From StatWiki

Average using Timeseries +

pdf(rpdf)
gdpa=readdataSK("GDPA.csv", "csv")
gdpa=data.frame(year=as.numeric(substr(gdpa$DATE,1,4)), gdp=gdpa$VALUE)
gdpa=subset(gdpa, year>=1946)
GDP.ts=ts(gdpa$gdp, start=1946)
GDP.ls <- log(GDP.ts)

S1 <- (lag(GDP.ls,k=-1)+GDP.ls+lag(GDP.ls,k=1))/3
plot(GDP.ls,type="p",xlab="Year",ylab="log(GDP)")
lines(S1,lwd=2)

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Using loops

pdf(rpdf)
gdpa=readdataSK("GDPA.csv", "csv")
gdpa=data.frame(year=as.numeric(substr(gdpa$DATE,1,4)), gdp=gdpa$VALUE)
gdpa=subset(gdpa, year>=1946)
GDP.ts=ts(gdpa$gdp, start=1946)
GDP.ls <- log(GDP.ts)

S7 <- GDP.ls
for (i in 1:7) S7 <- S7+lag(GDP.ls,k=-i)+lag(GDP.ls,k=i)

S7 <- S7/15
plot(GDP.ls,type="p",xlab="Year",ylab="log(GDP)")
lines(S7,lwd=2)

#

Using functions

pdf(rpdf)
gdpa=readdataSK("GDPA.csv", "csv")
gdpa=data.frame(year=as.numeric(substr(gdpa$DATE,1,4)), gdp=gdpa$VALUE)
gdpa=subset(gdpa, year>=1946)
GDP.ts=ts(gdpa$gdp, start=1946)
GDP.ls <- log(GDP.ts)

smooth <- function(y,k,col,eps)
{ 
c <- 2*k+1; y.sm <- y
for (i in 1:k) y.sm <- y.sm+lag(y,k=-i)+lag(y,k=i)
y.sm <- y.sm/c
lines(y.sm+eps,lwd=2,col=col)
}

plot(GDP.ls,type="p",xlab="Year",ylab="")
smooth(GDP.ls,k=1,col=1,0)
smooth(GDP.ls,k=7,col=2,-.5)
smooth(GDP.ls,k=10,col=3,.5)

legend(1955,9,c("k=1","k=7","k=10"), col=1:4, lty=1)

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