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?
Continue reading “Lab 12 – Questions of Words”
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.
Continue reading “Lab 11 – Mapping Data”
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.
Continue reading “Lab 10 – Graphing Data II”
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.
Continue reading “Lab 9 – Graphing 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.
Continue reading “Lab 8 – Cleaning Data”
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.
Continue reading “Lab 7 – Creating Data”
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.
Continue reading “Lab 6 – Assessing Data”
Today we are learning from Kevin Walker from the UA Libraries about strategies for finding and working different types of data.
For your final project, you will explore some aspect of religious culture in the US between 1980 and 2010 using the Longitudinal Religious Congregations and Membership File (state or county) and at least one other data source of your choice. It could be textual data, social media data, statistical or survey data, geospatial data …, whatever you think will help you better understanding and present your research.
For your lab, create a blog post that discusses project ideas and possible data sources. First, write up a 1-2 sentence idea for your research topic. Then explore the resources that we have covered today in class and come up with a short-list of 2-4 data sources that you think might be useful for your final project. For each source provide:
- the name of the data source (such as the Religious Landscape Study from the Pew Research Center, Twitter, or the Adventist Digital Library)
- a link to some example data (datafile, book or other textual resource, hashtag search results)
- a sentence or two describing what questions you think you might be able to explore with that data.
Your reading today was probably one of the more technical pieces you’ll read in this course. Tidy Data is written for statisticians to discuss the problem of organizing data. I find that this piece is useful one for introducing some of the concepts involved in data work and for introducing you to how statisticians think about data. These are the people we are stealing methods from, and it is important to understand their concerns and assumptions as we think about how we bring those methods and tools into the humanities.
So, my goals for today:
- Learn some of the vocabulary around data
- Practice thinking about data as something to be organized and operated on.
- Think a bit together about why one might one tidy data and what the costs might be of tidy data
Continue reading “Lab 4 – Tidy Data”
For our lab, we will be learning from Melissa Green, UA’s Technology Accessibility Specialist with CIT/OIT, about different ways people with disabilities access digital content.
Your homework is to evaluate the digital humanities site you have selected to review for accessibility. You do not need to do the whole site – chose a select page or two that includes core features of the site.
Your accessibility review should evaluate how well the site meets some of the basic web accessibility standards. Read the Introduction to Web Accessibility from WebAIM to learn about the core elements of accessible web documents. Use the WAVE Accessibility Tool or Accessibility Insights* to generate a report on how well your digital humanities site meets basic web accessibility standards. Both tools have video introductions that you should watch to learn how to use them: WAVE introduction video; Accessibility Insights introduction video.
*For Accessibility Insights, FastPass mode is sufficient for our purposes, but you are welcome to explore the full assessment.
Additionally, your accessibility review should evaluate the site in relation to inclusive design principles. Who is the assumed user? Who is excluded from the site? What types of changes might produce a more inclusive design?
Your review should be 500-1000 words and posted to your blog. Include the report generated by the accessibility audit tool (screen shots will work for our purposes).