Uniqueness of Baby Names between Males and Females
I chose to visualize the difference in uniqueness in baby names between both the male and female gender. Thus, I created a horizontal bar chart, coloring the bars based off whether they were female or male names to emphasize that male baby names tend to be more frequent and therefore less unique than female names. As shown by 14 of the 15 most frequent baby names each year from 2001-2010 being male, females arguably have the more unique baby names. Displayed in the graph, the same male names are overly used every year, with numerous “Jack” and “Joshua” bars being displayed as the most popular. This graph style is appropriate because you can clearly see which bars are male or female, which names are being measured for each year as being the most frequent, and a count of the name for that year is visible too (also essential as I had categorical names plotted against their quantitative counts).
I made a few changes to the visualization to improve its clarity. The original color palette had similar-colored, transparent bars for both the male and female names. So, I changed the color palette to one with more contrasting colors and I increased the opacity of the bars so they really stood out. I did this because I distinctly remember Lin’s lecture and her emphasis on making your message stand out in visualizations. I wanted to emphasize that there was one sole female baby name mentioned in the top 15 most common baby names, so I made sure its color was distinct and easily observable. I also changed the number of bars from 10 to 15, so I could emphasize that only one female name for one year was considered the most popular from 2001-2010 (Ella). I also added a title and heading to make my message clearer to the viewer.
Making simple yet distinct visualizations like the one I made is necessary in digital humanities. With seemingly endless data and messages to get across to viewers, representing findings or arguments in easily observable, simple graphs is the best way to connect the viewer to your work. Keeping graphs simple yet clean is more engaging than graphs that are complex and “hard on the eye”.
3 thoughts on “Lab Assignment: Exploratory Data Visualization”
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I really like the way you displayed the data. One of the graphs I made took on a similar approach but I used pie charts. I think this is the best way to get a larger understanding of the data although it does not tell the story of individual years. Regardless, I think this was the best approach to try and capture the overall message of the data. Good work!
I like the idea of looking at the “uniqueness” of names, it sounds super interesting! I’m not sure though that we have enough data to make that conclusion here. The dataset provided contained only raw counts, so the higher count of male names could be accounted for simply by a higher rate of male births in the relevant years. I think something could be worked out if we were provided the total number of births each year though. Neat idea!
Hey Chris, fantastic figure! Changing the color palette definitely helps improve the ease of understanding, as well as flipping the coordinates. I also agree that simple but distinct graphs is extremely important. You need to be able to hold the viewer’s attention while also getting your data out in a succinct way. Great work!