For this lab, I chose to use Rawgraphs.io in order to visualize the data for baby names in New Zealand from 2001 to 2010. In order to represent my data, I chose to use a line chart that maps the counts of the names across this time span; however, the dataset had too many names for the data to be of note, so I just took the top 3 boys name and top 3 girls names from 2001, and mapped how the counts of those names changed over the next 9 years. This felt more significant to me as it showed how the most popular names in a year continue to be popular or dip in popularity after that year.
For the style of this graph, I changed the x-axis so that there were no commas in the years since they would usually be recognized as thousands values. Additionally, I added dots to the data since I only had data for each year, so it was clear that the line in between each year was extrapolated and only there to show a trend and to represent individual data points. The last change that I made was changing the curve type for the graph, as the bumpy curve showed more movement between years as opposed to the monotone x curve which better represented abrupt changes from year to year.
This graph is interesting and significant because it shows how much each of the most popular baby names loses popularity after it gets into the top 3 names; this is likely due to the fact that once there are too many people with a certain name, people are less likely to name their kid that, even if they like the name. This kind of graph and visualization aligns with what Lin teaches on data visualization principles, as it makes sure to not overload the user with too much data and keeps it simple so that what is being represented is easy to understand and has a singular goal or purpose. Additionally, this visualization is a good example of how we can use specific data in digital arts and humanities in order to draw conclusions about a small subset of a large data set that may actually hold significance, as opposed to just getting lost in the data set. Even though my data set was not incredibly big, there were enough names that it made any kind of visualization pretty unusable or understandable. Finding niches in the data is an integral part of DH, which I think is evident in this specific case.
