[R] Speeding up R code - Apply a function to each row of a matrix using the dplyr package

[MacQueen, Don]

Without more study, I can only give some general pointers.

The as.vector() in X1 <- as.vector(coord[1]) is almost certainly not needed. It will add a little bit to your execution time.
Converting the output of func() to a one row matrix is almost certainly not needed. Just return c(res1, res2).

Your data frame appears to be entirely numeric, in which case you don't need to ever use a data frame. 

Try
  apply( tab, 1, func, a=40, b=5, c=1 )
instead of all that dplyr stuff.


Your function can be redefined as

func <- function(coord, a, b, c){
    
          X1 <- as.vector(coord[1])
          Y1 <- as.vector(coord[2])
          X2 <- as.vector(coord[3])
          Y2 <- as.vector(coord[4])
    
           res1 <- mean(c((X1 - a) : (X1 - 1), (Y1 + 1) : (Y1 + 40)))
           res2 <- mean(c((X2 - a) : (X2 - 1), (Y2 + 1) : (Y2 + 40)))    
    
            if (c==0) c(res1, res2) else c(res1, res2)*b
        }

I suspect you can operate on the entire matrix, without looping (which both the apply() method, and the split/rbind method do, in effect), and if so it will be much faster. But I can't say for sure without more study.

--
Don MacQueen
Lawrence Livermore National Laboratory
7000 East Ave., L-627
Livermore, CA 94550
925-423-1062
Lab cell 925-724-7509
 
 

On 11/1/18, 12:35 PM, "R-help on behalf of Nelly Reduan" <r-help-bounces using r-project.org on behalf of nell.redu using hotmail.fr> wrote:

    Hello,
    
    I have a input data frame with multiple rows. For each row, I want to apply a function. The input data frame has 1,000,000+ rows. How can I speed up my code ? I would like to keep the function "func".
    
    Here is a reproducible example with a simple function:
    
        library(tictoc)
        library(dplyr)
    
    func <- function(coord, a, b, c){
    
          X1 <- as.vector(coord[1])
          Y1 <- as.vector(coord[2])
          X2 <- as.vector(coord[3])
          Y2 <- as.vector(coord[4])
    
          if(c == 0) {
    
            res1 <- mean(c((X1 - a) : (X1 - 1), (Y1 + 1) : (Y1 + 40)))
            res2 <- mean(c((X2 - a) : (X2 - 1), (Y2 + 1) : (Y2 + 40)))
            res <- matrix(c(res1, res2), ncol=2, nrow=1)
    
          } else {
    
            res1 <- mean(c((X1 - a) : (X1 - 1), (Y1 + 1) : (Y1 + 40)))*b
            res2 <- mean(c((X2 - a) : (X2 - 1), (Y2 + 1) : (Y2 + 40)))*b
            res <- matrix(c(res1, res2), ncol=2, nrow=1)
    
          }
    
          return(res)
        }
    
        ## Apply the function
        set.seed(1)
        n = 10000000
        tab <- as.matrix(data.frame(x1 = sample(1:100, n, replace = T), y1 = sample(1:100, n, replace = T), x2 = sample(1:100, n, replace = T), y2 = sample(1:100, n, replace = T)))
    
    
      tic("test 1")
      test <- tab %>%
        split(1:nrow(tab)) %>%
        map(~ func(.x, 40, 5, 1)) %>%
        do.call("rbind", .)
      toc()
    
    test 1: 599.2 sec elapsed
    
    Thanks very much for your time
    Have a nice day
    Nell
    
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