Memory error in sapply for lagged variables

Question

Edit: fake data for example

df = matrix(runif(50*507), nrow = 50, ncol = 507)
df = data.frame(df)
df[,1] = seq(as.Date("2017/1/1"), as.Date("2017/2/19"), "days")
names(df) = paste0("var", 1:507)
names(df)[505:507] = c("mktrf", "smb", "hml")
names(df)[1] = "Date"

All the dep var

x = df[,505:507]

All the indep var

y <- df[,2:504]

I have a function called shift I'd like to apply to every column of a df. The function lags variables. The function is as follows, and shifts the specified column(s) by a specified number.

shift<-function(x,shift_by){
  stopifnot(is.numeric(shift_by))
  stopifnot(is.numeric(x))

  if (length(shift_by)>1)
    return(sapply(shift_by,shift, x=x))

  out<-NULL
  abs_shift_by=abs(shift_by)
  if (shift_by > 0 )
    out<-c(tail(x,-abs_shift_by),rep(NA,abs_shift_by))
  else if (shift_by < 0 )
    out<-c(rep(NA,abs_shift_by), head(x,-abs_shift_by))
  else 
    out<-x
  out
}

When I use the sapply function like this, where y is a dataframe consisting of time series variables I want to lag:

y_lag <- sapply(y,shift,-1 )

I get the following error:

Error: cannot allocate vector of size 54.2 Mb
In addition: Warning messages:
1: In unlist(x, recursive = FALSE) :
  Reached total allocation of 8072Mb: see help(memory.size)
2: In unlist(x, recursive = FALSE) :
  Reached total allocation of 8072Mb: see help(memory.size)
3: In unlist(x, recursive = FALSE) :
  Reached total allocation of 8072Mb: see help(memory.size)
4: In unlist(x, recursive = FALSE) :
  Reached total allocation of 8072Mb: see help(memory.size)
5: In unlist(x, recursive = FALSE) :
  Reached total allocation of 8072Mb: see help(memory.size)
6: In unlist(x, recursive = FALSE) :
  Reached total allocation of 8072Mb: see help(memory.size)

My question: can I use a different method to lag every element of a column, while still using the lm package? Or how do I address the memory issue I am having? I can't use a different computer.

Answer 1

I was able to get it to work using the lagpad function described in a different question here:

lagpad <- function(x, k=1) {
  i<-is.vector(x)
  if(is.vector(x)) x<-matrix(x) else x<-matrix(x,nrow(x))
  if(k>0) {
    x <- rbind(matrix(rep(NA, k*ncol(x)),ncol=ncol(x)), matrix(x[1:(nrow(x)-k),], ncol=ncol(x)))
  }
  else {
    x <- rbind(matrix(x[(-k+1):(nrow(x)),], ncol=ncol(x)),matrix(rep(NA, -k*ncol(x)),ncol=ncol(x)))
  }
  if(i) x[1:length(x)] else x
}

Which essentially does what r2evans described, shifting the whole df.