Lab 3

For my data diagram, I chose to visualize the most popular names during each year in a matrix plot. Out of all the plots options in RAWgraphs, I found the matrix plot to be the neatest for displaying the year, name, and popularity. Since the number of names correlates with the ranking, I chose to drop visualizing the ranking category and make the box sizes proportional to the count instead.

To improve clarity of the visualization, I changed the colors of the box depending on the gender of the name. One thing I’m unsure about how to fix is the awkward gap between the plot and the legend. I tried adjusting the margins, but I didn’t find a lot of success with reducing the gap. From Lin’s lecture, she mentioned that we should be making visualizations that make it easy for humans to differentiate (contrast). While the box sizes generally have similar sizes, it is still relatively easy to differentiate the count of the name between the 5 years represented in the data compared to the sunburst diagram I was considering using.

Data visualization allows clear representation of DH statistics for easier analysis of the relationship between variables. The relationship trends can bring to light insightful observations about events that could have happened at the time. For example, in the Rhythm of Food DH project, the graphs demonstrated unusual spikes in various keywords with google trends data. These unusual spikes brought up questions as to why such a trend occurred which allowed various conclusions to be drawn.  

Since I was unable to find a way to embedd an interactive map on RAWGraphs, I kept the names label on my diagram as that detail is more important. The year is on the x axis and the list of names is on the y axis. The legend shows what count corresponds to what size of box as well as the color corresponding to a gender.