Using RAWGraphs, I selected a line chart as how to visualize the list of baby names in New Zealand. I figured this would be the best way to go because it compares all names at once, and you can see the trend from year to year. This makes it easier to see which name was used the most in each year, and if names dropped out of the top 10 in a certain year, or if a name entered the top 10 in a certain year. A couple of things I changed for the graph to make it easier to read were to separate the graph into two series, grouped by gender, given in the .csv file. Then I decided to assign a different color to each name on the graph to really help them stand out. One other small change I made was the width of the graph, at first it felt pretty scrunched up with the original width, so I added some width to make it that much easier to read.
I tried to follow some of the main points from Lin’s lecture this past Thursday. One of the main things I gained from the talk was that making the graph easily readable is one of, if not the most important, aspects of making a great visual. You can make a graph that shows some great insights, but if it is hard to read, it might not be as useful as a simpler graph that is much more readable. Relating this to digital humanities, this graph was able to take a dataset that was a great piece of New Zealand culture, and put it in a way that was helpful to interpret as a reader. That is one of the many cool aspects of digital humanities, is showing different aspects of different cultures in meaningful ways.