Default parameters inherited by other documentation

Description

Default parameters inherited by other documentation

Arguments

See Also

stringdist::stringdist(), stringdist::stringsim() for details on the underlying functions and the additional options available.

Hotel room listings

Description

The dataset contains 85 descriptions of the same hotel rooms from Expedia and Booking.com.

Usage

hotel_rooms

Format

A data frame with the following columns:

expedia

The title of the room's listing on expedia.com

booking

The title of the room's listing on booking.com

Source

Based on a dataset compiled by Susan Li.

Internal functions

Description

lev_partial_ratio() and lev_token_set_ratio() are hard to vectorise in one go, so in the interests of lazy thinking these "internal" versions contain the logic to operate on single-length inputs, and the calling functions just apply() them as needed.

Usage

internal_lev_token_set_ratio(a, b, pairwise = TRUE, useNames = !pairwise, ...)

internal_lev_partial_ratio(a, b, pairwise = TRUE, useNames = !pairwise, ...)

Arguments

Functions

• internal_lev_token_set_ratio(): See lev_token_set_ratio().

• internal_lev_partial_ratio(): See lev_partial_ratio().

Get the best matched string from a list of candidates

Description

Given an input string and multiple candidates, return the candidate with the best score as calculated by .fn.

Usage

lev_best_match(input, candidates, .fn = lev_ratio, ..., decreasing = TRUE)

Arguments

Value

A string

See Also

lev_score_multiple()

Examples

lev_best_match("bilbo", c("frodo", "gandalf", "legolas"))

String distance metrics

Description

Uses stringdist::stringdistmatrix() to compute a range of string distance metrics.

Usage

lev_distance(a, b, pairwise = TRUE, useNames = TRUE, ...)

Arguments

Value

A numeric scalar, vector or matrix depending on the length of the inputs. See "Details".

Details

This is a thin wrapper around stringdist::stringdistmatrix() and mainly exists to coerce the output into the simplest possible format (via lev_simplify_matrix()).

The function will return the simplest possible data structure permitted by the length of the inputs a and b. This will be a scalar if a and b are length 1, a vector if either (but not both) is length > 1, and a matrix otherwise.

Other options

In addition to useNames stringdist::stringdistmatrix() provides a range of options to control the matching, which can be passed using .... Refer to the stringdist documentation for more information.

Examples

lev_distance("Bilbo", "Frodo")

lev_distance("Bilbo", c("Frodo", "Merry"))

lev_distance("Bilbo", c("Frodo", "Merry"), useNames = FALSE)

lev_distance(c("Bilbo", "Gandalf"), c("Frodo", "Merry"))

Ratio of the best-matching substring

Description

Find the best lev_ratio() between substrings.

Usage

lev_partial_ratio(a, b, pairwise = TRUE, useNames = TRUE, ...)

Arguments

Value

A numeric scalar, vector or matrix depending on the length of the inputs.

Details

If string a has length len_a and is shorter than string b, this function finds the highest lev_ratio() of all the len_a-long substrings of b (and vice versa).

Examples

lev_ratio("Bruce Springsteen", "Bruce Springsteen and the E Street Band")

# Here the two "Bruce Springsteen" strings will match perfectly.
lev_partial_ratio("Bruce Springsteen", "Bruce Springsteen and the E Street Band")

String similarity ratio

Description

String similarity ratio

Usage

lev_ratio(a, b, pairwise = TRUE, useNames = TRUE, ...)

Arguments

Value

A numeric scalar, vector or matrix depending on the length of the inputs.

Details

This is a thin wrapper around stringdist::stringsimmatrix() and mainly exists to coerce the output into the simplest possible format (via lev_simplify_matrix()).

The function will return the simplest possible data structure permitted by the length of the inputs a and b. This will be a scalar if a and b are length 1, a vector if either (but not both) is length > 1, and a matrix otherwise.

Examples

lev_ratio("Bilbo", "Frodo")

lev_ratio("Bilbo", c("Frodo", "Merry"))

lev_ratio("Bilbo", c("Frodo", "Merry"), useNames = FALSE)

lev_ratio(c("Bilbo", "Gandalf"), c("Frodo", "Merry"))

Score multiple candidate strings against a single input

Description

Given a single input string and multiple candidates, compute scores for each candidate.

Usage

lev_score_multiple(input, candidates, .fn = lev_ratio, ..., decreasing = TRUE)

Arguments

Value

A list where the keys are candidates and the values are the scores. The list is sorted according to the decreasing parameter, so by default higher scores are first.

