Plotting Primaries
library(jsonlite) # for CNN JSON
library(purrr) # for cleaner list ops
library(dplyr) # data munging
library(sp) # geo
library(maptools) # geo
library(rgeos) # geo
library(tigris) # tx map
library(ggplot2) # plotting
library(ggthemes) # plotting themes
library(scales) # plotting formatting
library(svglite) # output SVGs
library(DT) # nicer data frame outputCNN has surprisingly good primary/election data that one can get w/o much effort. We can use it to show how what percentage of each county a (Republican) candidate won (that shows how a county leans a bit better IMO).
tx <- fromJSON("http://data.cnn.com/ELECTION/2016primary/TX/county/R.json",
flatten=TRUE)We build a data.frame from the CNN JSON, only keeping the person with the most votes (%) per county
tx_heat <- mutate(map_df(tx$counties$race.candidates, function(x) {
head(arrange(x, desc(vpct)), 1)
}), FIPS=tx$counties$countycode, vpct=vpct/100)This is a perfect use-case for tigris. Get the county map and make it a bit more lightweight than it already is.
tx_shp <- counties("Texas", TRUE)
tx_shp <- SpatialPolygonsDataFrame(gSimplify(tx_shp, 0.01, TRUE), tx_shp@data)
tx_map <- fortify(tx_shp, region="GEOID")Now, plot:
gg <- ggplot()
gg <- gg + geom_map(data=tx_map, map=tx_map,
aes(x=long, y=lat, map_id=id),
color="#2b2b2b99", fill=NA, size=0.1)
gg <- gg + geom_map(data=tx_heat, map=tx_map,
aes(fill=lname, alpha=vpct, map_id=FIPS),
color="#2b2b2b99", size=0.1)
gg <- gg + scale_fill_brewer(name="County\nwinner", palette="Dark2")
gg <- gg + scale_alpha_continuous(name="% Votes", labels=percent)
gg <- gg + coord_map()
gg <- gg + theme_map()
gg <- gg + theme(legend.position="right")
gg
And let folks explore all the results
datatable(select(bind_rows(tx$counties$race.candidates),
Candidate=lname, Won=winner, `%`=vpct, `# Votes`=votes,
-id, -fname, -mname, -suffix, -usesuffix, -inrace,
-party, -nominee, -pctDecimal, -inc, -cvotes))