R/peers.R

@@ -133,7 +133,9 @@ cog_find_peers <- function(target_govid,
 #' [cog_find_peers()] result or a character vector of `canonical_govid`) and
 #' appends peer-distribution summary rows (`summary_p25`, `summary_p50`,
 #' `summary_p75`) so the result can be faceted by `role` in a single ggplot
-#' call.
+#' call. Those summary rows are quantiles **within each category**, not
+#' quantiles of each peer's total — see the `@return` section before summing
+#' them.
 #'
 #' @param target_govid Character scalar.
 #' @param peers A tibble from [cog_find_peers()] or a character vector of
@@ -155,6 +157,32 @@ cog_find_peers <- function(target_govid,
 #'   `attr(peers, "cohort_year")`; `NA` when `peers` was a bare character
 #'   vector). Provenance reports `verb = "cog_peer_compare"`, `peer_count`,
 #'   `cohort_year`, and `cohort_govids`.
+#'
+#'   **The `summary_*` rows are per-category quantiles: they are not additive.**
+#'   Each one is computed **within each `(year, spend_subtype,
+#'   category)` cell** across the peer set, so a `summary_p50` row is *the
+#'   median peer's value in that one category*, not *the value of the median
+#'   peer's total*. The median peer for Police and the median peer for Fire
+#'   are usually different governments, so summing `summary_*` rows across
+#'   categories does not give any peer's total and misstates the band it
+#'   appears to describe — measured at −32.7% to +251.0% across 24 years on
+#'   one cohort, with a sign flip at FY2012.
+#'
+#'   Facet by `role` **and** `category` (the documented use, and what the
+#'   rows are built for). For a genuine "median peer's total spending" line,
+#'   sum each peer's own categories first and take the quantile of those
+#'   per-government totals:
+#'
+#'   ```r
+#'   library(dplyr)
+#'   cmp |>
+#'     filter(role %in% c("target", "peer")) |>
+#'     group_by(year, role, canonical_govid) |>
+#'     summarise(total = sum(amt_per_capita_real, na.rm = TRUE), .groups = "drop") |>
+#'     filter(role == "peer") |>
+#'     group_by(year) |>
+#'     summarise(p50 = quantile(total, 0.5, na.rm = TRUE))
+#'   ```
 #' @export
 cog_peer_compare <- function(target_govid, peers, category, years,
                              per_capita = TRUE, adjust_to_year = NULL,

R/search.R

@@ -6,8 +6,11 @@
 #' the cross-vintage canonical-government registry. Operates in two modes:
 #'
 #' * **Utility mode** (single `name`, the original behavior): returns all
-#'   rows whose `gov_name` matches the regex case-insensitively, sorted by
-#'   `population_acs` descending. Useful for exploratory lookups.
+#'   rows whose `gov_name` contains `name` as a **literal, case-insensitive
+#'   substring**, sorted by `population_acs` descending. Useful for
+#'   exploratory lookups. Regex metacharacters in `name` are escaped, so a
+#'   government is findable by its own complete name even when that name
+#'   contains parentheses or a period.
 #' * **Basket mode** (`length(name) > 1`): resolves each input row to a
 #'   single canonical govid and returns a tibble in input order, suitable
 #'   for piping straight into [cog_spending()] / [cog_revenue()] /
@@ -19,7 +22,8 @@
 #' 1. Filter `canonical_fips_xwalk` by `state` and (if non-NA) `type`.
 #' 2. **Exact pass:** case-insensitive equality against `gov_name`.
 #'    Single hit -> resolved. Multiple -> step 4.
-#' 3. **Substring fallback:** case-insensitive regex against `gov_name`.
+#' 3. **Substring fallback:** case-insensitive literal substring against
+#'    `gov_name` (metacharacters escaped).
 #'    Single hit -> resolved (`match_method = "substring"`). Zero hits ->
 #'    `status = "no_match"`. Multiple hits -> step 4.
 #' 4. **Disambiguation:** if matches share one `govs_type`, pick the
@@ -48,7 +52,7 @@
 #'   [cog_spending()], [cog_revenue()].
