context — read this first
+
+**Before doing any work in this repo, read `~/.claude/memory/values/civilytics.md`.**
+It carries the purpose, direction, and constraints for this domain. It is not optional
+context — read it before planning or writing code, not after. (An `@` import will not
+work here; project-level imports don't preload. The read is the mechanism.)

CONTRIBUTING.md

@@ -0,0 +1,113 @@
+# Contributing to uscogdata
+
+Thanks for reading this — a package like this gets better mostly through people
+noticing that a number looks wrong.
+
+## Where the code lives
+
+Development happens on **Gitea**, at
+`gitea.civilytics.org/Civilytics/uscogdata`. The repository at
+`github.com/civilytics/uscogdata` is a **mirror** that accepts issues and pull
+requests.
+
+## What happens to a GitHub pull request
+
+Open it normally. Behind the scenes it is fetched and landed on the canonical
+Gitea repository, then syncs back:
+
+```sh
+git fetch github refs/pull/42/head:pr-42
+git switch main && git merge --no-ff pr-42
+git push origin main            # Gitea -> mirror -> GitHub
+```
+
+Because the merge preserves your commits at their original SHAs, **GitHub marks
+your PR merged on its own** as soon as the mirror syncs. So:
+
+> If your pull request closes as "Merged" without anyone visibly clicking
+> Merge, that is the normal, successful outcome — not a rejection.
+
+Substantial contributions get a `ctb` entry in `DESCRIPTION`, which surfaces in
+`citation("uscogdata")`.
+
+There is no CLA and no DCO sign-off requirement.
+
+## Running the tests
+
+```r
+devtools::test()   # bundled fixture; no network, no credentials
+```
+
+`tests/testthat/setup.R` points `USCOGDATA_URL` at
+`inst/extdata/fixture_corpus/` automatically — a four-year slice (2011, 2012,
+2019, 2020) covering all 50 states. That is the whole data setup.
+
+## Testing against the live corpus
+
+```sh
+USCOGDATA_LIVE_TEST=true Rscript -e 'devtools::test(filter = "live-corpus")'
+```
+
+This is worth understanding rather than skipping. Until 0.3.0 the package
+**could not read a remote corpus at all** — the partitioned view used a glob,
+and DuckDB cannot expand a glob over generic HTTP. It went unnoticed for months
+because every test path used a local corpus (the bundled fixture), and so did
+the production API (a host mount). Nothing exercised the package the way a new
+user does.
+
+`test-live-corpus.R` is the only test that runs with no `USCOGDATA_URL`, no
+option, and no fixture. If you change anything touching view registration,
+manifest handling, or configuration, run it.
+
+## Do not exclude the fixture from the build
+
+There is a temptation to add `^inst/extdata/fixture_corpus$` to
+`.Rbuildignore` because 15 MB feels large for a package. Don't:
+
+- `vignette("total-spending")` reads from it and would fail to build.
+- `R CMD check` on r-universe and GitHub Actions would have no corpus, so the
+  suite could not run without credentials.
+
+This package is not going to CRAN, so its 5 MB guidance does not apply. A
+package-size NOTE in `R CMD check` is expected and acceptable.
+
+## Downstream consumers
+
+`cog-api` depends on this package and its CI clones uscogdata at
+`USCOGDATA_REF`, **defaulting to `main`**. There is no pin. Anything merged
+here reaches the API's next build, so before merging a change to the reader,
+run the API suite against your branch:
+
+```sh
+Rscript -e "remotes::install_local('/path/to/uscogdata', upgrade = 'never')"
+cd /path/to/cog-api/api/tests/testthat
+Rscript -e 'testthat::test_dir(".", stop_on_failure = TRUE)'
+```
+
+The API calls only exported verbs, so internal refactors are usually safe —
+but "usually" is not a release gate.
+
+## Release checklist
+
+1. `devtools::test()` — green against the bundled fixture, offline.
+2. `USCOGDATA_LIVE_TEST=true devtools::test()` — green against the live corpus.
+3. cog-api suite green against this branch (above).
+4. `devtools::check(args = "--as-cran")` — 0 errors, 0 warnings.
+5. `pkgdown::build_site()` completes.
+6. Vignettes resolve from an installed copy:
+   `vignette("total-spending", package = "uscogdata")`.
+7. **Cold-start check**: on a machine that has never had this package,
+   install it and run the README quickstart verbatim with no environment
+   variables set. This is the only check that catches a
+   corpus-unreachable defect, and its absence is why 0.3.0 needed fixing.
+8. Bump `Version` and add a `NEWS.md` section.
+9. Tag on **Gitea** (`git tag -a vX.Y.Z && git push origin vX.Y.Z`). The mirror
+   workflow carries tags to GitHub on its own — confirm the tag appears at
+   `github.com/civilytics/uscogdata/tags` before continuing.
+10. Update the r-universe registry pin at
+    `github.com/civilytics/civilytics.r-universe.dev` — edit `packages.json`'s
+    `branch` to the new tag. **r-universe will not pick up a release until this
+    is edited**: the pin is a tag, deliberately, so a mid-refactor `main` is
+    never published as a release. `"branch": "*release"` would track releases
+    automatically, but it needs a GitHub *Release* object and the mirror pushes
+    tags only — so it would silently never update.

DESCRIPTION

@@ -1,14 +1,21 @@
 Package: uscogdata
 Type: Package
 Title: Curated Reader for the Civilytics US Census of Governments Finance Corpus
-Version: 0.1.0
-Authors@R:
-    person("Civilytics", , , "jknowles@gmail.com", role = c("aut", "cre"))
+Version: 0.4.0
+Authors@R: c(
+    person(c("Jared", "E."), "Knowles",
+           email = "jared@civilytics.com",
+           role = c("aut", "cre"),
+           comment = c(ORCID = "0000-0003-0005-9478")),
+    person("Civilytics Consulting LLC", role = c("cph", "fnd")))
 Description: Curated R verbs over 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.
 License: MIT + file LICENSE
+URL: https://github.com/civilytics/uscogdata,
+    https://civilytics.r-universe.dev/uscogdata
+BugReports: https://github.com/civilytics/uscogdata/issues
 Encoding: UTF-8
 LazyData: false
 Depends: R (>= 4.1)
@@ -32,4 +39,4 @@ Config/testthat/edition: 3
 VignetteBuilder: knitr
 RoxygenNote: 7.3.3
 MinCorpusSchema: 4
-MaxCorpusSchema: 5
+MaxCorpusSchema: 7

LICENSE

@@ -1,2 +1,2 @@
 YEAR: 2026
-COPYRIGHT HOLDER: Civilytics
+COPYRIGHT HOLDER: Civilytics Consulting LLC

LICENSE.md

@@ -0,0 +1,21 @@
+# MIT License
+
+Copyright (c) 2026 Civilytics Consulting LLC
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

NEWS.md

@@ -1,225 +1,230 @@
-# uscogdata 0.1.0 (development)
+# uscogdata 0.4.0
 
-## New: `cog_balances()` for cash-and-security holdings
+## Cohorts can be named by predicate, not just by id
 
-* New `cog_balances()` exposes the 14 cash-and-security holding codes
-  (`category_type = "balance"`): fund balances, retirement system holdings and
-  insurance trust balances (#25). Holdings are a stock, not a flow, so the verb
-  has no `expenditure_concept` / `revenue_concept` / `complete` arguments, and
-  no `subtype` argument either -- for holdings, `category` is a strict
-  coarsening of `balance_subtype`, so `category = "Fund Balances"` is exactly
-  the `general` family (`W01`/`W31`/`W61`).
-* `cog_balances()` results carry `provenance$balance_caveats`, recording that
-  Census holdings are gross rather than GAAP fund balance, and the measured
-  coverage window of each subtype family.
+`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.
 
-## Multi-government aggregates now disclose their reporting coverage
+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:
 
-* 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. On
-  the bundled fixture, Wisconsin's 608-city universe rolls up **597**
-  governments in FY2012 and **112** in FY2019 — an 18%-to-98% swing the
-  return value said nothing about, so a statewide total resting on a fifth of
-  the universe looked exactly like one resting on all of it.
-* `cog_geographic_rollup()`, `cog_peer_compare()` and `cog_find_peers()` gain
-  `coverage`:
+```r
+# 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)
 
-  | value | effect |
-  |---|---|
-  | `"all"` (default) | every unit that reported that year — unchanged behaviour |
-  | `"census"` | census years only; aborts if the range holds none rather than returning nothing |
-  | `"consistent"` | only units reporting in *every* requested year — a balanced panel |
+# now: the cohort never leaves the database
+cog_spending(years = 2022, state = "CA", type = "city")
+```
 
-* **Regardless of mode**, every result now carries `provenance$coverage` with
-  per-year `n_units_reporting`, `n_units_expected` and `is_census_year`, plus
-  `provenance$coverage_mode`. `cog_explain()` prints a "Reporting coverage"
-  section. So the default mode can no longer mislead silently.
-* `is_census_year` is 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. `n_units_reporting` is the number that tells the truth.
-* On `cog_peer_compare()` the target is exempt from `"consistent"` balancing —
-  it is the subject of the comparison, not a member of the cohort — and the
-  `summary_*` quantiles are computed after the filter, so they describe the
-  cohort actually returned. `n_units_reporting` counts peers only, against the
-  cohort size.
-* On `cog_find_peers()`, `coverage` governs the cohort **vintage** when `year`
-  is `NULL`: `"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.
+Measured against the production corpus, same FY2022 aggregate over the
+20,106-government `type = "city"` cohort:
 
-## `complete = TRUE`: absent cells, labelled with why they are absent
+| 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 |
 
-* `cog_spending()` and `cog_revenue()` gain `complete`, defaulting to `FALSE`
-  (today's behaviour). With `complete = TRUE` the requested grid is filled
-  from the corpus's `code_set` table and every row carries a new
-  `value_source` column:
+**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.
 
-  | `value_source` | meaning | `amt_nominal` |
-  |---|---|---|
-  | `reported` | the corpus carries this cell | as published |
-  | `census_zero` | dense-source year (≤ FY2011), cell absent — Census published `$0` | `0` |
-  | `not_reported` | sparse-source year (≥ FY2012), cell absent — unknown | `NA` |
+`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"`).
 
-  The `NA` is deliberate and is the whole point: filling a modern absence
-  with `0` would invent data, which is precisely the error the corpus's
-  representation contract exists to prevent.
-* This restores information the reader lost when the corpus was sparsified
-  (`SB194`, cog_pipeline#64) — a wide-era query whose cells were all `$0`
-  had begun returning nothing at all — and improves on what came before it,
-  since the pre-sparsification corpus could not distinguish a published zero
-  from an unreported cell either.
-* The grid is scoped to each government's **own type**, so a county is never
-  filled with cells only a state can report.
-* Needs a corpus published from 2026-07-29 onward (when `representation` and
-  `code_set` began shipping); aborts with class
-  `uscogdata_representation_unavailable` otherwise. Gated on the manifest
-  listing those tables rather than on `schema_version`, which was never
-  bumped for the change. Not available with `recipe` or
-  `expenditure_concept = "total"` — neither draws its cells from `code_set`.
-* `provenance$completion` reports `applied`, `rows_filled`, and the per-year
-  `absence_means` rule; `cog_explain()` prints a "Completion" section.
+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.
 
-## Corpus-wide series breaks now reach users (`corpus_break_refs`)
+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.
 
-* Four catalogued series breaks carry `fin_code = "ALL"` — caveats about the
-  corpus as a whole rather than about one item code. `series_break_refs` is
-  built by matching `fin_code` against the item codes in the result, and no
-  row's `item_code` is ever the literal `"ALL"`, so **none of them could ever
-  be surfaced**: `SB085` (dollar precision across the 1976/1977 boundary),
-  `SB087` (imputation exclusion from FY2002), `SB194` (the dense → sparse
-  representation change at FY2012) and `SB086` (the government id scheme
-  change at FY2017).
-* Provenance gains `corpus_break_refs`, selected on the break-year window
-  alone and disjoint from `series_break_refs` by construction, so a consumer
-  can tell a whole-result caveat from a break in one series. `cog_explain()`
-  prints them under their own "Corpus-wide caveats" heading. cog-api passes
-  provenance through verbatim, so the field appears there without an API
-  change.
-* `SB194` is the one that made this urgent: a query spanning FY2011 → FY2012
-  crosses the boundary where an absent cell stops meaning "Census published
-  `$0`" and starts meaning "not reported", and until now nothing said so.
+## `cog_gov_search()` and `cog_balances()` gain `limit`/`offset`
 
-## Bundled fixture regenerated against the sparsified corpus
+Pagination arrived on `cog_spending()`/`cog_revenue()` in 0.3.0; the other two
+verbs were left materializing everything and slicing in R. Both now take
+`limit`/`offset` with the same semantics: `NULL` default, the page applied in
+SQL behind a deterministic `ORDER BY`, and the unpaginated count returned as a
+`total_rows` attribute computed by `COUNT(*) OVER()` in the same scan rather
+than a second query.
 
-* `inst/extdata/fixture_corpus/` now tracks the corpus published on
-  2026-07-29 (`pipeline_commit 83f9715`, schema v6). The wide era no longer
-  stores explicit zeros: FY2011 fell from 2,864,212 rows to 496,004, of
-  which none are `$0`. **Absence now means two different things** — in a
-  `dense_source` year (≤ FY2011) an absent cell means Census published `$0`;
-  in a `sparse_source` year (≥ FY2012) it means not reported. The corpus
-  carries that rule in two new tables the fixture now ships,
-  `representation.parquet` and `code_set.parquet`, alongside
-  `census_collection_coverage.parquet` and `lineage_events.parquet`
-  (all ten publish-tree metadata tables, up from six). Catalogued upstream
-  as series break `SB194`.
-* `cog_categories()` gains an `assistance` spending subtype: the J-prefix
-  aid/benefit codes (`J19`, `J67`, `J68`, `J85`) are categorised now that
-  the upstream crosswalk covers every flow code carrying dollars.
-* Two consequences worth knowing about, both visible in provenance rather
-  than in returned dollars. The harmonization block's `na_rows_excluded`
-  counts only rows that exist, so wide-era codes that were zero-padded no
-  longer appear there. Coverage-gap `suggestions` are presence-based for the
-  same reason, so a recipe whose component codes were all `$0` for a given
-  government-year is no longer suggested for it.
-* `tests/testthat/test-fixture-vintage.R` pins these structural facts, so a
-  fixture left behind by a future publish fails loudly instead of letting the
-  suite pass against a corpus that no longer exists.
+`cog_gov_search()` had no `LIMIT` at all, which made it the one verb that
+returns the entire 40,336-row crosswalk when called with no filter.
 
-## Breaking: corpus schema_version 4 (Phase P canonical ids)
+Two refusals rather than silent surprises:
 
-* The package now requires corpus `schema_version = 4` (`MinCorpusSchema` /
-  `MaxCorpusSchema` in `DESCRIPTION` are both `4`); older corpora built
-  against schema 3 are rejected by `cog_open()` with a clear version-mismatch
-  error. `canonical_govid` is now uniformly 12 characters across every
-  vintage the corpus covers (previously a mix of 9-char legacy ids and
-  12-char FIPS ids depending on source year) — **every hardcoded
-  `canonical_govid` literal from a pre-Phase-P corpus is now invalid** and
-  must be re-resolved via `cog_gov_search()` or the new `canonical_alias`
-  lookup table. `canonical_fips_xwalk` gains four columns
-  (`legacy_govs_id`, `census_geoid`, `id_source`; `confidence` is renamed to
-  `pop_confidence`) and a companion `canonical_alias` table ships in the
-  corpus for mapping legacy/alternate ids onto the current canonical
-  namespace. The bundled fixture corpus (`inst/extdata/fixture_corpus/`) has
-  been regenerated against the Phase P publish tree, now ships the full
-  `canonical_fips_xwalk` and `canonical_alias` master tables alongside the
-  2019-2020 long partitions, and is reproducible via
-  `data-raw/regenerate_fixture_corpus.R`.
+* `cog_balances(recipe = , limit = )` aborts with class
+  `uscogdata_recipe_pagination_conflict` -- a recipe's result comes from a
+  separate query that pagination is not wired into.
+* `cog_gov_search()` in basket mode (`length(name) > 1`) aborts with class
+  `uscogdata_basket_pagination_conflict`. Basket mode returns one resolved row
+  per requested name with a sidecar covering all of them; a page of that is not
+  a page of anything the caller asked for.
 
-## Clearer errors when `USCOGDATA_URL` is unconfigured or returns non-JSON
+## DuckDB's resource budget is configurable
 
-* `cog_open()` now aborts with the `uscogdata_url_not_configured` error
-  class when the resolved corpus URL still contains the placeholder
-  `REPLACE_WITH_SHARE_TOKEN` sentinel (or is empty). The message lists both
-  remediation paths (`Sys.setenv(USCOGDATA_URL = ...)` and
-  `options(uscogdata.url = ...)`) and points at the bundled fixture for
-  offline testing. Previously the package proceeded to fetch the placeholder
-  URL, cached the resulting HTML welcome page, and failed downstream with a
-  cryptic `jsonlite` lexical-error.
-* `.fetch_or_cache_manifest()` now parses the HTTP response body before
-  persisting it. Non-JSON responses (login pages, 404 HTML) raise
-  `uscogdata_invalid_manifest` with the URL, Content-Type, and underlying
-  parse error — and never write to the on-disk cache.
-* Manifest cache writes are now atomic (write to `manifest.json.tmp.<pid>`
-  in `cache_dir`, then `file.rename` over the target), so an interrupted
-  fetch cannot replace a previously-good cache.
-* Existing caches with non-JSON content (poisoned by the prior code path)
-  are silently refetched instead of returning a parse error to the caller.
-* Local `USCOGDATA_URL` paths whose `manifest.json` is not valid JSON now
-  surface the same `uscogdata_invalid_manifest` class with file context.
+`USCOGDATA_DUCKDB_THREADS` and `USCOGDATA_DUCKDB_MEMORY_LIMIT` (with matching
+`options(uscogdata.duckdb_threads = )` / `options(uscogdata.duckdb_memory_limit = )`
+spellings) cap the DuckDB connection the package opens. Both follow the same
+env-var > option > default precedence as `USCOGDATA_URL`.
 
-## Per-capita denominators now use per-year Census F-33 population
+Unset, **no pragma is issued at all** and DuckDB's own defaults apply exactly as
+before -- every visible core. That is right for one interactive session on a
+dedicated machine and wrong for a server: where several readers share a host, each
+otherwise claims the whole machine and they contend. Capping measured ~5% on a
+single-government all-years query (502 ms at 2 threads vs 475 ms uncapped on 16
+cores), which is cheap enough that a server should always cap.
 
-* `cog_spending()` and `cog_revenue()` previously divided all years' amounts
-  by a single ACS 2018-2022 estimate (`canonical_fips_xwalk.population_acs`),
-  producing biased per-capita values for time-series analysis. They now
-  divide by the F-33 `population` recorded on each gov-year via the new
-  `gov_population_yearly` view. Result tibbles gain a `pop_source` column
-  with values `"census_f33"` or `"unavailable"`. `notes` is updated to
-  concatenate multiple notes with `"; "`.
+This replaces a workaround in which a consumer reached into the package namespace
+at boot -- `getFromNamespace(".ensure_session", "uscogdata")()` followed by a manual
+`SET threads` -- depending both on a private name and on the session already being
+open.
 
-## Peer cohorts can be set to a chosen year
+## Documentation: the corpus-access table is re-measured and honest
 
-* `cog_find_peers()` adds a `year` argument (default: most recent year for
-  which the target has an observed population in `gov_population_yearly`).
-  The returned column previously named `population_acs` is now `population`
-  and reflects the cohort year's vintage. The cohort year is attached to the
-  returned tibble as `attr(x, "cohort_year")`.
-* `cog_peer_compare()` now stamps a `cohort_year` column on its result (read
-  from the peers tibble's attribute) and records `cohort_year` plus
-  `cohort_govids` in provenance. When the caller supplies a bare character
-  vector instead of a `cog_find_peers()` result, `cohort_year` is `NA`.
+The README's "two ways to read the corpus" table carried figures taken before
+the corpus was re-chunked into row groups (cog_pipeline#93, published
+2026-08-09) and reported the mirrored column as "local speed" with no number at
+all. Re-measured 2026-08-10 against the published corpus (`pipeline_commit
+3d28ddd`), fresh R session per arm:
 
-## Rollups exclude govs missing population
+* **A local mirror is roughly 60-80x faster.** A one-off question costs ~12 s
+  end to end remotely against ~0.15 s mirrored. That is the largest single
+  difference available to a user and it is now stated outright rather than left
+  as "local speed".
+* **Opening the session is the largest remote cost** (~7.5 s -- manifest fetch
+  plus 23 view registrations over HTTPS), larger than any individual query, and
+  it lands on the first query rather than on `library(uscogdata)`. The old table
+  did not account for it anywhere.
+* **The remote cost is round-trips, not scanning.** A repeat query over
+  already-touched partitions is ~1.5 s against ~4 s cold, and a full-history
+  query costs ~7 s whether it runs first or last.
+* The corpus size is **~201 MB**, not 190.6 MB -- row-group chunking added ~3.4%
+  and the old figure was ambiguous between MB and MiB besides.
+* Documented that a burst of remote queries can be rate-limited by the host
+  (`HTTP 429`), which is another reason to mirror for real work.
 
-* `cog_geographic_rollup(per_capita = TRUE)` drops rows whose government has
-  `pop_source == "unavailable"` and records the dropped govids in
-  `provenance$rollup$excluded_govids`. This excludes special districts
-  (type 4) and school districts (type 5) from per-capita rollups by design.
+## Fixes
 
-## New: vignette and provenance metadata
+* `cog_gov_search()` now orders by `population_acs DESC NULLS LAST,
+  canonical_govid`. **`population_acs` alone is not a total order** -- ties, and
+  the entire `NULLS LAST` block, came back in whatever order the scan produced.
+  That was invisible while every call returned the full result set, but it makes
+  a paged sweep unsound: two requests can order tied rows differently, so a row
+  is duplicated on one page and missing from the next. Unpaginated results are
+  unchanged except for the relative order of rows that were already tied.
 
-* New vignette `population-denominators` covers the four population sources,
-  the type-4/5 coverage gap, the popyear quirk, and how to build moving-window
-  peer cohorts manually.
-* Provenance gains `transformations$per_capita$popyear_range` and
-  `pop_source_counts`. `cog_explain()` renders both.
+* An unknown `state` abbreviation now aborts with "Unknown state abbreviation"
+  (class `uscogdata_unknown_state`) instead of base R's "subscript out of
+  bounds". `.state_abbrev_to_fips` is 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 a `state`.
+
+# 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$coverage` with per-year
+  `n_units_reporting`.
+* **Absence means two different things.** Before FY2012 an absent cell means
+  Census published $0; from FY2012 it means not reported. `complete = TRUE`
+  labels 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_reporting` is 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_gov_search()` gains a **basket mode**: passing vector `name`
-  / `state` / `type` arguments resolves multiple place names in one
-  call and returns a tibble of canonical rows in input order, ready
-  to pipe into `cog_spending()` / `cog_revenue()`. Per-row resolution
-  follows an exact-then-substring matching algorithm with deterministic
-  disambiguation; ambiguous and missing entries are surfaced via a
-  sidecar audit tibble plus a single console summary message.
-* New exports `cog_basket_resolution()` and `cog_basket_unresolved()`
-  expose the basket sidecar for iterative query refinement.
+* `cog_spending()` and `cog_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 to
+  `spend_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).
 
-## Breaking changes
+  `"All Categories"` is not the same thing as `expenditure_concept = "total"`.
+  The concept chooses which subtypes are in scope; `"All Categories"` chooses
+  whether the rows inside that scope are broken out or summed.
 
-* The first formal of `cog_gov_search()` was renamed from `pattern`
-  to `name`. All existing call sites in `cog_explorer/` and the
-  package itself use positional first-arg, so this rename is
-  non-breaking in practice. Callers that pass `pattern = ...` by name
-  must update to `name = ...`.
+* `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 by `category`,
+  which is never a match for the reserved `"All Categories"` value, so
+  `provenance$suggestions` always 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 $271,589,000 of aggregate-published Public
+  Welfare (`E68`), but now names `recipe = "welfare_cash_e68_wide"` to
+  recover it instead of reporting zero suggestions.
+
+## Documentation
+
+* `cog_geographic_rollup()` and `cog_peer_compare()` now document that
+  `provenance$coverage`'s `n_units_reporting` is **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).

R/balances.R

@@ -18,7 +18,9 @@
 #' comparable to a GAAP fund balance from an ACFR.
 #'
 #' @param govid Canonical govid(s): a character vector, or a data frame with a
-#'   `canonical_govid` column (e.g. from [cog_gov_search()]).
+#'   `canonical_govid` column (e.g. from [cog_gov_search()]). `NULL` to name
+#'   the cohort by `state`/`type` instead.
+#' @inheritParams cog_spending
 #' @param years Integer vector of fiscal years.
 #' @param category Optional character vector of categories to keep. One of
 #'   `"Fund Balances"`, `"Insurance Trust Balances"`,
@@ -28,7 +30,12 @@
 #'   every combination would be either redundant or empty.
 #'   `category = "Fund Balances"` is exactly the `general` family
 #'   (`W01`/`W31`/`W61`). `balance_subtype` is returned, so a finer split is
-#'   one `dplyr::filter()` away.
+#'   one `dplyr::filter()` away. The reserved pseudo-category
+#'   `"All Categories"` (see [cog_spending()]) is **not** supported here and
+#'   errors with class `uscogdata_all_categories_unsupported`: it sums a
+#'   concept's subtype scope, and holdings are a stock with no concept
+#'   vocabulary to sum across. Omit `category` to get every category broken
+#'   out instead.
 #' @param per_capita Divide holdings by population. Note this is a **stock per
 #'   resident** (reserves per person), which is *not* comparable to
 #'   [cog_spending()]'s per-capita figures -- those are a flow per person.
@@ -40,6 +47,12 @@
 #' @param recipe Optional harmonization recipe id (see [cog_recipes()]).
 #'   `"cash_securities_z77_wide"` and `"cash_securities_z78_wide"` bridge the
 #'   wide era to the modern one.
+#' @param limit Maximum number of result rows to return, pushed into the SQL
+#'   rather than applied after materializing every row. `NULL` (default)
+#'   returns everything. Cannot be combined with `recipe` -- see `offset` and
+#'   `total_rows`.
+#' @param offset Rows to skip before `limit` starts counting (0-based).
+#'   Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.
 #'
 #' @return Tibble with columns `year`, `canonical_govid`, `gov_name`,
 #'   `balance_subtype`, `category`, `amt_nominal`, `codes_included`,
@@ -57,16 +70,22 @@
 #'   and `truncated` (the observed subtypes whose coverage falls short of the
 #'   requested years). `expenditure_concept`/`revenue_concept` are `NA` --
 #'   holdings are a stock, not a flow, so neither concept vocabulary applies.
+#'
+#'   When `limit` is set, also carries a `total_rows` attribute: the full
+#'   unpaginated row count, computed by the same query (`COUNT(*) OVER()`)
+#'   rather than a second scan.
 #' @export
-cog_balances <- function(govid, years, category = NULL,
+cog_balances <- function(govid = NULL, years, category = NULL,
                          per_capita = FALSE, adjust_to_year = NULL,
-                         basis = c("harmonized", "raw"), recipe = NULL) {
+                         basis = c("harmonized", "raw"), recipe = NULL,
+                         state = NULL, type = NULL,
+                         limit = NULL, offset = NULL) {
   call <- match.call()
   basis <- match.arg(basis, c("harmonized", "raw"))
   # Coerce FIRST, validate second: .validate_verb_inputs() asserts
   # is.character(govid), and a data-frame govid (cog_gov_search() output) has
   # not been unwrapped yet at this point.
-  govid <- .coerce_govid_input(govid)
+  govid <- if (is.null(govid)) NULL else .coerce_govid_input(govid)
   # The money verbs' validator, reused rather than re-implemented (R/spending.R).
   # It covers the exact superset cog_balances() needs -- including the
   # recipe/category mutual-exclusivity guard -- so a second local copy would
@@ -74,11 +93,36 @@ cog_balances <- function(govid, years, category = NULL,
   # helper reuse as .build_verb_sql()/.attach_per_capita() below; it does NOT
   # route the verb through .verb_spendrev(), which stays deliberately unused
   # here because its flow vocabulary is meaningless for a stock.
+  #
+  # allow_all_categories is left at its FALSE default (contrast
+  # .verb_spendrev(), which passes TRUE): the all-categories mode's "sum"
+  # only means something in terms of a concept's subtype scope, and holdings
+  # have no concept vocabulary. The reuse above is exactly why this can be a
+  # one-line default rather than a second bespoke check -- see the
+  # validator's own doc comment for the incident that made that matter.
   .validate_verb_inputs(govid, years, category, per_capita, adjust_to_year,
                         recipe)
+
+  # Same semantics as the money verbs (R/pagination.R). Only the `recipe`
+  # conflict applies here: cog_balances() has no `complete` argument, and a
+  # recipe's result comes from .run_recipe()'s own query, which pagination is
+  # not wired into.
+  paging <- .validate_pagination(limit, offset)
+  limit  <- paging$limit
+  offset <- paging$offset
+  if (!is.null(limit) && !is.null(recipe)) {
+    cli::cli_abort(c(
+      "`limit`/`offset` cannot be combined with `recipe`.",
+      "i" = "A recipe's result comes from a separate query (`.run_recipe()`) that pagination is not wired into yet.",
+      "*" = "Drop `limit`/`offset`, or drop `recipe`."
+    ), class = "uscogdata_recipe_pagination_conflict")
+  }
+
   years <- as.integer(years)
   if (!is.null(adjust_to_year)) adjust_to_year <- as.integer(adjust_to_year)
 
+  cohort <- .make_cohort(govid, state, type)
+
   con <- .ensure_session()
   .require_balance_support(con)
   scope <- .check_govids_in_scope(govid)
@@ -91,13 +135,14 @@ cog_balances <- function(govid, years, category = NULL,
   manifest <- .uscogdata_env$manifest
   recipe_block <- NULL
   category_for_prov <- category
+  total_rows <- NULL  # set below only when limit is non-NULL (non-recipe path)
 
   if (!is.null(recipe)) {
     .require_schema_v5(con, manifest, "recipe =")
     .validate_recipe_id(con, recipe)
     comps <- .recipe_components(con, recipe)
     recipe_label <- comps$label[[1]]
-    result <- .run_recipe(con, recipe, govid, years)
+    result <- .run_recipe(con, recipe, cohort, years)
     sql <- attr(result, "sql_query")
     result <- .shape_recipe_result(result, "balance_subtype", recipe_label)
     recipe_block <- list(
@@ -107,15 +152,25 @@ cog_balances <- function(govid, years, category = NULL,
     category_for_prov <- recipe_label
   } else {
     sql <- .build_verb_sql("balance_annotated", "balance_subtype",
-                           govid, years, category,
-                           ig_view = NULL, subtype_scope = NULL)
+                           cohort, years, category,
+                           ig_view = NULL, subtype_scope = NULL,
+                           limit = limit, offset = offset)
     result <- tibble::as_tibble(DBI::dbGetQuery(con, sql))
+    if (!is.null(limit)) {
+      paged <- .take_pagination_total(result, con, function() {
+        .build_verb_sql("balance_annotated", "balance_subtype",
+                        cohort, years, category,
+                        ig_view = NULL, subtype_scope = NULL)
+      })
+      result     <- paged$result
+      total_rows <- paged$total_rows
+    }
   }
 
   # Order matters (matches .verb_spendrev()): per-capita first, so
   # .attach_real_dollars() deflates the nominal per-capita column into
   # amt_per_capita_real rather than needing amt_per_capita_nominal recomputed.
-  if (isTRUE(per_capita)) result <- .attach_per_capita(result, con, govid)
+  if (isTRUE(per_capita)) result <- .attach_per_capita(result, con)
   if (!is.null(adjust_to_year)) {
     result <- .attach_real_dollars(result, adjust_to_year, per_capita)
   }
@@ -133,6 +188,7 @@ cog_balances <- function(govid, years, category = NULL,
   )
   prov$scope$govids_found   <- scope$found
   prov$scope$govids_missing <- scope$missing
+  prov$scope$cohort <- .cohort_provenance(con, cohort)
 
   prov$balance_caveats <- .balance_caveats(
     con, prov$codes_summed$observed, years
@@ -140,6 +196,7 @@ cog_balances <- function(govid, years, category = NULL,
   .emit_balance_caveats(prov$balance_caveats)
 
   attr(result, "provenance") <- prov
+  if (!is.null(limit)) attr(result, "total_rows") <- total_rows
   result
 }
 

R/basis.R

@@ -54,7 +54,7 @@
 #' expenditure_concept = "total": ig_long_harmonized COALESCEs rather than
 #' drops NULL-harmonized rows, so harmonization never excludes an IG row.
 #' @noRd
-.build_harmonization_block <- function(con, govid, years, resolved,
+.build_harmonization_block <- function(con, cohort, years, resolved,
                                        subtype_col, subtype_scope) {
   if (!identical(resolved$basis, "harmonized")) {
     return(list(
@@ -68,12 +68,12 @@
   sql <- sprintf(
     "SELECT COUNT(*) AS n, COALESCE(SUM(amt), 0) * 1000.0 AS amt
      FROM long
-     WHERE canonical_govid IN (%s) AND year IN (%s)
+     WHERE %s AND year IN (%s)
        AND NOT is_aggregate AND harmonized_code IS NULL
        AND item_code IN (
          SELECT item_code FROM summary_categories WHERE %s IN (%s)
        )",
-    .sql_lit_chr(govid), paste(as.integer(years), collapse = ","),
+    .cohort_sql(cohort), paste(as.integer(years), collapse = ","),
     subtype_col, .sql_lit_chr(subtype_scope)
   )
   na <- DBI::dbGetQuery(con, sql)

R/categories.R

@@ -22,7 +22,10 @@
 #'   `category` column (e.g. `"Police"` or `"Tax"`).
 #' @return Tibble with columns `category`, `category_type`, `subtype`,
 #'   `n_codes`, `item_codes` (comma-separated, alphabetical). Sorted by
-#'   `category_type`, `category`, `subtype`.
+#'   `category_type`, `category`, `subtype`. Includes one row per flow for the
+#'   reserved pseudo-category `"All Categories"`, which carries `NA` for
+#'   `subtype`, `n_codes` and `item_codes` because it is a query mode rather
+#'   than a crosswalk entry — see [cog_spending()]'s `category` argument.
 #' @export
 cog_categories <- function(type = NULL, pattern = NULL) {
   if (!is.null(type)) {
@@ -63,5 +66,28 @@ cog_categories <- function(type = NULL, pattern = NULL) {
     "GROUP BY category, category_type, subtype
      ORDER BY category_type, category, subtype"
   )
-  tibble::as_tibble(DBI::dbGetQuery(con, sql))
+  out <- tibble::as_tibble(DBI::dbGetQuery(con, sql))
+
+  # The reserved pseudo-category is a query mode, not a crosswalk row, so it
+  # has no item codes to report -- hence NA rather than 0 for n_codes. It is
+  # emitted for the two FLOW vocabularies only: cog_balances() returns a stock
+  # and has no concept argument to sum within.
+  pseudo <- tibble::tibble(
+    category      = .ALL_CATEGORIES,
+    category_type = c("expenditure", "revenue"),
+    subtype       = NA_character_,
+    n_codes       = NA_integer_,
+    item_codes    = NA_character_
+  )
+  if (!is.null(type)) {
+    db_type <- if (type == "spending") "expenditure" else type
+    pseudo <- pseudo[pseudo$category_type == db_type, , drop = FALSE]
+  }
+  if (!is.null(pattern) && nrow(pseudo) > 0L) {
+    keep <- grepl(pattern, pseudo$category, ignore.case = TRUE)
+    pseudo <- pseudo[keep, , drop = FALSE]
+  }
+  if (nrow(pseudo) == 0L) return(out)
+  out <- rbind(out, pseudo)
+  out[order(out$category_type, out$category, out$subtype), , drop = FALSE]
 }

