Package 'fuzzyjoin' reference manual

Title:	Join Tables Together on Inexact Matching
Description:	Join tables together based not on whether columns match exactly, but whether they are similar by some comparison. Implementations include string distance and regular expression matching.
Authors:	David Robinson [aut, cre], Jennifer Bryan [ctb], Joran Elias [ctb]
Maintainer:	David Robinson <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.6
Built:	2024-10-22 03:15:00 UTC
Source:	https://github.com/dgrtwo/fuzzyjoin

Join two tables based on absolute difference between their columns

Description

Join two tables based on absolute difference between their columns

Usage

difference_join(
  x,
  y,
  by = NULL,
  max_dist = 1,
  mode = "inner",
  distance_col = NULL
)

difference_inner_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

difference_left_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

difference_right_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

difference_full_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

difference_semi_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

difference_anti_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)
difference_join(
  x,
  y,
  by = NULL,
  max_dist = 1,
  mode = "inner",
  distance_col = NULL
)

difference_inner_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

difference_left_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

difference_right_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

difference_full_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

difference_semi_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

difference_anti_join(x, y, by = NULL, max_dist = 1, distance_col = NULL)

Arguments

`x`	A tbl
`y`	A tbl
`by`	Columns by which to join the two tables
`max_dist`	Maximum distance to use for joining
`mode`	One of "inner", "left", "right", "full" "semi", or "anti"
`distance_col`	If given, will add a column with this name containing the difference between the two

Examples


library(dplyr)

head(iris)
sepal_lengths <- data_frame(Sepal.Length = c(5, 6, 7), Type = 1:3)

iris %>%
  difference_inner_join(sepal_lengths, max_dist = .5)

library(dplyr)

head(iris)
sepal_lengths <- data_frame(Sepal.Length = c(5, 6, 7), Type = 1:3)

iris %>%
  difference_inner_join(sepal_lengths, max_dist = .5)

Join two tables based on a distance metric of one or more columns

Description

This differs from difference_join in that it considers all of the columns together when computing distance. This allows it to use metrics such as Euclidean or Manhattan that depend on multiple columns. Note that if you are computing with longitude or latitude, you probably want to use geo_join.

Usage

distance_join(
  x,
  y,
  by = NULL,
  max_dist = 1,
  method = c("euclidean", "manhattan"),
  mode = "inner",
  distance_col = NULL
)

distance_inner_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

distance_left_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

distance_right_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

distance_full_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

distance_semi_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

distance_anti_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)
distance_join(
  x,
  y,
  by = NULL,
  max_dist = 1,
  method = c("euclidean", "manhattan"),
  mode = "inner",
  distance_col = NULL
)

distance_inner_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

distance_left_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

distance_right_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

distance_full_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

distance_semi_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

distance_anti_join(
  x,
  y,
  by = NULL,
  method = "euclidean",
  max_dist = 1,
  distance_col = NULL
)

Arguments

`x`	A tbl
`y`	A tbl
`by`	Columns by which to join the two tables
`max_dist`	Maximum distance to use for joining
`method`	Method to use for computing distance, either euclidean (default) or manhattan.
`mode`	One of "inner", "left", "right", "full" "semi", or "anti"
`distance_col`	If given, will add a column with this name containing the distance between the two

Examples


library(dplyr)

head(iris)
sepal_lengths <- data_frame(Sepal.Length = c(5, 6, 7),
                            Sepal.Width = 1:3)

iris %>%
  distance_inner_join(sepal_lengths, max_dist = 2)

library(dplyr)

head(iris)
sepal_lengths <- data_frame(Sepal.Length = c(5, 6, 7),
                            Sepal.Width = 1:3)

iris %>%
  distance_inner_join(sepal_lengths, max_dist = 2)

Join two tables based not on exact matches, but with a function describing whether two vectors are matched or not

Description

The match_fun argument is called once on a vector with all pairs of unique comparisons: thus, it should be efficient and vectorized.

