DGAH 110—Midterm: How Chris Claremont’s Uncanny X-Men run uses death to drive a plot
“Claremont wrote X-men comics for 16 years! Those 16 years form one of the greatest epics ever created. All modern X-men comics use Claremont’s run as a foundation. “
— The Comic Book Herald (link)
For my midterm project, I chose not to stick with my metaphorical guns and run with a technology I’d already learned. I’ve always been fascinated by data visualization and I used to love the Uncanny X-Men comics as a kid, so I figured this would be a perfect time to revisit one of my favorite comic book runs of all time: the Chris Claremont 10-saga, 16 year X-Men series.
The data I used wasn’t one of the cut-down datasets made available to us on the Midterm page, but rather one of the original large data sets, specifically The Claremont Run. TCR is a humanities-based data compilation of the events of the entire 185-issue Claremont run. For this project, I specifically looked at two categories: “declared dead” and “depowered.” My reasoning behind this is quite simple: the best authors know how to use death as a tool to raise the stakes of their stories. Claremont’s run is, as stated above, the foundation of all modern X-Men comics; so how did he use character deaths to accomplish that?
Before I answer that question, I first need to explain my methodology. I used three primary tools to modify my data down into what I needed it for: Open Refine for data processing, Google Sheets for totaling and RawGraphs for data visualization. Open Refine was an excellent tool for cutting away the chaff, as I ended up going from over five thousand rows of data down to around forty-two. From there, I used Google Sheets more arithmetic-friendly interfact to total deaths within the same issue and format my data for RawGraphs. Finally, I deposited my data into RawGraphs and built two corresponding bar charts. See below, incidences of de-powerings (mutants losing their superpowers):
In between graphs, I should explain why these two aren’t formatted as iframes; they’re scaled vector graphics, which are, more or less, fancier JPG files. These were a challenge to get into WordPress as they required installation of a specialized plugin in order to upload into the WordPress library. Though not interactive, the images are scalable, so there’s no blur as you zoom in. Now, the more complex Deaths by Issue chart:
Now, the question remains, why did I choose to visualize the data that I did? The answer to that lies in the fact that the X-Men have always been seen as a grittier, more outcast type of superhero group. Claremont understood this, and so organized the story accordingly; he does not go longer than thirty issues without writing in a major death (e.g. one of the X-Men dying). It’s also notable that Claremont coupled multiple deaths with de-powerings, without making them mutually exclusive. Issue 150 features two de-powerings and two deaths, while issue 133 features two de-powerings and one death.
The ultimate layout of these graphs also gives us an impression of how Claremont staged his long-running plot; initial, high-casualty conflict at the earlier end of the run, followed by a series of smaller but toll-inducing events, all capped off by another huge spike in deaths before another sudden drop-off. My theory on Claremont, as a reader, but also as a data scientist is that he adopted the motto of “It’s always darkest before the dawn,” to drive his interpretation of the X-men odyssey.
In the end, however, this is all subjective; it’s an analysis of a single set of plot-points in a much broader and complex storyline. I think there’s intrigue in that, however—and there’s more I’d like to do with respect to comic book runs. A couple, far bloodier, runs come to mind, such as Robert Kirkman’s Invincible, which is one of the greatest comic book runs of all time (the Amazon TV adaptation of the show is spot-on). I, one day, would like to see what the death/issue distribution is like for other famous runs, to try to formulize the structure of conflict in a good story. That, to me, would seem like an incredible mix of data science and humanities work.
