Added option to get the pheno data as factors or not.

DESCRIPTION

@@ -6,7 +6,7 @@ Author: Leon-Charles Tranchevent
 Maintainer: Leon-Charles Tranchevent <leon-charles.tranchevent@uni.lu>
 Description: This package contains functions to analyse microarray data.
     It is more a set of useful functions than a real package.
-License: The Unlicense (see LICENSE)
+License: file LICENSE
 Encoding: UTF-8
 LazyData: true
 Imports:

R/load_clinical_data.R

@@ -8,11 +8,13 @@
 #'
 #' @param data_dir A string representing the folder that contains the clinical data.
 #' @param clinical_file_name A string containing the file name. By default, this is 'ClinicalData.tsv'
+#' @param use_factors A boolean stating whether the columns should be read as factors (default FALSE).
 #' @param verbose A boolean representing whether the function should display log information. This
 #'  is TRUE by default.
 #' @return An annotated data-frame that contains the clinical data.
 load_clinical_data <- function(data_dir,
                                clinical_file_name = "ClinicalData.tsv",
+                               use_factors        = TRUE,
                                verbose            = TRUE) {
 
   # We define the I/Os.
@@ -22,9 +24,16 @@ load_clinical_data <- function(data_dir,
   }
 
   # We load the clinical data.
-  pheno_data <- Biobase::AnnotatedDataFrame(utils::read.delim(file       = clinical_data_file,
-                                                              row.names  = 1,
-                                                              colClasses = "factor"))
+  pheno_data <- NULL
+  if (use_factors) {
+    pheno_data <- Biobase::AnnotatedDataFrame(utils::read.delim(file       = clinical_data_file,
+                                                                row.names  = 1,
+                                                                colClasses = "factor"))
+  } else {
+    # We do not use factors.
+    pheno_data <- Biobase::AnnotatedDataFrame(utils::read.delim(file       = clinical_data_file,
+                                                                row.names  = 1))
+  }
 
   # We clean up and log information.
   rm(clinical_data_file)