public
Last active

  • Download Gist
points.r
R
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
mid_range <- function(x) mean(range(x, na.rm = TRUE))
centres <- ddply(county_df, c("state", "county"), summarise,
lat = mid_range(lat),
long = mid_range(long)
)
 
bubbles <- merge(centres, unemp, by = c("state", "county"))
ggplot(bubbles, aes(long, lat)) +
geom_polygon(aes(group = group), data = state_df,
colour = "white", fill = NA) +
geom_point(aes(size = rate), alpha = 1/2) +
scale_area(to = c(0.5, 3), breaks = c(5, 10, 20, 30))
 
ggplot(bubbles, aes(long, lat)) +
geom_polygon(aes(group = group), data = state_df,
colour = "white", fill = NA) +
geom_point(aes(color = rate_d)) +
scale_colour_brewer(pal = "PuRd")
polygons.r
R
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
library(ggplot2)
library(maps)
 
# First (and most annoying) task - get matching state and county variables
# for both datasets. And unfortauntely it's not quite right, as you can
# see from the finish product - some counties are missing.
unemp <- read.csv("unemployment09.csv", header = F, stringsAsFactors = F)
names(unemp) <- c("id", "state_fips", "county_fips", "name", "year",
"?", "?", "?", "rate")
unemp$county <- tolower(gsub(" County, [A-Z]{2}", "", unemp$name))
unemp$state <- gsub("^.*([A-Z]{2}).*$", "\\1", unemp$name)
 
county_df <- map_data("county")
names(county_df) <- c("long", "lat", "group", "order", "state_name", "county")
county_df$state <- state.abb[match(county_df$state_name, tolower(state.name))]
county_df$state_name <- NULL
 
state_df <- map_data("state")
 
# Combine together
choropleth <- merge(county_df, unemp, by = c("state", "county"))
choropleth <- choropleth[order(choropleth$order), ]
# Discretise rate to use with Brewer colour scheme - many options here
# choropleth$rate_d <- cut_number(choropleth$rate, 5)
# choropleth$rate_d <- cut_interval(choropleth$rate, 5)
# Nathan's choice is a little odd:
choropleth$rate_d <- cut(choropleth$rate, breaks = c(seq(0, 10, by = 2), 35))
 
# Once you have the data in the right format, recreating the plot is straight
# forward.
 
ggplot(choropleth, aes(long, lat, group = group)) +
geom_polygon(aes(fill = rate_d), colour = alpha("white", 1/2), size = 0.2) +
geom_polygon(data = state_df, colour = "white", fill = NA) +
scale_fill_brewer(pal = "PuRd")
 
# Takes a while to draw because ggplot2 not very efficient with large numbers
# of polygons :(

Hi.

I came across this page via the chloropleth challenge page. I've not yet understood all of the code above, but as you surely put a lot of effort into the challenge, I'd still like to ask you one question:
In the meantime, have you found one package that was particularly useful for displaying (and generally working with) maps in R? Or have you spent more time on developping your own maps-related projects?
I'm asking because I'd like to work with maps in the near future and I'm still not sure which package to use.

Cheers,
Markus

I had trouble getting the scale_fill_brewer to work. Using "palette" rather than "pal" made the code run.

This was a very useful comment. Thanks!
@hadley: Thx for the code; I'm very new to maps and right at the moment don't find the time to dive in properly with a book, so working examples are a real time saver.

Please sign in to comment on this gist.

Something went wrong with that request. Please try again.