Pie charts get a lot of hate. They are often associated with mass-market journalism that intends to obfuscate and trick the reader rather than to accurately convey information. The knock on them is that they make it difficult to compare proportions by not placing them side-by-side and by turning one-dimensional quantities into two-dimensional ones. However, there is one case in which I think pie charts have a place. On maps, if you want to associate a variety of numbers with a single point location, the conveniently circular form of the pie chart lends itself to being centered on the point of interest, more so than a histogram or stacked-bar chart. I wanted to make a map with pie charts at different points to show the land cover proportions of some of the sites in the National Ecological Observatory Network (NEON).
Here are a blank state map, the pies, and the two combined together:
Below, I present the R code that I used to make the map, using the ggplot2 , plyr, and grid packages.
# Pies On A Map # Demonstration script # By QDR # Uses NLCD land cover data for different sites in the National Ecological Observatory Network. # Each site consists of a number of different plots, and each plot has its own land cover classification. # On a US map, plot a pie chart at the location of each site with the proportion of plots at that site within each land cover class. # For this demo script, I've hard coded in the color scale, and included the data as a CSV linked from dropbox. # Custom color scale (taken from the official NLCD legend) nlcdcolors <- structure(c("#7F7F7F", "#FFB3CC", "#00B200", "#00FFFF", "#006600", "#E5CC99", "#00B2B2", "#FFFF00", "#B2B200", "#80FFCC"), .Names = c("unknown", "cultivatedCrops", "deciduousForest", "emergentHerbaceousWetlands", "evergreenForest", "grasslandHerbaceous", "mixedForest", "pastureHay", "shrubScrub", "woodyWetlands")) # NLCD data for the NEON plots nlcdtable_long <- read.csv(file='https://www.dropbox.com/s/x95p4dvoegfspax/demo_nlcdneon.csv?raw=1', row.names=NULL, stringsAsFactors=FALSE) library(ggplot2) library(plyr) library(grid) # Create a blank state map. The geom_tile() is included because it allows a legend for all the pie charts to be printed, although it does not statemap <- ggplot(nlcdtable_long, aes(decimalLongitude,decimalLatitude,fill=nlcdClass)) + geom_tile() + borders('state', fill='beige') + coord_map() + scale_x_continuous(limits=c(-125,-65), expand=c(0,0), name = 'Longitude') + scale_y_continuous(limits=c(25, 50), expand=c(0,0), name = 'Latitude') + scale_fill_manual(values = nlcdcolors, name = 'NLCD Classification') # Create a list of ggplot objects. Each one is the pie chart for each site with all labels removed. pies <- dlply(nlcdtable_long, .(siteID), function(z) ggplot(z, aes(x=factor(1), y=prop_plots, fill=nlcdClass)) + geom_bar(stat='identity', width=1) + coord_polar(theta='y') + scale_fill_manual(values = nlcdcolors) + theme(axis.line=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks=element_blank(), axis.title.x=element_blank(), axis.title.y=element_blank(), legend.position="none", panel.background=element_blank(), panel.border=element_blank(), panel.grid.major=element_blank(), panel.grid.minor=element_blank(), plot.background=element_blank())) # Use the latitude and longitude maxima and minima from the map to calculate the coordinates of each site location on a scale of 0 to 1, within the map panel. piecoords <- ddply(nlcdtable_long, .(siteID), function(x) with(x, data.frame( siteID = siteID, x = (decimalLongitude+125)/60, y = (decimalLatitude-25)/25 ))) # Print the state map. statemap # Use a function from the grid package to move into the viewport that contains the plot panel, so that we can plot the individual pies in their correct locations on the map. downViewport('panel.3-4-3-4') # Here is the fun part: loop through the pies list. At each iteration, print the ggplot object at the correct location on the viewport. The y coordinate is shifted by half the height of the pie (set at 10% of the height of the map) so that the pie will be centered at the correct coordinate. for (i in 1:length(pies)) print(pies[[i]], vp=dataViewport(xData=c(-125,-65), yData=c(25,50), clip='off',xscale = c(-125,-65), yscale=c(25,50), x=piecoords$x[i], y=piecoords$y[i]-.06, height=.12, width=.12))