Electricity sources by state

With Joe Biden calling for 100% clean electricity, John Muyskens and Juliet Eilperin for The Washington Post looked at where states are at now in terms of electricity generation.

The variable width bar chart above uses a column for each state. Clean electricity stacks on the top and fossil fuels stack on the bottom, each representing a percentage of total generation. Column width represents total electricity for each state.

It reminds me of the spending graphic by Interactive Things in 2010. I think variable width is about to be a thing again.

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If the unemployed lose $600 per week

A $600 per week benefit expires for the unemployed at the end of July. Congress is still deciding what to do after. For The New York Times, Ella Koeze highlights the percentage of usual income the unemployed will receive as a function of annual earnings and weekly benefit amount if the benefit goes away.

Each dot represents a state. The percentage ranges in the background provide a point of reference for where each income group and state falls. The scrollytelling format starts with individual points and then tours through the shifts.

An aside: I might just be imagining things, but I feel like there’s been more scrollytelling lately. Is this a function of doomscrolling on our phones? Also, whenever a chart anchors in the background and a text frame scrolls over, I think of Snow Fall from 2012.

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Defining ’90s music, based on song recognition

In search of songs that define music in the 1990s, Matt Daniels and Ilia Blinderman for The Pudding look for songs that that Gen Z still recognizes. Also, the songs that are mostly foreign to the younger generation:

In 1999, “Wild Wild West” was the song of the summer. Yet it is fading far faster than any other ’90s hit with comparable starting popularity. Twenty years ago, it was inescapable. Maybe Millennials are still too sick of it, even for nostalgia rotation. Perhaps it wasn’t even that great of a song to begin with, artificially inflated by Smith’s celebrity and cross-promotion with the film Wild Wild West.

Sorry, Will Smith.

The results are based on data gathered by The Pudding in an interactive survey.

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Comparing U.S. coronavirus case rates to other hot spots

The numbers are high here in the United States, and at this point, they look bad on their own. But compare it to other countries that are currently hit hard, the U.S. looks even worse. For The New York Times, Lauren Leatherby makes the comparisons.

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Remote work and industry

Some industries are more compatible with remote work than others. Jonathan I. Dingel and Brent Neiman at the University of Chicago estimated the scale of the differences. For Reuters, Sarah Slobin reports using a variable width bar chart to show likelihood of close contact with others against likelihood of in person work:

Professional, management and technology jobs run the gamut from accountants and architects to lawyers, insurance underwriters and web developers. This group is much more likely to retain the privilege of collecting a paycheck while working remotely, and is based in major metropolitan areas, like New York and Los Angeles.

See additional breakdowns by geography and job loss.

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Unemployment for different groups

Unemployment has hit the United States hard over the past several months, for some harder than others. Lena Groeger reporting for ProPublica:

Part of the reason for this disparity is that many workers of color, especially Black workers, didn’t come into the crisis on equal footing. At the beginning of 2020, when the U.S. was at what most would have considered peak economic prosperity, the unemployment rate for Black workers was more than double that of their white counterparts. “The classic fact about Black unemployment,” said William Darity Jr., an economist at Duke University who studies racial inequality, “is that it’s been two times the white rate since we started measuring it.”

Each line represents a different subpopulation, so you can scroll over specific lines or select specific groups with the buttons. It’s similar to this New York Times interactive from 2009.

But this is 2020, so Groeger uses the overview as the initial view and then it shifts into scrollytelling. Groups highlight and the time frame expands as you read. This eventually takes you back to the initial view, where you’re invited to explore the data.

The overview first provides an opportunity for the reader to set a baseline as it relates to their own demographic. Then you see how specific groups are different or similar to that baseline. At the end, with a different baseline set, you can compare one more time.

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Bitcoin scam, Twitter hacked

Twitter was hacked yesterday. Over a few hours, prominent accounts were tweeting that they were feeling generous during these times, and that if you sent them Bitcoin, they would send double the amount back. For The New York Times, Matthew Conlen and Lazaro Gamio show the Bitcoin scammed as more tweets flowed in.

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Increase in cases since states reopened

Using the now all too familiar baseline chart, where all of the time series line up relative to to starting point, The New York Times shows how case rates have fared since states reopened. Up, up, and away.

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Cases vs. testing

There have been assertions that increased case counts are all from increased testing. As you might expect, it’s not so clear cut. Andrew Witherspoon and Caitlin Owens for Axios show the changes in testing against changes in cases.

So in the wideout view of every state, the more-testing-more-cases assertion isn’t so straightforward.

ProPublica provided a similar comparison a couple of weeks ago, but I like the difference charts here for every state. They make the gaps more obvious.

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Gaps between black and white America

New York Times Opinion compared several demographics, such as unemployment and income, between majority-black and majority-white neighborhoods in the United States.

They come back to the zipper chart technique where the dots start together and then separate to emphasize the gaps. Horizontally, dots are sorted by smallest to largest difference.

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