Basic lag in R vector/dataframe

Question

Will most likely expose that I am new to R, but in SPSS, running lags is very easy. Obviously this is user error, but what I am missing?

x <- sample(c(1:9), 10, replace = T) y <- lag(x, 1) ds <- cbind(x, y) ds 

Results in:

      x y  [1,] 4 4  [2,] 6 6  [3,] 3 3  [4,] 4 4  [5,] 3 3  [6,] 5 5  [7,] 8 8  [8,] 9 9  [9,] 3 3 [10,] 7 7 

I figured I would see:

     x y  [1,] 4   [2,] 6 4  [3,] 3 6  [4,] 4 3  [5,] 3 4  [6,] 5 3  [7,] 8 5  [8,] 9 8  [9,] 3 9 [10,] 7 3 

Any guidance will be much appreciated.

Answer 1

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 

Answer 2

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  y 1  3 NA 2  8  3 3  4  8 4  8  4 5  9  8 6  1  9 7  5  1 8  9  5 9  5  9 10 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))