Today is our last lab and we are going to end by applying some computational algorithms to words.
Your reading for today was a blog post by Ted Underwood talking about the ways text analysis can be used in humanities research. Key point: Text mining is, at the root, counting words and identifying patterns in the ways words are used. What are the different ways Underwood identifies that we can use our word counts?
For your final assignment, you get to bring together all the different elements of the course to produce an essay using data and visualizations to discuss some aspect of religious practice in the US between 1980 and the present. You will need to use our course data set (though you do not have to limit to the state of Alabama) and at least one supplemental data source of your choice.
Alright everyone! Welcome back.
I hope you enjoyed your 48 hours of data visualizations, many of which involved maps and all of which communicated lots of uncertainty.
My plan for today is to talk briefly about making maps as a type of visualization in Tableau, maps that are models vs maps that are descriptive (which shaped some of the election visualizations that I assume you saw.)
I also want to talk about using maps (and other visualizations) as part of digital storytelling, looking outside of Tableau for a little bit.
Question: How do different modes of analysis transform data? What do those transformations reveal and conceal?
Write a 500-1000 word blog post reflecting on the role of “computational analysis” in digital humanities projects. Use course readings and your technical work to propose an answer to the question of the advantages and disadvantages of computational analysis for the study of religion. Be sure to use complete sentences, correct grammar, and citations.
We are going to change up our plan a little for the labs. We will come back to mapping next week, bumping our other forms of analysis down a week.
You each did great experimenting with the visualizations, and I think we could do with a little more time with Tableau and with the charts. So, today we are going to talk through the graphs you made as well as think about other ways to represent the data.
For new things, we are going to walk through how to create dashboards and stories and talk about what they are useful for. We will also walk through how to publish your work onto Tableau Online to the course server and then embed a view into your website.
Your primary resource for today is Visual Analytics with Tableau, chapters 1 and 3.
Today, our hard work with data preparation begins to pay off as we start to visualize our data as a way of understanding what is going on in the data and as a way to communicate about the data.
Your reading for today is Katie Rawson and Trevor Muñoz, “Against Cleaning.”
As you work with the class data and with your own data, Prepare Your Data for Tableau, chapters 6 – 10 provide further details on the different “cleaning” steps that we will discuss here.
So, what is data cleaning and why do you need to do it?
Think of data cleaning like editing and revising. The data file you are working with, whether you created it or downloaded it from someone else, was the first draft. Now you need to focus in on your thesis, rearrange paragraphs to make the argument easier to follow, clean up your grammar, and polish your prose.
Similarly, with data, you clean to focus in on the columns (variables) that help us answer our research question, reorganize to make it tidy, make the data consistent, and add some finishing touches, like data types, so that the data works well for visualization.
We are going to practice with a couple forms of data cleaning in class and your homework will be to practice on one of your datasets for your final project.
If you want to bring demographic information into your analysis, you will want to check out the National Historical GIS site, part of IPUMS. Even if you don’t think your data is “geographic,” you should double check with their holdings.
Today we are going to work through two different ways to create data: combining existing data and constructing data from unstructured sources.
A third method is to create new data through the use of surveys or methods such as ethnography, oral history, or narrative inquiry. This is beyond our scope, but you can learn these techniques from a variety of disciplines, including history, anthropology, and religious studies.
Chapters 4 and 5 of Prepare Your Data for Tableau cover combining data in greater detail. Use these as a resource as you work on your homework.
The goal for today’s lab is to walk through steps for assessing a data set, in terms of the quality or messiness of the data and in terms of the social, ethical, and contextual aspects of the data.
You will need Tableau Prep for this lab.