I’ve been experimenting with different ways to represent all the variables in the 10 Most Popular Baby Names dataset without making it seem too visually cluttered. I tried out several types of graphs on RawGraphs and most of the graphs ended up too complex and hard to interpret. Additionally, for some reason, the data seemed to be imported correctly but was not being implemented in the graph correctly. For example, when I tried to use color to represent ranking, the color scale would be an inaccurate representation because the 10 rankings would come directly after 1 for some reason. I tried my best to work around the challenges, the graph I created below is the clearest and most effective representation I’ve managed to achieve so far.
This graph is a treemap that shows the ranking of names by popularity over several years (from 2001 to 2010), grouped by ranking, which is then divided by gender. Each large box represents a specific year. Under each ranking of the year, there are two boxes with distinct colors-pink for females (F) and orange for males (M). The size of each rectangle represents the popularity/count of the name (bigger rectangles mean the name was more popular).
A treemap is one of the few graph styles that allows you to visualize multiple variables while comparing hierarchies and proportions. Although using hierarchies might initially seem like an unusual choice—correlations might appear more intuitive for this dataset—it actually works remarkably well for dividing the graph into distinct subcategories and making the data easier to interpret
In order to make this graph more readable, I had to change the colors of the boxes to be less saturated and more contrasted with the previous setting colors. I added the name labels(though they’re definitely not perfect) and modified the graph’s margins for better clarity. While I wish I could include a legend to give a comparison to approximate the size of the boxes, the graphing tool unfortunately didn’t provide that option.
The goal of data visualization is to help us better understand trends and correlations within a dataset. In digital humanities, we leverage technology to create these visualizations, aiming to make them as clear and accessible as possible. By organizing this dataset using RawGraph, we are practicing a form of digital humanities.