Usage

fuzzy_join(
  x,
  y,
  by = NULL,
  match_fun = NULL,
  multi_by = NULL,
  multi_match_fun = NULL,
  index_match_fun = NULL,
  mode = "inner",
  ...
)

fuzzy_inner_join(x, y, by = NULL, match_fun, ...)

fuzzy_left_join(x, y, by = NULL, match_fun, ...)

fuzzy_right_join(x, y, by = NULL, match_fun, ...)

fuzzy_full_join(x, y, by = NULL, match_fun, ...)

fuzzy_semi_join(x, y, by = NULL, match_fun, ...)

fuzzy_anti_join(x, y, by = NULL, match_fun, ...)
fuzzy_join(
  x,
  y,
  by = NULL,
  match_fun = NULL,
  multi_by = NULL,
  multi_match_fun = NULL,
  index_match_fun = NULL,
  mode = "inner",
  ...
)

fuzzy_inner_join(x, y, by = NULL, match_fun, ...)

fuzzy_left_join(x, y, by = NULL, match_fun, ...)

fuzzy_right_join(x, y, by = NULL, match_fun, ...)

fuzzy_full_join(x, y, by = NULL, match_fun, ...)

fuzzy_semi_join(x, y, by = NULL, match_fun, ...)

fuzzy_anti_join(x, y, by = NULL, match_fun, ...)

Arguments

`x`	A tbl
`y`	A tbl
`by`	Columns of each to join
`match_fun`	Vectorized function given two columns, returning TRUE or FALSE as to whether they are a match. Can be a list of functions one for each pair of columns specified in `by` (if a named list, it uses the names in x). If only one function is given it is used on all column pairs.
`multi_by`	Columns to join, where all columns will be used to test matches together
`multi_match_fun`	Function to use for testing matches, performed on all columns in each data frame simultaneously
`index_match_fun`	Function to use for matching tables. Unlike `match_fun` and `index_match_fun`, this is performed on the original columns and returns pairs of indices.
`mode`	One of "inner", "left", "right", "full" "semi", or "anti"
`...`	Extra arguments passed to match_fun

Details

match_fun should return either a logical vector, or a data frame where the first column is logical. If the latter, the additional columns will be appended to the output. For example, these additional columns could contain the distance metrics that one is filtering on.

Note that as of now, you cannot give both match_fun and multi_match_fun- you can either compare each column individually or compare all of them.

Like in dplyr's join operations, fuzzy_join ignores groups, but preserves the grouping of x in the output.

Join two tables based on overlapping genomic intervals: both a

Description

This is an extension of interval_join specific to genomic intervals. Genomic intervals include both a chromosome ID and an interval: items are only considered matching if the chromosome ID matches and the interval overlaps. Note that there must be three arguments to by, and that they must be in the order c("chromosome", "start", "end").

Usage

genome_join(x, y, by = NULL, mode = "inner", ...)

genome_inner_join(x, y, by = NULL, ...)

genome_left_join(x, y, by = NULL, ...)

genome_right_join(x, y, by = NULL, ...)

genome_full_join(x, y, by = NULL, ...)

genome_semi_join(x, y, by = NULL, ...)

genome_anti_join(x, y, by = NULL, ...)
genome_join(x, y, by = NULL, mode = "inner", ...)

genome_inner_join(x, y, by = NULL, ...)

genome_left_join(x, y, by = NULL, ...)

genome_right_join(x, y, by = NULL, ...)

genome_full_join(x, y, by = NULL, ...)

genome_semi_join(x, y, by = NULL, ...)

genome_anti_join(x, y, by = NULL, ...)

Arguments

`x`	A tbl
`y`	A tbl
`by`	Names of columns to join on, in order c("chromosome", "start", "end"). A match will be counted only if the chromosomes are equal and the start/end pairs overlap.
`mode`	One of "inner", "left", "right", "full" "semi", or "anti"
`...`	Extra arguments passed on to `findOverlaps`

Details

All the extra arguments to interval_join, which are passed on to findOverlaps, work for genome_join as well. These include maxgap and minoverlap.