R/cohort.R

@@ -0,0 +1,127 @@
+# How a verb names the set of governments it queries.
+#
+# Historically there was one way: a `govid` character vector, rendered by
+# .sql_lit_chr() into a quoted IN list. That is fine for a handful of
+# governments and pathological for a fleet. Measured against the production
+# corpus, the 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, and within 7% of the no-filter floor. The rendered IN list is 301,591
+# characters and .verb_spendrev() embeds it in 5-8 separate statements per
+# call, so the parse-and-plan cost is paid over and over (uscogdata#58).
+#
+# A cohort therefore has two independent halves, and a query can carry either
+# or both:
+#
+#   ids        an explicit canonical_govid vector      -> literal IN list
+#   predicate  state/type over canonical_fips_xwalk    -> IN (SELECT ...)
+#
+# Both together is an INTERSECTION -- "these ids, narrowed to that state/type"
+# -- never a precedence rule where one silently wins.
+
+#' Build the internal cohort object shared by every query verb.
+#'
+#' `state` and `type` are coerced with the SAME helpers `cog_gov_search()`
+#' uses. That is load-bearing, not tidiness: the public argument is a postal
+#' abbreviation (`"WI"`) while `canonical_fips_xwalk.fips_state` holds a FIPS
+#' code (`"55"`), and `type` is a label (`"city"`) against an integer
+#' `govs_type`. A predicate written against the raw parameter matches nothing
+#' and returns an empty result indistinguishable from "this government
+#' reported nothing" -- cog-api hit exactly that trap optimizing this path.
+#' One definition of the translation, not two.
+#'
+#' @param govid Already-coerced character vector of canonical_govids, or NULL.
+#' @param state Postal abbreviation or FIPS code, or NULL.
+#' @param type Type label or integer code, or NULL.
+#' @noRd
+.make_cohort <- function(govid = NULL, state = NULL, type = NULL) {
+  if (is.null(govid) && is.null(state) && is.null(type)) {
+    cli::cli_abort(c(
+      "A cohort must be named.",
+      "*" = "Pass {.arg govid} for specific governments, or {.arg state}/{.arg type} for every government matching a predicate.",
+      "i" = "Passing both intersects them: the governments in {.arg govid} that also match {.arg state}/{.arg type}."
+    ), class = "uscogdata_no_cohort")
+  }
+  structure(
+    list(
+      ids        = govid,
+      state      = state,
+      type       = type,
+      state_fips = if (is.null(state)) NULL else .coerce_state_to_fips(state),
+      type_int   = if (is.null(type)) NULL else .coerce_type(type)
+    ),
+    class = "uscogdata_cohort"
+  )
+}
+
+#' Is any part of this cohort expressed as an xwalk predicate?
+#' @noRd
+.cohort_by_predicate <- function(cohort) {
+  !is.null(cohort$state_fips) || !is.null(cohort$type_int)
+}
+
+#' Render the cohort as a SQL boolean expression over `col`.
+#'
+#' `col` may be qualified (`"l.canonical_govid"`, `"x.canonical_govid"`) --
+#' several call sites join the xwalk under an alias. The subquery's own
+#' projected column stays unqualified: it selects from canonical_fips_xwalk,
+#' not from the outer relation.
+#' @noRd
+.cohort_sql <- function(cohort, col = "canonical_govid") {
+  preds <- character(0)
+
+  if (!is.null(cohort$ids)) {
+    preds <- c(preds, sprintf("%s IN (%s)", col, .sql_lit_chr(cohort$ids)))
+  }
+
+  if (.cohort_by_predicate(cohort)) {
+    xwalk_preds <- character(0)
+    if (!is.null(cohort$state_fips)) {
+      xwalk_preds <- c(xwalk_preds,
+                       sprintf("fips_state = %s", .sql_lit_chr(cohort$state_fips)))
+    }
+    if (!is.null(cohort$type_int)) {
+      xwalk_preds <- c(xwalk_preds, sprintf("govs_type = %d", cohort$type_int))
+    }
+    preds <- c(preds, sprintf(
+      "%s IN (SELECT canonical_govid FROM canonical_fips_xwalk WHERE %s)",
+      col, paste(xwalk_preds, collapse = " AND ")
+    ))
+  }
+
+  paste(preds, collapse = " AND ")
+}
+
+#' How many governments the cohort covers.
+#'
+#' One COUNT against the crosswalk, used only to populate the provenance
+#' `scope$cohort` block. Deliberately a count rather than the id list: a
+#' fleet-scale cohort would otherwise put 20,000 ids into every response body,
+#' which is the cost this issue exists to remove.
+#' @noRd
+.cohort_count <- function(con, cohort) {
+  sql <- sprintf(
+    "SELECT COUNT(*) AS n FROM canonical_fips_xwalk WHERE %s",
+    .cohort_sql(cohort)
+  )
+  as.integer(DBI::dbGetQuery(con, sql)$n[[1]])
+}
+
+#' The provenance `scope$cohort` block for a predicate cohort, or NULL when
+#' the cohort was named by id alone (in which case `govids_found`/
+#' `govids_missing` already describe it exactly).
+#' @noRd
+.cohort_provenance <- function(con, cohort) {
+  if (!.cohort_by_predicate(cohort)) return(NULL)
+  list(
+    state         = if (is.null(cohort$state)) NA_character_ else as.character(cohort$state),
+    type          = if (is.null(cohort$type)) NA_character_ else as.character(cohort$type),
+    n_governments = .cohort_count(con, cohort)
+  )
+}

R/complete.R

@@ -57,7 +57,7 @@
 #' aggregate rows. Without it the grid would offer cells the verb structurally
 #' never returns, so every one of them would fill as a phantom $0.
 #' @noRd
-.completion_grid_sql <- function(subtype_col, govid, years, category,
+.completion_grid_sql <- function(subtype_col, cohort, years, category,
                                  subtype_scope) {
   category_pred <- if (is.null(category)) {
     ""
@@ -76,13 +76,13 @@
      JOIN canonical_fips_xwalk x ON x.govs_type = cs.type
      JOIN summary_categories c   ON c.item_code = cs.item_code
      JOIN representation r       ON r.year = cs.year
-     WHERE x.canonical_govid IN (%2$s)
+     WHERE %2$s
        AND cs.year IN (%3$s)
        AND NOT cs.is_aggregate
        AND c.category IS NOT NULL
        AND c.%1$s IN (%4$s)
        %5$s",
-    subtype_col, .sql_lit_chr(govid),
+    subtype_col, .cohort_sql(cohort, "x.canonical_govid"),
     paste(as.integer(years), collapse = ","),
     .sql_lit_chr(subtype_scope), category_pred
   )
@@ -94,10 +94,10 @@
 #' provenance block. Reported rows are passed through untouched -- filling
 #' must never alter or drop what the corpus actually published.
 #' @noRd
-.complete_result <- function(result, con, subtype_col, govid, years, category,
+.complete_result <- function(result, con, subtype_col, cohort, years, category,
                              subtype_scope) {
   grid <- tibble::as_tibble(DBI::dbGetQuery(
-    con, .completion_grid_sql(subtype_col, govid, years, category, subtype_scope)
+    con, .completion_grid_sql(subtype_col, cohort, years, category, subtype_scope)
   ))
 
   result$value_source <- rep("reported", nrow(result))

R/config.R

@@ -5,9 +5,28 @@
 .uscogdata_env <- new.env(parent = emptyenv())
 
 .uscogdata_defaults <- list(
-  url               = "https://cloud.civilytics.org/s/REPLACE_WITH_SHARE_TOKEN/download/",
+  # Public HuggingFace mirror of the published corpus: CC-BY-4.0, no
+  # credential, CDN-backed. This is the default so `library(uscogdata)`
+  # followed by a verb works with zero configuration -- previously the
+  # default was a REPLACE_WITH_SHARE_TOKEN sentinel and no document in the
+  # package supplied a working URL, so a new user had no path to a session.
+  #
+  # The trailing slash is required: every consumer concatenates onto this
+  # (see .resolve_url(), which enforces it anyway).
+  #
+  # Override with USCOGDATA_URL or options(uscogdata.url=) to read a
+  # Nextcloud share or a local copy made by cog_mirror().
+  url               = "https://huggingface.co/datasets/civilytics/us-cog-finance/resolve/main/",
   cache_dir         = NULL,
-  manifest_ttl_secs = 3600L
+  manifest_ttl_secs = 3600L,
+  # NULL means "emit no pragma", which leaves DuckDB's own defaults intact:
+  # every visible core, and 80% of RAM. That is right for one interactive
+  # session on a dedicated machine and wrong for a server, where several
+  # readers share a box and each would otherwise claim all of it. See
+  # .resolve_duckdb_threads() for why this is a supported option rather than
+  # something a consumer reaches into the namespace to set.
+  duckdb_threads      = NULL,
+  duckdb_memory_limit = NULL
 )
 
 #' Resolve a config value: env var > option > default
@@ -50,3 +69,54 @@
   v <- .cfg("cache_dir")
   if (is.null(v)) tools::R_user_dir("uscogdata", "cache") else v
 }
+
+#' Resolve the DuckDB thread cap, or NULL to leave DuckDB's default alone.
+#'
+#' `cog_open()` used to connect with a bare `dbConnect()` and set no `threads`
+#' pragma, so DuckDB claimed every core it could see. cog-api works around that
+#' by reaching into this namespace at boot --
+#' `getFromNamespace(".ensure_session", "uscogdata")()` followed by a manual
+#' `SET threads` -- which depends on a private name and on the session already
+#' being open. Making it a resolved option removes the reason to do that.
+#'
+#' `.cfg()` returns an environment variable as CHARACTER, so this coerces
+#' rather than trusting the type: `USCOGDATA_DUCKDB_THREADS=4` arrives as "4",
+#' and `sprintf("SET threads TO %d", "4")` would abort inside the connection
+#' path with an error about the pragma rather than about the setting.
+#' @noRd
+.resolve_duckdb_threads <- function() {
+  v <- .cfg("duckdb_threads")
+  if (is.null(v) || (is.character(v) && !nzchar(v))) return(NULL)
+  n <- suppressWarnings(as.integer(v))
+  if (length(n) != 1L || is.na(n) || n < 1L) {
+    cli::cli_abort(c(
+      "{.envvar USCOGDATA_DUCKDB_THREADS} must be a single positive integer.",
+      x = "Got {.val {v}}.",
+      i = "Unset it (or {.code options(uscogdata.duckdb_threads = NULL)}) to use DuckDB's default of every visible core."
+    ), class = "uscogdata_invalid_duckdb_threads")
+  }
+  n
+}
+
+#' Resolve the DuckDB memory limit, or NULL to leave DuckDB's default alone.
+#'
+#' The value is a DuckDB size string (`"4GB"`, `"512MB"`). Only its SHAPE is
+#' checked here -- DuckDB owns the unit vocabulary, and re-implementing that
+#' parse would be a second definition free to drift from the engine's. An
+#' unrecognised unit therefore surfaces as DuckDB's own error at `SET` time,
+#' which names the setting correctly; the check here exists to reject the
+#' inputs that would otherwise reach the connection as a SQL fragment.
+#' @noRd
+.resolve_duckdb_memory_limit <- function() {
+  v <- .cfg("duckdb_memory_limit")
+  if (is.null(v) || (is.character(v) && !nzchar(v))) return(NULL)
+  if (length(v) != 1L || !is.character(v) ||
+      !grepl("^[0-9]+(\\.[0-9]+)?\\s*[A-Za-z]{0,3}$", v)) {
+    cli::cli_abort(c(
+      "{.envvar USCOGDATA_DUCKDB_MEMORY_LIMIT} must be a single DuckDB size string.",
+      x = "Got {.val {v}}.",
+      i = "Examples: {.val 4GB}, {.val 512MB}, {.val 1.5GB}."
+    ), class = "uscogdata_invalid_duckdb_memory_limit")
+  }
+  trimws(v)
+}

R/coverage.R

@@ -84,22 +84,92 @@
 #' `n_units_reporting = 0`, which is precisely the disclosure a silently
 #' missing year fails to make.
 #'
-#' `n_units_reporting` describes the result the caller actually received, so
-#' under `coverage = "consistent"` it reports the balanced count. `is_census_year`
-#' is 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.
-#' `n_units_reporting` is the number that tells the truth.
+#' Three counters are returned, each answering a different question:
+#'
+#'   * `n_units_expected` -- the universe the caller named (govids passed in,
+#'     or peers for cog_peer_compare). "How many governments did you ask
+#'     about?"
+#'   * `n_units_collected` -- how many of those appear in the corpus at all
+#'     that year, in ANY category. This is a statement about survey collection,
+#'     independent of what was asked for: "of the governments you named, how
+#'     many did Census actually collect data from this year?" It separates
+#'     sampling (not collected) from real zeros (collected but spends nothing
+#'     in your category).
+#'   * `n_units_reporting` -- how many of those appear with rows for the
+#'     SPECIFIC category you requested. This is always <= n_units_collected:
+#'     a government can be collected but have no rows for "Police" because it
+#'     contracts policing to the county sheriff, not because it wasn't
+#'     surveyed.
+#'
+#' `n_units_reporting` therefore conflates two very different things: a unit
+#' that was not collected (sampling) and a unit that was collected but spends
+#' nothing in that category. The ratio n_units_collected / n_units_expected is
+#' the true collection rate; n_units_reporting / n_units_collected measures
+#' category participation among collected units.
+#'
+#' `is_census_year` is 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. The counters are what tell the truth.
+#'
+#' @param con Active DuckDB connection (used to look up n_units_collected).
+#' @param long_view The verb's own long view, used for the collection query;
+#'   NULL skips the lookup and leaves n_units_collected as NA_integer_.
+#' @param expected_ids The full EXPECTED cohort (govids the caller named),
+#'   used as the candidate list for the collection query. Required alongside
+#'   `con`/`long_view` for a correct count -- see the note below on why it
+#'   must not be derived from `result`/`rows`. `NULL`, or non-`NULL` but
+#'   empty after dropping `NA`/`""` entries, skips the lookup and leaves
+#'   n_units_collected as NA_integer_.
 #' @noRd
 .coverage_table <- function(result, years, n_expected,
-                            id_col = "canonical_govid", rows = NULL) {
+                            id_col = "canonical_govid", rows = NULL,
+                            con = NULL, long_view = NULL,
+                            expected_ids = NULL) {
   years <- sort(unique(as.integer(years)))
   src <- if (is.null(rows)) result else rows
   reporting <- vapply(years, function(y) {
     ids <- src[[id_col]][as.integer(src$year) == y]
     length(unique(ids[!is.na(ids)]))
   }, integer(1))
+
+  # n_units_collected: count EXPECTED cohort members present in the corpus
+  # for ANY category that year, not just the requested one. This separates
+  # sampling (not collected at all) from real zeros (collected but no rows
+  # for this category). Only computed when a connection, long_view, AND
+  # expected_ids are all provided; otherwise NA_integer_.
+  #
+  # The candidate list MUST be expected_ids, not derived from `result`/
+  # `rows`: a government with zero rows in the requested category across
+  # EVERY requested year never appears in `result` at all, so deriving
+  # candidates from it would silently exclude exactly the "collected but
+  # real zero" governments this counter exists to count -- collapsing
+  # n_units_collected back to n_units_reporting for precisely the case #36
+  # was filed over.
+  if (!is.null(con) && !is.null(long_view) && length(expected_ids) > 0L) {
+    cohort_chr <- .sql_lit_chr(unique(expected_ids[!is.na(expected_ids) &
+                                                       nzchar(expected_ids)]))
+    years_lit <- paste(years, collapse = ",")
+    collected_q <- sprintf(
+      "SELECT year, COUNT(DISTINCT canonical_govid) AS n
+       FROM %s
+       WHERE canonical_govid IN (%s)
+         AND year IN (%s)
+       GROUP BY year",
+      long_view, cohort_chr, years_lit
+    )
+    collected_df <- DBI::dbGetQuery(con, collected_q)
+    collected_map <- setNames(collected_df$n, as.integer(collected_df$year))
+    collected <- vapply(years, function(y) {
+      val <- collected_map[as.character(y)]
+      if (is.na(val)) 0L else as.integer(val)
+    }, integer(1))
+  } else {
+    collected <- rep(NA_integer_, length(years))
+  }
+
   tibble::tibble(
     year              = years,
+    n_units_collected = collected,
     n_units_reporting = as.integer(reporting),
     n_units_expected  = rep(as.integer(n_expected), length(years)),
     is_census_year    = .is_census_year(years)

R/explain.R

@@ -11,6 +11,30 @@
 #'   returns `result` invisibly for chaining. `"list"` returns the raw
 #'   provenance list (identical to `attr(result, "provenance")`).
 #' @return Either `result` (invisibly) or the provenance list.
+#' @section Two kinds of series break:
+#' Catalogued breaks reach you without being asked for, in two disjoint
+#' fields, because a caveat about one series and a caveat about the whole
+#' corpus are different claims:
+#'
+#' * **`series_break_refs`** — breaks matched against the item codes actually
+#'   present in this result. A break in one code you queried.
+#' * **`corpus_break_refs`** — breaks catalogued with `fin_code = "ALL"`,
+#'   which are statements about the corpus rather than about any one code:
+#'   dollar precision across the 1976/1977 boundary (`SB085`), imputation
+#'   exclusion from FY2002 (`SB087`), the FY2012 dense-to-sparse
+#'   representation change (`SB194`), and the FY2017 government-identifier
+#'   change (`SB086`). These are selected on the break-year window alone.
+#'
+#' `SB194` is the one most likely to matter: a query spanning FY2011 to FY2012
+#' crosses the boundary where an absent cell stops meaning "Census published
+#' $0" and starts meaning "not reported".
+#' @section Other provenance blocks:
+#' `transformations$units_conversion` records the `$1,000s`-to-dollars
+#' multiply that every amount column has already had applied.
+#' `transformations$per_capita` records the population denominator and its
+#' year range. `coverage` and `coverage_mode` appear on multi-government
+#' results (see [cog_geographic_rollup()]). `completion` appears when
+#' `complete = TRUE`. `balance_caveats` appears on [cog_balances()] results.
 #' @export
 cog_explain <- function(result, format = c("print", "list")) {
   format <- match.arg(format)
@@ -125,8 +149,15 @@ cog_explain <- function(result, format = c("print", "list")) {
   if (length(prov$suggestions) > 0L) {
     cli::cli_h2("Suggestions")
     sugg_lines <- vapply(prov$suggestions, function(s) {
-      sprintf("%s -- %s (years %s-%s): %s", s$recipe_id, s$label,
+      line <- sprintf("%s -- %s (years %s-%s): %s", s$recipe_id, s$label,
               s$available_years[1], s$available_years[2], s$hint)
+      if (isTRUE(s$suppressed_amount > 0)) {
+        line <- paste0(line, sprintf(" [$%s excluded from %s: %s]",
+          formatC(s$suppressed_amount, format = "f", digits = 0, big.mark = ","),
+          paste0("FY", s$suppressed_years, collapse = ", "),
+          paste(s$suppressed_codes, collapse = ", ")))
+      }
+      line
     }, character(1))
     cli::cli_ul(sugg_lines)
   }
@@ -135,12 +166,30 @@ cog_explain <- function(result, format = c("print", "list")) {
     cli::cli_h2("Reporting coverage")
     cli::cli_text("Mode: {prov$coverage_mode %||% 'all'}")
     cov <- prov$coverage
-    cli::cli_ul(sprintf(
-      "%d: %d of %d units reporting (%.0f%%) -- %s year",
-      cov$year, cov$n_units_reporting, cov$n_units_expected,
-      100 * cov$n_units_reporting / pmax(cov$n_units_expected, 1L),
-      ifelse(cov$is_census_year, "census", "sample")
-    ))
+    has_collected <- "n_units_collected" %in% names(cov)
+    if (has_collected) {
+      # Three counters: collected separates sampling from real zeros;
+      # reporting is category-conditional and never a response rate.
+      cli::cli_ul(sprintf(
+        "%d: %d of %d units collected, %d reporting in this category -- %s year",
+        cov$year,
+        cov$n_units_collected,
+        cov$n_units_expected,
+        cov$n_units_reporting,
+        ifelse(cov$is_census_year, "census", "sample")
+      ))
+    } else {
+      cli::cli_ul(sprintf(
+        "%d: %d of %d units reporting (%.0f%%) -- %s year",
+        cov$year, cov$n_units_reporting, cov$n_units_expected,
+        100 * cov$n_units_reporting / pmax(cov$n_units_expected, 1L),
+        ifelse(cov$is_census_year, "census", "sample")
+      ))
+    }
+    # Explains what the per-row "-- sample year" tag means, regardless of
+    # which branch above rendered it -- not gated on has_collected, which
+    # would make this permanently unreachable now that both real callers
+    # (cog_geographic_rollup(), cog_peer_compare()) always supply it.
     if (any(!cov$is_census_year)) {
       cli::cli_text(
         "Note: the Census of Governments is a complete census only in years ending in 2 or 7; every other year is a sample."

R/manifest.R

@@ -28,7 +28,7 @@
       "*" = "{.code Sys.setenv(USCOGDATA_URL = \"<url-or-local-path>/\")}",
       "*" = "{.code options(uscogdata.url = \"<url-or-local-path>/\")}",
       i = "For an offline smoke test, use the bundled fixture: {.code system.file(\"extdata/fixture_corpus\", package = \"uscogdata\")}.",
-      i = "For the live Civilytics corpus, request the Nextcloud share URL from the package maintainer."
+      i = "The public corpus is the default: unset USCOGDATA_URL to use it, or point it at a local copy made by {.code cog_mirror()}."
     ), class = "uscogdata_url_not_configured")
   }
   invisible(url)

R/pagination.R

@@ -0,0 +1,81 @@
+# R/pagination.R
+#
+# Shared limit/offset machinery. #39 established the semantics inside
+# .verb_spendrev(); #57 extends them to cog_gov_search() and cog_balances(),
+# which is what made a single definition worth having: three inline copies of
+# "coerce, refuse, unwrap the count" would be three places for the meaning of
+# `total_rows` to drift.
+#
+# The SQL side stays in .build_verb_sql() (R/spending.R) -- it already wraps
+# the aggregate in an outer SELECT so COUNT(*) OVER() sees the post-GROUP-BY
+# row count rather than the pre-aggregation one, and that is the subtle part
+# worth not duplicating either.
+
+#' Coerce and check a limit/offset pair.
+#'
+#' Returns the coerced pair, or NULL for `limit` when no page was requested.
+#' `offset` defaults to 0 whenever `limit` is set, so a caller can supply just
+#' `limit` and get the first page.
+#'
+#' Conflicts with other arguments are deliberately NOT checked here: they
+#' differ per verb (`complete`/`recipe` for the money verbs, basket mode for
+#' `cog_gov_search()`, `recipe` alone for `cog_balances()`), and a shared
+#' function taking a list of conflict flags would be harder to read than the
+#' three explicit refusals at the call sites.
+#' @noRd
+.validate_pagination <- function(limit, offset) {
+  if (is.null(limit)) {
+    return(list(limit = NULL, offset = NULL))
+  }
+  limit <- as.integer(limit)
+  if (length(limit) != 1L || is.na(limit) || limit < 0L) {
+    cli::cli_abort("`limit` must be a single non-negative integer.",
+                   class = "uscogdata_invalid_pagination")
+  }
+  offset <- if (is.null(offset)) 0L else as.integer(offset)
+  if (length(offset) != 1L || is.na(offset) || offset < 0L) {
+    cli::cli_abort("`offset` must be a single non-negative integer.",
+                   class = "uscogdata_invalid_pagination")
+  }
+  list(limit = limit, offset = offset)
+}
+
+#' Wrap a query so one page comes back carrying the unpaginated total.
+#'
+#' `COUNT(*) OVER()` rides along as an ordinary column, so the caller gets the
+#' true total from the SAME scan instead of a second round trip. The outer
+#' `SELECT *` matters: appending LIMIT/OFFSET directly to a grouped query would
+#' have the window function count pre-aggregation rows.
+#' @noRd
+.paginate_sql <- function(base_sql, limit, offset) {
+  if (is.null(limit)) return(base_sql)
+  sprintf(
+    "SELECT *, COUNT(*) OVER() AS pagination_total_rows
+     FROM (%s) AS _paged
+     LIMIT %d OFFSET %d",
+    base_sql, limit, offset
+  )
+}
+
+#' Strip the count column back out and report the unpaginated total.
+#'
+#' Returns `list(result = , total_rows = )`.
+#'
+#' An empty page -- an offset past the end -- carries no row to read the window
+#' function off, so that one case falls back to a second, unpaginated
+#' `COUNT(*)` rather than reporting a wrong zero. `unpaged_sql` is passed as a
+#' function so the fallback query is only BUILT when it is actually needed;
+#' every caller's unpaginated SQL is otherwise constructed on every paged call
+#' and thrown away.
+#' @noRd
+.take_pagination_total <- function(result, con, unpaged_sql) {
+  if (nrow(result) > 0L) {
+    total <- result$pagination_total_rows[[1]]
+    result$pagination_total_rows <- NULL
+    return(list(result = result, total_rows = as.integer(total)))
+  }
+  count_sql <- sprintf("SELECT COUNT(*) AS n FROM (%s) AS _uncounted",
+                       if (is.function(unpaged_sql)) unpaged_sql() else unpaged_sql)
+  list(result = result,
+       total_rows = as.integer(DBI::dbGetQuery(con, count_sql)$n[[1]]))
+}

R/peers.R

@@ -195,11 +195,13 @@ cog_find_peers <- function(target_govid,
 #'     a balanced panel.
 #'
 #'   Regardless of mode, `provenance$coverage` always carries per-year
-#'   `n_units_reporting`, `n_units_expected` and `is_census_year`, and
-#'   `provenance$coverage_mode` records the mode. `is_census_year` is 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. `n_units_reporting` is the number that tells the truth.
+#'   `n_units_expected`, `n_units_collected`, `n_units_reporting` and
+#'   `is_census_year`, and `provenance$coverage_mode` records the mode.
+#'   `is_census_year` is a statement about the **survey calendar**, never a
+#'   claim of completeness: FY1967 is a census year in which only 97 of
+#'   Wisconsin's 608 cities report. `n_units_reporting` is
+#'   category-conditional and is not a response rate on its own -- see
+#'   "Reading `coverage`" below for what each counter answers.
 #'
 #'   The comparison target is exempt from `"consistent"` balancing -- it is the
 #'   subject of the comparison, not a member of the cohort -- and the
@@ -240,6 +242,32 @@ cog_find_peers <- function(target_govid,
 #'     group_by(year) |>
 #'     summarise(p50 = quantile(total, 0.5, na.rm = TRUE))
 #'   ```
+#' @section Reading `coverage`:
+#' `provenance$coverage` carries three per-year counters:
+#'
+#'   * `n_units_expected` -- how many governments you asked about.
+#'   * `n_units_collected` -- how many of those appear in the corpus at all
+#'     that year (in ANY category), separating sampling from real zeros.
+#'   * `n_units_reporting` -- how many have rows for the SPECIFIC category you
+#'     requested. This is always <= n_units_collected: a government can be
+#'     collected but have no rows for "Police" because it contracts policing
+#'     to the county sheriff, not because it wasn't surveyed.
+#'
+#' **`n_units_reporting` is category-conditional** and therefore **not a
+#' response rate**: `n_units_reporting / n_units_expected` conflates sampling
+#' (never collected) with real zeros (collected but spends nothing in your
+#' category). Use `n_units_collected / n_units_expected` for the true
+#' collection rate, and `n_units_reporting / n_units_collected` for category
+#' participation among collected units.
+#'
+#' In FY2022 — a complete census year — Georgia reports 393 of 567 cities for
+#' `category = "Police"`; the 174-city gap is overwhelmingly cities that
+#' contract policing to the county sheriff, not non-response.
+#'
+#' The comparison that *is* valid is the same category across a census year
+#' (ending in 2 or 7) and a sample year, where the real-zero component is
+#' roughly constant and the difference reflects the survey cycle. `is_census_year`
+#' marks which is which.
 #' @export
 cog_peer_compare <- function(target_govid, peers, category, years,
                              per_capita = TRUE, adjust_to_year = NULL,
@@ -308,10 +336,20 @@ cog_peer_compare <- function(target_govid, peers, category, years,
   # Counted over PEER rows only, against the cohort size: "3 of your 15 peers
   # reported in FY2019". Including the target would inflate every count by one
   # and make a cohort that has entirely stopped reporting look non-empty.
+  # n_units_collected is looked up against the spending long view matching
+  # whatever basis cog_spending() actually resolved above (prov$basis) --
+  # NOT hardcoded to spending_long_harmonized, which does not exist on a
+  # corpus with schema_version < 5 (R/basis.R resolves basis = "raw" there,
+  # and only *_long, not *_long_harmonized, is registered; see R/views.R).
+  # The con comes from .ensure_session() already called inside cog_spending().
+  con <- .ensure_session()
   prov$coverage_mode <- coverage
   prov$coverage <- .coverage_table(
     out, years, length(peer_govids),
-    rows = r[r$role == "peer", , drop = FALSE]
+    rows = r[r$role == "peer", , drop = FALSE],
+    con = con,
+    long_view = .select_long_view("spending_annotated", prov$basis),
+    expected_ids = peer_govids
   )
   attr(out, "provenance") <- prov
   out

R/provenance.R

@@ -67,7 +67,15 @@
     basis_note = basis_note,
     expenditure_concept = expenditure_concept,
     expenditure_concept_note = expenditure_concept_note,
-    expenditure_concept_direct_suppressed = isTRUE(expenditure_concept_direct_suppressed),
+    # isTRUE() alone would collapse a deliberate NA (all-categories mode,
+    # where suppression detection cannot run -- see .verb_spendrev()) down to
+    # FALSE, turning "we don't know" back into the false claim this field
+    # exists to avoid. Preserve NA; otherwise normalize to a strict logical.
+    expenditure_concept_direct_suppressed = if (isTRUE(is.na(expenditure_concept_direct_suppressed))) {
+      NA
+    } else {
+      isTRUE(expenditure_concept_direct_suppressed)
+    },
     revenue_concept = revenue_concept,
     harmonization = harmonization %||% list(
       applied = FALSE, na_rows_excluded = 0L, na_amount_excluded = 0,

R/recipes.R

@@ -113,7 +113,7 @@ cog_recipes <- function(pattern = NULL) {
 #' `year_min`/`year_max` -- so there is no double-counting. (Checkpoint
 #' review docs/phase_r_harmonization_review.md § 0.2.)
 #' @noRd
-.run_recipe <- function(con, recipe_id, govid, years) {
+.run_recipe <- function(con, recipe_id, cohort, years) {
   sql <- sprintf(
     "SELECT l.year, l.canonical_govid,
             COALESCE(x.gov_name, l.gov_name) AS gov_name,
@@ -128,11 +128,11 @@ cog_recipes <- function(pattern = NULL) {
            OR (r.gov_type_scope = 'local' AND l.type BETWEEN 1 AND 3))
      LEFT JOIN canonical_fips_xwalk x USING (canonical_govid)
      WHERE r.recipe_id = %1$s
-       AND l.canonical_govid IN (%2$s)
+       AND %2$s
        AND l.year IN (%3$s)
      GROUP BY 1, 2, 3
      ORDER BY 1, 2",
-    .sql_lit_chr(recipe_id), .sql_lit_chr(govid),
+    .sql_lit_chr(recipe_id), .cohort_sql(cohort, "l.canonical_govid"),
     paste(as.integer(years), collapse = ",")
   )
   result <- tibble::as_tibble(DBI::dbGetQuery(con, sql))

R/revenue.R

@@ -8,6 +8,17 @@
 #' multiplies by 1000 and records the conversion in `provenance`).
 #'
 #' @inheritParams cog_spending
+#' @param category Character vector of category names (from
+#'   `summary_categories.category`), or `NULL` for all categories broken out
+#'   one row each. The reserved value `"All Categories"` instead returns a
+#'   single summed row per `(year, canonical_govid, subtype)`, covering every
+#'   category inside the requested concept's subtype scope. It cannot be
+#'   combined with other category names, and it is not the same thing as
+#'   `revenue_concept = "total"`: the concept chooses which subtypes are in
+#'   scope, `"All Categories"` chooses whether rows inside that scope are
+#'   broken out or summed. Because the result keeps one row per
+#'   `revenue_subtype`, filtering the returned frame to
+#'   `revenue_subtype == "own_source"` gives an own-source revenue total.
 #' @param revenue_concept Which of Census's two published revenue concepts to
 #'   return. Concepts are defined as sets of the crosswalk's `revenue_subtype`
 #'   values -- never as item-code first letters, which cannot classify
@@ -39,11 +50,12 @@
 #'   optional `pop_source`, `codes_included`, `aggregate_fallback`, `notes`,
 #'   and `value_source` when `complete = TRUE`.
 #' @export
-cog_revenue <- function(govid, years, category = NULL,
+cog_revenue <- function(govid = NULL, years, category = NULL,
                         per_capita = FALSE, adjust_to_year = NULL,
                         basis = c("harmonized", "raw"), recipe = NULL,
                         revenue_concept = c("general", "total"),
-                        complete = FALSE) {
+                        complete = FALSE, limit = NULL, offset = NULL,
+                        state = NULL, type = NULL) {
   # flow_prefixes no longer classifies rows (crosswalk revenue_subtype
   # membership does -- General Revenue, i.e. everything except
   # insurance_trust) -- it only scopes the recipe-suggestion machinery to
@@ -62,6 +74,10 @@ cog_revenue <- function(govid, years, category = NULL,
     basis          = basis,
     recipe         = recipe,
     revenue_concept = revenue_concept,
-    complete       = complete
+    complete       = complete,
+    limit          = limit,
+    offset         = offset,
+    state          = state,
+    type           = type
   )
 }

R/rollup.R

@@ -19,7 +19,11 @@
 #'   `state`, `county`, `city`. Each element is a character vector of
 #'   `canonical_govid` values. At least one layer required.
 #' @param category Single category name or character vector (passed through
-#'   to [cog_spending()]).
+#'   to [cog_spending()]), or the reserved `"All Categories"` for one summed
+#'   row per `(year, canonical_govid, subtype)` covering every category in the
+#'   concept's scope. `"All Categories"` is the efficient way to build a
+#'   geographic total: without it a caller must issue one rollup per category
+#'   and sum the results themselves.
 #' @param years Integer vector of years.
 #' @param per_capita If `TRUE`, per-capita uses each gov's own per-year
 #'   population from `gov_population_yearly`. Govs with missing population
@@ -45,17 +49,45 @@
 #'     a balanced panel.
 #'
 #'   Regardless of mode, `provenance$coverage` always carries per-year
-#'   `n_units_reporting`, `n_units_expected` and `is_census_year`, and
-#'   `provenance$coverage_mode` records the mode. `is_census_year` is 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. `n_units_reporting` is the number that tells the truth.
+#'   `n_units_expected`, `n_units_collected`, `n_units_reporting` and
+#'   `is_census_year`, and `provenance$coverage_mode` records the mode.
+#'   `is_census_year` is a statement about the **survey calendar**, never a
+#'   claim of completeness: FY1967 is a census year in which only 97 of
+#'   Wisconsin's 608 cities report. `n_units_reporting` is
+#'   category-conditional and is not a response rate on its own -- see
+#'   "Reading `coverage`" below for what each counter answers.
 #' @return Tibble with columns `year`, `layer`, `canonical_govid`, `gov_name`,
 #'   `spend_subtype`, `category`, `amt_nominal`, optional `amt_real` /
 #'   `amt_per_capita_nominal` / `amt_per_capita_real`, optional `pop_source`,
 #'   `codes_included`, `aggregate_fallback`, `scope_note`, `notes`. Carries a
 #'   `provenance` attribute with `verb = "cog_geographic_rollup"`, `layers`,
 #'   and `rollup$included_govids` / `rollup$excluded_govids`.
+#' @section Reading `coverage`:
+#' `provenance$coverage` carries three per-year counters:
+#'
+#'   * `n_units_expected` -- how many governments you asked about.
+#'   * `n_units_collected` -- how many of those appear in the corpus at all
+#'     that year (in ANY category), separating sampling from real zeros.
+#'   * `n_units_reporting` -- how many have rows for the SPECIFIC category you
+#'     requested. This is always <= n_units_collected: a government can be
+#'     collected but have no rows for "Police" because it contracts policing
+#'     to the county sheriff, not because it wasn't surveyed.
+#'
+#' **`n_units_reporting` is category-conditional** and therefore **not a
+#' response rate**: `n_units_reporting / n_units_expected` conflates sampling
+#' (never collected) with real zeros (collected but spends nothing in your
+#' category). Use `n_units_collected / n_units_expected` for the true
+#' collection rate, and `n_units_reporting / n_units_collected` for category
+#' participation among collected units.
+#'
+#' In FY2022 — a complete census year — Georgia reports 393 of 567 cities for
+#' `category = "Police"`; the 174-city gap is overwhelmingly cities that
+#' contract policing to the county sheriff, not non-response.
+#'
+#' The comparison that *is* valid is the same category across a census year
+#' (ending in 2 or 7) and a sample year, where the real-zero component is
+#' roughly constant and the difference reflects the survey cycle. `is_census_year`
+#' marks which is which.
 #' @export
 cog_geographic_rollup <- function(govids, category, years,
                                   per_capita = FALSE, adjust_to_year = NULL,
@@ -116,8 +148,20 @@ cog_geographic_rollup <- function(govids, category, years,
   # n_units_expected is the universe the CALLER named -- the govids passed in
   # -- not the national universe. That is what makes the ratio meaningful:
   # "597 of the 608 Wisconsin cities you asked about reported in FY2012".
+  # n_units_collected is looked up against the spending long view matching
+  # whatever basis cog_spending() actually resolved above (prov$basis) --
+  # NOT hardcoded to spending_long_harmonized, which does not exist on a
+  # corpus with schema_version < 5 (R/basis.R resolves basis = "raw" there,
+  # and only *_long, not *_long_harmonized, is registered; see R/views.R).
+  # The con comes from .ensure_session() already called inside cog_spending().
+  con <- .ensure_session()
   prov$coverage_mode <- coverage
-  prov$coverage <- .coverage_table(r, years, length(unique(all_govids)))
+  prov$coverage <- .coverage_table(
+    r, years, length(unique(all_govids)),
+    con = con,
+    long_view = .select_long_view("spending_annotated", prov$basis),
+    expected_ids = all_govids
+  )
   attr(r, "provenance") <- prov
 
   r

R/search.R

@@ -44,10 +44,22 @@
 #'   in basket mode (recycles from length 1). Excluded types `4`/`5` (or
 #'   `"special_district"` / `"school_district"`) trigger an explanatory
 #'   message and an empty result.
+#' @param limit Maximum number of rows to return, applied in SQL. `NULL`
+#'   (default) returns every match -- which, with no other filter, is the
+#'   entire crosswalk. Utility mode only: pagination has no meaning in basket
+#'   mode, where the result is one resolved row per requested name in input
+#'   order, and is refused there with class
+#'   `uscogdata_basket_pagination_conflict`.
+#' @param offset Rows to skip before `limit` starts counting (0-based).
+#'   Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.
 #' @return A tibble of `canonical_fips_xwalk` rows. In utility mode, all
-#'   matches sorted by `population_acs` desc. In basket mode, resolved
-#'   rows in input order, with `attr(., "resolution")` set to the
-#'   sidecar tibble.
+#'   matches sorted by `population_acs` desc, ties broken by
+#'   `canonical_govid`. In basket mode, resolved rows in input order, with
+#'   `attr(., "resolution")` set to the sidecar tibble.
+#'
+#'   When `limit` is set, carries a `total_rows` attribute: the full
+#'   unpaginated match count, computed by the same query (`COUNT(*) OVER()`)
+#'   rather than a second scan.
 #' @seealso [cog_basket_resolution()], [cog_basket_unresolved()],
 #'   [cog_spending()], [cog_revenue()].
 #' @examples
@@ -82,7 +94,12 @@
 #' )
 #' }
 #' @export
-cog_gov_search <- function(name = NULL, state = NULL, type = NULL) {
+cog_gov_search <- function(name = NULL, state = NULL, type = NULL,
+                           limit = NULL, offset = NULL) {
+  paging <- .validate_pagination(limit, offset)
+  limit  <- paging$limit
+  offset <- paging$offset
+
   if (!is.null(type) && length(type) == 1L && .is_excluded_type(type)) {
     cli::cli_inform(c(
       i = "v0.1 covers gov_types 0-3 (state/county/city/township) only.",
@@ -94,6 +111,18 @@ cog_gov_search <- function(name = NULL, state = NULL, type = NULL) {
   con <- .ensure_session()
 
   if (length(name) > 1L) {
+    # Basket mode returns one resolved row per requested name, in input order,
+    # with a resolution sidecar describing how each was matched. A page of that
+    # is not a page of anything the caller asked for -- the sidecar would still
+    # describe every name -- so refuse rather than silently ignoring the
+    # arguments. Same shape as the recipe/complete refusals in .verb_spendrev().
+    if (!is.null(limit)) {
+      cli::cli_abort(c(
+        "`limit`/`offset` cannot be combined with basket mode.",
+        "i" = "Basket mode ({.code length(name) > 1}) returns one resolved row per requested name, in input order, with a resolution sidecar covering all of them.",
+        "*" = "Drop `limit`/`offset`, or search one name at a time."
+      ), class = "uscogdata_basket_pagination_conflict")
+    }
     return(.resolve_basket(name = name, state = state, type = type, con = con))
   }
 
