Vectorized approach for the following task

Question

The following code works, but as expected, it takes ages to execute for large vectors.

What would be the vectorised way to accomplish the same task:

x <- seq(0,10,0.01)
y <- seq(0,10,0.01)
df <- data.frame(vector1 = rnorm(10000), vector2 = rnorm(10000), vector3 = rnorm(10000))


m.out <- matrix(nrow=length(x),ncol = length(y))

a <- df$vector1
b <- df$vector2
c <- df$vector3

for (i in 1:length(x)){
  for(j in 1:length(y)){
    m.out[i,j] <- cor((x[i]*a + y[j]*b),c,use="complete.obs",method = "pearson")
  }
}

Thanks,

Your example is not working, can you fix it? It would be nice to have `seed` and data size too. — pogibas, Sep 13 '18 at 13:54
This [H E R E](https://stackoverflow.com/questions/30686904/apply-a-function-over-all-combinations-of-arguments-output-as-list) seems to be what you're after??? — Andre Elrico, Sep 13 '18 at 14:04

score 0 · Answer 1 · answered Sep 13 '18 at 22:06

Please see vectorized version below, you can use mapply and expand.grid. To return to wide dataset format you can use dcast of reshape2 package (however it still takes some time):

set.seed(123)
x <- seq(0, 10, 0.01)
y <- seq(0, 10, 0.01)

# simulation
df <- data.frame(vector1 = rnorm(length(x)), vector2 = rnorm(length(x)), vector3 = rnorm(length(x)))
a <- df$vector1
b <- df$vector2
c <- df$vector3

v <- expand.grid(x, y)
v$out <- mapply(function(n, m) cor(n * a + m * b, c, use = "complete.obs", method = "pearson"), v[, 1], v[, 2])
library(reshape2)
z <- dcast(v, Var1 ~ Var2)
rownames(z) <- z$Var1
z <- z[, -1]
head(z[, 1:5])

Output:

               0          0.01          0.02          0.03          0.04
0             NA  0.0140699293  0.0140699293  0.0140699293  0.0140699293
0.01 -0.01383734  0.0003350528  0.0065542508  0.0090938390  0.0103897953
0.02 -0.01383734 -0.0059841841  0.0003350528  0.0042062076  0.0065542508
0.03 -0.01383734 -0.0086178379 -0.0035752709  0.0003350528  0.0031310581
0.04 -0.01383734 -0.0099713568 -0.0059841841 -0.0024814273  0.0003350528
0.05 -0.01383734 -0.0107798236 -0.0075458061 -0.0045052606 -0.0018627055

Thanks for the reply @Artem; however, after some bench marking, my original approach takes roughly the same amount of time than your version. Cheers — Mario Reyes, Sep 14 '18 at 05:01

Vectorized approach for the following task

1 Answers1