babynames.R

library(tidyverse)
library(babynames)
library(glue)

# plot total US births using a stacked area chart
applicants_simple <- applicants %>%
  mutate(
    sex = if_else(sex == "F", "Female", "Male"),
    n_all = n_all / 1e06)
ggplot(applicants_simple, aes(x = year, y = n_all, fill = sex)) +
  geom_area() +
  scale_fill_brewer(type = "qual") +
  labs(
    title = "Total US births",
    x = "Year",
    y = "Millions",
    fill = NULL,
    caption = "Source: Social Security Administration"
  ) +
  theme_minimal()

# write function to show trends over time for specific name
name_trend <- function(person_name) {
  babynames %>%
    filter(name == person_name) %>%
    ggplot(mapping = aes(x = year, y = n, color = sex)) +
    geom_line() +
    scale_color_brewer(type = "qual") +
    labs(
      title = glue("Name: ", {person_name}),
      x = "Year",
      y = "Number of births",
      color = NULL
    ) +
    theme_minimal()
}

name_trend("Benjamin")

# write function to show trends over time for top N names in a specific year
top_n_trend <- function(n_year, n_rank = 5) {
  # create lookup table
  top_names <- babynames %>%
    group_by(year) %>%
    filter(n > 1000)
  
  # get the top N names from n_year
  top_names <- babynames %>%
    filter(year == n_year) %>%
    group_by(sex) %>%
    mutate(rank = min_rank(desc(n))) %>% 
    filter(rank <= n_rank) %>%
    arrange(sex, rank) %>%
    select(name, sex, rank)
  
  # keep just the top N names over time and plot
  babynames %>%
    inner_join(top_names, by = c("name", "sex")) %>%
    ggplot(mapping = aes(x = year, y = n, color = name)) +
    facet_wrap(~sex, ncol = 1) +
    geom_line() +
    scale_color_brewer(type = "qual", palette = "Set3") +
    labs(
      title = glue("Most Popular Names of {n_year}"),
      x = "Year",
      y = "Number of births",
      color = "Name"
    ) +
    theme_minimal()
}

top_n_trend(n_year = 1986)
top_n_trend(n_year = 2014)
top_n_trend(n_year = 1986, n_rank = 10)


# compare naming trends to disney princess film releases
disney <- tribble(
  ~princess,  ~film, ~release_year,
  "Snow White", "Snow White and the Seven Dwarfs", 1937,
  "Cinderella", "Cinderella", 1950,
  "Aurora", "Sleeping Beauty", 1959,
  "Ariel", "The Little Mermaid", 1989,
  "Belle", "Beauty and the Beast", 1991,
  "Jasmine", "Aladdin", 1992,
  "Pocahontas", "Pocahontas", 1995,
  "Mulan", "Mulan", 1998,
  "Tiana", "The Princess and the Frog", 2009,
  "Rapunzel", "Tangled", 2010,
  "Merida", "Brave", 2012,
  "Elsa", "Frozen", 2013,
  "Moana", "Moana", 2016
)

## join together the data frames
babynames %>%
  inner_join(disney, by = c("name" = "princess")) %>%
  mutate(name = fct_reorder(.f = name, .x = release_year)) %>%
  # plot the trends over time, indicating release year
  ggplot(mapping = aes(x = year, y = n)) +
  facet_wrap(~ name + film, scales = "free_y", labeller = label_both) +
  geom_line() +
  geom_vline(mapping = aes(xintercept = release_year), linetype = 2, alpha = .5) +
  scale_x_continuous(breaks = c(1880, 1940, 2000)) +
  theme_minimal() +
  labs(title = "Popularity of Disney princess names",
       x = "Year",
       y = "Number of births")