Everyday Data: A Blog

Welcome to my blog: A place for random, interesting tidbits and ideas on how to look at data in everyday life

Statistics, or Stories?

Politicians make me queasy. So do self-help books, Twitter wars, linguistics professors, even TED talks. It’s not that they lack value—Twitter excepted, of course—it’s that they rely too heavily on examples and stories to persuade. Why? It’s effective. As Chip and Dan Heath put it in Made to Stick (one of those self-help books), anecdotes—simple,Continue reading “Statistics, or Stories?”

Corruption of the Youth! Profanity in Music, 1958–2019

There are many neat studies on the internet of lyrical content in popular songs. People have claimed that music is getting more repetitive and sexual, among many other things. Here’s a cool analysis of word sophistication, n-grams, and lyric sentiments. Wanting to do something similar, I scraped the weekly Billboard 100 list of songs sinceContinue reading “Corruption of the Youth! Profanity in Music, 1958–2019”

A Timeline Visualization Tool from the Future

Quick follow-up to my last post: my father’s been developing a professional tool called TimeStory, built for visualizing timelines easily and aesthetically. It’s perfect for the Billboard analysis I was doing: Timelines represent the span between artists’ first and last weeks on the Billboard 100, up to last month. Artists are ranked by their totalContinue reading “A Timeline Visualization Tool from the Future”

Lil Nas X vs. The Beatles? The Billboard 100’s Most Successful Artists

There are many ways to judge an artist, and the best ways are probably entirely nonstatistical. But add up all these millions of ways that different people rate music, and you get something pretty close to metrics like total album sales, YouTube streams, and other quantitative popularity measures. In this post, and hopefully in aContinue reading “Lil Nas X vs. The Beatles? The Billboard 100’s Most Successful Artists”


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