React for Data Visualization
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How data visualization saved 40,000+ lives this spring

Last week Slovenia became the first EU country to beat Covid-19 with just 105 deaths – 0.05 per 1000. My mum was excited as heck.

"Yay we're going back to normal! No more of this bullshit, I hate wearing masks".

Meanwhile in San Francisco we're still heming and hawing. Should we? Shouldn't we? Maybe a little? One of the first to shelter-in-place we're now at "Yeah maybe".

Work from home if possible, stay inside please. Wear masks always.

Sweden never went into lockdown at all.

Now Sweden has the highest death rate per capita and San Francisco barely registers with 40 deaths – 0.04 per 1000. New York went into full lockdown just 4 days after SF and they're one of America's strongest hotspots. 2239 deaths (1.3/1000) in Manhattan alone.

Exponential growth boggles the mind.

4 day difference in response plus a higher population density and you get orders of magnitude different results. 🀯

How dataviz saves lives

Remember back to March. What was it like? Who were you listening to? How'd you decide what to do?

I bet you remember "flatten the curve".

Slow the spread and maybe we can buy enough time to figure out what's going on. To really understand this novel disease. To avoid swamping hospitals and making it worse.

Above all, it was this image that said it all.

One image, instant understanding.

"Oh right, I should stay home. Let's not break the rules this time."

I remember mid January some of us in a Slack group were looking at the John Hopkins Dashboard and thinking: "This doesn't look so good. It's spreading fast in China"

By January 23rd Wuhan was in lockdown. January 31st Italy had its first case. Soon after it's everywhere.

By mid February online activists urge people to stay home. The #StayTheFuckHome movement is born.

One by one countries start banning large gatherings. First 100+, then 50+, then 5+. Conferences got cancelled, concerts banned, movie theaters shut down. Grocery stores limit attendance. Toilet paper runs out.

Life goes into limbo. We wait. Standing 6ft apart.

All because of graphs like this

And this

And simulations like the Washington Post's that shows the difference between viral spread with and without measures.

Or the wonderful People of the Pandemic game that takes lockdown measures and translates them into "Here's what happens if you actually listen. Or if you don't"

Ultimately everyone made their own bets.

Some wore masks, some didn't. Some left the house, others stayed inside for 7 weeks. Some disinfected everything, others were blasΓ© about it.

And it's these data visualizations that helped us make our choices.

Did it work?

We'll never know for sure. You can't A/B test reality.

But we can guess. Run some math on the back of a napkin. Compare countries with and without stay-at-home orders.

Sweden famously didn't lock down.

4029 deaths since January 24th according to wikipedia. That's 33/day. 0.003/day/1000people.

Compare that to USA with a rolling lockdown response as each state saw fit. 91,941 deaths since January 13th according to wikipedia. That's 691/day. 0.002/day/1000people.

It might not look like much but that 0.001/day difference means USA saved 40,000 lives with social distancing.

That's despite crazy stories of people protesting against lockdowns, holding mass gatherings on purpose, and the general human tendency to not quite listen to what your parents say.

We only listen when we understand why my friend.

And that's how I think dataviz saved 40,000 lives in the US alone. πŸ’ͺ

Did dataviz help you? Hit reply

Cheers,
~Swizec

PS: this article uses back-of-the-napkin math. We'll know real numbers once it's all over and data scientists do their thing.

About the Author

Hi, I’m Swizec Teller. I help coders become software engineers.

Story time πŸ‘‡

React+D3 started as a bet in April 2015. A friend wanted to learn React and challenged me to publish a book. A month later React+D3 launched with 79 pages of hard earned knowledge.

In April 2016 it became React+D3 ES6. 117 pages and growing beyond a single big project it was a huge success. I kept going, started live streaming, and publishing videos on YouTube.

In 2017, after 10 months of work, React + D3v4 became the best book I'd ever written. At 249 pages, many examples, and code to play with it was designed like a step-by-step course. But I felt something was missing.

So in late 2018 I rebuilt the entire thing as React for Data Visualization β€” a proper video course. Designed for busy people with real lives like you. Over 8 hours of video material, split into chunks no longer than 5 minutes, a bunch of new chapters, and techniques I discovered along the way.

React for Data Visualization is the best way to learn how to build scalable dataviz components your whole team can understand.

Some of my work has been featured in πŸ‘‡

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