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]
 }

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

tests/testthat/test-all-categories.R

@@ -96,3 +96,30 @@ test_that('"All Categories" combines with subtype to give operating totals', {
   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)
+})

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