See Also

lev_best_match()

Examples

lev_score_multiple("bilbo", c("frodo", "gandalf", "legolas"))

Simplify a matrix

Description

Given an input matrix, try and simplify it to a scalar or vector. This requires that one or both of the dimensions are 1. If the matrix has dimnames() and the output has more than one item, name the elements according to the longest dimname.

Usage

lev_simplify_matrix(m)

Arguments

Value

A scalar, vector or matrix as described above.

Matching based on common tokens

Description

Compare stings based on shared tokens.

Usage

lev_token_set_ratio(a, b, pairwise = TRUE, useNames = TRUE, ...)

Arguments

Value

A numeric scalar, vector or matrix depending on the length of the inputs.

Details

Similar to lev_token_sort_ratio() this function breaks the input down into tokens. It then identifies any common tokens between strings and creates three new strings:

x <- {common_tokens}
y <- {common_tokens}{remaining_unique_tokens_from_string_a}
z <- {common_tokens}{remaining_unique_tokens_from_string_b}

and performs three pairwise lev_ratio() calculations between them (x vs y, y vs z and x vs z). The highest of those three ratios is returned.

See Also

lev_token_sort_ratio()

Examples

x <- "the quick brown fox jumps over the lazy dog"
y <- "my lazy dog was jumped over by a quick brown fox"

lev_ratio(x, y)

lev_token_sort_ratio(x, y)

lev_token_set_ratio(x, y)

Ordered token matching

Description

Compares strings by tokenising them, sorting the tokens alphabetically and then computing the lev_ratio() of the result. This means that the order of words is irrelevant which can be helpful in some circumstances.

Usage

lev_token_sort_ratio(a, b, pairwise = TRUE, useNames = TRUE, ...)

Arguments

Value

A numeric scalar, vector or matrix depending on the length of the inputs.

See Also

lev_token_set_ratio()

Examples

x <- "Episode IV - Star Wars: A New Hope"
y <- "Star Wars Episode IV - New Hope"

# Because the order of words is different the simple approach gives a low match ratio.
lev_ratio(x, y)

# The sorted token approach ignores word order.
lev_token_sort_ratio(x, y)

Weighted token similarity measure

Description

Computes similarity but allows you to assign weights to specific tokens. This is useful, for example, when you have a frequently-occurring string that doesn't contain useful information. See examples.

Usage

lev_weighted_token_ratio(a, b, weights = list(), ...)

Arguments

Value

A float

Details

The algorithm used here is as follows:

• Tokenise the input strings

• Compute the edit distance between each pair of tokens

• Compute the maximum edit distance between each pair of tokens

• Apply any weights from the weights argument

• Return 1 - (sum(weighted_edit_distances) / sum(weighted_max_edit_distance))

See Also

Other weighted token functions: lev_weighted_token_set_ratio(), lev_weighted_token_sort_ratio()

Examples

lev_weighted_token_ratio("jim ltd", "tim ltd")

lev_weighted_token_ratio("tim ltd", "jim ltd", weights = list(ltd = 0.1))

Weighted version of lev_token_set_ratio()

Description

Weighted version of lev_token_set_ratio()

Usage

lev_weighted_token_set_ratio(a, b, weights = list(), ...)

Arguments

Value

Float

See Also

lev_token_set_ratio()

Other weighted token functions: lev_weighted_token_ratio(), lev_weighted_token_sort_ratio()

Weighted version of lev_token_sort_ratio()

Description

This function tokenises inputs, sorts tokens and computes similarities for each pair of tokens. Similarity scores are weighted based on the weights argument, and a total similarity score is returned in the same manner as lev_weighted_token_ratio().

Usage

lev_weighted_token_sort_ratio(a, b, weights = list(), ...)

Arguments

Value

Float

See Also

lev_token_sort_ratio()

Other weighted token functions: lev_weighted_token_ratio(), lev_weighted_token_set_ratio()

Find all substrings of a given length

Description

Find all substrings of a given length

Usage

str_all_substrings(x, n)

Arguments

Value

A character vector containing all the length n substrings of x. If x has length > then a list is returned containing an entry for each input element.

Tokenise and sort a string

Description

Given an input string, tokenise it (using str_tokenise()), sort the tokens alphabetically and return the result as a single space-separated string.

Usage

str_token_sort(x)

Arguments

Value

A character vector the same length as x containing the sorted tokens.

Tokenise a string

Description

Splits an input string into tokens. A wrapper for strsplit() with a predefined split regex of ⁠[^[:alnum:]]⁠, i.e. anything except ⁠[a-zA-Z0-9]⁠.

Usage

str_tokenise(x, split = "[^[:alnum:]]+")

Arguments

Value

A list containing one character vector for each element of x.