hashmap: Hashing for 64bit integers

Description

This is an explicit implementation of hash functionality that underlies matching and other functions in R. Explicit means that you can create, store and use hash functionality directly. One advantage is that you can re-use hashmaps, which avoid re-building hashmaps again and again.

Usage

hashfun(x, …)
# S3 method for integer64
hashfun(x, minfac=1.41, hashbits=NULL, …)
hashmap(x, …)
# S3 method for integer64
hashmap(x, nunique=NULL, minfac=1.41, hashbits=NULL, cache=NULL, …)
hashpos(cache, …)
# S3 method for cache_integer64
hashpos(cache, x, nomatch = NA_integer_, …)
hashrev(cache, …)
# S3 method for cache_integer64
hashrev(cache, x, nomatch = NA_integer_, …)
hashfin(cache, …)
# S3 method for cache_integer64
hashfin(cache, x, …)
hashrin(cache, …)
# S3 method for cache_integer64
hashrin(cache, x, …)
hashdup(cache, …)
# S3 method for cache_integer64
hashdup(cache, …)
hashuni(cache, …)
# S3 method for cache_integer64
hashuni(cache, keep.order=FALSE, …)
hashmapuni(x, …)
# S3 method for integer64
hashmapuni(x, nunique=NULL, minfac=1.5, hashbits=NULL, …)
hashupo(cache, …)
# S3 method for cache_integer64
hashupo(cache, keep.order=FALSE, …)
hashmapupo(x, …)
# S3 method for integer64
hashmapupo(x, nunique=NULL, minfac=1.5, hashbits=NULL, …)
hashtab(cache, …)
# S3 method for cache_integer64
hashtab(cache, …)
hashmaptab(x, …)
# S3 method for integer64
hashmaptab(x, nunique=NULL, minfac=1.5, hashbits=NULL, …)

Arguments

an integer64 vector

hashmap

an object of class 'hashmap' i.e. here 'cache_integer64'

minfac

minimum factor by which the hasmap has more elements compared to the data x, ignored if hashbits is given directly

hashbits

length of hashmap is 2^hashbits

cache

an optional cache object into which to put the hashmap (by default a new cache is created)

nunique

giving correct number of unique elements can help reducing the size of the hashmap

nomatch

the value to be returned if an element is not found in the hashmap

keep.order

determines order of results and speed: FALSE (the default) is faster and returns in the (pseudo)random order of the hash function, TRUE returns in the order of first appearance in the original data, but this requires extra work

…

further arguments, passed from generics, ignored in methods

Value

see details

Details

function	see also	description
`hashfun`	`digest`	export of the hash function used in `hashmap`
`hashmap`	`match`	return hashmap
`hashpos`	`match`	return positions of `x` in `hashmap`
`hashrev`	`match`	return positions of `hashmap` in `x`
`hashfin`	`%in%.integer64`	return logical whether `x` is in `hashmap`
`hashrin`	`%in%.integer64`	return logical whether `hashmap` is in `x`
`hashdup`	`duplicated`	return logical whether hashdat is duplicated using hashmap
`hashuni`	`unique`	return unique values of hashmap
`hashmapuni`	`unique`	return unique values of `x`
`hashupo`	`unique`	return positions of unique values in hashdat
`hashmapupo`	`unique`	return positions of unique values in `x`
`hashtab`	`table`	tabulate values of hashdat using hashmap in `keep.order=FALSE`
`hashmaptab`	`table`	tabulate values of `x` building hasmap on the fly in `keep.order=FALSE`

Examples

Run this code

# NOT RUN {
x <- as.integer64(sample(c(NA, 0:9)))
y <- as.integer64(sample(c(NA, 1:9), 10, TRUE))
hashfun(y)
hx <- hashmap(x)
hy <- hashmap(y)
ls(hy)
hashpos(hy, x)
hashrev(hx, y)
hashfin(hy, x)
hashrin(hx, y)
hashdup(hy)
hashuni(hy)
hashuni(hy, keep.order=TRUE)
hashmapuni(y)
hashupo(hy)
hashupo(hy, keep.order=TRUE)
hashmapupo(y)
hashtab(hy)
hashmaptab(y)

stopifnot(identical(match(as.integer(x),as.integer(y)),hashpos(hy, x)))
stopifnot(identical(match(as.integer(x),as.integer(y)),hashrev(hx, y)))
stopifnot(identical(as.integer(x) %in% as.integer(y), hashfin(hy, x)))
stopifnot(identical(as.integer(x) %in% as.integer(y), hashrin(hx, y)))
stopifnot(identical(duplicated(as.integer(y)), hashdup(hy)))
stopifnot(identical(as.integer64(unique(as.integer(y))), hashuni(hy, keep.order=TRUE)))
stopifnot(identical(sort(hashuni(hy, keep.order=FALSE)), sort(hashuni(hy, keep.order=TRUE))))
stopifnot(identical(y[hashupo(hy, keep.order=FALSE)], hashuni(hy, keep.order=FALSE)))
stopifnot(identical(y[hashupo(hy, keep.order=TRUE)], hashuni(hy, keep.order=TRUE)))
stopifnot(identical(hashpos(hy, hashuni(hy, keep.order=TRUE)), hashupo(hy, keep.order=TRUE)))
stopifnot(identical(hashpos(hy, hashuni(hy, keep.order=FALSE)), hashupo(hy, keep.order=FALSE)))
stopifnot(identical(hashuni(hy, keep.order=FALSE), hashtab(hy)$values))
stopifnot(identical(as.vector(table(as.integer(y), useNA="ifany"))
, hashtab(hy)$counts[order.integer64(hashtab(hy)$values)]))
stopifnot(identical(hashuni(hy, keep.order=TRUE), hashmapuni(y)))
stopifnot(identical(hashupo(hy, keep.order=TRUE), hashmapupo(y)))
stopifnot(identical(hashtab(hy), hashmaptab(y)))

	
# }
# NOT RUN {
	message("explore speed given size of the hasmap in 2^hashbits and size of the data")
	message("more hashbits means more random access and less collisions")
	message("i.e. more data means less random access and more collisions")
	bits <- 24
	b <- seq(-1, 0, 0.1)
	tim <- matrix(NA, length(b), 2, dimnames=list(b, c("bits","bits+1")))
    for (i in 1:length(b)){
	  n <- as.integer(2^(bits+b[i]))
	  x <- as.integer64(sample(n))
	  tim[i,1] <- repeat.time(hashmap(x, hashbits=bits))[3]
	  tim[i,2] <- repeat.time(hashmap(x, hashbits=bits+1))[3]
	  print(tim)
      matplot(b, tim)
	}
	message("we conclude that n*sqrt(2) is enough to avoid collisions")
	
# }