lib/statsample/converter/csv.rb in statsample-1.4.1 vs lib/statsample/converter/csv.rb in statsample-1.4.2

@@ -1,78 +1,65 @@
+require 'csv'
+
 module Statsample
   class CSV < SpreadsheetBase
-    if RUBY_VERSION<"1.9"
-      require 'fastercsv'
-      CSV_klass=::FasterCSV  
-    else
-      require 'csv'
-      CSV_klass=::CSV  
-    end    
-    class << self
+    # Default options for processing CSV files. Accept the same options as
+    # Ruby's `CSV#new`.
+    DEFAULT_OPTIONS = {
+      converters: [:numeric]
+    }
 
-      def read19(filename,ignore_lines=0,csv_opts=Hash.new)
-        #default first line is header
-        csv_opts.merge!(:headers=>true, :header_converters => :symbol)
-        csv = CSV_klass::Table.new(CSV_klass::read(filename,'r',csv_opts))
-        csv_headers = if csv_opts[:headers]
-          csv.headers
-        else
-          #as in R, if no header we name the headers as V1,V2,V3,V4,..
-          1.upto(csv.first.length).collect { |i| "V#{i}" }
-        end
-        #we invert row -> column. It means csv[0] is the first column and not row. Similar to R
-        csv.by_col!
-        thash = {}
-        csv_headers.each_with_index do |header,idx|
-          thash[header] = Statsample::Vector.new(csv[idx].drop(ignore_lines))
-        end
-        Statsample::Dataset.new(thash)
-      end
-      # Returns a Dataset  based on a csv file
+    class << self
+      # Return a Dataset created from a csv file.
       #
       # USE:
-      #     ds=Statsample::CSV.read("test_csv.csv")
-      def read(filename, empty=[''],ignore_lines=0,csv_opts=Hash.new)        
-        first_row=true
-        fields=[]
-        #fields_data={}
-        ds=nil
-        line_number=0
-        csv=CSV_klass.open(filename,'rb', csv_opts)
+      #     ds = Statsample::CSV.read('test_csv.csv')
+      def read(filename, empty = [''], ignore_lines = 0, opts = {})
+        first_row = true
+        fields = []
+        ds = nil
+        line_number = 0
+        options = DEFAULT_OPTIONS.merge(opts)
+
+        csv = ::CSV.open(filename, 'rb', options)
+
         csv.each do |row|
-          line_number+=1
-          if(line_number<=ignore_lines)
-            #puts "Skip line"
+          line_number += 1
+
+          if (line_number <= ignore_lines)
             next
           end
-          row.collect!{|c| c.to_s }
+
           if first_row
-            fields=extract_fields(row)
-            ds=Statsample::Dataset.new(fields)
-            first_row=false
+            fields = extract_fields(row)
+            ds = Statsample::Dataset.new(fields)
+            first_row = false
           else
-            rowa=process_row(row,empty)
-            ds.add_case(rowa,false)
+            rowa = process_row(row, empty)
+            ds.add_case(rowa, false)
           end
         end
-        convert_to_scale_and_date(ds,fields)
+
+        convert_to_scale_and_date(ds, fields)
         ds.update_valid_data
         ds
       end
-      # Save a Dataset on a csv file
+
+      # Save a Dataset on a csv file.
       #
       # USE:
-      #     Statsample::CSV.write(ds,"test_csv.csv")
-      def write(dataset,filename, convert_comma=false,*opts)
-        
-        writer=CSV_klass.open(filename,'w',*opts)
+      #     Statsample::CSV.write(ds, 'test_csv.csv')
+      def write(dataset, filename, convert_comma = false, opts = {})
+        options = DEFAULT_OPTIONS.merge(opts)
+
+        writer = ::CSV.open(filename, 'w', options)
         writer << dataset.fields
-        dataset.each_array do|row|
-          if(convert_comma)
-            row.collect!{|v| v.to_s.gsub(".",",")}
-          end
+
+        dataset.each_array do |row|
+          row.collect! { |v| v.to_s.gsub('.', ',') } if convert_comma
           writer << row
         end
+
         writer.close
       end
     end
   end
 end