How to find the last or next record using R.table package data and rolling connections

Lets say I have a data table like this.

   customer_id time_stamp value
1:           1        223     4
2:           1        252     1
3:           1        456     3
4:           2        455     5
5:           2        632     2

So customer_id and time_stamp together form a unique key. I want to add some new columns indicating the previous and last values ​​of "value". That is, I want the result to be like this.

  customer_id time_stamp value value_PREV value_NEXT
1:           1        223     4         NA          1
2:           1        252     1          4          3
3:           1        456     3          1         NA
4:           2        455     5         NA          2
5:           2        632     2          5         NA

I want it to be fast and work with a rare, irregular time. I thought a data connection. However, the sliding connection seems to find the last time OR at the same time. Therefore, if you perform a sliding join on two copies of the same table (after adding _PREV to the column names of the copy), this does not quite work. You can lure it out by adding a tiny number to the copy time variable, but that's awkward.

rollin join data.table? , 40 R. , , , . , , .

.

data=data.table(customer_id=c(1,2,1,1,2),time_stamp=c(252,632,456,223,455),value=c(1,2,3,4,5))
data_sorted=data[order(customer_id,time_stamp)]

, . , , NA customer_id , , . . - ?

add_prev_next_cbind<-function(data,ident="customer_id",timecol="time_stamp",prev_tag="PREV",
                   next_tag="NEXT",sep="_"){
  o=order(data[[ident]],data[[timecol]])
  uo=order(o)
  data=data[o,]
  Nrow=nrow(data)
  Ncol=ncol(data)
  #shift it, put any junk in the first row
  data_prev=data[c(1,1:(Nrow-1)),]
  #shift it, put any junk in the last row
  data_next=data[c(2:(Nrow),Nrow),]
  #flag the rows where the identity changes, these get NA
  prev_diff=data[[ident]] != data_prev[[ident]]
  prev_diff[1]=T
  next_diff=data[[ident]] != data_next[[ident]]  
  next_diff[Nrow]=T
  #change names
  names=names(data)
  names_prev=paste(names,prev_tag,sep=sep)
  names_next=paste(names,next_tag,sep=sep)
  setnames(data_prev,names,names_prev)
  setnames(data_next,names,names_next)
  #put NA in rows where prev and next are from a different ident
  #replace the next two lines with something else
  #data_prev[prev_diff,]<-NA
  #data_next[next_diff,]<-NA
  data_all=cbind(data,data_prev,data_next)
  data_all=data_all[uo,]
  return(data_all)
}