@@ -123,12 +152,26 @@ cog_gov_search <- function(name = NULL, state = NULL, type = NULL) {
   }
 
   where <- if (length(preds) == 0L) "" else paste("WHERE", paste(preds, collapse = " AND "))
-  sql <- paste(
+  # canonical_govid breaks ties. population_acs alone is NOT a total order --
+  # governments sharing a population, and the whole NULLS LAST block, came back
+  # in whatever order the scan produced. That was invisible while every call
+  # returned the full result set, but it makes a paged sweep unsound: two
+  # requests can order the tied rows differently, so a row is duplicated on one
+  # page and missing from the next. Any pagination has to sit on a total order.
+  base_sql <- paste(
     "SELECT * FROM canonical_fips_xwalk",
     where,
-    "ORDER BY population_acs DESC NULLS LAST"
+    "ORDER BY population_acs DESC NULLS LAST, canonical_govid"
   )
-  tibble::as_tibble(DBI::dbGetQuery(con, sql))
+  result <- tibble::as_tibble(
+    DBI::dbGetQuery(con, .paginate_sql(base_sql, limit, offset))
+  )
+  if (is.null(limit)) return(result)
+
+  paged <- .take_pagination_total(result, con, base_sql)
+  out <- paged$result
+  attr(out, "total_rows") <- paged$total_rows
+  out
 }
 
 #' @noRd
@@ -184,11 +227,18 @@ cog_gov_search <- function(name = NULL, state = NULL, type = NULL) {
   if (!is.character(state) || length(state) != 1L) {
     cli::cli_abort("`state` must be a 2-letter USPS abbrev or a FIPS integer.")
   }
-  fips <- .state_abbrev_to_fips[[toupper(state)]]
-  if (is.null(fips)) {
-    cli::cli_abort("Unknown state abbreviation: {state}.")
+  # Membership tested before the lookup, not after: `.state_abbrev_to_fips` is
+  # a named CHARACTER vector, and `[[` on a name it does not carry throws
+  # base R's "subscript out of bounds" rather than returning NULL -- which
+  # made the curated message below unreachable dead code. Reported as a bare
+  # subscript error, `cog_gov_search(state = "ZZ")` gave no hint that the
+  # argument wants a postal abbreviation.
+  key <- toupper(state)
+  if (!key %in% names(.state_abbrev_to_fips)) {
+    cli::cli_abort("Unknown state abbreviation: {state}.",
+                   class = "uscogdata_unknown_state")
   }
-  fips
+  .state_abbrev_to_fips[[key]]
 }
 
 # USPS state / territory abbreviation -> 2-digit FIPS code.

R/session.R

@@ -2,13 +2,23 @@
 
 #' Internal: open session, register views, cache manifest.
 #' Not exported. Called lazily by verbs via .ensure_session().
+#'
+#' `threads` and `memory_limit` default to the resolved configuration and are
+#' applied as pragmas on the new connection. When both resolve to NULL -- which
+#' is the case unless the operator sets one -- NO pragma is issued at all, so an
+#' unconfigured session connects exactly as it did before this argument existed.
 #' @noRd
 cog_open <- function(url = .resolve_url(),
-                     cache_dir = .resolve_cache_dir()) {
+                     cache_dir = .resolve_cache_dir(),
+                     threads = .resolve_duckdb_threads(),
+                     memory_limit = .resolve_duckdb_memory_limit()) {
   .check_url_configured(url)
   if (!dir.exists(cache_dir)) dir.create(cache_dir, recursive = TRUE)
 
   con <- DBI::dbConnect(duckdb::duckdb())
+  # Before anything else touches the connection: httpfs reads the corpus, and
+  # a remote read should already be bound by whatever budget the operator set.
+  .apply_duckdb_limits(con, threads, memory_limit)
   DBI::dbExecute(con, "INSTALL httpfs; LOAD httpfs;")
 
   manifest <- .fetch_or_cache_manifest(url, cache_dir)
@@ -25,6 +35,26 @@ cog_open <- function(url = .resolve_url(),
   invisible(con)
 }
 
+#' Apply the operator's DuckDB resource budget to a fresh connection.
+#'
+#' Split out from cog_open() so the "unset changes nothing" property is one
+#' readable branch rather than two conditionals buried in the connection path.
+#' Both settings are session-scoped in DuckDB, so this must run per connection;
+#' cog_close() discards the connection and the next cog_open() re-resolves,
+#' which is what makes a changed option take effect on the next session.
+#' @noRd
+.apply_duckdb_limits <- function(con, threads, memory_limit) {
+  if (!is.null(threads)) {
+    DBI::dbExecute(con, sprintf("SET threads TO %d", threads))
+  }
+  if (!is.null(memory_limit)) {
+    # Quoted as a string literal: DuckDB's memory_limit takes '4GB', not 4GB.
+    DBI::dbExecute(con, sprintf("SET memory_limit TO %s",
+                                .sql_lit_chr(memory_limit)))
+  }
+  invisible(con)
+}
+
 #' @noRd
 .ensure_session <- function() {
   if (is.null(.uscogdata_env$con) ||

R/spending.R

@@ -19,6 +19,14 @@
 .spend_subtypes_primary <- c("operations", "capital", "assistance")
 .spend_subtypes_direct  <- c(.spend_subtypes_primary, "interest", "insurance_benefits")
 
+# The reserved pseudo-category. Deliberately NOT "Total": `category = "Total"`
+# would sit one argument away from `expenditure_concept = "total"` and mean
+# something different -- the concept selects WHICH SUBTYPES are in scope, this
+# selects whether the rows inside that scope are broken out by category or
+# summed. "All Categories" states the operation and cannot be misread as the
+# concept.
+.ALL_CATEGORIES <- "All Categories"
+
 #' @noRd
 .expenditure_concept_subtypes <- function(concept) {
   switch(concept,
@@ -60,10 +68,21 @@
 #' millions/billions). The conversion is recorded in the provenance attribute
 #' under `transformations$units_conversion`.
 #'
-#' @param govid Character vector of `canonical_govid` values.
+#' @param govid Character vector of `canonical_govid` values, or `NULL` to name
+#'   the cohort by `state`/`type` instead. One of `govid`, `state`, or `type`
+#'   is required.
 #' @param years Integer vector of years.
 #' @param category Character vector of category names (from
-#'   `summary_categories.category`), or `NULL` for all categories.
+#'   `summary_categories.category`), or `NULL` for all categories broken out
+#'   one row each. The reserved value `"All Categories"` instead returns a
+#'   single summed row per `(year, canonical_govid, subtype)`, covering every
+#'   category inside the requested concept's subtype scope. It cannot be
+#'   combined with other category names, and it is not the same thing as
+#'   `expenditure_concept = "total"`: the concept chooses which subtypes are in
+#'   scope, `"All Categories"` chooses whether rows inside that scope are
+#'   broken out or summed. Because the result keeps one row per
+#'   `spend_subtype`, filtering the returned frame to
+#'   `spend_subtype == "operations"` gives an operating-expenditure total.
 #' @param per_capita If `TRUE`, adds `amt_per_capita_nominal` (and
 #'   `amt_per_capita_real` when `adjust_to_year` is set) using the per-year
 #'   Census F-33 population from `gov_population_yearly`. Result also gains
@@ -133,7 +152,12 @@
 #'   component (when one exists), and
 #'   `provenance$expenditure_concept_direct_suppressed` is `TRUE` -- the
 #'   figure in those rows is the intergovernmental leg alone, not Direct +
-#'   IG.
+#'   IG. When `category = "All Categories"` is combined with
+#'   `expenditure_concept = "total"`, this detection cannot run (it keys on
+#'   per-category rows, which all-categories mode collapses to one literal
+#'   value), so `expenditure_concept_direct_suppressed` is `NA` rather than a
+#'   possibly-false `FALSE`; query an explicit `category` to get a real
+#'   answer.
 #' @param complete If `TRUE`, fill the requested grid so that a cell the
 #'   corpus does not carry still appears, labelled with **why** it is
 #'   missing, and add a `value_source` column to every row:
@@ -156,6 +180,36 @@
 #'   `recipe` or with `expenditure_concept = "total"` (class
 #'   `uscogdata_complete_unsupported`) — neither draws its cells from
 #'   `code_set`.
+#' @param limit Maximum number of result rows to return, pushed into the SQL
+#'   query itself (`LIMIT`/`OFFSET`) rather than applied after the full
+#'   result is materialized. `NULL` (the default) returns every matching row,
+#'   exactly as before this parameter existed. Mutually exclusive with
+#'   `recipe` and with `complete = TRUE` -- see `offset` and `total_rows`.
+#' @param offset Rows to skip before `limit` starts counting (0-based).
+#'   Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.
+#' @param state,type Name the cohort by predicate instead of by id: `state` is
+#'   a 2-letter USPS abbreviation (or a FIPS code) and `type` is one of
+#'   `"state"`, `"county"`, `"city"`, `"township"` (or the integer `0:3`) --
+#'   the same vocabulary, and the same internal coercion, as
+#'   [cog_gov_search()]. Both default to `NULL`.
+#'
+#'   The cohort is then expressed as a subquery against `canonical_fips_xwalk`
+#'   inside each statement rather than round-tripped through R as a literal id
+#'   list. For a fleet-scale cohort that is the difference between a
+#'   301,591-character `IN` list re-parsed in 5--8 statements per call and a
+#'   constant-size predicate: measured at **94 ms versus 449 ms** for the same
+#'   FY2022 aggregate over the 20,106-government `type = "city"` cohort, within
+#'   7% of the no-filter floor.
+#'
+#'   Supplying `govid` **and** `state`/`type` INTERSECTS them -- the
+#'   governments in `govid` that also match the predicate -- rather than one
+#'   silently taking precedence. Naming no cohort at all (`govid`, `state` and
+#'   `type` all `NULL`) aborts with class `uscogdata_no_cohort`.
+#'
+#'   When the cohort is named by predicate, `provenance$scope$govids_found`
+#'   and `govids_missing` are empty -- there is no id list to report against --
+#'   and `provenance$scope$cohort` carries `state`, `type` and
+#'   `n_governments` instead. A `govid`-named cohort reports exactly as before.
 #' @return Tibble with columns `year`, `canonical_govid`, `gov_name`,
 #'   `spend_subtype`, `category`, `amt_nominal`, optional `amt_real`,
 #'   optional `amt_per_capita_nominal`, optional `amt_per_capita_real`,
@@ -163,13 +217,18 @@
 #'   and `value_source` when `complete = TRUE`.
 #'   Carries a `provenance` attribute matching `inst/schemas/provenance-v1.json`,
 #'   whose `completion` block reports `applied`, `rows_filled`, and the
-#'   per-year `absence_means` rule that was applied.
+#'   per-year `absence_means` rule that was applied. When `limit` is set,
+#'   also carries a `total_rows` attribute: the full unpaginated row count,
+#'   computed by the same query (`COUNT(*) OVER()`) rather than a second
+#'   round trip -- so a caller walking pages never has to ask "how many are
+#'   there" separately.
 #' @export
-cog_spending <- function(govid, years, category = NULL,
+cog_spending <- function(govid = NULL, years, category = NULL,
                          per_capita = FALSE, adjust_to_year = NULL,
                          basis = c("harmonized", "raw"), recipe = NULL,
                          expenditure_concept = c("primary", "direct", "total"),
-                         complete = FALSE) {
+                         complete = FALSE, limit = NULL, offset = NULL,
+                         state = NULL, type = NULL) {
   # flow_prefixes no longer classifies rows (crosswalk subtype membership
   # does, per expenditure_concept) -- it only scopes the recipe-suggestion
   # machinery to this verb's recipe families (see R/suggestions.R; the
@@ -188,7 +247,11 @@ cog_spending <- function(govid, years, category = NULL,
     basis         = basis,
     recipe        = recipe,
     expenditure_concept = expenditure_concept,
-    complete      = complete
+    complete      = complete,
+    limit         = limit,
+    offset        = offset,
+    state         = state,
+    type          = type
   )
 }
 
@@ -216,7 +279,8 @@ cog_spending <- function(govid, years, category = NULL,
                            basis = c("harmonized", "raw"), recipe = NULL,
                            expenditure_concept = c("primary", "direct", "total"),
                            revenue_concept = c("general", "total"),
-                           complete = FALSE) {
+                           complete = FALSE, limit = NULL, offset = NULL,
+                           state = NULL, type = NULL) {
   basis_explicit <- length(basis) == 1L
   basis <- match.arg(basis, c("harmonized", "raw"))
   # match.arg() itself throws a base `simpleError`, not an rlang-classed
@@ -256,9 +320,32 @@ cog_spending <- function(govid, years, category = NULL,
     .revenue_concept_subtypes(revenue_concept)
   }
 
-  govid <- .coerce_govid_input(govid, arg = "govid")
+  # NULL `govid` means "the cohort is named by predicate"; anything else is
+  # coerced and validated exactly as before, so an empty or wrong-typed vector
+  # still fails with its original message rather than being read as absent.
+  govid <- if (is.null(govid)) NULL else .coerce_govid_input(govid, arg = "govid")
+  # allow_all_categories = TRUE: cog_spending()/cog_revenue() are the two
+  # verbs the reserved pseudo-category is defined for. cog_balances() shares
+  # this validator but leaves the argument at its FALSE default, so it
+  # rejects "All Categories" instead of silently returning zero rows
+  # (finding 3, all-categories review).
   .validate_verb_inputs(govid, years, category, per_capita, adjust_to_year,
-                        recipe)
+                        recipe, allow_all_categories = TRUE)
+
+  # Built after validation so the argument-shape errors above keep firing
+  # first, and before .ensure_session() so a bad state/type costs no I/O.
+  cohort <- .make_cohort(govid, state, type)
+
+  # Recognize the reserved pseudo-category. Detected after type validation so a
+  # non-character `category` still fails with the ordinary type error.
+  all_categories <- !is.null(category) && .ALL_CATEGORIES %in% category
+  if (all_categories && length(category) > 1L) {
+    cli::cli_abort(c(
+      "{.val {(.ALL_CATEGORIES)}} cannot be combined with other categories.",
+      "i" = "It already sums every category in the requested concept's scope.",
+      "*" = "Ask for it alone, or list the specific categories you want."
+    ), class = "uscogdata_all_categories_not_combinable")
+  }
 
   if (!is.null(recipe) && identical(expenditure_concept, "total")) {
     cli::cli_abort(c(
@@ -299,6 +386,37 @@ cog_spending <- function(govid, years, category = NULL,
       "Use `expenditure_concept = \"direct\"` with `complete = TRUE`, or drop `complete`."
     )
   }
+  if (complete && all_categories) {
+    .abort_complete_unsupported(
+      "`category = \"All Categories\"` collapses the category dimension that `code_set` grids over (see `.completion_grid_sql()`), so there is no per-category grid left to fill -- filling a summed row has no defined semantics.",
+      "Drop `complete`, or use `complete = TRUE` with an explicit `category` (or `category = NULL` for every category)."
+    )
+  }
+
+  # limit/offset push the page into the SQL itself (see .build_verb_sql()),
+  # so the two things that would make "a page of what" ambiguous are refused
+  # up front rather than silently ignored: complete = TRUE fills a grid over
+  # the FULL requested (year, category) space, and a recipe's result comes
+  # from .run_recipe()'s own query, which this function does not touch.
+  paging <- .validate_pagination(limit, offset)
+  limit  <- paging$limit
+  offset <- paging$offset
+  if (!is.null(limit)) {
+    if (complete) {
+      cli::cli_abort(c(
+        "`limit`/`offset` cannot be combined with `complete = TRUE`.",
+        "i" = "`complete` fills a grid over the FULL requested (year, category) space; paginating a slice of already-grouped rows has no defined meaning for the cells it would fill.",
+        "*" = "Drop `limit`/`offset`, or drop `complete`."
+      ), class = "uscogdata_complete_pagination_conflict")
+    }
+    if (!is.null(recipe)) {
+      cli::cli_abort(c(
+        "`limit`/`offset` cannot be combined with `recipe`.",
+        "i" = "A recipe's result comes from a separate query (`.run_recipe()`) that pagination is not wired into yet.",
+        "*" = "Drop `limit`/`offset`, or drop `recipe`."
+      ), class = "uscogdata_recipe_pagination_conflict")
+    }
+  }
 
   years   <- as.integer(years)
   if (!is.null(adjust_to_year)) adjust_to_year <- as.integer(adjust_to_year)
@@ -312,12 +430,13 @@ cog_spending <- function(govid, years, category = NULL,
 
   recipe_block <- NULL
   category_for_prov <- category
+  total_rows <- NULL  # set below only when limit is non-NULL (non-recipe path)
   if (!is.null(recipe)) {
     .require_schema_v5(con, manifest, "recipe =")
     .validate_recipe_id(con, recipe)
     comps <- .recipe_components(con, recipe)
     recipe_label <- comps$label[[1]]
-    result <- .run_recipe(con, recipe, govid, years)
+    result <- .run_recipe(con, recipe, cohort, years)
     sql <- attr(result, "sql_query")
     result <- .shape_recipe_result(result, subtype_col, recipe_label)
     recipe_block <- list(
@@ -333,9 +452,22 @@ cog_spending <- function(govid, years, category = NULL,
     } else {
       NULL
     }
-    sql <- .build_verb_sql(view, subtype_col, govid, years, category, ig_view,
-                           subtype_scope)
+    sql <- .build_verb_sql(view, subtype_col, cohort, years,
+                           if (all_categories) NULL else category,
+                           ig_view, subtype_scope,
+                           all_categories = all_categories,
+                           limit = limit, offset = offset)
     result <- tibble::as_tibble(DBI::dbGetQuery(con, sql))
+    if (!is.null(limit)) {
+      paged <- .take_pagination_total(result, con, function() {
+        .build_verb_sql(view, subtype_col, cohort, years,
+                        if (all_categories) NULL else category,
+                        ig_view, subtype_scope,
+                        all_categories = all_categories)
+      })
+      result     <- paged$result
+      total_rows <- paged$total_rows
+    }
   }
 
   # Fill BEFORE per_capita / inflation so the added cells get the same
@@ -344,13 +476,13 @@ cog_spending <- function(govid, years, category = NULL,
   # spurious 0.
   completion <- list(applied = FALSE, rows_filled = 0L, absence_means = list())
   if (complete) {
-    result <- .complete_result(result, con, subtype_col, govid, years,
+    result <- .complete_result(result, con, subtype_col, cohort, years,
                                category, subtype_scope)
     completion <- attr(result, ".completion")
     attr(result, ".completion") <- NULL
   }
 
-  if (per_capita) result <- .attach_per_capita(result, con, govid)
+  if (per_capita) result <- .attach_per_capita(result, con)
   if (!is.null(adjust_to_year)) {
     result <- .attach_real_dollars(result, adjust_to_year, per_capita)
   }
@@ -378,7 +510,7 @@ cog_spending <- function(govid, years, category = NULL,
     basis_for_prov <- resolved$basis
     basis_note_for_prov <- resolved$note
     harmonization <- .build_harmonization_block(
-      con, govid, years, resolved, subtype_col, subtype_scope
+      con, cohort, years, resolved, subtype_col, subtype_scope
     )
     # C1(a): gap detection must run against the Direct leg alone. `result`
     # can also carry UNION'd intergovernmental rows (expenditure_concept =
@@ -393,9 +525,13 @@ cog_spending <- function(govid, years, category = NULL,
     } else {
       result
     }
-    suggestions <- .build_suggestions(con, govid, years, category,
+    suggestions <- .build_suggestions(con, cohort, years, category,
                                        direct_leg_result,
-                                       resolved$basis, flow_prefixes)
+                                       resolved$basis, flow_prefixes,
+                                       .select_long_view(view_base, resolved$basis),
+                                       all_categories = all_categories,
+                                       subtype_col = subtype_col,
+                                       subtype_scope = subtype_scope)
   }
 
   # C1(b): when expenditure_concept = "total", flag any row where the IG
@@ -407,13 +543,32 @@ cog_spending <- function(govid, years, category = NULL,
   # direct spending in that category, which is correct, ordinary data). When
   # a covering recipe is found, both the row-level notes and the provenance
   # say so rather than pass silently as a plausible Total.
-  direct_suppressed_info <- if (identical(expenditure_concept, "total")) {
+  #
+  # In all-categories mode this cannot run at all: .detect_direct_suppressed()
+  # keys on (year, canonical_govid, category), and every row shares the same
+  # literal "All Categories" value, so the key collides across every real
+  # category for that (year, govid) -- an IG-only row for a suppressed
+  # category becomes indistinguishable from one sharing a key with an
+  # unrelated category's ordinary Direct row. `has_direct` would then read
+  # TRUE whenever the government has ANY direct spending at all, and the
+  # detector could never fire. Rather than run it and report a false FALSE,
+  # skip it and record NA -- the provenance must stop making a claim it
+  # cannot support (finding 1, all-categories review).
+  suppression_unavailable <- all_categories &&
+    identical(expenditure_concept, "total")
+  direct_suppressed_info <- if (suppression_unavailable) {
+    list(flag = rep(NA, nrow(result)), notes = rep(NA_character_, nrow(result)))
+  } else if (identical(expenditure_concept, "total")) {
     .detect_direct_suppressed(con, result, subtype_col)
   } else {
     list(flag = rep(FALSE, nrow(result)), notes = rep(NA_character_, nrow(result)))
   }
   direct_suppressed <- direct_suppressed_info$flag
-  direct_suppressed_flag <- isTRUE(any(direct_suppressed))
+  direct_suppressed_flag <- if (suppression_unavailable) {
+    NA
+  } else {
+    isTRUE(any(direct_suppressed))
+  }
 
   result$notes <- .notes_column(result, direct_suppressed_info$notes)
 
@@ -422,9 +577,20 @@ cog_spending <- function(govid, years, category = NULL,
   # leg is suppressed for at least one requested (year, category), append an
   # explicit warning rather than let the base note's "Total = Direct + IG"
   # framing stand unqualified for rows where that arithmetic didn't happen.
+  # When suppression detection itself is unavailable (all-categories mode),
+  # say so instead of silently reusing the unqualified base note.
   expenditure_concept_note_for_prov <- if (identical(expenditure_concept, "total")) {
     base_note <- "Total = Direct + intergovernmental (M to local govts + L to state govts). Legacy-era IG is assembled from aggregate-flagged rows, which are year-disjoint from their modern leaf components; the L-- family total is excluded."
-    if (direct_suppressed_flag) {
+    if (suppression_unavailable) {
+      paste0(
+        base_note,
+        " NOTE: direct-leg-suppression detection is unavailable when ",
+        "`category = \"All Categories\"` -- it keys on per-category rows, ",
+        "which this mode collapses. `expenditure_concept_direct_suppressed` ",
+        "is NA here rather than a possibly-false FALSE; query an explicit ",
+        "`category` (or `category = NULL`) to get a real answer."
+      )
+    } else if (isTRUE(direct_suppressed_flag)) {
       paste0(
         base_note,
         " NOTE: for at least one requested (year, category) the Direct leg ",
@@ -464,18 +630,45 @@ cog_spending <- function(govid, years, category = NULL,
   )
   prov$scope$govids_found   <- scope$found
   prov$scope$govids_missing <- scope$missing
+  # A predicate-named cohort has no id list to report found/missing against
+  # (both stay empty), so it describes itself instead. Deliberately a COUNT
+  # rather than the resolved ids: enumerating them would put 20,000 govids in
+  # every fleet-scale response body, which is the cost this path exists to
+  # remove. NULL for a govid-named cohort, so that output is untouched.
+  prov$scope$cohort <- .cohort_provenance(con, cohort)
   attr(result, "provenance") <- prov
   attr(result, ".popyear_range") <- NULL
+  # Attached here, after every downstream transform (per_capita/real-dollar
+  # joins, notes, subtype filtering), the same way provenance is -- an
+  # attribute set before those runs is not guaranteed to survive them.
+  if (!is.null(limit)) attr(result, "total_rows") <- total_rows
 
   if (length(suggestions) > 0L) .inform_suggestions(suggestions)
 
   result
 }
 
+#' Shared input validation for the money/holdings verbs.
+#'
+#' `allow_all_categories` gates the reserved pseudo-category
+#' `.ALL_CATEGORIES` ("All Categories"). It is meaningful only where a
+#' concept's subtype scope defines what "all" sums over --
+#' `cog_spending()`/`cog_revenue()`, via `.verb_spendrev()`, pass `TRUE`.
+#' `cog_balances()` leaves it at the `FALSE` default: holdings are a stock
+#' with no concept vocabulary to sum across (see R/balances.R), and before
+#' this guard existed `cog_balances(category = "All Categories")` silently
+#' matched zero crosswalk rows and returned an empty result with no error
+#' (finding 3, all-categories review). This validator is shared specifically
+#' so the three verbs cannot drift apart on this again.
 #' @noRd
 .validate_verb_inputs <- function(govid, years, category,
-                                  per_capita, adjust_to_year, recipe = NULL) {
-  if (!is.character(govid) || length(govid) == 0L) {
+                                  per_capita, adjust_to_year, recipe = NULL,
+                                  allow_all_categories = FALSE) {
+  # NULL is allowed only because the caller has already established that the
+  # cohort is named some other way (`state`/`type`); .make_cohort() is what
+  # refuses a call that names no cohort at all. A supplied-but-empty `govid`
+  # still fails here, exactly as before.
+  if (!is.null(govid) && (!is.character(govid) || length(govid) == 0L)) {
     cli::cli_abort("`govid` must be a non-empty character vector.")
   }
   if (!(is.integer(years) || is.numeric(years)) || length(years) == 0L) {
@@ -484,6 +677,14 @@ cog_spending <- function(govid, years, category = NULL,
   if (!is.null(category) && !is.character(category)) {
     cli::cli_abort("`category` must be character or NULL.")
   }
+  if (!allow_all_categories && !is.null(category) &&
+      .ALL_CATEGORIES %in% category) {
+    cli::cli_abort(c(
+      "{.val {(.ALL_CATEGORIES)}} is not supported here.",
+      i = "It sums a spending or revenue concept's subtype scope; this verb has no concept vocabulary to sum across.",
+      i = "Use {.fn cog_spending} or {.fn cog_revenue} for an all-categories total."
+    ), class = "uscogdata_all_categories_unsupported")
+  }
   if (!is.logical(per_capita) || length(per_capita) != 1L) {
     cli::cli_abort("`per_capita` must be a length-1 logical.")
   }
@@ -512,6 +713,18 @@ cog_spending <- function(govid, years, category = NULL,
   if (identical(basis, "harmonized")) paste0(view_base, "_harmonized") else view_base
 }
 
+#' The `*_long`/`*_long_harmonized` view behind an annotated view base --
+#' `"spending_annotated"` -> `"spending_long_harmonized"`. `.build_suggestions()`
+#' anti-joins the LONG view rather than the annotated one: they have identical
+#' row membership (the annotated views are the long views plus LEFT JOINs, see
+#' inst/sql/42-spending_annotated_harmonized.sql), but the long view is the
+#' one that actually owns the `NOT is_aggregate` + crosswalk-membership rule
+#' the suppression test is asking about.
+#' @noRd
+.select_long_view <- function(view_base, basis) {
+  .select_view(sub("_annotated$", "_long", view_base), basis)
+}
+
 #' @noRd
 .select_ig_view <- function(basis) {
   if (identical(basis, "harmonized")) "ig_annotated_harmonized" else "ig_annotated"
@@ -556,11 +769,17 @@ cog_spending <- function(govid, years, category = NULL,
 }
 
 #' @noRd
-.build_verb_sql <- function(view, subtype_col, govid, years, category,
-                            ig_view = NULL, subtype_scope = NULL) {
-  govid_lit <- .sql_lit_chr(govid)
+.build_verb_sql <- function(view, subtype_col, cohort, years, category,
+                            ig_view = NULL, subtype_scope = NULL,
+                            all_categories = FALSE, limit = NULL, offset = NULL) {
+  cohort_pred <- .cohort_sql(cohort)
   years_lit <- paste(as.integer(years), collapse = ",")
-  category_pred <- if (is.null(category)) {
+  # In all-categories mode there is no category filter: the sum is defined by
+  # the concept's SUBTYPE allowlist (subtype_pred below), which is the real
+  # concept boundary. Filtering by category as well would be a no-op at best
+  # and, if the crosswalk ever gained an uncategorized code, a silent
+  # under-count of the very total this mode exists to guarantee.
+  category_pred <- if (all_categories || is.null(category)) {
     ""
   } else {
     sprintf("AND category IN (%s)", .sql_lit_chr(category))
@@ -599,29 +818,59 @@ cog_spending <- function(govid, years, category = NULL,
   # though its dollars came entirely from an aggregate row, silently
   # suppressing the "Aggregate fallback applied" note on exactly the rows
   # this feature exists to surface.
-  sprintf(
+
+  # Collapse the category dimension. subtype is deliberately KEPT: it is what
+  # lets a caller filter the result to `spend_subtype == "operations"` and
+  # get an operating-expenditure total, the measure a fiscal comparison
+  # actually wants. (There is no `subtype` argument -- this is a post-hoc
+  # filter on the returned column, not a query parameter.)
+  category_select <- if (all_categories) {
+    sprintf("%s AS category", .sql_lit_chr(.ALL_CATEGORIES))
+  } else {
+    "category"
+  }
+  category_group <- if (all_categories) "" else ", category"
+
+  base_sql <- sprintf(
     "SELECT
        year,
        canonical_govid,
        COALESCE(xwalk_gov_name, gov_name) AS gov_name,
        %1$s,
-       category,
+       %7$s,
        SUM(amt) * 1000.0 AS amt_nominal,
        string_agg(DISTINCT item_code, ',' ORDER BY item_code) AS codes_included,
        bool_or(is_aggregate) AS aggregate_fallback
      FROM %2$s
-     WHERE canonical_govid IN (%3$s)
+     WHERE %3$s
        AND year IN (%4$s)
        %5$s
        %6$s
-     GROUP BY year, canonical_govid, gov_name, xwalk_gov_name, %1$s, category
-     ORDER BY year, canonical_govid, %1$s, category",
-    subtype_col, source_expr, govid_lit, years_lit, category_pred, subtype_pred
+     GROUP BY year, canonical_govid, gov_name, xwalk_gov_name, %1$s%8$s
+     ORDER BY year, canonical_govid, %1$s%8$s",
+    subtype_col, source_expr, cohort_pred, years_lit, category_pred, subtype_pred,
+    category_select, category_group
   )
+
+  # limit/offset push the page into the query itself instead of pulling every
+  # matching row across the network only to slice and discard most of it
+  # afterward (the pattern behind the 2026-08-06 production incident: a
+  # 193,105-row/194-page sweep re-ran the full query and re-listified every
+  # row on EVERY page). See .paginate_sql() in R/pagination.R for why the
+  # wrapping is an outer SELECT rather than a bare LIMIT on base_sql.
+  .paginate_sql(base_sql, limit, offset)
 }
 
+#' Join population onto a result and derive the per-capita columns.
+#'
+#' The population lookup is keyed on the govids PRESENT IN `result`, not on the
+#' cohort that produced it. Those are the only ones the LEFT JOIN below can
+#' match, so the joined output is identical either way -- but it means this
+#' works unchanged for a cohort named by predicate (where no id list exists in
+#' R at all), and on a paginated call it looks up one page's governments
+#' instead of the whole fleet's.
 #' @noRd
-.attach_per_capita <- function(result, con, govid) {
+.attach_per_capita <- function(result, con) {
   if (nrow(result) == 0L) {
     result$amt_per_capita_nominal <- numeric(0)
     result$pop_source <- character(0)
@@ -634,7 +883,7 @@ cog_spending <- function(govid, years, category = NULL,
      FROM gov_population_yearly
      WHERE canonical_govid IN (%s)
        AND year IN (%s)",
-    .sql_lit_chr(govid), years_lit
+    .sql_lit_chr(unique(result$canonical_govid)), years_lit
   )
   pops <- tibble::as_tibble(DBI::dbGetQuery(con, sql))
   result <- dplyr::left_join(result, pops,

R/suggestions.R

@@ -1,8 +1,16 @@
 # R/suggestions.R
-# Recipe-component-driven signposting: when a basis = "harmonized" query for
-# a category comes back with a coverage gap in some requested years (the
-# result has no rows at all in that year) that a harmonization recipe would
-# actually fill for this government, surface that recipe as a suggestion.
+# Recipe-component-driven signposting. When a basis = "harmonized" query for
+# a category comes back incomplete in some requested year -- and a
+# harmonization recipe would actually fill it for this government -- surface
+# that recipe as a suggestion. "Incomplete" has two forms, and a recipe
+# qualifies on either:
+#   1. empty_year          -- the result has no rows at all in that year.
+#   2. suppressed_component -- the result HAS rows, but a component code
+#      carries dollars the verb's own long view structurally excludes
+#      (aggregate-published, or absent from summary_categories). This is
+#      uscogdata#9: Public Welfare kept returning E74/E79 rows while dropping
+#      aggregate-only E67/E68, so form 1 never fired and the caller got a
+#      number a third too low with no signpost at all.
 #
 # This is deliberately keyed off the recipe catalog's component codes, not
 # off harmonization_map rows: no live map row carries a non-blank
@@ -34,8 +42,8 @@
 #' verb call: recipes whose generic join would fill a real gap in `result`.
 #'
 #' @param con Active DuckDB connection.
-#' @param govid Character vector of canonical_govid values (the verb's raw
-#'   `govid`).
+#' @param cohort The verb's cohort object (see `.make_cohort()`), naming the
+#'   governments by id, by state/type predicate, or both.
 #' @param years Integer vector of requested years.
 #' @param category `category` argument as passed to the verb (character
 #'   vector or `NULL`; suggestions are only computed when non-NULL).
@@ -48,11 +56,54 @@
 #'   "D")` for `cog_revenue()` -- see `.verb_spendrev()`). Passed through to
 #'   `.attach_ig_counterparts()` to keep the intergovernmental-counterpart
 #'   lookup scoped to the calling verb's own flow family.
+#' @param long_view Name of the verb's own long view (from
+#'   `.select_long_view()`), passed through to `.suppressed_components()` to
+#'   measure the second qualifying path (uscogdata#9).
+#' @param all_categories `TRUE` when the caller's `category` is the reserved
+#'   pseudo-category (`.ALL_CATEGORIES`). Defaults to `FALSE` so no other
+#'   caller's behaviour changes. When `TRUE`, the candidate-recipe sub-select
+#'   is scoped by `subtype_col`/`subtype_scope` instead of by `category` --
+#'   symmetric with `.build_verb_sql()`'s own all-categories branch (see
+#'   R/spending.R): the concept's subtype allowlist is the real scope
+#'   boundary, not any literal category value, and
+#'   `.ALL_CATEGORIES` ("All Categories") is never itself a row in
+#'   `summary_categories.category`, so leaving the category-keyed sub-select
+#'   in place here always returned zero candidates and silently disabled
+#'   signposting in all-categories mode (final whole-branch review, finding
+#'   6).
+#' @param subtype_col Name of the `summary_categories` subtype column to
+#'   scope by when `all_categories = TRUE` (`"spend_subtype"` or
+#'   `"revenue_subtype"` -- the same value `.build_verb_sql()` already
+#'   receives as its own `subtype_col`). Ignored when `all_categories =
+#'   FALSE`. `NULL` by default.
+#' @param subtype_scope Character vector of subtype values to scope by when
+#'   `all_categories = TRUE` (the same value `.build_verb_sql()` already
+#'   receives as its own `subtype_scope` -- the concept's subtype allowlist,
+#'   e.g. `.expenditure_concept_subtypes(expenditure_concept)`). Ignored when
+#'   `all_categories = FALSE`. `NULL` by default.
 #' @return List of `list(recipe_id, label, available_years, hint,
-#'   ig_recipe_id)`, possibly empty.
+#'   ig_recipe_id, trigger, suppressed_amount, suppressed_years,
+#'   suppressed_codes)`, possibly empty.
+#'
+#' Decomposed (Issue #33) into three extracted helpers to stay within the
+#' project's "functions under 50 lines" convention:
+#' \itemize{
+#'   \item `.query_candidate_recipes()` -- candidate recipe lookup by
+#'     category/subtype scope + `category_type` filter (#34) + M/L exclusion.
+#'   \item `.query_recipe_meta()` -- metadata (label, year spans).
+#'   \item `.query_covered_years()` -- Path 1 gap-year coverage via the
+#'     recipe's own generic join.
+#' }
+#' The for-loop that merges covered-years + suppressed-components into
+#' suggestion objects stays inline here because it interleaves
+#' empty_hit/supp_hit precedence with field assembly. Likewise kept inline:
+#' the M/L-exclusion design-comment block and the final
+#' `.attach_ig_counterparts()` call.
 #' @noRd
-.build_suggestions <- function(con, govid, years, category, result, basis,
-                                flow_prefixes) {
+.build_suggestions <- function(con, cohort, years, category, result, basis,
+                                flow_prefixes, long_view,
+                                all_categories = FALSE,
+                                subtype_col = NULL, subtype_scope = NULL) {
   if (!identical(basis, "harmonized") || is.null(category)) return(list())
 
