Babynames Project MEA 329

Introduction

This report will be an exploration of 10 unisex babynames found from https://www.kaggle.com/benhamner/most-gender-neutral-names-in-2014. This report will explore if unisex/gender-neutral names are more popular between boys or girls within different generations. It will look into how much growth there was of these names per generation. It will see if unisex names have become more popular over time.

First, import necessary packages

library(babynames)
library(tidyverse)

All boys with these names:

babynames %>% 
  filter(name %in% c("Charlie", "Emerson", "Hayden",
  "Riley", "Peyton", "Alexis", "Parker", "Avery", "Jordan") & sex == "M") -> UnisexB
head(UnisexB)

## # A tibble: 6 x 5
##    year sex   name        n      prop
##   <dbl> <chr> <chr>   <int>     <dbl>
## 1  1880 M     Charlie   730 0.00617  
## 2  1880 M     Riley      41 0.000346 
## 3  1880 M     Emerson    25 0.000211 
## 4  1880 M     Jordan     23 0.000194 
## 5  1880 M     Parker     14 0.000118 
## 6  1880 M     Avery       9 0.0000760

1,186 rows

All girls with these names:

babynames %>% 
  filter(name %in% c("Charlie", "Emerson", "Hayden",
  "Riley", "Peyton", "Alexis", "Parker", "Avery", "Jordan") & sex == "F") -> UnisexG
head(UnisexG)

## # A tibble: 6 x 5
##    year sex   name        n      prop
##   <dbl> <chr> <chr>   <int>     <dbl>
## 1  1881 F     Charlie     5 0.0000506
## 2  1882 F     Charlie     6 0.0000519
## 3  1883 F     Charlie    10 0.0000833
## 4  1884 F     Charlie     7 0.0000509
## 5  1885 F     Charlie     9 0.0000634
## 6  1886 F     Charlie    11 0.0000716

616 rows

Looking into gender neutral (unisex) names per generation.

Compare boys & girls within each generation.

Boys from 1996-2017 (gen X):

babynames %>% 
  filter(year > 1996 & year < 2017 & name %in% c("Charlie", "Emerson", "Hayden",
  "Riley", "Peyton", "Alexis", "Parker", "Avery", "Jordan") & sex == "M") ->genXboys 
genXboys %>% 
  ggplot(aes(year, prop, color = name)) + geom_line()

Prop high is at .006. A lot of growth shown in this graph, Jordan declines from most popular over time.

Girls from 1996-2017 (gen X):

babynames %>% 
  filter(year > 1996 & year < 2017 & name %in% c("Charlie", "Emerson", "Hayden",
  "Riley", "Peyton", "Alexis", "Parker", "Avery", "Jordan") & sex == "F") -> genXgirls
genXgirls %>% 
  ggplot(aes(year, prop, color = name)) + geom_line() 

Prop high is at .0100. Still a lot of growth in this graph, Alexis declines from most popular name.

Boys from 1977-1995 (Millenials):

babynames %>% 
  filter(year > 1977 & year < 1995 & name %in% c("Charlie", "Emerson", "Hayden",
   "Riley", "Peyton", "Alexis", "Parker", "Avery", "Jordan") & sex == "M") -> MillenialBoys
MillenialBoys %>% 
  ggplot(aes(year, prop, color = name)) + geom_line() 

Prop @ .006. Less growth for the names during this generation, expect for increase of the name Jordan in males.

Girls from 1977-1995 (Millenials):

babynames %>% 
  filter(year > 1977 & year < 1995 & name %in% c("Charlie", "Emerson", "Hayden",
 "Riley", "Peyton", "Alexis", "Parker", "Avery", "Jordan") & sex == "F") -> MillenialGirls
MillenialGirls %>% 
  ggplot(aes(year, prop, color = name)) + geom_line()

Prop @ .006. Not a lot of growth for the names during this generation, expect for increase in the names Alexis and Jordan for girls.

Gender nuetral names are more common in gen X than in millenials.

In millenials Jordan rises for both boys and girls. In gen X, there is more overall change for the names between both boys and girls.

babynames %>%
  filter(year > 1996 & year < 2017 & name %in% c("Charlie", "Emerson", "Hayden",
  "Riley", "Peyton", "Alexis", "Parker", "Avery", "Jordan")) %>%
  ggplot(aes(year, n, colour=sex)) + stat_summary(fun=sum, geom="line")

For gen X (1996-2017), girls with unisex names stayed steady while boys with those names started to decline around 2007.

babynames %>%
  filter(year > 1977 & year < 1995 & name %in% c("Charlie", "Emerson", "Hayden",
 "Riley", "Peyton", "Alexis", "Parker", "Avery", "Jordan")) %>%
  ggplot(aes(year, n, colour=sex)) + stat_summary(fun=sum, geom="line")

For millennials, unisex names increase over time for both males and females.

Which of these names is the most popular amoung boys and girls in 2000:

babynames %>% 
  filter(name %in% c("Charlie", "Emerson", "Hayden",
  "Riley", "Peyton", "Alexis", "Parker", "Avery", "Jordan") & year == 2000) %>% 
  group_by(sex) %>% 
  arrange(desc(prop))

## # A tibble: 18 x 5
## # Groups:   sex [2]
##     year sex   name        n      prop
##    <dbl> <chr> <chr>   <int>     <dbl>
##  1  2000 F     Alexis  17629 0.00884  
##  2  2000 M     Jordan  12167 0.00583  
##  3  2000 F     Jordan   5808 0.00291  
##  4  2000 M     Riley    3420 0.00164  
##  5  2000 M     Parker   3100 0.00149  
##  6  2000 M     Hayden   3044 0.00146  
##  7  2000 M     Alexis   2714 0.00130  
##  8  2000 F     Riley    2552 0.00128  
##  9  2000 F     Peyton   1967 0.000986 
## 10  2000 M     Peyton   2001 0.000959 
## 11  2000 F     Avery    1832 0.000918 
## 12  2000 M     Avery    1370 0.000656 
## 13  2000 M     Charlie   528 0.000253 
## 14  2000 F     Hayden    348 0.000174 
## 15  2000 F     Parker    254 0.000127 
## 16  2000 M     Emerson   170 0.0000814
## 17  2000 F     Emerson   136 0.0000682
## 18  2000 F     Charlie   129 0.0000647

Alexis was first for girls with Jordan in first for boys and second for girls.

