My project was to create a data visualization of which Marvel character was visualized most often by cover artists behind Chris Claremont’s Issues #200 – #249 of the Uncanny X-Men comics, and who among those artists was the most active in visualizing them.
The dataset behind this analysis was derived from the works by The Claremont Run, a project solely dedicated to exploring Claremont’s work on the comic. Originally, it had many fields such as Issue #, Cover Artist(s), Narrative Captions, Characters Visualized, Character(s) Speaking, etc. but as part of the cleaning process, I deleted most of them, leaving only the Cover Artist(s) and Characters Visualized columns behind. Then, I split up the cells in Characters Visualized so they would only have 1 name per cell, then used OpenRefine to fix the various spelling errors such as “Sabretooth” erroneously becoming “Sabertooth”, or “Nightcrawler” becoming “Nightcralwer”. Following that, I exported it into an Excel document to create a new column for the number of unique combinations of each Cover Artist and Character Visualized. I then imported the result into RawGraphs.io to visualize the result.
I decided to use a Matrix Plot to visualize my data. On the one hand, it provides a very intuitive view of my goal, with the most frequently visualized artist-character pair being located at the top left with clear labels about who the character is, who is their cover artist and the number of times they have been labeled by that artist in particular. On the other hand, my cleaned data having 3 vectors of data meant I had quite a limited number of options to choose from, and the Matrix Plot was the most suitable of them all thanks to the reasons I’ve mentioned. Due to its sheer size, I’ve opted to include a grid system to make each pair easier to be found. However, it unfortunately also included a second set of labels which I couldn’t remove no matter how much I tried. Due to its size, I have left the result at the bottom of the page to keep all the texts together.
Through this project, I have been able to intuitively see multiple pieces of information. Firstly, I could see which Marvel Comics character has been represented the most across the various issues of the Comics. Secondly, I could see which cover artist was the most active (or most frequently hired) in the creation of the Comics’ characters. Finally, I could see the number of times each artist-character pair occurred over the issues. Looking deeper, this translated to being able to see the trend of which character and art style was favored by the consumers of the issues. This is related to Digital Arts and Humanities because it provided insight into a part of human nature, their enjoyment of factors in their life, it was done using technology, and it was visualized in an artistic way that guides the mind of any viewer in understanding the processed result. On the other hand, data science in general only involves analyzing data and visualizing them with no focus on a particular field, making it less focused than the field of Digital Arts and Humanities.
