The dataset Unisex Babynames provides the most popular naming conventions for US babies. I want to determine the frequency of Unisex names. I hypothesized that unisex baby names would be on the rise in recent decades due to gender neutrality movements.

library(tidyverse)
library(babynames)
library(ggthemes)
library(readr)
unisex_names <- read_csv("/Users/rileykunzmann/Desktop/R/unisex_names_table.csv")

unisex_names %>% 
left_join(babynames, by = "name") -> unisex_babynames

Unisex names have been on the rise for decades now. Below is a break down of the top 10 unisex names and a range of the name in a male or female context.

unisex_babynames %>% 
  filter(year > 1950) %>% 
  group_by(name) %>%  
  summarize(total = sum(n)) %>% 
  arrange(desc(total))
## # A tibble: 918 × 2
##    name     total
##    <chr>    <int>
##  1 Riley   186238
##  2 Casey   184224
##  3 Avery   166765
##  4 Peyton  116074
##  5 Jaime   115675
##  6 Jackie  103852
##  7 Jessie  100592
##  8 Kerry    90368
##  9 Kendall  88420
## 10 Jody     83861
## # … with 908 more rows
unisex_babynames %>% 
  filter(year > 1950) -> babynames_1950

Displayed below is the overall analysis of top gender neutral names since 1950 among males and females. Names like Jessie, Riley, Casey, Jaime, Peyton and Kendall are all examples of names we have seen on the rise over the past several decades.

babynames_1950 %>%  
  filter(name %in% c('Jessie', 'Riley', 'Casey', 'Avery', 'Jackie', 'Jaime', 'Peyton', 'Kerry', 'Kendall', 'Jody')) %>% 
  group_by(name, year) %>% 
  summarise(prop = mean(prop)) %>% 
  ggplot(aes(year, prop, color = name)) + geom_line()

Going one step further, we are able to identify the popularity of these names by sex and plot the trends of each name over time.

babynames_1950 %>%  
  filter(name %in% c('Jessie', 'Riley', 'Casey', 'Avery', 'Jackie', 'Jaime', 'Peyton', 'Kerry', 'Kendall', 'Jody')) %>% 
  ggplot(aes(year, prop, color = name)) + geom_line() + 
  facet_wrap(~sex)

As seen below, the name Jessie has experienced a steep decline across the female names front, but a steadier downward trend for males. Although, the name Casey has had a very similar trend in both male and female names. First with very few individuals, then transitioning to a popular name for approximately 50 years.

babynames %>%  
  filter(name %in% c('Jessie', 'Casey')) %>% 
  ggplot(aes(year, prop, color = name)) + geom_line() + 
  facet_wrap(~sex)

The graph displays a spike in popularity in both names Jaime and Riley, however Jaime had a precipitous decline in the early 2000s. The naming conventions Jaime and Riley have both proven to be unfavorable in the male baby naming category as seen in the graph. Riley has shown relative similarity in both sexes.

babynames %>%  
  filter(name %in% c('Riley', 'Jaime')) %>% 
  ggplot(aes(year, prop, color = name)) + geom_line() + 
  facet_wrap(~sex)

Overall, there has been a steady rise of unisex names over the last 50 years. Interestingly enough, this data shows that most gender neutral names are favored for female babies and not so much male. The graphs display that the majority of the 10 most popular unisex names are on the decline with a few like Riley, Avery and Peyton on the rise. Contrary to our societies context, unisex names are on in fact on the decline in popularity.