For this lab, I found the two tools we were allowed to use to both be a bit difficult. I am used to using excel to make data visualizations, and I found both RAWGraphs and Flourish felt limited and confusing in comparison. I feel like I have more control on how to present the data and compare it in excel.
I did find one option on Flourish that seemed interesting, but because of how the tool wants the data formatted, I was unable to get it in the way I needed it for it to display the pattern I was trying to show. I was hoping to use the line chart race visualization as I thought it would be interesting to use it to show the popularity of the names as they changed through the years. However, because I only wanted to focus on the names that were always in the top ten, that left some gaps in the ranks. Flourish also automatically calculates ranks, so if I typed in the ranks it would interpret it as counts and if I put in the count, it would not reflect the other names that dipped in and out of the top ten.
For my final visualization, I wanted to look at the girls’ names that were in the top ten for every year between 2001 and 2010. I found Emma, Emily, Olivia, and Sophie made the list every year. After I cleaned up the data and imported it, I found at a glance the line chart seemed to show the opposite of what I was trying to communicate. Flourish was looking at the numbers in the rank and putting the highest number at the top, but because the data is in a countdown format, I wanted the smallest number (the highest rank) to be at the top. I was unable to find an option to do that, so I flipped the numbers instead so it would read how I wanted it to at a glance. So then 10 became 1, 9 became 2, 8 became 3, 7 became 4, 6 became 5, 5 became 6, 4 became 7, 3 became 8, 2 became 9, and 1 became 10. If I had been able to find a way to make the smallest number go at the top, I would not have done this and I understand doing this is slightly confusing, I deemed it more important for the graph to easily read as one name being more popular than another at a certain date and the greater height is the fastest way to communicate that. Having done that, I then needed to add a legend explaining how I modified the data.
The reason I chose to only look at names that never left the top ten list, and a possible link to DH is that this information might serve as a jumping-off point to look at what is important and popular in a country’s culture. For example, after looking at a country’s most popular names, a historian might then go back and trace the origins of those names. This could offer insight into the country’s history and values.
