First set of changes identified by check()

R/atlas_media.R

@@ -107,12 +107,15 @@ build_media_id <- function(df, media_fields){
   }else{
     purrr::map(media_fields, .f = \(a){
      df |>
-        tidyr::unnest_longer(col = a) |>
+       tidyr::unnest_longer(col = tidyselect::any_of(a)) |>
        dplyr::mutate(media_id = as.character(.data[[a]]),
                      media_type = as.character(a),
                     .before = 1) |>
        dplyr::filter(!is.na(.data$media_id)) |>
        dplyr::select(- tidyselect::any_of(media_fields))
+        # break pipe at this point because tidyselect can't handle -any_of()
+       # keep_cols <- colnames(result)[!(colnames(result) %in% media_fields)]
+       # dplyr::select(result, tidyselect::any_of(keep_cols))
     }) |>
     dplyr::bind_rows()
   }

R/capture_species.R

@@ -30,7 +30,7 @@ capture_species_atlas <- function(.query){
       if(!is.null(.query$group_by)){
         .query$distinct$name <- .query$group_by$name
       }else{
-        cli::cli_error("No variable supplied to `distinct()`")
+        cli::cli_abort("No variable supplied to `distinct()`")
       }
     }
   }

R/check.R

@@ -277,8 +277,8 @@ check_fields_gbif_counts <- function(.query){
   # First get filters
   # set fields that can be queried using predicates or downloaded
   valid_download_fields <- .query[["metadata/fields"]] |>
-    filter(download_field == TRUE) |>
-    dplyr::pull(id)
+    dplyr::filter(.data$download_field == TRUE) |>
+    dplyr::pull("id")
   valid_assertions <- .query[["metadata/assertions"]]$id
   valid_any <- c(valid_download_fields, valid_assertions)
 
@@ -302,13 +302,12 @@ check_fields_gbif_counts <- function(.query){
     facets <- .query$body$group_by$name
     # check for invalid facets
     valid_search_fields <- .query[["metadata/fields"]] |>
-      filter(search_field == TRUE) |>
+      dplyr::filter(.data$search_field == TRUE) |>
       dplyr::pull(id)
      if (!all(facets %in% valid_search_fields)) {
        invalid_facets <- facets[!(facets %in% valid_search_fields)]
        group_by_invalid <- glue::glue_collapse(invalid_facets, sep = ", ")
      }
-    # }
   }
 
   c(filter_invalid, group_by_invalid)

R/collect_distributions.R

@@ -5,8 +5,8 @@ collect_distributions <- function(.query){
   result <- query_API(.query)
   # NOTE: below is consistent with `collect_distributions_metadata()` (+ geometry)
   result <- result |>
-    bind_rows() |>
-    select("gid", 
+    dplyr::bind_rows() |>
+    dplyr::select("gid", 
            "family", 
            "genus_name", 
            "scientific", 
@@ -16,14 +16,14 @@ collect_distributions <- function(.query){
            "area_km",
            "data_resource_uid",
            "geometry") |>
-    rename(
+    dplyr::rename(
       "id" = "gid", # this is chosen as ID because it is called by later APIs
       "genus" = "genus_name",
       "species" = "scientific",
       "taxon_concept_id" = "lsid",
       "label" = "area_name",
       "common_name" = "common_nam")  |>
-    mutate("common_name" = trimws(.data$common_name))
+    dplyr::mutate("common_name" = trimws(.data$common_name))
   result$geometry <- sf::st_as_sfc(result$geometry, crs=4326)
   return(sf::st_as_sf(result))
 }

R/collect_metadata.R

@@ -40,7 +40,7 @@ tidy_list_columns <- function(x){
     list_names <- names(x)[list_check]
     list_tibbles <- purrr::map(list_names,
                               \(a){
-                                tibble::tibble({{a}} := list(x[[a]]))
+                                tibble::tibble({{a}} == list(x[[a]]))
                               })
   }else{
     list_tibbles <- NULL
@@ -51,7 +51,7 @@ tidy_list_columns <- function(x){
     make_nulls_safe() |>
     tibble::as_tibble() |>
     dplyr::bind_cols(list_tibbles) |>
-    dplyr::select(!!!names(x)) # reorder columns to same as `x`
+    dplyr::select(tidyselect::any_of(names(x))) # reorder columns to same as `x`
 }
 
