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

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.}
 

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

@@ -0,0 +1,40 @@
+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")
+  )
+})