For this week’s blog post, I decided to use our project data and create a visualization that would help understand our assigned data better. For our final project, we have been assigned the Marvel and DC database of superheroes. However, for this visualization I only used the DC database. I created an alluvial diagram on RAM to show the correlation between the genders of superheroes and their eye color.
We notice how the distribution of eye color between both genders is pretty similar. The affinity for blue eyes is high in both male and female characters. We also notice many varied colors that we would normally not think of eye colors, such as violet, amber and pink. This visualization is also interactive, that is, if you hover over any of the relations it tells you exactly how many heroes in that gender have that specific eye color. Additionally we notice the sheer difference in the number of male characters as compared to female or genderless or transgender characters.
Creating this simple visualization really helped me look at my data in a different way. It even helped me realize that there were discrepancies in my data, and gave me direction to clean and filter it. For example, I noticed there is a segment of auburn hair in the eye color column, which means some data must have been entered in the wrong column by error. Although, the purpose of this visualization was not to find discrepancies in your data, rather to provide further insight. I think it really helped me understand my data better as a person with no prior knowledge handling data or any visualizations, especially in such large quantities.
Great visualization! I really liked learning about the alluvial diagram in class because it was easy to see how different categories were distributed and correlated. I didn’t even know that some comics included transgendered characters! Very interesting thing I learned from your post. I like how you noted that visualizations are a great tool to provide insight on your data that can’t be pointed out when looking at the original spreadsheet.
I liked that you used an alluvial diagram, as many data sets don’t usually have the capability to do so and they are very nice to look at. It interested me that you pointed out that creating the visualization helped you find errors in your data. It seems as though that attempting an initial visualization gives you a starting point in seeing what cleaning work needs to be done before delving straight into data cleaning.
It’s such a coincidence that you, Shreya and I worked with the DC data set (because we have 2 sheets)! Your alluvial diagram was great in terms of showing the relationship between gender and eye color. It isn’t difficult to interpret and makes the information more visually appealing. I definitely agree with what you said this visualization can tell us about our data set. I think we are making a great start in analyzing and understanding our data a bit more.
I personally really love the alluvial diagrams, since it really helps me understand the representation of each group in a dynamic, spatio-visual way. I appreciate that we can draw some social implication of representation from this specific data visualization, seeing that blue eyed male heroes are the most represented in your data. It seems that there the heroines only have a presence in your data that is a third of the heroes. One can also see that transgender representation is minuscule; I almost didn’t even see their group at the bottom left-hand corner of the visualization. This was a great way to present your group’s data!