gender: Predict gender from first names using historical data

In gender: Predict Gender from Names Using Historical Data

Description

This function predicts the gender of a first name given a year or range of years in which the person was born. The prediction can use one of several data sets suitable for different time periods or geographical regions. See the package vignette for suggestions on using this function with multiple names and for a discussion of which data set is most suitable for your research question. When using certain methods, the genderdata data package is required; you will be prompted to install it if it is not already available.

Usage

gender(
  names,
  years = c(1932, 2012),
  method = c("ssa", "ipums", "napp", "kantrowitz", "genderize", "demo"),
  countries = c("United States", "Canada", "United Kingdom", "Denmark", "Iceland",
    "Norway", "Sweden")
)

Arguments

names	First names as a character vector. Names are case insensitive.
years	The birth year of the name whose gender is to be predicted. This argument can be either a single year, a range of years in the form c(1880, 1900). If no value is specified, then for the "ssa" method it will use the period 1932 to 2012; acceptable years for the SSA method range from 1880 to 2012, but for years before 1930 the IPUMS method is probably more accurate. For the "ipums" method the default range is the period 1789 to 1930, which is also the range of acceptable years. For the "napp" method the default range is the period 1758 to 1910, which is also the range of acceptable years. If a year or range of years is specified, then the names will be looked up for that period.
method	This value determines the data set that is used to predict the gender of the name. The "ssa" method looks up names based from the U.S. Social Security Administration baby name data. (This method is based on an implementation by Cameron Blevins.) The "ipums" method looks up names from the U.S. Census data in the Integrated Public Use Microdata Series. (This method was contributed by Ben Schmidt.) The "napp" method uses census microdata from Canada, Great Britain, Denmark, Iceland, Norway, and Sweden from 1801 to 1910 created by the North Atlantic Population Project. The "kantrowitz" method uses the Kantrowitz corpus of male and female names. The "genderize" method uses the Genderize.io <https://genderize.io/> API, which is based on "user profiles across major social networks." The "demo" method is uses the top 100 names in the SSA method; it is provided only for demonstration purposes when the genderdata package is not installed and it is not suitable for research purposes.
countries	The countries for which datasets are being used. For the "ssa" and "ipums" methods, the only valid option is "United States" which will be assumed if no argument is specified. For the "napp" method, you may specify a character vector with any of the following countries: "Canada", "United Kingdom", "Denmark", "Iceland", "Norway", "Sweden". For the "kantrowitz" and "genderize" methods, no country should be specified.

Value

Returns a data frame containing the results of predicting the gender. The exact components of the returned list will depend on the specific method used. They include the following:

name	The name for which the gender has been predicted.
proportion_male	The proportion of male names for the given range of years.
proportion_female	The proportion of female names for the given range of years.
gender	The predicted gender based on the proportion of male and female names. Possible values are "male" and "female" for proportions above 0.5, "either" for proportions that are exactly 0.5, and NA for combinations of names and years for which a gender cannot be predicted using the given method.
year_min	The lower bound (inclusive) of the year range used for the prediction.
year_max	The upper bound (inclusive) of the year range used for the prediction.

Examples

gender("madison", method = "demo", years = 1985)
gender("madison", method = "demo", years = c(1900, 1985))
# SSA method
## Not run: gender("madison", method = "demo", years = c(1900, 1985))
# IPUMS method
## Not run: gender("madison", method = "ipums", years = 1860)
# NAPP method
## Not run: gender("madison", method = "napp", countries = c("Sweden", "Denmark"))

Example output

# A tibble: 1 x 6
     name proportion_male proportion_female gender year_min year_max
    <chr>           <dbl>             <dbl>  <chr>    <dbl>    <dbl>
1 madison          0.2137            0.7863 female     1985     1985
# A tibble: 1 x 6
     name proportion_male proportion_female gender year_min year_max
    <chr>           <dbl>             <dbl>  <chr>    <dbl>    <dbl>
1 madison          0.9028            0.0972   male     1900     1985

gender documentation built on Oct. 13, 2021, 5:08 p.m.