Merge pull request #12 from e-kotov/5-add-safe-guards-and-warnings-wh…

…en-requesting-more-then-10k-entries

5 add safe guards and warnings when requesting more then 10k entries

R/data.R

@@ -8,7 +8,7 @@
 #' @param level A `character` string specifying the NUTS level ("0", "1", "2", or "3").
 #' @param x_filters A `named list` where the names are the filter fields for the x variable and the values are the selected values for those fields. Default is an empty list. To find out which filters to use, use \code{\link{mi_source_filters}} with the desired `source_name`.
 #' @param y_filters (Optional) A `named list` where the names are the filter fields for the y variable and the values are the selected values for those fields. Default is `NULL`. To find out which filters to use, use \code{\link{mi_source_filters}} with the desired `source_name`.
-#' @param limit An `integer` specifying the maximum number of results to return. Default is 2500.
+#' @param limit An `integer` specifying the maximum number of results to return. Default is 2500. This default should be enough for most uses, as it is well above the number of NUTS 3 regions in the EU. The maximum limited by the API is 10000.
 #'
 #' @return A `tibble` with the following columns:
 #' 
@@ -21,6 +21,7 @@
 #' * `x`: the value of the univariate variable.
 #' * `y` (optional): the value of the y variable (only included when `y_source` is provided).
 #' 
+#' @importFrom rlang .data
 #' @export
 #'
 #' @examples
@@ -30,7 +31,7 @@
 #'   x_source = "TGS00010",
 #'   year = 2020,
 #'   level = "2",
-#'   x_filters = list(isced11 = "TOTAL", unit = "PC", age = "Y_GE15", freq = "A")
+#'   x_filters = list(isced11 = "TOTAL", sex = "F")
 #' )
 #'
 #' # Bivariate example
@@ -39,8 +40,8 @@
 #'   y_source = "DEMO_R_MLIFEXP",
 #'   year = 2020,
 #'   level = "2",
-#'   x_filters = list(isced11 = "TOTAL", unit = "PC", age = "Y_GE15", freq = "A"),
-#'   y_filters = list(unit = "YR", age = "Y_LT1", freq = "A")
+#'   x_filters = list(isced11 = "TOTAL", sex = "F"),
+#'   y_filters = list(age = "Y2", sex = "F")
 #' )
 #' }
 mi_data <- function(
@@ -58,6 +59,7 @@ mi_data <- function(
   checkmate::assert_list(x_filters, types = c("character", "NULL"))
   checkmate::assert_integerish(year, null.ok = TRUE, max.len = 1)
   checkmate::assert_list(y_filters, types = c("character", "NULL"), null.ok = TRUE)
+  checkmate::assert_number(limit, lower = 1, upper = 10000)
   if (!is.null(y_source)) checkmate::assert_string(y_source)
 
   # Build filter JSONs for X and Y
@@ -69,7 +71,10 @@ mi_data <- function(
     source = x_source,
     conditions = x_conditions
   )
-  x_json_string <- jsonlite::toJSON(x_json, auto_unbox = TRUE)
+  # Minify JSON to remove extra whitespace/newlines
+  x_json_string <- jsonlite::minify(
+    jsonlite::toJSON(x_json, auto_unbox = TRUE)
+  )
 
   # Check if it's bivariate (Y filters are provided)
   if (!is.null(y_source) && !is.null(y_filters)) {
@@ -80,7 +85,9 @@ mi_data <- function(
       source = y_source,
       conditions = y_conditions
     )
-    y_json_string <- jsonlite::toJSON(y_json, auto_unbox = TRUE)
+    y_json_string <- jsonlite::minify(
+      jsonlite::toJSON(y_json, auto_unbox = TRUE)
+    )
   }
 
   # Build API endpoint
@@ -104,26 +111,94 @@ mi_data <- function(
     query_params$`_outcome_year` <- as.character(year)
   }
 
-  # Add JSON parameters as proper strings without URL encoding issues
-  query_params$`X_JSON` <- I(x_json_string)
+  # Add JSON parameters as proper strings so that httr2 can URL encode them automatically
+  query_params$`X_JSON` <- x_json_string
   if (!is.null(y_source) && !is.null(y_filters)) {
-    query_params$`Y_JSON` <- I(y_json_string)
+    query_params$`Y_JSON` <- y_json_string
   }
 
