Finding the minimum difference between each element of one vector and another vector
r
Solution
I would use a step function sorted on the first vector. This will avoid loops and is pretty fast in R.
x <- rnorm(1000)
y <- rnorm(1000)
sorted.x <- sort(x)
myfun <- stepfun(sorted.x, 0:length(x))
Now `myfun(1)` will give you the index of the largest element of `sorted.x` whose value is less than `1`. In my case,
> myfun(1)
[1] 842
> sorted.x[842]
[1] 0.997574
> sorted.x[843]
[1] 1.014771
So you know that the closest element is either `sorted.x[myfun(1)]` or `sorted.x[myfun(1) + 1]`. Consequently (and padding for 0),
indices <- pmin(pmax(1, myfun(y)), length(sorted.x) - 1)
mindist <- pmin(abs(y - sorted.x[indices]), abs(y - sorted.x[indices + 1]))
Problem
I have two vectors of integers, and for each element of the second vector I want to find the minumum distance to any element of the first vector - for example ``` obj1 <- seq(0, 1000, length.out=11) obj2 <- 30:50 min_diff <- sapply(obj2, function(x) min(abs(obj1-x))) min_diff ``` returns ``` [1] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 ``` Is there a more efficient way? I want to scale this up to thousands (millions?) of both obj1 & obj2. Thanks, Aaron