Jordan is the most popular unisex name among boys and girls.

Looking at an individual name:

babynames %>% 
  filter(name %in% c("Jordan") & sex == "M") %>% 
  arrange(desc(prop)) %>% 
  head(10)

## # A tibble: 10 x 5
##     year sex   name       n    prop
##    <dbl> <chr> <chr>  <int>   <dbl>
##  1  1991 M     Jordan 16013 0.00756
##  2  1990 M     Jordan 16133 0.00750
##  3  1997 M     Jordan 14759 0.00739
##  4  1993 M     Jordan 14753 0.00714
##  5  1998 M     Jordan 14409 0.00711
##  6  1994 M     Jordan 14160 0.00695
##  7  1996 M     Jordan 13852 0.00691
##  8  1995 M     Jordan 13567 0.00675
##  9  1992 M     Jordan 14029 0.00668
## 10  1999 M     Jordan 13051 0.00640

Jordan as a boy name is most popular during the 90’s (1990-1999). This makes sense when comparing to the graphs showing unisex names for boys because it had a lot of growth in the 90’s, especially the name Jordan.

babynames %>% 
  filter(name %in% c("Jordan") & sex == "F") %>% 
  arrange(desc(prop)) %>% 
  head(10)

## # A tibble: 10 x 5
##     year sex   name       n    prop
##    <dbl> <chr> <chr>  <int>   <dbl>
##  1  1997 F     Jordan  7166 0.00375
##  2  1998 F     Jordan  7112 0.00367
##  3  1995 F     Jordan  6479 0.00337
##  4  1996 F     Jordan  6297 0.00329
##  5  1994 F     Jordan  6257 0.00321
##  6  2002 F     Jordan  6151 0.00312
##  7  1993 F     Jordan  5824 0.00295
##  8  1999 F     Jordan  5730 0.00294
##  9  2001 F     Jordan  5803 0.00293
## 10  2000 F     Jordan  5808 0.00291

Jordan as a girl name is more popular later into the 90’s and early 2000’s (1993-2000). This makes sense when comparing to the graphs showing unisex names for girls because the gorwth for girls started later in the 90’s and platued in the early 2000’s before dropping in the later 2000’s

Start, Middle, & End of Generations

Start of millenials:

babynames %>% 
  filter(year == 1977 & name %in% c("Charlie", "Emerson", "Hayden", "Riley", 
  "Peyton", "Alexis", "Parker", "Avery", "Jordan")) %>%
    group_by(sex, name) %>% 
  ggplot(aes(name, n, fill = sex)) + geom_col()

Middle of millenials:

babynames %>% 
  filter(year == 1986 & name %in% c("Charlie", "Emerson", "Hayden", "Riley", 
  "Peyton", "Alexis", "Parker", "Avery", "Jordan")) %>%
  group_by(sex, name) %>% 
  ggplot(aes(name, n, fill = sex)) + geom_col()

End of millenials:

babynames %>% 
  filter(year == 1995 & name %in% c("Charlie", "Emerson", "Hayden", "Riley", 
  "Peyton", "Alexis", "Parker", "Avery", "Jordan")) %>%
  group_by(sex, name) %>% 
  ggplot(aes(name, n, fill = sex)) + geom_col()

For millenials, while certain names were outweighed by females the majority of the names were popularized by males.

Start of gen X:

babynames %>% 
  filter(year == 1996 & name %in% c("Charlie", "Emerson", "Hayden", "Riley", 
  "Peyton", "Alexis", "Parker", "Avery", "Jordan")) %>%
  group_by(sex, name) %>% 
  ggplot(aes(name, n, fill = sex)) + geom_col()

Middle of gen X:

babynames %>% 
  filter(year == 2006 & name %in% c("Charlie", "Emerson", "Hayden", "Riley", 
  "Peyton", "Alexis", "Parker", "Avery", "Jordan")) %>%
  group_by(sex, name) %>% 
  ggplot(aes(name, n, fill = sex)) + geom_col()

End of gen X:

babynames %>% 
  filter(year == 2017 & name %in% c("Charlie", "Emerson", "Hayden", "Riley", 
  "Peyton", "Alexis", "Parker", "Avery", "Jordan")) %>%
  group_by(sex, name) %>% 
  ggplot(aes(name, n, fill = sex)) + geom_col()

For genX, unisex names in general grew a lot from 1996 to 2017, but grew more popular amongst girls in total than compared to boys.

Summary (Conclusion)

Unisex names are more popular within in boys between millennials and gen x. Unisex names in general are more popular (have more growth) for gen x than for millenials. Of the 10 unisex names Jordan was the most popular unisex name betweeen boys and girls. Jordan was more popular for boys during the decade of the 90’s. Jordan was more popular for girls later into the 90’s into the eary 2000’s. All 10 unisex names stayed low and were run by boys throughout years categorized by millenials. All 10 unisex names experienced overall large growth and were taken over by girls throughout years categorized by gen X.