adist
Approximate String Distances
Compute the approximate string distance between character vectors. The distance is a generalized Levenshtein (edit) distance, giving the minimal possibly weighted number of insertions, deletions and substitutions needed to transform one string into another.
 Keywords
 character
Usage
adist(x, y = NULL, costs = NULL, counts = FALSE, fixed = TRUE, partial = !fixed, ignore.case = FALSE, useBytes = FALSE)
Arguments
 x
 a character vector. Long vectors are not supported.
 y
 a character vector, or
NULL
(default) indicating takingx
asy
.  costs
 a numeric vector or list with names partially matching
insertions, deletions and substitutions giving
the respective costs for computing the Levenshtein distance, or
NULL
(default) indicating using unit cost for all three possible transformations.  counts
 a logical indicating whether to optionally return the
transformation counts (numbers of insertions, deletions and
substitutions) as the
"counts"
attribute of the return value.  fixed
 a logical. If
TRUE
(default), thex
elements are used as string literals. Otherwise, they are taken as regular expressions andpartial = TRUE
is implied (corresponding to the approximate string distance used byagrep
withfixed = FALSE
.  partial
 a logical indicating whether the transformed
x
elements must exactly match the completey
elements, or only substrings of these. The latter corresponds to the approximate string distance used byagrep
(by default).  ignore.case
 a logical. If
TRUE
, case is ignored for computing the distances.  useBytes
 a logical. If
TRUE
distance computations are done bytebybyte rather than characterbycharacter.
Details
The (generalized) Levenshtein (or edit) distance between two strings
s and t is the minimal possibly weighted number of
insertions, deletions and substitutions needed to transform s
into t (so that the transformation exactly matches t).
This distance is computed for partial = FALSE
, currently using
a dynamic programming algorithm (see, e.g.,
http://en.wikipedia.org/wiki/Levenshtein_distance) with space
and time complexity $O(mn)$, where $m$ and $n$ are the
lengths of s and t, respectively. Additionally computing
the transformation sequence and counts is $O(\max(m, n))$.
The generalized Levenshtein distance can also be used for approximate
(fuzzy) string matching, in which case one finds the substring of
t with minimal distance to the pattern s (which could be
taken as a regular expression, in which case the principle of using
the leftmost and longest match applies), see, e.g.,
http://en.wikipedia.org/wiki/Approximate_string_matching. This
distance is computed for partial = TRUE
using tre by
Ville Laurikari (http://http://laurikari.net/tre/) and
corresponds to the distance used by agrep
. In this
case, the given cost values are coerced to integer.
Note that the costs for insertions and deletions can be different, in which case the distance between s and t can be different from the distance between t and s.
Value

A matrix with the approximate string distances of the elements of
x
and y
, with rows and columns corresponding to x
and y
, respectively.If counts
is TRUE
, the transformation counts are
returned as the "counts"
attribute of this matrix, as a
3dimensional array with dimensions corresponding to the elements of
x
, the elements of y
, and the type of transformation
(insertions, deletions and substitutions), respectively.
Additionally, if partial = FALSE
, the transformation sequences
are returned as the "trafos"
attribute of the return value, as
character strings with elements M, I, D and
S indicating a match, insertion, deletion and substitution,
respectively. If partial = TRUE
, the offsets (positions of
the first and last element) of the matched substrings are returned as
the "offsets"
attribute of the return value (with both offsets
$1$ in case of no match).
See Also
agrep
for approximate string matching (fuzzy matching)
using the generalized Levenshtein distance.
Examples
library(utils)
## Cf. http://en.wikipedia.org/wiki/Levenshtein_distance
adist("kitten", "sitting")
## To see the transformation counts for the Levenshtein distance:
drop(attr(adist("kitten", "sitting", counts = TRUE), "counts"))
## To see the transformation sequences:
attr(adist(c("kitten", "sitting"), counts = TRUE), "trafos")
## Cf. the examples for agrep:
adist("lasy", "1 lazy 2")
## For a "partial approximate match" (as used for agrep):
adist("lasy", "1 lazy 2", partial = TRUE)