| Version: | 1.1-6 |
| Title: | Fast 'match()' Function |
| Author: | Simon Urbanek 
[aut, cre, cph] (https://urbanek.org) |
| Maintainer: | Simon Urbanek <simon.urbanek@r-project.org> |
| Description: | Package providing a fast match() replacement for cases that require repeated look-ups. It is slightly faster that R's built-in match() function on first match against a table, but extremely fast on any subsequent lookup as it keeps the hash table in memory. |
| License: | GPL-2 |
| Depends: | R (≥ 2.3.0) |
| URL: | http://www.rforge.net/fastmatch |
| NeedsCompilation: | yes |
| Packaged: | 2024-12-20 08:06:22 UTC; rforge |
| Repository: | CRAN |
| Date/Publication: | 2024-12-23 17:50:34 UTC |
Create an index that groups unique values together
Description
coalesce makes sure that a given index vector is coalesced,
i.e., identical values are grouped into contiguous blocks. This can be
used as a much faster alternative to sort.list where the
goal is to group identical values, but not necessarily in a
pre-defined order. The algorithm is linear in the length of the vector.
Usage
coalesce(x)
Arguments
x |
character, integer or real vector to coalesce |
Details
The current implementation takes two passes through the vector. In the
first pass it creates a hash table for the values of x counting
the occurrences in the process. In the second pass it assigns indices
for every element based on the index stored in the hash table.
The order of the groups of unique values is defined by the first
occurence of each unique value, hence it is identical to the order of
unique.
One common use of coalesce is to allow the use of arbitrary
vectors in ctapply via
ctapply(x[coalesce(x)], ...).
Value
Integer vector with the resulting permutation. x[coalesce(x)]
gives x with contiguous unique values.
Author(s)
Simon Urbanek
See Also
Examples
i = rnorm(2e6)
names(i) = as.integer(rnorm(2e6))
## compare sorting and coalesce
system.time(o <- i[order(names(i))])
system.time(o <- i[coalesce(names(i))])
## more fair comparison taking the coalesce time (and copy) into account
system.time(tapply(i, names(i), sum))
system.time({ o <- i[coalesce(names(i))]; ctapply(o, names(o), sum) })
## in fact, using ctapply() on a dummy vector is faster than table() ...
## believe it or not ... (that that is actually wasteful, since coalesce
## already computed the table internally anyway ...)
ftable <- function(x) {
t <- ctapply(rep(0L, length(x)), x[coalesce(x)], length)
t[sort.list(names(t))]
}
system.time(table(names(i)))
system.time(ftable(names(i)))
Fast tapply() replacement functions
Description
ctapply is a fast replacement of tapply that assumes
contiguous input, i.e. unique values in the index are never speparated
by any other values. This avoids an expensive split step since
both value and the index chungs can be created on the fly. It also
cuts a few corners to allow very efficient copying of values. This
makes it many orders of magnitude faster than the classical
lapply(split(), ...) implementation.
Usage
ctapply(X, INDEX, FUN, ..., MERGE=c)
Arguments
X |
an atomic object, typically a vector |
INDEX |
numeric or character vector of the same length as |
FUN |
the function to be applied |
... |
additional arguments to |
MERGE |
function to merge the resulting vector or |
Details
Note that ctapply supports either integer, real or character
vectors as indices (note that factors are integer vectors and thus
supported, but you do not need to convert character vectors). Unlike
tapply it does not take a list of factors - if you want to use
a cross-product of factors, create the product first, e.g. using
paste(i1, i2, i3, sep='\01') or multiplication - whetever
method is convenient for the input types.
ctapply requires the INDEX to contiguous. One (slow) way
to achieve that is to use sort or order.
Author(s)
Simon Urbanek
See Also
Examples
i = rnorm(4e6)
names(i) = as.integer(rnorm(1e6))
i = i[order(names(i))]
system.time(tapply(i, names(i), sum))
system.time(ctapply(i, names(i), sum))
Fast match() replacement
Description
fmatch is a faster version of the built-in match()
function. It is slightly faster than the built-in version because it
uses more specialized code, but in addition it retains the hash table
within the table object such that it can be re-used, dramatically reducing
the look-up time especially for large tables.
