Zoomerjoin

Zoomerjoin: Superlatively-Fast Fuzzy Joins - Published in JOSS (2023)

https://github.com/beniaminogreen/zoomerjoin

Science Score: 98.0%

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Published in Journal of Open Source Software

Keywords

blazinglyfast fuzzyjoin join r r-package rstats rust zoomer

Last synced: 6 months ago · JSON representation ·

Repository

Superlatively-fast fuzzy-joins in R

Basic Info

Host: GitHub
Owner: beniaminogreen
License: gpl-3.0
Language: R
Default Branch: main
Homepage: https://beniamino.org/zoomerjoin/
Size: 77.5 MB

Statistics

Stars: 105
Watchers: 5
Forks: 6
Open Issues: 9
Releases: 2

Topics

blazinglyfast fuzzyjoin join r r-package rstats rust zoomer

Created about 3 years ago · Last pushed 11 months ago

Metadata Files

Readme Changelog Contributing License Citation Codemeta

README.Rmd

---
output: github_document
always_allow_html: true
---

```{r, include=F}
library(tidyverse)
library(microbenchmark)
library(fuzzyjoin)

# rextendr::document()
devtools::load_all()
```


# zoomerjoin 

[![DOI](https://joss.theoj.org/papers/10.21105/joss.05693/status.svg)](https://doi.org/10.21105/joss.05693)
[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[![Codecov test coverage](https://codecov.io/gh/beniaminogreen/zoomerjoin/branch/main/graph/badge.svg)](https://app.codecov.io/gh/beniaminogreen/zoomerjoin?branch=main)


zoomerjoin is an R package that empowers you to fuzzy-join massive datasets
rapidly, and with little memory consumption. It is powered by high-performance
implementations of [Locality Sensitive
Hashing](https://en.wikipedia.org/wiki/Locality-sensitive_hashing), an
algorithm that finds the matches records between two datasets without having to
compare all possible pairs of observations. In practice, this means zoomerjoin
can fuzzily-join datasets days, or even years faster than other matching
packages. zoomerjoin has been used in-production to join datasets of hundreds
of millions of names or vectors in a matter of hours.

## Installation

### Installing from CRAN:

You can install from the CRAN as you would with any other package. Please be
aware that you will have to have Cargo (the rust toolchain and compiler) installed to build
the package from source.

```r
install.packages(zoomerjoin)
```


### Installing Rust

If your operating system or version of R is not installed, you must have the
[Rust compiler](https://www.rust-lang.org/tools/install) installed to compile
this package from sources. After the package is compiled, Rust is no longer
required, and can be safely uninstalled.

#### Installing Rust on Linux or Mac:

To install Rust on Linux or Mac, you can simply run the following snippet in
your terminal.

``` sh
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
```

#### Installing Rust on Windows:

To install Rust on windows, you can use the Rust installation wizard,
`rustup-init.exe`, found [at this
site](https://forge.rust-lang.org/infra/other-installation-methods.html).
Depending on your version of Windows, you may see an error that looks something like this:

```
error: toolchain 'stable-x86_64-pc-windows-gnu' is not installed
```

In this case, you should run `rustup install stable-x86_64-pc_windows-gnu` to
install the missing toolchain. If you're missing another toolchain, simply type
this in the place of `stable-x86_64-pc_windows-gnu` in the command above.

### Installing Package from Github:

Once you have rust installed Rust, you should be able to install the package
with either the install.packages function as above, or using the
`install_github` function from the `devtools` package or with the `pkg_install`
function from the `pak` package.

``` r
## Install with devtools
# install.packages("devtools")
devtools::install_github("beniaminogreen/zoomerjoin")

## Install with pak
# install.packages("pak")
pak::pkg_install("beniaminogreen/zoomerjoin")
```

### Loading The Package

Once the package is installed, you can load it into memory as usual by typing:

```{r, warning = FALSE, message = FALSE}
library(zoomerjoin)
```

