A couple of years back The New York Times asked readers to draw on a blank plot the relationship between income and college attendance. It was a way to get you to think about your own preconceptions and compare them against reality. The Times recently applied the same mechanic to the changes during Barack Obama’s presidency.

Bonus: Here’s how to make your own you-draw-it graph.

Focusing on immigrant characters in television shows, The Guardian US’s data editor Mona Cholabi interviewed Sesame Street’s Count von Count. It only lasts a few minutes but will put a smile on your face.

I have an affinity for new things designed as old things, so this brief history of data visualization by RJ Andrews hits the spot.

I have placed cartoons representing important works of data visualization along a fictitious scroll map in the style of Ogilby’s atlas. The road marches you through time, passing many charts, through towns (named for key contributors whose charts make up said towns), and over waterways that mark important dates.

If you’re interested in a comprehensive compendium, you should check out Michael Friendly’s Milestones project.

There was a time when the best way to get a view from above was to hop into a hot air balloon, which eventually led to the so-called “balloon map.” Cara Giaimo for Atlas Obscura starts the story with a ballooner named Thomas Baldwin.

In an age of transatlantic flights and Google Earth, Baldwin’s suggestions seem a bit quaint. But in his time, when almost everyone was stuck on the ground, Baldwin’s attempts to pin down an accurate sky-view were heroic. Over the following century, entrepreneurs, military spies, and tourist boards alike would follow his lead, transforming some of the world’s most vital views into lovely, quirky “balloon maps.”

Hand-drawn, detailed maps and graphics like this — before computers — always blows my mind.

Infographics devolved a bit in recent years, but there was a time the term wasn’t immediately associated with content marketing. (And there is still plenty of good infographic work that actually informs.) National Geographic is one such source of inspiration, and now you can get a best-of collection in book form that spans over a century.

With an essay by Nigel Holmes, charting the evolution of National Geographic over the decades and its pioneering use of graphics, as well as four fold-outs mimicking original pull-outs or inserts in the magazine, the book stands as a defining record of one of the world’s best-known publications as much as it is a beautifully presented repository of discovery and learning.

Get it on Amazon.

Data can be fact and analytical. It can help you make objective decisions. Data can also evoke the feels, helping you understand and relate to something that used to be foreign. Lisa Charlotte Rost, currently a Knight-Mozilla OpenNews fellow at NPR, makes the case in this talk.

Sometimes data can look really noisy when you zoom in close, and so you zoom out to get a better look at an overall trend. Sometimes it goes the other way. The Guardian looks at gun violence data at the local, census tract level, to focus on not just cities, but the clusters within the cities.

America’s gun policy debate is usually driven by high-profile mass shootings that seem to strike at random, and it focuses on sweeping federal gun control or mental health policies. But much of America’s gun homicide problem happens in a relatively small number of predictable places, often driven by predictable groups of high-risk people, and its burden is anything but random.

I like the scroll action that first shows the map with randomly distributed dots and then transitions to reality. Drives the point home.

It’s always fun to go back to sports articles and graphics that were a lead-up to a game the day after. The newest addition: this graphic from The New York Times that shows wide receiver Odell Beckham Jr.’s catches this season. It shows route patterns, the catch, yards after the catch, and touchdown paths. If you’re not into football, just take it in as a small multiples example.

In planning for the upcoming inauguration, the U.S. military is using a giant multi-part rollout map to do walkthroughs. Yep.

In some areas of the United States it poured down rain, which caused historic floods, and in other areas there was a lack of rain, which caused historic drought. The Washington Post has a map for that. Purple means less rain than usual, and green means more.

By definition, heartland is some central place of importance of a country. But ask people where to find America’s heartland, and the actual boundaries of this so-called area grows fuzzy. The Upshot asks its readers the same question with a multiple-choice poll.

First, it gets you to think about your concept of the heartland. Second, it provides a baseline to compare against others. Third, it goes into more detail for each option. And by the end, well, you still don’t quite know where the heartland is, but at least you learn something.

I have a feeling we’ll see this story format more this year.

We tend to think of demographics on a large scale. Countries, counties, and cities. Then we look at trends over time for thousands or millions of people. But it can be equally, if not more, interesting to look at the same trends at a personal level. This is what Dorothy Gambrell did. She charted her ten closest friends in New York.

I like how even though the charts are for only ten people, we see similar patterns that we might see for millions.

That was fast. Just when you get used to dating with 2016, 2017 comes along. A big thank you to all of you who make this site possible. I thank my lucky stars that this is what I get to do every day, and I hope I get to do it for years to come.

This year I made a conscious effort to learn to visualize data with d3.js. I still used R for exploration and data preparation, but on the presentation side of things, I always tried to go native in the browser first.

The popularity of my experiments and learning exercises took me by surprise. This was the ninth year of FlowingData, and from a pageviews perspective, it was almost twice that of any other year. And keep in mind that blogs were supposed to have died multiple times over already.
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Map who “likes” television shows on Facebook, by ZIP code, and you get a good idea of cultural boundaries. This is what Josh Katz for the Upshot did for 50 of the most liked shows in the United States, finding three distinct regions: “cities and their suburbs; rural areas; and what we’re calling the extended Black Belt.”

Nikhil Sonnad for Quartz mapped the top 100,000 words used in tweets. Search to your heart’s content.

The data for these maps are drawn from billions of tweets collected by geographer Diansheng Guo in 2014. Jack Grieve, a forensic linguist at Aston University in the United Kingdom, along with Andrea Nini of the University of Manchester, identified the top 100,000 words used in these tweets and how often they are used in every county in the continental United States, based on location data from Twitter.

See also the dialect quiz and maps by Josh Katz from a few years back.

You might remember Bayes mentioned a few times in your introduction to statistics course. Or maybe you hear it every now and then in the news, and maybe you’re not quite sure what people are talking about. Here’s an introduction video to Bayesian statistics by Brandon Rohrer.

There’s also a text version, if you prefer that over video. [via Revolutions]

The tweenr package in R, by Thomas Lin Pedersen, helps you interpolate data for easier animated transitions.

tweenr is a small package that makes it easy to interpolate your data between different states, specifying the length of each change, the easing of the transition and how many intermediary steps should be generated. tweenr works particularly well with gganimate but can be used for any case where interpolation of data is needed.

Why I’m just now learning about this, I have no clue. I thought we were friends.

From Google Research, a look at how discrimination in machine learning can lead to poor results and what might be done to combat:

Here we discuss “threshold classifiers,” a part of some machine learning systems that is critical to issues of discrimination. A threshold classifier essentially makes a yes/no decision, putting things in one category or another. We look at how these classifiers work, ways they can potentially be unfair, and how you might turn an unfair classifier into a fairer one.