In API mode, convert list to data.table (#255)

* Return data.table in API mode for .readClusterDesc()

* Define function .convert_list_clusterDesc_to_datatable() and call it in .readClusterDesc()

NEWS.md

@@ -24,7 +24,7 @@ BUGFIXES :
 
 * `readInputThermal()` return data from file data.txt with `thermalData` parameter
 * `setSimulationPath()` has also the parameter **areasWithSTClusters** in 'output' mode
-
+* `readClusterDesc()` / `readClusterResDesc()` / `readClusterSTDesc()` return a data.table in API mode
 
 # antaresRead 2.7.0
 

R/readClusterDesc.R

@@ -104,18 +104,17 @@ readClusterSTDesc <- function(opts = simOptions()) {
  "st-storage/clusters" = "st-storages"
  )
 
- if(api_study){
-
+ if (api_study) {
  # api request with all columns
- list_clusters = api_get(
+ list_clusters <- api_get(
  opts = opts,
  endpoint = paste0(opts$study_id, "/table-mode/", table_type),
- query = list(
- columns = ""
- )
+ query = list(columns = "")
  )
 
- return(list_clusters)
+ dt_clusters <- .convert_list_clusterDesc_to_datatable(list_clusters)
+
+ return(dt_clusters)
  }
 
  # "text" mode
@@ -124,7 +123,7 @@ readClusterSTDesc <- function(opts = simOptions()) {
  # READ cluster properties
  properties <- get_input_cluster_properties(table_type = table_type, 
  opts = opts)
-
+  
  # read properties for each area
  res <- plyr::llply(areas, function(x, prop_ref=properties) {
  clusters <- readIniFile(file.path(path, x, "list.ini"))
@@ -225,3 +224,29 @@ get_input_cluster_properties <- function(table_type, opts){
 
  return(wide_ref)
 }
+
+
+.convert_list_clusterDesc_to_datatable <- function(list_clusters) {
+
+ if (length(list_clusters) == 0) {
+ return(data.table())
+ } 
+
+ rows_cluster <- lapply(names(list_clusters), FUN = function(cl_name) {
+
+ row_cluster <- as.data.frame(list_clusters[[cl_name]])
+ row_cluster[,c("area", "cluster")] <- unlist(strsplit(cl_name, split = " / "))
+
+ return(row_cluster)
+ }
+ )
+
+ df_clusters <- do.call("rbind", rows_cluster)
+ id_cols <- intersect(c("area", "cluster", "group"), colnames(df_clusters))
+ additional_cols <- setdiff(colnames(df_clusters), id_cols) 
+ df_clusters <- df_clusters[,c(id_cols, additional_cols)]
+ df_clusters$cluster <- as.factor(tolower(df_clusters$cluster))
+ colnames(df_clusters) <- tolower(colnames(df_clusters))
+
+ return(as.data.table(df_clusters))
+}