Tutorials, natural language processing

Text Mining and Self Organizing Maps: Visualizing Shakespeare

After my previous exploration of Self Organizing Maps, I decided to use the tool for an application of text mining: Can we visualize how Shakespeare’s characters and plays are similar or different from each other based on an analysis of their words?

This tutorial walks through a couple examples using R and suggests some further exploration. It’s split into two sequential parts:

Self Organizing Maps and Text Mining – Visualizing Shakespeare (Part 1)

Self Organizing Maps and Text Mining – Visualizing Shakespeare (Part 2)

 

Tutorials

Introduction to Self Organizing Maps in R

This semester I’ve been playing around with Self Organizing Maps (SOMs) using the “kohonen” package in R. SOMs allow you to visualize very high dimensional data in a simplified two dimensional map which preserves proximity. I’ve written up an introductory tutorial on getting started making SOMs using the kohonen package:

https://clarkdatalabs.github.io/soms/SOM_NBA

This workshop plays around with NBA player stats from the 2015/2016 season. Disclaimer: I know next to nothing about basketball.

Self Organizing Map depicting NBA Player Position Predictions

If you like this post, keep an eye out for the next one. In the next month I’ll put out a tutorial on using SOMs to visualize the text-mined works of Shakespeare. Disclaimer: I know next to nothing about the works of Shakespeare.

 

projects, Tutorials

Creating R Tutorials Using RMarkdown: Code Chunk Options

Two of us here in the Digital Project Studio have recently been working through an R script developed for a workshop on doing some basic mapping in R. The goal was to turn the script, which was used alongside in-person instruction, into a usable self-directed tutorial.  To do this we used R Markdown, an authoring platform that turns R scripts into reproducible and dynamic documents, presentations, and webpages. Our introductory tutorial will get you set up and started to using R Markdown. On this post we’ll share some of the additional features we’ve learned using this platform.

To find the actual R mapping workshop we created, the instructions and file downloads are accessible here: http://clarkdatalabs.github.io/mapping_R/

In the zipped file package you can find our R Markdown file for creating the instructions of the workshop: script_markup.Rmd

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Tutorials

Customizing Applications in Django

This post is a follow-up to the introduction to the Field Notebook and the demo notebook, ‘Monumental Gifts’. I will go over how to install the app and start customizing your own web-based Field Notebook. This post will focus on how to start tailoring the models and appearance of your Notebook to suit your needs for your research. If you are interested (or discover later that you are interested) in building your own original application from scratch, I recommend working through the Beginner’s Tutorial on Django’s website. In fact, even if you don’t plan on building your own application, I still recommend the tutorial. You’ll have better understanding of how to modify and use your Field Notebook if you become familiar with how Django works as a framework.

Installing the app

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