Examples


library(dplyr)

x1 <- tibble(id1 = 1:4,
             chromosome = c("chr1", "chr1", "chr2", "chr2"),
             start = c(100, 200, 300, 400),
             end = c(150, 250, 350, 450))

x2 <- tibble(id2 = 1:4,
             chromosome = c("chr1", "chr2", "chr2", "chr1"),
             start = c(140, 210, 400, 300),
             end = c(160, 240, 415, 320))

if (requireNamespace("IRanges", quietly = TRUE)) {
  # note that the the third and fourth items don't join (even though
  # 300-350 and 300-320 overlap) since the chromosomes are different:
  genome_inner_join(x1, x2, by = c("chromosome", "start", "end"))

  # other functions:
  genome_full_join(x1, x2, by = c("chromosome", "start", "end"))
  genome_left_join(x1, x2, by = c("chromosome", "start", "end"))
  genome_right_join(x1, x2, by = c("chromosome", "start", "end"))
  genome_semi_join(x1, x2, by = c("chromosome", "start", "end"))
  genome_anti_join(x1, x2, by = c("chromosome", "start", "end"))
}

library(dplyr)

x1 <- tibble(id1 = 1:4,
             chromosome = c("chr1", "chr1", "chr2", "chr2"),
             start = c(100, 200, 300, 400),
             end = c(150, 250, 350, 450))

x2 <- tibble(id2 = 1:4,
             chromosome = c("chr1", "chr2", "chr2", "chr1"),
             start = c(140, 210, 400, 300),
             end = c(160, 240, 415, 320))

if (requireNamespace("IRanges", quietly = TRUE)) {
  # note that the the third and fourth items don't join (even though
  # 300-350 and 300-320 overlap) since the chromosomes are different:
  genome_inner_join(x1, x2, by = c("chromosome", "start", "end"))

  # other functions:
  genome_full_join(x1, x2, by = c("chromosome", "start", "end"))
  genome_left_join(x1, x2, by = c("chromosome", "start", "end"))
  genome_right_join(x1, x2, by = c("chromosome", "start", "end"))
  genome_semi_join(x1, x2, by = c("chromosome", "start", "end"))
  genome_anti_join(x1, x2, by = c("chromosome", "start", "end"))
}

Join two tables based on a geo distance of longitudes and latitudes

Description

This allows joining based on combinations of longitudes and latitudes. If you are using a distance metric that is *not* based on latitude and longitude, use distance_join instead. Distances are calculated based on the distHaversine, distGeo, distCosine, etc methods in the geosphere package.

Usage

geo_join(
  x,
  y,
  by = NULL,
  max_dist,
  method = c("haversine", "geo", "cosine", "meeus", "vincentysphere",
    "vincentyellipsoid"),
  unit = c("miles", "km"),
  mode = "inner",
  distance_col = NULL,
  ...
)

geo_inner_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

geo_left_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

geo_right_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

geo_full_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

geo_semi_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

geo_anti_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)
geo_join(
  x,
  y,
  by = NULL,
  max_dist,
  method = c("haversine", "geo", "cosine", "meeus", "vincentysphere",
    "vincentyellipsoid"),
  unit = c("miles", "km"),
  mode = "inner",
  distance_col = NULL,
  ...
)

geo_inner_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

geo_left_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

geo_right_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

geo_full_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

geo_semi_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

geo_anti_join(
  x,
  y,
  by = NULL,
  method = "haversine",
  max_dist = 1,
  distance_col = NULL,
  ...
)

Arguments

`x`	A tbl
`y`	A tbl
`by`	Columns by which to join the two tables
`max_dist`	Maximum distance to use for joining
`method`	Method to use for computing distance: one of "haversine" (default), "geo", "cosine", "meeus", "vincentysphere", "vincentyellipsoid"
`unit`	Unit of distance for threshold (default "miles")
`mode`	One of "inner", "left", "right", "full" "semi", or "anti"
`distance_col`	If given, will add a column with this name containing the geographical distance between the two
`...`	Extra arguments passed on to the distance method

Details

"Haversine" was chosen as default since in some tests it is approximately the fastest method. Note that by far the slowest method is vincentyellipsoid, and on fuzzy joins should only be used when there are very few pairs and accuracy is imperative.

If you need to use a custom geo method, you may want to write it directly with the multi_by and multi_match_fun arguments to fuzzy_join.

