Whatever I want to do, there are always intrepid explorers who’ve been there and blogged it, and so the satisfaction of my long held desire to get to know more about how Nottinghamshire Healthcare’s services are spread geographically has been wonderfully expedited by these amazing blog posts.
Special thanks, of course, go to David Kahle and Hadley Wickham, progenitors of the mighty ggmap package and also to the fine folk at geonames who freely distribute postcodes from around the world in .csv format.
With the thanks out of the way, there’s almost no work for me to do at all, and I’ve produced this lovely heatmap with absolutely minimal coding. I can’t tell you what it represents, I’m afraid, because I haven’t cleared the data for release, and actually it doesn’t represent anything particularly interesting at the moment. I need to do some preparation of the data but I naturally did this bit first because it’s more fun.
library(ggmap) myUni=mydata[!duplicated(mydata$ClientID),] # produce dataframe with unique individuals mywhere=merge(myUni, mycodes, by.x="ClientHomePostcode", by.y="Postcode", all=FALSE) # merge with postcode data ### Plot! map.center = geocode("Nottingham, UK") # Centre map on Nottingham myMap = qmap(c(lon=map.center$lon, lat=map.center$lat), source="google", zoom=10) # download map from Google myMap + stat_bin2d(bins=80, aes(x=Long, y=Lat), alpha=.6, data=mywhere) + scale_fill_gradient(low = "blue", high ="red") # plot with a bit of transparency
Note finally that you can use Google’s map API to give you latitudes and longitudes from postcode data (using the geocode() function), but you are limited to 2500 queries per day. I had many more than that so I needed to download the postcode data.