Is the more efficient pitching connected backward rather than forward?

data.table

implements asof

(also known as rolling

or LOCF

). I found this related question:

Filling in missing (gaps) in the data table, for each category - back and forth

but this question contains NA data. In my case, I am following the advice to save data irregularly and join it with roll=TRUE

. What I would like to do, and not the last observation carried forward, is the following observation, which should be carried back as far as possible.

This is what I tried, using first time:=-time

to try and trick it. Can I do it better? Can I make it faster?

llorJoin <- function(A,B){
    B <- copy(B);
    keys <- key(A);
    if( !identical(key(A), key(B)) | is.null(keys) ){
       stop("llorJoin::ERROR; A and B should have the same non-empty keys");
    }

    lastKey <- tail(keys,1L);
    myStr <- parse(text=paste0(lastKey,":=-as.numeric(",lastKey,")"));
    A <- A[,eval(myStr)]; setkeyv(A,keys);
    B <- B[,eval(myStr)]; setkeyv(B,keys);

    origin <- "1970-01-01 00:00.00 UTC";
    A <- B[A,roll=T];
    myStr2 <- parse(text=paste0(lastKey,":=as.POSIXct(-",lastKey,",origin=origin)"));
    A <- A[,eval(myStr2)]; setkeyv(A,keys);
    return(A);
}

      

        
        
        
      

    

library(data.table)
A <- data.table(time=as.POSIXct(c("10:01:01","10:01:02","10:01:04","10:01:05","10:01:02","10:01:01","10:01:01"),format="%H:%M:%S"),
                b=c("a","a","a","a","b","c","c"),
                d=c(1,1.9,2,1.8,5,4.1,4.2));
B <- data.table(time=as.POSIXct(c("10:01:01","10:01:03","10:01:00","10:01:01"),format="%H:%M:%S"),b=c("a","a","c","d"), e=c(1L,2L,3L,4L));
setkey(A,b,time)
setkey(B,b,time)

      

        
        
        
      

    

library(rbenchmark)
benchmark(llorJoin(A,B),B[A,roll=T],replications=10)
            test replications elapsed relative user.self sys.self user.child sys.child
1 llorJoin(A, B)           10   0.045        1     0.048        0          0         0
2 B[A, roll = T]           10   0.009        1     0.008        0          0         0

   b                time  e   d
1: a 2013-01-12 09:01:01  1 1.0
2: a 2013-01-12 09:01:02  2 1.9
3: a 2013-01-12 09:01:04 NA 2.0
4: a 2013-01-12 09:01:05 NA 1.8
5: b 2013-01-12 09:01:02 NA 5.0
6: c 2013-01-12 09:01:01 NA 4.1
7: c 2013-01-12 09:01:01 NA 4.2

      

        
        
        
      

    

So, as a comparadion, compared to the original data 5 times faster.