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gender

Predict Gender from Names Using Historical Data

https://github.com/lmullen/gender

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    1 of 8 committers (12.5%) from academic institutions
  • ○
    Institutional organization owner
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (13.1%) to scientific vocabulary

Keywords

r r-package rstats

Keywords from Contributors

genome geocode biodiversity disease-networks darwincore helminth taxize taxonomy parasites ebird-api
Last synced: 10 months ago · JSON representation

Repository

Predict Gender from Names Using Historical Data

Basic Info
  • Host: GitHub
  • Owner: lmullen
  • License: other
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 48.9 MB
Statistics
  • Stars: 191
  • Watchers: 14
  • Forks: 27
  • Open Issues: 8
  • Releases: 0
Topics
r r-package rstats
Created over 12 years ago · Last pushed almost 5 years ago
Metadata Files
Readme License

README.Rmd 

---
output: github_document
pagetitle: "Predict Gender from Names Using Historical Data"
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# gender 

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/gender)](https://CRAN.R-project.org/package=gender)
[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/gender)](https://CRAN.R-project.org/package=gender)

## Guidelines and warnings

This package attempts to infer gender (or more precisely, sex assigned at birth) based on first names using historical data, typically data that was gathered by the state. This method has many limitations, and before you use this package be sure to take into account the following guidelines.

(1) Your analysis and the way you report it should take into account the limitations of this method, which include its reliance of data created by the state and its inability to see beyond the state-imposed gender binary. At a minimum, be sure to read our article explaining the limitations of this method, as well as the review article that is critical of this sort of methodology, both cited below.

(2) Do not use this package to study individuals: it is at most useful for studying populations in the aggregate.

(3) Resort to this method only when the alternative is not a more nuanced and justifiable approach to studying gender, but where the alternative is not studying gender at all. For instance, for many historical sources this approach might be the only way to get a sense of the sex ratios in a population. But ask whether you really need to use this method, whether you are using it responsibly, or whether you could use a better approach instead. 

Blevins, Cameron, and Lincoln A. Mullen, “Jane, John … Leslie? A Historical Method for Algorithmic Gender Prediction,” *Digital Humanities Quarterly* 9, no. 3 (2015).  http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html

Mihaljević, Helena, Marco Tullney, Lucía Santamaría, and Christian Steinfeldt. “Reflections on Gender Analyses of Bibliographic Corpora.” *Frontiers in Big Data* 2 (August 28, 2019): 29. https://doi.org/10.3389/fdata.2019.00029.

## Description

Data sets, historical or otherwise, often contain a list of first names but seldom identify those names by gender. Most techniques for finding gender programmatically rely on lists of male and female names. However, the gender associated with names can vary over time. Any data set that covers the normal span of a human life will require a historical method to find gender from names. This [R](https://www.r-project.org/) package uses historical datasets from the U.S. Social Security Administration, the U.S. Census Bureau (via [IPUMS USA](https://usa.ipums.org/usa/)), and the [North Atlantic Population Project](https://www.nappdata.org/napp/) to provide predictions of gender for first names for particular countries and time periods.

## Installation

You can install [this package from CRAN](https://cran.r-project.org/package=gender):

```{r eval=FALSE}
install.packages("gender")
```

The first time you use the package you will be prompted to install the accompanying [genderdata package](https://github.com/lmullen/genderdata). Alternatively, you can install this package for yourself.

```{r eval = FALSE}
# install.packages("remotes")
remotes::install_github("lmullen/genderdata")
```

## Using the package

The `gender()` function takes a character vector of names and a year or range of years and uses various datasets to predict the gender of names. Here we predict the gender of the names Madison and Hillary in 1930 and again in the 2000s using Social Security data.

```{r}
library(gender)
gender(c("Madison", "Hillary"), years = 1930, method = "ssa")
gender(c("Madison", "Hillary"), years = c(2000, 2010), method = "ssa")
```

See the package vignette for a fuller introduction and suggestions on how to use the `gender()` function efficiently with large datasets.

```{r eval=FALSE}
vignette(topic = "predicting-gender", package = "gender")
```