   # Exclude any recipe that is ITSELF an intergovernmental (M/L) recipe --
@@ -67,17 +118,24 @@
   # flow-prefix gate below/in `.attach_ig_counterparts()`: an M/L recipe
   # should never be suggested as a coverage-gap filler for EITHER verb, not
   # just kept from being named as the *counterpart* of another suggestion.
-  candidates <- DBI::dbGetQuery(con, sprintf(
-    "SELECT DISTINCT recipe_id FROM harmonization_recipes
-     WHERE component_code IN (
-       SELECT DISTINCT item_code FROM summary_categories WHERE category IN (%s)
-     )
-     AND recipe_id NOT IN (
-       SELECT DISTINCT recipe_id FROM harmonization_recipes
-       WHERE LEFT(component_code, 1) IN ('M', 'L')
-     )",
-    .sql_lit_chr(category)
-  ))$recipe_id
+  #
+  # The inner sub-select is the concept boundary (finding 6, final
+  # whole-branch review): in all-categories mode it is scoped by
+  # `subtype_col`/`subtype_scope` -- the same allowlist `.build_verb_sql()`
+  # applies as a WHERE predicate to make the summed result a *concept*, not
+  # by `category` (`.ALL_CATEGORIES` is never a row in
+  # `summary_categories.category`, so a category-keyed sub-select always
+  # came back empty here). The M/L exclusion below is unchanged either way.
+  #
+  # Issue #34: scope the candidate query by `category_type` ('expenditure'
+  # vs 'revenue') to prevent cross-flow-family leakage -- e.g.
+  # `cog_revenue(category = "Corrections")` must not surface
+  # expenditure-only recipes (E04/E05) merely because they share the same
+  # category name in summary_categories. The type is derived from
+  # flow_prefixes: E/F/G -> 'expenditure', anything else -> 'revenue'.
+  candidates <- .query_candidate_recipes(con, category, flow_prefixes,
+                                          all_categories, subtype_col,
+                                          subtype_scope)
   if (length(candidates) == 0L) return(list())
 
   result_years <- if (is.null(result) || nrow(result) == 0L) {
@@ -86,22 +144,170 @@
     unique(as.integer(result$year))
   }
   gap_years <- setdiff(as.integer(years), result_years)
-  if (length(gap_years) == 0L) return(list())
 
-  meta <- tibble::as_tibble(DBI::dbGetQuery(con, sprintf(
-    "SELECT recipe_id, any_value(label) AS label,
-            MIN(year_min) AS year_min, MAX(year_max) AS year_max
-     FROM harmonization_recipes
-     WHERE recipe_id IN (%s)
-     GROUP BY recipe_id",
-    .sql_lit_chr(candidates)
-  )))
+  # Path 2 (uscogdata#9): component dollars this government holds that the
+  # verb's own view structurally excludes. Measured across ALL requested
+  # years, not just gap years -- the whole point is that a year with rows can
+  # still be missing dollars. Scoped to the calling verb's own flow_prefixes
+  # (I1) -- see `.suppressed_components()`'s own roxygen for why.
+  #
+  # This runs unconditionally whenever there are candidates -- an earlier
+  # revision of this fix wave tried a free, in-memory pre-check
+  # (`.needs_suppression_query()`) to skip the round trip on an already-
+  # covered path, but a scoped re-review measured it against the fixture and
+  # found it didn't pay for itself (it skipped ~3% of healthy calls, ~0% of
+  # the multi-govid batch shape it was meant to help, at a net cost increase
+  # once its own always-run metadata query was counted) while adding an
+  # untested exactness invariant -- that `result$codes_included` and this
+  # anti-join share the harmonized `item_code` space -- whose silent
+  # violation would kill signposting, the exact failure class uscogdata#9
+  # exists to prevent. Owner's call: keep this simple; a batch-aware
+  # optimization, if one is worth building, is a separate issue.
+  supp <- .suppressed_components(con, candidates, cohort, years, long_view, flow_prefixes)
 
-  # Which (recipe_id, year) pairs the recipe's own generic join actually
-  # covers for this government, restricted to the gap years -- the same
-  # join .run_recipe() uses (component year_min/year_max + gov_type_scope,
-  # no is_aggregate filter), just checking existence instead of summing.
-  covered <- DBI::dbGetQuery(con, sprintf(
+  if (length(gap_years) == 0L && nrow(supp) == 0L) return(list())
+
+  meta <- .query_recipe_meta(con, candidates)
+
+  # Path 1 (unchanged): (recipe, year) pairs the recipe's own generic join
+  # covers for this government, restricted to the gap years.
+  covered <- .query_covered_years(con, candidates, cohort, gap_years)
+
+  suggestions <- list()
+  for (rid in candidates) {
+    empty_hit <- rid %in% covered$recipe_id
+    s_rows <- supp[supp$recipe_id == rid, , drop = FALSE]
+    supp_hit <- nrow(s_rows) > 0L
+    if (!empty_hit && !supp_hit) next
+    m <- meta[meta$recipe_id == rid, ]
+    suggestions[[length(suggestions) + 1L]] <- list(
+      recipe_id = rid,
+      label = m$label[[1]],
+      available_years = c(as.integer(m$year_min), as.integer(m$year_max)),
+      hint = sprintf("re-run with recipe = '%s'", rid),
+      # An empty year is the stronger claim -- the category returned nothing
+      # at all -- so it wins when both paths qualify. The suppressed_* fields
+      # are still populated, so an empty_year fire also reports its dollars.
+      trigger = if (empty_hit) "empty_year" else "suppressed_component",
+      suppressed_amount = if (supp_hit) sum(s_rows$suppressed_amount) else 0,
+      suppressed_years = if (supp_hit) {
+        sort(unique(as.integer(s_rows$year)))
+      } else {
+        integer(0)
+      },
+      suppressed_codes = if (supp_hit) {
+        sort(unique(unlist(strsplit(s_rows$suppressed_codes, ",", fixed = TRUE))))
+      } else {
+        character(0)
+      }
+    )
+  }
+  .attach_ig_counterparts(con, suggestions, flow_prefixes)
+}
+
+#' Query candidate harmonization recipe IDs for a coverage-gap suggestion.
+#'
+#' Selects recipes whose component codes fall within the requested scope
+#' (category or subtype allowlist), excluding any recipe that is ITSELF an
+#' intergovernmental (M/L) recipe -- i.e. every one of its own component
+#' codes is M/L-prefixed. Without this exclusion, a category whose
+#' summary_categories rows span both a Direct family (e.g. E04/E05,
+#' "Corrections") and its M/L counterpart (M04/M05) makes the M/L recipe
+#' itself a raw top-level candidate for a plain `cog_spending()` call --
+#' following that hint would silently return intergovernmental dollars
+#' under `expenditure_concept = "direct"` provenance.
+#'
+#' In all-categories mode (`all_categories = TRUE`) the inner sub-select is
+#' scoped by `subtype_col`/`subtype_scope` -- the same allowlist
+#' `.build_verb_sql()` applies as a WHERE predicate to make the summed
+#' result a *concept* (see R/spending.R), not by `category`.
+#' `.ALL_CATEGORIES` ("All Categories") is never itself a row in
+#' `summary_categories.category`, so a category-keyed sub-select always
+#' returns zero candidates and silently disables signposting.
+#'
+#' Scope is also by `category_type` ('expenditure' vs 'revenue', Issue #34)
+#' to prevent cross-flow-family leakage: `cog_revenue(category =
+#' "Corrections")` must not surface expenditure-only recipes (E04/E05)
+#' merely because they share the same category name in summary_categories.
+#' The type is derived from flow_prefixes: E/F/G -> 'expenditure', anything
+#' else -> 'revenue'.
+#'
+#' @param con Active DuckDB connection.
+#' @param category Category name, or `NULL`.
+#' @param flow_prefixes The calling verb's own flow-type prefixes (see
+#'   `.build_suggestions()`). Used to derive `category_type` (#34).
+#' @param all_categories `TRUE` when the caller used `.ALL_CATEGORIES`.
+#' @param subtype_col Name of the summary_categories subtype column to
+#'   scope by when `all_categories = TRUE`; ignored otherwise.
+#' @param subtype_scope Character vector of subtype values to scope by
+#'   when `all_categories = TRUE`; ignored otherwise.
+#' @return Character vector of recipe IDs (possibly empty).
+#' @noRd
+.query_candidate_recipes <- function(con, category, flow_prefixes,
+                                      all_categories = FALSE,
+                                      subtype_col = NULL,
+                                      subtype_scope = NULL) {
+  # Issue #34: derive category_type from flow_prefixes to prevent
+  # cross-flow-family leakage -- e.g. cog_revenue(category = "Corrections")
+  # must not surface expenditure-only recipes merely because they share the
+  # same category name in summary_categories.
+  category_type <- if (all(flow_prefixes %in% c("E", "F", "G"))) {
+    "expenditure"
+  } else {
+    "revenue"
+  }
+
+  candidate_scope_sql <- if (isTRUE(all_categories)) {
+    sprintf(
+      "SELECT DISTINCT item_code FROM summary_categories
+       WHERE %s IN (%s) AND category_type = '%s'",
+      subtype_col, .sql_lit_chr(subtype_scope), category_type
+    )
+  } else {
+    sprintf(
+      "SELECT DISTINCT item_code FROM summary_categories
+       WHERE category IN (%s) AND category_type = '%s'",
+      .sql_lit_chr(category), category_type
+    )
+  }
+
+  DBI::dbGetQuery(con, sprintf(
+    "SELECT DISTINCT recipe_id FROM harmonization_recipes
+     WHERE component_code IN (
+       %s
+     )
+     AND recipe_id NOT IN (
+       SELECT DISTINCT recipe_id FROM harmonization_recipes
+       WHERE LEFT(component_code, 1) IN ('M', 'L')
+     )",
+    candidate_scope_sql
+  ))$recipe_id
+}
+
+#' Query gap-year coverage: which (recipe, year) pairs the recipe's own
+#' generic join covers for this government, restricted to `gap_years`.
+#'
+#' This is Path 1 of a suggestion (unchanged): it finds recipes whose
+#' component codes' generic join produces at least one row for this
+#' government in each gap year -- i.e. the category returned nothing in
+#' that year but a recipe would fill it.
+#'
+#' @param con Active DuckDB connection.
+#' @param candidates Character vector of recipe IDs to check coverage for.
+#' @param cohort The verb's cohort object (see `.make_cohort()`), rendered
+#'   into the govid predicate on the joined `long` scan via `.cohort_sql()`.
+#' @param gap_years Integer vector of requested years absent from the
+#'   result.
+#' @return Data frame with columns `recipe_id` (character) and `year`
+#'   (integer). Returns an empty data frame (`recipe_id = character(0)`,
+#'   `year = integer(0)`) when `gap_years` or `candidates` is empty, so
+#'   callers can safely reference `$recipe_id`.
+#' @noRd
+.query_covered_years <- function(con, candidates, cohort, gap_years) {
+  if (length(gap_years) == 0L || length(candidates) == 0L) {
+    return(data.frame(recipe_id = character(0), year = integer(0)))
+  }
+  res <- DBI::dbGetQuery(con, sprintf(
     "SELECT DISTINCT r.recipe_id, l.year
      FROM long l
      JOIN harmonization_recipes r
@@ -111,24 +317,32 @@
            OR (r.gov_type_scope = 'state' AND l.type = 0)
            OR (r.gov_type_scope = 'local' AND l.type BETWEEN 1 AND 3))
      WHERE r.recipe_id IN (%s)
-       AND l.canonical_govid IN (%s)
+       AND %s
        AND l.year IN (%s)",
-    .sql_lit_chr(candidates), .sql_lit_chr(govid),
+    .sql_lit_chr(candidates), .cohort_sql(cohort, "l.canonical_govid"),
     paste(gap_years, collapse = ",")
   ))
+  res$year <- as.integer(res$year)
+  res
+}
 
-  suggestions <- list()
-  for (rid in candidates) {
-    if (!rid %in% covered$recipe_id) next
-    m <- meta[meta$recipe_id == rid, ]
-    suggestions[[length(suggestions) + 1L]] <- list(
-      recipe_id = rid,
-      label = m$label[[1]],
-      available_years = c(as.integer(m$year_min), as.integer(m$year_max)),
-      hint = sprintf("re-run with recipe = '%s'", rid)
-    )
-  }
-  .attach_ig_counterparts(con, suggestions, flow_prefixes)
+#' Query recipe metadata: labels and year spans for a set of candidate
+#' recipes.
+#'
+#' @param con Active DuckDB connection.
+#' @param candidates Character vector of recipe IDs to look up.
+#' @return Tibble with columns `recipe_id`, `label`, `year_min` (int), and
+#'   `year_max` (int).
+#' @noRd
+.query_recipe_meta <- function(con, candidates) {
+  tibble::as_tibble(DBI::dbGetQuery(con, sprintf(
+    "SELECT recipe_id, any_value(label) AS label,
+            MIN(year_min) AS year_min, MAX(year_max) AS year_max
+     FROM harmonization_recipes
+     WHERE recipe_id IN (%s)
+     GROUP BY recipe_id",
+    .sql_lit_chr(candidates)
+  )))
 }
 
 #' Attach `ig_recipe_id` to each suggestion: the intergovernmental-expenditure
@@ -176,7 +390,7 @@
 #'      `R/basis.R`). This blocks a recipe surfaced through a mis-scoped
 #'      category from ever reaching the M/L search, e.g. `cog_spending()`'s
 #'      flow_prefixes are `c("E","F","G")`, which `ig_federal_b47_wide`'s own
-#'      `"B"` is not part of.
+#'      "B" is not part of.
 #'   2. `own_prefix %in% c("E","F","G")`: M/L only ever pairs with the
 #'      DIRECT-expenditure family, never with revenue (`cog_revenue()`'s
 #'      flow_prefixes already fold B/C/D in as ordinary revenue -- there is
@@ -184,10 +398,6 @@
 #'      adds one for spending) and never with ANOTHER M/L recipe (without
 #'      this check, `ige_local_m47_wide` would wrongly match sibling
 #'      `ige_state_l47_wide` on their shared {"47","94"} suffix set).
-#'      Condition 1 alone does not catch this: under `cog_revenue()`,
-#'      `ig_federal_b47_wide`'s own `"B"` IS inside revenue's own
-#'      `flow_prefixes`, so only this second, family-specific check blocks
-#'      the search.
 #' @noRd
 .attach_ig_counterparts <- function(con, suggestions, flow_prefixes) {
   if (length(suggestions) == 0L) return(suggestions)
@@ -232,12 +442,25 @@
 #' expressions. When a suggestion has an `ig_recipe_id`, one indented
 #' continuation line is appended naming the intergovernmental counterpart
 #' recipe (embedded `\n` renders as a hanging-indent continuation of the
-#' same bullet under cli, not a new bullet).
+#' same bullet under cli, not a new bullet). Same treatment for
+#' `suppressed_amount` (uscogdata#9): only present when dollars were
+#' actually measured as excluded (an `empty_year` fire can carry them too --
+#' see `.build_suggestions()` -- so this keys off the amount, not `trigger`).
 #' @noRd
 .inform_suggestions <- function(suggestions) {
   bullets <- vapply(suggestions, function(s) {
     bullet <- sprintf("%s (%d-%d): %s", s$recipe_id,
             s$available_years[1], s$available_years[2], s$hint)
+    # Only present when dollars were actually measured as excluded. An
+    # empty_year fire can carry them too -- the year had no rows AND the
+    # component was suppressed -- which is strictly more informative.
+    if (isTRUE(s$suppressed_amount > 0)) {
+      bullet <- paste0(bullet, sprintf(
+        "\n  $%s excluded from %s (%s), published as an aggregate or outside the crosswalk",
+        formatC(s$suppressed_amount, format = "f", digits = 0, big.mark = ","),
+        paste0("FY", s$suppressed_years, collapse = ", "),
+        paste(s$suppressed_codes, collapse = ", ")))
+    }
     if (!is.null(s$ig_recipe_id)) {
       bullet <- paste0(bullet, sprintf(
         "\n  intergovernmental counterpart: recipe = '%s'", s$ig_recipe_id))
@@ -245,7 +468,7 @@
     bullet
   }, character(1))
   cli::cli_inform(c(
-    i = "Coverage gap detected for the requested years; a harmonization recipe may fill it:",
+    i = "Incomplete coverage for the requested years; a harmonization recipe may fill it:",
     stats::setNames(bullets, rep("*", length(bullets)))
   ))
 }

R/suppression.R

@@ -0,0 +1,117 @@
+# R/suppression.R
+# Split out of R/suggestions.R (2026-08-05) to keep files under the project's
+# 400-line limit. Owns the second qualifying path for coverage signposting
+# (uscogdata#9): measuring, per government, the component dollars the
+# calling verb's own long view structurally excludes (aggregate-published,
+# or absent from summary_categories). See R/suggestions.R for the
+# orchestrator (`.build_suggestions()`) that calls this and the full
+# uscogdata#9 background.
+
+#' Measure, per (recipe, year), the component dollars this government holds
+#' that the calling verb's own long view structurally excludes.
+#'
+#' This is the second qualifying path for a suggestion (uscogdata#9). The
+#' first -- row absence -- only fires when a category returns NOTHING in a
+#' requested year, which is how Corrections behaves in the wide era. Public
+#' Welfare is the failure mode it misses: E74/E75/E77/E79 still return rows,
+#' so there is no absence to detect, while E67/E68 (aggregate-flagged 1967-
+#' 2011, and absent from `summary_categories` entirely) are dropped. The
+#' caller gets a plausible number a third too low, silently.
+#'
+#' "Structurally excluded" is decided by anti-joining the verb's REAL long
+#' view rather than restating its WHERE clause, so this stays correct if
+#' `spending_long_harmonized` / `revenue_long_harmonized` ever change. That
+#' anti-join is keyed on `item_code`, which is sound only because
+#' harmonization never renames a recipe component -- asserted by the "no
+#' recipe component is ever renamed by harmonization" test in
+#' tests/testthat/test-recipes.R.
+#'
+#' Note what this deliberately does NOT count as suppressed: a component
+#' excluded from the RESULT for scoping reasons -- because it belongs to a
+#' different `category`, or because `expenditure_concept` narrowed the
+#' subtypes -- is still present in the view, so it never fires. Suggesting a
+#' recipe is a coverage fix, not a category redefinition.
+#'
+#' `flow_prefixes` (uscogdata#9 review, finding I1) restricts the measured
+#' components to the CALLING VERB's own flow family (`c("E","F","G")` for
+#' spending, `c("T","A","U","B","C","D")` for revenue). Without this, a
+#' candidate recipe belonging to the OTHER flow family is always absent from
+#' this verb's view (by construction -- `cog_revenue()`'s view never carries
+#' an E-coded row) and so was always reported as "suppressed", fabricating a
+#' dollar claim across flow families (`cog_revenue(category = "Corrections")`
+#' claimed $3.63B excluded that `cog_spending()` reports and fully accounts
+#' for). Filtering on `LEFT(r.component_code, 1)` also drops M/L-prefixed
+#' components from measurement under `cog_spending()` (`flow_prefixes` never
+#' includes "M"/"L") -- harmless today, because a recipe's own M/L components
+#' (e.g. `corrections_ig_local_combined`'s M04/M05) are present in the view
+#' in every year they exist and so never fired as suppressed anyway, but
+#' worth recording since this filter is now the thing relied on to prevent
+#' it.
+#'
+#' @param con Active DuckDB connection.
+#' @param candidates Character vector of recipe ids to measure.
+#' @param cohort The verb's cohort object (see `.make_cohort()`), rendered
+#'   into the govid predicate on both the outer scan and the restated
+#'   NOT EXISTS filter.
+#' @param years Integer vector of requested years.
+#' @param long_view Name of the verb's long view, from `.select_long_view()`.
+#' @param flow_prefixes The calling verb's own flow-type prefixes (see
+#'   `.build_suggestions()`). Only recipe components whose first character is
+#'   in this set are measured.
+#' @return Tibble of `recipe_id`, `year`, `suppressed_amount` (full US
+#'   dollars), `suppressed_codes` (comma-joined, sorted). Zero rows when
+#'   nothing is suppressed.
+#' @noRd
+.suppressed_components <- function(con, candidates, cohort, years, long_view,
+                                    flow_prefixes) {
+  empty <- tibble::tibble(
+    recipe_id = character(0), year = numeric(0),
+    suppressed_amount = numeric(0), suppressed_codes = character(0)
+  )
+  if (length(candidates) == 0L) return(empty)
+
+  # long_view is interpolated as a SQL IDENTIFIER, not a literal, so it can
+  # never be quoted safely. It is always internally derived from a fixed
+  # view_base, so an off-allowlist value is a programming error, not input.
+  if (!long_view %in% c("spending_long", "spending_long_harmonized",
+                        "revenue_long", "revenue_long_harmonized")) {
+    cli::cli_abort(
+      "Internal error: unexpected `long_view` {.val {long_view}}.",
+      class = "uscogdata_internal_error"
+    )
+  }
+
+  sql <- sprintf(
+    "SELECT r.recipe_id,
+            l.year,
+            SUM(l.amt) * 1000.0 AS suppressed_amount,
+            string_agg(DISTINCT l.item_code, ',' ORDER BY l.item_code)
+              AS suppressed_codes
+     FROM long l
+     JOIN harmonization_recipes r
+       ON l.item_code = r.component_code
+      AND l.year BETWEEN r.year_min AND r.year_max
+      AND (r.gov_type_scope = 'all'
+           OR (r.gov_type_scope = 'state' AND l.type = 0)
+           OR (r.gov_type_scope = 'local' AND l.type BETWEEN 1 AND 3))
+     WHERE r.recipe_id IN (%1$s)
+       AND %2$s
+       AND l.year IN (%3$s)
+       AND l.amt <> 0
+       AND LEFT(r.component_code, 1) IN (%5$s)
+       AND NOT EXISTS (
+         SELECT 1 FROM %4$s v
+         WHERE v.canonical_govid = l.canonical_govid
+           AND v.year = l.year
+           AND v.item_code = l.item_code
+           AND v.year IN (%3$s)              -- restated: enables partition pruning (I3a)
+           AND %6$s   -- restated: pushes the cohort filter (I3a)
+       )
+     GROUP BY 1, 2
+     ORDER BY 1, 2",
+    .sql_lit_chr(candidates), .cohort_sql(cohort, "l.canonical_govid"),
+    paste(as.integer(years), collapse = ","), long_view,
+    .sql_lit_chr(flow_prefixes), .cohort_sql(cohort, "v.canonical_govid")
+  )
+  tibble::as_tibble(DBI::dbGetQuery(con, sql))
+}

R/views.R

@@ -78,6 +78,71 @@
   file %in% basename(paths)
 }
 
+#' Build the SQL path expression for the partitioned `long` table.
+#'
+#' DuckDB cannot expand a glob over generic HTTP: there is no directory
+#' listing to expand against, and `allow_asterisks_in_http_paths` only
+#' forwards the literal `**/*` as a filename, which 404s. Measured against
+#' the published corpus on 2026-08-08, an explicit file list returns the
+#' same 46,148,034 rows the (working) `hf://` glob does, and
+#' `hive_partitioning = true` still recovers `year` from the paths.
+#'
+#' The manifest already enumerates every partition, so we build the list
+#' from it. This is host-agnostic -- Nextcloud, HuggingFace and a local
+#' fixture take the same path -- where an `hf://` URL would tie the reader
+#' to one vendor's protocol and still need special-casing, since manifest
+#' fetching goes through httr2, which cannot speak `hf://`.
+#'
+#' Falls back to the glob when the manifest carries no partition list: a
+#' hand-built manifest in a test (see test-views.R) or a corpus predating
+#' the field. Both are local, where globbing works.
+#' @noRd
+.long_files_sql <- function(url, manifest) {
+  parts <- manifest$files$long_partitions %||% list()
+  if (length(parts) == 0L) {
+    return(.sql_lit_chr(paste0(url, "data/long/**/*.parquet")))
+  }
+  paths <- vapply(parts, function(p) as.character(p$path), character(1))
+  paste0("[", .sql_lit_chr(paste0(url, paths)), "]")
+}
+
+#' Substitute the corpus-location tokens in a view's SQL text.
+#'
+#' One place knows the token vocabulary. `.register_views()` and the tests
+#' that execute a view file directly both route through here. This exists
+#' because four test sites had hand-rolled the `{url}` substitution -- one
+#' of them commented as doing it "exactly as .register_views() does" -- and
+#' every one of them broke the moment a second token was introduced.
+#'
+#' `{long_files}` must be substituted BEFORE `{url}`: it expands to a string
+#' that itself contains the url, so the reverse order leaves the token in
+#' place and DuckDB's parser fails on the brace.
+#'
+#' `manifest` defaults to empty, which routes `.long_files_sql()` to its glob
+#' fallback -- correct for the local temp corpora the direct-execution tests
+#' build.
+#' @noRd
+#' `fixed = TRUE` is load-bearing, not a style choice.
+#'
+#' In regex mode, `gsub()` interprets backslashes in the REPLACEMENT string as
+#' escape sequences and silently drops them. A Windows corpus path is full of
+#' them, so `C:\Users\RUNNER\AppData\...` was substituted in as
+#' `C:UsersRUNNERAppData...` and every DuckDB read failed with "No files found
+#' that match the pattern". `fixed = TRUE` treats pattern and replacement as
+#' literal text, which is what a filesystem path needs.
+#'
+#' This is why the package could not read a LOCAL corpus on Windows at all --
+#' including the test fixture, hence the entire suite, and any `cog_mirror()`
+#' copy. Remote https URLs were unaffected, having no backslashes, which is
+#' part of why it stayed hidden: the bug predates the `{long_files}` token and
+#' lived in the original `{url}` substitution, unnoticed because nothing ever
+#' ran on Windows until the mirror's check matrix existed.
+#' @noRd
+.render_view_sql <- function(sql, url, manifest = list()) {
+  sql <- gsub("{long_files}", .long_files_sql(url, manifest), sql, fixed = TRUE)
+  gsub("{url}", url, sql, fixed = TRUE)
+}
+
 #' Register DuckDB views from inst/sql/ SQL files
 #' @noRd
 .register_views <- function(con, url, manifest) {
@@ -91,7 +156,7 @@
         !.corpus_has_table(manifest, .representation_view_files[[base]])) next
     if (base %in% .balance_view_files && !.corpus_has_balance_subtype(con)) next
     sql <- paste(readLines(f, warn = FALSE), collapse = "\n")
-    sql <- gsub("\\{url\\}", url, sql, fixed = FALSE)
+    sql <- .render_view_sql(sql, url, manifest)
     DBI::dbExecute(con, sql)
   }
 }

README.md

@@ -1,24 +1,152 @@
 # uscogdata
 
-Curated R reader for the Civilytics US Census of Governments finance corpus.
+<!-- badges: start -->
+[![R-CMD-check](https://github.com/civilytics/uscogdata/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/civilytics/uscogdata/actions/workflows/R-CMD-check.yaml)
+[![r-universe](https://civilytics.r-universe.dev/badges/uscogdata)](https://civilytics.r-universe.dev/uscogdata)
+[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE.md)
+<!-- badges: end -->
 
-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.
+A curated R reader for the Civilytics US Census of Governments finance corpus —
+every dollar that US state, county, municipal and township governments reported
+raising and spending, from **FY1967 to FY2024**, in one queryable place.
 
-## Status
+The Census of Governments is the only nationwide source for local government
+finance, and it is hard to use: item codes change meaning across vintages,
+government identifiers were renumbered in 2017, and an absent value means
+"published zero" in one era and "not reported" in the next. This package
+handles each of those problems, and it tells you when it has — every result
+carries provenance describing what was converted, what was aggregated, and
+which known series breaks intersect your query.
 
-Under active development (Phase 2 of the cog_pipeline project). See
-`../cog_pipeline/docs/reader-specification.md` for the reader contract this
-package implements.
+**Scope:** government types 0–3 (state, county, municipality, township).
+56 fiscal years, 46,148,034 rows, ~201 MB. There is no source data for FY1968
+or FY1969. Special districts (type 4) and school districts (type 5) are
+excluded pending validation.
 
-## Installation
+## Where the data comes from
+
+The corpus is published and documented at the **[US Census of Governments
+Finance API](https://pages.civilytics.org/cog-api/)**. Start there for how the
+data was built, how the identifier and item-code reconciliation works, and what
+the corpus does and does not cover.
+
+- **[API documentation and walkthroughs](https://pages.civilytics.org/cog-api/)**
+  — reference, data dictionary, and worked examples such as the
+  [Southern states guide](https://pages.civilytics.org/cog-api/cog-api-south-guide.html)
+- **[Live API](https://cog-api.civilytics.org/api/v1/)** — the same corpus over
+  HTTP, for Tableau, Python, or anything that isn't R
+- **[Bulk corpus on Hugging Face](https://huggingface.co/datasets/civilytics/us-cog-finance)**
+  — CC-BY-4.0; the same parquet files this package reads
+- **[Census Bureau source data](https://www.census.gov/programs-surveys/gov-finances.html)**
+  — the underlying public files
+
+## Install
 
 ```r
-# pak::pkg_install("gitea.civilytics.org/Civilytics/uscogdata")
+install.packages("uscogdata",
+  repos = c("https://civilytics.r-universe.dev",
+            "https://cloud.r-project.org"))
 ```
 
+Or from source:
+
+```r
+pak::pkg_install("git::https://gitea.civilytics.org/Civilytics/uscogdata.git")
+```
+
+## Quickstart
+
+No configuration, no credentials, no download. The package reads the published
+corpus over HTTPS by default.
+
+```r
+library(uscogdata)
+
+# Resolve a place name to a canonical government id
+madison <- cog_gov_search(name = "Madison", state = "WI", type = 2)
+madison$canonical_govid
+#> [1] "552025209777"
+
+# Police spending, inflation-adjusted and per capita
+spend <- cog_spending(
+  madison$canonical_govid,
+  years          = 2012:2022,
+  category       = "Police",
+  per_capita     = TRUE,
+  adjust_to_year = 2023
+)
+
+# What did that result do to the numbers, and what should you know about them?
+cog_explain(spend)
+```
+
+`years` is required — there is no implicit full-history default.
+
+## Two ways to read the corpus
+
+| | Remote (default) | Mirrored |
+|---|---|---|
+| Setup | none | `cog_mirror(dest)`, ~201 MB once |
+| Disk used | **0 MB** — HTTP range requests only | ~201 MB |
+| Opening a session | ~7.5 s | ~0.1 s |
+| One government, one year | ~4 s | ~0.05 s |
+| One government, full history | ~7 s | ~0.1 s |
+| Later queries, same session | ~1.5 s | ~0.05 s |
+| Good for | trying it out, teaching, one-off questions | repeated analysis, offline work, reproducibility |
+
+**A local mirror is roughly 60–80x faster, and it is one function call.** That is
+by far the largest difference any of these settings makes. If you are going to
+ask more than a handful of questions, mirror first.
+
+Measured 2026-08-10 on a 16-core Linux workstation against the published corpus
+(schema v7, `pipeline_commit 3d28ddd`), fresh R session per arm. A one-off
+question costs about **12 seconds end to end remotely and 0.15 seconds
+mirrored**, session setup included.
+
+Two things the per-query rows hide:
+
+- **Opening the session is the single largest remote cost** — larger than any
+  one query. It fetches the manifest and registers 23 SQL views over HTTPS, and
+  it lands on your first query, not on `library(uscogdata)`.
+- **The cost is network round-trips, not scanning.** A repeat query against
+  partitions this session has already touched is ~1.5 s rather than ~4 s, and a
+  full-history query costs ~7 s whether it runs first or last. What you are
+  paying for is reaching each of the 56 yearly files over HTTPS the first time.
+
+Nothing is written to disk in remote mode: DuckDB fetches the parquet footer,
+works out which row groups it needs, and reads only those. Nothing is cached
+between sessions either, so every query goes back to the network — and a session
+that issues many remote queries in quick succession can be rate-limited by the
+host (`HTTP Error: ... 429`). Both are further reasons to mirror for real work.
+
+The default points at a public HuggingFace mirror of the corpus. If you would
+rather not depend on a third party — for reproducibility, for an air-gapped
+environment, or on principle — **the escape hatch is one function call**:
+
+```r
+cog_mirror("~/cog-corpus")
+Sys.setenv(USCOGDATA_URL = "~/cog-corpus/")
+```
+
+After that, nothing in your analysis touches an external service.
+
+### Configuration
+
+- `USCOGDATA_URL` — corpus root: an HTTPS URL or a local path, **trailing slash required**
+- `USCOGDATA_CACHE_DIR` — where the manifest is cached (default: user cache dir)
+- `USCOGDATA_MANIFEST_TTL_SECS` — manifest re-fetch interval (default 3600)
+- `USCOGDATA_DUCKDB_THREADS` — cap DuckDB's thread count (default: every visible core)
+- `USCOGDATA_DUCKDB_MEMORY_LIMIT` — cap DuckDB's memory, e.g. `"4GB"` (default: DuckDB's own)
+
+Each also has an `options()` spelling — `uscogdata.url`, `uscogdata.duckdb_threads`,
+and so on — and the environment variable wins where both are set.
+
+The two DuckDB caps exist for **servers**, not laptops. Unset, DuckDB claims every
+core it can see, which is right for one interactive session on your own machine and
+wrong when several readers share a box: each claims the whole machine and they fight.
+Capping costs roughly 5% on a single query and is worth it anywhere the process is
+sharing hardware.
+
 ## Amounts are in full US dollars
 
 Every amount column this package returns — `amt_nominal`, `amt_real`,
@@ -29,111 +157,134 @@ 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
-r <- cog_spending("552025209777", 2020L)
-attr(r, "provenance")$transformations$units_conversion
-#> $applied TRUE  $source_unit "$1,000s (raw Census)"  $target_unit "$USD"  $multiplier 1000
+attr(spend, "provenance")$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.
+`$1,000s` — which is true of the raw Census files and of the corpus's own `amt`
+column — 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
+## Concepts worth understanding before you publish a number
 
-- `USCOGDATA_URL` — corpus root URL (public Nextcloud share, trailing slash)
-- `USCOGDATA_CACHE_DIR` — optional override for the manifest cache directory
-- `USCOGDATA_MANIFEST_TTL_SECS` — optional manifest re-fetch TTL (default 3600)
-
-## Primary vs Direct vs Total spending
+### Primary vs Direct vs Total spending
 
 `cog_spending(..., expenditure_concept = c("primary", "direct", "total"))`
-controls whose spending a result counts. Concepts are defined as sets of the
-crosswalk's `spend_subtype` values — never item-code first letters, which
-cannot classify correctly (the letter `Y` alone spans revenue, expenditure,
-and balance codes):
+controls *whose* spending a result counts. Concepts are defined as sets of the
+crosswalk's `spend_subtype` values, never item-code first letters — the letter
+`Y` alone spans revenue, expenditure and balance codes.
 
-- `"primary"` (the default) is the government's own service provision:
-  current operations, capital outlay, and assistance payments.
-- `"direct"` is Census's published Direct Expenditure: `primary` plus
-  interest on debt and insurance trust benefit payments (e.g. pensions).
-- `"total"` additionally adds the intergovernmental leg — money handed to
-  other governments to spend (`M`/`L` codes plus `Q11`/`Q12`/`Q18` state
-  payments to school systems) — 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).
+- **`"primary"`** (default) — the government's own service provision: current
+  operations, capital outlay, assistance payments.
+- **`"direct"`** — Census's published Direct Expenditure: `primary` plus
+  interest on debt and insurance trust benefits (e.g. pensions).
+- **`"total"`** — adds the intergovernmental leg, money handed to other
+  governments to spend. Meaningful for one government's own budget over time,
+  but it 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
-`primary` or `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.
+**Rule of thumb: any figure spanning more than one government uses `primary`
+or `direct`.** `cog_geographic_rollup()` and `cog_peer_compare()` enforce that
+by refusing `"total"` outright. Worked examples in
+`vignette("total-spending", package = "uscogdata")`.
 
-## General vs Total revenue
+### General vs Total revenue
 
-`cog_revenue(..., revenue_concept = c("general", "total"))` selects between
-Census's two published revenue concepts, again defined as crosswalk
-`revenue_subtype` sets rather than item-code prefixes:
+`cog_revenue(..., revenue_concept = c("general", "total"))`:
 
-- `"general"` (the default) is Census **General Revenue**: own-source
-  (taxes, charges, miscellaneous) plus federal, state and local
-  intergovernmental aid.
-- `"total"` is Census **Total Revenue**: `general` plus utility revenue
-  (`A91`–`A94`), liquor store revenue (`A90`), and insurance trust revenue
-  (unemployment and workers' compensation `Y` codes plus the
-  employee-retirement `X` codes).
+- **`"general"`** (default) — Census General Revenue: own-source taxes,
+  charges and miscellaneous, plus federal, state and local aid.
+- **`"total"`** — General plus utility revenue (`A91`–`A94`), liquor store
+  revenue (`A90`), and insurance trust revenue.
 
