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r - How can I automatically create n lags in a timeseries?

I have a dataframe with a column t. I want to create n lagged columns that has names like t-1,t-2 etc..

  year      t  t-1 t-2
19620101    1   NA  NA
19630102    2   1   NA
19640103    3   2   1
19650104    4   3   2
19650104    5   4   3
19650104    6   5   4

My idea is that I will do it in four steps:

  • A loop for the column names using "paste"
  • A loop for the temporary dataframes for lagged columns using "paste"
  • A loop for creating the lagged columns
  • cbind them.

But I am not able to proceed with the code. Something rough:

df_final<-lagged(df="odd",n=3)

lagged<-function(df,n){
   df<-zoo(df)
   lags<-paste("A", 1:n, sep ="_")
   for (i in 1:5) {
     odd<-as.data.frame(lag(odd$OBS_Q,-1*i,na.pad =  TRUE))

   #Cbind here
   } 

I am stuck in writing this function. Could you please show some way? Or a different simpler way of doing this....

Reference: Basic lag in R vector/dataframe


Addendum:

Real data:

x<-structure(list(DATE = 19630101:19630104, PRECIP = c(0, 0, 0,0), 
               OBS_Q = c(1.61, 1.48, 1.4, 1.33), swb = c(1.75, 1.73, 1.7,1.67), 
               gr4j = c(1.9, 1.77, 1.67, 1.58), isba = c(0.83, 0.83,0.83, 0.83), 
               noah = c(1.31, 1.19, 1.24, 1.31), sac = c(1.99,1.8, 1.66, 1.57), 
               swap = c(1.1, 1.05, 1.08, 0.99), vic.mm.day. = c(2.1,1.75, 1.55, 1.43)), 
          .Names = c("DATE", "PRECIP", "OBS_Q", "swb","gr4j", "isba", "noah", "sac", "swap", "vic.mm.day."), 
          class = c("data.table","data.frame"), row.names = c(NA, -4L))

The column to be lagged is OBS_Q.

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I might build something around base R's embed()

x <- c(rep(NA,2),1:6)
embed(x,3)
#      [,1] [,2] [,3]
# [1,]    1   NA   NA
# [2,]    2    1   NA
# [3,]    3    2    1
# [4,]    4    3    2
# [5,]    5    4    3
# [6,]    6    5    4

Perhaps something like this:

f <- function(x, dimension, pad) {
    if(!missing(pad)) {
        x <- c(rep(pad, dimension-1), x)
    }
    embed(x, dimension)
}
f(1:6, dimension=3, pad=NA)
#      [,1] [,2] [,3]
# [1,]    1   NA   NA
# [2,]    2    1   NA
# [3,]    3    2    1
# [4,]    4    3    2
# [5,]    5    4    3
# [6,]    6    5    4

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