To read the documentation for the datasets, install the [genderdata package](https://github.com/lmullen/genderdata) then examine the included datasets.

```{r eval=FALSE}
library(genderdata)
data(package = "genderdata")
```

## Citation

If you use this package, I would appreciate a citation.

```{r}
citation("gender")
```

Owner

  • Name: Lincoln Mullen
  • Login: lmullen
  • Kind: user
  • Location: Virginia
  • Company: @chnm / George Mason University

Religious history, digital history, and nineteenth-century US history. History faculty at @chnm and George Mason University.

GitHub Events

Total
  • Watch event: 3
  • Issue comment event: 1
Last Year
  • Watch event: 3
  • Issue comment event: 1

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 315
  • Total Committers: 8
  • Avg Commits per committer: 39.375
  • Development Distribution Score (DDS): 0.044
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Lincoln Mullen l****n@l****m 301
Hadley Wickham h****m@g****m 4
Benjamin Schmidt b****t@g****m 4
Karthik Ram k****m@g****m 2
Scott Chamberlain m****s@g****m 1
Oliver Keyes I****s 1
Fangzhou Xie 4****e 1
rOpenSci Bot m****t@g****m 1
Committer Domains (Top 20 + Academic)
lincolnmullen.com: 1

Issues and Pull Requests

Last synced: 12 months ago

All Time
  • Total issues: 57
  • Total pull requests: 14
  • Average time to close issues: 7 months
  • Average time to close pull requests: about 2 months
  • Total issue authors: 28
  • Total pull request authors: 10
  • Average comments per issue: 2.04
  • Average comments per pull request: 1.36
  • Merged pull requests: 8
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • lmullen (22)
  • bmschmidt (4)
  • sckott (4)
  • soodoku (2)
  • Ironholds (2)
  • albnd (1)
  • sarahhatherly (1)
  • mpadge (1)
  • AtisK (1)
  • 31YY88 (1)
  • trinker (1)
  • Moohan (1)
  • jtribbia (1)
  • antagomir (1)
  • romainfrancois (1)
Pull Request Authors
  • bmschmidt (4)
  • Moohan (2)
  • steveYeah (1)
  • fangzhou-xie (1)
  • hadley (1)
  • estesoymio (1)
  • Ironholds (1)
  • lmullen (1)
  • karthik (1)
  • MarHai (1)
Top Labels
Issue Labels
enhancement (6) wontfix (2) bug (2)
Pull Request Labels
enhancement (1)

Packages

  • Total packages: 2
  • Total downloads:
    • cran 1,708 last-month
  • Total docker downloads: 132,022
  • Total dependent packages: 5
    (may contain duplicates)
  • Total dependent repositories: 17
    (may contain duplicates)
  • Total versions: 16
  • Total maintainers: 1
cran.r-project.org: gender

Predict Gender from Names Using Historical Data

  • Versions: 9
  • Dependent Packages: 5
  • Dependent Repositories: 17
  • Downloads: 1,708 Last month
  • Docker Downloads: 132,022
Rankings
Docker downloads count: 0.0%
Stargazers count: 2.3%
Forks count: 2.8%
Average: 4.8%
Dependent repos count: 6.9%
Dependent packages count: 8.1%
Downloads: 8.9%
Maintainers (1)
lincoln@lincolnmullen.com
Last synced: 11 months ago
proxy.golang.org: github.com/lmullen/gender
  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.5%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 4.0.0 depends
  • stats * depends
  • utils * depends
  • dplyr >= 0.8.5 imports
  • httr >= 1.4.1 imports
  • jsonlite >= 1.6.1 imports
  • remotes >= 2.2.0 imports
  • covr * suggests
  • genderdata >= 0.6.0 suggests
  • ggplot2 >= 3.3.0 suggests
  • knitr >= 1.28 suggests
  • rmarkdown >= 2.1 suggests
  • testthat >= 2.3.2 suggests