-The manual defines the first by subtracting the other three from the second,
-so the two are related by Census's own identity:
+Census defines these by its own identity:
 
 ```
 Total Revenue = General + Utility + Liquor Store + Insurance Trust
 ```
 
-Two things worth knowing before switching to `"total"`:
+Two things to know before switching to `"total"`. **Utility revenue is large
+for cities** — measured on the bundled fixture, utility plus liquor store is
+15.9% of city revenue, against 1.2% for states and 1.7% for counties. And the
+**employee-retirement (`X`) codes stop at FY2016**, when those systems moved to
+the separate Annual Survey of Public Pensions, so a `"total"` series steps down
+at the FY2016/FY2017 boundary for reasons of collection scope, not revenue
+(series breaks `SB197`–`SB209`).
 
-- **Utility revenue is large for cities.** Measured on the bundled fixture,
-  utility plus liquor store revenue is 15.9% of city (type 2) revenue, versus
-  1.2% for states and 1.7% for counties. `general` excludes it by definition.
-- **The employee-retirement (`X`) codes stop at FY2016**, when those systems
-  moved out of the annual finance file into the separate Annual Survey of
-  Public Pensions. A `"total"` series therefore steps down at the
-  FY2016/FY2017 seam for reasons of collection scope, not revenue (series
-  breaks `SB197`–`SB202`, in the corpus's `series_breaks` table).
+### Reporting coverage: the Census is only sometimes a census
 
-## Developer notes
+**The Census of Governments is a complete enumeration only in years ending in
+2 and 7.** Every other year is a sample, and the sample varies enormously —
+measured on the bundled fixture, Wisconsin's 608-city universe rolls up 597
+governments in FY2012 and 112 in FY2019.
 
-### 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:
+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
-devtools::test()   # uses bundled fixture, no credentials required
+attr(rollup, "provenance")$coverage
+# per-year n_units_expected, n_units_collected, n_units_reporting, is_census_year
 ```
 
-### Releasing against the live corpus
+`cog_geographic_rollup()`, `cog_peer_compare()` and `cog_find_peers()` take a
+`coverage` argument — `"all"` (default), `"census"` (census years only), or
+`"consistent"` (only units reporting in every requested year, a balanced
+panel).
 
-Before cutting a release, run the test suite against the published corpus to
-catch any drift between the fixture and the real data:
+`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
+
+Before FY2012, an absent cell means Census published `$0`. From FY2012 on, it
+means not reported. `cog_spending(..., complete = TRUE)` fills the requested
+grid and labels every row with which it is, via `value_source`:
+
+| `value_source` | meaning | `amt_nominal` |
+|---|---|---|
+| `reported` | the corpus carries this cell | as published |
+| `census_zero` | dense-source year (≤ FY2011), absent — Census published `$0` | `0` |
+| `not_reported` | sparse-source year (≥ FY2012), absent — unknown | `NA` |
+
+That `NA` is deliberate. Filling a modern absence with `0` would invent data.
+
+### Series breaks surface on their own
+
+Catalogued breaks that intersect your query appear in provenance whether or not
+you went looking for them — `series_break_refs` for breaks in a specific item code, and
+`corpus_break_refs` for caveats about the corpus as a whole (dollar precision
+across the 1976/1977 boundary, the FY2017 identifier change, the FY2012
+dense→sparse representation change). `cog_explain()` prints both.
+
+## How to cite
 
