This project aims to analyze the popularity of unisex names over generational time periods.

I found on namberry.com that Avery, Riley, Ryan and Parker are the most popular unisex names calculated by using social security data. For my project I used the babynames package and I used https://www.beresfordresearch.com/age-range-by-generation/ to find out the different generations by years and also used https://nameberry.com/unisex-names to find out the 4 most popular unisex names.

Overtime, I believe that Avery, Riley, Ryan, and Parker will increase in popularity starting with the Baby Boomers generation leading all the way to Gen Z generation.

library(tidyverse)
library(babynames)

From the generation website, I decided to combine both Boomers 1 and Boomers 2 together and look at Baby Boomers overall.

First I will create two bar graphs. One for use of Avery, Riley, Ryan and Parker in the Baby Boomers generation as a female.

babynames %>%  
  filter(year > 1946 & year < 1964 & sex == "F") %>% 
  filter(name %in% c("Avery", "Riley", "Ryan", "Parker")) %>% 
  group_by(name) %>% 
  summarize(total = sum(n)) %>%
  ggplot(aes(name, total, fill = name)) + geom_col()

The previous bar graph showed that Parker was not a name used within the Baby Boomers generation.

This is most likely due to Parker not being a popular name at the time for females.

The following graph shows the use of the 4 unisex names during the Baby Boomers generation as a male.

babynames %>%  
  filter(year > 1946 & year < 1964 & sex == "M") %>% 
  filter(name %in% c("Avery", "Riley", "Ryan", "Parker")) %>% 
  group_by(name) %>% 
  summarize(total = sum(n)) %>%
  ggplot(aes(name, total, fill = name)) + geom_col()

Now I will create a bar graph looking at a new generational time, the Gen X generation.

The following bar graph shows the popularity of the 4 unisex names for females during the Gen X generation.

babynames %>%  
  filter(year > 1965 & year < 1980 & sex == "F") %>% 
  filter(name %in% c("Avery", "Riley", "Ryan", "Parker")) %>% 
  group_by(name) %>% 
  summarize(total = sum(n)) %>% 
  ggplot(aes(name, total, fill = name)) + geom_col()

This next bar graph shows the popularity of the 4 unisex names for males during the Gen X generation.

babynames %>%  
  filter(year > 1965 & year < 1980 & sex == "M") %>% 
  filter(name %in% c("Avery", "Riley", "Ryan", "Parker")) %>% 
  group_by(name) %>% 
  summarize(total = sum(n)) %>% 
  ggplot(aes(name, total, fill = name)) + geom_col()

Finally I will create two bar graphs looking at the popularity of the 4 unisex names within the Gen Z generation.

The first within females.

babynames %>%  
  filter(year > 1997 & year < 2012 & sex == "F") %>% 
  filter(name %in% c("Avery", "Riley", "Ryan", "Parker")) %>% 
  group_by(name) %>% 
  summarize(total = sum(n)) %>% 
  ggplot(aes(name, total, fill = name)) + geom_col()

Now I will create a bar graph graph looking at the popularity of the 4 unisex names within the Gen Z generation for males.

babynames %>%  
  filter(year > 1997 & year < 2012 & sex == "M") %>% 
  filter(name %in% c("Avery", "Riley", "Ryan", "Parker")) %>% 
  group_by(name) %>% 
  summarize(total = sum(n)) %>% 
  ggplot(aes(name, total, fill = name)) + geom_col()

After this, I wanted to see the change in the use of the 4 unisex names over the start of the Baby Boomers generation all the way to Gen Z in a line graph.

First graph shows the increase in the 4 unisex names for females over time on a line graph.

babynames %>% 
  filter(year > 1946 & year < 2012 & sex =="F") %>% 
  filter(name %in% c("Avery", "Riley", "Ryan", "Parker")) %>% 
  ggplot(aes(year, prop, color = name)) + geom_line()

The following graph shows the increase in the 4 unisex names over time for males on a line graph.

babynames %>% 
  filter(year > 1946 & year < 2012 & sex =="M") %>% 
  filter(name %in% c("Avery", "Riley", "Ryan", "Parker")) %>% 
  ggplot(aes(year, prop, color = name)) + geom_line()

Then I wanted to create 4 separate line graphs combined showing specifically the increase in the popularity for males and females of the 4 unisex names.

babynames %>% 
  filter(name %in% c("Avery", "Riley", "Ryan", "Parker") & year > 1946) %>% 
           ggplot(aes(year, prop, color = sex)) + geom_line() +
           facet_wrap(~name)

Although some of the unisex names have not increased dramatically over generational time period, but there is a small increase within Avery, Parker, and Riley over time.

The name Ryan has decreased in use in the most recent generation for males, most likely due to a decrease in popularity.

The data shown on the graphs portrays that over time, the 4 unisex names have increased in popularity by generational time period for both males and females.