Speed up the loop operation in R

Question

I have a big performance problem in R. I wrote a function that iterates over a data.frame object. It simply adds a new column to a data.frame and a

Answer 1

Processing with data.table is a viable option:

n <- 1000000
df <- as.data.frame(matrix(sample(1:10, n*9, TRUE), n, 9))
colnames(df) <- paste("col", 1:9, sep = "")

library(data.table)

dayloop2.dt <- function(df) {
  dt <- data.table(df)
  dt[, Kumm. := {
    res <- .I;
    ifelse (res > 1,             
      ifelse ((col6 == shift(col6, fill = 0)) & (col3 == shift(col3, fill = 0)) , 
        res <- col9 + shift(res)                   
      , # else
        res <- col9                                 
      )
     , # else
      res <- col9
    )
  }
  ,]
  res <- data.frame(dt)
  return (res)
}

res <- dayloop2.dt(df)

m <- microbenchmark(dayloop2.dt(df), times = 100)
#Unit: milliseconds
#       expr      min        lq     mean   median       uq      max neval
#dayloop2.dt(df) 436.4467 441.02076 578.7126 503.9874 575.9534 966.1042    10

If you ignore the possible gains from conditions filtering, it is very fast. Obviously, if you can do the calculation on the subset of data, it helps.