Developing Geographic Thematic Maps with R

Question

There are clearly a number of packages in R for all sorts of spatial analysis. That can by seen in the CRAN Task View: Analysis of Spatial Data. These packages are numerous

Answer 1

The R Graphics Gallery has a very similar map which should make for a good starting point. The code is here: www.ai.rug.nl/~hedderik/R/US2004 . You'd need to add a legend with the legend() function.

Answer 2

Check out the packages

library(sp)
library(rgdal)

which are nice for geodata, and

library(RColorBrewer)  

is useful for colouring. This map is made with the above packages and this code:

VegMap <- readOGR(".", "VegMapFile")
Veg9<-brewer.pal(9,'Set2')
spplot(VegMap, "Veg", col.regions=Veg9,
 +at=c(0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5),
 +main='Vegetation map')

"VegMapFile" is a shapefile and "Veg" is the variable displayed. Can probably be done better with a little work. I don`t seem to be allowed to upload image, here is an link to the image:

Answer 3

It is just three lines!

library(maps);
colors = floor(runif(63)*657);
map("state", col = colors, fill = T, resolution = 0)

Done!! Just change the second line to any vector of 63 elements (each element between 0 and 657, which are members of colors())

Now if you want to get fancy you can write:

library(maps);
library(mapproj);
colors = floor(runif(63)*657);
map("state", col = colors, fill = T, projection = "polyconic", resolution = 0);

The 63 elements represent the 63 regions whose names you can get by running:

map("state")$names;

Answer 4

The following code has served me well. Customize it a little and you are done.
_{(source: eduardoleoni.com)}

library(maptools)
substitute your shapefiles here
state.map <- readShapeSpatial("BRASIL.shp")
counties.map <- readShapeSpatial("55mu2500gsd.shp")
## this is the variable we will be plotting
counties.map@data$noise <- rnorm(nrow(counties.map@data))

heatmap function

plot.heat <- function(counties.map,state.map,z,title=NULL,breaks=NULL,reverse=FALSE,cex.legend=1,bw=.2,col.vec=NULL,plot.legend=TRUE) {
  ##Break down the value variable
  if (is.null(breaks)) {
    breaks=
      seq(
          floor(min(counties.map@data[,z],na.rm=TRUE)*10)/10
          ,
          ceiling(max(counties.map@data[,z],na.rm=TRUE)*10)/10
          ,.1)
  }
  counties.map@data$zCat <- cut(counties.map@data[,z],breaks,include.lowest=TRUE)
  cutpoints <- levels(counties.map@data$zCat)
  if (is.null(col.vec)) col.vec <- heat.colors(length(levels(counties.map@data$zCat)))
  if (reverse) {
    cutpointsColors <- rev(col.vec)
  } else {
    cutpointsColors <- col.vec
  }
  levels(counties.map@data$zCat) <- cutpointsColors
  plot(counties.map,border=gray(.8), lwd=bw,axes = FALSE, las = 1,col=as.character(counties.map@data$zCat))
  if (!is.null(state.map)) {
    plot(state.map,add=TRUE,lwd=1)
  }
  ##with(counties.map.c,text(x,y,name,cex=0.75))
  if (plot.legend) legend("bottomleft", cutpoints, fill = cutpointsColors,bty="n",title=title,cex=cex.legend)
  ##title("Cartogram")
}

plot it

plot.heat(counties.map,state.map,z="noise",breaks=c(-Inf,-2,-1,0,1,2,Inf))

Answer 5

Take a look at the PBSmapping package (see borh the vignette/manual and the demo) and this O'Reilly Data Mashups in R article (unfortunately it is not free of charge but it worth 4.99$ to download, according Revolutions blog ).

Answer 6

Thought I would add some new information here since there has been some activity around this topic since the posting. Here are two great links to "Choropleth Map R Challenge" on the Revolutions blog:

Choropleth Map R Challenge

Choropleth Challenge Results

Hopefully these are useful for people viewing this question.

All the best,

Jay