Exploratory PCA

R/exploratory_pca_report.R

@@ -0,0 +1,29 @@
+#' Exploratory PCA Report
+#'
+#' @description
+#' <What the function does, 1 or 2 sentences>
+#'
+#' @details
+#' <Any text written here goes into the "Details" section>
+#'
+#'
+#' @param argument1 < Explain type of argument and what it does here. If you set
+#' a default value, give a reason why it is default unless this is obvious.>
+#' @param argument2 integer, <doing stuff>. The default value is 42.
+#' @param ... Further arguments passed to <name functions>.
+#'
+#' @references
+#' <include here any relevant literature references>
+#'
+#' @returns
+#' <Describe the output of the function. This part goes into the "Value" section>
+#'
+#' @export # remove if the function should not be visible to the user
+#'
+#' @examples
+#' <write here any example code. These are small simple examples on how to use
+#' the function or to highlight specific features>
+
+add_pca_coordinates <- function(data, columns, id_column="id", use_all_numeric_columns =FALSE) {
+
+}

R/utils.R

@@ -11,15 +11,18 @@ check_columns_exist <- function(df, columns) {
   TRUE
 }
 
-
-filter_columns_with_id <- function(df, columns, id_column = "id") {
+#' @export
+filter_columns_with_id <- function(df, columns, id_column_name = "id", all = FALSE) {
   # all data is supposed to have an id column
-  required_cols <- c(id_column, columns)
-
+  if (all){
+    required_cols <- c(id_column_name, columns)
+  } else {
+    required_cols = colnames(df)
+  }
   check_columns_exist(df, required_cols)
 
-  if (length(unique(df[[id_column]])) != length(df[[id_column]])) {
-    stop(paste("Column ", id_column, " is not unique and therefore cannot be used as id"))
+  if (length(unique(df[[id_column_name]])) != length(df[[id_column_name]])) {
+    stop(paste("Column ", id_column_name, " is not unique and therefore cannot be used as id"))
   }
 
   df_filtered <- df[, required_cols]