Introduction
I chose to work with this data set as I was a huge fan of the X-Men as a child. The Family Video down the block from where I lived at the time had a section made up of kids’ movies that could be rented for free. I would alternate between renting a DVD with a season of ‘Young Justice’ and a DVD with a season ‘X-Men: The Animated Series’ over and over. That was a really fond and foundational aspect of my childhood. While cleaning up the data set and reading the list of familiar names, I could hear the intro music on loop in my head. Its not something I had thought about in a while and I felt really happy and nostalgic while cleaning the data, even if I did spend quite a bit of time doing that.
I also chose to look at instances of unconsciousness, captures, and times one is declared dead as I felt those three categories related together the most. I also thought it would be interesting to see the ratios between the three for a particular character, which characters were written about in this way more often than others, and if there were any characters declared dead multiple times.
Sources
I used the Claremont Run Project data to make the visualization. I spent quite a bit of time cleaning the data. I first removed the other categories and the characters that don’t have any counts for the three categories I was looking at. After that, I decided I was not super concerned with the issue the occurrence happened in, so I had to clean up the data where these things would happen to the same character but in a different issue. There was likely an easier way to do this, but I ended up doing this all by hand. I looked through over 100 rows one-by-one to consolidate this data.
Processes
After cleaning the data, I uploaded the data into Flourish and made a grouped column chart. That was instances of the three occurrences could be compared to each other for the same character and then all the different characters could also be compared as well. I also struggled with formatting, as I did the first time using the tool for an earlier lab. This time, however, I was able to solve the problem. I was having trouble getting the values to read as I wanted them to. It was not allowing me to insert some columns in that category as they were ‘text’ despite not actually being text. I then saw an option to change the data type that I had not seen before and was very happy to have solved this.
Presentation
I decided to use a subdomain on WordPress as opposed to making my own with html as I thought it would look better using this existing tool than it would with my very limited knowledge of html. I would also like to save making my own with html for a more personal project that I am more attached to. Using it for a class assignment would feel slightly like a betrayal for what I have planned and I did not want to do that.
For the data visualization, I was happy with the simplicity of the chart. I also like having Angel at the rightmost as he had two instance of being rendered unconscious and two instances of being captured and was only declared dead once. I think the chart is easy to read and understand at a glance, but I think also having those smaller numbers at the side also makes for a quick way to tell scale if a viewer needs a little more help.
Significance
With this visualization, the viewer can get a quick understanding of which characters were frequently involved in conflicts during this comic run. Cyclops, Nightcrawler, Phoenix, Wolverine, and Storm are involved more than the others and are characters more people in the general public would be familiar with. It relates to the digital arts and humanities more than general data science as it hints at which of these comic characters are written about more. It is, however, unclear if these characters were popular with audiences and then were written about more, of if they were written about more which then made them more popular characters.
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