Examples


library(dplyr)
data("state")

# find pairs of US states whose centers are within
# 200 miles of each other
states <- data_frame(state = state.name,
                     longitude = state.center$x,
                     latitude = state.center$y)

s1 <- rename(states, state1 = state)
s2 <- rename(states, state2 = state)

pairs <- s1 %>%
 geo_inner_join(s2, max_dist = 200) %>%
 filter(state1 != state2)

pairs

# plot them
library(ggplot2)
ggplot(pairs, aes(x = longitude.x, y = latitude.x,
                  xend = longitude.y, yend = latitude.y)) +
  geom_segment(color = "red") +
  borders("state") +
  theme_void()

# also get distances
s1 %>%
  geo_inner_join(s2, max_dist = 200, distance_col = "distance")

library(dplyr)
data("state")

# find pairs of US states whose centers are within
# 200 miles of each other
states <- data_frame(state = state.name,
                     longitude = state.center$x,
                     latitude = state.center$y)

s1 <- rename(states, state1 = state)
s2 <- rename(states, state2 = state)

pairs <- s1 %>%
 geo_inner_join(s2, max_dist = 200) %>%
 filter(state1 != state2)

pairs

# plot them
library(ggplot2)
ggplot(pairs, aes(x = longitude.x, y = latitude.x,
                  xend = longitude.y, yend = latitude.y)) +
  geom_segment(color = "red") +
  borders("state") +
  theme_void()

# also get distances
s1 %>%
  geo_inner_join(s2, max_dist = 200, distance_col = "distance")

Join two tables based on overlapping (low, high) intervals

Description

Joins tables based on overlapping intervals: for example, joining the row (1, 4) with (3, 6), but not with (5, 10). This operation is sped up using interval trees as implemented in the IRanges package. You can specify particular relationships between intervals (such as a maximum gap, or a minimum overlap) through arguments passed on to findOverlaps. See that documentation for descriptions of such arguments.

Usage

interval_join(x, y, by, mode = "inner", ...)

interval_inner_join(x, y, by = NULL, ...)

interval_left_join(x, y, by = NULL, ...)

interval_right_join(x, y, by = NULL, ...)

interval_full_join(x, y, by = NULL, ...)

interval_semi_join(x, y, by = NULL, ...)

interval_anti_join(x, y, by = NULL, ...)
interval_join(x, y, by, mode = "inner", ...)

interval_inner_join(x, y, by = NULL, ...)

interval_left_join(x, y, by = NULL, ...)

interval_right_join(x, y, by = NULL, ...)

interval_full_join(x, y, by = NULL, ...)

interval_semi_join(x, y, by = NULL, ...)

interval_anti_join(x, y, by = NULL, ...)

Arguments

`x`	A tbl
`y`	A tbl
`by`	Columns by which to join the two tables. If provided, this must be two columns: start of interval, then end of interval
`mode`	One of "inner", "left", "right", "full" "semi", or "anti"
`...`	Extra arguments passed on to `findOverlaps`

Details

This allows joining on date or datetime intervals. It throws an error if the type of date/datetime disagrees between the two tables.

This requires the IRanges package from Bioconductor. See here for installation: https://bioconductor.org/packages/release/bioc/html/IRanges.html.

Examples


if (requireNamespace("IRanges", quietly = TRUE)) {
  x1 <- data.frame(id1 = 1:3, start = c(1, 5, 10), end = c(3, 7, 15))
  x2 <- data.frame(id2 = 1:3, start = c(2, 4, 16), end = c(4, 8, 20))

  interval_inner_join(x1, x2)

  # Allow them to be separated by a gap with a maximum:
  interval_inner_join(x1, x2, maxgap = 1)   # let 1 join with 2
  interval_inner_join(x1, x2, maxgap = 20)  # everything joins each other

  # Require that they overlap by more than a particular amount
  interval_inner_join(x1, x2, minoverlap = 3)

  # other types of joins:
  interval_full_join(x1, x2)
  interval_left_join(x1, x2)
  interval_right_join(x1, x2)
  interval_semi_join(x1, x2)
  interval_anti_join(x1, x2)
}

if (requireNamespace("IRanges", quietly = TRUE)) {
  x1 <- data.frame(id1 = 1:3, start = c(1, 5, 10), end = c(3, 7, 15))
  x2 <- data.frame(id2 = 1:3, start = c(2, 4, 16), end = c(4, 8, 20))

  interval_inner_join(x1, x2)

  # Allow them to be separated by a gap with a maximum:
  interval_inner_join(x1, x2, maxgap = 1)   # let 1 join with 2
  interval_inner_join(x1, x2, maxgap = 20)  # everything joins each other

