Taxes are due today in the U.S. (yay). Geoffrey A. Fowler for The Washington Post on the part when tax services like TurboTax and H&R Block ask for your data:

What he discovered is a little-discussed evolution of the tax-prep software industry from mere processors of returns to profiteers of personal data. It’s the Facebook-ization of personal finance.

America’s most-popular online tax-prep service, Intuit’s TurboTax, also asks you to grant it additional access to the data in your return to “enrich your financial profile, communicate with you about Intuit’s services, and provide insights to you and others.”

[…]

The good news is because of Internal Revenue Service rules, this is one data request you can actually say “no” to while continuing to do your taxes online. And if you already clicked “agree” and now have changed your mind, there are some steps you can take, too.

NZ Herald talked to Ross Ihaka, one of the creators of R:

Today, R is depended upon around the world by analysts, data scientists and big-name companies like Facebook, Google, Amazon and the New York Times, and it’s garnered Ihaka something of a rockstar status in the field of data science and statistics.

He’s received numerous accolades over the years recognising his work, such as the Royal Society of New Zealand’s prestigious Pickering Medal, and the Statistical Computing and Graphics Award from the American Statistical Association.

Asked how many people use R on a daily basis, Ihaka’s guess is in the millions but he’s not quite sure how many million.

One of the reasons R is called R is because Ihaka and co-creator Robert Gentleman both had first names that started with the letter.

Earlier this year, an underwater volcano erupted in the island nation of Tonga. For The New York Times, Aatish Bhatia and Henry Fountain describe the effects of the eruption, which lasted for days and rippled around the world. The introductory animated globe shows the pressure wave and gives a good sense of the eruption’s massive scale.

Manually editing charts is worthwhile, despite the possibility of manually making mistakes.

Based on leaked IRS data for the 400 wealthiest Americans, ProPublica provides a comparison of their incomes and the lower taxes they paid between 2013 and 2018. This might be best piece so far from ProPublica’s IRS series in terms of understanding the big picture from their dataset. Also, that “smaller than a pixel” note for the average American is doing some heavy lifting.

This map by @loverofgeography shows the usual dinner times for countries in Europe. There’s no source listed, so I’m not sure if this is based on actual data or just anecdotal, but I think the latter. From my meager experience, this seems right? I might have to check out European time use data.

Jeff Bezos’ wealth is difficult to understand conceptually, because the scale is just so much more than what any of us are used to. So for NYT Magazine, Mona Chalabi took a more abstract approach, focusing less on monetary values and more on how many multiples more Bezos has compared to the median household.

See also The Washington Post’s comparison from a couple of years ago, scaling things down to spending equivalencies. I think Chalabi’s comparison works better. It’s abstract compared with abstract.

Georgios Karamanis plotted the ratio of girls-to-boys over time for all the names in the Social Security Administration dataset. You can see the more gender-specific names at the edges and more gender-neutral names clustering in the middle.

Those dips in 1989 and 2004 are curious. Otherwise, the increase in gender-neutral names seems to match up with my analysis from a while back.

More lines can mean more patterns, more cycles, and more context.

Microsoft researchers analyzed keystrokes by time of day, for a sample of Microsoft employees during this past summer. You can see the typical peaks during work hours with a dip for lunch. But among 30% of workers in the sample, there was a third peak starting around 9 o’clock in the evening.

That third peak felt too close to home for me.

The 2022 Oscars came and went, and it was like all anyone could talk about was how outfits paired with public health charts. William Lopez has the collection.

For Nature, Lynne Peeples spoke to the people behind many of the popular covid dashboards and the lessons learned:

Among the shared themes for the dashboards were simplicity and clarity. Whether you are producing visuals and analytical tools for policymakers or for the public, Blauer says, the same rules of thumb apply. “Don’t overcomplicate your visualization, make the conclusions as clear as possible, and speak in the most basic of plain-language terms,” she says.

Yet, as other data scientists point out, presenting data simply might not be enough to ensure viewers get the message. For one thing, attention to detail matters. Ritchie recalls how she and her team spent hours focused on the titles and subtitles of charts, “because that is ultimately what most people will look at”. And in those titles and subtitles, the analysts made sure to specify ‘confirmed’ deaths or ‘confirmed’ cases. “An emphasis on ‘confirmed’ is really important because we know that it’s an underestimate of the total,” says Ritchie. “It might seem very basic, but it’s really crucial to how you understand the data and the scale of the pandemic.”

The New York Times shows how Russia has tried to take over and how Ukraine continues to stop the offensives. The mixed media piece pulls you in to how different strategies have worked and have not, at least the best you can through a screen.

For privacy reasons, there’s a 72-year restriction on individual Census records, which include names and addresses. It’s 72 years today since the release of the 1950 Census. The scanned paper records are available for browsing and downloading.

For The Washington Post, Bonnie Berkowitz and Artur Galocha report on several facets of Russia’s logistics, from poor protection, to poor communication, to vehicle breakdowns.

Here’s the good stuff for March.

Say what you will about circular visualization, but the spiral plays. This one from NASA shows global temperature change over time:

The visualization presents monthly global temperature anomalies between the years 1880-2021. These temperatures are based on the GISS Surface Temperature Analysis (GISTEMP v4), an estimate of global surface temperature change. Anomalies are defined relative to a base period of 1951-1980. The data file used to create this visualization can be accessed here.

This is based on Ed Hawkins’ chart originally from 2016, but watch to the end for some extra sauce.

Selecting a color palette for a single chart can be tricky, which is why we see so many charts that just go with defaults. Selecting a color palette for all your charts is a bigger challenge. For Datawrapper, Lisa Charlotte Muth has you covered with a detailed guide that describes the important bits, existing color palettes in the wild, and consideration for your readers.

You’ll want to save this for later. It just stops short of picking the colors for you.