zoomerjoin (version 0.2.1)

hamming_inner_join: Fuzzy joins for Hamming distance using Locality Sensitive Hashing

Description

Find similar rows between two tables using the hamming distance. The hamming distance is equal to the number characters two strings differ by, or is equal to infinity if two strings are of different lengths

Usage

hamming_inner_join(
  a,
  b,
  by = NULL,
  n_bands = 100,
  band_width = 8,
  threshold = 2,
  progress = FALSE,
  clean = FALSE,
  similarity_column = NULL
)
hamming_anti_join(
  a,
  b,
  by = NULL,
  n_bands = 100,
  band_width = 100,
  threshold = 2,
  progress = FALSE,
  clean = FALSE,
  similarity_column = NULL
)
hamming_left_join(
  a,
  b,
  by = NULL,
  n_bands = 100,
  band_width = 100,
  threshold = 2,
  progress = FALSE,
  clean = FALSE,
  similarity_column = NULL
)
hamming_right_join(
  a,
  b,
  by = NULL,
  n_bands = 100,
  band_width = 100,
  threshold = 2,
  progress = FALSE,
  clean = FALSE,
  similarity_column = NULL
)
hamming_full_join(
  a,
  b,
  by = NULL,
  n_bands = 100,
  band_width = 100,
  threshold = 2,
  progress = FALSE,
  clean = FALSE,
  similarity_column = NULL
)

Value

A tibble fuzzily-joined on the basis of the variables in by. Tries to adhere to the same standards as the dplyr-joins, and uses the same logical joining patterns (i.e. inner-join joins and keeps only observations in both datasets).

Arguments

a, b

The two dataframes to join.

by

A named vector indicating which columns to join on. Format should be the same as dplyr: by = c("column_name_in_df_a" = "column_name_in_df_b"), but two columns must be specified in each dataset (x column and y column). Specification made with dplyr::join_by() are also accepted.

n_bands

The number of bands used in the locality sensitive hashing algorithm (default is 100). Use this in conjunction with the band_width to determine the performance of the hashing. Generally speaking, a higher number of bands leads to greater recall at the cost of higher runtime.

band_width

The length of each band used in the minihashing algorithm (default is 8). Use this in conjunction with the n_bands to determine the performance of the hashing. Generally speaking a wider number of bands decreases the number of false positives, decreasing runtime at the cost of lower sensitivity (true matches are less likely to be found).

threshold

The Hamming distance threshold below which two strings should be considered a match. A distance of zero corresponds to complete equality between strings, while a distance of 'x' between two strings means that 'x' substitutions must be made to transform one string into the other.

progress

Set to TRUE to print progress.

clean

Should the strings that you fuzzy join on be cleaned (coerced to lower-case, stripped of punctuation and spaces)? Default is FALSE.

similarity_column

An optional character vector. If provided, the data frame will contain a column with this name giving the Hamming distance between the two fields. Extra column will not be present if anti-joining.

Examples

# load baby names data
# install.packages("babynames")
library(babynames)

baby_names <- data.frame(name = tolower(unique(babynames$name))[1:500])
baby_names_mispelled <- data.frame(
  name_mispelled = gsub("[aeiouy]", "x", baby_names$name)
)

# Run the join and only keep rows that have a match:
hamming_inner_join(
  baby_names,
  baby_names_mispelled,
  by = c("name" = "name_mispelled"),
  threshold = 3,
  n_bands = 150,
  band_width = 10,
  clean = FALSE # default
)

# Run the join and keep all rows from the first dataset, regardless of whether
# they have a match:
hamming_left_join(
  baby_names,
  baby_names_mispelled,
  by = c("name" = "name_mispelled"),
  threshold = 3,
  n_bands = 150,
  band_width = 10,
)