## Usage:

The flagship feature of zoomerjoins are the jaccard_join and euclidean family
of functions, which are designed to be near drop-ins for the corresponding
dplyr/fuzzyjoin commands:

* `jaccard_left_join()`
* `jaccard_right_join()`
* `jaccard_inner_join()`
* `jaccard_full_join()`
* `euclidean_left_join()`
* `euclidean_right_join()`
* `euclidean_inner_join()`
* `euclidean_full_join()`

The `jaccard_join` family of functions provide fast fuzzy-joins for strings
using the Jaccard distance while the `euclidean_join` family provides
fuzzy-joins for points or vectors using the Euclidean distance.

### Example: Joining rows of the Database on Ideology, Money in Politics, and Elections
(DIME)

Here's a snippet showing off how to use the `jaccard_inner_join()` merge two
lists of political donors in the [Database on Ideology, Money in Politics,
and Elections (DIME)](https://data.stanford.edu/dime). You can see a more
detailed example of this vignette in the [introductory vignette](https://beniamino.org/zoomerjoin/articles/guided_tour.html).

I start with two corpuses I would like to combine, `corpus_1`:

```{r}
corpus_1 <- dime_data %>%
  head(500)
names(corpus_1) <- c("a", "field")
corpus_1
```

And `corpus_2`:

```{r}
corpus_2 <- dime_data %>%
  tail(500)
names(corpus_2) <- c("b", "field")
corpus_2
```
Both corpuses have an observation ID column, and a donor name column. We would
like to join the two datasets on the donor names column, but the two can't be
directly joined because of misspellings. Because of this, we will use the
jaccard_inner_join function to fuzzily join the two on the donor name column.

Importantly, Locality Sensitive Hashing is a [probabilistic
algorithm](https://en.wikipedia.org/wiki/Randomized_algorithm), so it may fail
to identify some matches by random chance. I adjust the hyperparameters
`n_bands` and `band_width` until the chance of true matches being dropped is
negligible. By default, the package will issue a warning if the chance of a
true match being discovered is less than 95%. You can use the
`jaccard_probability` and `jaccard_hyper_grid_search` to help understand the
probability any true matches will be discarded by the algorithm.

More details and a more thorough description of how to tune the hyperparameters
can be can be found in the [guided tour
vignette](https://beniamino.org/zoomerjoin/articles/guided_tour.html).

```{r}
set.seed(1)
start_time <- Sys.time()
join_out <- jaccard_inner_join(corpus_1, corpus_2, n_gram_width = 6, n_bands = 20, band_width = 6)
print(Sys.time() - start_time)
print(join_out)
```

Zoomerjoin is able to quickly find the matching columns without comparing all
pairs of records. This saves more and more time as the size of each list
increases, so it can scale to join datasets with millions or hundreds of
millions of rows.

# Contributing

Thanks for your interest in contributing to Zoomerjoin!

I am using a gitub-centric workflow to manage the package; You can file a bug report, request a new feature, or ask a question about the package by [filing
an issue on the issues page](https://github.com/beniaminogreen/zoomerjoin/issues), where you will also
find a range of templates to help you out. If you'd like to make changes to the code, you can write and file a [pull
request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests)
on [this page](https://github.com/beniaminogreen/zoomerjoin/pulls). I'll try to
respond to all of these in a timely manner (within a week) although
occasionally I may take longer to respond to a complicated question or issue.

Please also be aware of the [contributor code of
conduct](https://github.com/beniaminogreen/zoomerjoin/blob/main/CONTRIBUTING.md)
for contributing to the repository.

## Acknowledgments:


The Zoomerjoin was made using [this SQL join
illustration](https://commons.wikimedia.org/wiki/File:SQL_Join_-_08_A_Cross_Join_B.svg)
by [Germanx](https://commons.wikimedia.org/wiki/User:GermanX) and [this speed
limit sign](https://commons.wikimedia.org/wiki/File:Speed_limit_75_sign.svg) from the
Federal Highway Administration - MUTCD.

