diff --git a/R/as_wide_table.R b/R/as_wide_table.R
index 88657be..eec13b9 100644
--- a/R/as_wide_table.R
+++ b/R/as_wide_table.R
@@ -1,6 +1,6 @@
-#' Create a single wide table from the AusTraits data object
+#' Create a single wide table from a traits.build data object
 #'
-#' @param austraits austraits data object
+#' @param database traits.build database (list object)
 #'
 #' @return A single wide table with collapsed contexts and locations text and with 
 #' some cols renamed for alignment with other resources
@@ -8,19 +8,18 @@
 #'
 #' @examples
 #' \dontrun{
-#' data <- austraits
-#' data %>% as_wide_table()
+#' austraits %>% as_wide_table()
 #' }
 #' @importFrom stats family
 #' @importFrom utils methods
 
-as_wide_table <- function(austraits){
+as_wide_table <- function(database){
   # Check compatability
-  status <- check_compatibility(austraits)
+  status <- check_compatibility(database)
   
   # If compatible
   if(!status){
-    function_not_supported(austraits)
+    function_not_supported(database)
   }
 
   # Function to collapse columns in locations and contexts into single column
@@ -33,39 +32,39 @@ as_wide_table <- function(austraits){
   }
   
   ################################################################################
-  # Define and adapt each table in the list of austraits to prepare for the wide table format 
+  # Define and adapt each table in the list of a traits.build database to prepare for the wide table format 
 
     # The contexts table needs the contexts collapsed to one context name per site
-  austraits %>% 
-    join_contexts_old(collapse_context = TRUE) -> austraits
+  database %>% 
+    join_contexts_old(collapse_context = TRUE) -> database
 
   # Getting rid of the columns that will soon be deleted in the next austraits release and renaming the description column
-  austraits$methods <- 
-    austraits$methods %>% 
+  database$methods <- 
+    database$methods %>% 
     dplyr::rename(dataset_description = "description")  %>% 
     dplyr::distinct()
   
   # collapse into one column
-  austraits$locations <- 
-    austraits$locations %>% 
+  database$locations <- 
+    database$locations %>% 
     dplyr::filter(value!="unknown") %>% 
     dplyr::rename("property" = "location_property") %>%
     split(., .$dataset_id) %>%
     purrr::map_dfr(process_table3)
 
   # rename taxonomic_dataset field to reflect the APC/APNI name matching process better
-  austraits$taxa <- 
-    austraits$taxa %>% 
+  database$taxa <- 
+    database$taxa %>% 
     dplyr::distinct()
   
-  austraits_wide <- 
-    austraits$traits %>% 
-    dplyr::left_join(by=c("dataset_id", "location_id"), austraits$locations) %>%
-    dplyr::left_join(by=c("dataset_id", "method_id", "trait_name"), austraits$methods) %>%
-    dplyr::left_join(by=c("taxon_name"), austraits$taxa)
+  database_wide <- 
+    database$traits %>% 
+    dplyr::left_join(by=c("dataset_id", "location_id"), database$locations) %>%
+    dplyr::left_join(by=c("dataset_id", "method_id", "trait_name"), database$methods) %>%
+    dplyr::left_join(by=c("taxon_name"), database$taxa)
 
     # reorder the names to be more intuitive
-    austraits_wide %>% dplyr::select(
+    database_wide %>% dplyr::select(
       
     # The most useful (if you are filtering for just one taxon_name)
       "dataset_id", "observation_id", "trait_name", "taxon_name", "value", "unit", 
@@ -92,7 +91,7 @@ as_wide_table <- function(austraits){
     "taxon_rank", "genus", "family"
     )
   
-  austraits_wide
+  database_wide
 }
 
 #' Collapse columns into text string
diff --git a/R/bind_databases.R b/R/bind_databases.R
index ac65c0f..a90eec7 100644
--- a/R/bind_databases.R
+++ b/R/bind_databases.R
@@ -4,7 +4,7 @@
 #' database object as a large list.
 #'
 #' @param ... Arguments passed to other functions
-#' @param databases List of traits.build databases to bind together (lists of tibbles)
+#' @param databases List of traits.build databases to bind together
 #'
 #' @return Compiled database as a single large list
 #' @importFrom rlang .data
diff --git a/R/bind_trait_values.R b/R/bind_trait_values.R
index 717e1a3..7b17005 100644
--- a/R/bind_trait_values.R
+++ b/R/bind_trait_values.R
@@ -3,7 +3,7 @@
 #' @description This function condenses data for studies that have multiple observations for a given trait into a single row. 
 #' This function concatenates multiple values into a single cell
 #' @usage bind_trait_values(trait_data)
-#' @param trait_data The trait data frame generated from austraits - see example
+#' @param trait_data the traits table in a traits.build database -- see example
 #' @return tibble that is condensed down where multiple observations in value, value_type and replicates are collapsed down and separated by '--' 
 #'
 #' @examples 
diff --git a/R/check_compatibility.R b/R/check_compatibility.R
index 8691fc2..d8e6d3a 100644
--- a/R/check_compatibility.R
+++ b/R/check_compatibility.R
@@ -1,19 +1,19 @@
 #' @title Check compatibility of traits.build object
 #' @description Function to check whether the data object has been compiled by the traits.build workflow and 
 #' therefore has a data structure that is appropriate for use with austraits functions.
-#' @param austraits dataframe generated by traits build
+#' @param database traits.build database (list object)
 #'
 #' @return logical (T/F) output and messaging for uncompatible versions
 #'
 #' @examples
 #' \dontrun{
-#' check_compatibility(austraits)
+#' check_compatibility(database)
 #' }
 #' @author Elizabeth Wenk - e.wenk@unsw.edu.au
 
-check_compatibility <- function(austraits) {
+check_compatibility <- function(database) {
   
-  if (is.null(austraits$metadata)) {
+  if (is.null(database$metadata)) {
     
     compatible <- FALSE
     
@@ -22,9 +22,9 @@ check_compatibility <- function(austraits) {
   } else {
     
     compiled_by_traits.build <-
-      austraits$metadata$related_identifiers %>% 
+      database$metadata$related_identifiers %>% 
       convert_list_to_df2() %>%
-      dplyr::filter(relation_type == "isCompiledBy") |> 
+      dplyr::filter(relation_type == "isCompiledBy") %>% 
       dplyr::filter(stringr::str_detect(identifier, "github.com/traitecoevo/traits.build"))
     
     if(is.null(compiled_by_traits.build) | nrow(compiled_by_traits.build) > 0) {
@@ -46,13 +46,13 @@ check_compatibility <- function(austraits) {
 
 #' Check compatibility of traits table
 #'
-#' @param traits table in traits.build object
+#' @param trait_data the traits table in a traits.build database
 #'
 #' @return logical, TRUE indicating version traits table came from traits.build version > 1.0
 
