# Simple example using optimal string alignment
stringdist("ca","abc")
# The same 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 lcm (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')Run the code above in your browser using DataLab