Remove the dead, duplicate-keyed fetch_assets() (#20)

fetch_assets() built its result with `sites =` twice: the second entry
fetched the `forms` table but stored it under `sites`. R keeps both
entries and `$` returns the first, so `$forms` was unreachable and
`$sites` silently resolved to landing_sites.

The function has no callers — in coasts or in any downstream pipeline.
ingest_assets() is the live producer of the assets snapshot and has a
correct `forms =` entry. Mozambique and Kenya each carry their own local
fetch_assets() with a single `sites`, so nothing resolves to this one.

Delete it rather than fix the key: an exported, broken near-namesake of
the function actually in use is a trap for the next caller. fetch_asset()
(singular), which ingest_assets() calls nine times, is untouched.

Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>

Author: langbart

NAMESPACE

@@ -43,7 +43,6 @@ export(export_pds_spatial)
 export(export_portal)
 export(expr)
 export(fetch_asset)
-export(fetch_assets)
 export(fetch_track_for_prediction)
 export(generate_app_usage_report)
 export(generate_fleet_analysis)

R/airtable.R

@@ -668,64 +668,3 @@ fetch_asset <- function(
     janitor::clean_names() |>
     dplyr::select(dplyr::all_of(select_cols))
 }
-
-#' Fetch Multiple Asset Tables from Airtable
-#'
-#' @description
-#' Fetches taxa, gear, vessels, and landing sites data from Airtable.
-#' Returns distinct records for each table.
-#'
-#' @param conf Configuration object from read_config().
-#'
-#' @return A named list containing four data frames:
-#'   \itemize{
-#'     \item \code{taxa}: Contains survey_label, alpha3_code, and scientific_name columns
-#'     \item \code{gear}: Contains survey_label and standard_name columns
-#'     \item \code{vessels}: Contains survey_label and standard_name columns
-#'     \item \code{sites}: Contains site and site_code columns
-#'   }
-#'
-#' @details
-#' Each table is fetched separately using `fetch_asset()` and filtered to return
-#' only distinct rows to avoid duplicates in the mapping tables.
-#'
-#' @keywords preprocessing helper
-#' @export
-fetch_assets <- function(conf = NULL) {
-  assets_list <-
-    list(
-      taxa = fetch_asset(
-        table_name = "taxa",
-        select_cols = c(
-          "form_id",
-          "survey_label",
-          "alpha3_code",
-          "scientific_name",
-          "english_name"
-        ),
-        conf = conf
-      ),
-      gear = fetch_asset(
-        table_name = "gears",
-        select_cols = c("form_id", "survey_label", "standard_name"),
-        conf = conf
-      ),
-      vessels = fetch_asset(
-        table_name = "vessels",
-        select_cols = c("form_id", "survey_label", "standard_name"),
-        conf = conf
-      ),
-      sites = fetch_asset(
-        table_name = "landing_sites",
-        select_cols = c("form_id", "site", "site_code"),
-        conf = conf
-      ),
-      sites = fetch_asset(
-        table_name = "forms",
-        select_cols = c("form_id", "form_name", "site_code"),
-        conf = conf
-      )
-    )
-
-  purrr::map(assets_list, ~ dplyr::distinct(.x))
-}