• About This Project
• Getting Started
  • Files In This Repository
• Why Are We Doing This?
• Iterations

We're going to write a set of simple command-line tools to display basic statistics about a text file or set of text files. Some basic statistics include...

1. Character count, word count, and sentence count
2. Letter frequency
3. Word frequency, e.g., most common and least common words

We'll also work towards adding the ability to...

1. Download data from an arbitrary URL
2. Extract the text from a web page for analysis
3. Display the results in different formats, e.g., charts, histograms, etc.
4. Export the results to a spreadsheet

Here's a screenshot of a program that downloads the entire text of Moby Dick from Project Gutenberg and prints out a histogram of the letter frequencies.

It turns out that the letter "t" makes up 9.25% of all the letters in Moby Dick. The more you know!

To get started, you'll need to...

1. Fork this repository to your own GitHub account
2. Open a Terminal and clone this repository to your local computer
3. Navigate to the repository on your local computer
4. Open the repository in Sublime Text 3, Atom, or your editor of choice.
5. Read the Why Are We Doing This? section
6. Start working on the Iterations

1. textalyze.py is the source code for this project
2. sample_data is a directory containing sample text files to analyze, mostly from Project Gutenberg.

The textalyze.py file that comes with this repository is filled with comments designed to help you get started. You should feel free to delete them in order to make the program easier to read.

If you can believe it, the program above that downloads text from a URL, computes the letter frequencies, and displays a histogram is fewer than 50 lines of Ruby. Think about the questions you'd need to be able to answer in order to make it work, though:

1. How do I open and read data contained in file on my computer?

2. How do I download and read data on the web?

3. How to I pass in an arbitrary URL to my Ruby program? That is, how do I make something like this work?

   # This downloads and analyzes the text file at the supplied URL
   $ ruby textalyze.rb http:https://some-website.com/books/moby-dick.txt
   

   Note: the $ represents the terminal prompt.

4. Once I have the data (from a file or a URL), how do I go about calculating the relevant statistics?

5. Once I've calculated the relevant statistics, how do I display them in a user-friendly way?

These questions run the gamut from nitty-gritty Ruby to user experience, while also starting us down the path of becoming comfortable with how the web works.

This project is structured as a sequence of iterations, each of which builds on previous iterations. We do not expect students to complete each and every iteration. Rather, they serve three important roles:

1. Models for good engineering and product management, i.e., what do we build, in what order, and why?
2. Natural checkpoints to ask for a code review or other feedback
3. The ability to accommodate students with different interests, skill levels, and time constraints.

Using hard-coded examples, write a method that takes a list containing arbitrary and possibly duplicated items as input and returns a dict containing item/count pairs. Print out those pairs in a sensible way.

That is, if the input has 100 entries and 20 of the are letter "a" then then resulting dict should contain

{'a': 20}

"Sensible" is up to you to define, but here's a suggested format, pretending we hard-coded the input as ["a", "a", "a", "b", "b", "c"].

user@host text-analysis-python $ python textalyzer.py
The counts for ["a", "a", "a", "b", "b", "c"] are...
a   3
b   2
c   1
user@host text-analysis-python $

Using hard-coded examples, write a method that takes an arbitrary str as input and returns an list of all the characters in the string, including spaces and punctuation.

Feed this into the array-counting method from the previous iteration to get a dict containing letter/count pairs. Print out those pairs in a sensible way.

Write a method called sanitize that takes an arbitrary str — perhaps containing spaces, punctuation, line breaks, etc. — and returns a "sanitized" string that replaces all upper-case letters with their lower-case equivalent. This will ensure that the letters 'A' and 'a' are not treated as two distinct letters when we analyze our text. We'll handle punctuation and other bits in a later iteration.

It should work like this

sanitize("This is a sentence.")        # => "this is a sentence."
sanitize("WHY AM I YELLING?")          # => "why am i yelling?"
sanitize("HEY: ThIs Is hArD tO rEaD!") # => "hey: this is hard to read!"

Lucky for us, Python comes with a built-in method to help us: str.lower.

Integrate this method into current program so that the dict of results contains, e.g.,

{'a': 25}

instead of

{'a': 19, 'A': 6}

Oftentimes the data we want isn't in a format that makes it easy to analyze. The process of taking poorly-formatted data and transforming it into something we can make use of is called sanitizing our data.

What counts as "sanitizing" varies depending on the underlying data and our needs. For example, if we wanted to look at all the text in an HTML document, we wouldn't want to be counting all the HTML tags. Conversely, if we wanted a report on the most commonly-used tags in an HTML document, we'd want to keep the tags but remove the text.

In our case, we've designed our program such that it treats upper-case letters and lower-case letters as distinct letters, i.e., our results dict might contain

{'a': 20, 'A': 5}

but we'd probably rather it just contain

{'a': 25}

Likewise, we probably don't care about punctuation, although this is harder to deal with than differences between upper-case and lower-case letters.

The base repository contains a directory called sample_data that contains a handful of text files. Hard-code the name of one of these files into your program and read the contents of that file into a string. Pass that string into your current program, so that it now prints out the letter-count statistics for that specific file instead of the hard-coded strings you had in the previous iteration.

To read the contents of a file into a string, use File.read.

Note: Look at the examples/file_read.rb file in the ruby-examples repository to see an example of File.read in action.

If you're feeling adventurous, look at the Ruby documentation for File and IO.read. The IO class is a more abstract class encompassing all kinds of input/output, including reading/writing from network connections. This is where the general read method is defined, however. Don't worry if the documentation is overwhelming or doesn't make sense — reading technical documentation is a skill in its own right and like any skill, one starts improving by becoming comfortable with what it looks like.

We don't want to edit our Ruby code every time we need to change the file from which we're reading data. Let's change it so that the user running the program can pass in the name of the file from which to read. We'll do this using command-line arguments.

This iteration marks v1.0 of our program. As it stands, our program — although limited — is self-contained enough that you could give it to another person and they could use it as you intended without having to know how to edit Ruby code.

Congrats!

Consider the following command run from the Terminal:

$ ruby some-program.rb first_argument second_argument banana

The command-line arguments are first_argument, second_argument, and banana, with a space denoting the separation between each argument. first_argument is the first command-line argument and banana is the third command-line argument.

Look at the examples/args.rb file in the ruby-examples repository for a demonstration of how command-line arguments work.

Using hard-coded examples, write a method that takes an list containing arbitrary and possibly duplicated entries as input and returns a dict containing item/frequency pairs. Print out those pairs in a sensible way.

That is, if the input has 100 entries and 20 of the are letter "a" then then resultant dict should have

{'a': 0.20}

You've already written a method that takes an list and returns a dict containing entry/count pairs and you'll need these counts (one way or another) in order to calculate the overall frequency. If you want to stretch yourself, try writing your "frequency statistics" method in a way that makes use of your "counting statistics" method, so that you don't have to duplicate as much code or work in your program.

This is a "stretch approach," which means it's absolutely not necessary for you to write your program this way. Like we've been saying, it's much better to write something and get feedback on it than get stuck while trying to puzzle out a "better", "faster", "more elegant", etc. approach.

Print out a histogram of letter frequencies, a la the original screenshot. Hint: You can use the frequency for each item as a way to scale the length of the histogram.