-check_traits_compatibility <- function(traits){
+check_traits_compatibility <- function(trait_data){
   # Check compatibility using column 
-  if(any(names(traits) %in% c("treatment_context_id", "repeat_measurements_id"))){
+  if(any(names(trait_data) %in% c("treatment_context_id", "repeat_measurements_id"))){
     compatible <- TRUE
   } else
     compatible <- FALSE
diff --git a/R/extract_data.R b/R/extract_data.R
index 28acc8f..5d42cdf 100644
--- a/R/extract_data.R
+++ b/R/extract_data.R
@@ -6,7 +6,7 @@
 #' The output a traits.build formatted database with all tables subset 
 #' based on the specified table, column (variable) and column value.
 #'
-#' @param database traits.build database
+#' @param database traits.build database (list object)
 #' @param table Table within a traits.build database
 #' @param col Column name within the specified table.
 #' @param col_value Value (of column, from with a table) that is used to subset database. This can be a single value or a vector. It includes partial string matches. 
diff --git a/R/extract_dataset.R b/R/extract_dataset.R
index dc92865..06df0be 100644
--- a/R/extract_dataset.R
+++ b/R/extract_dataset.R
@@ -3,7 +3,7 @@
 #' @description Function to subset all data associated with a particular dataset from a traits.build relational database.
 #' 
 #' @usage extract_dataset(database, dataset_id)
-#' @param database a large list of tibbles built by `traits.build` workflow
+#' @param database traits.build database (list object)
 #' @param dataset_id character string that matches a `dataset_id` in the database
 #' @return List of tibbles containing all traits.build data and metadata for the specified dataset(s).
 #' @details
diff --git a/R/extract_taxa.R b/R/extract_taxa.R
index d95343a..0db5ca5 100644
--- a/R/extract_taxa.R
+++ b/R/extract_taxa.R
@@ -2,7 +2,7 @@
 #'
 #' @description Function to subset of all data associated with a particular taxon from a traits.build relational database.
 #' 
-#' @param database a large list of tibbles built by `traits.build` workflow
+#' @param database traits.build database (list object)
 #' @param family character string of family or families
 #' @param genus character string of genus or genera
 #' @param taxon_name character string of taxon name(s)
@@ -19,8 +19,8 @@
 #'
 #' @examples 
 #' \dontrun{
-#'extract_taxa(austraits, family = "Proteaceae")
-#'extract_taxa(austraits, genus = "Acacia")
+#'extract_taxa(database = austraits, family = "Proteaceae")
+#'extract_taxa(database = austraits, genus = "Acacia")
 #' }
 #' @author Fonti Kar - f.kar@unsw.edu.au
 #' @export
diff --git a/R/extract_trait.R b/R/extract_trait.R
index c5a1af1..d4f5941 100644
--- a/R/extract_trait.R
+++ b/R/extract_trait.R
@@ -3,7 +3,7 @@
 #' @description Function to subset all data associated with a particular trait from a traits.build relational database.
 #' 
 #' @usage extract_trait(database, trait_names, taxon_names)
-#' @param database a large list of tibbles built by `traits.build`
+#' @param database traits.build database (list object)
 #' @param trait_names character string of trait(s) for which data will be extracted
 #' @param taxon_names optional argument, specifying taxa for which data will be extracted 
 #' @return List of tibbles containing all traits.build data and metadata for the specified trait(s).
@@ -19,7 +19,7 @@
 #'
 #' @examples 
 #' \dontrun{
-#'extract_trait(database, "wood_density", taxon_name = "Acacia celsa")
+#'extract_trait(database = austraits, trait_names = "wood_density", taxon_names = "Acacia celsa")
 #' }
 #' @author Daniel Falster - daniel.falster@unsw.edu.au
 #' @export
diff --git a/R/flatten_database.R b/R/flatten_database.R
index 83d06ac..9789a7d 100644
--- a/R/flatten_database.R
+++ b/R/flatten_database.R
@@ -6,7 +6,7 @@
 #' but all measurement-related metadata (methods, location properties, context properties, contributors)
 #' are now included as additional columns in a single table.
 #'
-#' @param database A traits.build database
+#' @param database traits.build database (list object)
 #' @param format A parameter for the locations, contexts and data contributors tables specifying how data are packed. 
 #' All three can be formatted as a single compacted column(s) will have a human readable column ("single_column_pretty")
 #' or using json ("single_column_json") syntax. For location properties or context properties  there is also 
diff --git a/R/join_all.R b/R/join_.R
similarity index 85%
rename from R/join_all.R
rename to R/join_.R
index e78df05..729a628 100644
--- a/R/join_all.R
+++ b/R/join_.R
@@ -2,7 +2,7 @@
 #' @description Function to merge geographic coordinates (latitude/longitude) 
 #' stored in the locations table of a traits.build database into the core traits table.
 #' 
-#' @param austraits traits.build database
+#' @param database traits.build database (list object)
 #' @return traits.build list object, but with additional fields (columns) 
 #' for latitude and longitude appended to `traits` dataframe
 #' @details
@@ -17,34 +17,34 @@
 #'
 #' @examples
 #' \dontrun{
-#' (austraits %>% join_location_coordinates)$traits
+#' (database %>% join_location_coordinates)$traits
 #' }
 #' 
 #' @export
-join_location_coordinates <- function(austraits) {
+join_location_coordinates <- function(database) {
 
   # Check compatibility
-  if(!check_compatibility(austraits)){
-    function_not_supported(austraits)
+  if(!check_compatibility(database)){
+    function_not_supported(database)
   }
 
   location_coordinates <-
-    austraits$locations %>%
+    database$locations %>%
     dplyr::filter(location_property %in% c("latitude (deg)", "longitude (deg)")) %>%
     tidyr::pivot_wider(names_from = location_property, values_from = value)
 
   # variables to join_ by depends on if location_name already in traits table
   # from joining coordinates for instances
-  join_vars <- intersect(names(austraits$traits), c("dataset_id", "location_id", "location_name"))
+  join_vars <- intersect(names(database$traits), c("dataset_id", "location_id", "location_name"))
 
   if (any(stringr::str_detect(names(location_coordinates), "latitude "))) {
-    austraits$traits <- 
-      austraits$traits %>%
+    database$traits <- 
+      database$traits %>%
       dplyr::left_join(by = join_vars, location_coordinates)
 
   } else {
-    austraits$traits <- 
-      austraits$traits %>%
+    database$traits <- 
+      database$traits %>%
       dplyr::mutate(
         location_name = NA_character_,
         `latitude (deg)` = NA_character_,
@@ -52,7 +52,7 @@ join_location_coordinates <- function(austraits) {
       )
   }
 
-  austraits
+  database
 }
 
 
@@ -60,7 +60,7 @@ join_location_coordinates <- function(austraits) {
 
 #' @description Function to merge metadata from the taxa table of a traits.build database into the core traits table.
 #' 
-#' @param austraits traits.build database
+#' @param database traits.build database (list object)
 #' @param vars Columns from the taxa table to be joined to the traits table, defaulting to c("family", "genus", "taxon_rank", "establishment_means").
 #' 
 #' @return traits.build list object, but with additional fields (columns) for the specified variables from the taxa table appended to the traits table.
@@ -79,27 +79,27 @@ join_location_coordinates <- function(austraits) {
 #' @examples
 #' \dontrun{
 #' #Append taxonomic details
-#' (austraits %>% join_taxa)$traits
+#' (database %>% join_taxa)$traits
 #' }
-join_taxa <- function(austraits, 
+join_taxa <- function(database, 
                       vars =  c("family", "genus", "taxon_rank", "establishment_means")) {
 
   # Check compatibility
-  if(!check_compatibility(austraits)){
-    function_not_supported(austraits)
+  if(!check_compatibility(database)){
+    function_not_supported(database)
   }
 
   # If all columns to be added, create `vars` vector
   if (vars[1] == "all" & length(vars == 1)){
-    vars <- names(austraits$taxa)
+    vars <- names(database$taxa)
   }
 
