Stories, Stats & Scatterplots: Inside data visualization at the Financial Times

Four years ago, John Burn-Murdoch was a journalist who had never written a line of code. These days, the senior data visualization journalist at the Financial Times says he rarely writes anything but code.

While what Burn-Murdoch dubs a “cool journey” sounds a little extreme (from zero to R, on the job, seriously?), he was never strictly a text journalist. He holds advanced degrees in data science and interactive journalism. Before landing at the FT, he worked as data journalist at The Guardian for two years.

On loan from the London headquarters to focus on bias in artificial intelligence and geographical inequality, the spiky-haired, elfin Burn-Murdoch offered a peek inside the workings of the FT data newsroom for about 50 members of the Bay Area d3 User Group.

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Crooked! Donald Trump’s most recent insults as a word cloud

UPDATE: The Times is still tracking the list of insults — as of January 2017 it grew to 305 — and added a visualization that shows the kinds of people and things most frequently insulted. (Spoiler alert: journalists and Democrats.)

The reporters at the New York Times combed through Republican presidential nominee Donald Trump’s Twitter feed for the most recent 250 insults to nations, people and random things – including a podium.

NYtimesThis is the kind of story that cries out for a visual representation – there has to be a better way to process the information than listing names of the people he insulted in alphabetical order and the tweets as quotes underneath them. What story does that tell?

Most commonly used words in Trump insults, by frequency.

Most commonly used words in Trump insults, by frequency. By Nicole Martinelli, via Wordle.

A quick word cloud will tell you that the most common insult for the straight-talking New Yorker is “crooked” (his go-to insult for rival Hillary Clinton) followed by “dishonest,” “bad,” and “failing.”

A couple of necessary caveats: this cloud was made with a tool called Wordle and the size of the word corresponds to the number of times it appears in the text. The text in the graphic was copied and pasted from the article on the NYT site without any additional weighting or manipulation. The program automatically cuts out common words (i.e. articles) but it would be interesting to see how the cloud shifts by cutting some filler words like “new” “news” “many” “another” etc.

Digital publishing gives public figures so many ways to broadcast a message – it’s our job as journalists to make sense of it. What would you trawl through other political figures tweets to understand?

Mapping the term ‘fiscal cliff’ with the New York Times API

8262373294_99fd141ed2_bAs time goes by, it looks like we’re all going to fall off the fiscal cliff.

When thinking about ideas to test out the New York Times API, which lets you dig into everything from campaign finance to geographic tags in the old gray lady’s formidable database, I wanted to keep it simple. (The learning curve was already steep for me, to be honest.)

The chart is my first pass at using the NYT API, based in part on Jer Thorp’s chapter about it in “Beautiful Visualization,” which you can find at Amazon  or a public library.

The result is a simple cliff graphic that mimics the momentum the term gained along with the budget crisis. Wikipedia dates the term “fiscal cliff” to 2011, but Federal reserve chairman Ben Bernanke gave it heft in February 2012 with testimony before the House Committee on Financial Services, the first mention is here: nyti.ms/Tcd7Lq