DemografixeR

‘DemografixeR’ allows to estimate gender, age & nationality from a name. The package is an API wrapper of all 3 ‘Demografix’ API’s - all three APIs supported in one package:

Documentation

You can find all the necessary documentation about the package here:

Installation

You can install the CRAN release version of DemografixeR following this R command:

install.packages("DemografixeR")

You can also install the development version of DemografixeR following these R commands:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("matbmeijer/DemografixeR")

Examples

These are basic examples, which shows you how to estimate nationality, gender and age by a given name with & without specifying a country. The package takes care of multiple background tasks:

  • API pagination
  • Duplicated names (one request made per name)
  • Missing values
  • Workflow integration (e.g. with dplyr or data.table)
library(DemografixeR)

#Simple example without country_id
names<-c("Ben", "Allister", "Lucie", "Paula")
genderize(name = names)
#> [1] "male"   "male"   "female" "female"
nationalize(name = names)
#> [1] "AU" "ZA" "CZ" "PT"
agify(name = names)
#> [1] 48 44 24 50

#Simple example with
genderize(name = names, country_id = "US")
#> [1] "male"   "male"   "female" "female"
agify(name = names, country_id = "US")
#> [1] 67 46 65 70

#Workflow example with dplyr with missing values and multiple different countries
df<-data.frame(names=c("Ana", NA, "Pedro",
                       "Francisco", "Maria", "Elena"),
                 country=c(NA, NA, "ES",
                           "DE", "ES", "NL"), stringsAsFactors = FALSE)

df %>% dplyr::mutate(guessed_nationality=nationalize(name = names),
                guessed_gender=genderize(name = names, country_id = country),
                guessed_age=agify(name = names, country_id = country)) %>%
  knitr::kable()
names country guessed_nationality guessed_gender guessed_age
Ana NA PT female 58
NA NA NA NA NA
Pedro ES PT male 69
Francisco DE CL male 58
Maria ES CY NA 59
Elena NL CC female 69 
#Detailed data.frame example:
genderize(name = names, simplify = FALSE, meta = TRUE) %>% knitr::kable()
name type gender probability count api_rate_limit api_rate_remaining api_rate_reset api_request_timestamp
2 Ben gender male 0.95 77991 1000 959 46192 2020-05-14 11:10:07
1 Allister gender male 0.98 129 1000 959 46192 2020-05-14 11:10:07
3 Lucie gender female 0.99 85580 1000 959 46192 2020-05-14 11:10:07
4 Paula gender female 0.98 74130 1000 959 46192 2020-05-14 11:10:07

Disclaimer

  • This package is in no way affiliated to the Demografix ApS company, the owner of the ‘genderize.io’, ‘agify.io’ and ‘nationalize.io’ APIs.
  • An open mind towards gender & gender diversity is promoted, warning that the results from the ‘genderize.io’ API reflect an oversimplification of gender identity, gender roles and the meaning of ‘gender’. For more information visit the active discussion in the following Wikipedia article.

Code of Conduct

Please note that the ‘DemografixeR’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.