Getting rid of notes

R/as_wide_table.R

@@ -33,7 +33,7 @@ as_wide_table3 <- function(austraits){
   # Function to collapse columns in locations and contexts into single column
   process_table3 <- function(data) {
     data %>% 
-      tidyr::pivot_wider(names_from = property, values_from = value) %>% 
+      tidyr::pivot_wider(names_from = .data$property, values_from = .data$value) %>% 
       tidyr::nest(data=-dplyr::any_of(c("dataset_id", "location_id", "latitude (deg)", "longitude (deg)"))) %>%
       dplyr::mutate(location = purrr::map_chr(data, collapse_cols)) %>%
       dplyr::select(-data) 
@@ -55,7 +55,7 @@ as_wide_table3 <- function(austraits){
   # collapse into one column
   austraits$locations <- 
     austraits$locations %>% 
-    dplyr::filter(value!="unknown") %>% 
+    dplyr::filter(.data$value!="unknown") %>% 
     dplyr::rename(c("property" = "location_property")) %>%
     split(., .$dataset_id) %>%
     purrr::map_dfr(process_table3)
@@ -76,29 +76,29 @@ as_wide_table3 <- function(austraits){
     austraits_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, 
-      entity_type, population_id, individual_id,
-      value_type, basis_of_value, 
-      replicates, 
+      .data$dataset_id, .data$observation_id, .data$trait_name, .data$taxon_name, .data$value, .data$unit, 
+      .data$entity_type, .data$population_id, .data$individual_id,
+      .data$value_type, .data$basis_of_value, 
+      .data$replicates, 
     # tissue, trait_category,  # Add after new zenodo release
 
     # More stuff you can filter on
-    collection_date, basis_of_record, life_stage, sampling_strategy, 
-    treatment_context_id, temporal_context_id, 
+    .data$collection_date, .data$basis_of_record, .data$life_stage, .data$sampling_strategy, 
+    .data$treatment_context_id, .data$temporal_context_id, 
 
     #stuff relating to locations
-    `latitude (deg)`, `longitude (deg)`, location, plot_context_id,
+    .data$`latitude (deg)`, .data$`longitude (deg)`, .data$location, .data$plot_context_id,
 
     #stuff relating to contexts and methods
-    context, methods, method_id, method_context_id, original_name,
+    .data$context, .data$methods, .data$method_id, .data$method_context_id, .data$original_name,
 
     #the citations
-    dataset_description, source_primary_citation, source_secondary_citation,
+    .data$dataset_description, .data$source_primary_citation, .data$source_secondary_citation,
 
     #the taxa details
-    taxonomic_status, taxon_distribution, 
-    taxon_rank, genus, family, #accepted_name_usage_id, 
-    scientific_name_authorship
+    .data$taxonomic_status, .data$taxon_distribution, 
+    .data$taxon_rank, .data$genus, .data$family, #accepted_name_usage_id, 
+    .data$scientific_name_authorship
     )
 
   austraits_wide
@@ -112,7 +112,7 @@ as_wide_table2 <- function(austraits){
   # Function to collapse columns in locations and contexts into single column
   process_table2 <- function(data) {
     data %>% 
-      tidyr::pivot_wider(names_from = property, values_from = value) %>% 
+      tidyr::pivot_wider(names_from = .data$property, values_from = .data$value) %>% 
       tidyr::nest(data=-dplyr::any_of(c("dataset_id", "location_id", "latitude (deg)", "longitude (deg)"))) %>%
       dplyr::mutate(location = purrr::map_chr(data, collapse_cols)) %>%
       dplyr::select(-data) 
@@ -134,7 +134,7 @@ as_wide_table2 <- function(austraits){
   # collapse into one column
   austraits$locations <- 
     austraits$locations %>% 
-    dplyr::filter(value!="unknown") %>% 
+    dplyr::filter(.data$value!="unknown") %>% 
     dplyr::rename(c("property" = "location_property")) %>%
     split(., .$dataset_id) %>%
     purrr::map_dfr(process_table2)
@@ -155,29 +155,29 @@ as_wide_table2 <- function(austraits){
   austraits_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, 
-    entity_type, population_id, individual_id,
-    value_type, basis_of_value, 
-    replicates, 
+    .data$dataset_id, .data$observation_id, .data$trait_name, .data$taxon_name, .data$value, .data$unit, 
+    .data$entity_type, .data$population_id, .data$individual_id,
+    .data$value_type, .data$basis_of_value, 
+    .data$replicates, 
     # tissue, trait_category,  # Add after new zenodo release
 
     # More stuff you can filter on
-    collection_date, basis_of_record, life_stage, sampling_strategy, 
-    treatment_id, temporal_id, 
+    .data$collection_date, .data$basis_of_record, .data$life_stage, .data$sampling_strategy, 
+    .data$treatment_id, .data$temporal_id, 
 
