My data visualization is a boxplot that shows the distribution of counts for each popular name used from 2001 to 2010 in New Zealand. Using this boxplot, I hope to visualize the differences in distribution for each name so that we can see which name varies more in terms of frequency compared to others. To be honest, my initial idea was to create a bar chart that shows which name was used most frequently throughout the 10 years, but as I skimmed through the posts, I noticed Jeremy had already taken that idea, which forced me to come up with a new one (lol).
As seen in the graph, the name “Joshua” varies a lot—its smallest count is 298, while its largest count is 590. At first, the graph was unclear because the dataset contains too many names, which made the x-axis messy. To solve this problem, I went to the “artboard” tab and changed the width from the default value to 1600. By expanding the x-axis, I successfully separated each name so they no longer overlapped, making the whole plot more readable.
However, there was one trade-off I had to make: balancing clarity and retaining information. I found that having all the numbers (i.e., minimum, Q1, median, Q3, maximum) around the boxplot made the whole plot less clean, as it is now. But removing the numbers made the plot less informative. This reminded me of the inclusivity discussed in the reading—I believe there are viewers who would be particularly interested in comparing the distributions of two specific names. In that case, having the relative statistics beside the boxplots would definitely help. As a result, I ultimately chose to prioritize information in this case. And to compensate clarity, I extended the height to leave more space for each statistic, aiming to maintain the best possible balance.
2 thoughts on “Lab 3 Data Visualization”
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This is a really interesting chart. I just learned about box plots earlier this trimester, and this chart has a very unique way of showing the usage counts of different popular names in each year. Unlike my chart, this one focuses more on the changes in usage counts for the same name, which is a different but very meaningful approach.
This is a super neat data visualization Peter! I hadn’t thought about doing a boxplot for this dataset, but I think it does a great job presenting the data in a clear and precise manner. I also like that you separated the genders by color, I feel that it makes the graph much more readable. I do support your decision to include the numbers in the boxplot to make the graph more informative. However, I would have enjoyed seeing the other plot as well just so I could visualize the comparison!