  # Require that they overlap by more than a particular amount
  interval_inner_join(x1, x2, minoverlap = 3)

  # other types of joins:
  interval_full_join(x1, x2)
  interval_left_join(x1, x2)
  interval_right_join(x1, x2)
  interval_semi_join(x1, x2)
  interval_anti_join(x1, x2)
}

A corpus of common misspellings, for examples and practice

Description

This is a codetbl_df mapping misspellings of their words, compiled by Wikipedia, where it is licensed under the CC-BY SA license. (Three words with non-ASCII characters were filtered out). If you'd like to reproduce this dataset from Wikipedia, see the example code below.

Usage

misspellings
misspellings

Format

An object of class tbl_df (inherits from tbl, data.frame) with 4505 rows and 2 columns.

Source

https://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings/For_machines

Examples


## Not run: 
library(rvest)
library(readr)
library(dplyr)
library(stringr)
library(tidyr)

u <- "https://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings/For_machines"
h <- read_html(u)

misspellings <- h %>%
  html_nodes("pre") %>%
  html_text() %>%
  readr::read_delim(col_names = c("misspelling", "correct"), delim = ">",
                    skip = 1) %>%
  mutate(misspelling = str_sub(misspelling, 1, -2)) %>%
  unnest(correct = str_split(correct, ", ")) %>%
  filter(Encoding(correct) != "UTF-8")

## End(Not run)

## Not run: 
library(rvest)
library(readr)
library(dplyr)
library(stringr)
library(tidyr)

u <- "https://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings/For_machines"
h <- read_html(u)

misspellings <- h %>%
  html_nodes("pre") %>%
  html_text() %>%
  readr::read_delim(col_names = c("misspelling", "correct"), delim = ">",
                    skip = 1) %>%
  mutate(misspelling = str_sub(misspelling, 1, -2)) %>%
  unnest(correct = str_split(correct, ", ")) %>%
  filter(Encoding(correct) != "UTF-8")

## End(Not run)

Join two tables based on a regular expression in one column matching the other

Description

Join a table with a string column by a regular expression column in another table

Usage

regex_join(x, y, by = NULL, mode = "inner", ignore_case = FALSE)

regex_inner_join(x, y, by = NULL, ignore_case = FALSE)

regex_left_join(x, y, by = NULL, ignore_case = FALSE)

regex_right_join(x, y, by = NULL, ignore_case = FALSE)

regex_full_join(x, y, by = NULL, ignore_case = FALSE)

regex_semi_join(x, y, by = NULL, ignore_case = FALSE)

regex_anti_join(x, y, by = NULL, ignore_case = FALSE)
regex_join(x, y, by = NULL, mode = "inner", ignore_case = FALSE)

regex_inner_join(x, y, by = NULL, ignore_case = FALSE)

regex_left_join(x, y, by = NULL, ignore_case = FALSE)

regex_right_join(x, y, by = NULL, ignore_case = FALSE)

regex_full_join(x, y, by = NULL, ignore_case = FALSE)

regex_semi_join(x, y, by = NULL, ignore_case = FALSE)

regex_anti_join(x, y, by = NULL, ignore_case = FALSE)

Arguments

`x`	A tbl
`y`	A tbl
`by`	Columns by which to join the two tables
`mode`	One of "inner", "left", "right", "full" "semi", or "anti"
`ignore_case`	Whether to be case insensitive (default no)

Examples


library(dplyr)
library(ggplot2)
data(diamonds)

diamonds <- tbl_df(diamonds)

d <- data_frame(regex_name = c("^Idea", "mium", "Good"),
                type = 1:3)

# When they are inner_joined, only Good<->Good matches
diamonds %>%
  inner_join(d, by = c(cut = "regex_name"))

# but we can regex match them
diamonds %>%
 regex_inner_join(d, by = c(cut = "regex_name"))

library(dplyr)
library(ggplot2)
data(diamonds)

diamonds <- tbl_df(diamonds)

d <- data_frame(regex_name = c("^Idea", "mium", "Good"),
                type = 1:3)

# When they are inner_joined, only Good<->Good matches
diamonds %>%
  inner_join(d, by = c(cut = "regex_name"))

# but we can regex match them
diamonds %>%
 regex_inner_join(d, by = c(cut = "regex_name"))

Join two tables based on fuzzy string matching of their columns

Description

Join two tables based on fuzzy string matching of their columns. This is useful, for example, in matching free-form inputs in a survey or online form, where it can catch misspellings and small personal changes.