 ```r
-Sys.setenv(USCOGDATA_URL = "<published-corpus-url-with-trailing-slash>")
-devtools::test()
+citation("uscogdata")
 ```
 
-When the live-corpus run is clean, strip the fixture from the built package by
-adding this line to `.Rbuildignore`:
+The corpus itself is published under CC-BY-4.0. Cite it as:
 
-```
-^inst/extdata/fixture_corpus$
-```
+> Civilytics Consulting. US Census of Governments finance corpus.
+> https://huggingface.co/datasets/civilytics/us-cog-finance
 
-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.
+## Contributing
+
+Development happens on [Gitea](https://gitea.civilytics.org/Civilytics/uscogdata);
+[GitHub](https://github.com/civilytics/uscogdata) is a mirror that accepts
+issues and pull requests. See [CONTRIBUTING.md](CONTRIBUTING.md) for how a
+patch gets from there to here.
+
+## License
+
+MIT © Civilytics Consulting LLC. See [LICENSE.md](LICENSE.md).

_pkgdown.yml

@@ -1,4 +1,5 @@
-url: ~
+url: https://civilytics.r-universe.dev/uscogdata
+
 template:
   bootstrap: 5
 
@@ -15,11 +16,26 @@ reference:
       - cog_gov_search
       - cog_basket_resolution
       - cog_basket_unresolved
-  - title: Session
+  - title: Comparison & aggregation
+    desc: Peer cohorts and geographic aggregates.
     contents:
-      - has_keyword("internal")
+      - cog_find_peers
+      - cog_peer_compare
+      - cog_geographic_rollup
+  - title: Corpus metadata
+    desc: >
+      What the corpus contains, where a given result came from, and how to
+      hold a local copy of it.
+    contents:
+      - cog_categories
+      - cog_recipes
+      - cog_manifest
+      - cog_explain
+      - cog_mirror
 
 articles:
-  - title: Getting started
+  - title: Concepts
     navbar: ~
-    contents: []
+    contents:
+      - total-spending
+      - population-denominators

inst/extdata/fixture_corpus/manifest.json

@@ -1,7 +1,7 @@
 {
   "schema_version": 6,
-  "built_at": "2026-07-31T00:47:27Z",
-  "pipeline_commit": "aadb46b",
+  "built_at": "2026-08-03T16:51:32Z",
+  "pipeline_commit": "e7394a4",
   "fixture_note": "Four-year (2011, 2012, 2019, 2020) fixture for uscogdata tests. Full corpus available via USCOGDATA_URL. Regenerated from the sparsified schema-v6 corpus: the wide era (<= FY2011) no longer stores explicit zeros, so FY2011 absence means Census published $0 while FY2012+ absence means not reported. representation.parquet and code_set.parquet carry that rule and ship in full, as do every other metadata table in the publish tree. 2011/2012 straddle both the wide-aggregate -> modern-leaf format boundary (exercised by basis=\"harmonized\" and recipe= queries) and the dense -> sparse representation boundary (SB194); 2019/2020 retain the prior per-capita/CPI regression anchors. Regenerated via data-raw/regenerate_fixture_corpus.R.",
   "data_vintage": {
     "source_vintages": {
@@ -105,7 +105,7 @@
       },
       {
         "path": "data/series_breaks.parquet",
-        "sha256": "06dcc995ff533e57cc65fa25086cc9bf83ba592c58bf7cc99269dc2576f69944",
+        "sha256": "731998516cd802f63fcf7fb66053c7a62b7be955ab0794cad4a4979cb7628b87",
         "description": "series_breaks.parquet"
       },
       {

inst/schemas/provenance-v1.json

@@ -22,8 +22,8 @@
       "description": "How the intergovernmental leg was assembled; null for 'primary' and 'direct'."
     },
     "expenditure_concept_direct_suppressed": {
-      "type": "boolean",
-      "description": "TRUE when expenditure_concept = 'total' and at least one requested (year, category) has intergovernmental rows but NO Direct rows in this corpus (typically a legacy aggregate-only family) -- those result rows report the intergovernmental leg alone, not Direct + IG. Always FALSE for expenditure_concept = 'primary' or 'direct'. See the affected rows' `notes` for the recovering recipe, if any."
+      "type": ["boolean", "null"],
+      "description": "TRUE when expenditure_concept = 'total' and at least one requested (year, category) has intergovernmental rows but NO Direct rows in this corpus (typically a legacy aggregate-only family) -- those result rows report the intergovernmental leg alone, not Direct + IG. Always FALSE for expenditure_concept = 'primary' or 'direct'. null (NA) when expenditure_concept = 'total' AND category = 'All Categories': the detector keys on per-category rows, which that mode collapses, so suppression cannot be computed -- see `expenditure_concept_note`. See the affected rows' `notes` for the recovering recipe, if any."
     },
     "revenue_concept": {
       "type": "string",
@@ -33,7 +33,48 @@
     },
     "harmonization":  { "type": "object" },
     "recipe":         { "type": ["object", "null"] },
-    "suggestions":    { "type": "array" },
+    "suggestions": {
+      "type": "array",
+      "description": "Harmonization recipes that would fill incomplete coverage in the requested years for this government. Empty on a healthy query, on an un-scoped (category = NULL) query, on basis = 'raw', and on a recipe = query (which resolves its own coverage).",
+      "items": {
+        "type": "object",
+        "required": ["recipe_id", "label", "available_years", "hint", "ig_recipe_id",
+                     "trigger", "suppressed_amount", "suppressed_years", "suppressed_codes"],
+        "properties": {
+          "recipe_id": { "type": "string" },
+          "label": { "type": "string" },
+          "available_years": {
+            "type": "array",
+            "items": { "type": "integer" },
+            "description": "[year_min, year_max] of the recipe's component coverage."
+          },
+          "hint": { "type": "string" },
+          "ig_recipe_id": {
+            "type": ["string", "null"],
+            "description": "The intergovernmental (M/L) counterpart recipe covering the same function suffixes, or null. Never set for revenue recipes."
+          },
+          "trigger": {
+            "type": "string",
+            "enum": ["empty_year", "suppressed_component"],
+            "description": "Why this fired. 'empty_year': the result has no rows at all in a requested year. 'suppressed_component': the result HAS rows, but a component code carries dollars this government reports in the requested years that the verb's underlying long view structurally excludes -- aggregate-published, carrying no harmonized code, or absent from summary_categories. This is NOT the same thing as 'excluded from the result': a component present in the view under a different category (a scoping choice, e.g. a different `category` or a narrower `expenditure_concept`) contributes 0 and never fires. 'empty_year' wins when both apply, being the stronger claim; the suppressed_* fields are populated either way, using the same underlying-view measurement, and can be 0 even on an 'empty_year' fire."
+          },
+          "suppressed_amount": {
+            "type": "number",
+            "description": "Full US dollars this government reports, in the recipe's component codes, in the requested years, that the verb's underlying long view structurally excludes (aggregate-published, carrying no harmonized code, or absent from summary_categories) -- summed across those years. This is NOT the same quantity as 'what the result excludes': a component present in the view under a different category or a narrower `expenditure_concept` is scoped out on purpose, counts as 0 here, and is not suppression. 0 does not always mean full coverage -- see 'trigger' and 'empty_year'. May be negative where Census publishes a negative `amt` for the excluded rows."
+          },
+          "suppressed_years": {
+            "type": "array",
+            "items": { "type": "integer" },
+            "description": "The requested years contributing to suppressed_amount."
+          },
+          "suppressed_codes": {
+            "type": "array",
+            "items": { "type": "string" },
+            "description": "The excluded component item codes, sorted."
+          }
+        }
+      }
+    },
     "scope":          { "type": "object" },
     "codes_summed":   { "type": "object" },
     "aggregate_fallback": { "type": ["object", "null"] },

inst/sql/10-long.sql

@@ -1,3 +1,7 @@
 CREATE OR REPLACE VIEW long AS
 SELECT *
-FROM read_parquet('{url}data/long/**/*.parquet', hive_partitioning = true);
+-- {long_files} carries its own quoting: a bracketed list of every partition
+-- the manifest enumerates, or a single quoted glob on fallback. Do NOT wrap
+-- it in quotes. See .long_files_sql() in R/views.R for why a glob alone
+-- cannot work over HTTP.
+FROM read_parquet({long_files}, hive_partitioning = true);

man/cog_balances.Rd

@@ -5,18 +5,23 @@
 \title{Cash and security holdings for one or more governments}
 \usage{
 cog_balances(
-  govid,
+  govid = NULL,
   years,
   category = NULL,
   per_capita = FALSE,
   adjust_to_year = NULL,
   basis = c("harmonized", "raw"),
-  recipe = NULL
+  recipe = NULL,
+  state = NULL,
+  type = NULL,
+  limit = NULL,
+  offset = NULL
 )
 }
 \arguments{
 \item{govid}{Canonical govid(s): a character vector, or a data frame with a
-`canonical_govid` column (e.g. from [cog_gov_search()]).}
+`canonical_govid` column (e.g. from [cog_gov_search()]). `NULL` to name
+the cohort by `state`/`type` instead.}
 
 \item{years}{Integer vector of fiscal years.}
 
@@ -28,7 +33,12 @@ argument: for holdings, `category` is a strict coarsening of
 every combination would be either redundant or empty.
 `category = "Fund Balances"` is exactly the `general` family
 (`W01`/`W31`/`W61`). `balance_subtype` is returned, so a finer split is
-one `dplyr::filter()` away.}
+one `dplyr::filter()` away. The reserved pseudo-category
+`"All Categories"` (see [cog_spending()]) is **not** supported here and
+errors with class `uscogdata_all_categories_unsupported`: it sums a
+concept's subtype scope, and holdings are a stock with no concept
+vocabulary to sum across. Omit `category` to get every category broken
+out instead.}
 
 \item{per_capita}{Divide holdings by population. Note this is a **stock per
 resident** (reserves per person), which is *not* comparable to
@@ -44,6 +54,38 @@ and raw space are identical for holdings. Reported in
 \item{recipe}{Optional harmonization recipe id (see [cog_recipes()]).
 `"cash_securities_z77_wide"` and `"cash_securities_z78_wide"` bridge the
 wide era to the modern one.}
+
+\item{state, type}{Name the cohort by predicate instead of by id: `state` is
+  a 2-letter USPS abbreviation (or a FIPS code) and `type` is one of
+  `"state"`, `"county"`, `"city"`, `"township"` (or the integer `0:3`) --
+  the same vocabulary, and the same internal coercion, as
+  [cog_gov_search()]. Both default to `NULL`.
+
+  The cohort is then expressed as a subquery against `canonical_fips_xwalk`
+  inside each statement rather than round-tripped through R as a literal id
+  list. For a fleet-scale cohort that is the difference between a
+  301,591-character `IN` list re-parsed in 5--8 statements per call and a
+  constant-size predicate: measured at **94 ms versus 449 ms** for the same
+  FY2022 aggregate over the 20,106-government `type = "city"` cohort, within
+  7% of the no-filter floor.
+
+  Supplying `govid` **and** `state`/`type` INTERSECTS them -- the
+  governments in `govid` that also match the predicate -- rather than one
+  silently taking precedence. Naming no cohort at all (`govid`, `state` and
+  `type` all `NULL`) aborts with class `uscogdata_no_cohort`.
+
+  When the cohort is named by predicate, `provenance$scope$govids_found`
+  and `govids_missing` are empty -- there is no id list to report against --
+  and `provenance$scope$cohort` carries `state`, `type` and
+  `n_governments` instead. A `govid`-named cohort reports exactly as before.}
+
+\item{limit}{Maximum number of result rows to return, pushed into the SQL
+rather than applied after materializing every row. `NULL` (default)
+returns everything. Cannot be combined with `recipe` -- see `offset` and
+`total_rows`.}
+
+\item{offset}{Rows to skip before `limit` starts counting (0-based).
+Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.}
 }
 \value{
 Tibble with columns `year`, `canonical_govid`, `gov_name`,
@@ -62,6 +104,10 @@ Tibble with columns `year`, `canonical_govid`, `gov_name`,
   and `truncated` (the observed subtypes whose coverage falls short of the
   requested years). `expenditure_concept`/`revenue_concept` are `NA` --
   holdings are a stock, not a flow, so neither concept vocabulary applies.
+
+  When `limit` is set, also carries a `total_rows` attribute: the full
+  unpaginated row count, computed by the same query (`COUNT(*) OVER()`)
+  rather than a second scan.
 }
 \description{
 Returns Census cash-and-security holdings (`category_type = "balance"`):

man/cog_categories.Rd

@@ -16,7 +16,10 @@ balance), `"spending"`, `"revenue"`, or `"balance"`.}
 \value{
 Tibble with columns `category`, `category_type`, `subtype`,
   `n_codes`, `item_codes` (comma-separated, alphabetical). Sorted by
-  `category_type`, `category`, `subtype`.
+  `category_type`, `category`, `subtype`. Includes one row per flow for the
+  reserved pseudo-category `"All Categories"`, which carries `NA` for
+  `subtype`, `n_codes` and `item_codes` because it is a query mode rather
+  than a crosswalk entry — see [cog_spending()]'s `category` argument.
 }
 \description{
 Returns the category taxonomy exposed by the corpus's

man/cog_explain.Rd

@@ -21,3 +21,33 @@ Prints the structured provenance attached to a tibble returned by any
 `cog_*` verb, or returns it as a list for downstream use (MCP tools,
 dashboards, JSON export).
 }
+\section{Two kinds of series break}{
+
+Catalogued breaks reach you without being asked for, in two disjoint
+fields, because a caveat about one series and a caveat about the whole
+corpus are different claims:
+
+* **`series_break_refs`** — breaks matched against the item codes actually
+  present in this result. A break in one code you queried.
+* **`corpus_break_refs`** — breaks catalogued with `fin_code = "ALL"`,
+  which are statements about the corpus rather than about any one code:
+  dollar precision across the 1976/1977 boundary (`SB085`), imputation
+  exclusion from FY2002 (`SB087`), the FY2012 dense-to-sparse
+  representation change (`SB194`), and the FY2017 government-identifier
+  change (`SB086`). These are selected on the break-year window alone.
+
+`SB194` is the one most likely to matter: a query spanning FY2011 to FY2012
+crosses the boundary where an absent cell stops meaning "Census published
+$0" and starts meaning "not reported".
+}
+
+\section{Other provenance blocks}{
+
+`transformations$units_conversion` records the `$1,000s`-to-dollars
+multiply that every amount column has already had applied.
+`transformations$per_capita` records the population denominator and its
+year range. `coverage` and `coverage_mode` appear on multi-government
+results (see [cog_geographic_rollup()]). `completion` appears when
+`complete = TRUE`. `balance_caveats` appears on [cog_balances()] results.
+}
+

man/cog_geographic_rollup.Rd

@@ -20,7 +20,11 @@ cog_geographic_rollup(
 `canonical_govid` values. At least one layer required.}
 
 \item{category}{Single category name or character vector (passed through
-to [cog_spending()]).}
+to [cog_spending()]), or the reserved `"All Categories"` for one summed
+row per `(year, canonical_govid, subtype)` covering every category in the
+concept's scope. `"All Categories"` is the efficient way to build a
+geographic total: without it a caller must issue one rollup per category
+and sum the results themselves.}
 
 \item{years}{Integer vector of years.}
 
@@ -51,11 +55,13 @@ Direct spending); `"primary"` and `"direct"` combine safely.}
     a balanced panel.
 
   Regardless of mode, `provenance$coverage` always carries per-year
-  `n_units_reporting`, `n_units_expected` and `is_census_year`, and
-  `provenance$coverage_mode` records the mode. `is_census_year` is 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. `n_units_reporting` is the number that tells the truth.}
+  `n_units_expected`, `n_units_collected`, `n_units_reporting` and
+  `is_census_year`, and `provenance$coverage_mode` records the mode.
+  `is_census_year` is a statement about the **survey calendar**, never a
+  claim of completeness: FY1967 is a census year in which only 97 of
+  Wisconsin's 608 cities report. `n_units_reporting` is
+  category-conditional and is not a response rate on its own -- see
+  "Reading `coverage`" below for what each counter answers.}
 }
 \value{
 Tibble with columns `year`, `layer`, `canonical_govid`, `gov_name`,
@@ -80,3 +86,32 @@ the result. The dropped govids are recorded in
 (gov type 4) and school districts (gov type 5) from per-capita rollups
 by design — see `vignette('population-denominators')`.
 }
+\section{Reading `coverage`}{
+
+`provenance$coverage` carries three per-year counters:
+
+  * `n_units_expected` -- how many governments you asked about.
+  * `n_units_collected` -- how many of those appear in the corpus at all
+    that year (in ANY category), separating sampling from real zeros.
+  * `n_units_reporting` -- how many have rows for the SPECIFIC category you
+    requested. This is always <= n_units_collected: a government can be
+    collected but have no rows for "Police" because it contracts policing
+    to the county sheriff, not because it wasn't surveyed.
+
+**`n_units_reporting` is category-conditional** and therefore **not a
+response rate**: `n_units_reporting / n_units_expected` conflates sampling
+(never collected) with real zeros (collected but spends nothing in your
+category). Use `n_units_collected / n_units_expected` for the true
+collection rate, and `n_units_reporting / n_units_collected` for category
+participation among collected units.
+
+In FY2022 — a complete census year — Georgia reports 393 of 567 cities for
+`category = "Police"`; the 174-city gap is overwhelmingly cities that
+contract policing to the county sheriff, not non-response.
+
+The comparison that *is* valid is the same category across a census year
+(ending in 2 or 7) and a sample year, where the real-zero component is
+roughly constant and the difference reflects the survey cycle. `is_census_year`
+marks which is which.
+}
+

man/cog_gov_search.Rd

@@ -4,7 +4,13 @@
 \alias{cog_gov_search}
 \title{Search for governments by name, state, and/or type}
 \usage{
-cog_gov_search(name = NULL, state = NULL, type = NULL)
+cog_gov_search(
+  name = NULL,
+  state = NULL,
+  type = NULL,
+  limit = NULL,
+  offset = NULL
+)
 }
 \arguments{
 \item{name}{Character vector of place name(s). Length 1 = utility mode;
@@ -19,12 +25,26 @@ all entries; otherwise must match `length(name)`.}
 in basket mode (recycles from length 1). Excluded types `4`/`5` (or
 `"special_district"` / `"school_district"`) trigger an explanatory
 message and an empty result.}
+
+\item{limit}{Maximum number of rows to return, applied in SQL. `NULL`
+(default) returns every match -- which, with no other filter, is the
+entire crosswalk. Utility mode only: pagination has no meaning in basket
+mode, where the result is one resolved row per requested name in input
+order, and is refused there with class
+`uscogdata_basket_pagination_conflict`.}
+
+\item{offset}{Rows to skip before `limit` starts counting (0-based).
+Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.}
 }
 \value{
 A tibble of `canonical_fips_xwalk` rows. In utility mode, all
-  matches sorted by `population_acs` desc. In basket mode, resolved
-  rows in input order, with `attr(., "resolution")` set to the
-  sidecar tibble.
+  matches sorted by `population_acs` desc, ties broken by
+  `canonical_govid`. In basket mode, resolved rows in input order, with
+  `attr(., "resolution")` set to the sidecar tibble.
+
+  When `limit` is set, carries a `total_rows` attribute: the full
+  unpaginated match count, computed by the same query (`COUNT(*) OVER()`)
+  rather than a second scan.
 }
 \description{
 Resolves human-readable place names into rows of `canonical_fips_xwalk`,

man/cog_peer_compare.Rd

@@ -50,11 +50,13 @@ safely.}
     a balanced panel.
 
   Regardless of mode, `provenance$coverage` always carries per-year
-  `n_units_reporting`, `n_units_expected` and `is_census_year`, and
-  `provenance$coverage_mode` records the mode. `is_census_year` is 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. `n_units_reporting` is the number that tells the truth.
+  `n_units_expected`, `n_units_collected`, `n_units_reporting` and
+  `is_census_year`, and `provenance$coverage_mode` records the mode.
+  `is_census_year` is 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. `n_units_reporting` is
+  category-conditional and is not a response rate on its own -- see
+  "Reading `coverage`" below for what each counter answers.
 
   The comparison target is exempt from `"consistent"` balancing -- it is the
   subject of the comparison, not a member of the cohort -- and the
@@ -107,3 +109,32 @@ call. Those summary rows are quantiles **within each category**, not
 quantiles of each peer's total — see the `@return` section before summing
 them.
 }
+\section{Reading `coverage`}{
+
+`provenance$coverage` carries three per-year counters:
+
+  * `n_units_expected` -- how many governments you asked about.
+  * `n_units_collected` -- how many of those appear in the corpus at all
+    that year (in ANY category), separating sampling from real zeros.
+  * `n_units_reporting` -- how many have rows for the SPECIFIC category you
+    requested. This is always <= n_units_collected: a government can be
+    collected but have no rows for "Police" because it contracts policing
+    to the county sheriff, not because it wasn't surveyed.
+
+**`n_units_reporting` is category-conditional** and therefore **not a
+response rate**: `n_units_reporting / n_units_expected` conflates sampling
+(never collected) with real zeros (collected but spends nothing in your
+category). Use `n_units_collected / n_units_expected` for the true
+collection rate, and `n_units_reporting / n_units_collected` for category
+participation among collected units.
+
+In FY2022 — a complete census year — Georgia reports 393 of 567 cities for
+`category = "Police"`; the 174-city gap is overwhelmingly cities that
+contract policing to the county sheriff, not non-response.
+
+The comparison that *is* valid is the same category across a census year
+(ending in 2 or 7) and a sample year, where the real-zero component is
+roughly constant and the difference reflects the survey cycle. `is_census_year`
+marks which is which.
+}
+

man/cog_revenue.Rd

@@ -5,7 +5,7 @@
 \title{Summarized revenue by category}
 \usage{
 cog_revenue(
-  govid,
+  govid = NULL,
   years,
   category = NULL,
   per_capita = FALSE,
@@ -13,16 +13,31 @@ cog_revenue(
   basis = c("harmonized", "raw"),
   recipe = NULL,
   revenue_concept = c("general", "total"),
-  complete = FALSE
+  complete = FALSE,
+  limit = NULL,
+  offset = NULL,
+  state = NULL,
+  type = NULL
 )
 }
 \arguments{
-\item{govid}{Character vector of `canonical_govid` values.}
+\item{govid}{Character vector of `canonical_govid` values, or `NULL` to name
+the cohort by `state`/`type` instead. One of `govid`, `state`, or `type`
+is required.}
 
 \item{years}{Integer vector of years.}
 
 \item{category}{Character vector of category names (from
-`summary_categories.category`), or `NULL` for all categories.}
+`summary_categories.category`), or `NULL` for all categories broken out
+one row each. The reserved value `"All Categories"` instead returns a
+single summed row per `(year, canonical_govid, subtype)`, covering every
+category inside the requested concept's subtype scope. It cannot be
+combined with other category names, and it is not the same thing as
+`revenue_concept = "total"`: the concept chooses which subtypes are in
+scope, `"All Categories"` chooses whether rows inside that scope are
+broken out or summed. Because the result keeps one row per
+`revenue_subtype`, filtering the returned frame to
+`revenue_subtype == "own_source"` gives an own-source revenue total.}
 
 \item{per_capita}{If `TRUE`, adds `amt_per_capita_nominal` (and
 `amt_per_capita_real` when `adjust_to_year` is set) using the per-year
@@ -106,6 +121,39 @@ possibly-misleading `"harmonized"`/`"raw"` value.}
   `recipe` or with `expenditure_concept = "total"` (class
   `uscogdata_complete_unsupported`) — neither draws its cells from
   `code_set`.}
+
+\item{limit}{Maximum number of result rows to return, pushed into the SQL
+query itself (`LIMIT`/`OFFSET`) rather than applied after the full
+result is materialized. `NULL` (the default) returns every matching row,
+exactly as before this parameter existed. Mutually exclusive with
+`recipe` and with `complete = TRUE` -- see `offset` and `total_rows`.}
+
+\item{offset}{Rows to skip before `limit` starts counting (0-based).
+Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.}
+
+\item{state, type}{Name the cohort by predicate instead of by id: `state` is
+  a 2-letter USPS abbreviation (or a FIPS code) and `type` is one of
+  `"state"`, `"county"`, `"city"`, `"township"` (or the integer `0:3`) --
+  the same vocabulary, and the same internal coercion, as
+  [cog_gov_search()]. Both default to `NULL`.
+
+  The cohort is then expressed as a subquery against `canonical_fips_xwalk`
+  inside each statement rather than round-tripped through R as a literal id
+  list. For a fleet-scale cohort that is the difference between a
+  301,591-character `IN` list re-parsed in 5--8 statements per call and a
+  constant-size predicate: measured at **94 ms versus 449 ms** for the same
+  FY2022 aggregate over the 20,106-government `type = "city"` cohort, within
+  7% of the no-filter floor.
+
+  Supplying `govid` **and** `state`/`type` INTERSECTS them -- the
+  governments in `govid` that also match the predicate -- rather than one
+  silently taking precedence. Naming no cohort at all (`govid`, `state` and
+  `type` all `NULL`) aborts with class `uscogdata_no_cohort`.
+
+  When the cohort is named by predicate, `provenance$scope$govids_found`
+  and `govids_missing` are empty -- there is no id list to report against --
+  and `provenance$scope$cohort` carries `state`, `type` and
+  `n_governments` instead. A `govid`-named cohort reports exactly as before.}
 }
 \value{
 Tibble with columns `year`, `canonical_govid`, `gov_name`,

man/cog_spending.Rd

@@ -5,7 +5,7 @@
 \title{Summarized spending by category}
 \usage{
 cog_spending(
-  govid,
+  govid = NULL,
   years,
   category = NULL,
   per_capita = FALSE,
@@ -13,16 +13,31 @@ cog_spending(
   basis = c("harmonized", "raw"),
   recipe = NULL,
   expenditure_concept = c("primary", "direct", "total"),
-  complete = FALSE
+  complete = FALSE,
+  limit = NULL,
+  offset = NULL,
+  state = NULL,
+  type = NULL
 )
 }
 \arguments{
-\item{govid}{Character vector of `canonical_govid` values.}
+\item{govid}{Character vector of `canonical_govid` values, or `NULL` to name
+the cohort by `state`/`type` instead. One of `govid`, `state`, or `type`
+is required.}
 
 \item{years}{Integer vector of years.}
 
 \item{category}{Character vector of category names (from
-`summary_categories.category`), or `NULL` for all categories.}
+`summary_categories.category`), or `NULL` for all categories broken out
+one row each. The reserved value `"All Categories"` instead returns a
+single summed row per `(year, canonical_govid, subtype)`, covering every
+category inside the requested concept's subtype scope. It cannot be
+combined with other category names, and it is not the same thing as
+`expenditure_concept = "total"`: the concept chooses which subtypes are in
+scope, `"All Categories"` chooses whether rows inside that scope are
+broken out or summed. Because the result keeps one row per
+`spend_subtype`, filtering the returned frame to
+`spend_subtype == "operations"` gives an operating-expenditure total.}
 
 \item{per_capita}{If `TRUE`, adds `amt_per_capita_nominal` (and
 `amt_per_capita_real` when `adjust_to_year` is set) using the per-year
@@ -97,7 +112,12 @@ possibly-misleading `"harmonized"`/`"raw"` value.}
   component (when one exists), and
   `provenance$expenditure_concept_direct_suppressed` is `TRUE` -- the
   figure in those rows is the intergovernmental leg alone, not Direct +
-  IG.}
+  IG. When `category = "All Categories"` is combined with
+  `expenditure_concept = "total"`, this detection cannot run (it keys on
+  per-category rows, which all-categories mode collapses to one literal
+  value), so `expenditure_concept_direct_suppressed` is `NA` rather than a
+  possibly-false `FALSE`; query an explicit `category` to get a real
+  answer.}
 
 \item{complete}{If `TRUE`, fill the requested grid so that a cell the
   corpus does not carry still appears, labelled with **why** it is
@@ -121,6 +141,39 @@ possibly-misleading `"harmonized"`/`"raw"` value.}
   `recipe` or with `expenditure_concept = "total"` (class
   `uscogdata_complete_unsupported`) — neither draws its cells from
   `code_set`.}
+
+\item{limit}{Maximum number of result rows to return, pushed into the SQL
+query itself (`LIMIT`/`OFFSET`) rather than applied after the full
+result is materialized. `NULL` (the default) returns every matching row,
+exactly as before this parameter existed. Mutually exclusive with
+`recipe` and with `complete = TRUE` -- see `offset` and `total_rows`.}
+
+\item{offset}{Rows to skip before `limit` starts counting (0-based).
+Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.}
+
+\item{state, type}{Name the cohort by predicate instead of by id: `state` is
+  a 2-letter USPS abbreviation (or a FIPS code) and `type` is one of
+  `"state"`, `"county"`, `"city"`, `"township"` (or the integer `0:3`) --
+  the same vocabulary, and the same internal coercion, as
+  [cog_gov_search()]. Both default to `NULL`.
+
+  The cohort is then expressed as a subquery against `canonical_fips_xwalk`
+  inside each statement rather than round-tripped through R as a literal id
+  list. For a fleet-scale cohort that is the difference between a
+  301,591-character `IN` list re-parsed in 5--8 statements per call and a
+  constant-size predicate: measured at **94 ms versus 449 ms** for the same
+  FY2022 aggregate over the 20,106-government `type = "city"` cohort, within
+  7% of the no-filter floor.
+
+  Supplying `govid` **and** `state`/`type` INTERSECTS them -- the
+  governments in `govid` that also match the predicate -- rather than one
+  silently taking precedence. Naming no cohort at all (`govid`, `state` and
+  `type` all `NULL`) aborts with class `uscogdata_no_cohort`.
+
+  When the cohort is named by predicate, `provenance$scope$govids_found`
+  and `govids_missing` are empty -- there is no id list to report against --
+  and `provenance$scope$cohort` carries `state`, `type` and
+  `n_governments` instead. A `govid`-named cohort reports exactly as before.}
 }
 \value{
 Tibble with columns `year`, `canonical_govid`, `gov_name`,
@@ -130,7 +183,11 @@ Tibble with columns `year`, `canonical_govid`, `gov_name`,
   and `value_source` when `complete = TRUE`.
   Carries a `provenance` attribute matching `inst/schemas/provenance-v1.json`,
   whose `completion` block reports `applied`, `rows_filled`, and the
-  per-year `absence_means` rule that was applied.
+  per-year `absence_means` rule that was applied. When `limit` is set,
+  also carries a `total_rows` attribute: the full unpaginated row count,
+  computed by the same query (`COUNT(*) OVER()`) rather than a second
+  round trip -- so a caller walking pages never has to ask "how many are
+  there" separately.
 }
 \description{
 One row per `(year, canonical_govid, spend_subtype, category)`. Amounts are

pm/JOURNAL.md

@@ -0,0 +1,14 @@
+# Project journal
+
+Append-only, newest first. **Entries are never edited** — the value of this file is
+that it records what was believed at the time, including the parts that turned out
+wrong. Where things stand *today* is in `STATUS.md`, which is generated.
+
+Four lines per entry. The analysis belongs in the issue or the decision record; this
+file carries the reasoning and the pointers.
+
+- **Why** — the driver. The one line git cannot reconstruct later.
+- **Obligates** — issues this change created elsewhere. Numbers, not prose.
+- **Refs** — commits, issues, decision records.
+
+---

pm/STATUS.md

@@ -0,0 +1,67 @@
+# Project status
+
+> Between the compass markers is generated. Edit the sources, not this.
+
+<!-- compass:begin -->
+<!-- compass:board -->
+
+## Where this stands
+
+uscogdata is at 0.4.0 and its public surface is settled: the query verbs, the cohort
+predicates added in this release, and the provenance contract every verb returns.
+
+The six open issues split cleanly. Two are API work carried out of the #9 review pass
+and deliberately deferred there rather than fixed in that branch. Three concern the
+corpus layer, and the largest of them, partition-level caching, was named the single
+highest-leverage change on the remote path before being deferred. One, the
+data-correction intake (#52), is a decision rather than a task: it was parked during
+the 0.3.0 design, and the API announcement waits on it, because without it the corpus
+cannot make the "traceable and correctable" claim that most distinguishes it from
+Census's own files.
+
+Nothing here is blocked on anything else, so the ordering is a judgement about value
+rather than a dependency graph.
+
+Compass's own files moved out of `docs/` this session. They were sitting inside
+pkgdown's output directory, and `pkgdown::clean_site()` deletes every top-level entry
+there except `CNAME` and `dev` — asked directly, it listed `docs/pm` and
+`docs/decisions` among the 28 it would remove, with the guard that would have stopped
+it satisfied by `docs/pkgdown.yml`. They are in `pm/` now. Nothing was lost: the
+journal had no entries and there were no decision records yet, which made this the
+cheapest moment to move. The `.gitignore` workaround that re-included two children of
+an excluded `docs/` is gone with it.
+
+## Ready to work on next
+
+- **#34** cog_revenue() offers expenditure recipes as suggestions: scope the candidate query by category_type · `ws/api` — nothing is blocking it; something is currently wrong
+- **#36** n_units_reporting is category-conditional and cannot be read as a response rate · `ws/corpus` — nothing is blocking it; owed work from an earlier change
+- **#2** Extend population data to be households as an alternate spending denominator · `ws/corpus` — nothing is blocking it
+- **#33** Decompose .build_suggestions() (106 lines) into named helpers · `ws/api` — nothing is blocking it
+- **#52** Release 11/11: design the data-correction intake (deferred; gates the API announcement) · `ws/corpus` — nothing is blocking it
+- **#64** Partition-level caching: R/cache.R is still a stub, and the remote path pays for it every session · `ws/corpus` — nothing is blocking it
+
+## Workstreams
+
+| Stream | Commits since | Open | Debt | Owes docs |
+|---|---|---|---|---|
+| Query verbs and results | 77 | 2 | 0 | no |
+| Corpus, mirror, provenance | 39 | 4 | 1 | no |
+| Vignettes and guides | 34 | 0 | 0 | **yes** |
+
+## CI
+
+![R-CMD-check](https://gitea.civilytics.org/Civilytics/uscogdata/actions/workflows/ci.yml/badge.svg?branch=main)
+![Mirror to GitHub](https://gitea.civilytics.org/Civilytics/uscogdata/actions/workflows/mirror-github.yml/badge.svg?branch=main)
+
+<details>
+<summary>Dependency graph and detail</summary>
+
+_Nothing blocks anything else, so there is no graph to draw._
+
+- Marker: `none` (no journal entry yet)
+- Commits since: 165
+- Open issues: 6
+
+</details>
+
+<!-- compass:end -->

pm/compass.toml

@@ -0,0 +1,44 @@
+[project]
+name  = "uscogdata"
+forge = "Civilytics/uscogdata"
+
+# Three strands that go stale independently: what the verbs return, what the
+# corpus is and how it is mounted, and how both are explained to a reader.
+
+[[workstream]]
+id    = "api"
+title = "Query verbs and results"
+paths = [
+  "R/revenue.R", "R/spending.R", "R/balances.R", "R/peers.R", "R/search.R",
+  "R/categories.R", "R/recipes.R", "R/rollup.R", "R/explain.R", "R/basket.R",
+  "R/suggestions.R", "R/suppression.R", "R/complete.R", "R/cohort.R",
+  "R/basis.R", "R/adjust.R", "R/pagination.R",
+]
+docs  = ["vignettes/*.Rmd", "README.md"]
+
+[[workstream]]
+id    = "corpus"
+title = "Corpus, mirror, provenance"
+paths = [
+  "R/manifest.R", "R/mirror.R", "R/cache.R", "R/session.R", "R/provenance.R",
+  "R/coverage.R", "R/config.R", "R/views.R", "R/series_breaks.R",
+  "R/balance_caveats.R", "R/zzz.R", "data-raw/**", "inst/sql/**",
+]
+docs  = ["vignettes/*.Rmd", "NEWS.md"]
+
+[[workstream]]
+id    = "docs"
+title = "Vignettes and guides"
+paths = ["vignettes/**", "README.md", "_pkgdown.yml", "NEWS.md"]
+docs  = []
+
+[roborev]
+project_guidelines = [
+  "Every verb calls .ensure_session() first, then queries via DBI::dbGetQuery().",
+  "A verb's return value is always a tbl_df carrying a provenance attribute.",
+  "govid inputs always go through .coerce_govid_input(); it accepts a character vector or a data frame.",
+  "SQL has two layers: view definitions are numbered .sql files in inst/sql/ registered by .register_views(); query construction is inline sprintf() in R. Add a view as a file; build a query in R.",
+  "No arrow dependency -- DuckDB reads parquet natively.",
+  "withr is Suggests-only and must appear in tests alone.",
+  "Tests must pass offline against the bundled fixture; tests/testthat/setup.R sets USCOGDATA_URL for that.",
+]

pm/decisions/README.md

@@ -0,0 +1,12 @@
+# Decisions
+
+One file per decision, numbered and immutable. A decision that changes is superseded
+by a new record, never edited in place — the old reasoning is the point.
+
+The table below is **generated** by `compass:decide`. Do not hand-edit it.
+
+<!-- compass:begin decisions -->
+| # | Date | Decision | Status |
+|---|---|---|---|
+| — | — | *No decisions recorded yet.* | — |
+<!-- compass:end decisions -->

specs/2026-08-08-public-release-design.md

@@ -0,0 +1,296 @@
+# `uscogdata` 0.3.0 — public release
+
+**Date:** 2026-08-08 · **Status:** design, awaiting approval
+**Scope:** release-readiness, README, NEWS. Distribution mechanics recorded here as
+decided, sequenced after the package is clean.
+
+`uscogdata` is feature-complete and the corpus it reads has been public on
+HuggingFace since 2026-08-07 (294 downloads as of this writing). The API built on
+it is live. What does not exist is a public *package*: the repo is private, there
+is no install path, and — measured, not assumed — **a stranger who installed it
+today could not read the corpus at all.**
+
+This spec covers making that untrue.
+
+## Decisions locked
+
+| Decision | Choice |
+|---|---|
+| Canonical source | `gitea.civilytics.org/Civilytics/uscogdata`, flipped public |
+| Public mirror | `github.com/civilytics/uscogdata` — issues, PRs, multi-OS check, CDN |
+| Mirror mechanism | Gitea Actions non-force `git push` (not a push mirror) |
+| Binaries | `civilytics.r-universe.dev`, registry pinned to a release tag |
+| Author of record | Jared E. Knowles `<jared@civilytics.com>`, ORCID `0000-0003-0005-9478` |
+| Copyright | Civilytics Consulting LLC (`cph`, `fnd`) |
+| License | MIT (package) · CC-BY-4.0 (corpus) |
+| Corrections intake | Deferred — see *Out of scope* |
+| Other packages | Parked until this one walks the path end to end |
+
+## P0 — the corpus is unreachable
+
+Two independent faults, either of which alone is fatal.
+
+**No corpus URL exists.** `R/config.R` defaults to the literal
+`REPLACE_WITH_SHARE_TOKEN` sentinel, and no file in the repo supplies a working
+one. A new user calling any verb gets `uscogdata_url_not_configured` with no path
+to resolution.
+
+**Remote reads are broken regardless.** Every partitioned view globs:
+
+```sql
+FROM read_parquet('{url}data/long/**/*.parquet', hive_partitioning = true)
+```
+
+DuckDB 1.5.5 refuses globs over generic HTTP. Its suggested
+`allow_asterisks_in_http_paths` escape hatch does not help — it forwards the
+literal `**/*` as a filename and 404s, because plain HTTP exposes no directory
+listing to expand against.
+
+The package therefore works only against a **local path**. That is how the API
+runs it (`CORPUS_HOST_PATH` is a host mount on maxwell) and how the tests run
+(bundled fixture), which is why the fault went unnoticed. The README's headline
+claim — *"Reads the published corpus directly from Nextcloud via DuckDB httpfs —
+no local bulk downloads required"* — is currently false.
+
+### Fix: enumerate from the manifest, do not glob
+
+`manifest.json` already lists every partition under `files.long_partitions[]`
+with `path`, `year`, `sha256`, `row_count` and `size_bytes` — 56 of them.
+Substituting an explicit file list for the glob was measured against the
+published corpus on 2026-08-08:
+
+| Path | Result |
+|---|---|
+| `https://…/data/long/**/*.parquet` (default) | error — globs unsupported over HTTP |
+| same, `allow_asterisks_in_http_paths = true` | error — literal `**/*` 404s |
+| `hf://datasets/civilytics/us-cog-finance/…` glob | 46,148,034 rows |
+| **explicit list over plain https** | **46,148,034 rows** |
+
+`hive_partitioning = true` still recovers `year` from the paths under
+enumeration, so no downstream view or verb changes.
+
+Enumeration is preferred over `hf://` deliberately. It is **host-agnostic** —
+Nextcloud, HuggingFace, or any static server take the same code path — where
+`hf://` would tie the default to one vendor's protocol and still need
+special-casing, since manifest fetching goes through `httr2`, which cannot speak
+`hf://`. Enumeration also *removes* a dependency (globbing) rather than adding
+one, and the manifest's per-file `sha256` becomes available for integrity
+checking later.
+
+Views are registered from `inst/sql/` with `{url}` substitution in
+`R/views.R:.register_views()`. The list must be built once per session from the
+already-fetched manifest and substituted the same way, so the change is confined
+to view registration and does not touch verb code.
+
+### Fix: ship a working default
+
+`R/config.R`'s default becomes the public HuggingFace `resolve/main/` URL:
+CC-BY-4.0, no token to publish, CDN-backed, and it keeps maxwell's uplink out of
+the path — the same reasoning behind the GitHub mirror and r-universe.
+
+This means `library(uscogdata)` followed by a verb works with **zero
+configuration**, which is what makes the package demonstrable in a README and
+later in a post. `USCOGDATA_URL` and `options(uscogdata.url=)` continue to
+override, so the Nextcloud copy and local mirrors are unaffected.
+
+The `uscogdata_url_not_configured` error class stays — it still fires for an
+explicitly-set empty or placeholder URL — but ceases to be the default
+experience.
+
+### Consequence: `cog_mirror()` is promoted
+
+Measured cost of the remote default, from efron on a good connection:
+
+| | |
+|---|---|
+| Whole corpus | **190.6 MB**, 56 partitions, 46,148,034 rows, FY1967–FY2024 |
+| One government, one year | 1.5 s |
+| One government, all 56 years | 2.8 s |
+| Disk written | **0.00 MB** — range requests only; `external_file_cache` is in-memory |
+
+Nothing persists locally beyond the shared `httpfs` extension in `~/.duckdb` (a
+few MB, once per machine, across all DuckDB use). Costs are RAM and per-query
+bandwidth, since nothing caches between sessions.
+
+Those timings are raw scans. Real verbs additionally join crosswalks, resolve
+categories and assemble provenance, so end-to-end verb latency will be higher and
+**must be re-measured once the fix lands** — it cannot be measured today.
+
+The corpus being only 190.6 MB makes `cog_mirror()` a first-class option rather
+than a developer footnote. The README presents **both paths**:
+
+- **Remote (default, zero setup)** — trying it out, teaching, one-off questions.
+- **Mirrored (`cog_mirror()`, 190 MB once)** — repeated or heavy analysis,
+  offline work, reproducibility, or preferring not to depend on HuggingFace.
+
+The second is also the honest answer to the vendor-dependency question raised by
+defaulting to HuggingFace: **the escape hatch is one function call and 190 MB**,
+after which no analysis touches an external service. The README says so
+explicitly. That is the difference between a convenience default and lock-in.
+
+## Release-readiness fixes
+
+| # | Issue | Fix |
+|---|---|---|
+| 1 | `MaxCorpusSchema: 5` in DESCRIPTION; `.validate_schema()` accepts `4,5,6,7`; published corpus is **7** | `MaxCorpusSchema: 7` |
+| 2 | `^vignettes$` in `.Rbuildignore` — both vignettes absent from the installed package, while README tells users to run `vignette("total-spending")` | Remove `^vignettes$`, `^doc$`, `^Meta$`. Both vignettes build offline (`total-spending` reads the bundled fixture; `population-denominators` is `eval = FALSE`) |
+| 3 | `_pkgdown.yml` reference index covers 6 of 14 exports — pkgdown errors on missing topics | Add `cog_categories`, `cog_explain`, `cog_find_peers`, `cog_geographic_rollup`, `cog_manifest`, `cog_mirror`, `cog_peer_compare`, `cog_recipes`; set `url:` |
+| 4 | No `URL:` / `BugReports:` in DESCRIPTION | Add both, pointing at the GitHub mirror |
+| 5 | No `LICENSE.md`; `LICENSE` holder reads `Civilytics` | `usethis::use_mit_license("Civilytics Consulting LLC")` |
+| 6 | README instructs stripping the fixture at release | Delete that section — see below |
+| 7 | `Authors@R` is an org with no human | Jared E. Knowles `aut`/`cre` + ORCID; Civilytics Consulting LLC `cph`/`fnd` |
+
+**On #6.** The advice to add `^inst/extdata/fixture_corpus$` to `.Rbuildignore`
+is CRAN-sized thinking (5 MB limit) and this package is not going to CRAN.
+Stripping the 15 MB fixture would break `total-spending.Rmd`, which reads from
+it, and would leave r-universe and GitHub Actions unable to run the 28 test files
+without a corpus credential. **The fixture is what lets `R CMD check` pass
+anywhere with zero secrets** — precisely what public CI needs. It ships.
+
+## README
+
+The current README addresses someone standing inside the repo tree: status reads
+"Under active development (Phase 2 of the cog_pipeline project)", it points at
+`../cog_pipeline/docs/reader-specification.md`, the install line is commented
+out, and developer, testing and release sections sit above anything a user needs.
+
+Restructured around a stranger, in this order:
+
+1. **What this is** — one paragraph, and what the corpus covers (types 0–3,
+   FY1967–FY2024, 46M rows, 190.6 MB).
+2. **Install** — r-universe first (binaries), git second.
+3. **Quickstart that actually runs** — resolve a government, get its history,
+   print provenance. No configuration step.
+4. **Two ways to read the corpus** — remote default vs `cog_mirror()`, with the
+   measured numbers and the independence note.
+5. **Amounts are in full US dollars** — kept near the top. This is the errata
+   most likely to produce a wrong answer that looks plausible.
+6. **Concepts** — primary/direct/total spending, general/total revenue,
+   coverage. Condensed, linking to the vignettes for the full treatment.
+7. **How to cite** — `citation("uscogdata")`, corpus CC-BY-4.0 attribution.
+8. **Contributing** — canonical-on-Gitea PR flow.
+
+Developer notes, testing instructions and release procedure move to
+`CONTRIBUTING.md`. Every path reference to a sibling repo is removed or replaced
+with a URL that resolves for someone who has only this repo.
+
+## NEWS.md
+
+`NEWS.md` currently holds two sections. `0.2.0` is a legitimate changelog — the
+`"All Categories"` reserved value, the coverage-signposting fix, the
+`n_units_reporting` documentation — and it stays. Beneath it,
+`0.1.0 (development)` is a pre-release churn log: changes described relative to
