natural language processing, Tutorials

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)

 

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From Networks to Scrapbooks: A Case Study of Data Visualization Consulting (Part 2)

Part 2: Writing and Visualizing the Data Narrative

In contrast to the tens of thousands of records associated with the collection as a whole, the Bentley Student Scrapbooks consists of 88 scrapbooks documenting student experiences at the University of Michigan spanning the 1860s to the 1940s, with most scrapbooks falling between about 1906 to 1919. These scrapbooks covered a fascinating cross-section of life on campus – everything from student athletics to cross-dressing to secret societies to dance cards appeared in the Subjects field of the metadata.

When I asked the (admittedly naive) question “what do you mean by scrapbooks?” the archivist team had a lot of stories to share. For instance, I had no idea that a fraternity in 1910 might keep track of their beloved top athlete in painstaking detail and then put it all into a scrapbook for posterity. It was genuinely lovely to experience their enthusiasm about this collection, which often focused on specific backstories to the creation or legacy of these scrapbooks that fell outside the metadata itself. How then, I wondered, might a data visualization narrative support these passionate archivists in their public lectures and workshops? What types of patterns should we focus on revealing? Continue reading

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From Networks to Scrapbooks: A Case Study of Data Visualization Consulting (Part 1)

Part 1: Finding the Story in Data

Introduction

When you set out to tell a story with data, how do you determine its scope and focus? What kind of relationship do you want to cultivate between your viewers and the data being visualized? If there is a “best” or “most effective” story lurking in the data for the audience at hand, how do you pick it apart from the others?

Data visualization refers to a set of tools and practices, but also a deeper struggle to find a way to craft meaning from representations of reality, and share that meaning with others via narrative. In this post, I’ll explore how I grappled with identifying and framing a data visualization story in the context of a semester-long consulting project with the Bentley Historical Library.

 

The Bentley

According to the Bentley’s website:

The Bentley Historical Library collects the materials for and promotes the study of the histories of two great, intertwined institutions, the State of Michigan and the University of Michigan. The Library is open without fee to the public, and we welcome researchers regardless of academic or professional affiliation.

The Bentley is home to a massive, diverse trove of items spread across 11,000 collections. When the Bentley reached out to the Digital Project Studio last fall, they had a central goal in mind: helping researchers understand the collections better, and engage with these collections in ways beyond the affordances of simple keyword searches or browsing alphabetical lists. They hoped data visualization could provide something special to spur that process – a new kind of insight or way of interacting. Continue reading

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.

 

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Introduction to Mapping in R

One of the goals of the Digital Project Studio is to develop a set of helpful materials for people interested in various visualization tasks. Part of working towards this goal is cleaning up some already existing notes for in person workshops into usable standalone tutorials.

This workshop is designed to introduce you to some of the geospatial packages that can be used in R through a series of examples. It requires some experience with either the R language or with working with geospatial data. All the data necessary are provided in the download links at the top of the workshop page. The instructions are both on this webpage and in a pdf named script_markup.pdf in the downloaded files.

Mapping R Workshop

If you are interested in how we made the instructions for this workshop, check out our blog post:

Creating R Tutorials Using RMarkdown: Code Chunk Options

 

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

Continue reading