Engaging and popular visualisations are not always the easiest to read.
Andrew Gelman always has useful things to say. In a Wired article he talks about different types of data visualisation. The best ones often drawn in the viewer and make them engage in the scientific process as if discovering something for themselves.
“The most effective graphs both anticipate and shape expectations. Regardless of how complicated the graph, the same general principle holds. We make graphs for two reasons: to learn the unexpected (“exploratory data analysis,” in statistics jargon) and to communicate findings to others. Exploratory data analysis works off of models almost by definition—it is only through expectations that “the unexpected” is defined. “
I like the parallels drawn here between good data visualisation and art. Its not always about clearly stating your ideas, as much as wining over and exciting your audience. Everything is sales!📖 Read more here (2,447 words) 📖