CaPReT (great circles) map

gistfile1.r

# Based on Nathan Yau's How to map connections with great circles
# http://flowingdata.com/2011/05/11/how-to-map-connections-with-great-circles/


# Prelim stages
library(maps)
library(geosphere)

# Google Spreadsheet key (must be published to the web)
key='0AqGkLMU9sHmLdDd5UXFGdEJGUjEyN3M5clU1X2R5V0E'
# Sheet gid name
gid=2

# Read data from spreadsheet
dataset = read.csv(paste('https://docs.google.com/spreadsheet/pub?hl=en_US&hl=en_US&key=', key ,'&single=true&gid=', gid, '&output=csv', sep=""), header = T)

# Colors
pdf("capret.pdf", width=11, height=7)
map("world", col="#F2F2F2", fill=TRUE, bg="#ffffff", lwd=0.05)
pal <- colorRampPalette(c("#333333", "white", "#1292db"))
colors <- pal(100)

# get a domain frquency table of text source domains
sourceFreq <- as.data.frame(table(dataset$domain)) 
# get the maximum frquency
maxcnt <- max(sourceFreq$Freq)
#order by domain frequency - most last
dataset <- [order(dataset$domain),]

for (j in 1:nrow(dataset)) {
	inter <- gcIntermediate(c(dataset[j,]$source_long, dataset[j,]$source_lat), c(dataset[j,]$target_long, dataset[j,]$target_lat), n=100, addStartEnd=TRUE)
	colindex <- round( (subset(sourceFreq,Var1==dataset[j,]$domain)$Freq / maxcnt) * length(colors) )
	lines(inter, col=colors[colindex], lwd=0.8)
}
dev.off()