match.integer64 {bit64}R Documentation

64-bit integer matching


match returns a vector of the positions of (first) matches of its first argument in its second.

%in% is a more intuitive interface as a binary operator, which returns a logical vector indicating if there is a match or not for its left operand.


## S3 method for class 'integer64'
match(x, table, nomatch = NA_integer_, nunique = NULL, method = NULL, ...)
## S3 method for class 'integer64'
x %in% table, ...



integer64 vector: the values to be matched, optionally carrying a cache created with hashcache


integer64 vector: the values to be matched against, optionally carrying a cache created with hashcache or sortordercache


the value to be returned in the case when no match is found. Note that it is coerced to integer.


NULL or the number of unique values of table (including NA). Providing nunique can speed-up matching when table has no cache. Note that a wrong nunique can cause undefined behaviour up to a crash.


NULL for automatic method selection or a suitable low-level method, see details




These functions automatically choose from several low-level functions considering the size of x and table and the availability of caches.

Suitable methods for %in%.integer64 are hashpos (hash table lookup), hashrev (reverse lookup), sortorderpos (fast ordering) and orderpos (memory saving ordering). Suitable methods for match.integer64 are hashfin (hash table lookup), hashrin (reverse lookup), sortfin (fast sorting) and orderfin (memory saving ordering).


A vector of the same length as x.

match: An integer vector giving the position in table of the first match if there is a match, otherwise nomatch.

If x[i] is found to equal table[j] then the value returned in the i-th position of the return value is j, for the smallest possible j. If no match is found, the value is nomatch.

%in%: A logical vector, indicating if a match was located for each element of x: thus the values are TRUE or FALSE and never NA.


Jens Oehlschlägel <>

See Also



x <- as.integer64(c(NA, 0:9), 32)
table <- as.integer64(c(1:9, NA))
match.integer64(x, table)
"%in%.integer64"(x, table)

x <- as.integer64(sample(c(rep(NA, 9), 0:9), 32, TRUE))
table <- as.integer64(sample(c(rep(NA, 9), 1:9), 32, TRUE))
stopifnot(identical(match.integer64(x, table), match(as.integer(x), as.integer(table))))
stopifnot(identical("%in%.integer64"(x, table), as.integer(x) %in% as.integer(table)))

## Not run: 
	message("check when reverse hash-lookup beats standard hash-lookup")
	e <- 4:24
	timx <- timy <- matrix(NA, length(e), length(e), dimnames=list(e,e))
	for (iy in seq_along(e))
	for (ix in 1:iy){
		nx <- 2^e[ix]
		ny <- 2^e[iy]
		x <- as.integer64(sample(ny, nx, FALSE))
		y <- as.integer64(sample(ny, ny, FALSE))
		#hashfun(x, bits=as.integer(5))
		timx[ix,iy] <- repeat.time({
		hx <- hashmap(x)
		py <- hashrev(hx, y)
		timy[ix,iy] <- repeat.time({
		hy <- hashmap(y)
		px <- hashpos(hy, x)
		#identical(px, py)
		print(round(timx[1:iy,1:iy]/timy[1:iy,1:iy], 2), na.print="")

	message("explore best low-level method given size of x and table")
	B1 <- 1:27
	B2 <- 1:27
	tim <- array(NA, dim=c(length(B1), length(B2), 5)
 , dimnames=list(B1, B2, c("hashpos","hashrev","sortpos1","sortpos2","sortpos3")))
	for (i1 in B1)
	for (i2 in B2)
	  b1 <- B1[i1]
	  b2 <- B1[i2]
	  n1 <- 2^b1
	  n2 <- 2^b2
	  x1 <- as.integer64(c(sample(n2, n1-1, TRUE), NA))
	  x2 <- as.integer64(c(sample(n2, n2-1, TRUE), NA))
	  tim[i1,i2,1] <- repeat.time({h <- hashmap(x2);hashpos(h, x1);rm(h)})[3]
	  tim[i1,i2,2] <- repeat.time({h <- hashmap(x1);hashrev(h, x2);rm(h)})[3]
	  s <- clone(x2); o <- seq_along(s); ramsortorder(s, o)
	  tim[i1,i2,3] <- repeat.time(sortorderpos(s, o, x1, method=1))[3]
	  tim[i1,i2,4] <- repeat.time(sortorderpos(s, o, x1, method=2))[3]
	  tim[i1,i2,5] <- repeat.time(sortorderpos(s, o, x1, method=3))[3]
	  print(apply(tim, 1:2, function(ti)if(any( else which.min(ti)))

## End(Not run)

[Package bit64 version 4.0.5 Index]