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Generate a Random Data Frame of Names and Genders
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# dependencies | |
library(dplyr) # data wrangling | |
library(gender) # assess gender | |
library(randomNames) # generate names | |
library(stringr) # work with strings | |
# create data frame of random names | |
names <- randomNames(30, which.names = "both", name.order = "first.last", name.sep = " ") | |
names <- as.data.frame(names, stringsAsFactors = FALSE) | |
# parse full name into first and last name variables | |
names %>% | |
rename(fullName = names) %>% | |
mutate(firstName = word(fullName,1)) %>% | |
mutate(lastName = word(fullName,-1)) -> names | |
# evaluate names for typical gender | |
gender <- gender(names$firstName) | |
# trim gender data frame | |
gender %>% | |
rename(firstName = name) %>% | |
select(firstName, gender) -> gender | |
# combine name and gender data | |
sampleData <- left_join(names, gender, by = "firstName") | |
# make sure all names have a gender and there are no duplicates | |
sampleData <- filter(sampleData, is.na(gender) == FALSE) | |
sampleData <- distinct(sampleData, fullName, .keep_all = TRUE) | |
# take random sample of names | |
index <- sample(1:nrow(sampleData), 20) | |
# filter based on random sample | |
sampleData %>% | |
mutate(row = row_number()) %>% | |
filter(row %in% index == TRUE) %>% | |
select(-row) -> sampleData | |
# save as tibble | |
sampleData <- as_tibble(sampleData) |
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