docs: n_units_reporting is category-conditional, not a response rate

Closes uscogdata#36. It counts governments with rows for the requested
category, so a surveyed government that genuinely spends nothing there
is indistinguishable from one never surveyed. In FY2022, a complete
census year, Georgia reports 393 of 567 cities for Police -- the gap is
cities that contract to the sheriff.

Documents the comparison that IS valid: same category, census year vs
sample year.

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,