     #stuff relating to locations
-    `latitude (deg)`, `longitude (deg)`, location, plot_id,
+    .data$`latitude (deg)`, .data$`longitude (deg)`, .data$location, .data$plot_id,
 
     #stuff relating to contexts and methods
-    context, methods, method_id, original_name,
+    .data$context, .data$methods, .data$method_id, .data$original_name,
 
     #the citations
-    dataset_description, source_primary_citation, source_secondary_citation,
+    .data$dataset_description, .data$source_primary_citation, .data$source_secondary_citation,
 
     #the taxa details
-    taxonomic_status, taxon_distribution, 
-    taxon_rank, genus, family, #accepted_name_usage_id, 
-    scientific_name_authorship
+    .data$taxonomic_status, .data$taxon_distribution, 
+    .data$taxon_rank, .data$genus, .data$family, #accepted_name_usage_id, 
+    .data$scientific_name_authorship
 
   )
 
@@ -209,7 +209,7 @@ as_wide_table1 <- function(austraits){
   process_table <- function(data) {
 
     data %>% 
-      tidyr::pivot_wider(names_from = property, values_from = value) %>% 
+      tidyr::pivot_wider(names_from = .data$property, values_from = .data$value) %>% 
       tidyr::nest(data=-dplyr::any_of(c("dataset_id", "site_name", "context_name", "latitude (deg)", "longitude (deg)"))) %>%
       dplyr::mutate(site = purrr::map_chr(data, collapse_cols)) %>%
       dplyr::select(-data) 
@@ -238,19 +238,19 @@ as_wide_table1 <- function(austraits){
   # TODO: this section can be removed for next release
   # Some studies have multiple records per traits. This breaks things when joining
   # For now select first
-  dplyr::group_by(dataset_id, trait_name) %>% 
+  dplyr::group_by(.data$dataset_id, .data$trait_name) %>% 
     dplyr::slice(1) %>%
     dplyr:: ungroup() %>%
     #------------
-  dplyr::select(-c(year_collected_start, year_collected_end)) %>% 
+  dplyr::select(-c(.data$year_collected_start, .data$year_collected_end)) %>% 
     dplyr::rename(c("dataset_description" = "description"))  
 
   # collapse into one column
   austraits$sites <- 
     austraits$sites %>% 
-    dplyr::filter(value!="unknown") %>% 
+    dplyr::filter(.data$value!="unknown") %>% 
     # next line is a fix -- one dataset in 3.0.2 has value "site_name"
-    dplyr::mutate(site_property = gsub("site_name", "name", site_property)) %>%
+    dplyr::mutate(site_property = gsub("site_name", "name", .data$site_property)) %>%
     dplyr::rename(c("property" = "site_property")) %>%
     split(., .$dataset_id) %>%
     purrr::map_dfr(process_table)
@@ -271,26 +271,26 @@ as_wide_table1 <- function(austraits){
     dplyr::select(
 
       # The most useful (if you are filtering for just one taxon_name)
-      dataset_id, observation_id, trait_name, taxon_name, trait_value, unit, 
-      value_type, replicates, 
+      .data$dataset_id, .data$observation_id, .data$trait_name, .data$taxon_name, .data$trait_value, .data$unit, 
+      .data$value_type, .data$replicates, 
       # tissue, trait_category,  # Add after new zenodo release
 
       # More stuff you can filter on
-      date, collection_type, sample_age_class, sampling_strategy, 
+      .data$date, .data$collection_type, .data$sample_age_class, .data$sampling_strategy, 
 
       #stuff relating to sites
-      `latitude (deg)`, `longitude (deg)`, site_name, site,
+      .data$`latitude (deg)`, .data$`longitude (deg)`, .data$site_name, .data$site,
 
       #stuff relating to contexts and methods
-      context_name, context, methods, original_name,
+      .data$context_name, .data$context, .data$methods, .data$original_name,
 
       #the citations
-      dataset_description, source_primary_citation, source_secondary_citation,
+      .data$dataset_description, .data$source_primary_citation, .data$source_secondary_citation,
 
       #the taxa details
-      taxonomicStatus, taxonDistribution, 
-      taxonRank, genus, family, acceptedNameUsageID, 
-      scientificNameAuthorship, ccAttributionIRI
+      .data$taxonomicStatus, .data$taxonDistribution, 
+      .data$taxonRank, .data$genus, .data$family, .data$acceptedNameUsageID, 
+      .data$scientificNameAuthorship, .data$ccAttributionIRI
     )
 
   austraits_wide

R/bind_trait_values.R

@@ -26,18 +26,18 @@ bind_trait_values <- function(trait_data) {
     if(nrow(.data) > 1) {
       return(
         .data %>% 
-          dplyr::mutate(value = bind_x(value),
-                        value_type = bind_x(value_type),
-                        replicates = bind_x(replicates)) %>%
+          dplyr::mutate(value = bind_x(.data$value),
+                        value_type = bind_x(.data$value_type),
+                        replicates = bind_x(.data$replicates)) %>%
           dplyr::filter(dplyr::row_number()==1) 
       )
     }
     .data
   }
 
   trait_data  %>% 
-    dplyr::group_by(observation_id, trait_name) %>% 
+    dplyr::group_by(.data$observation_id, .data$trait_name) %>% 
     bind_values_worker() %>% 
     dplyr::ungroup() %>% 
-    dplyr::arrange(observation_id, trait_name, value_type)
+    dplyr::arrange(.data$observation_id, .data$trait_name, .data$value_type)
 }

