Quick look at the Global WITS data Gary sent us.

explore-global-wits.R

setwd("/Volumes/political-science/shared/ICEWS Project/C-IED/Data/Global_WITS")

# Read as txt chunk
# The first 16 lines are meta data
wits.text <- readLines("WITS.csv")
head(wits.text, n=16)

# Get actual data
wits <- read.csv(text=wits.text, header=TRUE, skip=16)

wits$event_start_date <- as.Date(wits$event_start_date, format="%d-%b-%y")
wits$event_end_date <- as.Date(wits$event_end_date, format="%d-%b-%y")

library(plyr)
wits$longitude <- revalue(wits$longitude, c("#N/A"="NA"))
wits$latitude  <- revalue(wits$latitude, c("#N/A"="NA"))
wits$longitude <- as.numeric(as.character(wits$longitude))
wits$latitude  <- as.numeric(as.character(wits$latitude))

# Daily count
daily <- ddply(wits, .(event_start_date), summarize, count=length(event_start_date))

library(zoo)

png(file="~/Desktop/wits-daily.png", width=600, height=400)
plot(daily, type="l")
lines(daily$event_start_date, rollmean(daily$count, k=30, fill=NA), col="red")
dev.off()

# Map it
library(cshapes)

world <- cshp(as.Date("2011-01-01"))

points <- wits[complete.cases(wits[, c("latitude", "longitude")]), c("latitude", "longitude")]

library(scales)  # for alpha()

png(file="~/Desktop/wits-map.png", width=1200, heigh=800)
plot(world, col="wheat", bg="lightblue1", border="gray70")
points(points$longitude, points$latitude, pch=20, col=alpha("red", 0.2))
dev.off()