 #' @examples
 #' \dontrun{
-#' # Utility mode — exploratory regex lookup
+#' # Utility mode — exploratory substring lookup
 #' cog_gov_search("broward", state = "FL")
 #'
 #' # Basket mode — resolve a known cohort
@@ -98,9 +102,16 @@ cog_gov_search <- function(name = NULL, state = NULL, type = NULL) {
     if (!is.character(name) || length(name) != 1L) {
       cli::cli_abort("`name` must be a length-1 character string.")
     }
+    # Escaped, so `name` is a literal case-insensitive substring -- the same
+    # treatment basket mode has always given it. Interpolating it raw made a
+    # government unfindable by its own name whenever that name contains a
+    # metacharacter (FREDONIA (BRISCOE) CITY), turned a bare "." into a
+    # match-everything wildcard, and let malformed pattern text reach the
+    # engine as an error -- which cog-api surfaced as a 500, reachable by
+    # typing a real name one character at a time (uscogdata#16, F-025).
     preds <- c(preds,
                sprintf("regexp_matches(gov_name, %s, 'i')",
-                       .sql_lit_chr(name)))
+                       .sql_lit_chr(.escape_regex(name))))
   }
   if (!is.null(state)) {
     st_fips <- .coerce_state_to_fips(state)
@@ -136,8 +147,9 @@ cog_gov_search <- function(name = NULL, state = NULL, type = NULL) {
 #' @noRd
 .escape_regex <- function(x) {
   # Backslash-escape POSIX regex metacharacters so `name` is treated as a
-  # literal substring in the DuckDB regexp_matches call (substring fallback
-  # only; utility-mode intentionally preserves regex behavior).
+  # literal substring in the DuckDB regexp_matches call. Used by BOTH modes:
+  # utility mode used to interpolate raw, which was a defect rather than a
+  # feature -- see the call site and uscogdata#16.
   gsub("([\\^$.|?*+(){}\\[\\]])", "\\\\\\1", x, perl = TRUE)
 }
 

README.md

@@ -19,6 +19,27 @@ package implements.
 # pak::pkg_install("gitea.civilytics.org/Civilytics/uscogdata")
 ```
 
+## Amounts are in full US dollars
+
+Every amount column this package returns — `amt_nominal`, `amt_real`,
+`amt_per_capita_nominal`, `amt_per_capita_real` — is in **full US dollars**.
+
+The raw Census source files report **thousands of dollars**, and the corpus's
+own `amt` column preserves that. The verbs multiply by 1000 on the way out, so
+you never have to. The conversion is recorded in every result:
+
+```r
+r <- cog_spending("552025209777", 2020L)
+attr(r, "provenance")$transformations$units_conversion
+#> $applied TRUE  $source_unit "$1,000s (raw Census)"  $target_unit "$USD"  $multiplier 1000
+```
+
+**Do not multiply again.** If you have read elsewhere that COG amounts are in
+`$1,000s` — true of the raw corpus, and of `cog_explorer`'s conventions doc —
+that rule does not apply to anything a `cog_*()` verb hands you. Applying it
+twice overstates every figure by 1000x, and the result looks plausible rather
+than obviously wrong.
+
 ## Configuration
 
 - `USCOGDATA_URL` — corpus root URL (public Nextcloud share, trailing slash)

man/cog_gov_search.Rd

@@ -32,8 +32,11 @@ the cross-vintage canonical-government registry. Operates in two modes:
 }
 \details{
 * **Utility mode** (single `name`, the original behavior): returns all
-  rows whose `gov_name` matches the regex case-insensitively, sorted by
-  `population_acs` descending. Useful for exploratory lookups.
+  rows whose `gov_name` contains `name` as a **literal, case-insensitive
+  substring**, sorted by `population_acs` descending. Useful for
+  exploratory lookups. Regex metacharacters in `name` are escaped, so a
+  government is findable by its own complete name even when that name
+  contains parentheses or a period.
