Merge branch 'generalise_extract_off_develop' of https://github.com/t…

…raitecoevo/austraits into generalise_extract_off_develop

R/austraits-package.R

@@ -17,46 +17,60 @@ if(getRversion() >= "2.15.1")  utils::globalVariables(c(".", "dplyr::n()"))
 ## usethis namespace: end
 NULL
 
-utils::globalVariables(c("..density..",
-                       ".data",
-                       "Group",
-                       "abort",
-                       "australia",
-                       "colour",
-                       "context", 
-                       "context_name",
-                       "context_property",
-                       "dataset_id",
-                       "latitude (deg)", 
-                       "link_id", 
-                       "link_vals",
-                       "location_name", 
-                       "location_property",
-                       "longitude (deg)",
-                       "method_context_id",
-                       "method_id", 
-                       "n", 
-                       "n_vals", 
-                       "n_value_type", 
-                       "observation_id", 
-                       "original_name", 
-                       "percent",
-                       "percent_total",
-                       "repeat_measurements_id",
-                       "replicates",
-                       "shapes",
-                       "site_name",
-                       "site_property",
-                       "source_id",
-                       "taxon_name", 
-                       "text", 
-                       "trait_name",
-                       "value",
-                       "value_type",
-                       "x",
-                       "y", 
-                       "publication_date",
-                       "doi",
-                       "relation_type",
-                       "identifier")
+utils::globalVariables(c(
+  "aus_traits",
+  "..density..",
+  "description",
+  ".data",
+  "doi",
+  "given_name",
+  "Group",
+  "abort",
+  "australia",
+  "basis_of_value",
+  "category",
+  "colour",
+  "context", 
+  "context_name",
+  "context_property",
+  "contributor",
+  "data",
+  "dataset_id",
+  "data_contributors",
+  "identifier",
+  "last_name",
+  "latitude (deg)", 
+  "link_id", 
+  "link_vals",
+  "location_id",
+  "location_name", 
+  "location_property",
+  "location_properties",
+  "longitude (deg)",
+  "method_context_id",
+  "method_id", 
+  "n", 
+  "name",
+  "n_vals", 
+  "n_value_type", 
+  "observation_id", 
+  "original_name", 
+  "percent",
+  "percent_total",
+  "publication_date",
+  "repeat_measurements_id",
+  "relation_type",
+  "replicates",
+  "shapes",
+  "site_name",
+  "site_property",
+  "source_id",
+  "taxon_name", 
+  "text", 
+  "trait_name",
+  "value",
+  "value_type",
+  "x",
+  "y"
+)
 )

R/extract_data.R

@@ -16,7 +16,8 @@
 #'
 #' @examples 
 #' \dontrun{
-#' extract_data(database = traits.build_database, table = "traits", col = "trait_name", col_value = "leaf_area")
+#' extract_data(database = traits.build_database, table = "traits", 
+#' col = "trait_name", col_value = "leaf_area")
 #' }
 extract_data <- function(database, table, col, col_value) {
 
@@ -155,7 +156,7 @@ extract_data <- function(database, table, col, col_value) {
       # Trim traits, based on the columns identified as being common between the traits table and target table
       cc_traits <- database[[table[[i]]]] |>
         dplyr::slice(found_indicies) |>
-        dplyr::select(all_of(columns_to_select)) |> 
+        dplyr::select(tidyselect::all_of(columns_to_select)) |> 
         dplyr::distinct()
 
       # Filtering join
@@ -175,11 +176,11 @@ extract_data <- function(database, table, col, col_value) {
           dplyr::distinct() 
 
         cut_traits <- cut_traits |>
-          dplyr::filter(if_all(everything(), ~ !is.na(.)))
+          dplyr::filter(dplyr::if_all(tidyselect::everything(), ~ !is.na(.)))
 
         cut_table <- eval(parse(text = tables_tmp$tables_complete_path[[j]])) |>
           dplyr::semi_join(cut_traits, by = get(tables_tmp$cookie_cutters[[j]])) %>%
-          dplyr::rename(link_vals = contains("context_id"))
+          dplyr::rename(link_vals = tidyselect::contains("context_id"))
 
         assign(paste0("ret_tmp[[\"", tables_tmp$tables_to_cut[[j]], "\"]]"), cut_table )