Folks who’ve been tracking this blog on R-bloggers probably remember [this post](https:\/\/rud.is\/b\/2014\/11\/16\/moving-the-earth-well-alaska-hawaii-with-r\/) where I showed how to create a composite U.S. map with an Albers projection (which is commonly referred to as AlbersUSA these days thanks to D3).<\/p>\n
I’m not sure why I didn’t think of this earlier, but you don’t _need_ to do those geographical machinations every time you want a prettier & more inclusive map (Alaska & Hawaii have been states for a while, so perhaps we should make more of an effort to include them in both data sets and maps). After doing the map transformations, the composite shape can be saved out to a shapefile, preferably GeoJSON since (a) you can use `geojsonio::geojson_write()` to save it and (b) it’s a single file vs a ZIP\/directory.<\/p>\n
I did just that and saved both state and country maps out with FIPS codes and other useful data slot bits and created a small data package : [`albersusa`](https:\/\/github.com\/hrbrmstr\/albersusa) : with some helper functions. It’s not in CRAN yet so you need to `devtools::install_github(“hrbrmstr\/albersusa”)` to use it. The github repo has some basic examples, heres a slightly more complex one.<\/p>\n
### Mapping Obesity<\/p>\n
I grabbed an [obesity data set](http:\/\/www.cdc.gov\/diabetes\/data\/county.html) from the CDC and put together a compact example for how to make a composite U.S. county choropleth to show obesity rates per county (for 2012, which is the most recent data). I read in the Excel file, pull out the county FIPS code and 2012 obesity rate, then build the choropleth. It’s not a whole lot of code, but that’s one main reason for the package!<\/p>\n
library(readxl)\r\nlibrary(rgeos)\r\nlibrary(maptools)\r\nlibrary(ggplot2) # devtools::install_github(\"hadley\/ggplot2\") only if you want subtitles\/captions\r\nlibrary(ggalt)\r\nlibrary(ggthemes)\r\nlibrary(albersusa) # devtools::install_github(\"hrbrmstr\/albersusa\")\r\nlibrary(viridis)\r\nlibrary(scales)\r\n\r\n# get the data and be nice to the server and keep a copy of the data for offline use\r\n\r\nURL <- \"http:\/\/www.cdc.gov\/diabetes\/atlas\/countydata\/OBPREV\/OB_PREV_ALL_STATES.xlsx\"\r\nfil <- basename(URL)\r\nif (!file.exists(fil)) download.file(URL, fil)\r\n\r\n# it's not a horrible Excel file, but we do need to hunt for the data\r\n# and clean it up a bit. we just need FIPS & 2012 percent info\r\n\r\nwrkbk <- read_excel(fil)\r\nobesity_2012 <- setNames(wrkbk[-1, c(2, 61)], c(\"fips\", \"pct\"))\r\nobesity_2012$pct <- as.numeric(obesity_2012$pct) \/ 100\r\n\r\n# I may make a version of this that returns a fortified data.frame but\r\n# for now, we just need to read the built-in saved shapefile and turn it\r\n# into something ggplot2 can handle\r\n\r\ncmap <- fortify(counties_composite(), region=\"fips\")\r\n\r\n# and this is all it takes to make the map below\r\n\r\ngg <- ggplot()\r\ngg <- gg + geom_map(data=cmap, map=cmap,\r\n aes(x=long, y=lat, map_id=id),\r\n color=\"#2b2b2b\", size=0.05, fill=NA)\r\ngg <- gg + geom_map(data=obesity_2012, map=cmap,\r\n aes(fill=pct, map_id=fips),\r\n color=\"#2b2b2b\", size=0.05)\r\ngg <- gg + scale_fill_viridis(name=\"Obesity\", labels=percent)\r\ngg <- gg + coord_proj(us_laea_proj)\r\ngg <- gg + labs(title=\"U.S. Obesity Rate by County (2012)\",\r\n subtitle=\"Content source: Centers for Disease Control and Prevention\",\r\n caption=\"Data from http:\/\/www.cdc.gov\/diabetes\/atlas\/countydata\/County_ListofIndicators.html\")\r\ngg <- gg + theme_map(base_family=\"Arial Narrow\")\r\ngg <- gg + theme(legend.position=c(0.8, 0.25))\r\ngg <- gg + theme(plot.title=element_text(face=\"bold\", size=14, margin=margin(b=6)))\r\ngg <- gg + theme(plot.subtitle=element_text(size=10, margin=margin(b=-14)))\r\ngg<\/pre>\n![\"Fullscreen_3_29_16__9_06_AM\"](\"https:\/\/i0.wp.com\/rud.is\/b\/wp-content\/uploads\/2016\/03\/Fullscreen_3_29_16__9_06_AM.png?ssl=1\")
<\/a><\/p>\n