   # Join selected columns to traits table
-  austraits$traits <- 
-    austraits$traits %>%
-    dplyr::left_join(by="taxon_name", austraits$taxa %>% dplyr::select("taxon_name", tidyselect::any_of(vars)))
+  database$traits <- 
+    database$traits %>%
+    dplyr::left_join(by="taxon_name", database$taxa %>% dplyr::select("taxon_name", tidyselect::any_of(vars)))
 
-  austraits
+  database
 }
 
 
@@ -107,7 +107,7 @@ join_taxa <- function(austraits,
 #' 
 #' @description Function to merge metadata from the taxonomic_updates table of a traits.build database into the core traits table.
 #' 
-#' @param austraits traits.build database
+#' @param database traits.build database (list object)
 #' @param vars Columns from the taxa table to be joined to the traits table, defaulting to c("aligned_name").
 #' 
 #' @return traits.build list object, but with additional fields (columns) for the specified variables from the taxonomic_updates table appended to the traits table.
@@ -126,36 +126,36 @@ join_taxa <- function(austraits,
 #' @examples
 #' \dontrun{
 #' #Append taxonomic update details
-#' (austraits %>% join_taxonomic_updates)$traits
+#' (database %>% join_taxonomic_updates)$traits
 #' }
-join_taxonomic_updates <- function(austraits, vars =  c("aligned_name")) {
+join_taxonomic_updates <- function(database, vars =  c("aligned_name")) {
 
   # Check compatibility
-  if(!check_compatibility(austraits)){
-    function_not_supported(austraits)
+  if(!check_compatibility(database)){
+    function_not_supported(database)
   }
 
   # If all columns to be added, create `vars` vector
   if (vars[1] == "all" & length(vars == 1)){
-    vars <- names(austraits$taxonomic_updates)
+    vars <- names(database$taxonomic_updates)
   }
 
   # Join selected columns to traits table
-  austraits$traits <- 
-    austraits$traits %>%
+  database$traits <- 
+    database$traits %>%
     dplyr::left_join(by = c("taxon_name", "dataset_id", "original_name"),
-        austraits$taxonomic_updates %>%
+        database$taxonomic_updates %>%
           dplyr::select("taxon_name", "dataset_id", "original_name", 
                         tidyselect::any_of(vars)))
 
-  austraits
+  database
 }
 
 #' @title Joining methodological information to traits table
 #' 
 #' @description Function to merge metadata from the methods table of a traits.build database into the core traits table.
 #' 
-#' @param austraits traits.build database
+#' @param database traits.build database (list object)
 #' @param vars Columns from the taxa table to be joined to the traits table, defaulting to c("methods").
 #' 
 #' @return traits.build list object, but with additional fields (columns) for the specified variables from the methods table appended to the traits table.
@@ -173,30 +173,30 @@ join_taxonomic_updates <- function(austraits, vars =  c("aligned_name")) {
 #' 
 #' @examples
 #' \dontrun{
-#' (austraits %>% join_methods)$traits
+#' (database %>% join_methods)$traits
 #' }
-join_methods <- function(austraits, vars =  c("methods")) {
+join_methods <- function(database, vars =  c("methods")) {
 
   # Check compatibility
-  if(!check_compatibility(austraits)){
-    function_not_supported(austraits)
+  if(!check_compatibility(database)){
+    function_not_supported(database)
   }
 
   # If all columns to be added, create `vars` vector
   if (vars[1] == "all" & length(vars == 1)){
-    vars <- names(austraits$methods)
+    vars <- names(database$methods)
   }
 
   # Join selected columns to traits table
-  austraits$traits <- 
-    austraits$traits %>%
+  database$traits <- 
+    database$traits %>%
     dplyr::left_join(by=c("dataset_id", "trait_name", "method_id"),
-      austraits$methods %>% 
+      database$methods %>% 
       dplyr::select(c("dataset_id", "trait_name", "method_id"), tidyselect::any_of(vars)) %>% 
       dplyr::distinct()
     )
 
-  austraits
+  database
 }
 
 
@@ -204,7 +204,7 @@ join_methods <- function(austraits, vars =  c("methods")) {
 #' 
 #' @description Function to merge metadata from the data contributors table of a traits.build database into the core traits table.
 #' 
-#' @param austraits traits.build database
+#' @param database traits.build database (list object)
 #' @param format Specifies whether metadata from the contributors table is output in a human readable format ("single_column_pretty"; default) or using json syntax ("single_column_json").
 #' @param vars Columns from the taxa table to be joined to the traits table, defaulting to all columns (vars = "all").
 #' 
@@ -226,25 +226,25 @@ join_methods <- function(austraits, vars =  c("methods")) {
 #' (database %>% join_contributors(format = "single_column_pretty", 
 #' vars = c("last_name", "first_name", "ORCID")))$traits
 #' }
-join_contributors <- function(austraits,
+join_contributors <- function(database,
                               format = "single_column_pretty",
                               vars =  "all") {
 
   # Check compatibility
-  if(!check_compatibility(austraits)){
-    function_not_supported(austraits)
+  if(!check_compatibility(database)){
+    function_not_supported(database)
   }
 
   # Work out which vars to retain and create a dataframe for compacting
   if (vars[1] == "all") {
-    contributors_tmp <- austraits$contributors
+    contributors_tmp <- database$contributors
   } else {
     # Create vector that is combination of selected columns and required columns
     vars_tmp <- c("dataset_id", "last_name", "given_name", vars)
     # Determine which columns aren't wanted
-    vars_remove <- setdiff(names(austraits$contributors), vars_tmp)
+    vars_remove <- setdiff(names(database$contributors), vars_tmp)
     # Remove unwanted columns from contributors dataframe
-    contributors_tmp <- austraits$contributors %>% dplyr::select(-dplyr::any_of(vars_remove))
+    contributors_tmp <- database$contributors %>% dplyr::select(-dplyr::any_of(vars_remove))
   }
 
   # Different options for how data are compacted and joined depending on `format` argument
@@ -279,17 +279,17 @@ join_contributors <- function(austraits,
   } else if (format == "single_column_json") {
 
     compacted_contributors_column <-
-      contributors_tmp |> 
-      tidyr::nest(-dplyr::all_of("dataset_id")) |>
-      dplyr::mutate(data_contributors = purrr::map_chr(data, jsonlite::toJSON)) |>
-      dplyr::select(-dplyr::any_of("data")) |>
+      contributors_tmp %>% 
+      tidyr::nest(-dplyr::all_of("dataset_id")) %>%
+      dplyr::mutate(data_contributors = purrr::map_chr(data, jsonlite::toJSON)) %>%
+      dplyr::select(-dplyr::any_of("data")) %>%
       dplyr::ungroup()
   }
 
-  austraits$traits <- austraits$traits %>%
+  database$traits <- database$traits %>%
     dplyr::left_join(by = c("dataset_id"), compacted_contributors_column)
 
-  austraits
+  database
 }
 
 
@@ -297,7 +297,7 @@ join_contributors <- function(austraits,
 #' 
 #' @description Function to merge metadata from the locations table of a traits.build database into the core traits table.
 #' 
-#' @param austraits traits.build database
+#' @param database traits.build database (list object)
 #' @param format Specifies whether metadata from the locations is output in a human readable format ("single_column_pretty"; default), with each location property added as a separate column ("many_columns") or using json syntax ("single_column_json").
 #' @param vars Location properties for which data is to be appended to the traits table, defaulting to all location properties (vars = "all").
 #' 
@@ -318,20 +318,20 @@ join_contributors <- function(austraits,
 #' \dontrun{
 #' (database %>% join_location_properties(format = "single_column_pretty", vars = "all"))$traits
 #' }
-join_location_properties <- function(austraits,
+join_location_properties <- function(database,
                                      format = "single_column_pretty",
                                      vars =  "all") {
 
