Plotting bar charts on map using ggplot2?

I would like to produce a barplot for each location on a map using ggplot2, as was done by xingmowang with base graphics and a number of packages:

http://nzprimarysectortrade.wordpress.com/2011/10/02/let-r-fly-visualizing-export-data-using-r/

enter image description here

This is related to Embedding a miniature plot within a plot.

For the moment, the best I can do is match value to point size in a jittered point plot:

require(ggplot2)
require(maps)

#Get world map info
world_map <- map_data("world")

#Creat a base plot
p <- ggplot() + coord_fixed()

#Add map to base plot
base_world <- p + geom_polygon(data=world_map,
                               aes(x=long,
                                   y=lat,
                                   group=group))


#Create example data
geo_data <- data.frame(long=c(20,20,100,100,20,20,100,100),
                       lat=c(0,0,0,0,0,0,0,0),
                       value=c(10,30,40,50,20,20,100,100),
                       Facet=rep(c("Facet_1", "Facet_2"), 4),
                       colour=rep(c("colour_1", "colour_2"), each=4))

#Creat an example plot
map_with_jitter <- base_world+geom_point(data=geo_data,
                                            aes(x=long,
                                                y=lat,
                                                colour=colour,
                                                size=value),
                                         position="jitter",
                                         alpha=I(0.5))

#Add faceting
map_with_jitter <- map_with_jitter + facet_wrap(~Facet)

map_with_jitter <- map_with_jitter + theme(legend.position="none")

print(map_with_jitter)

Example map plot with ggplot2

With an inelegant workaround:

subset_data <- geo_data[geo_data$Facet=="Facet_1" &
                          geo_data$long=="20",]
subplot <- qplot(data=subset_data,
                 x=colour,
                 y=value,
                 fill=colour,
                 geom="bar",
                 stat="identity")+theme(legend.position="none")

print(base_world)
print(subplot, vp=viewport((200+mean(subset_data$long))/400,(100+mean(subset_data$lat))/200 , .2, .2))

enter image description here

library(ggsubplot) library(ggplot2) library(maps) library(plyr) #Get world map info world_map <- map_data("world") #Create a base plot p <- ggplot() + geom_polygon(data=world_map,aes(x=long, y=lat,group=group)) # Calculate the mean longitude and latitude per region, these will be the coördinates where the plots will be placed, so you can tweak them where needed. # Create simulation data of the age distribution per region and merge the two. centres <- ddply(world_map,.(region),summarize,long=mean(long),lat=mean(lat)) mycat <- cut(runif(1000), c(0, 0.1, 0.3, 0.6, 1), labels=FALSE) mycat <- as.factor(mycat) age <- factor(mycat,labels=c("<15","15-30","20-60",">60")) simdat <- merge(centres ,age) colnames(simdat) <- c( "region","long","lat","Age" ) # Select the countries where you want a subplot for and plot simdat2 <- subset(simdat, region %in% c("USA","China","USSR","Brazil", "Australia")) (testplot <- p+geom_subplot2d(aes(long, lat, subplot = geom_bar(aes(Age, ..count.., fill = Age))), bins = c(15,12), ref = NULL, width = rel(0.8), data = simdat2))

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