The traditional approach is to preprocess the data
and put it in a data structure, often a K-d tree,
for which the "nearest point" query is very fast.
There is an implementation in the nnclust
package.
library(nnclust)
foo <- cbind(x=c(1,2,4,4,10),y=c(1,2,4,4,10))
i <- nnfind(foo)$neighbour
plot(foo)
arrows( foo[,1], foo[,2], foo[i,1], foo[i,2] )
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