   # Check compatibility
-  if(!check_compatibility(austraits)){
-    function_not_supported(austraits)
+  if(!check_compatibility(database)){
+    function_not_supported(database)
   }
 
   # If all location properties to be added, create `vars` vector that is unique list 
   # of location properties in the database
   if (vars[1] == "all") {
 
-    vars_tmp <- austraits$locations %>%
+    vars_tmp <- database$locations %>%
       dplyr::distinct(location_property) %>%
       dplyr::filter(!location_property %in% c("latitude (deg)", "longitude (deg)"))
 
@@ -342,12 +342,12 @@ join_location_properties <- function(austraits,
   vars <- setdiff(vars, c("latitude (deg)", "longitude (deg)"))
 
   locations <- 
-    austraits$locations %>% 
+    database$locations %>% 
     dplyr::filter(location_property %in% vars)
 
   # Variables to join_ by depends on if location_name already in traits table
   # from joining coordinates for instances
-  join_vars <- intersect(names(austraits$traits), c("dataset_id", "location_id", "location_name"))
+  join_vars <- intersect(names(database$traits), c("dataset_id", "location_id", "location_name"))
 
   # Different options for how data are compacted and joined depending on `format` argument
   if (format == "many_columns") {
@@ -359,8 +359,8 @@ join_location_properties <- function(austraits,
       tidyr::pivot_wider(names_from = location_property)
 
     # Join locations, based on appropriate columns
-    austraits$traits <- 
-      austraits$traits %>%
+    database$traits <- 
+      database$traits %>%
       dplyr::left_join(by = join_vars, locations)
 
   } else if (format == "single_column_pretty") {
@@ -376,32 +376,32 @@ join_location_properties <- function(austraits,
       dplyr::ungroup() %>%
       dplyr::distinct()
 
-    austraits$traits <- 
-      austraits$traits %>%
+    database$traits <- 
+      database$traits %>%
       dplyr::left_join(by = join_vars, compacted_locations_column)
 
   } else if (format == "single_column_json") {
 
     compacted_locations_column <-
-      locations |> 
-      tidyr::nest(data = -dplyr::all_of(c("dataset_id", "location_id"))) |>
-      dplyr::mutate(location_properties = purrr::map_chr(data, jsonlite::toJSON)) |>
-      dplyr::select(-dplyr::any_of("data")) |>
+      locations %>% 
+      tidyr::nest(data = -dplyr::all_of(c("dataset_id", "location_id"))) %>%
+      dplyr::mutate(location_properties = purrr::map_chr(data, jsonlite::toJSON)) %>%
+      dplyr::select(-dplyr::any_of("data")) %>%
       dplyr::ungroup()
     
-    austraits$traits <- austraits$traits %>%
+    database$traits <- database$traits %>%
       dplyr::left_join(by = join_vars, compacted_locations_column)
 
   }
 
-  austraits
+  database
 }
 
 #' @title Joining context properties to traits table
 #' 
 #' @description Function to merge metadata from the contexts table of a traits.build database into the core traits table.
 #' 
-#' @param austraits traits.build database
+#' @param database traits.build database (list object)
 #' @param format Specifies whether metadata from the contexts is output in a human readable format ("single_column_pretty"; default), with each context property added as a separate column ("many_columns") or using json syntax ("single_column_json").
 #' @param vars Location properties for which data is to be appended to the traits table, defaulting to all context properties (vars = "all").
 #' @param include_description A logical indicating whether to include (TRUE) or omit (FALSE) the context_property descriptions.
@@ -424,25 +424,25 @@ join_location_properties <- function(austraits,
 #' (database %>% join_context_properties(
 #' format = "many_columns", vars = "all", include_description = TRUE))$traits
 #' }
-join_context_properties <- function(austraits,
+join_context_properties <- function(database,
                                     format = "single_column_pretty",
                                     vars =  "all",
                                     include_description = TRUE) {
   
   # Check compatibility
-  if(!check_compatibility(austraits)){
-    function_not_supported(austraits)
+  if(!check_compatibility(database)){
+    function_not_supported(database)
   }
 
   # If all context properties to be added, create `vars` vector that is unique list 
   # of context properties in the database
   if (vars[1] == "all") {
-    vars <- austraits$contexts$context_property %>% unique()
+    vars <- database$contexts$context_property %>% unique()
   }
 
   # Create dataframe of contexts to use & add `context_property:` to context properties
   contexts_tmp <- 
-    austraits$contexts %>% 
+    database$contexts %>% 
     dplyr::filter(context_property %in% vars) %>%
     dplyr::mutate(context_property = paste0(category, ": ", context_property))
 
@@ -481,13 +481,13 @@ join_context_properties <- function(austraits,
   } else if (format == "single_column_json") {
     
     contexts_tmp <-
-      contexts_tmp |> 
-      tidyr::separate_longer_delim(link_vals, ", ") |>
-      dplyr::distinct() |> 
-      dplyr::mutate(description = ifelse(!is.na(description) & include_description, description, NA)) |> 
-      tidyr::nest(data = -dplyr::all_of(c("dataset_id", "link_id", "link_vals"))) |> 
-      dplyr::mutate(value = purrr::map_chr(data, jsonlite::toJSON)) |>
-      dplyr::select(-dplyr::any_of("data")) |>
+      contexts_tmp %>% 
+      tidyr::separate_longer_delim(link_vals, ", ") %>%
+      dplyr::distinct() %>% 
+      dplyr::mutate(description = ifelse(!is.na(description) & include_description, description, NA)) %>% 
+      tidyr::nest(data = -dplyr::all_of(c("dataset_id", "link_id", "link_vals"))) %>% 
+      dplyr::mutate(value = purrr::map_chr(data, jsonlite::toJSON)) %>%
+      dplyr::select(-dplyr::any_of("data")) %>%
       dplyr::ungroup()
     
     pivot <- FALSE
@@ -496,7 +496,7 @@ join_context_properties <- function(austraits,
     stop("format not supported: ", format)
   }
 
-  # Merge contexts to austraits$traits
+  # Merge contexts to database$traits
 
   # Defines a function to further reformat specific columns of the context table
   reformat_contexts <- function(data, context_id, pivot) {
@@ -521,8 +521,8 @@ join_context_properties <- function(austraits,
   }
 
 
-  austraits$traits <- 
-    austraits$traits %>%
+  database$traits <- 
+    database$traits %>%
     dplyr::left_join(
       by = c("dataset_id", "treatment_context_id"),
       reformat_contexts(contexts_tmp, "treatment_context_id", pivot)
@@ -544,7 +544,7 @@ join_context_properties <- function(austraits,
       reformat_contexts(contexts_tmp, "method_context_id", pivot)
     )
 
-  austraits
+  database
 
 }
 
diff --git a/R/load_austraits.R b/R/load_austraits.R
index e016865..020fd06 100644
--- a/R/load_austraits.R
+++ b/R/load_austraits.R
@@ -62,7 +62,7 @@ load_austraits <- function(doi = NULL, version = NULL, path = "data/austraits",
   version_name <- paste0("v", version)
   
   # Getting specific version
-  id <- ret[which(ret$version == version), "id"] |> as.character()
+  id <- ret[which(ret$version == version), "id"] %>% as.character()
   
   target <- res$hits$hits$files[[version_name]]
   
