# 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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