IN THIS POST I’M JUST GOING TO share a few data visualizations I’ve been working on today. For most, no code, but these build off my previous posts here and here so you can check there for hints and I’ll post some more examples with code later. Population maps This one shows the evolution of population by county without the distribution plots I included last time. We discussed these data in our last post.
SOMETIMES YOU ACTUALLY LEARN SOMETHING from social media. Today on Twitter I happened across this Tweet via @kyle_e_walker: Seems somebody posted estimates of the U.S. population by county (defined by 2010 county definitions) going back to 1790. This is a perfect dataset to practice my mapping with R. The data are conveniently available via the University of Minnesota. The data come in a nice spreadsheet that we can easily import into R and manipulate.
IN THIS POST I WANT TO DOCUMENT some R code I’ve recently been working on combining maps and distribution plots. As I discussed earlier lots of interesting data will be released in the fall and I want to be ready for it. Some of these snippets can be recycled when the new data is available. Maps One area of data visualization with R I haven’t explored much is mapping. Part of this reason is because I’ve had other tools to use, but usually it’s because I’m in a hurry.
No code, just some gifs: Comparing mortgage rates by week (2014, 2015,2016) Annotated history of mortgage rates
AUGUST IS ALMOST OVER, and it’s nearly back to school season. And that means one thing. No, not that we’re about to get a chance to watch the #1 NCAA football program of all time dominate the gridiron (though that’s awesome too). No, it’s data release season! A data swarm is on its way. From American Community Survey to the American Housing Survey to the annual Home Mortgage Disclosure Act Data many statistical data releases come out in September and October.
A FEW DAYS AGO I POSTED on trends in household debt using data from the the New York Federal Reserve Bank’s Consumer Credit Panel. The post got many responses, some observing that while student debt has grown a lot the absolute level of it is small relative to mortgage debt. I had made that point in my post, but the pictures caught the attention of many who didn’t read the post or catch the point.
OVER THE PAST THREE MONTHS I HAVE MADE several new house price visualizations. In these meditations I’ll consider some recent graphs and provide R code for them. For reference, prior meditations are available at: Part 1: data wrangling Part 2: sparklines and dots (animated) Part 3: bubbles and bounce Part 4: graph gallery Meditation 1: Median sales price trends Earlier this week, the National Association of Realtors (NAR) released their quarterly update on metro area median house prices (data here).
TODAY the NEW YORK FEDERAL RESERVE BANK released its Quarterly Report on Household Debt and Credit. These data come from the Center for Microeconomic Data based on credit records from Equifax. R code for the graphs are posted at bottom of page Trends in household debt balances One of the key statistics tracked in the report (full data can be found here) is household debt balances. They break debt balances out by loan type:
See you later I’m going to be away from the grid (web, twitter etc.) for a few weeks. Back later this summer! {% include JB/setup
IT WAS A BUSY WEEK FOR ECONOMIC AND HOUSING DATA. Existing and new home sales came out, the Joint Center for Housing Studies of Harvard University released their annual State of the Nation’s Housing, and the U.K. voted to leave the European Union (the Brexit). We’ll recap these data and events through charts I’ve created and shared throughout the week. In this post, I’ll share each of the charts with some commentary, and then below, I’ll include the R code I used to generate the charts.