@@ -119,15 +119,15 @@ load_json <- function(path, update){
 
 create_metadata <- function(res){
   # Version table
-  ret <- res$hits$hits$metadata |> 
-    dplyr::select(tidyselect::all_of(c("publication_date", "doi", "version"))) |>  
-    dplyr::mutate(version = gsub("v", "", version) |> numeric_version(),
+  ret <- res$hits$hits$metadata %>% 
+    dplyr::select(tidyselect::all_of(c("publication_date", "doi", "version"))) %>%  
+    dplyr::mutate(version = gsub("v", "", version) %>% numeric_version(),
                   id = stringr::str_remove_all(doi, stringr::fixed("10.5281/zenodo."))
-                  )|>  # set as numeric version for easier filtering
-    dplyr::filter(version >= "3.0.2") |> # exclude everything pre 3.0.2
+                  )%>%  # set as numeric version for easier filtering
+    dplyr::filter(version >= "3.0.2") %>% # exclude everything pre 3.0.2
     dplyr::mutate(version = as.character(version),
-                  publication_date = lubridate::ymd(publication_date)) |>  # change back as character
-    dplyr::tibble() |> 
+                  publication_date = lubridate::ymd(publication_date)) %>%  # change back as character
+    dplyr::tibble() %>% 
     dplyr::arrange(dplyr::desc(publication_date))
 
   ret
@@ -228,10 +228,10 @@ get_version_latest <- function(path = "data/austraits", update = TRUE){
   metadata <- create_metadata(res) 
   
   # Sort old to new
-  metadata <- metadata |> 
+  metadata <- metadata %>% 
   dplyr::arrange(dplyr::desc(publication_date))
   
   # Grab the first version
-  dplyr::first(metadata$version) |> as.character()
+  dplyr::first(metadata$version) %>% as.character()
 }
 
diff --git a/R/lookup.R b/R/lookup_.R
similarity index 70%
rename from R/lookup.R
rename to R/lookup_.R
index 382cc29..3c0c1e6 100644
--- a/R/lookup.R
+++ b/R/lookup_.R
@@ -1,6 +1,6 @@
 #' Look up a particular trait term
 #'
-#' @param austraits austraits list
+#' @param database traits.build database (list object)
 #' @param term character string for trait search term 
 #'
 #' @return vector containing traits that contains search term
@@ -8,11 +8,11 @@
 #'
 #' @examples
 #' \dontrun{
-#' austraits %>% lookup_trait("leaf") %>% extract_trait(austraits, .)
+#' austraits %>% lookup_trait("leaf") %>% extract_trait(database = austraits, .)
 #' }
-lookup_trait <- function(austraits, term){
+lookup_trait <- function(database, term){
   
-  all_traits <- austraits$traits$trait_name %>% unique()
+  all_traits <- database$traits$trait_name %>% unique()
 
    ret <-  stringr::str_subset(all_traits, term)
   
@@ -30,7 +30,7 @@ lookup_trait <- function(austraits, term){
 #' Look up location properties that contain a specific search term.
 #' 
 #'
-#' @param austraits austraits list
+#' @param database traits.build database (list object)
 #' @param term character string for location property search term 
 #'
 #' @return vector containing location properties that contains search term
@@ -40,9 +40,9 @@ lookup_trait <- function(austraits, term){
 #' \dontrun{
 #' austraits %>% lookup_location_property("soil")
 #' }
-lookup_location_property <- function(austraits, term){
+lookup_location_property <- function(database, term){
   
-  all_location_properties <- austraits$locations$location_property %>% unique()
+  all_location_properties <- database$locations$location_property %>% unique()
   
   ret <-  stringr::str_subset(all_location_properties, term)
   
@@ -60,7 +60,7 @@ lookup_location_property <- function(austraits, term){
 #' Look up context properties that contain a specific search term.
 #' 
 #'
-#' @param austraits austraits list
+#' @param database traits.build database (list object)
 #' @param term character string for context property search term 
 #'
 #' @return vector containing context properties that contains search term
@@ -70,9 +70,9 @@ lookup_location_property <- function(austraits, term){
 #' \dontrun{
 #' austraits %>% lookup_context_property("temperature")
 #' }
-lookup_context_property <- function(austraits, term){
+lookup_context_property <- function(database, term){
   
-  all_context_properties <- austraits$contexts$context_property %>% unique()
+  all_context_properties <- database$contexts$context_property %>% unique()
   
   ret <-  stringr::str_subset(all_context_properties, term)
   
diff --git a/R/plot_locations.R b/R/plot_locations.R
index 17fdbde..0f69930 100644
--- a/R/plot_locations.R
+++ b/R/plot_locations.R
@@ -1,6 +1,6 @@
 #' @title Produce location maps of trait values 
 #' @description Plot location where trait data was collected from
-#' @param aus_traits austraits object OR traits table. Note location details must be joined. See join_location_coordinates and examples
+#' @param database traits.build database OR traits table from a traits.build database. Note location details must be joined. See join_location_coordinates and examples
 #' @param feature grouping/classification categories e.g trait_name, collection_type for <= v3.0.2, basis of record for >3.0.2
 #' @param ... arguments passed to ggplot()
 #' @author Dony Indiarto - d.indiarto@student.unsw.edu.au
@@ -18,22 +18,22 @@
 #' @export
 
 
-plot_locations <- function(aus_traits, feature="trait_name", ...){
+plot_locations <- function(database, feature="trait_name", ...){
   
   # Check if traits.build object or the traits table
   # If traits.build, check if traits table contains coordinate cols
-  if( is.null(dim(aus_traits)) ){
+  if( is.null(dim(database)) ){
     
     # Extract traits table if needed
-    traits <- get_traits_table(aus_traits)
+    traits <- get_traits_table(database)
     
     if( length(stringr::str_which(names(traits), "(deg)")) < 2 ){
       cli::cli_alert_info("Coordinate columns were not detected, joining location tables now.")
-      aus_traits <- aus_traits |> join_location_coordinates()
-      traits <- get_traits_table(aus_traits)
+      database <- database %>% join_location_coordinates()
+      traits <- get_traits_table(database)
     }
   } else {
-    traits <- aus_traits #If not traits.build, assign traits table as traits
+    traits <- database #If not traits.build, assign traits table as traits
     
     # Check if traits contains coordinate cols in traits table
     if( length(stringr::str_which(names(traits), "(deg)")) < 2 ) 
@@ -45,15 +45,15 @@ plot_locations <- function(aus_traits, feature="trait_name", ...){
 
 #' Location plot for AusTraits versions > 3.0.2
 #' @noRd
-plot_locations2 <- function(aus_traits, feature, ...){
+plot_locations2 <- function(database, feature, ...){
   au_map <- australia_map_raster %>%
     dplyr::mutate(australia = as.factor(australia))
   
   #Create site data
   sites <-
-    aus_traits |> 
-    dplyr::select(!!feature, tidyselect::any_of(c("site_name", "location_name", "latitude (deg)", "longitude (deg)"))) |> 
-    tidyr::drop_na() |> 
+    database %>% 
+    dplyr::select(!!feature, tidyselect::any_of(c("site_name", "location_name", "latitude (deg)", "longitude (deg)"))) %>% 
+    tidyr::drop_na() %>% 
     dplyr::mutate(dplyr::across(c("longitude (deg)","latitude (deg)"), as.numeric)) %>% 
     dplyr::filter(
       `latitude (deg)` > (-45), `latitude (deg)` < (-9.5),
@@ -101,21 +101,21 @@ plot_locations2 <- function(aus_traits, feature, ...){
 #' @description `r lifecycle::badge('deprecated')`
 #'
 #'Plot location where trait data was collected from
-#' @param traits  traits table with site details appended. See join_location_coordinates and examples
+#' @param trait_data traits table in a traits.build database with site details appended. See join_location_coordinates and examples
 #' @param feature grouping/classification categories e.g trait_name, collection_type for <= v3.0.2
 #' @param ... arguments passed to ggplot()
 #' @author Dony Indiarto - d.indiarto@student.unsw.edu.au
 #' @return ggplot of sites
 #' @export
 
