Normalizing raw text in different formats to create objects in Ruby

Question

I have three text files with the exact same type of information but with different delimiters. One is a CSV, one uses spaces as delimiters, and the last one uses | (pipe) as the delimiter. The delimiters are different, but each row in all of the files has exactly the same format. So in the pipe-delimited file, the format is FirstName | LastName | DOB | City | State | ZIP (there is a space before and after each pipe). The other two files use the exact same order but with the other delimiters. All rows are unique. The files do not have headers.

I want to go through all of these files and create an instance of my Person object for each row. The class looks like this:

class Person
  attr_reader :first_name, :last_name, :d_o_b, :city, :state, :zip

  def initialize(first_name, last_name, ...)
    @first_name = first_name
    @last_name = last_name
    ...
  end

  ...

  etc.

end

I want to parse this data and create the objects in the cleanest and most readable way -- performance/scaling/etc. are unimportant here. What approach would be best for doing this? My initial idea is to convert all of the files to CSV somehow (perhaps with a gsub), then make a nested array from this data, and then iterate over the array to create the objects, but I am looking for any possible better/cleaner ideas.

mgrant · Accepted Answer · 2015-08-01 01:27:21Z

2

Something along these lines should work. You'll have to be careful if your city names have commas or spaces.

files = ['file1.txt', 'file2.txt']

people = []
files.each do |f|
  File.open(f).each do |line|

    # Split line along any of the delimeters
    args = line.strip.split(/[ ,\|]+/)

    # The * (splat) unpacks the array into an argument list
    people << Person.new(*args)
  end
end

answered Aug 1, 2015 at 1:27

mgrant

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Borsunho · Accepted Answer · 2015-08-01 01:56:38Z

1

FasterCSV allows you to specify column delimiter

FasterCSV.read(path, { :headers => false, :col_sep => "|", :skip_blanks => true })

FasterCSV.read(path, {col_sep: " ", skip_blanks: false})

answered Aug 1, 2015 at 1:56

Borsunho

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