Since it was mentioned in the optional video, I figured I would mention it here. I haven't read through the forum posts to see if anyone else referenced it as well.
As far as the Deconstructing Data Viz reading goes, the Johns Hopkins COVID-19 visualization hs many of the elements talked about in the document.
Link for their dashboard here:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
With the help of the University's Systems Science and Engineering Co-Director Lauren Gardner during a briefing on Capitol Hill, I was able to understand the dashboard for the most part. Video of that can be found here:
https://www.c-span.org/video/?470092-1/johns-hopkins-university-coronavirus-briefing&fbclid=IwAR357GDN7VLUzgXD6EdZ46xoCm6f0vMK6yTlR05fn4pKp5it541y242cnOA
Comparisons: The resource does this well, via a line chart, when it comes to comparing mainland China to the rest of the world. What is interesting in the graph is the way they present the confirmed case vs the recovered cases. In the above-referenced video, Gardner explains the significance of that comparison.
Time-series: Again, the same mentioned line chart shows the number of cases over time, and you can compare early to current cases. An additional bar graph is provided to show new cases per day as well as newly recovered people, allowing you to compare them.
Geographical: This is the best part of the resource. I have yet to see any other source present it so well. It also uses ArcGIS. What it doesn't do well, depending on the zoom level, is how much landmass is affected. I don't know if that's because of the changing size of the dots or what. I am still trying to figure out how to accurately point something like that, without embellishing or downplaying data such as this. If you look at the map one way, it looks like entire states are affected, when in reality, only a small part of a state is.
Static or interactive: The resource has both. Again, the improvement to the interactive part, I mentioned above. Static parts are presented in a particular order (highest to lowest affected), which is good.
Design: I love the differentiating colors. I can't think of anything to improve it.
Overall, what the data doesn't show well, is the local level when it comes to geographical. It was mentioned in one of the course videos, one has to be careful when using geographical data and the misrepresentation of location. The dashboard only gives counties (for the U.S.) and plots it as such. Some counties can be huge. This isn't advantageous to local reporting. Additional info would be needed from other local resources to track affected people down.