Following Nathan Yau’s excellent tutorial for creating heat maps with time series data (he used vehicle accidents by day for a year), I visualized 3,559 of my tweets back to March 2009.
These maps, created with a modified R script from the tutorial, show how often I sent tweets (both personal and RT), with darker shades representing more activity. It’s fun to go back to the dark days and recall what sparked flurries of tweets:
Lately I’ve been experimenting with bubble charts in R based on Nathan Yau’s great tutorial. In this case, I wanted to see the relationship between higher education and marriage among women by state.
Some states — such as Idaho, Utah and Wyoming — have both high marriage rates and low higher education rates. But that really says more abou those states than whether marriage and higher education correlate. Washington, D.C., for example, has the highest higher education rate and the lowest marriage rate.
Still, it’s fun to see how states compare. View a larger version here.
Data source: U.S. Census Bureau, American Community Survey
Yesterday I posted a map that used proportional symbols to visualize the home cities of Online News Association conference attendees. Today’s version uses great circles to map the routes attendees took to Boston (assuming they had direct flights, of course). Red lines represent more attendees from a location:
Inspired by Nathan Yau’s great tutorial. (Thanks also, Nathan, for the generous help today).