Using the provided dataset, I first began this project by looking at the recommended Flourish graphs and charts for my uploaded dataset. Although this feature seemed like a cool idea, I quickly realized that our dataset had too many components to be easily filled into a generic template and thus the recommended graphs were no help. I had a couple of ideas floating in my head regarding what exact components I wanted my graph to have. After flipping through a couple options, like “Stacked Bar Graph” and “Percentage Line Graph,” I began to realize that I would need to cut down on some of the dataset’s components if I wanted this creation process to be approachable (at this time I did not know what a racing graph/chart was). At this point, part of me wanted to quit the Flourish website entirely, for it felt like I was wrestling for control with a weird robot entity, and just entirely format my chart on excel or R. However, Flourish’s pretty colors and easy website formatting drew me back in.
After a couple failed attempts at creating a readable chart, I decided to cut the 1-10 name rankings and just plotted the top (#1) baby name for each year. I had initially wanted to have the years as the x-axis with a stacked bar chart where each name was displayed its years bar section. This did not seem to be working and so I moved on. In the map above, you cannot see the names but, on the actual chart, the names pop up when you hover over each bar. I also grappled with whether to include gender on the same chart but, for the purpose of a clean data visualization, I ended up differentiating between M and F. The interesting thing with baby names, especially nowadays, is that gender is not exactly ascribed to each name. For example, Sam could be a boy but also a girl or even a nickname for Samantha. I am not sure how this dataset accounts for those ambiguities. Also, in the US we sometimes make very clear distinctions between gender identity and sex assigned at birth. How does this dataset work with those additional factors in mind? This sentiment is reflected in the “What Gets Counted Counts” article by D’Ignazio and Klein. When I was creating the notation for this graph, including the headers and footers, I also noticed that I new very little regarding how this data was collected. Was there a specific purpose or intention to its collection efforts? If so, should I be trying to highlight that aspect in my data visualization?
Originally all of the bars on the chart were the same blue color. This was beginning to confuse my eyes, however, and I decided to make each year a different color. As seen in the image above, the y-axis initially had the highest year at the top (2010) and the lowest (2001) at the bottom. I knew I wanted it the opposite, so I flipped the direction of the y-axis. However, this is of interest to me because, technically, the y-axis should vertically increase on a regular, positive x-y plane. For some reason though, the direction felt off for this particular graph. This prompts questions regarding how the public presentation of data might skew away from normative data practices based upon factors like readability, etc. One aspect of the “A Counterhistory of Data Visualization” article was the focus on the overall purpose of data visualization. The creation of this graph prompted me to consider how much of my own agenda is put into my charts versus how much of this is the visualization of raw data that allows viewers to draw their own conclusions. It seems like the transition from dataset to visualization, in this case, can mean the omission of data unbeknownst the eventual viewer. However, some of this may inherently be because of the limitations of data visualizing programs rather than the intentions of the creator.