-plot_site_locations <- function(traits, feature="trait_name", ...){
+plot_site_locations <- function(trait_data, feature="trait_name", ...){
   # Extract function name
   function_name <- as.character(sys.calls()[[1]])[1]
   
   # Determine if traits table or traits.build object
-  if( is.null(dim(traits))){
+  if( is.null(dim(trait_data))){
     # Extract AusTraits version
-    AusTraits_version <- print_version(aus_traits)
+    AusTraits_version <- print_version(database)
   } else
     AusTraits_version <- "< 5.0.0"
   
diff --git a/R/plot_trait_distribution_beeswarm.R b/R/plot_trait_distribution_beeswarm.R
index 16881a4..7c5eea6 100644
--- a/R/plot_trait_distribution_beeswarm.R
+++ b/R/plot_trait_distribution_beeswarm.R
@@ -1,11 +1,11 @@
 #' @title Beeswarm Trait distribution 
 #' @description Plots distribution of trait values by a  grouping variable using ggbeeswarm package  
 #'
-#' @param austraits austraits data object
+#' @param database traits.build database (list object)
 #' @param trait_name Name of trait to plot
 #' @param y_axis_category One of `dataset_id`, `family`
-#' @param highlight specify a group to highlight
-#' @param hide_ids add label on y_axis?
+#' @param highlight Specify a group to highlight
+#' @param hide_ids Logical for whether to add a label on y_axis?
 #'
 #' @export
 #'
@@ -17,21 +17,21 @@
 #' @export
 
 #
-plot_trait_distribution_beeswarm <- function(austraits,
+plot_trait_distribution_beeswarm <- function(database,
                                              trait_name,
                                              y_axis_category,
-                                             highlight=NA,
+                                             highlight = NA,
                                              hide_ids = FALSE) {
   
   # Check compatability
-  status <- check_compatibility(austraits)
+  status <- check_compatibility(database)
   
   # If compatible
   if(!status) {
-    function_not_supported(austraits)
+    function_not_supported(database)
   }
   # Subset data to this trait
-  austraits_trait <- extract_trait(austraits, trait_name)
+  database_trait <- extract_trait(database, trait_name)
   
   my_shapes <- c("_min" = 60, "_mean" = 16, "_max" = 62, "unknown" = 18)
   
@@ -44,10 +44,10 @@ plot_trait_distribution_beeswarm <- function(austraits,
     factor(p, levels=names(my_shapes))
   }
   
-  tax_info  <- austraits_trait$taxa %>% dplyr::select(taxon_name, family)
+  tax_info  <- database_trait$taxa %>% dplyr::select(taxon_name, family)
   
   data <-
-    austraits_trait$traits %>%
+    database_trait$traits %>%
     dplyr::mutate(shapes = as_shape(value_type)) %>%
     dplyr::left_join(by = "taxon_name", tax_info) %>%
     dplyr::mutate(value = as.numeric(value))
@@ -79,8 +79,8 @@ plot_trait_distribution_beeswarm <- function(austraits,
   }
   
 
-  vals <- list(minimum = purrr::pluck(austraits_trait, "definitions", trait_name, "allowed_values_min"),
-           maximum = purrr::pluck(austraits_trait, "definitions", trait_name, "allowed_values_max"))
+  vals <- list(minimum = purrr::pluck(database_trait, "definitions", trait_name, "allowed_values_min"),
+           maximum = purrr::pluck(database_trait, "definitions", trait_name, "allowed_values_max"))
   
   range <- (vals$maximum/vals$minimum)
   
diff --git a/R/print.austraits.R b/R/print.austraits.R
index 2a83330..58f14a2 100644
--- a/R/print.austraits.R
+++ b/R/print.austraits.R
@@ -1,19 +1,19 @@
 #' @title Generic for outputting a nice summary for austraits objects
 #'
 #' @name print.austraits
-#' @param x austraits list object 
+#' @param database traits.build database
 #' @param \dots passed to print
 #'
 #' @return nicely printed table
 #' @export
 
-print.austraits <- function(x, ...){
+print.austraits <- function(database, ...){
   
   # Setting up
-  version <- x$build_info$version %>% as.character()
-  nrecords <- nrow(x$traits)
-  nspecies <- unique(x$traits$taxon_name) %>% length()
-  ntraits <- unique(x$traits$trait_name) %>% length()
+  version <- database$build_info$version %>% as.character()
+  nrecords <- nrow(database$traits)
+  nspecies <- unique(database$traits$taxon_name) %>% length()
+  ntraits <- unique(database$traits$trait_name) %>% length()
   
   cat("This is version",
          version, 
@@ -26,7 +26,7 @@ print.austraits <- function(x, ...){
   if(package_version(version) <= '3.0.2'){
   
   cat("\nThis object is a 'list' with the following components:\n\n",
-  x$definitions$austraits$elements %>%
+  database$definitions$austraits$elements %>%
   purrr::map(~.x[["description"]]) %>% 
   as.vector() %>% 
   sprintf("- `%s`: %s", names(.), .) %>%
@@ -34,7 +34,7 @@ print.austraits <- function(x, ...){
   )
   } else{
     cat("\nThis object is a 'list' with the following components:\n\n",
-        x$schema$austraits$elements %>%
+        database$schema$austraits$elements %>%
           purrr::map(~.x[["description"]]) %>% 
           as.vector() %>% 
           sprintf("- `%s`: %s", names(.), .) %>%
diff --git a/R/seperate_trait_values.R b/R/separate_trait_values.R
similarity index 78%
rename from R/seperate_trait_values.R
rename to R/separate_trait_values.R
index 297df9e..4886f1c 100644
--- a/R/seperate_trait_values.R
+++ b/R/separate_trait_values.R
@@ -2,23 +2,23 @@
 #'
 #' @description This function reverts the action of bind_trait_values. 
 #' This function separates values that were concatenated so that studies that have multiple observations for a given trait will have seperate row for each observation.
-#' @usage separate_trait_values(data, definitions)
-#' @param data The trait data frame generated from austraits - see example
+#' @usage separate_trait_values(trait_data, definitions)
+#' @param trait_data The traits table in a traits.build database - see example
 #' @param definitions The austraits definitions data frame
 #' @return trait tibble
 #' @examples 
 #' \dontrun{
-#' traits <- austraits$traits %>% 
+#' trait_data <- austraits$traits %>% 
 #' dplyr::filter(dataset_id == "Falster_2005_1")
-#' traits
-#' traits_bind <- bind_trait_values(traits)
+#' trait_data
+#' traits_bind <- bind_trait_values(trait_data)
 #' separate_trait_values(traits_bind)
 #' }
 #' @author Daniel Falster - daniel.falster@unsw.edu.au
 #' @export
 
 #' 
-separate_trait_values <- function(data, definitions) {
+separate_trait_values <- function(trait_data, definitions) {
   
   separate_x <- function(x) strsplit(x, "--")[[1]]
   
