genderBR predicts gender from Brazilian first names
using data from the Instituto Brasileiro de Geografia e Estatistica’s
2010 Census.
genderBR’s main function is get_gender,
which takes a string with a Brazilian first name and predicts its gender
using data from the IBGE’s 2010 Census – specifically, from its API and
from an internal dataset.
More specifically, it uses data on the number of females and males
with the same name in Brazil, or in a given Brazilian state, and
calculates the proportion of female’s uses of it. The function then
classifies a name as male or female only when that proportion is higher
than a given threshold (e.g.,
female if proportion > 0.9, or
male if proportion <= 0.1); proportions below those
threshold are classified as missing (NA). An example:
library(genderBR)
#>
#> To cite genderBR in publications, use: citation('genderBR')
#> To learn more, visit: fmeireles.com/genderbr
get_gender("joão")
#> [1] "Male"
get_gender("ana")
#> [1] "Female"Multiple names can be passed at the same function call:
get_gender(c("pedro", "maria"))
#> [1] "Male" "Female"And both full names and names written in lower or upper case are accepted as inputs:
get_gender("Mario da Silva")
#> [1] "Male"
get_gender("ANA MARIA")
#> [1] "Female"Additionally, one can filter results by state with the argument
state; or get the probability that a given first name
belongs to a female person by setting the prob argument to
TRUE (defaults to FALSE).
# What is the probability that the name Ariel belongs to a female person in Brazil?
get_gender("Ariel", prob = TRUE)
#> [1] 0.09219289
# What about differences between Brazilian states?
get_gender("Ariel", prob = TRUE, state = "RJ") # RJ, Rio de Janeiro
#> [1] 0.2627399
get_gender("Ariel", prob = TRUE, state = "RS") # RS, Rio Grande do Sul
#> [1] 0.05144695
get_gender("Ariel", prob = TRUE, state = "SP") # SP, Sao Paulo
#> [1] 0.1294782Note that a vector with states’ abbreviations is a valid input for
get_gender function, so this also works:
name <- rep("Ariel", 3)
states <- c("rj", "rs", "sp")
get_gender(name, prob = T, state = states)
#> [1] 0.26273991 0.05144695 0.12947819This can be useful also to predict the gender of different individuals living in different states:
df <- data.frame(name = c("Alberto da Silva", "Maria dos Santos", "Thiago Rocha", "Paula Camargo"),
uf = c("AC", "SP", "PE", "RS"),
stringsAsFactors = FALSE
)
df$gender <- get_gender(df$name, df$uf)
df
#> name uf gender
#> 1 Alberto da Silva AC Male
#> 2 Maria dos Santos SP Female
#> 3 Thiago Rocha PE Male
#> 4 Paula Camargo RS FemaleThe genderBR package relies on Brazilian state
abbreviations (acronyms) to filter results. To get a complete dataset
with the full name, IBGE code, and abbreviations of all 27 Brazilian
states, use the get_states functions:
get_states()
#> # A tibble: 27 x 3
#> state abb code
#> <chr> <chr> <int>
#> 1 ACRE AC 12
#> 2 ALAGOAS AL 27
#> 3 AMAPA AP 16
#> 4 AMAZONAS AM 13
#> 5 BAHIA BA 29
#> 6 CEARA CE 23
#> 7 DISTRITO FEDERAL DF 53
#> 8 ESPIRITO SANTO ES 32
#> 9 GOIAS GO 52
#> 10 MARANHAO MA 21
#> # … with 17 more rowsThe genderBR package can also be used to get information
on the relative and total number of persons with a given name by gender
and by state in Brazil. To that end, use the map_gender
function:
map_gender("maria")
#> # A tibble: 27 x 6
#> nome uf freq populacao sexo prop
#> <chr> <int> <int> <int> <chr> <dbl>
#> 1 Piauí 22 363139 3118360 "" 11645.
#> 2 Ceará 23 967042 8452381 "" 11441.
#> 3 Paraíba 25 423026 3766528 "" 11231.
#> 4 Rio Grande do Norte 24 341940 3168027 "" 10793.
#> 5 Alagoas 27 321330 3120494 "" 10297.
#> 6 Pernambuco 26 838534 8796448 "" 9533.
#> 7 Sergipe 28 188619 2068017 "" 9121.
#> 8 Maranhão 21 574689 6574789 "" 8741.
#> 9 Acre 12 63172 733559 "" 8612.
#> 10 Minas Gerais 31 1307650 19597330 "" 6673.
#> # … with 17 more rowsTo specify gender in the consultation, use the optional argument
gender (valid inputs are f, for female;
m, for male; or NULL, the default option).
map_gender("iris", gender = "m")
#> # A tibble: 23 x 6
#> nome uf freq populacao sexo prop
#> <chr> <int> <int> <int> <chr> <dbl>
#> 1 Goiás 52 840 6003788 m 14.0
#> 2 Tocantins 17 156 1383445 m 11.3
#> 3 Bahia 29 422 14016906 m 3.01
#> 4 Mato Grosso 51 91 3035122 m 3
#> 5 Minas Gerais 31 512 19597330 m 2.61
#> 6 Distrito Federal 53 65 2570160 m 2.53
#> 7 Espírito Santo 32 69 3514952 m 1.96
#> 8 Rondônia 11 28 1562409 m 1.79
#> 9 Pará 15 129 7581051 m 1.7
#> 10 Rio de Janeiro 33 225 15989929 m 1.41
#> # … with 13 more rowsTo install genderBR’s last stable version on CRAN,
use:
install.packages("genderBR")To install a development version, use:
if (!require("devtools")) install.packages("devtools")
devtools::install_github("meirelesff/genderBR")The surveyed population in the Instituto Brasileiro de Geografia e Estatistica’s (IBGE) 2010 Census includes 190,8 million Brazilians – with more than 130,000 unique first names.
To extracts the numer of male or female uses of a given first name in Brazil, the package employs the IBGE’s API and, from in 1.1.0 version, also from an internal dataset containing all the names recorded in the IBGE’s Census. In this service, different spelling (e.g., Ana and Anna, or Marcos and Markos) implies different occurrences, and only names with more than 20 occurrences, or more than 15 occurrences in a given state, are included in the database.
For more information on the IBGE’s data, please check (in Portuguese): https://censo2010.ibge.gov.br/nomes/