Usage

stringdist_join(
  x,
  y,
  by = NULL,
  max_dist = 2,
  method = c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw",
    "soundex"),
  mode = "inner",
  ignore_case = FALSE,
  distance_col = NULL,
  ...
)

stringdist_inner_join(x, y, by = NULL, distance_col = NULL, ...)

stringdist_left_join(x, y, by = NULL, distance_col = NULL, ...)

stringdist_right_join(x, y, by = NULL, distance_col = NULL, ...)

stringdist_full_join(x, y, by = NULL, distance_col = NULL, ...)

stringdist_semi_join(x, y, by = NULL, distance_col = NULL, ...)

stringdist_anti_join(x, y, by = NULL, distance_col = NULL, ...)
stringdist_join(
  x,
  y,
  by = NULL,
  max_dist = 2,
  method = c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw",
    "soundex"),
  mode = "inner",
  ignore_case = FALSE,
  distance_col = NULL,
  ...
)

stringdist_inner_join(x, y, by = NULL, distance_col = NULL, ...)

stringdist_left_join(x, y, by = NULL, distance_col = NULL, ...)

stringdist_right_join(x, y, by = NULL, distance_col = NULL, ...)

stringdist_full_join(x, y, by = NULL, distance_col = NULL, ...)

stringdist_semi_join(x, y, by = NULL, distance_col = NULL, ...)

stringdist_anti_join(x, y, by = NULL, distance_col = NULL, ...)

Arguments

`x`	A tbl
`y`	A tbl
`by`	Columns by which to join the two tables
`max_dist`	Maximum distance to use for joining
`method`	Method for computing string distance, see `stringdist-metrics` in the stringdist package.
`mode`	One of "inner", "left", "right", "full" "semi", or "anti"
`ignore_case`	Whether to be case insensitive (default yes)
`distance_col`	If given, will add a column with this name containing the difference between the two
`...`	Arguments passed on to `stringdist`

Details

If method = "soundex", the max_dist is automatically set to 0.5, since soundex returns either a 0 (match) or a 1 (no match).

Examples


library(dplyr)
library(ggplot2)
data(diamonds)

d <- data_frame(approximate_name = c("Idea", "Premiums", "Premioom",
                                     "VeryGood", "VeryGood", "Faiir"),
                type = 1:6)

# no matches when they are inner-joined:
diamonds %>%
  inner_join(d, by = c(cut = "approximate_name"))

# but we can match when they're fuzzy joined
diamonds %>%
 stringdist_inner_join(d, by = c(cut = "approximate_name"))

library(dplyr)
library(ggplot2)
data(diamonds)

d <- data_frame(approximate_name = c("Idea", "Premiums", "Premioom",
                                     "VeryGood", "VeryGood", "Faiir"),
                type = 1:6)

# no matches when they are inner-joined:
diamonds %>%
  inner_join(d, by = c(cut = "approximate_name"))

# but we can match when they're fuzzy joined
diamonds %>%
 stringdist_inner_join(d, by = c(cut = "approximate_name"))

Package 'fuzzyjoin'

Help Index

Join two tables based on absolute difference between their columns

Description

Usage

Arguments

Examples

Join two tables based on a distance metric of one or more columns

Description

Usage

Arguments

Examples

Join two tables based not on exact matches, but with a function describing whether two vectors are matched or not

Description

Usage

Arguments

Details

Join two tables based on overlapping genomic intervals: both a

Description

Usage

Arguments

Details

Examples

Join two tables based on a geo distance of longitudes and latitudes

Description

Usage

Arguments

Details

Examples

Join two tables based on overlapping (low, high) intervals

Description

Usage

Arguments

Details

Examples

A corpus of common misspellings, for examples and practice

Description

Usage

Format

Source

Examples

Join two tables based on a regular expression in one column matching the other

Description

Usage

Arguments

See Also

Examples

Join two tables based on fuzzy string matching of their columns

Description

Usage

Arguments

Details

Examples