@@ -34,15 +34,15 @@ separate_trait_values <- function(data, definitions) {
   }
   
   # record the number of values in each row of data
-  data$n_vals <- 1 + stringr::str_count(data$value_type, "--")
+  trait_data$n_vals <- 1 + stringr::str_count(trait_data$value_type, "--")
   
   # separate out those rows requiring no modification
-  out_1 <- data %>% 
+  out_1 <- trait_data %>% 
     dplyr::filter(n_vals == 1)
   
-  if (nrow(dplyr::filter(data, n_vals > 1)) > 0) {
+  if (nrow(dplyr::filter(trait_data, n_vals > 1)) > 0) {
     # separate out those rows requiring modification & modify
-    out_2 <- data %>% 
+    out_2 <- trait_data %>% 
       dplyr::filter(n_vals > 1) %>% 
       dplyr::group_split(stringr::str_c(dataset_id, observation_id, trait_name, method_id, method_context_id, repeat_measurements_id, sep = " ")) %>%    
       lapply(separate_values_worker) %>% 
diff --git a/R/summarise_austraits.R b/R/summarise_austraits.R
index 0ea1bbf..519b50d 100644
--- a/R/summarise_austraits.R
+++ b/R/summarise_austraits.R
@@ -1,38 +1,38 @@
 #' @title Summarise counts for a particular variable of interest
 #'
 #' @name summarise_austraits
-#' @param austraits A large list of tibbles built from austraits
+#' @param database traits.build database (list object)
 #' @param var variable you use wish to see summary of (trait_name, genus, family)
 #'
 #' @return dataframe of unique levels of variable with counts and percentage
 #' @export
 #' @examples
 #' \dontrun{
-#' summarise_austraits(austraits, "trait_name")
-#' summarise_austraits(austraits, "family")
+#' summarise_austraits(database = austraits, "trait_name")
+#' summarise_austraits(database = austraits, "family")
 #' }
 
 
-summarise_austraits <- function(austraits, var){
+summarise_austraits <- function(database, var){
   
   if(!var %in% c("trait_name", "family", "genus")){
     stop(paste0("Print summary for ", var, " has not been implemented! see examples)"))
   }
   
   switch(var,
-         trait_name = summarise_austraits_traits(austraits, var),
-         genus =  summarise_austraits_taxa(austraits, var),
-         family = summarise_austraits_taxa(austraits, var)
+         trait_name = summarise_austraits_traits(database, var),
+         genus =  summarise_austraits_taxa(database, var),
+         family = summarise_austraits_taxa(database, var)
   )
 }
 
 #' @noRd
 #' @keywords internal
          
-summarise_austraits_traits <-function(austraits, var) {
+summarise_austraits_traits <-function(database, var) {
 
   ret <- 
-    austraits[["traits"]] %>% 
+    database[["traits"]] %>% 
     dplyr::pull({{var}}) %>% 
     sort() %>% 
     janitor::tabyl() 
@@ -46,7 +46,7 @@ summarise_austraits_traits <-function(austraits, var) {
                                percent_total = signif(percent, 3),
                                percent = NULL)
   # Summary statistics
-  sum_stats <- austraits[["traits"]] %>% 
+  sum_stats <- database[["traits"]] %>% 
     dplyr::group_by(trait_name) %>% 
     dplyr::summarise(n_dataset = length(unique(dataset_id)),
                      n_taxa = length(unique(taxon_name))) 
@@ -54,19 +54,19 @@ summarise_austraits_traits <-function(austraits, var) {
   ret <- dplyr::left_join(ret, sum_stats, by = "trait_name")
   
   # Organise
-  ret %>% dplyr::select(1, dplyr::starts_with("n_"), percent_total) |> tibble::tibble()
+  ret %>% dplyr::select(1, dplyr::starts_with("n_"), percent_total) %>% tibble::tibble()
 }
 
 #' @noRd
 #' @keywords internal
 
-summarise_austraits_taxa <-function(austraits, var) {
+summarise_austraits_taxa <-function(database, var) {
   
   #Join taxonomic info
-  austraits <- austraits %>% join_taxa()
+  database <- database %>% join_taxa()
   
   # Create table
-  ret <- austraits[["traits"]] %>% 
+  ret <- database[["traits"]] %>% 
     dplyr::pull(var) %>% 
     sort() %>% 
     janitor::tabyl() 
@@ -84,7 +84,7 @@ summarise_austraits_taxa <-function(austraits, var) {
 
   
   # Summary statistics (https://stackoverflow.com/questions/55425976/use-quoted-variable-in-group-by-mutate-function-call)
-  sum_stats <- austraits[["traits"]] %>% 
+  sum_stats <- database[["traits"]] %>% 
     dplyr::group_by(!!rlang::sym(var)) %>% 
     dplyr::summarise(n_dataset = length(unique(dataset_id)),
                      n_taxa = length(unique(taxon_name)))
@@ -92,6 +92,6 @@ summarise_austraits_taxa <-function(austraits, var) {
   ret <- dplyr::left_join(ret, sum_stats, by = var)
   
   # Organise
-  ret %>% dplyr::select(1, dplyr::starts_with("n_"), percent_total) |> tibble::tibble()
+  ret %>% dplyr::select(1, dplyr::starts_with("n_"), percent_total) %>% tibble::tibble()
   
 }
diff --git a/R/summarise_trait_values.R b/R/summarise_trait_values.R
index 912a3f7..b940e97 100644
--- a/R/summarise_trait_values.R
+++ b/R/summarise_trait_values.R
@@ -1,6 +1,6 @@
 #' Compute mean trait values for studies that have multiple observations for a given trait
 #'
-#' @param trait_data trait table for austraits list
+#' @param trait_data the traits table in a traits.build database
 #'
 #' @return A reduced trait table, mean values are flagged with the suffix '_summarised' in value_type
 #' @export
diff --git a/R/trait_pivot_wider.R b/R/trait_pivot_wider.R
index 38af169..b506f99 100644
--- a/R/trait_pivot_wider.R
+++ b/R/trait_pivot_wider.R
@@ -6,7 +6,7 @@
 #' 
 #' The function austraits::trait_pivot_longer reverts the actions of this function.
 #' 
-#' @param aus_traits The traits table from traits.build database list object
+#' @param database The traits tibble from a traits.build database
 #' @return traits.build traits table in wide format
 #' @details
 #' `trait_pivot_wider`` has been developed to pivot the traits table for a database build using the traits.build workflow. 
@@ -30,16 +30,16 @@
 #' @author Daniel Falster - daniel.falster@unsw.edu.au
 #' @export
 
-trait_pivot_wider <- function(aus_traits){
+trait_pivot_wider <- function(database){
   # Extract traits table if needed
-  traits <- get_traits_table(aus_traits)
+  traits <- get_traits_table(database)
   
   # Check compatibility
   status <- check_traits_compatibility(traits)
   
   # If compatible
   if(!status){
-    function_not_supported(aus_traits)
+    function_not_supported(database)
   }
 
   metadata_cols <- c("unit", "replicates", "measurement_remarks", "basis_of_value")
diff --git a/R/utils.R b/R/utils.R
index 3fe101b..508653a 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -62,21 +62,21 @@ convert_list_to_df2 <- function(my_list, as_character = TRUE, on_empty = NA) {
 
 #' Notify user the function they are using is no longer support
 #'
-#' @param austraits 
+#' @param database traits.build database (list object)
 #'
-#' @return cli messaging about the function name, the version of austraits they are using and their next options
+#' @return cli messaging about the function name, the version of AusTraits they are using and their next options
 #' @keywords internal
 #' @noRd
 
