Predict Gender from Names Using Historical Data

Encodes gender based on names and dates of birth using historical datasets. By using these datasets instead of lists of male and female names, this package is able to more accurately guess the gender of a name, and it is able to report the probability that a name was male or female.

gender

An R package for predicting gender from first names using historical data.

Author: Lincoln Mullen
License: MIT

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 package uses historical datasets from the U.S. Social Security Administration, the U.S. Census Bureau (via IPUMS USA), and the North Atlantic Population Project to provide predictions of gender for first names for particular countries and time periods.

Installation

You can install this package from CRAN:

install.packages("gender")

The first time you use the package you will be prompted to install the accompanying genderdata package. Alternatively, you can install this package for yourself from the rOpenSci package repository:

install.packages("genderdata", type = "source",
                 repos = "http://packages.ropensci.org")

If you prefer, you can install the development versions of both packages from the rOpenSci package repository:

install.packages(c("gender", "genderdata"),
                 repos = "http://packages.ropensci.org",
                 type = "source")

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.

library(gender)
gender(c("Madison", "Hillary"), years = 1930, method = "ssa")
#> # A tibble: 2 x 6
#>   name    proportion_male proportion_female gender year_min year_max
#>   <chr>             <dbl>             <dbl> <chr>     <dbl>    <dbl>
#> 1 Hillary              1.                0. male      1930.    1930.
#> 2 Madison              1.                0. male      1930.    1930.
gender(c("Madison", "Hillary"), years = c(2000, 2010), method = "ssa")
#> # A tibble: 2 x 6
#>   name    proportion_male proportion_female gender year_min year_max
#>   <chr>             <dbl>             <dbl> <chr>     <dbl>    <dbl>
#> 1 Hillary         0.00550             0.994 female    2000.    2010.
#> 2 Madison         0.00460             0.995 female    2000.    2010.

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

vignette(topic = "predicting-gender", package = "gender")

To read the documentation for the datasets, install the genderdata package then examine the included datasets.

library(genderdata)
data(package = "genderdata")

Citation

If you use this package, I would appreciate a citation. You can see an up to date citation information with citation("gender"). You can cite either the package or the accompanying journal article.

Historical Data. R package version 0.5.2. https://github.com/ropensci/gender

Cameron Blevins and Lincoln 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