## Population data from:
## http://en.wikipedia.org/wiki/Demographic_history_of_the_United_States

population <- read.table("pop.csv",header=T)
population$Date <- as.Date(ISOdate(population$year,6,15))

popspline <- splinefun(population$Date,population$uspop)


## Gas prices from: http://www.randomuseless.info/gasprice/usa.txt

gasprice <- read.table("usa_gasprice.txt",header=T)

gasprice$Date <- as.Date(strptime(as.character(gasprice$Date),"%m/%d/%Y"))

gasspline <- splinefun(gasprice$Date,gasprice$NomDollars)

## GDP data from: http://www.bea.gov/national/xls/gdplev.xls

gdp <- read.csv("gdp.txt",header=T)
gdp <- gdp[1:79,1:3]


gdpspline <- splinefun(as.Date(ISOdate(gdp$Year,6,15)),gdp$BilCur)

dates <- seq.Date(as.Date("1981/01/01"),as.Date("2008/06/01"),by="month")

png("gasanalyzed.png")

plot(dates,10*50*gasspline(dates)/(gdpspline(dates)*1e9/popspline(dates)),type="l",ylab="500 USGals gas as fraction of GDP/capita",xlab="Year",main="Cost of Commuting through time")

now <-as.Date("2008-06-15")

points(now,4.50*10*50/(gdpspline(now)*1e9/popspline(now)))
dev.off();