+states no user has ever seen ("Breaking: corpus schema_version 4", "the package
+now requires…"), spanning the package's entire pre-release development. To a
+newcomer deciding whether to depend on this, that section reads as instability.
+
+**A new `0.3.0` section is added at the top, framed as the first public
+release**: what the package does, what the corpus covers, and the caveats that
+are genuinely load-bearing. **`0.2.0` is kept verbatim.** **`0.1.0 (development)`
+is dropped** — that history stays in git, where it belongs.
+
+The version is `0.3.0` rather than `0.2.0` because this release changes
+user-visible behaviour: remote corpus reads go from broken to working, and the
+default URL from a dead placeholder to a live corpus. It is also not `1.0.0` —
+the corpus still excludes government types 4 and 5 pending validation, so a
+stability promise would overclaim. No git tag exists for any prior version;
+`chore: release 0.2.0` bumped `DESCRIPTION` and `NEWS` only.
+
+The substantive content is migrated, not deleted. These are hard-won and belong
+in documentation rather than buried in a changelog:
+
+| Content | Destination |
+|---|---|
+| Coverage disclosure on multi-government aggregates (census vs sample years) | README concepts + `cog_geographic_rollup()` docs |
+| `complete = TRUE` three-way absence semantics (`reported` / `census_zero` / `not_reported`) | `cog_spending()` / `cog_revenue()` docs |
+| Series-break and corpus-break surfacing | README + `cog_explain()` docs |
+| $1,000s → full dollars conversion | README, already prominent |
+| Per-year F-33 population denominators | `population-denominators` vignette, already there |
+
+This also makes NEWS reusable as raw material for the release announcement,
+which is the stated downstream purpose.
+
+## Distribution mechanics
+
+Recorded as decided; executed after the package is clean and checks are green.
+
+**Sequence matters.** r-universe publishes check results the moment a package is
+registered. Registering before the fixes above land means a red badge on day one,
+which is a worse first impression than a week's delay.
+
+1. `gitleaks` over full history. A coarse grep found nothing across 140 commits
+   and the default corpus URL is still the placeholder sentinel, but a proper
+   scan is the gate on an irreversible action.
+2. Flip the Gitea repo public. Disable Gitea issues on it, so there is exactly
+   one inbox.
+3. Create `github.com/civilytics/uscogdata`. Add `.github/workflows/` for the
+   Windows/macOS/Linux `R CMD check` matrix — the platforms the Gitea runner
+   cannot provide, and which this package has never been tested on despite
+   depending on duckdb and httr2. Gitea reads `.gitea/workflows`, GitHub reads
+   `.github/workflows`; both live in one tree without colliding.
+4. Gitea Actions workflow pushing to GitHub **without `--force`**, so divergence
+   fails loudly in CI rather than silently overwriting.
+5. Add `jared@civilytics.com` as a verified secondary email on the GitHub
+   account — r-universe links maintainer identity by matching DESCRIPTION's email
+   against registered GitHub emails, and the association only takes effect on the
+   next build.
+6. Tag `v0.3.0`. Create `github.com/civilytics/civilytics.r-universe.dev` with a
+   `packages.json` pinned to the tag, pointing at the GitHub mirror rather than
+   Gitea so clone traffic stays off maxwell. Install the r-universe app.
+
+### PR flow
+
+Never press Merge on GitHub. A merge there is overwritten by the next sync, the
+PR still displays "Merged", and nothing says otherwise.
+
+```sh
+git remote add github https://github.com/civilytics/uscogdata.git
+git config --add remote.github.fetch '+refs/pull/*/head:refs/remotes/github/pr/*'
+git fetch github
+git switch -c pr-42 github/pr/42      # test
+git switch main && git merge --no-ff pr-42
+git push origin main                   # Gitea -> mirror -> GitHub
+```
+
+GitHub auto-closes a PR as merged once its head commit becomes an ancestor of the
+base branch, so `--no-ff` — which preserves the contributor's SHAs — makes the PR
+close itself when the mirror pushes. **For external PRs, merge; do not squash or
+rebase.** Squashing rewrites the SHAs, the auto-close never fires, and closing by
+hand reads to a first-time contributor as rejection.
+
+`CONTRIBUTING.md` states this, and a GitHub Action comments it on incoming PRs.
+No CLA; no DCO.
+
+## Verification
+
+The release is not done until all of these pass:
+
+1. `R CMD check --as-cran` clean on Linux, and on Windows and macOS via the
+   GitHub matrix. This package has never been checked on the latter two.
+2. Full test suite (28 files) green against the **bundled fixture**, offline,
+   with no credentials — the property public CI depends on.
+3. Full test suite green against the **live corpus**, which additionally
+   exercises the enumeration fix that the fixture's local path cannot.
+4. `pkgdown::build_site()` completes.
+5. Both vignettes present in the built tarball and
+   `vignette("total-spending", package = "uscogdata")` resolves from an
+   installed copy.
+6. **Cold-start check on a machine that has never seen this package:** install
+   from r-universe, `library(uscogdata)`, run the README quickstart verbatim with
+   no environment variables set. This is the only test that catches the P0 class
+   of fault, and its absence is why the fault survived.
+7. End-to-end verb latency re-measured against the live corpus and the README's
+   numbers updated if they moved.
+
+## Out of scope
+
+- **Corrections intake.** Deferred by decision. Consequence: the release cannot
+  invite data-error reports or make the "traceable and correctable" claim that
+  most distinguishes this corpus from Census's own files. `BugReports:` points at
+  package issues only. A verified correction should eventually terminate as a
+  `lineage_event` or `series_break` row so it propagates through provenance to
+  every consumer — that design is unstarted.
+- **Announcement posts.** Deferred. The API announcement is gated on corrections
+  landing and merits a Civic Pulse edition.
+- **The rest of the R package backlog.** Parked until this one completes the path.
+- **`cog_pipeline` publication.** Stays private.

tests/testthat/test-all-categories-rollup.R

@@ -0,0 +1,58 @@
+test_that('cog_geographic_rollup() accepts "All Categories" and agrees with per-category sums', {
+  skip_if_no_corpus()
+  govs <- cog_gov_search(name = NULL, state = "WI", type = 2L)
+  expect_gt(nrow(govs), 1L)
+  ids <- list(city = utils::head(govs$canonical_govid, 25L))
+
+  by_cat <- cog_geographic_rollup(ids, category = NULL, years = 2019L)
+  total  <- cog_geographic_rollup(ids, category = "All Categories", years = 2019L)
+
+  expect_setequal(unique(total$category), "All Categories")
+  # one row per (govid, subtype) that appears in the per-category result
+  key_by_cat <- unique(paste(by_cat$canonical_govid, by_cat$spend_subtype))
+  key_total  <- paste(total$canonical_govid, total$spend_subtype)
+  expect_setequal(key_total, key_by_cat)
+
+  lhs <- tapply(by_cat$amt_nominal, paste(by_cat$canonical_govid, by_cat$spend_subtype), sum)
+  rhs <- tapply(total$amt_nominal,  key_total, sum)
+  expect_equal(as.numeric(rhs[names(lhs)]), as.numeric(lhs), tolerance = 1e-8)
+})
+
+test_that('"All Categories" survives per_capita and inflation adjustment through the rollup', {
+  skip_if_no_corpus()
+  govs <- cog_gov_search(name = NULL, state = "WI", type = 2L)
+  ids  <- list(city = utils::head(govs$canonical_govid, 10L))
+  r <- cog_geographic_rollup(ids, category = "All Categories", years = 2019L,
+                             per_capita = TRUE, adjust_to_year = 2020L)
+  expect_true(all(c("amt_per_capita_nominal", "amt_real", "amt_per_capita_real") %in% names(r)))
+  expect_setequal(unique(r$category), "All Categories")
+  expect_true(all(is.finite(r$amt_real)))
+})
+
+test_that('cog_geographic_rollup() still refuses expenditure_concept = "total" with "All Categories"', {
+  skip_if_no_corpus()
+  govs <- cog_gov_search(name = NULL, state = "WI", type = 2L)
+  ids  <- list(city = utils::head(govs$canonical_govid, 5L))
+  expect_error(
+    cog_geographic_rollup(ids, category = "All Categories", years = 2019L,
+                          expenditure_concept = "total")
+  )
+})
+
+test_that("n_units_reporting is category-conditional, not a response rate", {
+  skip_if_no_corpus()
+  govs <- cog_gov_search(name = NULL, state = "WI", type = 2L)
+  ids  <- list(city = govs$canonical_govid)
+
+  police <- cog_geographic_rollup(ids, category = "Police", years = 2012L)
+  allcat <- cog_geographic_rollup(ids, category = "All Categories", years = 2012L)
+
+  cov_police <- cog_explain(police, format = "list")$coverage
+  cov_all    <- cog_explain(allcat, format = "list")$coverage
+
+  # Same year, same requested govids, same collection -- yet a single category
+  # reports fewer units than the all-categories query. That gap is real zeros,
+  # not non-response, which is exactly why the ratio is not a response rate.
+  expect_lte(cov_police$n_units_reporting, cov_all$n_units_reporting)
+  expect_identical(cov_police$n_units_expected, cov_all$n_units_expected)
+})

tests/testthat/test-all-categories.R

@@ -0,0 +1,267 @@
+# Baseline at branch point: 843 PASS / 0 FAIL / 0 SKIP / 0 WARN (2026-08-05, origin/main 2fc9e75)
+
+test_that(".build_verb_sql emits a literal category and no category filter in all-categories mode", {
+  sql <- uscogdata:::.build_verb_sql(
+    view         = "spending_annotated",
+    subtype_col  = "spend_subtype",
+    cohort = uscogdata:::.make_cohort("552025209777"),
+    years        = 2019L,
+    category     = NULL,
+    subtype_scope = c("operations", "capital"),
+    all_categories = TRUE
+  )
+
+  expect_match(sql, "'All Categories' AS category", fixed = TRUE)
+  # no category filter of any kind
+  expect_false(grepl("AND category IN", sql, fixed = TRUE))
+  # category is not a grouping key
+  expect_false(grepl("GROUP BY year, canonical_govid, gov_name, xwalk_gov_name, spend_subtype, category",
+                     sql, fixed = TRUE))
+  # the subtype allowlist still applies -- this is what makes the sum a concept
+  expect_match(sql, "AND spend_subtype IN ('operations','capital')", fixed = TRUE)
+})
+
+test_that(".build_verb_sql is unchanged when all_categories is FALSE", {
+  args <- list(
+    view = "spending_annotated", subtype_col = "spend_subtype",
+    cohort = uscogdata:::.make_cohort("552025209777"), years = 2019L, category = NULL,
+    subtype_scope = c("operations", "capital")
+  )
+  old <- do.call(uscogdata:::.build_verb_sql, args)
+  new <- do.call(uscogdata:::.build_verb_sql, c(args, list(all_categories = FALSE)))
+  expect_identical(old, new)
+  expect_match(new, "GROUP BY year, canonical_govid, gov_name, xwalk_gov_name, spend_subtype, category",
+               fixed = TRUE)
+})
+
+test_that(".ALL_CATEGORIES is the exact reserved string", {
+  expect_identical(uscogdata:::.ALL_CATEGORIES, "All Categories")
+})
+
+test_that('cog_spending(category = "All Categories") sums to the per-category total', {
+  gov <- "552025209777"
+  by_cat <- cog_spending(gov, 2019L)
+  total  <- cog_spending(gov, 2019L, category = "All Categories")
+
+  expect_true(nrow(total) > 0L)
+  expect_setequal(unique(total$category), "All Categories")
+  # one row per subtype present in the by-category result
+  expect_setequal(unique(total$spend_subtype), unique(by_cat$spend_subtype))
+  expect_equal(nrow(total), length(unique(by_cat$spend_subtype)))
+
+  # the dollars agree, per subtype
+  lhs <- tapply(by_cat$amt_nominal, by_cat$spend_subtype, sum)
+  rhs <- tapply(total$amt_nominal,  total$spend_subtype,  sum)
+  expect_equal(as.numeric(rhs[names(lhs)]), as.numeric(lhs), tolerance = 1e-8)
+})
+
+test_that('"All Categories" respects expenditure_concept', {
+  gov <- "552025209777"
+  prim <- cog_spending(gov, 2019L, category = "All Categories",
+                       expenditure_concept = "primary")
+  dir  <- cog_spending(gov, 2019L, category = "All Categories",
+                       expenditure_concept = "direct")
+  # direct = primary plus interest and insurance benefits, so it is never smaller
+  expect_gte(sum(dir$amt_nominal), sum(prim$amt_nominal))
+})
+
+test_that('"All Categories" works on revenue and respects revenue_concept', {
+  gov <- "552025209777"
+  gen <- cog_revenue(gov, 2019L, category = "All Categories",
+                     revenue_concept = "general")
+  tot <- cog_revenue(gov, 2019L, category = "All Categories",
+                     revenue_concept = "total")
+  expect_setequal(unique(gen$category), "All Categories")
+  expect_gte(sum(tot$amt_nominal), sum(gen$amt_nominal))
+})
+
+test_that('"All Categories" cannot be combined with another category', {
+  expect_error(
+    cog_spending("552025209777", 2019L, category = c("All Categories", "Police")),
+    class = "uscogdata_all_categories_not_combinable"
+  )
+})
+
+test_that('"All Categories" is recorded in provenance', {
+  r <- cog_spending("552025209777", 2019L, category = "All Categories")
+  expect_identical(cog_explain(r, format = "list")$category, "All Categories")
+})
+
+test_that('"All Categories" combines with subtype to give operating totals', {
+  gov <- "552025209777"
+  ops_by_cat <- cog_spending(gov, 2019L)
+  ops_by_cat <- ops_by_cat[ops_by_cat$spend_subtype == "operations", ]
+  ops_total  <- cog_spending(gov, 2019L, category = "All Categories")
+  ops_total  <- ops_total[ops_total$spend_subtype == "operations", ]
+  expect_equal(sum(ops_total$amt_nominal), sum(ops_by_cat$amt_nominal),
+               tolerance = 1e-8)
+})
+
+test_that('cog_categories() advertises "All Categories" for both flows', {
+  all <- cog_categories()
+  rows <- all[all$category == "All Categories", ]
+  expect_setequal(rows$category_type, c("expenditure", "revenue"))
+  expect_true(all(is.na(rows$subtype)))
+  expect_true(all(is.na(rows$n_codes)))
+})
+
+test_that('cog_categories(type=) still scopes, including the pseudo-category', {
+  sp <- cog_categories(type = "spending")
+  expect_setequal(unique(sp$category_type), "expenditure")
+  expect_true("All Categories" %in% sp$category)
+
+  rev <- cog_categories(type = "revenue")
+  expect_setequal(unique(rev$category_type), "revenue")
+  expect_true("All Categories" %in% rev$category)
+
+  # balances have no concept vocabulary, so no pseudo-category
+  bal <- cog_categories(type = "balance")
+  expect_false("All Categories" %in% bal$category)
+})
+
+test_that('cog_categories(pattern=) matches the pseudo-category', {
+  hit <- cog_categories(pattern = "^All Categories$")
+  expect_equal(nrow(hit), 2L)
+})
+
+# --- final whole-branch review fixes ---------------------------------------
+
+test_that('complete = TRUE is refused when combined with "All Categories"', {
+  # .completion_grid_sql() would emit `AND c.category IN ('All Categories')`,
+  # match zero crosswalk rows, and the early return in .complete_result()
+  # would stamp completion$applied = TRUE, rows_filled = 0 -- reading as "the
+  # grid was checked and nothing was missing" when nothing was actually
+  # checked. Filling a summed row has no defined semantics, so the verb must
+  # refuse the combination outright (finding 2).
+  expect_error(
+    cog_spending("552025209777", 2019L, category = "All Categories",
+                complete = TRUE),
+    class = "uscogdata_complete_unsupported"
+  )
+  expect_error(
+    cog_revenue("552025209777", 2019L, category = "All Categories",
+               complete = TRUE),
+    class = "uscogdata_complete_unsupported"
+  )
+})
+
+test_that('cog_balances() rejects "All Categories" instead of silently returning zero rows', {
+  # cog_balances() reuses .validate_verb_inputs() but did not pass
+  # allow_all_categories = TRUE, so "All Categories" used to become
+  # `AND category IN ('All Categories')` against balance_annotated -- 0
+  # matching crosswalk rows, 0 rows back, no error (finding 3). Holdings are
+  # a stock with no concept vocabulary to sum across, so the honest answer is
+  # to refuse, the same way cog_spending()/cog_revenue() refuse other
+  # nonsensical combinations.
+  expect_error(
+    cog_balances("552025209777", 2019L, category = "All Categories"),
+    class = "uscogdata_all_categories_unsupported"
+  )
+  # An ordinary category still works -- this is not a blanket regression.
+  r <- suppressMessages(
+    cog_balances("552025209777", 2019L, category = "Fund Balances")
+  )
+  expect_gt(nrow(r), 0L)
+})
+
+test_that('expenditure_concept_direct_suppressed is NA, not FALSE, when categories are collapsed', {
+  # .detect_direct_suppressed() keys on
+  # paste(year, canonical_govid, category, sep = "\r"). In all-categories
+  # mode every row carries the literal "All Categories" value, so an IG-only
+  # row's key collides with any ordinary Direct row for the same
+  # (year, govid) -- has_direct reads TRUE whenever the government has ANY
+  # direct spending at all, candidate is always empty, and the detector can
+  # never fire. Before the fix this silently reported FALSE, an affirmative
+  # claim the code did not actually compute (finding 1). NA is the honest
+  # answer: cog_explain(x, format = "list") is required here, since without
+  # format = "list" it returns the result tibble, not the provenance list.
+  gov <- "552025209777"
+  t <- cog_spending(gov, 2019L, category = "All Categories",
+                    expenditure_concept = "total")
+  prov <- cog_explain(t, format = "list")
+  expect_true(is.na(prov$expenditure_concept_direct_suppressed))
+  expect_false(isTRUE(prov$expenditure_concept_direct_suppressed))
+  expect_match(prov$expenditure_concept_note, "unavailable", fixed = TRUE)
+
+  # A per-category "total" query on the same government/year is unaffected --
+  # the detector can still key correctly and reports a strict logical.
+  t_by_cat <- cog_spending(gov, 2019L, expenditure_concept = "total")
+  prov_by_cat <- cog_explain(t_by_cat, format = "list")
+  expect_false(is.na(prov_by_cat$expenditure_concept_direct_suppressed))
+})
+
+test_that('"All Categories" still signposts coverage gaps (finding 6, final whole-branch review)', {
+  # .build_suggestions()'s candidate sub-select used to be keyed on
+  # `category`, e.g. `WHERE category IN ('All Categories')`. Since
+  # .ALL_CATEGORIES is never itself a row in summary_categories.category,
+  # that sub-select always came back empty in all-categories mode, so
+  # `candidates` was empty and .build_suggestions() short-circuited to
+  # list() -- coverage signposting was structurally impossible for the one
+  # mode whose whole selling point is "you cannot sum the wrong scope"
+  # (uscogdata#9's entire point, silently defeated).
+  #
+  # AL state government, FY2011, category = "Corrections": this category has
+  # no legacy leaf rows in FY2011 (aggregate-flagged E04/E05 family), so the
+  # per-category query returns 0 rows and 3 recipe-hint suggestions fire
+  # (empty_year path). All-categories mode does not have an empty year --
+  # the government has other primary spending in FY2011 -- but the same
+  # suppressed Corrections dollars are still excluded from the summed total,
+  # so the fix (scoping the candidate sub-select by subtype_col/subtype_scope
+  # instead of by category, symmetric with .build_verb_sql()) must still
+  # surface them via the suppressed_component path.
+  gov <- "010000226085"
+
+  by_cat <- suppressMessages(cog_spending(gov, 2011L, category = "Corrections"))
+  sugg_by_cat <- cog_explain(by_cat, format = "list")$suggestions
+  expect_gt(length(sugg_by_cat), 0L)
+
+  all_cat <- suppressMessages(cog_spending(gov, 2011L, category = "All Categories"))
+  sugg_all_cat <- cog_explain(all_cat, format = "list")$suggestions
+  expect_gt(length(sugg_all_cat), 0L)
+
+  # The same Corrections recipe that fired per-category must also fire in
+  # all-categories mode -- not just some unrelated recipe.
+  ids_by_cat <- vapply(sugg_by_cat, function(s) s$recipe_id %||% "", character(1))
+  ids_all_cat <- vapply(sugg_all_cat, function(s) s$recipe_id %||% "", character(1))
+  expect_true("corrections_combined" %in% ids_by_cat)
+  expect_true("corrections_combined" %in% ids_all_cat)
+
+  # In all-categories mode the government DOES have other primary spending
+  # in FY2011 (the year itself is not a gap), so the suggestion can only have
+  # fired via the suppressed_component path, not empty_year.
+  corr_all <- sugg_all_cat[[which(ids_all_cat == "corrections_combined")]]
+  expect_identical(corr_all$trigger, "suppressed_component")
+  expect_gt(corr_all$suppressed_amount, 0)
+})
+
+test_that('"All Categories" candidate scoping is symmetric with .build_verb_sql() -- subtype, not category', {
+  # Direct assertion on the mechanism itself (finding 6): in all-categories
+  # mode .build_suggestions() must scope its candidate recipe sub-select by
+  # subtype_col/subtype_scope, not by the literal "All Categories" value.
+  # Passing all_categories = FALSE with the identical category value proves
+  # the branch -- not merely the subtype_col/subtype_scope arguments' mere
+  # presence -- is what changes the query.
+  con <- uscogdata:::.ensure_session()
+
+  none <- uscogdata:::.build_suggestions(
+    con, cohort = uscogdata:::.make_cohort("010000226085"), years = 2011L,
+    category = "All Categories", result = NULL, basis = "harmonized",
+    flow_prefixes = c("E", "F", "G"),
+    long_view = "spending_long_harmonized",
+    all_categories = FALSE,
+    subtype_col = "spend_subtype",
+    subtype_scope = c("operations", "capital", "assistance")
+  )
+  expect_length(none, 0L)
+
+  scoped <- uscogdata:::.build_suggestions(
+    con, cohort = uscogdata:::.make_cohort("010000226085"), years = 2011L,
+    category = "All Categories", result = NULL, basis = "harmonized",
+    flow_prefixes = c("E", "F", "G"),
+    long_view = "spending_long_harmonized",
+    all_categories = TRUE,
+    subtype_col = "spend_subtype",
+    subtype_scope = c("operations", "capital", "assistance")
+  )
+  expect_gt(length(scoped), 0L)
+})

tests/testthat/test-balances.R

@@ -66,7 +66,7 @@ test_that("inst/sql/26-balance_long.sql enforces NOT is_aggregate (real SQL text
   sql_dir <- system.file("sql", package = "uscogdata")
   .read_view_sql <- function(filename) {
     txt <- paste(readLines(file.path(sql_dir, filename), warn = FALSE), collapse = "\n")
-    gsub("\\{url\\}", paste0(tmp, "/"), txt, fixed = FALSE)
+    uscogdata:::.render_view_sql(txt, paste0(tmp, "/"))
   }
 
   con <- DBI::dbConnect(duckdb::duckdb())

tests/testthat/test-categories.R

@@ -29,7 +29,9 @@ test_that("cog_categories(type = 'spending') returns only expenditure rows", {
   # joined with the I/Q/Y flow batch -- the last two characters of Census's
   # expenditure taxonomy. `interest` is what makes the three-concept model
   # computable: primary = direct minus debt service.
-  expect_true(all(r$subtype %in%
+  # Exclude pseudo-category which has NA for subtype
+  r_crosswalk <- r[r$category != "All Categories", ]
+  expect_true(all(r_crosswalk$subtype %in%
                   c("operations", "capital", "intergovernmental", "assistance",
                     "interest", "insurance_benefits")))
 })
@@ -54,7 +56,9 @@ test_that("cog_categories(type = 'revenue') returns only revenue rows", {
   # plus the employee-retirement X codes), utility (A91-A94) and liquor store
   # (A90) revenue by definition, which is what makes both of its published
   # revenue concepts computable -- see `revenue_concept` in `?cog_revenue`.
-  expect_true(all(r$subtype %in%
+  # Exclude pseudo-category which has NA for subtype
+  r_crosswalk <- r[r$category != "All Categories", ]
+  expect_true(all(r_crosswalk$subtype %in%
                   c("own_source", "federal", "state", "local_aid",
                     "insurance_trust", "utility", "liquor_store")))
 })
@@ -69,6 +73,8 @@ test_that("cog_categories(pattern = ...) filters case-insensitively", {
 test_that("cog_categories has one row per (category, subtype)", {
   skip_if_no_corpus()
   r <- cog_categories()
+  # Exclude pseudo-category which is not a crosswalk entry
+  r <- r[r$category != "All Categories", ]
   key <- paste(r$category, r$subtype, sep = "|")
   expect_equal(length(key), length(unique(key)))
 })
@@ -76,6 +82,8 @@ test_that("cog_categories has one row per (category, subtype)", {
 test_that("cog_categories item_codes is non-empty comma-separated string", {
   skip_if_no_corpus()
   r <- cog_categories()
+  # Exclude pseudo-category which has NA for n_codes and item_codes
+  r <- r[r$category != "All Categories", ]
   expect_true(all(nzchar(r$item_codes)))
   expect_true(all(r$n_codes >= 1L))
   # n_codes should equal count of commas + 1

tests/testthat/test-cohort-verbs.R

@@ -0,0 +1,218 @@
+# The money verbs accepting a cohort by predicate (uscogdata#58).
+#
+# The load-bearing property is EQUIVALENCE: naming the same set of governments
+# by id and by state/type must return the same rows. Everything else here is
+# about the ways that equivalence could silently break -- the postal/FIPS
+# translation, the intersection rule, and provenance no longer having an id
+# list to describe.
+#
+# Fixture cohorts used: RI (fips 44) cities = 8 governments, DE (fips 10)
+# counties = 3. Small on purpose; the size of the win is measured against the
+# production corpus, not here.
+
+# --- equivalence -----------------------------------------------------------
+
+test_that("a predicate cohort returns exactly what the same ids return", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    ids <- cog_gov_search(NULL, state = "RI", type = "city")$canonical_govid
+    expect_gt(length(ids), 1L)
+
+    by_id   <- cog_spending(govid = ids, years = 2019)
+    by_pred <- cog_spending(years = 2019, state = "RI", type = "city")
+
+    # Compare the data itself, ignoring the provenance attribute -- which is
+    # SUPPOSED to differ (see the scope tests below).
+    expect_equal(
+      as.data.frame(by_id[order(by_id$canonical_govid, by_id$category), ]),
+      as.data.frame(by_pred[order(by_pred$canonical_govid, by_pred$category), ]),
+      ignore_attr = TRUE
+    )
+    expect_gt(nrow(by_pred), 0L)
+  })
+})
+
+test_that("equivalence holds for cog_revenue()", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    ids <- cog_gov_search(NULL, state = "DE", type = "county")$canonical_govid
+    by_id   <- cog_revenue(govid = ids, years = 2019)
+    by_pred <- cog_revenue(years = 2019, state = "DE", type = "county")
+    expect_equal(nrow(by_id), nrow(by_pred))
+    expect_equal(sum(by_id$amt_nominal), sum(by_pred$amt_nominal))
+  })
+})
+
+test_that("equivalence holds for cog_balances()", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    ids <- cog_gov_search(NULL, state = "DE", type = "county")$canonical_govid
+    by_id   <- cog_balances(govid = ids, years = 2019)
+    by_pred <- cog_balances(years = 2019, state = "DE", type = "county")
+    expect_equal(nrow(by_id), nrow(by_pred))
+    expect_equal(sum(by_id$amt_nominal), sum(by_pred$amt_nominal))
+  })
+})
+
+test_that("equivalence survives per_capita, adjust_to_year and pagination", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    ids <- cog_gov_search(NULL, state = "RI", type = "city")$canonical_govid
+
+    # per_capita now keys its population lookup on the rows in the result
+    # rather than on the requested cohort; these must stay identical.
+    by_id   <- cog_spending(govid = ids, years = 2019, per_capita = TRUE,
+                            adjust_to_year = 2020)
+    by_pred <- cog_spending(years = 2019, state = "RI", type = "city",
+                            per_capita = TRUE, adjust_to_year = 2020)
+    expect_equal(by_id$amt_per_capita_nominal, by_pred$amt_per_capita_nominal)
+    expect_equal(by_id$amt_per_capita_real, by_pred$amt_per_capita_real)
+    expect_equal(by_id$pop_source, by_pred$pop_source)
+
+    paged_id   <- cog_spending(govid = ids, years = 2019, limit = 5, offset = 5)
+    paged_pred <- cog_spending(years = 2019, state = "RI", type = "city",
+                               limit = 5, offset = 5)
+    expect_equal(as.data.frame(paged_id), as.data.frame(paged_pred),
+                 ignore_attr = TRUE)
+    expect_identical(attr(paged_id, "total_rows"), attr(paged_pred, "total_rows"))
+  })
+})
+
+test_that("a state-only predicate spans every type in that state", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    ids <- cog_gov_search(NULL, state = "DE", type = NULL)$canonical_govid
+    by_id   <- cog_spending(govid = ids, years = 2019)
+    by_pred <- cog_spending(years = 2019, state = "DE")
+    expect_equal(nrow(by_id), nrow(by_pred))
+  })
+})
+
+# --- the postal/FIPS trap --------------------------------------------------
+
+test_that("a postal abbreviation resolves to rows, not to silence", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    # canonical_fips_xwalk.fips_state holds "44", not "RI". A predicate built
+    # from the raw parameter matches nothing and returns an empty result that
+    # reads as "these governments reported nothing" -- the exact trap cog-api
+    # hit. A zero-row result here is the regression.
+    r <- cog_spending(years = 2019, state = "RI", type = "city")
+    expect_gt(nrow(r), 0L)
+
+    # And the FIPS form is accepted as the same cohort.
+    expect_equal(nrow(cog_spending(years = 2019, state = "44", type = "city")),
+                 nrow(r))
+  })
+})
+
+test_that("an unknown state abbreviation aborts with a message that names the problem", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    # Regression: `.state_abbrev_to_fips` is a named character vector, so `[[`
+    # on an absent name threw base R's "subscript out of bounds" and the
+    # curated message was unreachable.
+    expect_error(cog_spending(years = 2019, state = "ZZ"),
+                 class = "uscogdata_unknown_state")
+    expect_error(cog_spending(years = 2019, state = "ZZ"),
+                 "Unknown state abbreviation")
+  })
+})
+
+# --- naming the cohort -----------------------------------------------------
+
+test_that("naming no cohort at all is refused", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    expect_error(cog_spending(years = 2019), class = "uscogdata_no_cohort")
+    expect_error(cog_revenue(years = 2019), class = "uscogdata_no_cohort")
+    expect_error(cog_balances(years = 2019), class = "uscogdata_no_cohort")
+  })
+})
+
+test_that("an empty govid vector still fails as it always did", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    # Supplied-but-empty is a caller error, not "cohort named some other way".
+    expect_error(cog_spending(character(0), 2019),
+                 "must be a non-empty character vector")
+  })
+})
+
+test_that("govid and state/type together intersect", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    cities   <- cog_gov_search(NULL, state = "RI", type = "city")$canonical_govid
+    counties <- cog_gov_search(NULL, state = "DE", type = "county")$canonical_govid
+
+    # The documented rule: the governments in `govid` that ALSO match the
+    # predicate -- never one silently taking precedence over the other.
+    both <- cog_spending(govid = c(cities, counties), years = 2019,
+                         state = "RI", type = "city")
+    only <- cog_spending(govid = cities, years = 2019)
+    expect_equal(as.data.frame(both), as.data.frame(only), ignore_attr = TRUE)
+
+    # A disjoint intersection is empty, not "whichever one won".
+    none <- cog_spending(govid = counties, years = 2019,
+                         state = "RI", type = "city")
+    expect_equal(nrow(none), 0L)
+  })
+})
+
+# --- provenance ------------------------------------------------------------
+
+test_that("a govid cohort's provenance scope is untouched", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    ids <- cog_gov_search(NULL, state = "DE", type = "county")$canonical_govid
+    prov <- attr(cog_spending(govid = ids, years = 2019), "provenance")
+    expect_setequal(prov$scope$govids_found, ids)
+    expect_length(prov$scope$govids_missing, 0L)
+    # No cohort block: govids_found already describes this cohort exactly.
+    expect_null(prov$scope$cohort)
+  })
+})
+
+test_that("a predicate cohort describes itself instead of listing ids", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    prov <- attr(cog_spending(years = 2019, state = "DE", type = "county"),
+                 "provenance")
+    # Deliberately NOT the resolved id list: a fleet-scale cohort would put
+    # 20,000 govids into every response body.
+    expect_length(prov$scope$govids_found, 0L)
+    expect_length(prov$scope$govids_missing, 0L)
+    expect_identical(prov$scope$cohort$state, "DE")
+    expect_identical(prov$scope$cohort$type, "county")
+    expect_identical(prov$scope$cohort$n_governments, 3L)
+  })
+})
+
+test_that("the cohort block counts the intersection, not the predicate alone", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    counties <- cog_gov_search(NULL, state = "DE", type = "county")$canonical_govid
+    prov <- attr(
+      cog_spending(govid = counties[1], years = 2019, state = "DE", type = "county"),
+      "provenance"
+    )
+    expect_identical(prov$scope$cohort$n_governments, 1L)
+  })
+})
+
+# --- the SQL actually changed ----------------------------------------------
+
+test_that("a predicate cohort never renders the ids into the query", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    ids <- cog_gov_search(NULL, state = "RI", type = "city")$canonical_govid
+    prov <- attr(cog_spending(years = 2019, state = "RI", type = "city"),
+                 "provenance")
+    sql <- prov$sql_query %||% prov$sql
+    skip_if(is.null(sql), "provenance carries no SQL for this verb")
+    # The whole point: cohort size does not enter the SQL string.
+    for (id in ids) expect_false(grepl(id, sql, fixed = TRUE))
+    expect_match(sql, "SELECT canonical_govid FROM canonical_fips_xwalk",
+                 fixed = TRUE)
+  })
+})

tests/testthat/test-cohort.R

@@ -0,0 +1,83 @@
+# A cohort is how every verb names the set of governments it queries. It can be
+# named by explicit id, by a predicate over canonical_fips_xwalk, or by both
+# (intersection). These tests cover the SQL construction itself -- pure string
+# building, no corpus needed -- because that is where the postal/FIPS trap and
+# the 40k-literal blowup both live.
+
+test_that(".make_cohort() keeps an explicit id vector as ids", {
+  ch <- .make_cohort(govid = c("550000227544", "060000000001"))
+  expect_identical(ch$ids, c("550000227544", "060000000001"))
+  expect_null(ch$state_fips)
+  expect_null(ch$type_int)
+  expect_false(.cohort_by_predicate(ch))
+})
+
+test_that(".make_cohort() translates a postal abbreviation to FIPS", {
+  # The trap this whole issue exists to avoid: canonical_fips_xwalk.fips_state
+  # holds "55", not "WI". A predicate written against the raw parameter matches
+  # nothing and returns an empty result that reads as "reported nothing".
+  ch <- .make_cohort(state = "WI")
+  expect_identical(ch$state_fips, "55")
+  expect_true(.cohort_by_predicate(ch))
+})
+
+test_that(".make_cohort() translates a type label to its integer code", {
+  expect_identical(.make_cohort(type = "city")$type_int, 2L)
+  expect_identical(.make_cohort(type = "state")$type_int, 0L)
+  expect_identical(.make_cohort(type = 1)$type_int, 1L)
+})
+
+test_that(".make_cohort() reuses the search verb's coercers for invalid input", {
+  expect_error(.make_cohort(state = "ZZ"), "Unknown state abbreviation")
+  expect_error(.make_cohort(type = "special_district"), "Unknown type")
+  # Out-of-scope types (4 = special district, 5 = school district) are refused
+  # by the numeric branch, with the v0.1-scope message.
+  expect_error(.make_cohort(type = 4), "type must be 0, 1, 2, or 3")
+})
+
+test_that(".make_cohort() rejects naming no cohort at all", {
+  expect_error(.make_cohort(), class = "uscogdata_no_cohort")
+})
+
+test_that(".cohort_sql() renders an id cohort as a literal IN list", {
+  sql <- .cohort_sql(.make_cohort(govid = c("a", "b")))
+  expect_identical(sql, "canonical_govid IN ('a','b')")
+})
+
+test_that(".cohort_sql() renders a predicate cohort as an xwalk subquery", {
+  # The point of the issue: the cohort never becomes a literal list, so its
+  # size does not enter the SQL string at all.
+  sql <- .cohort_sql(.make_cohort(state = "WI", type = "city"))
+  expect_match(sql, "SELECT canonical_govid FROM canonical_fips_xwalk", fixed = TRUE)
+  expect_match(sql, "fips_state = '55'", fixed = TRUE)
+  expect_match(sql, "govs_type = 2", fixed = TRUE)
+  expect_false(grepl("'WI'", sql, fixed = TRUE))
+})
+
+test_that(".cohort_sql() renders ids and a predicate as an intersection", {
+  sql <- .cohort_sql(.make_cohort(govid = c("a", "b"), type = "city"))
+  expect_match(sql, "canonical_govid IN ('a','b')", fixed = TRUE)
+  expect_match(sql, "AND canonical_govid IN (SELECT", fixed = TRUE)
+})
+
+test_that(".cohort_sql() honours a column alias", {
+  # Several call sites join the xwalk under an alias (`l.`, `x.`, `v.`), so the
+  # predicate has to be able to name the qualified column.
+  sql <- .cohort_sql(.make_cohort(state = "WI"), col = "l.canonical_govid")
+  expect_match(sql, "l.canonical_govid IN (SELECT", fixed = TRUE)
+  # The subquery's own column stays unqualified -- it selects from the xwalk,
+  # not from the outer relation.
+  expect_match(sql, "SELECT canonical_govid FROM", fixed = TRUE)
+})
+
+test_that(".cohort_sql() escapes quotes in ids", {
+  sql <- .cohort_sql(.make_cohort(govid = "o'brien"))
+  expect_match(sql, "'o''brien'", fixed = TRUE)
+})
+
+test_that("a predicate cohort's SQL does not grow with cohort size", {
+  # The regression this guards: 20,106 ids rendered to a 301,591-character
+  # IN list, embedded in 5-8 statements per call.
+  wide <- .cohort_sql(.make_cohort(state = "CA", type = "city"))
+  expect_lt(nchar(wide), 200L)
+})

tests/testthat/test-config.R

@@ -64,3 +64,109 @@ test_that(".resolve_url does not invent a slash for an empty setting", {
   withr::local_options(uscogdata.url = "")
   expect_equal(.resolve_url(), "")
 })
+
+test_that("the default corpus URL is real, not a placeholder", {
+  # setup.R points USCOGDATA_URL at the bundled fixture for the whole suite,
+  # so both the env var and the option have to be cleared to see the default.
+  withr::local_envvar(USCOGDATA_URL = NA)
+  withr::local_options(uscogdata.url = NULL)
+  url <- .resolve_url()
+  expect_false(grepl("REPLACE_WITH", url, fixed = TRUE))
+  expect_match(url, "^https://")
+  expect_match(url, "/$")
+})
+
+test_that("an explicitly-set sentinel URL still aborts", {
+  # The guard must survive the default change: a user who half-edited a
+  # copied config still gets the actionable error.
+  withr::local_envvar(
+    USCOGDATA_URL = "https://other.example/s/REPLACE_WITH_SHARE_TOKEN/x/"
+  )
+  expect_error(
+    .check_url_configured(.resolve_url()),
+    class = "uscogdata_url_not_configured"
+  )
+})
+
+test_that("DESCRIPTION carries release metadata", {
+  skip_if_no_source_tree("DESCRIPTION")
+  d <- read.dcf(source_tree_path("DESCRIPTION"))
+  fields <- colnames(d)
+
+  expect_true(all(c("URL", "BugReports") %in% fields))
+  expect_match(d[1, "Authors@R"], "Knowles", fixed = TRUE)
+  expect_match(d[1, "Authors@R"], "0000-0003-0005-9478", fixed = TRUE)
+  expect_match(d[1, "Authors@R"], "Civilytics Consulting LLC", fixed = TRUE)
+
+  # The gate in .validate_schema() accepts up to 7 and the published corpus
+  # IS 7; DESCRIPTION must not claim otherwise.
+  expect_equal(as.integer(d[1, "MaxCorpusSchema"]), 7L)
+
+  # Authors@R must actually parse -- a malformed person() call is only
+  # caught at citation()/build time otherwise.
+  people <- eval(parse(text = d[1, "Authors@R"]))
+  expect_s3_class(people, "person")
+  expect_true("cre" %in% unlist(lapply(people, function(p) p$role)))
+})
+
+test_that("LICENSE and LICENSE.md name the same copyright holder", {
+  skip_if_no_source_tree("LICENSE", "LICENSE.md")
+  holder <- sub("^COPYRIGHT HOLDER:\\s*", "",
+                grep("^COPYRIGHT HOLDER:", readLines(source_tree_path("LICENSE"),
+                                                     warn = FALSE), value = TRUE))
+  full <- paste(readLines(source_tree_path("LICENSE.md"), warn = FALSE), collapse = "\n")
+
+  expect_equal(holder, "Civilytics Consulting LLC")
+  expect_match(full, holder, fixed = TRUE)
+  # usethis::use_mit_license() writes LICENSE.md but leaves an existing
+  # LICENSE alone, which is how the two came to disagree in the first place.
+  expect_match(full, "MIT License", fixed = TRUE)
+})
+
+test_that("vignettes are not excluded from the build", {
+  skip_if_no_source_tree(".Rbuildignore")
+  ignore <- readLines(source_tree_path(".Rbuildignore"), warn = FALSE)
+  expect_false(any(grepl("^\\^vignettes\\$$", ignore)))
+  # The fixture is what lets R CMD check run offline with no credentials on
+  # r-universe and GitHub Actions. It must never be excluded.
+  expect_false(any(grepl("fixture_corpus", ignore, fixed = TRUE)))
+  # doc/ and Meta/ ARE build artefacts of devtools::build_vignettes() and must
+  # stay excluded -- R CMD build regenerates inst/doc/ from vignettes/ on its
+  # own, and leaving them in earns a "non-standard file at top level" NOTE.
+  expect_true(any(grepl("^\\^doc\\$$", ignore)))
+  expect_true(any(grepl("^\\^Meta\\$$", ignore)))
+})
+
+test_that("_pkgdown.yml indexes every exported topic", {
+  skip_if_no_source_tree("_pkgdown.yml", "NAMESPACE")
+  exports <- grep("^export\\(", readLines(source_tree_path("NAMESPACE"), warn = FALSE),
+                  value = TRUE)
+  exports <- sub("^export\\((.*)\\)$", "\\1", exports)
+  yml <- paste(readLines(source_tree_path("_pkgdown.yml"), warn = FALSE), collapse = "\n")
+  missing <- exports[!vapply(exports,
+                             function(e) grepl(paste0("\\b", e, "\\b"), yml),
+                             logical(1))]
+  # pkgdown errors on topics missing from the index, so an unlisted export
+  # means the docs site does not build at all.
+  expect_equal(missing, character(0))
+})
+
+test_that("README is written for a stranger, not a repo insider", {
+  skip_if_no_source_tree("README.md")
+  r <- paste(readLines(source_tree_path("README.md"), warn = FALSE), collapse = "\n")
+
+  # No paths that only resolve inside a maintainer's checkout.
+  expect_false(grepl("../cog_pipeline", r, fixed = TRUE))
+  # A real, uncommented install line.
+  expect_match(r, "install.packages", fixed = TRUE)
+  expect_false(grepl("# pak::pkg_install", r, fixed = TRUE))
+  # The errata most likely to produce a plausible-looking wrong answer.
+  expect_match(r, "full US dollars", fixed = TRUE)
+  # The release advice that conflicts with public CI is gone.
+  expect_false(grepl("Rbuildignore", r, fixed = TRUE))
+  # Both read paths documented.
+  expect_match(r, "cog_mirror", fixed = TRUE)
+  # cog_spending() has no default for `years`; a quickstart that omits it
+  # errors on the reader's first call.
+  expect_match(r, "years\\s*=", perl = TRUE)
+})

tests/testthat/test-coverage-disclosure.R

@@ -48,6 +48,13 @@ test_that("multi-government aggregates disclose reporting coverage on every resu
   expect_equal(cov$n_units_reporting, c(152L, 597L, 112L, 114L))
   expect_equal(cov$is_census_year, c(FALSE, TRUE, FALSE, FALSE))
 
+  # uscogdata#36: with category = NULL (no category scope), "reported at
+  # all" and "collected" are the same question, so n_units_collected must
+  # equal n_units_reporting exactly here. This case alone cannot catch a
+  # regression in HOW n_units_collected is computed, though: see the
+  # category-scoped test below for that.
+  expect_equal(cov$n_units_collected, cov$n_units_reporting)
+
   # Cross-check against the raw partitions, scoped to the SAME universe the
   # rollup was given -- the 608 govids above. Scoping instead on the long
   # table's own `type`/`fips_state` asks a different question and answers 595:
@@ -80,6 +87,8 @@ test_that("multi-government aggregates disclose reporting coverage on every resu
   expect_equal(cov_peers$n_units_expected, rep(15L, 3L))
   expect_equal(cov_peers$n_units_reporting, c(15L, 3L, 3L))
   expect_equal(cov_peers$is_census_year, c(TRUE, FALSE, FALSE))
+  # uscogdata#36: same identity as the rollup case above, category = NULL.
+  expect_equal(cov_peers$n_units_collected, cov_peers$n_units_reporting)
 
   # -- the three coverage modes --------------------------------------------
   expect_equal(attr(cog_peer_compare(target_govid = chilton, peers = peers,
@@ -101,3 +110,102 @@ test_that("multi-government aggregates disclose reporting coverage on every resu
                                        coverage = "census")
   expect_equal(sort(unique(census_only$year)), 2012)
 })
+
+test_that("n_units_collected separates sampling from real zeros, category-scoped (uscogdata#36)", {
+  # The motivating case from the issue: Wisconsin cities, category = "Police".
+  # FY2012 is a complete census year -- collection is not partial -- yet a
+  # category-conditional n_units_reporting alone reads like a sampling gap.
+  # n_units_collected must diverge from n_units_reporting here, unlike the
+  # category = NULL cases above, because most of the FY2012 gap is cities
+  # that contract policing to the county sheriff (collected, real zero), not
+  # cities Census never surveyed.
+  wi <- cog_gov_search(name = NULL, state = "WI", type = "city")
+  roll <- suppressMessages(cog_geographic_rollup(
+    govids = list(city = wi$canonical_govid), category = "Police",
+    years = c(2011L, 2012L, 2019L, 2020L)))
+  cov <- wt_coverage(roll)
+
+  expect_equal(cov$n_units_expected, rep(608L, 4L))
+  expect_equal(cov$n_units_collected, c(152L, 597L, 112L, 114L))
+  expect_equal(cov$n_units_reporting, c(152L, 485L, 109L, 111L))
+
+  # The pair the issue actually wants: collected/expected is the true
+  # collection rate (98% in the FY2012 census year, matching the raw
+  # cross-check above); reporting/collected is category participation among
+  # collected units (81% -- most of the gap is real, not sampling).
+  expect_equal(round(cov$n_units_collected[cov$year == 2012] /
+                        cov$n_units_expected[cov$year == 2012], 2), 0.98)
+  expect_equal(round(cov$n_units_reporting[cov$year == 2012] /
+                        cov$n_units_collected[cov$year == 2012], 2), 0.81)
+
+  # Every year: collected is bounded between reporting and expected.
+  expect_true(all(cov$n_units_collected >= cov$n_units_reporting))
+  expect_true(all(cov$n_units_collected <= cov$n_units_expected))
+})
+
+test_that(".coverage_table() candidates a government collected-but-absent from the category result (uscogdata#36)", {
+  # Direct regression test for the mechanism itself: n_units_collected's
+  # candidate list must be the caller's full expected cohort (expected_ids),
+  # never derived from `result`/`rows`. A government with zero rows in the
+  # requested category across every requested year never appears in
+  # `result` at all, so deriving candidates from `result` would silently
+  # drop exactly the "collected but real zero" governments this counter
+  # exists to count -- collapsing it back to n_units_reporting.
+  con <- uscogdata:::.ensure_session()
+
+  # A real fixture govid, present in spending_long_harmonized for 2019 (in
+  # SOME category), but absent from this fake category-specific `result`.
+  govid <- "011021100004"
+  fake_result <- data.frame(canonical_govid = character(0), year = integer(0))
+
+  cov <- uscogdata:::.coverage_table(
+    fake_result, years = 2019L, n_expected = 1L,
+    con = con, long_view = "spending_long_harmonized",