 * **Basket mode** (`length(name) > 1`): resolves each input row to a
   single canonical govid and returns a tibble in input order, suitable
   for piping straight into [cog_spending()] / [cog_revenue()] /
@@ -45,7 +48,8 @@ the cross-vintage canonical-government registry. Operates in two modes:
 1. Filter `canonical_fips_xwalk` by `state` and (if non-NA) `type`.
 2. **Exact pass:** case-insensitive equality against `gov_name`.
    Single hit -> resolved. Multiple -> step 4.
-3. **Substring fallback:** case-insensitive regex against `gov_name`.
+3. **Substring fallback:** case-insensitive literal substring against
+   `gov_name` (metacharacters escaped).
    Single hit -> resolved (`match_method = "substring"`). Zero hits ->
    `status = "no_match"`. Multiple hits -> step 4.
 4. **Disambiguation:** if matches share one `govs_type`, pick the
@@ -58,7 +62,7 @@ inputs (`ambiguous` / `no_match`) appear only in the sidecar.
 }
 \examples{
 \dontrun{
-# Utility mode — exploratory regex lookup
+# Utility mode — exploratory substring lookup
 cog_gov_search("broward", state = "FL")
 
 # Basket mode — resolve a known cohort

man/cog_peer_compare.Rd

@@ -43,11 +43,39 @@ Tibble matching [cog_spending()]'s columns, plus a `role`
   `attr(peers, "cohort_year")`; `NA` when `peers` was a bare character
   vector). Provenance reports `verb = "cog_peer_compare"`, `peer_count`,
   `cohort_year`, and `cohort_govids`.
+
+  **The `summary_*` rows are per-category quantiles: they are not additive.**
+  Each one is computed **within each `(year, spend_subtype,
+  category)` cell** across the peer set, so a `summary_p50` row is *the
+  median peer's value in that one category*, not *the value of the median
+  peer's total*. The median peer for Police and the median peer for Fire
+  are usually different governments, so summing `summary_*` rows across
+  categories does not give any peer's total and misstates the band it
+  appears to describe — measured at −32.7% to +251.0% across 24 years on
+  one cohort, with a sign flip at FY2012.
+
+  Facet by `role` **and** `category` (the documented use, and what the
+  rows are built for). For a genuine "median peer's total spending" line,
+  sum each peer's own categories first and take the quantile of those
+  per-government totals:
+
+  ```r
+  library(dplyr)
+  cmp |>
+    filter(role %in% c("target", "peer")) |>
+    group_by(year, role, canonical_govid) |>
+    summarise(total = sum(amt_per_capita_real, na.rm = TRUE), .groups = "drop") |>
+    filter(role == "peer") |>
+    group_by(year) |>
+    summarise(p50 = quantile(total, 0.5, na.rm = TRUE))
+  ```
 }
 \description{
 Pulls spending for the target plus a peer set (either a
 [cog_find_peers()] result or a character vector of `canonical_govid`) and
 appends peer-distribution summary rows (`summary_p25`, `summary_p50`,
 `summary_p75`) so the result can be faceted by `role` in a single ggplot
-call.
+call. Those summary rows are quantiles **within each category**, not
+quantiles of each peer's total — see the `@return` section before summing
+them.
 }

tests/testthat/test-amount-units-documented.R

@@ -17,7 +17,6 @@
 # cog-api's llms.txt, which is silent on units).
 
 test_that("returned amounts are documented as full US dollars where readers meet the package", {
-  testthat::skip("Blocked on uscogdata#15 (finding F-004)")
 
   says_units <- function(path) {
     txt <- paste(readLines(path, warn = FALSE), collapse = " ")

tests/testthat/test-gov-search-literal-match.R

@@ -18,7 +18,6 @@
 # semantics, not a row the fix makes findable.
 
 test_that("cog_gov_search() matches name literally, not as an unescaped regex", {
-  testthat::skip("Blocked on uscogdata#16 (finding F-025)")
 
   # -- correctness (1): a government must be findable by its own exact name ---
   # FREDONIA (BRISCOE) CITY is real; today the parentheses are read as regex

tests/testthat/test-peer-summary-scope.R

