For my visualization of New Zealand’s most popular baby names from 2001-2010, I chose a simple line chart. A line chart is a great way to visualize change over time and to compare many different lines at the same time. The complete graphs (two as they are split by gender) are shown below:
By far the largest thing I worked on with these graphs was simplicity. A key part of Lin’s lecture on making good visualizations was removing as much clutter as possible, which was exactly what I tried to focus on. I started by separating the male and female names onto two separate graphs as that makes the amount of lines on each half of before (and also makes it easier to parse out names of the same gender for such comparison). I then did as much as I could do to have each name label be legible (ie. not overlapping with something else), but as you can see, I wasn’t completely successful with that. To do so, I did things such as increasing the height of the graphs to spread out the lines a bit, and making the range in count different for the male and female name graphs to better fit each one (so there wasn’t white space on the top of the female name graph). Additionally, I just changed the color scheme to ordinal so half the lines weren’t just gray.
There was also just some frustration with some names only being present for a certain length of time, creating random stops and starts on the graph, but it didn’t seem very easy to fix with Rawgraphs (I would have had to alter the data table itself in some way).
This visualization relates to digital humanities in general by making (as much as possible) a visually appealing showcase of a dataset of baby name popularity which would otherwise be difficult to make sense of (especially if just given the data table).