+    expected_ids = govid
+  )
+  expect_equal(cov$n_units_collected, 1L)
+  expect_equal(cov$n_units_reporting, 0L)
+
+  # Without a connection, long_view, or expected_ids, the lookup is skipped
+  # rather than silently wrong.
+  no_con <- uscogdata:::.coverage_table(fake_result, years = 2019L, n_expected = 1L)
+  expect_true(is.na(no_con$n_units_collected))
+
+  no_ids <- uscogdata:::.coverage_table(
+    fake_result, years = 2019L, n_expected = 1L,
+    con = con, long_view = "spending_long_harmonized"
+  )
+  expect_true(is.na(no_ids$n_units_collected))
+})
+
+test_that("n_units_collected uses the resolved basis's long view, not a hardcoded harmonized one (uscogdata#36)", {
+  # spending_long_harmonized only exists when schema_version >= 5 (R/views.R
+  # gates the harmonization views on it); on an older corpus cog_spending()
+  # resolves basis = "raw" and queries spending_long instead. The coverage
+  # lookup must follow the SAME resolved basis, not a literal
+  # "spending_long_harmonized", or it hard-errors with a DuckDB catalog
+  # error on every schema_version < 5 corpus -- a vintage the package
+  # otherwise explicitly still supports (see test-manifest.R's dual-accept
+  # tests).
+  skip_if_no_corpus()
+  with_doctored_schema_version(4L, {
+    con <- cog_open()
+    ids <- DBI::dbGetQuery(con,
+      "SELECT DISTINCT canonical_govid FROM spending_long WHERE year = 2011 LIMIT 3"
+    )$canonical_govid
+    expect_gte(length(ids), 3L)
+
+    roll <- suppressMessages(cog_geographic_rollup(
+      list(city = ids), category = NULL, years = 2011L))
+    expect_equal(attr(roll, "provenance")$basis, "raw")
+    cov <- attr(roll, "provenance")$coverage
+    expect_false(is.na(cov$n_units_collected))
+    expect_equal(cov$n_units_collected, length(ids))
+
+    cmp <- suppressMessages(cog_peer_compare(
+      target_govid = ids[1], peers = ids[-1], category = NULL, years = 2011L))
+    expect_equal(attr(cmp, "provenance")$basis, "raw")
+    cov_peers <- attr(cmp, "provenance")$coverage
+    expect_false(is.na(cov_peers$n_units_collected))
+  })
+})

tests/testthat/test-duckdb-limits.R

@@ -0,0 +1,134 @@
+# tests/testthat/test-duckdb-limits.R
+#
+# uscogdata#60. cog_open() used to connect with a bare dbConnect() and set no
+# resource pragmas, so DuckDB claimed every visible core. That is right for one
+# interactive session on a dedicated machine and wrong for a server: cog-api
+# runs two replicas on an 8-core host budgeted 4, and without a cap each
+# replica independently claims all 8 and they fight.
+#
+# The consumer-side workaround this replaces reached into the namespace at
+# boot -- getFromNamespace(".ensure_session", "uscogdata")() followed by a
+# manual SET threads -- which depends on a private name AND on the session
+# already being open.
+#
+# The load-bearing property is the NEGATIVE one: unset must emit no pragma at
+# all, so an unconfigured session is byte-identical to pre-#60 behaviour.
+
+# Open a session under a given configuration and read a DuckDB setting back.
+# Each call closes first, because both settings are session-scoped: an
+# already-open connection would be reused by .ensure_session() and report the
+# PREVIOUS test's value, which is exactly the false pass to avoid here.
+setting_under <- function(setting, envvars = character(0), opts = list()) {
+  uscogdata:::cog_close()
+  on.exit(uscogdata:::cog_close(), add = TRUE)
+  withr::with_envvar(envvars, {
+    withr::with_options(opts, {
+      con <- uscogdata:::cog_open()
+      DBI::dbGetQuery(
+        con, sprintf("SELECT current_setting('%s') AS v", setting)
+      )$v[[1]]
+    })
+  })
+}
+
+test_that("USCOGDATA_DUCKDB_THREADS caps the connection's thread count", {
+  skip_if_no_corpus()
+  expect_equal(
+    as.integer(setting_under("threads", c(USCOGDATA_DUCKDB_THREADS = "2"))),
+    2L
+  )
+})
+
+test_that("the option spelling works, and the env var beats it", {
+  skip_if_no_corpus()
+  expect_equal(
+    as.integer(setting_under("threads",
+                             c(USCOGDATA_DUCKDB_THREADS = NA),
+                             list(uscogdata.duckdb_threads = 3L))),
+    3L
+  )
+  # Same precedence .cfg() gives every other setting: env var > option.
+  expect_equal(
+    as.integer(setting_under("threads",
+                             c(USCOGDATA_DUCKDB_THREADS = "1"),
+                             list(uscogdata.duckdb_threads = 3L))),
+    1L
+  )
+})
+
+test_that("unset leaves DuckDB's own default in place", {
+  skip_if_no_corpus()
+  # Not asserting a specific number -- the default is core-count-dependent and
+  # a literal would fail on a different machine. The claim is that NO pragma
+  # was issued, so the session sees whatever DuckDB would have chosen on its
+  # own. Compared against a plain connection opened the pre-#60 way.
+  unset <- setting_under("threads",
+                         c(USCOGDATA_DUCKDB_THREADS = NA),
+                         list(uscogdata.duckdb_threads = NULL))
+  bare <- local({
+    con <- DBI::dbConnect(duckdb::duckdb())
+    on.exit(DBI::dbDisconnect(con, shutdown = TRUE), add = TRUE)
+    DBI::dbGetQuery(con, "SELECT current_setting('threads') AS v")$v[[1]]
+  })
+  expect_equal(as.integer(unset), as.integer(bare))
+})
+
+test_that("USCOGDATA_DUCKDB_MEMORY_LIMIT is applied", {
+  skip_if_no_corpus()
+  v <- setting_under("memory_limit", c(USCOGDATA_DUCKDB_MEMORY_LIMIT = "2GB"))
+  # DuckDB does not echo back the string it was given: it stores bytes and
+  # reports BINARY units, so "2GB" (2e9 bytes) comes back as "1.8 GiB". Assert
+  # the magnitude it actually means rather than the spelling this package sent
+  # -- matching on "2" passes for the wrong reason and fails on the right one.
+  expect_match(as.character(v), "GiB", fixed = TRUE)
+  # DuckDB also truncates the display to one decimal ("1.8 GiB" for 1.863), so
+  # the tolerance covers rounding, not slack in the setting itself.
+  gib <- as.numeric(sub("\\s*GiB$", "", as.character(v)))
+  expect_equal(gib, 2e9 / 1024^3, tolerance = 0.05)
+})
+
+# --- Validation -------------------------------------------------------------
+# .cfg() returns an env var as CHARACTER. Without coercion here,
+# sprintf("SET threads TO %d", "4") aborts inside the connection path with an
+# error about the pragma rather than about the setting the operator got wrong.
+
+test_that(".resolve_duckdb_threads coerces a character env var to integer", {
+  withr::local_envvar(USCOGDATA_DUCKDB_THREADS = "4")
+  expect_identical(uscogdata:::.resolve_duckdb_threads(), 4L)
+})
+
+test_that(".resolve_duckdb_threads returns NULL when unset or empty", {
+  withr::local_options(uscogdata.duckdb_threads = NULL)
+  withr::local_envvar(USCOGDATA_DUCKDB_THREADS = NA)
+  expect_null(uscogdata:::.resolve_duckdb_threads())
+
+  withr::local_envvar(USCOGDATA_DUCKDB_THREADS = "")
+  expect_null(uscogdata:::.resolve_duckdb_threads())
+})
+
+test_that(".resolve_duckdb_threads rejects values that are not positive integers", {
+  for (bad in c("0", "-1", "two", "1.5.2")) {
+    withr::local_envvar(USCOGDATA_DUCKDB_THREADS = bad)
+    expect_error(uscogdata:::.resolve_duckdb_threads(),
+                 class = "uscogdata_invalid_duckdb_threads")
+  }
+})
+
+test_that(".resolve_duckdb_memory_limit accepts size strings and rejects junk", {
+  withr::local_envvar(USCOGDATA_DUCKDB_MEMORY_LIMIT = "4GB")
+  expect_identical(uscogdata:::.resolve_duckdb_memory_limit(), "4GB")
+
+  withr::local_envvar(USCOGDATA_DUCKDB_MEMORY_LIMIT = "1.5GB")
+  expect_identical(uscogdata:::.resolve_duckdb_memory_limit(), "1.5GB")
+
+  # A SQL fragment must not reach the connection as one.
+  withr::local_envvar(USCOGDATA_DUCKDB_MEMORY_LIMIT = "4GB'; DROP TABLE x; --")
+  expect_error(uscogdata:::.resolve_duckdb_memory_limit(),
+               class = "uscogdata_invalid_duckdb_memory_limit")
+})
+
+test_that(".apply_duckdb_limits issues no statement when both are NULL", {
+  # The negative property, asserted directly rather than inferred: a connection
+  # that would ERROR on any statement proves none was sent.
+  expect_silent(uscogdata:::.apply_duckdb_limits(NULL, NULL, NULL))
+})

tests/testthat/test-expenditure-concept.R

@@ -315,15 +315,17 @@ test_that("a mis-scoped cog_spending() call never attaches an M/L counterpart to
   # (SB194, cog_pipeline#64), so the recipe stopped being a candidate there.
   # FL state carries a real FY2011 B47 amount, so this exercises the guard
   # against a suggestion that genuinely fires.
+  #
+  # Issue #34: "IG Federal" maps to B-prefixed codes in summary_categories
+  # with category_type = 'revenue'. A spending verb (flow_prefixes E/F/G)
+  # now scopes its candidate query by category_type = 'expenditure', so it
+  # correctly finds NO candidates for this revenue-only category -- the
+  # suggestion machinery cannot fire, and no M/L counterpart is attached.
   r <- suppressMessages(
     cog_spending("120000226351", years = c(2005, 2011), category = "IG Federal")
   )
   sugg <- attr(r, "provenance")$suggestions
-  expect_gt(length(sugg), 0L)
-  ids <- vapply(sugg, function(s) s$recipe_id %||% "", character(1))
-  expect_true("ig_federal_b47_wide" %in% ids)
-  ig <- unlist(lapply(sugg, function(s) s$ig_recipe_id))
-  expect_length(ig, 0L)
+  expect_length(sugg, 0L)
 })
 
 test_that("C1: 'total' on a legacy aggregate-only family reports the IG-only figure honestly, not as Direct + IG", {

tests/testthat/test-explain.R

@@ -118,3 +118,17 @@ test_that("cog_explain prints denominator + popyear_range + counts", {
     expect_false(grepl("popyear range: 19-20", out, fixed = TRUE))
   })
 })
+
+test_that("cog_explain reports units collected alongside units reporting (uscogdata#36)", {
+  skip_if_no_corpus()
+  wi <- cog_gov_search(name = NULL, state = "WI", type = "city")
+  roll <- suppressMessages(cog_geographic_rollup(
+    govids = list(city = wi$canonical_govid), category = "Police",
+    years = 2012L))
+  out <- paste(c(
+    capture.output(cog_explain(roll)),
+    capture.output(cog_explain(roll), type = "message")
+  ), collapse = "\n")
+  expect_true(grepl("597 of 608 units collected", out, fixed = TRUE))
+  expect_true(grepl("485 reporting in this category", out, fixed = TRUE))
+})

tests/testthat/test-live-corpus.R

@@ -0,0 +1,62 @@
+# Network-gated. Set USCOGDATA_LIVE_TEST=true to run.
+#
+# This file exists because the defect fixed for 0.3.0 -- no remote corpus was
+# readable at all, because DuckDB cannot expand a glob over generic HTTP --
+# survived precisely because every other test path used a LOCAL corpus (the
+# bundled fixture), and so did the API in production (a host mount). Nothing
+# ever exercised the package the way a new user does.
+skip_live <- function() {
+  testthat::skip_if_not(
+    identical(tolower(Sys.getenv("USCOGDATA_LIVE_TEST", "")), "true"),
+    "live-corpus test: set USCOGDATA_LIVE_TEST=true to run"
+  )
+}
+
+# The suite's setup.R pins USCOGDATA_URL to the bundled fixture, so reaching
+# the default requires clearing both the env var and the option.
+with_default_corpus <- function(code) {
+  withr::local_envvar(
+    USCOGDATA_URL = NA, USCOGDATA_FIXTURE_URL = NA,
+    .local_envir = parent.frame()
+  )
+  withr::local_options(uscogdata.url = NULL, .local_envir = parent.frame())
+  cog_close()
+  withr::defer(cog_close(), envir = parent.frame())
+  force(code)
+}
+
+test_that("the package reads the public corpus with no configuration at all", {
+  skip_live()
+  with_default_corpus({
+    g <- cog_gov_search(name = "Madison", state = "WI", type = 2)
+    expect_gt(nrow(g), 0)
+
+    s <- cog_spending(g$canonical_govid[1], years = 2022)
+    expect_gt(nrow(s), 0)
+    expect_true(all(c("amt_nominal", "year", "category") %in% names(s)))
+
+    # Amounts are full dollars, already x1000. A city's annual spending is
+    # millions, not thousands -- this catches a regression that dropped or
+    # doubled the conversion.
+    expect_gt(sum(s$amt_nominal, na.rm = TRUE), 1e6)
+
+    p <- attr(s, "provenance")
+    expect_true(isTRUE(p$transformations$units_conversion$applied))
+    expect_equal(p$transformations$units_conversion$multiplier, 1000)
+  })
+})
+
+test_that("a multi-decade query reads across many partitions", {
+  skip_live()
+  with_default_corpus({
+    g <- cog_gov_search(name = "Madison", state = "WI", type = 2)
+    # `years` is required on cog_spending() -- there is no full-history
+    # default at the reader level (the API's /profile route supplies one).
+    s <- cog_spending(g$canonical_govid[1], years = 2000:2022)
+    # Enumeration builds one read_parquet() path per requested partition. If
+    # the list were truncated, or silently collapsed to a single file, the
+    # returned span is what catches it.
+    expect_gt(diff(range(s$year)), 10)
+    expect_gt(length(unique(s$year)), 5)
+  })
+})

tests/testthat/test-long-files.R

@@ -0,0 +1,97 @@
+test_that(".long_files_sql enumerates every partition the manifest lists", {
+  manifest <- list(files = list(long_partitions = list(
+    list(year = 2011L, path = "data/long/year=2011/part-0.parquet"),
+    list(year = 2012L, path = "data/long/year=2012/part-0.parquet")
+  )))
+  expect_equal(
+    uscogdata:::.long_files_sql("https://example.org/corpus/", manifest),
+    paste0(
+      "['https://example.org/corpus/data/long/year=2011/part-0.parquet',",
+      "'https://example.org/corpus/data/long/year=2012/part-0.parquet']"
+    )
+  )
+})
+
+test_that(".long_files_sql falls back to the glob when no partition list is present", {
+  # test-views.R registers views with a hand-built manifest that has no
+  # `files` element. That must keep working: the glob is valid for the
+  # local paths such a manifest is used with.
+  expect_equal(
+    uscogdata:::.long_files_sql("/tmp/corpus/", list(schema_version = 4L)),
+    "'/tmp/corpus/data/long/**/*.parquet'"
+  )
+  expect_equal(
+    uscogdata:::.long_files_sql("/tmp/corpus/", list(files = list(long_partitions = list()))),
+    "'/tmp/corpus/data/long/**/*.parquet'"
+  )
+})
+
+test_that("the enumerated list matches the bundled fixture's partition count", {
+  skip_if_no_corpus()
+  m <- jsonlite::fromJSON(
+    file.path(fixture_corpus_path(), "manifest.json"), simplifyVector = FALSE
+  )
+  out <- uscogdata:::.long_files_sql(fixture_corpus_path(), m)
+  expect_equal(
+    lengths(regmatches(out, gregexpr("part-0\\.parquet", out))),
+    length(m$files$long_partitions)
+  )
+})
+
+test_that("no view SQL survives rendering with an unsubstituted token", {
+  # Introducing {long_files} broke four test sites that had hand-rolled the
+  # {url} substitution -- each failed with a DuckDB parser error on the
+  # surviving brace. This asserts the whole SQL directory renders clean, so
+  # a future token cannot reintroduce that silently.
+  sql_dir <- system.file("sql", package = "uscogdata")
+  for (f in list.files(sql_dir, pattern = "\\.sql$", full.names = TRUE)) {
+    rendered <- uscogdata:::.render_view_sql(
+      paste(readLines(f, warn = FALSE), collapse = "\n"), "/tmp/corpus/"
+    )
+    expect_false(grepl("\\{[a-z_]+\\}", rendered), label = basename(f))
+  }
+})
+
+test_that("registered `long` view reads through the enumerated list", {
+  skip_if_no_corpus()
+  with_fixture_corpus({
+    con <- uscogdata:::.ensure_session()
+    n <- DBI::dbGetQuery(con, "SELECT count(*) AS n FROM long")$n
+    expect_gt(n, 0)
+    yrs <- DBI::dbGetQuery(con, "SELECT DISTINCT year FROM long ORDER BY year")$year
+    expect_true(all(c(2011, 2012, 2019, 2020) %in% yrs))
+  })
+})
+
+test_that("a Windows-style corpus path survives token substitution", {
+  # gsub() in regex mode treats backslashes in the REPLACEMENT as escape
+  # sequences and silently drops them, so a Windows path went in as
+  # C:\Users\RUNNER\... and came out as C:UsersRUNNER..., after which every
+  # DuckDB read failed with "No files found that match the pattern".
+  #
+  # That made a LOCAL corpus unreadable on Windows -- the bundled fixture
+  # included, so the whole suite failed there -- while remote https URLs
+  # worked fine, having no backslashes. It went unnoticed for the life of the
+  # package because nothing ever ran on Windows.
+  #
+  # Reproducible on any platform: this is string handling, not a filesystem
+  # behaviour, so it does not need a Windows runner to catch.
+  win <- "C:\\Users\\RUNNER~1\\AppData\\Local\\Temp\\Rtmp123/"
+
+  out <- uscogdata:::.render_view_sql(
+    "FROM read_parquet('{url}data/summary_categories.parquet')", win
+  )
+  expect_true(grepl("C:\\Users\\RUNNER~1\\AppData", out, fixed = TRUE))
+  expect_false(grepl("C:Users", out, fixed = TRUE))
+
+  # The same must hold through the {long_files} path, which embeds the url
+  # once per enumerated partition.
+  manifest <- list(files = list(long_partitions = list(
+    list(year = 2011L, path = "data/long/year=2011/part-0.parquet")
+  )))
+  out2 <- uscogdata:::.render_view_sql(
+    "FROM read_parquet({long_files}, hive_partitioning = true)", win, manifest
+  )
+  expect_true(grepl("C:\\Users\\RUNNER~1\\AppData", out2, fixed = TRUE))
+  expect_false(grepl("C:Users", out2, fixed = TRUE))
+})

tests/testthat/test-manifest.R

@@ -4,12 +4,14 @@
 # protect users from silent failures when USCOGDATA_URL is misconfigured
 # or returns non-JSON content.
 
-test_that("cog_open aborts with actionable error when URL is the placeholder default", {
+test_that("cog_open aborts with actionable error when URL contains the sentinel", {
   uscogdata:::cog_close()
   on.exit(uscogdata:::cog_close(), add = TRUE)
 
-  placeholder <- "https://cloud.civilytics.org/s/REPLACE_WITH_SHARE_TOKEN/download/"
-  withr::with_envvar(c(USCOGDATA_URL = placeholder), {
+  # No longer the package default (that is the public HF corpus). This is a
+  # user who copied a config template and did not finish editing it.
+  sentinel_url <- "https://cloud.civilytics.org/s/REPLACE_WITH_SHARE_TOKEN/download/"
+  withr::with_envvar(c(USCOGDATA_URL = sentinel_url), {
     expect_error(
       uscogdata:::cog_open(),
       class = "uscogdata_url_not_configured"
@@ -35,8 +37,10 @@ test_that("placeholder guard error names both env var and option as remediation"
   uscogdata:::cog_close()
   on.exit(uscogdata:::cog_close(), add = TRUE)
 
-  placeholder <- "https://cloud.civilytics.org/s/REPLACE_WITH_SHARE_TOKEN/download/"
-  withr::with_envvar(c(USCOGDATA_URL = placeholder), {
+  # No longer the package default (that is the public HF corpus). This is a
+  # user who copied a config template and did not finish editing it.
+  sentinel_url <- "https://cloud.civilytics.org/s/REPLACE_WITH_SHARE_TOKEN/download/"
+  withr::with_envvar(c(USCOGDATA_URL = sentinel_url), {
     msg <- tryCatch(uscogdata:::cog_open(), error = conditionMessage)
     expect_match(msg, "USCOGDATA_URL", fixed = TRUE)
     expect_match(msg, "uscogdata.url", fixed = TRUE)
@@ -111,7 +115,7 @@ test_that("cog_manifest returns the active session's parsed manifest", {
   })
 })
 
-test_that(".validate_schema accepts schema_version 4, 5 and 6, rejects others", {
+test_that(".validate_schema accepts schema_version 4 through 7, rejects others", {
   expect_silent(uscogdata:::.validate_schema(list(schema_version = 4L)))
   expect_silent(uscogdata:::.validate_schema(list(schema_version = 5L)))
   # v6 = FIPS geography harmonization (2026-07-22): _code -> _asof rename +
@@ -119,12 +123,22 @@ test_that(".validate_schema accepts schema_version 4, 5 and 6, rejects others",
   # renamed columns and its geography comes from the xwalk, so v6 is accepted
   # without behavioural change -- see .validate_schema()'s note.
   expect_silent(uscogdata:::.validate_schema(list(schema_version = 6L)))
+  # v7 = `data_year` APPENDED as column 29 (cog_pipeline #80, 2026-08-03), the
+  # most recent fiscal year contributing to a collapsed key. Appended, never
+  # inserted: canonical_govid stays at position 26, so nothing this package
+  # reads shifts. Verified against the real v7 corpus before widening the
+  # allow-list -- cog_spending()/cog_balances() return correctly for FY2024 AND
+  # for FY2012, so the new column is inert here.
+  expect_silent(uscogdata:::.validate_schema(list(schema_version = 7L)))
   expect_error(
     uscogdata:::.validate_schema(list(schema_version = 3L)),
     "schema_version"
   )
+  # The upper bound still has to be ENFORCED, not just moved. Without this the
+  # test would no longer prove that an unknown future schema is refused, and a
+  # v8 corpus with a genuinely breaking change would sail through.
   expect_error(
-    uscogdata:::.validate_schema(list(schema_version = 7L)),
+    uscogdata:::.validate_schema(list(schema_version = 8L)),
     "schema_version"
   )
 })

tests/testthat/test-recipes.R

@@ -214,3 +214,266 @@ test_that("no signposting under basis = 'raw'", {
   prov <- attr(r, "provenance")
   expect_length(prov$suggestions, 0L)
 })
+
+# --- uscogdata#9: partial-coverage signposting ------------------------------
+
+test_that("no recipe component is ever renamed by harmonization", {
+  # The suppression trigger anti-joins the verb's long view on item_code.
+  # That is only sound because harmonization never rewrites a recipe
+  # component's code -- every component whose harmonized_code differs has
+  # harmonized_code IS NULL (and is aggregate-flagged). If this ever fails,
+  # .suppressed_components() would report reachable dollars as suppressed.
+  skip_if_no_corpus()
+  con <- uscogdata:::.ensure_session()
+  n <- DBI::dbGetQuery(con,
+    "SELECT COUNT(*) AS renamed FROM long
+     WHERE item_code IN (SELECT DISTINCT component_code FROM harmonization_recipes)
+       AND harmonized_code IS NOT NULL
+       AND harmonized_code <> item_code")$renamed
+  expect_equal(as.integer(n), 0L)
+})
+
+test_that(".select_long_view maps annotated view bases to their long views", {
+  expect_equal(
+    uscogdata:::.select_long_view("spending_annotated", "harmonized"),
+    "spending_long_harmonized")
+  expect_equal(
+    uscogdata:::.select_long_view("revenue_annotated", "harmonized"),
+    "revenue_long_harmonized")
+  expect_equal(
+    uscogdata:::.select_long_view("spending_annotated", "raw"),
+    "spending_long")
+})
+
+test_that(".suppressed_components measures the E67/E68 dollars Public Welfare drops", {
+  skip_if_no_corpus()
+  con <- uscogdata:::.ensure_session()
+  s <- uscogdata:::.suppressed_components(
+    con,
+    candidates = c("welfare_cash_e67_wide", "welfare_cash_e68_wide"),
+    cohort = uscogdata:::.make_cohort("061037123085"), years = 2011L,
+    long_view = "spending_long_harmonized",
+    flow_prefixes = c("E", "F", "G"))
+
+  expect_s3_class(s, "tbl_df")
+  expect_equal(nrow(s), 2L)
+  s <- s[order(s$recipe_id), ]
+  expect_equal(s$recipe_id, c("welfare_cash_e67_wide", "welfare_cash_e68_wide"))
+  expect_equal(s$suppressed_amount, c(1803872000, 271589000))
+  expect_equal(s$suppressed_codes, c("E67", "E68"))
+})
+
+test_that(".suppressed_components finds nothing in a modern year", {
+  skip_if_no_corpus()
+  con <- uscogdata:::.ensure_session()
+  s <- uscogdata:::.suppressed_components(
+    con,
+    candidates = c("welfare_cash_e67_wide", "welfare_cash_e68_wide"),
+    cohort = uscogdata:::.make_cohort("061037123085"), years = 2019L,
+    long_view = "spending_long_harmonized",
+    flow_prefixes = c("E", "F", "G"))
+  expect_equal(nrow(s), 0L)
+})
+
+test_that(".suppressed_components rejects a long_view outside the allowlist", {
+  skip_if_no_corpus()
+  con <- uscogdata:::.ensure_session()
+  expect_error(
+    uscogdata:::.suppressed_components(
+      con, candidates = "welfare_cash_e67_wide", cohort = uscogdata:::.make_cohort("061037123085"),
+      years = 2011L, long_view = "long; DROP TABLE x",
+      flow_prefixes = c("E", "F", "G")),
+    class = "uscogdata_internal_error")
+})
+
+test_that(".suppressed_components never measures a component from the other flow family (I1)", {
+  # uscogdata#9 review, finding I1: without the flow_prefixes filter, a
+  # candidate recipe entirely outside the calling verb's own flow family is
+  # ALWAYS absent from that verb's view (by construction), so it was always
+  # reported as "suppressed" -- fabricating a dollar claim. E67/E68 are
+  # Public Welfare EXPENDITURE codes; scoping the measurement to revenue's
+  # own flow_prefixes must find nothing for them.
+  skip_if_no_corpus()
+  con <- uscogdata:::.ensure_session()
+  s <- uscogdata:::.suppressed_components(
+    con,
+    candidates = c("welfare_cash_e67_wide", "welfare_cash_e68_wide"),
+    cohort = uscogdata:::.make_cohort("061037123085"), years = 2011L,
+    long_view = "revenue_long_harmonized",
+    flow_prefixes = c("T", "A", "U", "B", "C", "D"))
+  expect_equal(nrow(s), 0L)
+})
+
+test_that("uscogdata#9: Public Welfare signposts its suppressed E67/E68 dollars", {
+  # The bug: E74/E79 return rows for FY2011, so there is no row-absence gap,
+  # so nothing fired -- while E67 ($1,803,872,000) and E68 ($271,589,000) were
+  # dropped for being aggregate-published. LA County reports $3,185,943,000
+  # and omits $2,075,461,000, a 39% understatement, silently.
+  skip_if_no_corpus()
+  r <- suppressMessages(
+    cog_spending("061037123085", years = 2011L, category = "Public Welfare"))
+  sugg <- attr(r, "provenance")$suggestions
+
+  expect_length(sugg, 2L)
+  ids <- vapply(sugg, function(s) s$recipe_id, character(1))
+  expect_setequal(ids, c("welfare_cash_e67_wide", "welfare_cash_e68_wide"))
+
+  e67 <- sugg[[which(ids == "welfare_cash_e67_wide")]]
+  expect_equal(e67$trigger, "suppressed_component")
+  expect_equal(e67$suppressed_amount, 1803872000)
+  expect_equal(e67$suppressed_years, 2011L)
+  expect_equal(e67$suppressed_codes, "E67")
+  expect_equal(e67$hint, "re-run with recipe = 'welfare_cash_e67_wide'")
+
+  e68 <- sugg[[which(ids == "welfare_cash_e68_wide")]]
+  expect_equal(e68$trigger, "suppressed_component")
+  expect_equal(e68$suppressed_amount, 271589000)
+  expect_equal(e68$suppressed_codes, "E68")
+})
+
+test_that("uscogdata#9: an empty_year fire keeps its trigger and gains the dollars", {
+  # Corrections is the case that already worked: zero rows in FY2011, so the
+  # row-absence path fires. It must keep firing, keep trigger = "empty_year",
+  # keep its IG counterpart -- and now also report what was suppressed.
+  skip_if_no_corpus()
+  r <- suppressMessages(
+    cog_spending("061037123085", years = 2011L, category = "Corrections"))
+  sugg <- attr(r, "provenance")$suggestions
+
+  expect_length(sugg, 3L)
+  ids <- vapply(sugg, function(s) s$recipe_id, character(1))
+  expect_setequal(ids, c("corrections_combined", "corrections_capital_combined",
+                         "corrections_other_capital_combined"))
+  expect_true(all(vapply(sugg, function(s) s$trigger, character(1)) == "empty_year"))
+
+  cc <- sugg[[which(ids == "corrections_combined")]]
+  expect_equal(cc$suppressed_amount, 1371460000)
+  expect_equal(cc$suppressed_codes, "E05")
+  expect_equal(cc$ig_recipe_id, "corrections_ig_local_combined")
+})
+
+test_that("uscogdata#9: the revenue verb inherits the same trigger", {
+  # Alaska state FY2011 Miscellaneous Revenue reports $943,842,000 from
+  # U11/U20/U30 while dropping $1,899,995,000 of aggregate-published `U4-`
+  # rents and royalties -- the omission is LARGER than the reported figure.
+  skip_if_no_corpus()
+  r <- suppressMessages(
+    cog_revenue("020000227749", years = 2011L,
+                category = "Miscellaneous Revenue"))
+  sugg <- attr(r, "provenance")$suggestions
+
+  expect_length(sugg, 1L)
+  expect_equal(sugg[[1]]$recipe_id, "rents_royalties_u4_wide")
+  expect_equal(sugg[[1]]$trigger, "suppressed_component")
+  expect_equal(sugg[[1]]$suppressed_amount, 1899995000)
+  expect_equal(sugg[[1]]$suppressed_codes, "U4-")
+  # A revenue recipe must never be handed an M/L expenditure counterpart.
+  expect_null(sugg[[1]]$ig_recipe_id)
+})
+
+test_that("I1 + #34: cog_revenue never suggests expenditure-only recipes", {
+  # uscogdata#9 review, finding I1: Corrections is an expenditure-only
+  # category (E04/E05). Before the flow_prefixes fix (#9), .suppressed_components()
+  # measured E04/E05 against cog_revenue()'s OWN view and reported $3.6B as
+  # "suppressed" -- nothing was suppressed at all.
+  #
+  # Issue #34 builds on that: the candidate query now also filters by
+  # category_type ('revenue'), so expenditure-only recipes like corrections_combined
+  # (whose components E04/E05 are classified as 'expenditure' in summary_categories)
+  # are never even considered for a revenue verb. This is stronger than just
+  # suppressing the dollar claim -- it prevents the suggestion from firing at all.
+  skip_if_no_corpus()
+  r <- suppressMessages(
+    cog_revenue("061037123085", years = 2019:2020, category = "Corrections"))
+  sugg <- attr(r, "provenance")$suggestions
+  ids <- vapply(sugg, function(s) s$recipe_id %||% "", character(1))
+
+  # corrections_combined should NOT appear -- its components are expenditure-only.
+  expect_false("corrections_combined" %in% ids)
+})
+
+test_that(".query_candidate_recipes() scopes candidates by category_type (#34)", {
+  # Direct assertion on the mechanism the two tests above exercise
+  # end-to-end: corrections_combined's own components (E04/E05) are
+  # category_type = 'expenditure' in summary_categories, so an
+  # expenditure-flavored flow_prefixes call must surface it and a
+  # revenue-flavored one must not. This queries only summary_categories/
+  # harmonization_recipes (no government data), so it runs against the
+  # bundled fixture with no skip_if_no_corpus() needed.
+  con <- uscogdata:::.ensure_session()
+
+  expenditure <- uscogdata:::.query_candidate_recipes(
+    con, category = "Corrections", flow_prefixes = c("E", "F", "G"))
+  expect_true("corrections_combined" %in% expenditure)
+
+  revenue <- uscogdata:::.query_candidate_recipes(
+    con, category = "Corrections",
+    flow_prefixes = c("T", "A", "U", "B", "C", "D"))
+  expect_false("corrections_combined" %in% revenue)
+})
+
+test_that("uscogdata#9: no partial-coverage fire in a modern year", {
+  skip_if_no_corpus()
+  r <- cog_spending("061037123085", years = 2019L, category = "Public Welfare")
+  expect_length(attr(r, "provenance")$suggestions, 0L)
+})
+
+test_that("uscogdata#9: leaf-and-classified wide-era families never fire", {
+  # higher_ed_e18_wide and general_gov_e89_wide are the control group: their
+  # components (E16/E18, E85/E89) are ordinary classified leaves even in the
+  # wide era, so widening the trigger must leave them silent. This is the
+  # measurement that refutes "it would fire on every category in every legacy
+  # year" -- corpus-wide on the fixture, these two produce zero suppressed rows.
+  skip_if_no_corpus()
+  con <- uscogdata:::.ensure_session()
+  n <- DBI::dbGetQuery(con,
+    "SELECT COUNT(*) AS n
+     FROM long l
+     JOIN harmonization_recipes r
+       ON l.item_code = r.component_code
+      AND l.year BETWEEN r.year_min AND r.year_max
+     WHERE r.recipe_id IN ('higher_ed_e18_wide', 'general_gov_e89_wide')
+       AND l.amt <> 0
+       AND NOT EXISTS (
+         SELECT 1 FROM spending_long_harmonized v
+         WHERE v.canonical_govid = l.canonical_govid
+           AND v.year = l.year AND v.item_code = l.item_code)")$n
+  expect_equal(as.integer(n), 0L)
+})
+
+test_that("uscogdata#9: the cli message reports the suppressed dollars", {
+  skip_if_no_corpus()
+  expect_message(
+    cog_spending("061037123085", years = 2011L, category = "Public Welfare"),
+    "1,803,872,000", fixed = TRUE)
+  expect_message(
+    cog_spending("061037123085", years = 2011L, category = "Public Welfare"),
+    "FY2011", fixed = TRUE)
+  expect_message(
+    cog_spending("061037123085", years = 2011L, category = "Public Welfare"),
+    "E67", fixed = TRUE)
+})
+
+test_that("uscogdata#9: cog_explain() reports the suppressed dollars", {
+  # cog_explain()'s whole "print" output -- including the Suggestions
+  # section built from cli::cli_ul() -- is emitted on the message stream
+  # (verified empirically 2026-08-04: capture.output(..., type = "output")
+  # returns character(0) for this call; testthat::capture_messages() is what
+  # actually carries it), so that is the stream this test captures.
+  skip_if_no_corpus()
+  r <- suppressMessages(
+    cog_spending("061037123085", years = 2011L, category = "Public Welfare"))
+  out <- paste(testthat::capture_messages(cog_explain(r)), collapse = "")
+  expect_match(out, "271,589,000", fixed = TRUE)
+})
+
+test_that("the provenance schema documents the suggestion trigger fields", {
+  sch <- jsonlite::fromJSON(
+    system.file("schemas", "provenance-v1.json", package = "uscogdata"),
+    simplifyVector = FALSE)
+  props <- sch$properties$suggestions$items$properties
+  expect_true(all(c("trigger", "suppressed_amount", "suppressed_years",
+                    "suppressed_codes") %in% names(props)))
+  expect_setequal(unlist(props$trigger$enum),
+                  c("empty_year", "suppressed_component"))
+})

tests/testthat/test-revenue-pagination.R

@@ -0,0 +1,29 @@
+# Mirror of test-spending-pagination.R for cog_revenue(), which shares the
+# same .verb_spendrev()/.build_verb_sql() pushdown -- see that file for the
+# incident this fixes.
+
+test_that("cog_revenue limit/offset page correctly and report total_rows", {
+  skip_if_no_corpus()
+  full <- cog_revenue("121011212191", years = 2019:2020, category = NULL)
+  page <- cog_revenue("121011212191", years = 2019:2020, category = NULL,
+                      limit = 5L, offset = 3L)
+  expect_equal(nrow(page), 5L)
+  expect_equal(page[c("year", "canonical_govid", "revenue_subtype", "category")],
+              full[4:8, c("year", "canonical_govid", "revenue_subtype", "category")],
+              ignore_attr = TRUE)
+  expect_equal(attr(page, "total_rows"), nrow(full))
+})
+
+test_that("cog_revenue limit unset by default leaves total_rows absent", {
+  skip_if_no_corpus()
+  r <- cog_revenue("121011212191", 2020L, "Property Tax")
+  expect_null(attr(r, "total_rows"))
+})
+
+test_that("cog_revenue complete + limit conflict aborts the same way as cog_spending", {
+  skip_if_no_corpus()
+  expect_error(
+    cog_revenue("121011212191", 2020L, "Property Tax", complete = TRUE, limit = 5L),
+    class = "uscogdata_complete_pagination_conflict"
+  )
+})

tests/testthat/test-search-balances-pagination.R

@@ -0,0 +1,178 @@
+# tests/testthat/test-search-balances-pagination.R
+#
+# uscogdata#57. cog_spending()/cog_revenue() gained limit/offset in #39;
+# cog_gov_search() and cog_balances() did not, so every consumer of those two
+# was back to materialize-then-slice -- the exact pattern that wedged the
+# production API for hours on 2026-08-06.
+#
+# cog_gov_search() was also the one verb with no LIMIT at all, so an
+# unfiltered call returns the entire 40,336-row crosswalk by accident.
+
+# --- cog_gov_search() -------------------------------------------------------
+
+test_that("cog_gov_search() limit returns the first page of the unpaginated result", {
+  skip_if_no_corpus()
+  full <- cog_gov_search(state = "WI", type = "city")
+  skip_if(nrow(full) < 12L, "fixture has too few WI cities to page")
+
+  page <- cog_gov_search(state = "WI", type = "city", limit = 5L)
+  expect_equal(nrow(page), 5L)
+  expect_equal(page$canonical_govid, full$canonical_govid[1:5])
+})
+
+test_that("cog_gov_search() offset skips ahead without gaps or overlap", {
+  skip_if_no_corpus()
+  full <- cog_gov_search(state = "WI", type = "city")
+  skip_if(nrow(full) < 12L, "fixture has too few WI cities to page")
+
+  p1 <- cog_gov_search(state = "WI", type = "city", limit = 5L)
+  p2 <- cog_gov_search(state = "WI", type = "city", limit = 5L, offset = 5L)
+  expect_equal(p2$canonical_govid, full$canonical_govid[6:10])
+  expect_length(intersect(p1$canonical_govid, p2$canonical_govid), 0L)
+})
+
+test_that("walking every page reconstructs the unpaginated search exactly", {
+  skip_if_no_corpus()
+  full <- cog_gov_search(state = "WI", type = "city")
+  n <- nrow(full)
+  limit <- 7L
+  pages <- list()
+  offset <- 0L
+  repeat {
+    p <- cog_gov_search(state = "WI", type = "city", limit = limit, offset = offset)
+    if (nrow(p) == 0L) break
+    pages[[length(pages) + 1L]] <- p
+    offset <- offset + limit
+    if (offset > n + limit) stop("test runaway: paging did not terminate")
+  }
+  walked <- dplyr::bind_rows(pages)
+  expect_equal(nrow(walked), n)
+  expect_equal(walked$canonical_govid, full$canonical_govid)
+})
+
+test_that("cog_gov_search() total_rows reports the full unpaginated count", {
+  skip_if_no_corpus()
+  full <- cog_gov_search(state = "WI", type = "city")
+  page <- cog_gov_search(state = "WI", type = "city", limit = 3L)
+  expect_equal(attr(page, "total_rows"), nrow(full))
+})
+
+test_that("cog_gov_search() offset past the end reports the true total, not zero", {
+  skip_if_no_corpus()
+  full <- cog_gov_search(state = "WI", type = "city")
+  # No row survives to carry COUNT(*) OVER(), so this is the branch that has
+  # to fall back to a second count rather than reporting 0 rows out of 0.
+  page <- cog_gov_search(state = "WI", type = "city",
+                         limit = 5L, offset = nrow(full) + 50L)
+  expect_equal(nrow(page), 0L)
+  expect_equal(attr(page, "total_rows"), nrow(full))
+})
+
+test_that("cog_gov_search() bounds an otherwise-unfiltered crosswalk sweep", {
+  skip_if_no_corpus()
+  # The reason this verb needed a limit most: with no filter it returns the
+  # whole crosswalk.
+  page <- cog_gov_search(limit = 10L)
+  expect_equal(nrow(page), 10L)
+  expect_gt(attr(page, "total_rows"), 10L)
+})
+
+test_that("cog_gov_search() orders by a total order, not population alone", {
+  skip_if_no_corpus()
+  # population_acs is not unique -- NA in particular repeats across many rows
+  # -- so paging on it alone can duplicate a row on one page and drop it from
+  # the next. The tiebreaker is what makes the sequence reproducible.
+  full <- cog_gov_search(state = "WI")
+  skip_if(nrow(full) < 5L, "fixture has too few WI governments")
+  expect_equal(cog_gov_search(state = "WI")$canonical_govid,
+               full$canonical_govid)
+
+  ties <- full[is.na(full$population_acs), ]
+  skip_if(nrow(ties) < 2L, "no tied rows in the fixture to order")
+  expect_false(is.unsorted(ties$canonical_govid))
+})
+
+test_that("cog_gov_search() refuses pagination in basket mode", {
+  skip_if_no_corpus()
+  expect_error(
+    cog_gov_search(name = c("MADISON CITY", "MILWAUKEE CITY"),
+                   state = c("WI", "WI"), limit = 1L),
+    class = "uscogdata_basket_pagination_conflict"
+  )
+})
+
+test_that("cog_gov_search() rejects a malformed limit or offset", {
+  skip_if_no_corpus()
+  expect_error(cog_gov_search(state = "WI", limit = -1L),
+               class = "uscogdata_invalid_pagination")
+  expect_error(cog_gov_search(state = "WI", limit = 5L, offset = -1L),
+               class = "uscogdata_invalid_pagination")
+})
+
+# --- cog_balances() ---------------------------------------------------------
+
+test_that("cog_balances() limit/offset walk the unpaginated result exactly", {
+  skip_if_no_corpus()
+  full <- cog_balances(years = 2019:2020, state = "WI", type = "city")
+  skip_if(nrow(full) < 6L, "fixture has too few WI city balance rows to page")
+
+  key <- c("year", "canonical_govid", "balance_subtype", "amt_nominal")
+  p1 <- cog_balances(years = 2019:2020, state = "WI", type = "city", limit = 3L)
+  p2 <- cog_balances(years = 2019:2020, state = "WI", type = "city",
+                     limit = 3L, offset = 3L)
+
+  expect_equal(nrow(p1), 3L)
+  expect_equal(p1[key], full[1:3, key], ignore_attr = TRUE)
+  expect_equal(p2[key], full[4:6, key], ignore_attr = TRUE)
+  # The window-function column is an implementation detail and must not reach
+  # the caller's data frame.
+  expect_false("pagination_total_rows" %in% names(p1))
+})
+
+test_that("cog_balances() total_rows reports the full unpaginated count", {
+  skip_if_no_corpus()
+  full <- cog_balances(years = 2019:2020, state = "WI", type = "city")
+  page <- cog_balances(years = 2019:2020, state = "WI", type = "city", limit = 2L)
+  expect_equal(attr(page, "total_rows"), nrow(full))
+})
+
+test_that("cog_balances() offset past the end reports the true total", {
+  skip_if_no_corpus()
+  full <- cog_balances(years = 2019:2020, state = "WI", type = "city")
+  page <- cog_balances(years = 2019:2020, state = "WI", type = "city",
+                       limit = 5L, offset = nrow(full) + 50L)
+  expect_equal(nrow(page), 0L)
+  expect_equal(attr(page, "total_rows"), nrow(full))
+})
+
+test_that("cog_balances() refuses pagination alongside a recipe", {
+  skip_if_no_corpus()
+  expect_error(
+    cog_balances(years = 2011, state = "WI", type = "city",
+                 recipe = "cash_securities_z77_wide", limit = 5L),
+    class = "uscogdata_recipe_pagination_conflict"
+  )
+})
+
+test_that("cog_balances() rejects a malformed limit or offset", {
+  skip_if_no_corpus()
+  expect_error(cog_balances(years = 2019, state = "WI", type = "city", limit = -1L),
+               class = "uscogdata_invalid_pagination")
+  expect_error(cog_balances(years = 2019, state = "WI", type = "city",
+                            limit = 5L, offset = -1L),
+               class = "uscogdata_invalid_pagination")
+})
+
+# --- Unchanged without the arguments ----------------------------------------
+
+test_that("both verbs are unchanged when limit is not supplied", {
+  skip_if_no_corpus()
+  # The adoption contract for cog-api: NULL default, so a formals() probe can
+  # feature-detect without any call site changing behaviour.
+  s <- cog_gov_search(state = "WI", type = "city")
+  b <- cog_balances(years = 2019, state = "WI", type = "city")
+  expect_null(attr(s, "total_rows"))
+  expect_null(attr(b, "total_rows"))
+  expect_true(all(c("limit", "offset") %in% names(formals(cog_gov_search))))
+  expect_true(all(c("limit", "offset") %in% names(formals(cog_balances))))
+})

tests/testthat/test-spending-pagination.R

@@ -0,0 +1,95 @@
+# cog-api's paginate() used to slice an already-fully-materialized result:
+# every page of a deep sweep re-ran the whole query and re-listified every
+# row, just to keep 1000 and discard the rest. For a 193,105-row fleet-wide
+# query walked 194 pages deep, that repeated the full cost 194 times and
+# wedged the production server for hours (2026-08-06 incident). limit/offset
+# here push the slice into the SQL itself, so a page costs O(limit), not
+# O(full result).
+
+test_that("limit without offset returns the first page, matching the unpaginated head", {
+  skip_if_no_corpus()
+  full <- cog_spending("121011212191", years = 2019:2020, category = NULL)
+  page <- cog_spending("121011212191", years = 2019:2020, category = NULL,
+                       limit = 10L)
+  expect_equal(nrow(page), 10L)
+  expect_equal(page[c("year", "canonical_govid", "spend_subtype", "category")],
+              full[1:10, c("year", "canonical_govid", "spend_subtype", "category")],
+              ignore_attr = TRUE)
+})
+
+test_that("offset skips ahead without gaps or overlap", {
+  skip_if_no_corpus()
+  full <- cog_spending("121011212191", years = 2019:2020, category = NULL)
+  page2 <- cog_spending("121011212191", years = 2019:2020, category = NULL,
+                        limit = 10L, offset = 10L)
+  expect_equal(nrow(page2), 10L)
+  expect_equal(page2[c("year", "canonical_govid", "spend_subtype", "category")],
+              full[11:20, c("year", "canonical_govid", "spend_subtype", "category")],
+              ignore_attr = TRUE)
+})
+
+test_that("walking every page reconstructs the unpaginated result exactly", {
+  skip_if_no_corpus()
+  full <- cog_spending("121011212191", years = 2019:2020, category = NULL)
+  n <- nrow(full)
+  limit <- 7L
+  pages <- list()
+  offset <- 0L
+  repeat {
+    p <- cog_spending("121011212191", years = 2019:2020, category = NULL,
+                      limit = limit, offset = offset)
+    if (nrow(p) == 0L) break
+    pages[[length(pages) + 1L]] <- p
+    offset <- offset + limit
+    if (offset > n + limit) stop("test runaway: paging did not terminate")
+  }
+  walked <- dplyr::bind_rows(pages)
+  expect_equal(nrow(walked), n)
+  key_cols <- c("year", "canonical_govid", "spend_subtype", "category", "amt_nominal")
+  expect_equal(walked[key_cols], full[key_cols], ignore_attr = TRUE)
+})
+
+test_that("total_rows attribute reports the full unpaginated count", {
+  skip_if_no_corpus()
+  full <- cog_spending("121011212191", years = 2019:2020, category = NULL)
+  page <- cog_spending("121011212191", years = 2019:2020, category = NULL,
+                       limit = 5L, offset = 0L)
+  expect_equal(attr(page, "total_rows"), nrow(full))
+})
+
+test_that("offset past the end returns zero rows, not an error", {
+  skip_if_no_corpus()
+  full <- cog_spending("121011212191", years = 2019:2020, category = NULL)
+  page <- cog_spending("121011212191", years = 2019:2020, category = NULL,
+                       limit = 10L, offset = nrow(full) + 100L)
+  expect_equal(nrow(page), 0L)
+  expect_equal(attr(page, "total_rows"), nrow(full))
+})
+
+test_that("limit is unset by default -- unpaginated calls are unaffected", {
+  skip_if_no_corpus()
+  r <- cog_spending("121011212191", 2020L, "Corrections")
+  expect_null(attr(r, "total_rows"))
+})
+
+test_that("per_capita and adjust_to_year still apply correctly within a page", {
+  skip_if_no_corpus()
+  full <- cog_spending("121011212191", years = 2020L, category = NULL,
+                       per_capita = TRUE, adjust_to_year = 2022L)
+  page <- cog_spending("121011212191", years = 2020L, category = NULL,
+                       per_capita = TRUE, adjust_to_year = 2022L,
+                       limit = 3L, offset = 2L)
+  expect_equal(page[c("amt_nominal", "amt_real", "amt_per_capita_nominal",
+                      "amt_per_capita_real")],
+              full[3:5, c("amt_nominal", "amt_real", "amt_per_capita_nominal",
+                          "amt_per_capita_real")],
+              ignore_attr = TRUE)
+})
+
+test_that("complete = TRUE with limit aborts -- pagination over a partial grid is undefined", {
+  skip_if_no_corpus()
+  expect_error(
+    cog_spending("121011212191", 2020L, "Corrections", complete = TRUE, limit = 5L),
+    class = "uscogdata_complete_pagination_conflict"
+  )
+})

tests/testthat/test-views.R

@@ -103,7 +103,7 @@ test_that("inst/sql/22- and 23- harmonized views enforce every WHERE predicate (
   sql_dir <- system.file("sql", package = "uscogdata")
   .read_view_sql <- function(filename) {
     txt <- paste(readLines(file.path(sql_dir, filename), warn = FALSE), collapse = "\n")
-    gsub("\\{url\\}", paste0(tmp, "/"), txt, fixed = FALSE)
+    uscogdata:::.render_view_sql(txt, paste0(tmp, "/"))
   }
 
   con <- DBI::dbConnect(duckdb::duckdb())
@@ -183,7 +183,7 @@ test_that("inst/sql/24- and 25- IG views retain aggregates, COALESCE NULL harmon
   sql_dir <- system.file("sql", package = "uscogdata")
   .read_view_sql <- function(filename) {
     txt <- paste(readLines(file.path(sql_dir, filename), warn = FALSE), collapse = "\n")
-    gsub("\\{url\\}", paste0(tmp, "/"), txt, fixed = FALSE)
+    uscogdata:::.render_view_sql(txt, paste0(tmp, "/"))
   }
 
   con <- DBI::dbConnect(duckdb::duckdb())
@@ -335,7 +335,7 @@ test_that(".harmonization_view_files guard is necessary: registration against a
   sql_dir <- system.file("sql", package = "uscogdata")
   .read_view_sql <- function(filename) {
     txt <- paste(readLines(file.path(sql_dir, filename), warn = FALSE), collapse = "\n")
-    gsub("\\{url\\}", url, txt, fixed = FALSE)
+    uscogdata:::.render_view_sql(txt, url)
   }
   con2 <- DBI::dbConnect(duckdb::duckdb())
   on.exit(DBI::dbDisconnect(con2, shutdown = TRUE), add = TRUE)