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# i used these linses of code to scrap african gendernames from the internet for one of my projects, it may not help you | |
library(rvest) | |
library(mbanalytics) | |
url = "https://www.momjunction.com/baby-names/african/page/%s/" | |
Allnames = 1:20%>% | |
map_df(~sprintf(url,.) %>% | |
read_html() %>% html_table(fill = T) %>% as.data.frame() %>% | |
select(NAMES,GENDER) %>% filter(!grepl("googletag.cmd.",NAMES),GENDER != "Unisex") %>% | |
mutate(GENDER = ifelse(GENDER == "Boy","M","F"), NAMES = tolower(NAMES))) %>% | |
rename_all(tolower) | |
url2 = "https://www.behindthename.com/submit/names/usage/eastern-african/%s" | |
Allnames2 = 1:3 %>% | |
map(~sprintf(url2,.) %>% read_html()) %>% | |
map_df(~tibble(names = html_nodes(., ".listname") %>% html_text() %>% tolower(), | |
gender = html_nodes(., ".listgender") %>% html_text()%>% tolower() )) %>% filter(gender != "f & m") | |
url3 = 'http://www.firstnamesbaby.com/Baby-Boy-Names-Popular/Kenyan/%s/Boy/' | |
url4 = 'http://www.firstnamesbaby.com/Names-By-Country/Kenyan/Girl/page%s' | |
malenames = LETTERS %>% | |
map_df(~ sprintf(url3,.) %>% | |
read_html() %>% | |
html_table(fill = T) %>% enframe() %>% filter(name == 1) %>% unnest(value) %>% | |
separate(X1,into = c("names",'gender'),sep = "[|]") %>% select(names,gender) %>% | |
mutate_all(.funs = trimws) %>% mutate(gender = ifelse(gender == "Boy","M","F"))) | |
femalenames = 1:11 %>% | |
map_df(~sprintf(url4,.) %>% | |
read_html() %>% | |
html_table(fill = T) %>% enframe() %>% filter(name == 1) %>% unnest(value) %>% | |
separate(X1,into = c("names",'gender'),sep = "[|]") %>% select(names,gender) %>% | |
mutate_all(.funs = trimws) %>% mutate(gender = ifelse(gender == "Boy","M","F"))) | |
Allnames3 = malenames %>% rbind(femalenames) %>% | |
mutate(names = tolower(names)) | |
allnames = rbind(Allnames,Allnames2,Allnames3) | |
full_list = list(genderdata::ipums_usa,genderdata::napp,genderdata::ssa_national) %>% | |
map(~mutate(.,gender = ifelse(male > female ,"M","F"))) %>% | |
bind_rows(genderdata::ssa_state %>% mutate(.,gender = ifelse(M > `F` ,"M","F")), | |
genderdata::kantrowitz,allnames) %>% | |
select(name,gender) | |
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