Given a shapefile, how do I shape and use a data file in order to be able to plot thematic maps using identifiers that correspond to shape regions in the shapefile?
#Download English Government Office Network Regions (GOR) from:
#http://www.sharegeo.ac.uk/handle/10672/50
tmp_dir = tempdir()
url_data = "http://www.sharegeo.ac.uk/download/10672/50/English%20Government%20Office%20Network%20Regions%20(GOR).zip"
zip_file = sprintf("%s/shpfile.zip", tmp_dir)
download.file(url_data, zip_file)
unzip(zip_file, exdir = tmp_dir)
library(maptools)
#Load in the data file (could this be done from the downloaded zip file directly?
gor=readShapeSpatial(sprintf('%s/Regions.shp', tmp_dir))
#I can plot the shapefile okay...
plot(gor)
#and I can use these commands to get a feel for the data...
summary(gor)
attributes(gor@data)
gor@data$NAME
#[1] North East North West
#[3] Greater London Authority West Midlands
#[5] Yorkshire and The Humber South West
#[7] East Midlands South East
#[9] East of England
#9 Levels: East Midlands East of England ... Yorkshire and The Humber
#download data from http://www.justice.gov.uk/downloads/publications/statistics-and-data/courts-and-sentencing/csq-q3-2011-insolvency-tables.csv
#insolvency<- read.csv("~/Downloads/csq-q3-2011-insolvency-tables.csv")
insolvency=read.csv("http://www.justice.gov.uk/downloads/publications/statistics-and-data/courts-and-sentencing/csq-q3-2011-insolvency-tables.csv")
insolvencygor.2011Q3=subset(insolvency,Time.Period=='2011 Q3' & Geography.Type=='Government office region')
#tidy the data
require(gdata)
insolvencygor.2011Q3=drop.levels(insolvencygor.2011Q3)
names(insolvencygor.2011Q3)
#[1] "Time.Period" "Geography"
#[3] "Geography.Type" "Company.Winding.up.Petition"
#[5] "Creditors.Petition" "Debtors.Petition"
levels(insolvencygor.2011Q3$Geography)
#[1] "East" "East Midlands"
#[3] "London" "North East"
#[5] "North West" "South East"
#[7] "South West" "Wales"
#[9] "West Midlands" "Yorkshire and the Humber"
#So what next?
Having got that far, how do I take the next step in generating a thematic/choropleth map, that colours each region according to the the Debtors.Petition value, for example?
(I also just noticed a possible gotcha - there is a mismatch in the capitalisation GOR levels: "Yorkshire and the Humber" and "Yorkshire and The Humber" )
Having not seen the wood for the trees, to answer my own question, here's one way (code following on from code in the question):
#Convert factors to numeric [ http://stackoverflow.com/questions/4798343/convert-factor-to-integer ]
#There's probably a much better formulaic way of doing this/automating this?
insolvencygor.2011Q3$Creditors.Petition=as.numeric(levels(insolvencygor.2011Q3$Creditors.Petition))[insolvencygor.2011Q3$Creditors.Petition]
insolvencygor.2011Q3$Company.Winding.up.Petition=as.numeric(levels(insolvencygor.2011Q3$Company.Winding.up.Petition))[insolvencygor.2011Q3$Company.Winding.up.Petition]
insolvencygor.2011Q3$Debtors.Petition=as.numeric(levels(insolvencygor.2011Q3$Debtors.Petition))[insolvencygor.2011Q3$Debtors.Petition]
#Tweak the levels so they match exactly (really should do this via a lookup table of some sort?)
i2=insolvencygor.2011Q3
i2c=c('East of England','East Midlands','Greater London Authority','North East','North West','South East','South West','Wales','West Midlands','Yorkshire and The Humber')
i2$Geography=factor(i2$Geography,labels=i2c)
#Merge the data with the shapefile
gor@data=merge(gor@data,i2,by.x='NAME',by.y='Geography')
#Plot the data using a greyscale
plot(gor,col=gray(gor@data$Creditors.Petition/max(gor@data$Creditors.Petition)))
So what this approach does is merge the numeric data into the shapefile, and then plot it directly.
That said, wouldn't a cleaner way be to keep the data file and the shapefile separate? (I'm still not sure how to do that?)