Some people love the Tidyverse in R. Others are less fond of it. For me, the more tools the better.

When it comes to reading lists, we usually look for what’s popular, because if a lot of people read something, then there must be something good about it. Russell Goldenberg and Amber Thomas for The Pudding took it the other direction. Using checkout data from the Seattle Public Library, they looked for books that haven’t been checked out in decades.

Also: How cool is it that there’s an API to access library checkout data?

National Geographic went all out on their atlas of moons. Space. Orbits. Rotating and interactive objects in the sky. Ooo. You’ll want to bookmark this one for later, so you can spend time with it.

As you might expect, NASA collects a lot of data, and much of it is seasonal. Eleanor Lutz animated a few maps to show the detail:

To show a few examples, the NASA Earth Observations website includes data on seasonal fire incidence (1), vegetation (2), solar insolation, or the amount of sunlight (3), cloud fraction (4), ice sheet coverage (5), and processed satellite images (6). My own map (7) combines the ice sheet data and the Blue Marble satellite images (6). The NEO database also has many more interesting datasets not shown here, like rainfall, chlorophyll concentration, or Carbon Monoxide.

Check out the final result. And, if you want to make your own, Lutz published her code on GitHub.

The citizenship question for the upcoming Census is still stuck in limbo. One of the arguments against the question is that it could lead to a significant undercount in population, which can lead to less funding. For Reuters, Ally J. Levine and Ashlyn Still show how this might happen with a highlight on federal programs that rely on population estimates.

Roger Peng provides a lesson on the roots of R and how it got to where it is now:

Chambers was referring to the difficulty in naming and characterizing the S system. Is it a programming language? An environment? A statistical package? Eventually, it seems they settled on “quantitative programming environment”, or in other words, “it’s all the things.” Ironically, for a statistical environment, the first two versions did not contain much in the way of specific statistical capabilities. In addition to a more full-featured statistical modeling system, versions 3 and 4 of the language added the class/methods system for programming (outlined in Chambers’ Programming with Data).

I’m starting feel my age, as some of the “history” feels more like recent experience.

You can also watch Peng’s keynote in the video version.

It’s about purpose. It’s about who your work is for. It’s about what you’re trying to show.

Alfred Ng for CNET:

Sen. Chris Coons, a Democrat from Delaware, sent a letter to Amazon CEO Jeff Bezos in May, demanding answers on Alexa and how long it kept voice recordings and transcripts, as well as what the data gets used for. The letter came after CNET’s report that Amazon kept transcripts of interactions with Alexa, even after people deleted the voice recordings.

The deadline for answers was June 30, and Amazon’s vice president of public policy, Brian Huseman, sent a response on June 28. In the letter, Huseman tells Coons that Amazon keeps transcripts and voice recordings indefinitely, and only removes them if they’re manually deleted by users.

Marvelous.

Mark Rober, who is great at explaining and demonstrating math and engineering to a wide audience, gets into the gist of machine learning in his latest video:

I have a feeling we’re in for a lot of manipulated videos as we get closer to the election. The Washington Post provides a guide for the different types. I hope they keep building on this with a guide on how to spot the fakes, but as they say, knowing is half the battle.

Here is the good stuff for June 2019.

In some public places, such as schools and hospitals, microphones installed with software listen for noise that sounds like aggression. The systems alert the authorities. It sounds useful, but in practice, the detection algorithms might not be ready yet. For ProPublica, Jack Gillum and Jeff Kao did some testing:

Yet ProPublica’s analysis, as well as the experiences of some U.S. schools and hospitals that have used Sound Intelligence’s aggression detector, suggest that it can be less than reliable. At the heart of the device is what the company calls a machine learning algorithm. Our research found that it tends to equate aggression with rough, strained noises in a relatively high pitch, like D’Anna’s coughing. A 1994 YouTube clip of abrasive-sounding comedian Gilbert Gottfried (“Is it hot in here or am I crazy?”) set off the detector, which analyzes sound but doesn’t take words or meaning into account. Although a Louroe spokesman said the detector doesn’t intrude on student privacy because it only captures sound patterns deemed aggressive, its microphones allow administrators to record, replay and store those snippets of conversation indefinitely.

Marvelous.

In the biggest crossover event of the century, Tom Lum used the Wikipedia API to chart the number of views for every reference in Billy Joel’s We Didn’t Start the Fire. Yes. [via @waxpancake]

Sonja Kuijpers used abstract imagery to represent some sobering numbers:

You might be wondering what you are viewing here. This landscape, each element in it represents a person who committed suicide in the Netherlands in the year 2017.

The cityscape leads into more traditional views, which in turn feel much more heavy.

For The New York Times, Kevin Roose on the possibility of machines becoming your boss:

The goal of automation has always been efficiency, but in this new kind of workplace, A.I. sees humanity itself as the thing to be optimized. Amazon uses complex algorithms to track worker productivity in its fulfillment centers, and can automatically generate the paperwork to fire workers who don’t meet their targets, as The Verge uncovered this year. (Amazon has disputed that it fires workers without human input, saying that managers can intervene in the process.) IBM has used Watson, its A.I. platform, during employee reviews to predict future performance and claims it has a 96 percent accuracy rate.

Splendid.

Ben Fry using the “tropiest of design tropes”, describes his goals for visualization. On communication:

Communication is the most basic part: the table stakes of information design. If the piece doesn’t communicate, then it’s useless. A lot of time, attention, and effort goes into creating PDF documents that few people will ever read. In the past this was even more expensive, because the result was elegant printed reports that would go straight to someone’s shelf. If you create something that nobody uses, you’ve failed to communicate.

You know those sped up videos where there’s a long line for something and someone walks the length of it? The New York Times did the scrolly equivalent for the recent Hong Kong protest, using snaps from aerial video and stringing them together geographically. A lot of people showed up.

After you’ve made your carefully designed data graphics, you probably want to share them online at some point. This is how I do it.