For this assignment, we were given a dataset of the top ten male and female baby names in New Zealand from 2001 to 2010, and were asked to visualize this data in a meaningful way.
In her talk, Lin presented us with a useful framework for visualizing data:
- Exploration: finding the stories in the data. In this exploration phase, both numerical and visual summaries are important to reveal these stories.
- Explanation: presenting the stories in the data. This phase includes choosing an appropriate type of graph for the data, and organizing it so it is clear and visually appealing. The graphs are deliberately crafted to showcase the stories that were discovered.
As I opened the file and started to look over the data, two things were clear to me. Since half the data was male baby names, and half the data was female baby names, it would make the most sense to present two separate graphs. I also recognized that the story that the data is telling is the change in frequency of particular names over time.
In order to make the graph like I envisioned, I needed to rearrange the data. I created a new column with each name that appears in the graph, then entered the frequency of the name in each year. I then uploaded the data to Flourished, and selected line graph as I wanted the trends to be visible over time. All of the names together were too cluttered, so I chose the option to separate them into separate graphs by name. Some names have more meaningful data, as they appeared consistently in the top ten baby names but at different frequencies, while some only have one or two points where they appeared in the top ten.
Overall, I realized when selecting my graph and fiddling with the settings that there are so many different ways to visualize the same data, and when we, as consumers of information, see graphs we often are not aware of the authorial choices that the creator of the graph has applied. As Lin described, the “explanation” can certainly affect the story we interpret from the graph.