## References:

Bonica, Adam. 2016. Database on Ideology, Money in Politics, and Elections: Public version 2.0 [Computer file]. Stanford, CA: Stanford University Libraries.

Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman. 2014. Mining of Massive Datasets (2nd. ed.). Cambridge University Press, USA.

Broder, Andrei Z. (1997), "On the resemblance and containment of documents", Compression and Complexity of Sequences: Proceedings. Positano, Salerno, Italy

Owner

Name: Beniamino Green
Login: beniaminogreen
Kind: user
Location: New Haven, CT
Company: Yale University

Repositories: 7
Profile: https://github.com/beniaminogreen

Pre-doctoral Fellow

JOSS Publication

Zoomerjoin: Superlatively-Fast Fuzzy Joins

Published

September 26, 2023

DOI

10.21105/joss.05693

Volume 8, Issue 89, Page 5693

Authors

Beniamino Green

Yale University, USA

Editor

Samuel Forbes

Citation (CITATION.cff)

cff-version: "1.2.0"
authors:
- family-names: Green
  given-names: Beniamino
  orcid: "https://orcid.org/0009-0006-4501-597X"
contact:
- family-names: Green
  given-names: Beniamino
  orcid: "https://orcid.org/0009-0006-4501-597X"
doi: 10.5281/zenodo.8370652
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Green
    given-names: Beniamino
    orcid: "https://orcid.org/0009-0006-4501-597X"
  date-published: 2023-09-26
  doi: 10.21105/joss.05693
  issn: 2475-9066
  issue: 89
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 5693
  title: "Zoomerjoin: Superlatively-Fast Fuzzy Joins"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.05693"
  volume: 8
title: "Zoomerjoin: Superlatively-Fast Fuzzy Joins"

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GitHub Events

Total

Watch event: 4
Issue comment event: 1
Push event: 2
Pull request event: 1
Fork event: 1

Last Year

Watch event: 4
Issue comment event: 1
Push event: 2
Pull request event: 1
Fork event: 1

Committers

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Avg Commits per committer: 73.25
Development Distribution Score (DDS): 0.106

Past Year

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Committers: 2
Avg Commits per committer: 4.5
Development Distribution Score (DDS): 0.111

Top Committers

Name	Email	Commits
Beniamino Green	b**n@g**m	262
Etienne Bacher	5****r	28
Josiah Parry	j**y@g**m	2
Florian Caro	f**0@g**m	1

Committer Domains (Top 20 + Academic)

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Merged pull requests: 72
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Pull requests: 2
Average time to close issues: N/A
Average time to close pull requests: about 12 hours
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Pull request authors: 2
Average comments per issue: 6.0
Average comments per pull request: 1.0
Merged pull requests: 2
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etiennebacher (32)
JosiahParry (3)
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Total packages: 1
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Total versions: 4
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cran.r-project.org: zoomerjoin

Superlatively Fast Fuzzy Joins

Homepage: https://beniamino.org/zoomerjoin/
Documentation: http://cran.r-project.org/web/packages/zoomerjoin/zoomerjoin.pdf
License: GPL (≥ 3)
Latest release: 0.2.1
published 10 months ago

Versions: 4
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 225 Last month

Rankings

Dependent packages count: 28.2%

Dependent repos count: 36.1%

Average: 49.6%

Downloads: 84.5%

Maintainers (1)

beniamino.green@yale.edu

Last synced: 6 months ago

Dependencies

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DESCRIPTION cran

dplyr * imports
babynames * suggests
fuzzyjoin * suggests
knitr * suggests
preferably * suggests
rmarkdown * suggests
testthat >= 3.0.0 suggests
tibble * suggests

.github/workflows/test-coverage.yaml actions

actions/checkout v3 composite
actions/upload-artifact v3 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

src/rust/Cargo.toml cargo

Zoomerjoin

Science Score: 98.0%

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Zoomerjoin: Superlatively-Fast Fuzzy Joins

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cran.r-project.org: zoomerjoin

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