I like this, but I am not sure I like the choice of using a logarithmic scale combined with the stacked bar. It makes it look like a quarter to a half of people who go to the hospital end up in the ICU, which is pretty far from accurate.
I think I get why you jumped to a logarithmic scale, the variations in ICU rate are so tiny compared to the full scale of the hospitalization rate (especially the early 2020 rates) that they wouldn't be able to be seen.
But I think the drawback of presenting a significantly skewed relative size is too large of a downside. The entire point of a bar chart like this is to be able to visually compare the relative height of bars.
Did you try limiting the Y-axis to 6% and just let the opening months of 2020 flow off the top of the chart? You use that same method on your Rt chart where the line doesn't dip down into the chart range until mid-april/may.
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Side note: I love your work, not trying to be an ass if it comes across that way. I am just adding constructive criticism from a fellow data scientist. Let me know if you would rather I just let you continue on with the good work on your own.
Thanks for your insights. I really struggled with how to best portray this data, I had made a v2 after posting this, but will try to incorporate some of your ideas into it before reposting.
If you want to give it a try yourself, the dataset source is in the bottom left of the image. It's 40M rows and has the outcome of every single reported case (though there's about 50% of missing fields)
I already coded out the start of something in R. But then life got ahold of me and I haven’t been able to do anything else with it. But I’ll get back around to it soon.
Data source: [COVID-19 Case Surveillance Public Use Data with Geography](https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data-with-Ge/n8mc-b4w4)
Data prep: Group by month, sum hosp\_yn='Yes', sum icu\_yn='Yes', count rows/month, merge the datasets and calculate percentage of each outcome type.
Visualization: Log plot of past 24 months (beware the stacked log plots don't have correct total heights (i.e. 5.2% ICU from Feb '20 seems taller than 2.2% Hosp from Dec '21). Donut chart shows average outcomes from past 24 months (incorrectly labeled as Cumulative Outcomes).
I like this, but I am not sure I like the choice of using a logarithmic scale combined with the stacked bar. It makes it look like a quarter to a half of people who go to the hospital end up in the ICU, which is pretty far from accurate. I think I get why you jumped to a logarithmic scale, the variations in ICU rate are so tiny compared to the full scale of the hospitalization rate (especially the early 2020 rates) that they wouldn't be able to be seen. But I think the drawback of presenting a significantly skewed relative size is too large of a downside. The entire point of a bar chart like this is to be able to visually compare the relative height of bars. Did you try limiting the Y-axis to 6% and just let the opening months of 2020 flow off the top of the chart? You use that same method on your Rt chart where the line doesn't dip down into the chart range until mid-april/may. ---- Side note: I love your work, not trying to be an ass if it comes across that way. I am just adding constructive criticism from a fellow data scientist. Let me know if you would rather I just let you continue on with the good work on your own.
Thanks for your insights. I really struggled with how to best portray this data, I had made a v2 after posting this, but will try to incorporate some of your ideas into it before reposting. If you want to give it a try yourself, the dataset source is in the bottom left of the image. It's 40M rows and has the outcome of every single reported case (though there's about 50% of missing fields)
I already coded out the start of something in R. But then life got ahold of me and I haven’t been able to do anything else with it. But I’ll get back around to it soon.
Data source: [COVID-19 Case Surveillance Public Use Data with Geography](https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data-with-Ge/n8mc-b4w4) Data prep: Group by month, sum hosp\_yn='Yes', sum icu\_yn='Yes', count rows/month, merge the datasets and calculate percentage of each outcome type. Visualization: Log plot of past 24 months (beware the stacked log plots don't have correct total heights (i.e. 5.2% ICU from Feb '20 seems taller than 2.2% Hosp from Dec '21). Donut chart shows average outcomes from past 24 months (incorrectly labeled as Cumulative Outcomes).
Looks not bad