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dgrtwo/fuzzyjoin

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dgrtwo / fuzzyjoin

R

Join tables together on inexact matching


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fuzzyjoin: Join data frames on inexact matching

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The fuzzyjoin package is a variation on dplyr's join operations that allows matching not just on values that match between columns, but on inexact matching. This allows matching on:

  • Numeric values that are within some tolerance (difference_inner_join)
  • Strings that are similiar in Levenshtein/cosine/Jaccard distance, or other metrics from the stringdist package (stringdist_inner_join)
  • A regular expression in one column matching to another (regex_inner_join)
  • Euclidean or Manhattan distance across multiple columns (distance_inner_join)
  • Geographic distance based on longitude and latitude (geo_inner_join)
  • Intervals of (start, end) that overlap (interval_inner_join)
  • Genomic intervals, which include both a chromosome ID and (start, end) pairs, that overlap (genome_inner_join)

One relevant use case is for classifying freeform text data (such as survey responses) against a finite set of options.

The package also includes:

  • For each of regex_, stringdist_, difference_, distance_, geo_, and interval_, variations for the six dplyr "join" operations- for example,
    • regex_inner_join (include only rows with matches in each)
    • regex_left_join (include all rows of left table)
    • regex_right_join (include all rows of right table)
    • regex_full_join (include all rows in each table)
    • regex_semi_join (filter left table for rows with matches)
    • regex_anti_join (filter left table for rows without matches)
  • A general wrapper (fuzzy_join) that allows you to define your own custom fuzzy matching function.
  • The option to include the calculated distance as a column in your output, using the distance_col argument

Installation

Install from CRAN with:

install.packages("fuzzyjoin")

You can also install the development version from GitHub using devtools:

devtools::install_github("dgrtwo/fuzzyjoin")

Example of stringdist_inner_join: Correcting misspellings against a dictionary

Often you find yourself with a set of words that you want to combine with a "dictionary"- it could be a literal dictionary (as in this case) or a domain-specific category system. But you want to allow for small differences in spelling or punctuation.

The fuzzyjoin package comes with a set of common misspellings (from Wikipedia):

library(dplyr)
library(fuzzyjoin)
data(misspellings)

misspellings
#> # A tibble: 4,505 x 2
#>    misspelling    correct
#>          <chr>      <chr>
#>  1  abandonned  abandoned
#>  2   aberation aberration
#>  3    abilties  abilities
#>  4      abilty    ability
#>  5     abondon    abandon
#>  6      abbout      about
#>  7       abotu      about
#>  8      abouta    about a
#>  9     aboutit   about it
#> 10    aboutthe  about the
#> # ... with 4,495 more rows
# use the dictionary of words from the qdapDictionaries package,
# which is based on the Nettalk corpus.
library(qdapDictionaries)
words <- tbl_df(DICTIONARY)

words
#> # A tibble: 20,137 x 2
#>     word syllables
#>  * <chr>     <dbl>
#>  1    hm         1
#>  2   hmm         1
#>  3  hmmm         1
#>  4  hmph         1
#>  5 mmhmm         2
#>  6  mmhm         2
#>  7    mm         1
#>  8   mmm         1
#>  9  mmmm         1
#> 10   pff         1
#> # ... with 20,127 more rows

As an example, we'll pick 1000 of these words (you could try it on all of them though), and use stringdist_inner_join to join them against our dictionary.

set.seed(2016)
sub_misspellings <- misspellings %>%
  sample_n(1000)
joined <- sub_misspellings %>%
  stringdist_inner_join(words, by = c(misspelling = "word"), max_dist = 1)

By default, stringdist_inner_join uses optimal string alignment (Damerau–Levenshtein distance), and we're setting a maximum distance of 1 for a join. Notice that they've been joined in cases where misspelling is close to (but not equal to) word:

joined
#> # A tibble: 728 x 4
#>    misspelling correct    word syllables
#>          <chr>   <chr>   <chr>     <dbl>
#>  1       sould  should   could         1
#>  2       sould  should  should         1
#>  3       sould  should    sold         1
#>  4       sould  should    soul         1
#>  5       sould  should   sound         1
#>  6       sould  should   would         1
#>  7       fiels   feels   field         1
#>  8       fiels   feels    fils         1
#>  9    conscent consent consent         2
#> 10       fleed   freed   bleed         1
#> # ... with 718 more rows

