Data Storytelling and Dataviz Approach
I didn't end up loving this dataset as much as I had hoped. I originally picked it because of an article that I saw that stated that the divorce rate for Millenials was dropping. The article linked this stat to lower overall marriage rates over the past several years among Millenials. So, digging into the data, I was expecting to see the Millenials (I'm lumping them into the 20 to 34 age group here) as an anomaly, where divorce and marriage rates in other age groups increased or remained steady. I guess I shouldn't have approached the dataset with this preconceived notion.
With all of this in mind, and even with my original incorrect supposition, I decided that I wanted to be able to compare the metrics across the age groups. I decided to go with small multiples, stacking the relationship rates (never married, married, separated, etc.) for each age group vertically. I used color in an effort to show the trend for the overall time period (2005 to 2017), where blue indicates an overall increase and orange, a decrease.
The one downside to the small multiples is that the vertical axis gets scrunched a bit, and subtle changes in the data can be hard to see. To combat this, I used a parameter that drives a filter to show the user's chosen age bracket. I polished it up with some simple tooltips and called it a day. After having a hard time deciding how to approach the data, I ended up really liking my finished product.
We'd love to see what you can do with this week's dataset, create your viz and post your work to Tableau Public and Twitter with the hashtag #ThrowbackDataThursday, tagging @TThrowbackThurs. We'd really love to see what you can come up with!
This week's dataset comes from the U.S. Census Bureau, American Community Survey Estimates. Please be sure to cite the source on your viz.
The column Estimated Population was calculated by multiplying the estimated total population for the gender/age group with that gender/age group's estimated percentage. The values are approximate, and fall within the stated margin of error.