Publications by hrbrmstr
Overcoming D3 Cartographic Envy With R + ggplot
When I used one of the Scotland TopoJSON files for a recent post, it really hit me just how much D3 cartography envy I had/have as an R user. Don’t get me wrong, I can conjure up D3 maps pretty well [1] [2] and the utility of an interactive map visualization goes without saying, but we can make great static maps in R without a great deal of eff...
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Plot Me Like a Hurricane (a.k.a. animating historical North Atlantic basin tropical storm tracks)
Markus Gessman (@MarkusGesmann) did a beautiful job Visualising the seasonality of Atlantic windstorms using small multiples, which was inspired by both a post by Arthur Charpentier (@freakonometrics) on using Markov spatial processes to “generate” hurricanes—which was tweaked a bit by Robert Grant (@robertstats)—and Gaston Sanchez‘s Vi...
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Spending Seized Assets – A State-by-State Per-capita Comparison in R
The Washingon Post did another great story+vis, this time on states Spending seized assets. According to their sub-head: Since 2008, about 5,400 police agencies have spent $2.5 billion in proceeds from cash and property seized under federal civil forfeiture laws. Police suspected the assets were linked to crime, although in 81 percent of cases n...
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Moving The Earth (well, Alaska & Hawaii) With R
In a previous post we looked at how to use D3 TopoJSON files with R and make some very D3-esque maps. I mentioned that one thing missing was moving Alaska & Hawaii a bit closer to the continental United States and this post shows you how to do that. The D3 folks have it easy. They just use the built in modified Albers composite projection. We R f...
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Visualizing Historical & Most-likely First Snowfall Dates for U.S. Regions
UPDATE: You can now run this as a local Shiny app by entering shiny::runGist("95ec24c1b0cb433a76a5", launch.browser=TRUE) at an R prompt (provided all the dependent libraries (below) are installed) or use it interactively over at Shiny Apps. The impending arrival of the first real snowfall of the year in my part of Maine got me curious about what...
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Power Outage Impact Choropleths In 5 Steps in R (featuring rvest & RStudio “Projects”)
I and @awpiii were trading news about the power outages in Maine & New Hampshire last night and he tweeted the link to the @PSNH Outage Map. As if the Bing Maps tiles weren’t bad enough, the use of a categorical color scale instead of a sequential one[1] caused sufficient angst that I whipped up an alternate version in R between making pies and...
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Making Static & Interactive Maps With ggvis (+ using ggvis maps w/shiny)
Even though it’s still at version 0.4, the ggvis package has quite a bit of functionality and is highly useful for exploratory data analysis (EDA). I wanted to see how geographical visualizations would work under it, so I put together six examples that show how to use various features of ggvis for presenting static & interactive cartographic cr...
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New R Package: metricsgraphics
Mozilla released the MetricsGraphics.js library back in November of 2014 (gh repo) and was greeted with great fanfare. It’s primary focus is on crisp, clean layouts for interactive time-series data, but they have support for other chart types as well (though said support is far from comprehensive). I had been pondering building an...
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New R Package: cdcfluview — Retrieve Flu Data from CDC’s FluView Portal
Towards the end of 2014 I had been tinkering with flu data from the CDC’s FluView portal since flu reports began to look like this season was going to go the way of 2009. While you can track the flu over at The Washington Post, I like to work with data on my own. However the CDC’s portal is Flash-driven and there was no obvious way to get th...
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NASA GISS’s Annual Global Temperature Anomaly Trends (dplyr/ggplot version)
D Kelly O’Day did a great post on charting NASA’s Goddard Institute for Space Studies (GISS) temperature anomaly data, but it sticks with base R for data munging & plotting. While there’s absolutely nothing wrong with base R operations, I thought a modern take on the chart using dplyr, magrittr & tidyr for data manipulation and ggplot2 for...
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