acsv2.r

library(acs)
library(ggplot2)
library(plyr)
library(tidyr)
library(maps)
library(viridis)
library(hrbrthemes)

#key
api.key.install(key="pasteyourkeyhere")


# load the boundary data for all counties
county.df<-map_data("county")
# rename fields for later merge
names(county.df)[5:6]<-c("state","county")
state.df<-map_data("county")

us.county<-geo.make(state="*", county="*")
# .. and a single command to fetch the data...!
us.raw<-acs.fetch(endyear=2015,geography=us.county, table.number="B19001", col.names="pretty")
tests<-data.frame(us.raw@acs.colnames)
strings<-strsplit(as.character(tests$us.raw.acs.colnames),":")
col.names<-sapply(strings,"[",2)


income.brackets<-data.frame(county=geography(us.raw)[[1]], us.transport=as.numeric(estimate(us.raw)),
                            paste0(str_pad(us.raw@geography$state, 2, "left", pad="0"), 
                                              str_pad(us.raw@geography$county, 3, "left", pad="0"), 
                                              str_pad(us.raw@geography$tract, 6, "left", pad="0")), 
                                       us.raw@estimate, stringsAsFactors = FALSE)

names(income.brackets)[4:20]<-col.names
names(income.brackets)[3]<-'zip'
income.brackets[5:20] <- 100*(income.brackets[5:20]/income.brackets[,4])

#small & tidy frame
new<-income.brackets%>%gather(key=level,value=rate,4:20)
new$level<-trimws(new$level)
new<-subset(new,level == '$200,000 or more')

# clean up county names and find the states, the parish bits are for Louisiana
new$state<-tolower(gsub("^.*County, |^.*Parish, ", "", new$county))
new$county<-tolower(gsub(" County,.*| Parish,.*", "", new$county))
choropleth<-merge(county.df, new, by=c("state","county"))
choropleth<-choropleth[order(choropleth$order), ]

ggplot(choropleth, aes(long, lat, group = group)) +
  geom_polygon(aes(fill = rate), colour = alpha("gray", 1 / 2), size = 0.2) +
  geom_polygon(data = state.df, colour = "white", fill = NA,cex=.25)  +
  scale_fill_viridis(option="magma",direction = -1)+
  theme(legend.position = 'bottom')+
  labs(title='Income level by county',subtitle='Percent of total households with an income greater or equal to $200,000',caption="Data: American Community Survey\nU.S. Census Bureau")+
  theme_ipsum()