Classification accuracy

Note that there are some redundancies; words that could be multiple items in the dictionary. These end up with one row per "guess" in the output. How many words did we classify?

joined %>%
  count(misspelling, correct)
#> # A tibble: 455 x 3
#>    misspelling      correct     n
#>          <chr>        <chr> <int>
#>  1   abritrary    arbitrary     1
#>  2   accademic     academic     1
#>  3   accension    ascension     2
#>  4  accessable   accessible     1
#>  5    accidant     accident     1
#>  6 accidentaly accidentally     1
#>  7   accordeon    accordion     1
#>  8      addopt        adopt     1
#>  9   addtional   additional     1
#> 10  admendment    amendment     1
#> # ... with 445 more rows

So we found a match in the dictionary for about half of the misspellings. In how many of the ones we classified did we get at least one of our guesses right?

which_correct <- joined %>%
  group_by(misspelling, correct) %>%
  summarize(guesses = n(), one_correct = any(correct == word))

which_correct
#> # A tibble: 455 x 4
#> # Groups:   misspelling [?]
#>    misspelling      correct guesses one_correct
#>          <chr>        <chr>   <int>       <lgl>
#>  1   abritrary    arbitrary       1        TRUE
#>  2   accademic     academic       1        TRUE
#>  3   accension    ascension       2        TRUE
#>  4  accessable   accessible       1        TRUE
#>  5    accidant     accident       1        TRUE
#>  6 accidentaly accidentally       1       FALSE
#>  7   accordeon    accordion       1        TRUE
#>  8      addopt        adopt       1        TRUE
#>  9   addtional   additional       1        TRUE
#> 10  admendment    amendment       1        TRUE
#> # ... with 445 more rows

# percentage of guesses getting at least one right
mean(which_correct$one_correct)
#> [1] 0.8527473

# number uniquely correct (out of the original 1000)
sum(which_correct$guesses == 1 & which_correct$one_correct)
#> [1] 294

Not bad.

Reporting distance in the joined output

If you wanted to include the distance as a column in your output, you can use the distance_col argument. For example, we may be interested in how many words were two letters apart.

joined_dists <- sub_misspellings %>%
  stringdist_inner_join(words, by = c(misspelling = "word"), max_dist = 2,
                        distance_col = "distance")

joined_dists
#> # A tibble: 7,427 x 5
#>    misspelling    correct       word syllables distance
#>          <chr>      <chr>      <chr>     <dbl>    <dbl>
#>  1  charactors characters  character         3        2
#>  2  charactors characters charactery         4        2
#>  3       sould     should       auld         1        2
#>  4       sould     should       bold         1        2
#>  5       sould     should      bound         1        2
#>  6       sould     should       cold         1        2
#>  7       sould     should      could         1        1
#>  8       sould     should       fold         1        2
#>  9       sould     should       foul         1        2
#> 10       sould     should      found         1        2
#> # ... with 7,417 more rows

Note the extra distance column, which in this case will always be less than or equal to 2. We could then pick the closest match for each, and examine how many of our closest matches were 1 or 2 away:

closest <- joined_dists %>%
  group_by(misspelling) %>%
  top_n(1, desc(distance)) %>%
  ungroup()

closest
#> # A tibble: 1,437 x 5
#>     misspelling      correct        word syllables distance
#>           <chr>        <chr>       <chr>     <dbl>    <dbl>
#>  1   charactors   characters   character         3        2
#>  2   charactors   characters  charactery         4        2
#>  3        sould       should       could         1        1
#>  4        sould       should      should         1        1
#>  5        sould       should        sold         1        1
#>  6        sould       should        soul         1        1
#>  7        sould       should       sound         1        1
#>  8        sould       should       would         1        1
#>  9 incorportaed incorporated incorporate         4        2
#> 10         awya         away          aa         2        2
#> # ... with 1,427 more rows

closest %>%
  count(distance)
#> # A tibble: 3 x 2
#>   distance     n
#>      <dbl> <int>
#> 1        0     1
#> 2        1   725
#> 3        2   711

