Photographic evidence.

When they say
"The camera adds 10 pounds"
they're not kidding.
Here's the effect with different camera lenses: pic.twitter.com/xmwbsflVKd

— Jim Zub (@JimZub) July 26, 2016

Every four years, campaign finance data from the Federal Election Commission peeks its head out into the light of importance. Committees and officials must report significant contributions to campaigns, which in turn provides a view into who is on who’s side. The weird thing is that the data was oddly tough to access for an everyday user, which is why third-party APIs and news orgs were and still are the primary place to go for information.

There’s a new FEC site in beta now though. A collaboration between the FEC and 18F, it’s a lot better than undocumented files only accessible via FTP. Search, browse, and download data for a candidate or contributor.

You’re likely familiar with the state grid map form used these days. Instead of using geographic boundaries, you place states in a grid layout, giving an equal-sized cell to each state so that they all get the same visual weight. The Wall Street Journal combined it with a time series for each state in their field guide to shifting states.

The country is more than just red states and blue states. Some former battlegrounds have moved to the sidelines. Other once reliably Republican or Democratic states have come into play as the composition of their electorates change.

Red means more Republican than the national popular vote, and blue means more Democrat.

The slow, animated load makes the map. It reminds me of the New York Times’ “wind” map from the previous election. Life-like.

As we delve deeper into election season, politicians will spit out more and more statistics to lend some factitude to their talking points. Some are real, and others will be less real. David Spiegelhalter for the Guardian provides a nine-point guide on how to sift out the latter.

On estimates and margin of error:

Next time you hear a politician boasting that unemployment has dropped by 30,000 over the previous quarter, just remember that this is an estimate based on a survey. And that estimate has a margin of error of +/- 80,000, meaning that unemployment may well have gone down, but it may have gone up – the best we can say is that it hasn’t changed very much, but that hardly makes a speech. And to be fair, the politician probably has no idea that this is an estimate and not a head count.

Ah, that makes me feel so warm and fuzzy inside.

Speaking of virtual reality visualization, this Nasdaq roller coaster by Roger Kenny and Ana Asnes Becker for the Wall Street Journal is quite the ride. The underlying data is just the index’s price/earnings ratio over time, but you get to experience the climbs and dips as if you were to ride on top of the time series track.

Weeeeeee, bubble burst.

More of an experiment, this VR map, by the Google Trends Lab in collaboration with Pitch Interactive, shows what people asked about Brexit leading up to the vote. It’s basic data-wise, but you can see potential for more details and get a feel for how virtual reality data visualization might work.

And besides, I’ll accept any excuse these days to bust out the Google Cardboard. Even if it’s basic visually, it’s easy to see how this point of view might bring you closer to the data.

See also the details on what the makers learned from the experiment.

A nanosecond is a billionth of a second, but we’re not very good with really big or tiny numbers. So, Grace Hopper, the inventor of “the first compiler for a computer programming language”, explains to some eager, young minds with a piece of wire.

You might think piecing together satellite imagery is a straightforward task of lining up latitude and longitude points. But if you think that, you haven’t actually worked with these things. David Yanofsky, part of the Quartz Things Team, describes how he processes satellite images for one coherent image and how you can to.

He starts at downloading the data, moves into stitching together a mosaic, and then adjusting the color so that everything is smooth and continuous.

Finished workflow on GitHub.

Pokemon is everywhere these days. I think it’s just something the world really needs right now. I know very little about the universe, but I do like it when people analyze fictional worlds and characters. Joshua Kunst grabbed a data dump about all the Pokemon (seriously, I don’t even know if I’m referring to them/it/thing correctly) and clustered them algorithmically. The t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to be specific.

Many hate pie charts. Others love them. I think they’re useful but have limitations. Most of these are just feelings though, maybe accompanied by an Edward Tufte quote. We need facts. Robert Kosara and Drew Skau provide some in their recent studies on how we read pie charts. There appears to be a good chance people don’t read the things correctly.

But I found Kosara’s follow-up more interesting. He dug up a paper that he and his student Caroline Ziemkiewicz wrote a few years ago on square pie charts. Instead of filling a circle to represent proportion, the square pie chart fills a — wait for it — square.
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As an introduction to a series on gun deaths in America, FiveThirtyEight uses a straightforward grid view to show the breakdowns. Each square represents a single gun death, and as you click through, the squares are colored to show various groups. For example, the above represents gun deaths from homicide in blue, about half of which are young men and two-thirds of that subgroup are black.

Sometimes it’s more useful to break the data down to its elements.

Ben Orlin of Math With Bad Drawings explains the pitfalls of using summary statistics — mean, median, and mode — to make decisions in life. Aggregates like these are meant as wideout overviews. Look deeper for details.

Road trips are fun, but it can be hard to enjoy yourself when you end up in a place during its hottest or coldest day of the year. Wouldn’t it be nice to travel across the country and have nice weather every single day? This road trip is for you.
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R has found its way into a good number of news groups who do data journalism. Andrew Flowers for FiveThirtyEight talks about how they use the statistical computing language throughout their workflow.

R is used in every step of the data journalism process: for cleaning and processing data, for exploratory graphing and statistical analysis, for models deploying in real time as and to create publishable data visualizations. We write R code to underpin several of our popular interactives, as well, like the Facebook Primary and our historical Elo ratings of NBA and NFL teams. Heck, we’ve even styled a custom ggplot2 theme. We even use R code on long-term investigative projects.

Clive Thompson for Smithsonian Magazine gives a quick history lesson on infographics.

[D]ata visualization was rare because data was rare. That began to change rapidly in the early 19th century, because countries began to collect—and publish—reams of information about their weather, economic activity and population. “For the first time, you could deal with important social issues with hard facts, if you could find a way to analyze it,” says Michael Friendly, a professor of psychology at York University who studies the history of data visualization. “The age of data really began.”

Thompson uses “infographic” but really means “data visualization” most of the time, but still a good overview.

And while we’re on the topic of old visualization stuff, you should also check out Scott Klein’s newsletter, Above Chart. The history provides fine context for where visualization is at now.

Last year the New York Times interviewed Justin Bieber, Diplo, and Skrillex about how they put together their song Where Are Ü Now. NYT coupled the video with data visualization elements that helped you understand what the artists talked about. Pretty great.

Now here’s what happens when you switch out the original song and insert the Seinfeld theme song.

what if Bieber diplo and skrilex created the seinfeld themehttps://t.co/BUTOrNDwGQ

— Seinfeld Current Day (@Seinfeld2000) July 4, 2016

Also pretty great.