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-boun...@r-project.org on behalf of nell.r...@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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