-function_not_supported <- function(aus_traits, ...){
+function_not_supported <- function(database, ...){
   
   # Extract function name
   function_name <- as.character(sys.calls()[[1]])[1]
   
   # Determine if traits table or traits.build object
-  if( is.null(dim(aus_traits))){
+  if( is.null(dim(database))){
     # Extract AusTraits version
-    AusTraits_version <- print_version(aus_traits)
+    AusTraits_version <- print_version(database)
   } else
     AusTraits_version <- "< 5.0.0"
   
@@ -94,14 +94,14 @@ function_not_supported <- function(aus_traits, ...){
 
 #' Retrieve traits table if user passes traits.build object.
 #'
-#' @param aus_traits traits.build object or traits table
+#' @param database traits.build database or traits table in a traits.build database
 
 
-get_traits_table <- function(aus_traits){
-  if( is.null(dim(aus_traits)) ){
-    traits <- aus_traits$traits
+get_traits_table <- function(database){
+  if( is.null(dim(database)) ){
+    traits <- database$traits
   } else{
-    traits <- aus_traits
+    traits <- database
   }
   
   return(traits)
@@ -112,8 +112,8 @@ get_traits_table <- function(aus_traits){
 #'
 #' @description Helper function to convert character strings of NA into true NA
 #' @usage clean_NA(x)
-#' @param data The trait data frame generated from austraits - see example
-#' @param definitions The austraits definitions data frame
+#' @param trait_data The traits table in a traits.build database
+#' @param definitions The definitions tibble from a traits.build database
 #' @return vector where strings of NA are treated as true NA
 #' @examples 
 #' \dontrun{
diff --git a/R/what_version.R b/R/what_version.R
index 8d5eb25..9836ca7 100644
--- a/R/what_version.R
+++ b/R/what_version.R
@@ -1,12 +1,14 @@
-#' Determine whether version was pre or post 3.0.2
+#' Identify austraits.build or traits.build version
+#' 
+#' @description Determine whether database version was built by austraits.build (AusTraits pre 5.0.0) or traits.build (AusTraits post 5.0.0)
 #'
-#' @param austraits austraits list object
+#' @param database traits.build database (list object)
 #' @return binary version for switch statements
 #' @noRd
 #' @keywords internal
 #' 
-what_version <- function(austraits){
-  version <- austraits$build_info$version %>% as.character()
+what_version <- function(database){
+  version <- database$build_info$version %>% as.character()
 
   if(package_version(version) <= '3.0.2'){
     ret_version <- "3-series-earlier"
@@ -24,11 +26,11 @@ what_version <- function(austraits){
 
 #' Print version of AusTraits object
 #'
-#' @param austraits austraits list object
+#' @param database traits.build database (list object)
 #' @return binary version for switch statements
 #' @noRd
 #' @keywords internal
 #' 
-print_version <- function(austraits){
-  austraits$build_info$version %>% as.character()
+print_version <- function(database){
+  database$build_info$version %>% as.character()
 }
\ No newline at end of file
diff --git a/tests/testthat/test-extract_.R b/tests/testthat/test-extract_.R
index e292035..da60770 100644
--- a/tests/testthat/test-extract_.R
+++ b/tests/testthat/test-extract_.R
@@ -9,7 +9,7 @@ genus = "Eucalyptus"
 taxon_name = "Banskia serrata"
 
 test_that("Error message is triggered", {
-  expect_error(austraits_5.0.0_lite |> extract_taxa())
+  expect_error(austraits_5.0.0_lite %>% extract_taxa())
 })
 
 test_extract_error <- function(austraits){
diff --git a/tests/testthat/test-misc_bugs_.R b/tests/testthat/test-misc_bugs_.R
index 4b2c3ff..7540191 100644
--- a/tests/testthat/test-misc_bugs_.R
+++ b/tests/testthat/test-misc_bugs_.R
@@ -13,13 +13,13 @@ test_that("Dataframe is extracted correctly", {
     dplyr::filter(trait_name == "lifespan")
   
   # Extract trait after
-  veronica |>  extract_trait("lifespan") -> genus_first
+  veronica %>%  extract_trait("lifespan") -> genus_first
   
   # Extract trait first
-  austraits |> extract_trait("lifespan") -> lifespan
+  austraits %>% extract_trait("lifespan") -> lifespan
   
   # Extract taxa after
-  lifespan |> extract_taxa(genus = "Veronica") -> trait_first
+  lifespan %>% extract_taxa(genus = "Veronica") -> trait_first
   
   expect_setequal(trait_first$traits$value, veronica_lifespan$value)
   expect_setequal(trait_first$traits$value, genus_first$traits$value)
diff --git a/tests/testthat/test-plot_.R b/tests/testthat/test-plot_.R
index 0c5c596..4a3da21 100644
--- a/tests/testthat/test-plot_.R
+++ b/tests/testthat/test-plot_.R
@@ -16,14 +16,14 @@ test_that("Function doesn't throw error", {
 
 test_non_compatibile <- function(austraits){
   test_that("Throws correct errors for deprecated or non-compatible",{
-    expect_error(austraits |> 
-                   extract_dataset("Falster_2003") |> 
-                   purrr::pluck("traits") |> plot_locations()
+    expect_error(austraits %>% 
+                   extract_dataset("Falster_2003") %>% 
+                   purrr::pluck("traits") %>% plot_locations()
     )
     
-    expect_error(austraits |> 
-                   extract_dataset("Falster_2003") |> 
-                   purrr::pluck("traits") |> plot_lplot_site_locationsocations()
+    expect_error(austraits %>% 
+                   extract_dataset("Falster_2003") %>% 
+                   purrr::pluck("traits") %>% plot_lplot_site_locationsocations()
     )
   })
 }
diff --git a/tests/testthat/test-trait_bind_sep.R b/tests/testthat/test-trait_bind_sep.R
index d1d560f..6574996 100644
--- a/tests/testthat/test-trait_bind_sep.R
+++ b/tests/testthat/test-trait_bind_sep.R
@@ -2,7 +2,7 @@
 dataset_id <- c("ABRS_1981")
 subset <- extract_dataset(austraits_5.0.0_lite, dataset_id = dataset_id)
 bounded <- bind_trait_values(subset$traits)
-seperated <-separate_trait_values(data = bounded, austraits_5.0.0_lite$definitions)
+seperated <-separate_trait_values(trait_data = bounded, austraits_5.0.0_lite$definitions)
 
 test_that("binding/seperating was successful", {
   expect_true(grep("--", bounded$value) %>% any())
diff --git a/tests/testthat/test-trait_pivot_.R b/tests/testthat/test-trait_pivot_.R
index b95a19e..bd6595d 100644
--- a/tests/testthat/test-trait_pivot_.R
+++ b/tests/testthat/test-trait_pivot_.R
@@ -6,7 +6,7 @@ test_pivot_success <- function(austraits){
   test_that("pivot on subset of data", {
     
     expect_silent(
-      wide_data <- austraits |> trait_pivot_wider()
+      wide_data <- austraits %>% trait_pivot_wider()
     )
     
     expect_type(wide_data, "list")
@@ -19,7 +19,7 @@ purrr::walk(inputs,
 
 
 test_that("Widen structure is expected", {
-  wide_data <- austraits_5.0.0_lite |> trait_pivot_wider()
+  wide_data <- austraits_5.0.0_lite %>% trait_pivot_wider()
   names(wide_data)
   #Checking if widened data has the same length as variables that we are spreading
   expect_gt(ncol(wide_data), ncol(austraits_5.0.0_lite$traits))