DATA MINING
Desktop Survival Guide
by Graham Williams

# Map Displays

Map displays often provide further insights into patterns that vary according to region. In this plot we illustrate some basic colouring of a map. This can be used to translate a variable into a colour which is then plot for the state. We use the rainbow function to generate the bright colours to use to colour the states.

 ```library(maptools) aus <- readShapePoly("australia.shp") plot(aus, lwd=2, border="grey", xlim=c(115,155), ylim=c(-35,-20)) colours <- rainbow(8) # Must be a better way than this.... nsw <- aus; nsw@plotOrder <- as.integer(c(2)); plot(nsw,col=colours[2],add=TRUE) act <- aus; act@plotOrder <- as.integer(c(1)); plot(act,col=colours[1],add=TRUE) nt <- aus; nt@plotOrder <- as.integer(c(3)); plot(nt, col=colours[3],add=TRUE) qld <- aus; qld@plotOrder <- as.integer(c(4)); plot(qld,col=colours[4],add=TRUE) sa <- aus; sa@plotOrder <- as.integer(c(5)); plot(sa, col=colours[5],add=TRUE) tas <- aus; tas@plotOrder <- as.integer(c(6)); plot(tas,col=colours[6],add=TRUE) vic <- aus; vic@plotOrder <- as.integer(c(7)); plot(vic,col=colours[7],add=TRUE) ```

http://rattle.togaware.com/code/map-australia-states.R

Here's a great example using the ggplot2 package, based on examples from http://www.thisisthegreenroom.com/2009/choropleths-in-r/. These plots are called choropleths.

 ```> library(ggplot2) > library(maps) > # Dowload and clean up the data to plot > > # unemp <- read.csv("http://datasets.flowingdata.com/unemployment09.csv") > unemp <- read.csv("unemployment09.csv") > unemp_data\$counties <- tolower(paste(states,counties,sep=",")) > countyNames <- sapply(as.character(unemp_data\$CountyName),strsplit,", ") > counties <- sapply(countyNames ,"[",1) > states <- sapply(countyNames ,"[",2) > states <- as.character(stateAbbr[states,2]) > #parse out county titles & specifics > unemp_data\$counties <- sub(" county","",unemp_data\$counties) > unemp_data\$counties <- sub(" parish","",unemp_data\$counties) > unemp_data\$counties <- sub(" borough","",unemp_data\$counties) > unemp_data\$counties <- sub("miami-dade","dade",unemp_data\$counties) > #define color buckets > colors = c("#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", "#980043") > unemp_data\$colorBuckets <- as.factor(as.numeric(cut(unemp_data\$unemp,c(0,2,4,6,8,10,100)))) > # Extract the reference data > > mapcounties <- map_data("county") > mapstates <- map_data("state") > # Merge the data with ggplot county coordinates > > mapcounties\$county <- with(mapcounties , paste(region, subregion, sep = ",")) > mergedata <- merge(mapcounties, unemp, by.x = "county", by.y = "counties") > mergedata <- mergedata[order(mergedata\$order),] > #draw map > map <- ggplot(mergedata, aes(long,lat,group=group)) + geom_polygon(aes(fill=colorBuckets)) > map <- map + scale_fill_brewer(palette="PuRd") + coord_map(project="globular") + opts(legend.position = "none") > #add state borders > map <- map + geom_path(data = mapstates, colour = "white", size = .75) > #add county borders > map <- map + geom_path(data = mapcounties, colour = "white", size = .5, alpha = .1) > map ```