zoomerjoin (version 0.2.1)

euclidean_anti_join: Fuzzy joins for Euclidean distance using Locality Sensitive Hashing

Description

Fuzzy joins for Euclidean distance using Locality Sensitive Hashing

Usage

euclidean_anti_join(
  a,
  b,
  by = NULL,
  threshold = 1,
  n_bands = 30,
  band_width = 5,
  r = 0.5,
  progress = FALSE
)
euclidean_inner_join(
  a,
  b,
  by = NULL,
  threshold = 1,
  n_bands = 30,
  band_width = 5,
  r = 0.5,
  progress = FALSE
)
euclidean_left_join(
  a,
  b,
  by = NULL,
  threshold = 1,
  n_bands = 30,
  band_width = 5,
  r = 0.5,
  progress = FALSE
)
euclidean_right_join(
  a,
  b,
  by = NULL,
  threshold = 1,
  n_bands = 30,
  band_width = 5,
  r = 0.5,
  progress = FALSE
)
euclidean_full_join(
  a,
  b,
  by = NULL,
  threshold = 1,
  n_bands = 30,
  band_width = 5,
  r = 0.5,
  progress = FALSE
)

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.

threshold

The distance threshold below which units should be considered a match. Note that contrary to Jaccard joins, this value is about the distance and not the similarity. Therefore, a lower value means a higher similarity.

n_bands

The number of bands used in the minihash algorithm (default is 40). Use this in conjunction with the band_width to determine the performance of the hashing. The default settings are for a (.2, .8, .001, .999)-sensitive hash i.e. that pairs with a similarity of less than .2 have a >.1% chance of being compared, while pairs with a similarity of greater than .8 have a >99.9% chance of being compared.

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. The default settings are for a (.2, .8, .001, .999)-sensitive hash i.e. that pairs with a similarity of less than .2 have a >.1% chance of being compared, while pairs with a similarity of greater than .8 have a >99.9% chance of being compared.

r

Hyperparameter used to govern the sensitivity of the locality sensitive hash. Corresponds to the width of the hash bucket in the LSH algorithm. Increasing values of r mean more hash collisions and higher sensitivity (fewer false-negatives) at the cost of lower specificity (more false-positives and longer run time). For more information, see the description in tools:::Rd_expr_doi("10.1145/997817.997857").

progress

Set to TRUE to print progress.

References

Datar, Mayur, Nicole Immorlica, Pitor Indyk, and Vahab Mirrokni. "Locality-Sensitive Hashing Scheme Based on p-Stable Distributions" SCG '04: Proceedings of the twentieth annual symposium on Computational geometry (2004): 253-262

Examples

n <- 10

# Build two matrices that have close values
X_1 <- matrix(c(seq(0, 1, 1 / (n - 1)), seq(0, 1, 1 / (n - 1))), nrow = n)
X_2 <- X_1 + .0000001

X_1 <- as.data.frame(X_1)
X_2 <- as.data.frame(X_2)

X_1$id_1 <- 1:n
X_2$id_2 <- 1:n

# only keep observations that have a match
euclidean_inner_join(X_1, X_2, by = c("V1", "V2"), threshold = .00005)

# keep all observations from X_1, regardless of whether they have a match
euclidean_inner_join(X_1, X_2, by = c("V1", "V2"), threshold = .00005)