   # Perform API request
-  response <- httr2::request(url_endpoint) |>
+  request <- httr2::request(url_endpoint) |>
     httr2::req_headers(
       "Content-Type" = "application/json",
       "User-Agent" = getOption("mapineqr.user_agent")
     ) |>
     httr2::req_url_query(!!!query_params) |>
-    httr2::req_method("GET") |>
-    httr2::req_perform()
+    httr2::req_method("GET")
+
+  response <- request |> httr2::req_perform()
 
   # Parse response
   response_data <- httr2::resp_body_json(response, simplifyVector = TRUE) |> 
     tibble::as_tibble()
 
+  # Check for duplicate values within each geo for x and (if applicable) y.
+  duplicate_issues <- response_data |>
+    dplyr::group_by(.data$geo) |>
+    dplyr::summarise(
+      distinct_x = dplyr::n_distinct(.data$x),
+      distinct_y = if ("y" %in% names(response_data)) dplyr::n_distinct(.data$y) else NA_integer_,
+      .groups = "drop"
+    )
+
+  x_issue <- any(duplicate_issues$distinct_x > 1)
+  y_issue <- if ("y" %in% names(response_data)) any(duplicate_issues$distinct_y > 1) else FALSE
+
+  # Only perform additional filter checking if duplicate geos exist
+  if (x_issue || y_issue) {
+    # --- For the x variable ---
+    missing_x_filters <- character(0)
+    if (x_issue) {
+      available_filters <- mi_source_filters(source_name = x_source, year = year, level = level)
+      # Determine which filter fields have more than one option
+      multi_option_fields <- available_filters |>
+        dplyr::group_by(.data$field) |>
+        dplyr::summarise(n_options = dplyr::n_distinct(.data$value), .groups = "drop") |>
+        dplyr::filter(.data$n_options > 1) |>
+        dplyr::pull(.data$field)
+      # Only require filters for those fields with multiple options.
+      missing_x_filters <- setdiff(multi_option_fields, names(x_filters))
+    }
+
+    # --- For the y variable (if applicable) ---
+    missing_y_filters <- character(0)
+    if (y_issue) {
+      available_y_filters <- mi_source_filters(source_name = y_source, year = year, level = level)
+      multi_option_y_fields <- available_y_filters |>
+        dplyr::group_by(.data$field) |>
+        dplyr::summarise(n_options = dplyr::n_distinct(.data$value), .groups = "drop") |>
+        dplyr::filter(.data$n_options > 1) |>
+        dplyr::pull(.data$field)
+      missing_y_filters <- setdiff(multi_option_y_fields, names(y_filters))
+    }
+
+    # Only raise an error if any missing filter is found among fields with multiple options.
+    if (length(missing_x_filters) > 0 || length(missing_y_filters) > 0) {
+      msg <- "The API returned duplicate values for some geographic regions. This may indicate that not all necessary filters were specified."
+      if (length(missing_x_filters) > 0) {
+        msg <- paste0(
+          msg,
+          "\n\nFor the 'x' variable (source: '", x_source, "'):",
+          "\n  The following filter fields (with multiple available options) were not specified: ",
+          paste(missing_x_filters, collapse = ", "),
+          "\nYou can review available filters by running:\n  mi_source_filters(source_name = '", x_source, "', year = ", year, ", level = '", level, "')"
+        )
+      }
+      if (length(missing_y_filters) > 0) {
+        msg <- paste0(
+          msg,
+          "\n\nFor the 'y' variable (source: '", y_source, "'):",
+          "\n  The following filter fields (with multiple available options) were not specified: ",
+          paste(missing_y_filters, collapse = ", "),
+          "\nYou can review available filters by running:\n  mi_source_filters(source_name = '", y_source, "', year = ", year, ", level = '", level, "')"
+        )
+      }
+      stop(msg)
+    }
+  }
+
+
+
   # Define expected columns based on whether y_source is specified
   if (is.null(y_source)) {
     expected_columns <- c("geo", "geo_name", "geo_source", "geo_year", "data_year", "x")