Recently I saw this really cool visualization around the reliance of the North Korean economy on trade from China. This tree_map is striking in a number of ways. One is that it conveys a ton of information in an easy on the eyes and interactive way. Another is the data itself. North Korea really does have quite the reliance on China. This got me thinking how I would visualize the information with R and I then came across a really cool example of how someone else visualized the same data via a sort of stream graph.
It is summer here in Chicago which means tourists abound and Divvy bikes are everywhere. Awhile ago, and a whole site ago, I posted a little how-to on making calendar heatmaps using the publicly available Divvy data. While that site is gone there are still some links to it out on the internet, one being the awesome Revolution Analytics blog, so instead of leaving people with a 404 I decided to revisit it.
I am not sure if you have heard about it yet but there will be a solar eclipse on 8/21/17. If you are one of a very few people who this is news to, congrats! As the day nears there have been a lot of articles and posts on the subject, with more than a few really awesome visualizations. The unique part about the eclipse is its path of totality that cuts through the heart of the United States.
This is a page dedicated to weekly predictions for English Premier League. I am a fan of the Premier League and I support the Southampton Saints though try as I might no sort of projection I do can make them better. A lot of the data for this comes from the awesome engsoccerdata package available on github. My predictions are under constantly construction, but they are based on Poisson distributions, and you can read a little bit about those here.
Awhile ago I posted about plotting the temperatures of Lincoln Nebraska that was inspired by a FiveThirtyEight article visualization. Well the internet have been abuzz with a new package found on github by Claus Wilke called ggjoy. So I decided to do a quick little post playing with it. code Once you have the package installed from github, go ahead and download this csv of the 2016 temperatures in Lincoln, NE.
I guess what turned into one post about ACS data is now an installment series. The #rstats community is so productive with its output that as I finally figure out the extant of one package someone has made a streamlined, optimized, or shiny new one. Kyle Walker’s new tidycensus package is the latest in that long line and before you go any further I encourage you to follow the link to read his brief introductions.
Graph!? more like art Every once in a while, I run into an article with some data that really intrigues me, and sometimes I run into a data visualization that makes me think, “How can I do something like that?” Sometimes they both happen simultaneously and I have to drop everything to start working on it. That happened to me with the 538 article, The Most Conservative And Most Liberal Elite Law Schools.
UPDATE: I had mentioned that I did not believe ggplot2 was the right route for the four panel style presentation but see the R-Bloggers post on how to achieve it with ggplot2 and ggalt. It has been awhile since I have posted a tutorial, or anything for that matter, on my website so I decided to revisit some data from my old post. If you recall in that quick little visualization I just wanted to plot this great new data set.
Below is a tutorial that helps take ZIP code data and, with R, get rough latitude and longitude data from them as well as County. Then using ggplot2 we can create a nice visual of the data plotted at the county level. The first section was written as part of a larger project and I like to keep it around as it was one of the first tutorials on this website.