Basic lag in R vector/dataframe

I had the same problem, but I didn't want to use zoo or xts, so I wrote a simple lag function for data frames:

lagpad <- function(x, k) {  if (k>0) {    return (c(rep(NA, k), x)[1 : length(x)] );  }  else {    return (c(x[(-k+1) : length(x)], rep(NA, -k)));  }}

This can lag forward or backwards:

x<-1:3;(cbind(x, lagpad(x, 1), lagpad(x,-1)))     x      [1,] 1 NA  2[2,] 2  1  3[3,] 3  2 NA

Another way to deal with this is using the zoo package, which has a lag method that will pad the result with NA:

require(zoo)> set.seed(123)> x <- zoo(sample(c(1:9), 10, replace = T))> y <- lag(x, -1, na.pad = TRUE)> cbind(x, y)   x  y1  3 NA2  8  33  4  84  8  45  9  86  1  97  5  18  9  59  5  910 5  5

The result is a multivariate zoo object (which is an enhanced matrix), but easily converted to a data.frame via

> data.frame(cbind(x, y))

lag does not shift the data, it only shifts the "time-base". x has no "time base", so cbind does not work as you expected. Try cbind(as.ts(x),lag(x)) and notice that a "lag" of 1 shifts the periods forward.

I would suggesting using zoo / xts for time series. The zoo vignettes are particularly helpful.