In early Spring 2020, I was frustrated with the lack of informative COVID-19 data for my county, so I started building my own spreadsheet “dashboard.” I’ve collected the single numbers my county reports each day – e.g., new cases, hospitalizations, deaths, testing – added them to my spreadsheet, then began exploring different graph types to tell different stories.
Beginning with purpose
For me, the purpose of this work is twofold. First, it’s an opportunity to keep myself up-to-date and use local data to inform decisions to venture out in my community. Let’s be be honest here, too. Some charts scare the hell out of me with what’s going on pandemically speaking (yeah, I made up that word), while others put me more at ease. That’s the power of data visualization to tell a story, evoke emotions, and catalyze change. Whoever said “data are objective” or “numbers are just numbers” is simply wrong, but that’s a storyline for another day…
The world cannot be understood without numbers. But the world cannot be understood with numbers alone.
– Hans Rosling (physician, academic, and public speaker)
Second, this presents an opportunity to work with different datasets and experiment with chart types I don’t normally use, thus enhancing my data visualization skill set – getting ready for whatever new project lies just around the corner.
As mentioned in the first blog on this topic, right now, these graphs are for me (well, and you too now!), so I haven’t considered one of the most important questions in data visualization – who is the audience for these graphs? Who will read and consume them, and what exactly do they need in order to make meaning from them? Were I to share them for the purpose of communicating specifics about the pandemic in our area, my thinking would certainly change .
Experimenting with Data Visualization
I like experimenting with data visualization and writing about it. In a way, I’m making a map of my territory, knowing for sure that it will always be but a representation of reality – an abstraction – and always far from perfect. It’s clear that some of my graphs work well for the datasets while others clearly do not, and if I set out to craft a dashboard or report with a clear message for an identified audience, chart choices would likely change.
Instance charts can handle massive amounts of data and communicate trends over time without labeling individual data points. #ShowYourStripes for example, takes up to ~170 years of climate data for different places on earth and plots them in what they call “warming stripes.” In my first post, I used a tutorial from Evergreen Data Academy to learn to make an instance chart. I’ve since updated that chart with a different color scheme and figured out how to add a line chart on top of it for an additional visual of the same data.
Waffle charts, a version of an icon array, are great for visualizing a single number. I like this chart for its ability to put things in perspective. Just as I’m agonizing over the daily number of new cases and wishing it would go down to zero, I realize how lucky I am to live in the place that I do, even with its proximity to a former hotspot – New York City. This one is a simple “adjust and color” waffle. In other words, I eyeballed the column heights and row widths to get a (nearly) perfect square. I inserted a square shape on the sheet to use as a guide. I estimated the partially colored cell, and placed a little grey rectangle to hide part of the red cell. Low tech!
Stacked Bar Chart
Building on that theme of charts that make me feel better, I tried this 100% stacked bar to show the rate of infection by age and sex against total population estimate for my county. On the one hand, the tiny colored slivers in each stack give me hope, but that doesn’t mean the numbers are insignificant.
Vertical Dot Plot
To see other stories in the data, I tried plotting cases and some outcomes along a vertical dot plot for men and women (how my county reports this dataset). This allows me to see that women make up more cases, but that men seem to be beset with greater numbers of negative outcomes.
There are times I learn a chart type and think, “I’ll never have reason to use that.” So far I’ve been wrong EVERY. SINGLE. TIME. While treemaps can be visually appealing and informative, they can also be confusing and challenging to make sense of. Every now and then, I find an opportunity to create one. Here, I assigned labels to age groups and used color, data labels, and brackets to summarize, along with a descriptive title that highlights one of the conclusions.
Are you an amateur (or professional?) COVID-19 data tracker?
Are you experimenting with visualizing COVID-19 or other data? Feel free to share your work in progress here. I’d love to see it.
Here are some of my favorite resources for learning and experimenting with data visualization.