stringdist/examples/stringdist.R at master · linearregression/stringdist · GitHub

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# Simple example using optimal string alignment

stringdist("ca","abc")

# computing a 'dist' object

d <- stringdistmatrix(c('foo','bar','boo','baz'))

# try plot(hclust(d))

# The following gives a matrix

stringdistmatrix(c("foo","bar","boo"),c("baz","buz"))

# An example using Damerau-Levenshtein distance (multiple editing of substrings allowed)

stringdist("ca","abc",method="dl")

# string distance matching is case sensitive:

stringdist("ABC","abc")

# so you may want to normalize a bit:

stringdist(tolower("ABC"),"abc")

# stringdist recycles the shortest argument:

stringdist(c('a','b','c'),c('a','c'))

# stringdistmatrix gives the distance matrix (by default for optimal string alignment):

stringdist(c('a','b','c'),c('a','c'))

# different edit operations may be weighted; e.g. weighted substitution:

stringdist('ab','ba',weight=c(1,1,1,0.5))

# Non-unit weights for insertion and deletion makes the distance metric asymetric

stringdist('ca','abc')

stringdist('abc','ca')

stringdist('ca','abc',weight=c(0.5,1,1,1))

stringdist('abc','ca',weight=c(0.5,1,1,1))

# Hamming distance is undefined for

# strings of unequal lengths so stringdist returns Inf

stringdist("ab","abc",method="h")

# For strings of eqal length it counts the number of unequal characters as they occur

# in the strings from beginning to end

stringdist("hello","HeLl0",method="h")

# The lcs (longest common substring) distance returns the number of

# characters that are not part of the lcs.

# Here, the lcs is either 'a' or 'b' and one character cannot be paired:

stringdist('ab','ba',method="lcs")

# Here the lcs is 'surey' and 'v', 'g' and one 'r' of 'surgery' are not paired

stringdist('survey','surgery',method="lcs")

# q-grams are based on the difference between occurrences of q consecutive characters

# in string a and string b.

# Since each character abc occurs in 'abc' and 'cba', the q=1 distance equals 0:

stringdist('abc','cba',method='qgram',q=1)

# since the first string consists of 'ab','bc' and the second

# of 'cb' and 'ba', the q=2 distance equals 4 (they have no q=2 grams in common):

stringdist('abc','cba',method='qgram',q=2)

# Wikipedia has the following example of the Jaro-distance.

stringdist('MARTHA','MATHRA',method='jw')

# Note that stringdist gives a _distance_ where wikipedia gives the corresponding

# _similarity measure_. To get the wikipedia result:

1 - stringdist('MARTHA','MATHRA',method='jw')

# The corresponding Jaro-Winkler distance can be computed by setting p=0.1

stringdist('MARTHA','MATHRA',method='jw',p=0.1)

# or, as a similarity measure

1 - stringdist('MARTHA','MATHRA',method='jw',p=0.1)

# This gives distance 1 since Euler and Gauss translate to different soundex codes.

stringdist('Euler','Gauss',method='soundex')

# Euler and Ellery translate to the same code and have distance 0

stringdist('Euler','Ellery',method='soundex')

stringdist/examples/stringdist.R at master · linearregression/stringdist · GitHub

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