Other joining functions

Note that stringdist_inner_join is not the only function we can use. If we're interested in including the words that we couldn't classify, we could have use stringdist_left_join:

left_joined <- sub_misspellings %>%
  stringdist_left_join(words, by = c(misspelling = "word"), max_dist = 1)

left_joined
#> # A tibble: 1,273 x 4
#>     misspelling      correct   word syllables
#>           <chr>        <chr>  <chr>     <dbl>
#>  1   charactors   characters   <NA>        NA
#>  2   Brasillian    Brazilian   <NA>        NA
#>  3        sould       should  could         1
#>  4        sould       should should         1
#>  5        sould       should   sold         1
#>  6        sould       should   soul         1
#>  7        sould       should  sound         1
#>  8        sould       should  would         1
#>  9  belligerant  belligerent   <NA>        NA
#> 10 incorportaed incorporated   <NA>        NA
#> # ... with 1,263 more rows

left_joined %>%
  filter(is.na(word))
#> # A tibble: 545 x 4
#>     misspelling      correct  word syllables
#>           <chr>        <chr> <chr>     <dbl>
#>  1   charactors   characters  <NA>        NA
#>  2   Brasillian    Brazilian  <NA>        NA
#>  3  belligerant  belligerent  <NA>        NA
#>  4 incorportaed incorporated  <NA>        NA
#>  5         awya         away  <NA>        NA
#>  6     occuring    occurring  <NA>        NA
#>  7  surveilence surveillance  <NA>        NA
#>  8    abondoned    abandoned  <NA>        NA
#>  9     alledges      alleges  <NA>        NA
#> 10  deliberatly deliberately  <NA>        NA
#> # ... with 535 more rows

(To get just the ones without matches immediately, we could have used stringdist_anti_join). If we increase our distance threshold, we'll increase the fraction with a correct guess, but also get more false positive guesses:

left_joined2 <- sub_misspellings %>%
  stringdist_left_join(words, by = c(misspelling = "word"), max_dist = 2)

left_joined2
#> # A tibble: 7,691 x 4
#>    misspelling    correct       word syllables
#>          <chr>      <chr>      <chr>     <dbl>
#>  1  charactors characters  character         3
#>  2  charactors characters charactery         4
#>  3  Brasillian  Brazilian       <NA>        NA
#>  4       sould     should       auld         1
#>  5       sould     should       bold         1
#>  6       sould     should      bound         1
#>  7       sould     should       cold         1
#>  8       sould     should      could         1
#>  9       sould     should       fold         1
#> 10       sould     should       foul         1
#> # ... with 7,681 more rows

left_joined2 %>%
  filter(is.na(word))
#> # A tibble: 264 x 4
#>      misspelling       correct  word syllables
#>            <chr>         <chr> <chr>     <dbl>
#>  1    Brasillian     Brazilian  <NA>        NA
#>  2   belligerant   belligerent  <NA>        NA
#>  3      occuring     occurring  <NA>        NA
#>  4     abondoned     abandoned  <NA>        NA
#>  5  correponding corresponding  <NA>        NA
#>  6 archeaologist archaeologist  <NA>        NA
#>  7   emmediately   immediately  <NA>        NA
#>  8    possessess     possesses  <NA>        NA
#>  9       unahppy       unhappy  <NA>        NA
#> 10        Guilio        Giulio  <NA>        NA
#> # ... with 254 more rows

Most of the missing words here simply aren't in our dictionary.

You can try other distance thresholds, other dictionaries, and other distance metrics (see [stringdist-metrics] for more). This function is especially useful on a domain-specific dataset, such as free-form survey input that is likely to be close to one of a handful of responses.

Example of regex_inner_join: Classifying text based on regular expressions

Consider the book Pride and Prejudice, by Jane Austen, which we can access through the janeaustenr package.

