Baby Names, Revisited

Suffixation

One of the first trends I feel I’ve noticed is that the last letter of first names is changing over time. It seems like we’ve hit a trend with the -A suffix especially for female names. Let’s see if that holds true.

We use proportions rather than frequency to balance out for the increase in population over time.

We use the excellent gghighlight package to help seperate out the top letters from the rest of the pack.

library(stringr)
library(gghighlight)
library(scales)

last_count <- babynames %>%
  mutate(Last_Letter = str_extract(Name, "[A-Z]$")) %>%
  count(Gender, Year, Last_Letter, wt = Frequency, sort = TRUE) %>%
  group_by(Gender, Year) %>%
  mutate(Prop = n / sum(n))

source <- "Source: ServiceOntario Top Baby Names, January 1, 1917 - December 31, 2013"

g <- gghighlight_line(last_count, aes(Year, Prop, color = Last_Letter),
                 predicate = max(Prop) > 0.2) +
  facet_wrap(~ Gender) +
  scale_y_continuous(labels=percent) +
  labs(y = "Proportion", title = "Proportion of the Last Letter in Ontario Baby First Names",
       caption = source)
g

The trend is pretty interesting. With Females, names that end in -A have skyrocketed, the explosion starting in the early 60s, just as names ending in -E became less popular. Names that end with -Y or -N have not seen much of a change.

For males, nothing even comes close to names ending with -N. For a while names with -D were leading in the 40s, but that fell out of a fashion in the 50s, and -N names make up 35% of all baby names in Ontario.

Let’s add some representative names for effect. We’ll get the top names for the top 5 last letters and add them to our plot.

library(ggrepel)

top_letters <- c('A', 'D', 'E', 'N', 'Y')
last_letter_prop <- last_count %>%
    filter(Last_Letter %in% top_letters) %>%
    group_by(Gender, Last_Letter) %>%
    filter(Prop == max(Prop)) %>%
    select(Gender, Year, Last_Letter, Prop)

top_name_by_year <- babynames %>%
  mutate(Last_Letter = str_extract(Name, "[A-Z]$")) %>%
  group_by(Year, Gender, Last_Letter) %>%
  top_n(1, wt = Frequency)

last_letter_labels <- inner_join(last_letter_prop, top_name_by_year)

g +
  geom_label_repel(aes(x = Year, y = Prop, color = Last_Letter, label = Name),
                  data = last_letter_labels, nudge_x = -10, size = 3)

The Snowflake Effect

Another theory I had is that more and more, parents are giving their children unique names. There are a few ways to test this. We could look at how common the most common names are, or how many unique names are introduced in a given year.

snowflakes <- babynames %>%
  group_by(Gender, Year) %>%
  mutate(Prop = Frequency / sum(Frequency)) %>%
  top_n(1, wt = Prop)

ggplot(snowflakes, aes(Year, Prop, color = Gender)) +
  geom_line() +
  scale_y_continuous(labels = percent) +
  labs(y = "Top Name as % of All Names",
       title = "Trend of Most Popular Name as % of all Given Names in Ontario",
       caption = source)

name_year_count <- babynames %>%
  count(Name) %>%
  arrange(n)

first_name_occurence <- babynames %>%
  group_by(Name) %>%
  summarise(min_year = min(Year))

inner_join(name_year_count, first_name_occurence) %>%
  filter(n==1) %>%
  ggplot(aes(min_year)) +
  geom_bar()  +
  labs(x = "Year", y = "# of Unique Names Introduced",
       title = "Unique names introduced in a given year significantly increased since the 90s",
       subtitle = "Aggregate count of given names introduced in a given year",
       caption = source)

Gender Neutral

Another question I had was if names are becoming more or less gendered over time. One way to judge the gender-neutralness of a name would be to see if the same name appears in both Male and Female lists for a given year.

gendered_names <- babynames %>%
  count(Name, Year) %>%
  filter(n == 2) %>%
  count(Year, sort = TRUE)

gendered_names %>%
  ggplot(aes(Year, nn)) +
  geom_line() +
  labs(y = "# of gender-neutral names",
       title = "Increasing trend of gender-neutral names",
       subtitle = "Gender neutral identified as a name that appears in both
       male and female lists in a given year",
       caption = source)

We can see that starting in the mid-80s, there’s been a sharp rise in gender neutral names, however that trend seems to have peaked in 2008, and decreasing since.