Here are some of the latest and greatest Plotly plots! Check out our Tumblr to see this same post with the interactive plots embedded, and to find more awesome content.

Kids These Days Listen to Longer Songs

Rhett Allain at WIRED is killing it again, this time with a series of plots on music. We circulated one of his plots on song length when it debuted, but now Rhett has authored a whole article on the subject, with new visualizations!

Here’s a plot of MusicBrainz data proving that average song length has indeed been creeping up:

“Did new technologies influence song length?” Rhett asks. “I am going to say that it’s plausible but not for certain. I still like the graphs though.”

We do too, Rhett!

Plots by a German Robo-DJ

Continuing on the theme of music plots, we noticed the user sortify making many 2d line and scatter plots and couldn’t help but wonder what they were about. Track names? Two axis grids? Paired graphs with different dashed lines?

It turns out that Sortify is a web service that takes in a playlist, analyzes the first 50 seconds of every track, and then figures out how similar/dissimilar they are with respect to 125 different features. Then it organizes your playlist according to those similarities.

As usual, a really cool idea makes for some pretty cool plots. These plots project those 125 features onto two axes — they’re before-and-after illustrations!

Against the Odds, New York Beat Los Angeles

We’ve been digging FiveThirtyEight’s content, so we’ve been remaking a lot of their vizes as interactive plots! Here’s one about the past, as predicted by the slightly-more-distant past:

“The 1970s had been a horrendous decade for New York, marked by a near-bankruptcy, a daylong blackout and a crime epidemic,” Silver tells us in his article, explaining the relatively flat slope of the dotted red line. “[But] New York in 1981 was on the verge of remarkable rebound.”

Keep an eye out for a blogpost about all this content soon. And you can find more FiveThirtyEight graphs at the FiveThirtyEight profile.

Right Where it Hurts

Pain scales are fascinating, and the one used in this bee sting barchart by Dreamshot is no exception. Participants in the experiment were stung in the forearm, told to rate that pain as a ‘5' and rank their subsequent stings accordingly.

This presumably mitigates the problem described in section ‘5' of Eula Biss’ essay “The Pain Scale”:

“The problem with scales from zero to ten,” my father tells me, “is the tyranny of the mean.”

Overwhelmingly, patients tend to rate their pain as a five, unless they are in excruciating pain. At best, this renders the scale far less sensitive to gradations in pain. At worst, it renders the scale useless.

As someone who has been stung by a bee, but never in the nostril, all I can say is “Ouch!”

Plotly: Helping You Make Histograms of Hockey Players (Other Things Too)

We’ve posted two new tutorials! First up, a histogram tutorial that features data from hockey fan Justin Fisher’s NHL draftbook.

Histograms show how many entries in a dataset occur within specific ranges of data — i.e. each bar in this histogram represents as many hockey players as its bar height. See our histogram explainer to go deeper, or our tutorial to make your own.