November Blogroll: Visual Data Edition

Dear readers:

The last way I typically describe my scholarship is as economic history. It feels like a confession, a thing that someone who studies literature, including Shakespeare, shouldn’t be interested in. It is, however, perhaps the most true statement of the kinds of work I do, often counting large groups of data sets, considering the parameters and variables, and taking into account elements that would have affected the purchasing decisions made by sixteenth-century playgoers.

Ultimately, I find this very rewarding work because it tells me something about what priorities early moderns held when it came to memory, creativity, and novelty. It also challenges my assumptions about why people go/went to theatre, what experience they are/were hoping to have, et cetera. In order to do this kind of work, I am increasingly in need of better graphing and mapping tools to visualize the data for others and to draw inferences. About midway through the summer, in advance of a conference paper, I appealed to Facebook for recommendations beyond what MS Excel could deliver. Here are those responses.

Tableau

This piece of software, available as a desktop or mobile application, was widely responded to positively by others, “having heard good things.” No one attested to actually using it. Expert reviews are also very positive and it seems that many academic institutions are beginning to adopt it for campus use. If you are a teacher or scholar, you can request a free academic license, renewable on a year-to-year basis, here.

Plot.ly

Most folks I know who have actually had to use data visualization in their academic writing, namely for papers and dissertations, had experience with this web-based software. These including folks in Particle Physics and Italian History. The community version is free for use, stressing the share-ability of data rather than the design functions.

Seaborn

This seems to be the program that actual Big Data scientists are using in the private sector to streamline their aggregates gracefully with maximum control. With my limited skills, it was a bit further afield than I felt capable of using, but no doubt a savvier user could quickly come to grips with it.

Exaptive

This seems to be the new guy on the block, taking a web-interface studio approach to visualizing data. As with the others, there are plenty of tutorials and training resources. This one seems to be second-most intuitive after Tableau at first blush. I’ll have to play around with them both more (since they are both equally cost effective), in order to determine what my data needs.

All of these are likely only stop-gaps to my necessity to learn R , if I could find the time and money to do so. The University of Washington does offer an online professional certificate in Statistical Analysis with R Programming, but I am not sure if I am ready to take that on just yet. Any recommendations, either on software or ways to train in visualizing statistics (in the range of 100-300 data points), would of course be very welcome indeed!

Happy mapping!
Elizabeth

Jospeh Cornell, “Untitled,” 1967. San Francisco Museum of Modern Art.

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