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:

https://genderize.io - Gender estimation based on a name
https://agify.io - Age estimation based on a name
https://nationalize.io - Nationality estimation based on a name

Documentation

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

https://matbmeijer.github.io/DemografixeR

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	831	5214	2020-05-04 22:33:05
1	Allister	gender	male	0.98	129	1000	831	5214	2020-05-04 22:33:05
3	Lucie	gender	female	0.99	85580	1000	831	5214	2020-05-04 22:33:05
4	Paula	gender	female	0.98	74130	1000	831	5214	2020-05-04 22:33:05

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