Reflection
I used a beeswarm plot to visualize the distribution of male and female first names across the years 2001 to 2010 in New Zealand births. Beeswarm plots are great for showing how data is spread out and avoiding clutter when there are lots of data points. With this plot, I was able to easily see which names were popular each year, thanks to how the names are arranged along the years. By using colors to distinguish between boys’ and girls’ names and varying dot sizes to represent name popularity, the plot provides a clear picture of name trends over time.
To make the plot easier to understand, I made a couple of adjustments. First, I changed the background color to green to make the plot more visually appealing and help the names and dots stand out. Additionally, I added labels to each dot, making it simple to identify which name it represents and understand which names were popular in each year at a glance.
Hans Rosling’s TED talk
Hans Rosling’s video highlighted the crucial role of data visualization in Digital Humanities (DH) studies. By using graphs like the world’s respiration number and global income distribution, Rosling effectively conveyed his points on DH topics. These visualizations not only enhanced understanding but also strengthened arguments within the field. Rosling’s dynamic animations within the graphs helped illustrate gradual changes over time, revealing the purpose behind his observations. In summary, visualization emerged as a key feature in DH projects by offering clearer insights and facilitating effective communication of complex data trends.
5 thoughts on “Week6 – Data Visualization”
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Hey Hadi! I love the use of a beeswarm plot to visualize this date. It helps visualize what names are popular during what period and it helps tell that story. I was going to use a beeswarm as well, but I really couldn’t figure out how to work it. Great data visualisation makes it very easy to read with the different colors.
I love the colors on the graph. I struggled a lot playing with different colorizations when messing with my own graph, so I appreciate that someone was able to figure it out. Also, I felt the size differentiations really added to the visual to help understand by counting what names were the most popular besides rank – which, as we know, doesn’t tell the full story of the data.
I love the choice of graph and the color scheme you chose. This tells a different story than a line graph about which names were popular. I wish I could see more names to make the dimensions bigger next time. I’m super impressed with your visualization, and I think you made the right choice by splitting the names by gender.
Hi Hadi, what a unique implementation of a beeswarm plot! Like what you mentioned about “effective communication,” the primary goal of data visualization is to help people make sense of the message hidden behind the numbers and labels. Your graph did a great job embodying this concept. A reader can easily decipher what baby names were the most popular in New Zealand each year from the variably sized dots according to counts. I wonder, though, if there is a way to ensure that the top one or two names for both boys and girls are marked up for each year’s plot. The one for 2007, for example, only displays the two most popular boy names (count 499 and 296), when the top girl name “Ella” (count 418, greater than the count for “Ryan”) is omitted.
It is amazing how you were able to use the beeswarm plot! I wanted to choose a creative graph like you but I was not sure if they are appropriate or how to use them so I was not able to. I did not know that Beeswarm plots are great for showing how data is spread out and avoiding clutter when there are lots of data points. Your graph was so unique I had to click on it. I love the color options you selected too. Great work!