@@ -13,7 +13,6 @@
 # is unaffected, so the fix is documentation: one sentence in @return.
 
 test_that("cog_peer_compare() documents that summary_* rows are per-category quantiles", {
-  testthat::skip("Blocked on uscogdata#14 (finding F-021)")
 
   rd <- paste(readLines(testthat::test_path("..", "..", "man", "cog_peer_compare.Rd"),
                         warn = FALSE), collapse = " ")

tests/testthat/test-rollup.R

@@ -80,8 +80,11 @@ test_that("cog_geographic_rollup provenance reports the outer verb", {
 
 test_that("cog_geographic_rollup accepts data.frames per layer", {
   skip_if_no_corpus()
-  fl_state  <- cog_gov_search("^FLORIDA$", type = "state")
-  broward   <- cog_gov_search("^BROWARD COUNTY$", state = "FL", type = "county")
+  # Unanchored: utility mode matches literally now, so "^...$" would be
+  # searched for as characters rather than read as anchors (uscogdata#16).
+  # Both still resolve to exactly one row once scoped by type/state.
+  fl_state  <- cog_gov_search("FLORIDA", type = "state")
+  broward   <- cog_gov_search("BROWARD COUNTY", state = "FL", type = "county")
   r <- cog_geographic_rollup(
     govids = list(state = fl_state, county = broward),
     category = "Police", years = 2020L

tests/testthat/test-spending.R

@@ -98,7 +98,11 @@ test_that("cog_spending rejects invalid inputs", {
 
 test_that("cog_spending accepts a cog_gov_search result directly", {
   skip_if_no_corpus()
-  picks <- cog_gov_search("^BROWARD COUNTY$", state = "FL", type = "county")
+  # Unanchored: utility mode matches `name` as a literal substring now, so
+  # "^...$" would be searched for as those characters rather than read as
+  # anchors (uscogdata#16). Scoped by state and type, the bare name still
+  # resolves to exactly one row.
+  picks <- cog_gov_search("BROWARD COUNTY", state = "FL", type = "county")
   expect_gt(nrow(picks), 0L)
   r <- cog_spending(picks, 2020L, "Corrections")
   expect_equal(unique(r$canonical_govid), "121011212191")

vignettes/population-denominators.Rmd

@@ -13,6 +13,8 @@ knitr::opts_chunk$set(eval = FALSE, collapse = TRUE, comment = "#>")
 
 # Why per-year population matters
 
+A note on units first, since every figure below is a rate: the numerator is in **full US dollars**. The raw Census files report **thousands of dollars** and the corpus keeps them that way in its own `amt` column, but `cog_spending()` and `cog_revenue()` multiply by 1000 on the way out, so `amt_per_capita_nominal` is already dollars per person. Do not scale it again.
+
 Per-capita finance numbers divide each year's spending or revenue by a population denominator. The choice of denominator is a research decision, not an implementation detail: a 24-year corpus paired with a single 5-year ACS estimate produces biased per-capita values whose magnitude scales with each government's population change.
 
 `uscogdata` defaults to the **Census F-33 population value Census itself uses to compute its published per-capita tables.** That value is recorded on every COG row as `population`, with `popyear` indicating the vintage. For a city that grew from 200,000 to 300,000 between 2000 and 2023, this default reproduces the per-capita value Census published. A static ACS denominator would have understated 2000 per-capita by ~33%.

vignettes/total-spending.Rmd

@@ -29,6 +29,13 @@ controls which of these a query answers. This vignette walks through both
 questions with code that actually runs against the package's bundled fixture
 corpus, then explains why the second question refuses `"total"` outright.
 
+Before any of the numbers below: every amount column here — `amt_nominal`,
+`amt_real`, and their `amt_per_capita_*` counterparts — is in **full US
+dollars**. The raw Census files report **thousands of dollars** and the
+corpus preserves that in its own `amt` column, but the verbs multiply by 1000
+on the way out. So `amt_nominal = 1317000` means $1.317 million, not $1.317
+billion. Do not scale it again.
+
 ```{r}
 library(uscogdata)