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| library(ggplot2) | |
| library(scales) | |
| library(maps) | |
| library(plyr) | |
| 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(breaks = c(5, 10, 20, 30)) | |
| bubbles$rate_d <- cut(bubbles$rate, breaks = c(seq(0, 10, by = 2), 35)) | |
| 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(palette = "PuRd") |
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| library(ggplot2) | |
| library(scales) | |
| 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(palette = "PuRd") | |
| # Takes a while to draw because ggplot2 not very efficient with large numbers | |
| # of polygons :( |
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