 #' Internal function to ensure rows can be converted to tibble
@@ -264,7 +264,7 @@ collect_fields <- function(.query){
 
     if(is_gbif()){
       # we need to join some local metadata to GBIF info
-      df <- galah:::gbif_internal_archived$search_fields |>
+      df <- gbif_internal_archived$search_fields |>
         dplyr::mutate(search_field = TRUE)
 
       # organise information from the API

R/collect_metadata_unnest.R

@@ -18,7 +18,7 @@ collect_fields_unnest <- function(.query,
       purrr::pluck(!!!list("facets", 1, "counts")) |>
       dplyr::bind_rows() |>
       dplyr::rename_with(camel_to_snake_case) |>
-      dplyr::rename({{facet}} := "name") |>
+      dplyr::rename({{facet}} == "name") |>
       parse_select(.query)
 
   }else{ 
@@ -33,10 +33,10 @@ collect_fields_unnest <- function(.query,
     if(nrow(result) > 0){
       result |>
         dplyr::mutate(
-          field_name := stringr::str_extract(result$i18nCode, "(?<=\\.).*"),
+          field_name == stringr::str_extract(result$i18nCode, "(?<=\\.).*"),
           .before = 1) |>
         dplyr::rename_with(camel_to_snake_case) |>
-        dplyr::rename({{facet}} := "field_name") |>
+        dplyr::rename({{facet}} == "field_name") |>
         parse_select(.query)
     }else{ # i.e. catch empty results
       result
@@ -72,7 +72,6 @@ collect_lists_unnest <- function(.query){
 
   # extract additional raw fields columns
   clean_kvp_values <- function(df){
-    browser()
     if(any(colnames(df) == "kvpValues")){
       if(any(lengths(df$kvpValues) > 0)){
         df <- df |>

R/collect_taxa.R

@@ -189,7 +189,7 @@ collect_identifiers <- function(.query){
     # we avoid `is_gbif()` here because other atlases use GBIF APIs
     result$success <- TRUE
     result <- result |>
-      dplyr::relocate(success, .before = 1) |>
+      dplyr::relocate("success", .before = 1) |>
       parse_rename(.query)
   }
 

R/compound.R

@@ -66,7 +66,7 @@ compound.prequery <- function(x, mint_doi = FALSE, ...){
   if(stringr::str_detect(x$type, "^metadata")){
     build_query_set_metadata(x)
   }else if(stringr::str_detect(x$type, "^files")){
-    list(as_query_media_files(x, ...)) |>
+    list(capture_media_files(x, ...)) |>
       as_query_set()
   }else if(x$type == "data/distribtions"){
     build_query_set_distributions(x)
@@ -245,7 +245,7 @@ build_query_set_distributions <- function(x, ...){
   }else{
     if(!is.null(x$identify)){
       result <- list(
-        collapse_taxa(list(identify = x$identify)) # wrong syntax?
+        capture_taxa(list(identify = x$identify)) # wrong syntax?
       )
       result[[2]] <- capture_distributions_data(x) # NOTE: shouldn't call microfunctions directly
     }else{

R/dplyr-glimpse.R

@@ -5,7 +5,8 @@
 #' frame. It's a little like [str()] applied to a data frame but it tries to 
 #' show you as much data as possible. This implementation is specific 
 #' to `galah` and is evaluated lazily. `r lifecycle::badge("experimental")`
-#' 
+#' @param x An object of class `data_request`
+#' @param ... Other arguments, currently ignored
 #' @details
 #' This implementation of [glimpse()] actually involves changing the API call
 #' sent to the server, then returning a novel object class with it's own 
@@ -19,7 +20,7 @@ glimpse.data_request <- function(x, ...){
 #' @rdname glimpse.data_request
 #' @export
 print.occurrences_glimpse <- function(x, ...){
-  y <- capture.output(dplyr::glimpse(x))
+  y <- utils::capture.output(dplyr::glimpse(x))
   n_text <- attr(x, 'total_n') |>
     formatC(big.mark = ",")
   y[[1]] <- glue::glue("Rows: {n_text}")

R/filter_object_classes.R

@@ -2,8 +2,8 @@
 #' 
 #' In galah, there are several ways to provide filter information. To ensure
 #' these are handled and printed correctly, they are assigned classes
-#' @param x a list
-#' @rdname filter_object_classes
+#' @param x a list, or object of supported class
+#' @name filter_object_classes
 #' @order 1
 #' @export
 as_data_filter <- function(x){
@@ -44,6 +44,7 @@ as_files_filter <- function(x){
 # Print functions for the above
 
