remove the function delete_columns()

R/plot.R

@@ -47,10 +47,8 @@ plot_score_table <- function(scores,
   id_vars <- get_forecast_unit(scores)
   metrics <- get_metrics(scores)
 
-  scores <- delete_columns(
-    scores,
-    names(scores)[!(names(scores) %in% c(metrics, id_vars))]
-  )
+  cols_to_delete <- names(scores)[!(names(scores) %in% c(metrics, id_vars))]
+  suppressWarnings(scores[, eval(cols_to_delete) := NULL])
 
   # compute scaled values ------------------------------------------------------
   # scaling is done in order to colour the different scores
@@ -403,11 +401,7 @@ plot_predictions <- function(data,
   del_cols <-
     colnames(truth_data)[!(colnames(truth_data) %in% c(by, "observed", x))]
 
-  truth_data <- delete_columns(
-    truth_data,
-    del_cols,
-    make_unique = TRUE
-  )
+  truth_data <- unique(suppressWarnings(truth_data[, eval(del_cols) := NULL]))
 
   # find out what type of predictions we have. convert sample based to
   # range data

R/utils.R

@@ -10,31 +10,6 @@ available_metrics <- function() {
 }
 
 
-#' Safely delete Columns From a Data.table
-#'
-#' @description take a vector of column names and delete the columns if they
-#' are present in the data.table
-#' @param df A data.table or data.frame from which columns shall be deleted
-#' @param cols_to_delete character vector with names of columns to be deleted
-#' @param make_unique whether to make the data set unique after removing columns
-#' @importFrom data.table as.data.table
-#' @return A data.table
-#'
-#' @keywords internal
-#'
-delete_columns <- function(df, cols_to_delete, make_unique = FALSE) {
-  df <- data.table::as.data.table(df)
-  delete_columns <- names(df)[names(df) %in% cols_to_delete]
-  if (length(delete_columns) > 0) {
-    if (make_unique) {
-      df <- unique(df[, eval(delete_columns) := NULL])
-    } else {
-      df <- df[, eval(delete_columns) := NULL]
-    }
-  }
-  return(df)
-}
-
 remove_na_observed_predicted <- function(data) {
   # remove rows where predicted or observed value are NA -----------------------
   data <- data[!is.na(observed) & !is.na(predicted)]
@@ -45,9 +20,6 @@ remove_na_observed_predicted <- function(data) {
 }
 
 
-
-
-
 #' @title Collapse several messages to one
 #'
 #' @description Internal helper function to facilitate generating messages

R/utils_data_handling.R

@@ -223,7 +223,7 @@ quantile_to_interval.data.frame <- function(dt,
   }
 
   if (format == "wide") {
-    delete_columns(dt, "quantile")
+    suppressWarnings(dt[, "quantile" := NULL])
     dt <- dcast(dt, ... ~ boundary, value.var = "predicted")
     # if there are NA values in `predicted`, this introduces a column "NA"
     if ("NA" %in% colnames(dt) && all(is.na(dt[["NA"]]))) {