distance_join: Join two tables based on a distance metric of one or more...

In fuzzyjoin: Join Tables Together on Inexact Matching

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
)

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)

fuzzyjoin documentation built on July 1, 2020, 7:07 p.m.