We could split the books up into "passages" of 50 lines each.

library(dplyr)
library(stringr)
library(janeaustenr)

passages <- data_frame(text = prideprejudice) %>%
  group_by(passage = 1 + row_number() %/% 50) %>%
  summarize(text = paste(text, collapse = " "))

passages
#> # A tibble: 261 x 2
#>    passage
#>      <dbl>
#>  1       1
#>  2       2
#>  3       3
#>  4       4
#>  5       5
#>  6       6
#>  7       7
#>  8       8
#>  9       9
#> 10      10
#> # ... with 251 more rows, and 1 more variables: text <chr>

Suppose we wanted to divide the passages based on which character's name is mentioned in each. Character's names may differ in how they are presented, so we construct a regular expression for each and pair it with that character's name.

characters <- readr::read_csv(
"character,character_regex
Elizabeth,Elizabeth
Darcy,Darcy
Mr. Bennet,Mr. Bennet
Mrs. Bennet,Mrs. Bennet
Jane,Jane
Mary,Mary
Lydia,Lydia
Kitty,Kitty
Wickham,Wickham
Mr. Collins,Collins
Lady Catherine de Bourgh,de Bourgh
Mr. Gardiner,Mr. Gardiner
Mrs. Gardiner,Mrs. Gardiner
Charlotte Lucas,(Charlotte|Lucas)
")

Notice that for each character, we've defined a regular expression (sometimes allowing ambiguity, sometimes not) for detecting their name. Suppose we want to "classify" passages based on whether this regex is present.

With fuzzyjoin's regex_inner_join function, we do:

character_passages <- passages %>%
  regex_inner_join(characters, by = c(text = "character_regex"))

This combines the two data frames based on cases where the passages$text column is matched by the characters$character_regex column. (Note that the dataset with the text column must always come first). This results in:

character_passages %>%
  select(passage, character, text)
#> # A tibble: 1,126 x 3
#>    passage       character
#>      <dbl>           <chr>
#>  1       1      Mr. Bennet
#>  2       1            Jane
#>  3       2      Mr. Bennet
#>  4       2            Jane
#>  5       2           Lydia
#>  6       2 Charlotte Lucas
#>  7       3       Elizabeth
#>  8       3      Mr. Bennet
#>  9       3     Mrs. Bennet
#> 10       4      Mr. Bennet
#> # ... with 1,116 more rows, and 1 more variables: text <chr>

This shows that Mr. Bennet's name appears in passages 1, 2, 4, and 6, while Charlotte Lucas's appears in 3. Notice that having fuzzy-joined the datasets, some passages will end up duplicated (those with multiple names in them), while it's possible others will be missing entirely (those without names).

We could ask which characters are mentioned in the most passages:

character_passages %>%
  count(character, sort = TRUE)
#> # A tibble: 14 x 2
#>                   character     n
#>                       <chr> <int>
#>  1                Elizabeth   227
#>  2                    Darcy   159
#>  3                     Jane   134
#>  4              Mrs. Bennet    89
#>  5                  Wickham    89
#>  6                    Lydia    79
#>  7              Mr. Collins    75
#>  8          Charlotte Lucas    68
#>  9               Mr. Bennet    55
#> 10                    Kitty    42
#> 11            Mrs. Gardiner    35
#> 12 Lady Catherine de Bourgh    25
#> 13             Mr. Gardiner    25
#> 14                     Mary    24

The data is also well suited to discover which characters appear in scenes together, and to cluster them to find groupings of characters (like in [this analysis]).

passage_character_matrix <- character_passages %>%
  group_by(passage) %>%
  filter(n() > 1) %>%
  reshape2::acast(character ~ passage, fun.aggregate = length, fill = 0)

passage_character_matrix <- passage_character_matrix / rowSums(passage_character_matrix)

h <- hclust(dist(passage_character_matrix, method = "manhattan"))

plot(h)

plot of chunk character_passages_matrix

Other options for further analysis of this fuzzy-joined dataset include doing sentiment analysis on text surrounding each character's name, similar to Julia Silge's analysis here.

Future Work

A few things I'd like to work on:

  • Shortcuts on string distance matching: If two strings are more than 1 character apart in length, the method is osa, and max_dist is 1, you don't even need to compare them.

  • More examples: I've used this package in other powerful ways, but on proprietary data. I'm interested in ideas for use cases that can be provided as vignettes.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.