Standardising parameters throughout austraits

- replace all `@param austraits` and `@param aus_traits` with `@param database`
- replace all references to just the traits table (previously x, data, trait( with `@param trait_data`
- standardise description of parameters - currently using `@param database traits.build database (list object)` so people realise it is a relational database, but could change to `@param database traits.build database`

R/as_wide_table.R

@@ -1,26 +1,25 @@
-#' 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
 #' @export
 #'
 #' @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

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

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 

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 - [email protected]
 
-check_compatibility <- function(austraits) {
+check_compatibility <- function(database) {
 
-  if (is.null(austraits$metadata)) {
+  if (is.null(database$metadata)) {
 
     compatible <- FALSE
 
@@ -22,7 +22,7 @@ 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(stringr::str_detect(identifier, "github.com/traitecoevo/traits.build"))
@@ -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

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. 

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

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 - [email protected]
 #' @export

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 - [email protected]
 #' @export

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