Merge pull request 'feat: 'All Categories' pseudo-category + n_units_reporting semantics' (#37) from feat/all-categories-37 into main

Reviewed-on: #37

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: uscogdata
 Type: Package
 Title: Curated Reader for the Civilytics US Census of Governments Finance Corpus
-Version: 0.1.0
+Version: 0.2.0
 Authors@R:
     person("Civilytics", , , "jknowles@gmail.com", role = c("aut", "cre"))
 Description: Curated R verbs over the Civilytics US Census of Governments

NEWS.md

@@ -1,3 +1,45 @@
+# uscogdata 0.2.0
+
+## New features
+
+* `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).
+
+  `"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.
+
+* `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).
+
 # uscogdata 0.1.0 (development)
 
 ## Signposting now catches partially-suppressed categories

R/balances.R

@@ -28,7 +28,12 @@
 #'   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.
@@ -74,6 +79,13 @@ 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)
   years <- as.integer(years)

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/peers.R

@@ -240,6 +240,23 @@ cog_find_peers <- function(target_govid,
 #'     group_by(year) |>
 #'     summarise(p50 = quantile(total, 0.5, na.rm = TRUE))
 #'   ```
+#' @section Reading `coverage`:
+#' `provenance$coverage` reports `n_units_reporting` against
+#' `n_units_expected` per year. **`n_units_reporting` is category-conditional:
+#' it counts cohort members with rows for the category you asked for, not
+#' cohort members collected that year.** A government that was surveyed and
+#' genuinely spends nothing in that category is indistinguishable here from one
+#' that was never surveyed.
+#'
+#' The ratio is therefore **not a response rate** and must not be used as one.
+#' 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,

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/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

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
@@ -56,6 +60,23 @@
 #'   `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` reports `n_units_reporting` against
+#' `n_units_expected` per year. **`n_units_reporting` is category-conditional:
+#' it counts governments with rows for the category you asked for, not
+#' governments collected that year.** A government that was surveyed and
+#' genuinely spends nothing in that category is indistinguishable here from one
+#' that was never surveyed.
+#'
+#' The ratio is therefore **not a response rate** and must not be used as one.
+#' 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,

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,
@@ -63,7 +71,16 @@
 #' @param govid Character vector of `canonical_govid` values.
 #' @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 +150,12 @@
 #'   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:
@@ -257,8 +279,24 @@ cog_spending <- function(govid, years, category = NULL,
   }
 
   govid <- .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)
+
+  # 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 +337,12 @@ 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)."
+    )
+  }
 
   years   <- as.integer(years)
   if (!is.null(adjust_to_year)) adjust_to_year <- as.integer(adjust_to_year)
@@ -333,8 +377,10 @@ 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, govid, years,
+                           if (all_categories) NULL else category,
+                           ig_view, subtype_scope,
+                           all_categories = all_categories)
     result <- tibble::as_tibble(DBI::dbGetQuery(con, sql))
   }
 
@@ -396,7 +442,10 @@ cog_spending <- function(govid, years, category = NULL,
     suggestions <- .build_suggestions(con, govid, years, category,
                                        direct_leg_result,
                                        resolved$basis, flow_prefixes,
-                                       .select_long_view(view_base, resolved$basis))
+                                       .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
@@ -408,13 +457,32 @@ 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)
 
@@ -423,9 +491,20 @@ 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 ",
@@ -473,9 +552,22 @@ cog_spending <- function(govid, years, category = NULL,
   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) {
+                                  per_capita, adjust_to_year, recipe = NULL,
+                                  allow_all_categories = FALSE) {
   if (!is.character(govid) || length(govid) == 0L) {
     cli::cli_abort("`govid` must be a non-empty character vector.")
   }
@@ -485,6 +577,14 @@ 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.")
   }
@@ -570,10 +670,16 @@ cog_spending <- function(govid, years, category = NULL,
 
 #' @noRd
 .build_verb_sql <- function(view, subtype_col, govid, years, category,
-                            ig_view = NULL, subtype_scope = NULL) {
+                            ig_view = NULL, subtype_scope = NULL,
+                            all_categories = FALSE) {
   govid_lit <- .sql_lit_chr(govid)
   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))
@@ -612,13 +718,26 @@ 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.
+
+  # 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"
+
   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
@@ -627,9 +746,10 @@ cog_spending <- function(govid, years, category = NULL,
        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, govid_lit, years_lit, category_pred, subtype_pred,
+    category_select, category_group
   )
 }
 

R/suggestions.R

@@ -59,12 +59,36 @@
 #' @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, trigger, suppressed_amount, suppressed_years,
 #'   suppressed_codes)`, possibly empty.
 #' @noRd
 .build_suggestions <- function(con, govid, years, category, result, basis,
-                                flow_prefixes, long_view) {
+                                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 --
@@ -79,16 +103,35 @@
   # 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.
+  #
+  # 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.
+  candidate_scope_sql <- if (isTRUE(all_categories)) {
+    sprintf(
+      "SELECT DISTINCT item_code FROM summary_categories WHERE %s IN (%s)",
+      subtype_col, .sql_lit_chr(subtype_scope)
+    )
+  } else {
+    sprintf(
+      "SELECT DISTINCT item_code FROM summary_categories WHERE category IN (%s)",
+      .sql_lit_chr(category)
+    )
+  }
   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)
+       %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)
+    candidate_scope_sql
   ))$recipe_id
   if (length(candidates) == 0L) return(list())
 

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",

man/cog_balances.Rd

@@ -28,7 +28,12 @@ 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

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_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.}
 
@@ -80,3 +84,23 @@ 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` reports `n_units_reporting` against
+`n_units_expected` per year. **`n_units_reporting` is category-conditional:
+it counts governments with rows for the category you asked for, not
+governments collected that year.** A government that was surveyed and
+genuinely spends nothing in that category is indistinguishable here from one
+that was never surveyed.
+
+The ratio is therefore **not a response rate** and must not be used as one.
+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_peer_compare.Rd

@@ -107,3 +107,23 @@ 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` reports `n_units_reporting` against
+`n_units_expected` per year. **`n_units_reporting` is category-conditional:
+it counts cohort members with rows for the category you asked for, not
+cohort members collected that year.** A government that was surveyed and
+genuinely spends nothing in that category is indistinguishable here from one
+that was never surveyed.
+
+The ratio is therefore **not a response rate** and must not be used as one.
+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

@@ -22,7 +22,16 @@ cog_revenue(
 \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

man/cog_spending.Rd

@@ -22,7 +22,16 @@ cog_spending(
 \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 +106,12 @@ 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

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",
+    govid        = "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",
+    govid = "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, govid = "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, govid = "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-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