 #' @rdname filter_object_classes
+#' @param ... Additional arguments, currently ignored
 #' @order 5
 #' @export
 print.data_filter <- function(x, ...){

R/galah_bbox.R

@@ -87,10 +87,10 @@ galah_bbox <- function(...) {
     } else {
       if (inherits(query, c("sf", "sfc"))) {
         query <- query |> 
-          sf::st_bbox(crs = st_crs("WGS84"))
+          sf::st_bbox(crs = sf::st_crs("WGS84"))
         bbox_coords <- round(query, 5)
         query <- query |> 
-          sf::st_as_sfc(crs = st_crs("WGS84")) # FIXME: should we define the projection?
+          sf::st_as_sfc(crs = sf::st_crs("WGS84")) # FIXME: should we define the projection?
       }
     }
   }

R/galah_polygon.R

@@ -120,8 +120,8 @@ parse_polygon <- function(query,
   } else {
 
     # remove space after "POLYGON" if present
-    if(str_detect(query, "POLYGON \\(\\("))
-      query <- string::str_replace(query, "POLYGON \\(\\(", "POLYGON\\(\\(")
+    if(stringr::str_detect(query, "POLYGON \\(\\("))
+      query <- stringr::str_replace(query, "POLYGON \\(\\(", "POLYGON\\(\\(")
 
     if (stringr::str_detect(query, "POLYGON") &
         !stringr::str_detect(query, "MULTIPOLYGON")) {

R/handle_quosures.R

@@ -468,7 +468,7 @@ parse_in <- function(x, excl){
 #' @noRd
 #' @keywords internal
 parse_c <- function(x, excl){ 
-  if(length(quo_get_expr(x)) < 2L){
+  if(length(rlang::quo_get_expr(x)) < 2L){
     filter_error()
   }
   # convert to logical format using OR statements

R/print.R

@@ -182,10 +182,10 @@ print.prequery <- print.query
 print.computed_query <- function(x, ...){
   # calculate arrange/slice info
   if(!is.null(x$arrange)){
-    arrange <- galah_pale_green(glue("\n
+    arrange <- galah_pale_green(glue::glue("\n
                               arrange: {x$arrange$variable} ({x$arrange$direction})"))
     if(x$arrange$slice_called == TRUE){
-      slice <- galah_pale_green(glue("\n
+      slice <- galah_pale_green(glue::glue("\n
                               slice: {x$arrange$slice_n}"))
     }else{
       slice <- NULL

R/search_all.R

@@ -23,6 +23,8 @@
 #' @param all_fields `r lifecycle::badge("experimental")` If `TRUE`, 
 #'   `show_values()` also returns all columns available from the API, rather
 #'   than the 'default' columns traditionally provided via galah.
+#' @param One or more objects accepted by the taxonomic lookup services. See 
+#' [taxonomic_searches] for details
 #' @details There are six categories of information, each with their own 
 #' specific sub-functions to look-up each type of information. 
 #' The available types of information for `search_all()` are:

R/show_values.R

@@ -99,7 +99,7 @@ show_values <- function(df,
   if(isTRUE(all_fields)){
     request_metadata() |>
       filter({{type}} == {{match_name}}) |>
-      select(everything()) |>
+      select(tidyselect::everything()) |>
       unnest() |>
       collect()
   }else{

R/utilities_internal.R

@@ -483,8 +483,8 @@ reasons_supported <- function(){
   atlas <- potions::pour("atlas", "region")
   supported_atlases <- request_metadata(type = "apis") |>
     collect() |>
-    dplyr::filter(type == "metadata/reasons") |>
-    dplyr::pull(atlas)
+    dplyr::filter(.data$type == "metadata/reasons") |>
+    dplyr::pull("atlas")
   atlas %in% supported_atlases
 }
 

tests/testthat/test-atlas_species.R

@@ -157,6 +157,8 @@ test_that("atlas_species reformats column names when empty tibble is returned",
 })
 
 test_that("`atlas_species()` accepts `distinct()` to set the grouping variable", {
+  skip_if_offline(); skip_on_ci()
+
   genera <- galah_call() |>
     identify("Limnodynastidae") |>
     distinct(genusID) |>