Although fmatch can be used separately, in general it is also
safe to use: match <- fmatch since it is a drop-in
replacement. Any cases not directly handled by fmatch are passed
to match with a warning.
fmatch.hash is identical to fmatch but it returns the table
object with the hash table attached instead of the result, so it can be
used to create a table object in cases where direct modification is
not possible.
%fin% is a version of the built-in %in% function
that uses fmatch instead of match().
Usage
fmatch(x, table, nomatch = NA_integer_, incomparables = NULL)
fmatch.hash(x, table, nomatch = NA_integer_, incomparables = NULL)
x %fin% table
Arguments
x |
values to be matched |
table |
values to be matched against |
nomatch |
the value to be returned in the case when no match is
found. It is coerced to |
incomparables |
a vector of values that cannot be matched. Any
value other than |
Details
See match for the purpose and details of the
match function. fmatch is a drop-in replacement for
the match function with the focus on
performance. incomparables are not supported by fmatch
and will be passed down to match.
The first match against a table results in a hash table to be computed
from the table. This table is then attached as the ".match.hash"
attribute of the table so that it can be re-used on subsequent calls
to fmatch with the same table.
The hashing algorithm used is the same as the match function in
R, but it is re-implemented in a slightly different way to improve its
performance at the cost of supporting only a subset of types (integer,
real and character). For any other types fmatch falls back to
match (with a warning).
Value
fmatch: A vector of the same length as x - see
match for details.
fmatch.hash: table, possibly coerced to match the type
of x, with the hash table attached.
%fin%: A logical vector the same length as x - see
%in% for details.
Note
fmatch modifies the table by attaching an attribute to
it. It is expected that the values will not change unless that
attribute is dropped. Under normal circumstances this should not have
any effect from user's point of view, but there is a theoretical
chance of the cache being out of sync with the table in case the table
is modified directly (e.g. by some C code) without removing
attributes.
In cases where the table object cannot be modified (or such
modification would not survive) fmatch.hash can be used to build
the hash table and return table object including the hash
table. In that case no lookup is done and x is only used to
determine the type into which table needs to be coerced.
Also fmatch does not convert to a common encoding so strings
with different representation in two encodings don't match.
Author(s)
Simon Urbanek
See Also
Examples
# some random speed comparison examples:
# first use integer matching
x = as.integer(rnorm(1e6) * 1000000)
s = 1:100
# the first call to fmatch is comparable to match
system.time(fmatch(s,x))
# but the subsequent calls take no time!
system.time(fmatch(s,x))
system.time(fmatch(-50:50,x))
system.time(fmatch(-5000:5000,x))
# here is the speed of match for comparison
system.time(base::match(s, x))
# the results should be identical
identical(base::match(s, x), fmatch(s, x))
# next, match a factor against the table
# this will require both x and the factor
# to be cast to strings
s = factor(c("1","1","2","foo","3",NA))
# because the casting will have to allocate a string
# cache in R, we run a dummy conversion to take
# that out of the equation
dummy = as.character(x)
# now we can run the speed tests
system.time(fmatch(s, x))
system.time(fmatch(s, x))
# the cache is still valid for string matches as well
system.time(fmatch(c("foo","bar","1","2"),x))
# now back to match
system.time(base::match(s, x))
identical(base::match(s, x), fmatch(s, x))
# finally, some reals to match
y = rnorm(1e6)
s = c(y[sample(length(y), 100)], 123.567, NA, NaN)
system.time(fmatch(s, y))
system.time(fmatch(s, y))
system.time(fmatch(s, y))
system.time(base::match(s, y))
identical(base::match(s, y), fmatch(s, y))
# this used to fail before 0.1-2 since nomatch was ignored
identical(base::match(4L, 1:3, nomatch=0), fmatch(4L, 1:3, nomatch=0))