require 'yaml' module Statsample # This module aids to code open questions # * Select one or more vectors of a dataset, to create a yaml files, on which each vector is a hash, which keys and values are the vector's factors . If data have Statsample::SPLIT_TOKEN on a value, each value will be separated on two or more hash keys. # * Edit the yaml and replace the values of hashes with your codes. If you need to create two or mores codes for an answer, use the separator (default Statsample::SPLIT_TOKEN) # * Recode the vectors, loading the yaml file: # * recode_dataset_simple!() : The new vectors have the same name of the original plus "_recoded" # * recode_dataset_split!() : Create equal number of vectors as values. See Vector.add_vectors_by_split() for arguments # # Usage: # recode_file="recodification.yaml" # phase=:first # flag # if phase==:first # File.open(recode_file,"w") {|fp| # Statsample::Codification.create_yaml(ds,%w{vector1 vector2}, ",",fp) # } # # Edit the file recodification.yaml and verify changes # elsif phase==:second # File.open(recode_file,"r") {|fp| # Statsample::Codification.verify(fp,['vector1']) # } # # Add new vectors to the dataset # elsif phase==:third # File.open(recode_file,"r") {|fp| # Statsample::Codification.recode_dataset_split!(ds,fp,"*") # } # end # module Codification class << self # Create a hash, based on vectors, to create the dictionary. # The keys will be vectors name on dataset and the values # will be hashes, with keys = values, for recodification def create_hash(dataset, vectors, sep=Statsample::SPLIT_TOKEN) raise ArgumentError,"Array should't be empty" if vectors.size==0 pro_hash=vectors.inject({}){|h,v_name| raise Exception, "Vector #{v_name} doesn't exists on Dataset" if !dataset.fields.include? v_name v=dataset[v_name] split_data=v.splitted(sep).flatten.collect {|c| c.to_s}.find_all {|c| !c.nil?} factors=split_data.uniq.compact.sort.inject({}) {|ac,val| ac[val]=val;ac } h[v_name]=factors h } pro_hash end # Create a yaml to create a dictionary, based on vectors # The keys will be vectors name on dataset and the values # will be hashes, with keys = values, for recodification # # v1=%w{a,b b,c d}.to_vector # ds={"v1"=>v1}.to_dataset # Statsample::Codification.create_yaml(ds,['v1']) # => "--- \nv1: \n a: a\n b: b\n c: c\n d: d\n" def create_yaml(dataset, vectors, io=nil, sep=Statsample::SPLIT_TOKEN) pro_hash=create_hash(dataset, vectors, sep) YAML.dump(pro_hash,io) end # Create a excel to create a dictionary, based on vectors. # Raises an error if filename exists # The rows will be: # * field: name of vector # * original: original name # * recoded: new code def create_excel(dataset, vectors, filename, sep=Statsample::SPLIT_TOKEN) require 'spreadsheet' if File.exist?(filename) raise "Exists a file named #{filename}. Delete ir before overwrite." end book = Spreadsheet::Workbook.new sheet = book.create_worksheet sheet.row(0).concat(%w{field original recoded}) i=1 create_hash(dataset, vectors, sep).sort.each do |field, inner_hash| inner_hash.sort.each do |k,v| sheet.row(i).concat([field.dup,k.dup,v.dup]) i+=1 end end book.write(filename) end # From a excel generates a dictionary hash # to use on recode_dataset_simple!() or recode_dataset_split!(). # def excel_to_recoded_hash(filename) require 'spreadsheet' h={} book = Spreadsheet.open filename sheet= book.worksheet 0 row_i=0 sheet.each do |row| row_i+=1 next if row_i==1 or row[0].nil? or row[1].nil? or row[2].nil? h[row[0]]={} if h[row[0]].nil? h[row[0]][row[1]]=row[2] end h end def inverse_hash(h, sep=Statsample::SPLIT_TOKEN) h.inject({}) do |a,v| v[1].split(sep).each do |val| a[val]||=[] a[val].push(v[0]) end a end end def dictionary(h, sep=Statsample::SPLIT_TOKEN) h.inject({}) {|a,v| a[v[0]]=v[1].split(sep); a } end def recode_vector(v,h,sep=Statsample::SPLIT_TOKEN) dict=dictionary(h,sep) new_data=v.splitted(sep) new_data.collect do |c| if c.nil? nil else c.collect{|value| dict[value] }.flatten.uniq end end end def recode_dataset_simple!(dataset, dictionary_hash ,sep=Statsample::SPLIT_TOKEN) _recode_dataset(dataset,dictionary_hash ,sep,false) end def recode_dataset_split!(dataset, dictionary_hash, sep=Statsample::SPLIT_TOKEN) _recode_dataset(dataset, dictionary_hash, sep,true) end def _recode_dataset(dataset, h , sep=Statsample::SPLIT_TOKEN, split=false) v_names||=h.keys v_names.each do |v_name| raise Exception, "Vector #{v_name} doesn't exists on Dataset" if !dataset.fields.include? v_name recoded=recode_vector(dataset[v_name], h[v_name],sep).collect { |c| if c.nil? nil else c.join(sep) end }.to_vector if(split) recoded.split_by_separator(sep).each {|k,v| dataset[v_name+"_"+k]=v } else dataset[v_name+"_recoded"]=recoded end end end def verify(h, v_names=nil,sep=Statsample::SPLIT_TOKEN,io=$>) require 'pp' v_names||=h.keys v_names.each{|v_name| inverse=inverse_hash(h[v_name],sep) io.puts "- Field: #{v_name}" inverse.sort{|a,b| -(a[1].count<=>b[1].count)}.each {|k,v| io.puts " - \"#{k}\" (#{v.count}) :\n -'"+v.join("\n -'")+"'" } } end end end end