R/join_all.R

@@ -1,7 +1,6 @@
 #' @title Join study details into main `traits` dataset
 #' @description Function to append all study information (method, location, taxonomic, context) variables into trait database
 #' @param austraits dataframe generated by austraits build
-#' @param vars variables to select from the respective table where information is being joined from. Not available for contexts table 
 #' @param ... arguments passed to `vars` to subset the columns
 #' @return austraits list object, but with additional variables appended to `traits` dataframe
 #' @rdname join_all
@@ -17,7 +16,7 @@
 #' (austraits %>% join_contexts)$traits
 #' 
 #' # Append methods
-#' (austraits %>% join_methods)$traits
+#' (austraits %>% join_methods(vars = c("method_id")))$traits
 #' 
 #' #Append taxonomic details
 #' (austraits %>% join_taxonomy)$traits
@@ -168,7 +167,8 @@ join_locations1 <- function(austraits, vars =  c("longitude (deg)","latitude (de
 #' @title  Joining location info for AusTraits versions <= 3.0.2
 #' @description `r lifecycle::badge('deprecated')`
 #' Joining location info for AusTraits versions <= 3.0.2
-#' @inheritParams join_locations
+#' @param austraits austraits object
+#' @param vars  variables from site table to join 
 #' @export
 
 join_sites <- function(austraits, vars =  c("longitude (deg)","latitude (deg)")) {

R/summarise_austraits.R

@@ -47,9 +47,9 @@ summarise_austraits_traits <-function(austraits, var) {
                                percent = NULL)
   # Summary statistics
   sum_stats <- austraits[["traits"]] %>% 
-    dplyr::group_by(trait_name) %>% 
-    dplyr::summarise(n_dataset = length(unique(dataset_id)),
-                     n_taxa = length(unique(taxon_name))) 
+    dplyr::group_by(.data$trait_name) %>% 
+    dplyr::summarise(n_dataset = length(unique(.data$dataset_id)),
+                     n_taxa = length(unique(.data$taxon_name))) 
 
   ret <- dplyr::left_join(ret, sum_stats, by = "trait_name")
 
@@ -86,8 +86,8 @@ 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"]] %>% 
     dplyr::group_by(!!rlang::sym(var)) %>% 
-    dplyr::summarise(n_dataset = length(unique(dataset_id)),
-                     n_taxa = length(unique(taxon_name)))
+    dplyr::summarise(n_dataset = length(unique(.data$dataset_id)),
+                     n_taxa = length(unique(.data$taxon_name)))
 
   ret <- dplyr::left_join(ret, sum_stats, by = var)
 

R/summarise_trait_values.R

@@ -15,23 +15,23 @@
 summarise_trait_means <- function(trait_data){
   suppressWarnings(
     trait_data  %>% 
-      dplyr::mutate(value = as.numeric(value),
-                    replicates = as.numeric(replicates)) -> trait_data
+      dplyr::mutate(value = as.numeric(.data$value),
+                    replicates = as.numeric(.data$replicates)) -> trait_data
   )
 
   # Identify which ones need summarising
   target <- trait_data %>% 
-    dplyr::group_by(trait_name, observation_id) %>% 
+    dplyr::group_by(.data$trait_name, .data$observation_id) %>% 
     dplyr::summarise(dplyr::n()) %>% 
     dplyr::filter(`dplyr::n()` > 1) %>%
-    dplyr::select(trait_name, observation_id)
+    dplyr::select(.data$trait_name, .data$observation_id)
 
   # # Identify which ones that don't need to change
   original <- trait_data %>%
-    dplyr::group_by(trait_name, observation_id) %>%
+    dplyr::group_by(.data$trait_name, .data$observation_id) %>%
     dplyr::summarise(dplyr::n()) %>%
     dplyr::filter(! `dplyr::n()`  > 1) %>%
-    dplyr::select(trait_name, observation_id)
+    dplyr::select(.data$trait_name, .data$observation_id)
 
   original_df <- purrr::map2_dfr(original$trait_name, original$observation_id,
                                  ~ dplyr::filter(trait_data, trait_name == .x & observation_id == .y))
@@ -57,5 +57,5 @@ summarise_trait_means <- function(trait_data){
   ret <- dplyr::bind_rows(original_df, target_bound)
 
   # Sort by observation_id and return
-  ret %>% dplyr::arrange(observation_id)
+  ret %>% dplyr::arrange(.data$observation_id)
 }