Intro
For this lab, I created a data visualization of the most common baby names in New Zealand. From the beginning, I knew I wanted to make a bar graph race because I think they are a good way to visualize change over time. For this prompt it works particularly well, because it offers a dynamic and easy-to-read way to analyze the data.
Process
After selecting a template, I realized that the data didn’t match the template because of the way it was organized. The first part of the assignment was very tedious, because I had to reorganize the data in a way that was similar to the original template. Then I deleted the “rank” column because the count already puts the names in order. The result was the tentative visualization below.
Then began the design element of the project. As you can see from the final result, I made a lot of edits. Keeping in mind principles from Lin’s lecture, such as contrast and proximity, I went through every setting and started making changes. To improve the contrast, some changes I made were lowering the opacity of the labels, changing the text size, and color-coding the gender category. To improve the proximity, I got rid of the total count display, made the axis fixed, and added headers.
Conclusion
Data visualization relates to digital humanities because digital humanists often rely on quantitative methods to reach humanistic conclusions and find trends. In this visualization, data was used to analyze cultural trends. An interesting trend that I found as a result of this visualization is that there are more common male names than common female names in New Zealand. At first, I found this assignment to be difficult, but after I the animation was set up correctly, the visual-editing process was very enjoyable.
3 thoughts on “Creating a Dynamic Chart”
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You make a really powerful and wonderful graph, which can really clearly show the change in the name every year! I was really compelled by it. Also, I really agree with your idea that digital humanists often rely on quantitative methods to research humanistic conclusions. You can talk more about it.
Yoel, I really enjoyed how you chose to format the dataset with the race feature. I was not aware of this when I was making mine and I think your post has demonstrated how efficient this more dynamic format can be for viewers. I think the development of the race feature versus the time slider is an interesting question regarding the viewers experience with how data changes over time!
Hi Yoel,
I also did a bar chart race with a similar reasoning. I can resonate with how you had to reorganize the data because I also needed to make some modifications in how I needed the data to look for Flourish to compose the data into a bar chart race. I really appreciate your thoughtfulness as you incorporated contrast and proximity from Lin’s lecture into your data visualization. Overall, when thinking about digital humanities, I think your work of using quantitative methods for humanistic conclusions is well represented.