# NOT RUN {
map() # low resolution map of the world
map(wrap = c(0,360), fill = TRUE, col = 2) # pacific-centered map of the world
map(wrap = c(0, 360, NA), fill = TRUE, col = 2) # idem, without Antarctica
map('usa') # national boundaries
map('county', 'new jersey') # county map of New Jersey
map('state', region = c('new york', 'new jersey', 'penn')) # map of three states
map("state", ".*dakota", myborder = 0) # map of the dakotas
map.axes() # show the effect of myborder = 0
if(require(mapproj))
map('state', proj = 'bonne', param = 45) # Bonne equal-area projection of states
# names of the San Juan islands in Washington state
map('county', 'washington,san', names = TRUE, plot = FALSE)
# national boundaries in one linetype, states in another
# (figure 5 in the reference)
map("state", interior = FALSE)
map("state", boundary = FALSE, lty = 2, add = TRUE)
# plot the ozone data on a base map
# (figure 4 in the reference)
data(ozone)
map("state", xlim = range(ozone$x), ylim = range(ozone$y))
text(ozone$x, ozone$y, ozone$median)
box()
if(require(mapproj)) { # mapproj is used for projection="polyconic"
# color US county map by 2009 unemployment rate
# match counties to map using FIPS county codes
# Based on J's solution to the "Choropleth Challenge"
# http://blog.revolutionanalytics.com/2009/11/choropleth-challenge-result.html
# load data
# unemp includes data for some counties not on the "lower 48 states" county
# map, such as those in Alaska, Hawaii, Puerto Rico, and some tiny Virginia
# cities
data(unemp)
data(county.fips)
# define color buckets
colors = c("#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", "#980043")
unemp$colorBuckets <- as.numeric(cut(unemp$unemp, c(0, 2, 4, 6, 8, 10, 100)))
leg.txt <- c("<2%", "2-4%", "4-6%", "6-8%", "8-10%", ">10%")
# align data with map definitions by (partial) matching state,county
# names, which include multiple polygons for some counties
cnty.fips <- county.fips$fips[match(map("county", plot=FALSE)$names,
county.fips$polyname)]
colorsmatched <- unemp$colorBuckets [match(cnty.fips, unemp$fips)]
# draw map
map("county", col = colors[colorsmatched], fill = TRUE, resolution = 0,
lty = 0, projection = "polyconic")
map("state", col = "white", fill = FALSE, add = TRUE, lty = 1, lwd = 0.2,
projection="polyconic")
title("unemployment by county, 2009")
legend("topright", leg.txt, horiz = TRUE, fill = colors)
# Choropleth Challenge example, based on J's solution, see:
# http://blog.revolutionanalytics.com/2009/11/choropleth-challenge-result.html
# To see the faint county boundaries, use RGui menu: File/SaveAs/PDF
}
# }
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