I would like to explore the influence that the characters on the popular TV show “Gilmore Girls” may have had on the names of children born after the show aired in 2000. To do so, I have selected the names of the characters that are present in over 80% of the 158 total episodes aired (including the 2016 reboot), which was done using IMDB to gathe data on the number of episodes each character appeared in throughout the duration of the show (https://www.imdb.com/title/tt0238784/).This leaves us with a list of the 10 most promiment characters on the show who will be included in this analysis. Based off of this list, I hypothesize that due to the uniquness of some of these names,the data will show an increase in the prevalence of a few of these namesafter the show aired.

First, I will load the neccessary packages.

library(tidyverse)
library(babynames)

I nextcreated a variable of all the character names I plan to analyze, listed in order of prevalence in the show.

top10<- c("Lorelai","Rory","Lane","Luke","Michel","Emily","Richard","Sookie","Kirk","Paris")

To get an overall look at how popular these names have been over time, two visualzations can be created that seperates the graph by sex. To do so, I used to filter() function to use the names in the top10 variable that I created and then used ggplot to create the visualization, which in this case is a line graph with #acet_wrap() used to seperate the graph by sex.

babynames %>% 
  filter(name %in% top10) %>%
  ggplot(aes(year,prop,color=name))+geom_line()+facet_wrap(~sex)+labs(y="Proportion",x="Year",title="Popularity of All Names Over Time by Gender")

Looking at this visualization, there are two distinct names that are prominent even before the show aired in 2000. For females, “Emily” appears to be the most popular while “Richard” is for the male population. Since these proportions are so high, it is hard to get a clear picture for the rest of the names since it affects the scale of the graph. Also,there does not appear to be a significant spike in popularity in the years following 2000. In fact, there is a consistent downward trend among these two names. For this reason, they will be taken out of the dataset for the remainder of this analysis since is does not appear that there will be conclusive evidence that the show impacted the popularity of these names and so that the scale of the proportions on the visualizations are not so drmataically impacted.

Next, a new variable can be be created without the names “Richard” and “Emily”

top8<-c("Lorelai","Rory","Lane","Luke","Michel","Sookie","Kirk","Paris")

A visualization can now be created with these two names excluded to get a bietter picture of overall popularity. The same process was used to create a similar visualization as the previous one, only this time useing the top8 variable.

babynames %>% 
  filter(name %in% top8) %>%
  ggplot(aes(year,prop,color=name))+geom_line()+facet_wrap(~sex)+labs(y="Proportion",x="Year",title="Popularity of Top 8 Names Over Time by Gender",subtitle = "Excludes 'Richard' and 'Emily'")

These plots show that these 8 names have existed for males longer than they have for females. For females, many of these names did not become prevalent until after the 1920s whereas for males, they show up in the dataset starting in 1880. There are also two names that stand out, ‘Luke’ for males and ‘Paris’ for females. ‘Paris’ appears to have spiked after 2000, but ‘Luke’ started its upward trend in what appears to be the 1970s. These two names will be look at in more detail further in this analysis and why it is they have been so popular. For now, I just wanted a general overview of what might be important to look out for as we continue this analyis. This graph also shows that some names exist for one gender but not the other, which will be looked at next.

The following code can be run to generate visualizations for each gender seperately to look at which names are present among both genders. Again, the same process is used, except this time sex is included in the filter() so that we have one graph for each gender.

babynames %>% 
  filter(name %in% top8 & sex =="F") %>% 
  ggplot(aes(year,prop,color=name))+geom_line()+labs(y="Proportion",x="Year",title="Female Names")

babynames %>% 
  filter(name %in% top8 & sex =="M") %>% 
  ggplot(aes(year,prop,color=name))+geom_line()+labs(y="Proportion",x="Year",title="Male Names")

Only two names, ‘Lorelai’ and ‘Sookie’ do not exist for both genders. Since the rest of the names do show up among both females and males, I will not break the names down by the gender of character in the show, in the event that maybe we see an increase in popularity for one of these names for the opposite gender. for example, while ‘Rory’ is a female in the show, it could very well be a possibility parents name their male children ‘Rory’ even after a female character. Therfore, I would like to look at both genders for all names.

The following visualizations will look at naming conventions up until and after the year 2000, when the show first aired, broken down by sex. This time, the filter() statement will only include names after the year 1980. This will zoom in on our previous graph to get a better look at the trends of these names.

First, we will look at females:

  babynames %>% 
  filter(name %in% top8 & sex=="F" & year > 1980) %>% 
  ggplot(aes(year,prop,color=name))+geom_line()+labs(y="Proportion",x="Year",title="Female Names after Gilmore Girls Aired",subtitle = "Air date 2000")

As seen in previous visualizations, most of the names for females were steadily unpopular, expect for a few spikes here and there after 1960. This remained the case prior to 2000, but we see more of an increase afterwards. 3 names in particular stand out to have had more of an increase: ‘Lorelai’, ‘Rory’, who are the two main characters, and ‘Paris’. While ‘Paris’ was already on an upward trend, there is a significant spike after the year 2000, though it dips back down again around the year 2004 before picking up again. Though, this is not neccessarily accredited to ‘Gilmore Girls’. Popular celebrity Paris Hilton had a reality show called ‘The Simple Life’, which aired from 2003 to 2007 (https://www.imdb.com/title/tt0362153/). This time period appears to match the spike seen in the name ‘Paris’ and so it would be hard to say based on this data that’Gilmore Girls’ had an effect on parents naming their children ‘Paris’.

‘Lorelai’ and ‘Rory’, on the other hand, were two names that had extremely low proportions prior to and leading up to 2000. Afterwards, though, there does appear to be an increase in the popularity of these names. Since the data shows that these names have historically been very unique, the surge in popularity after the year 2000 could very well be attributed to the TV show. The following visualizations explore these names further from the year 2000 and on. Overall, the names has been on a steady increase since the show aired.

The following code creates a bar chart depicting the popularity of the name ‘Lorelai’ for each year after and including 2000. It also uses the group_by() function to sort the data year by year.

babynames %>% 
  filter(name == "Lorelai" & year >= 2000) %>% 
  group_by(year) %>% 
  ggplot(aes(year,prop))+geom_col(fill="light blue")+labs(x="Year",y="Proportion",title="Popularity of 'Lorelai' since 2000")

This creates a similar bar chart for the name ‘Rory’.

babynames %>% 
  filter(name == "Rory" & year >= 2000) %>% 
  group_by(year) %>% 
  ggplot(aes(year,prop))+geom_col(fill="pink")+labs(x="Year",y="Proportion",title="Popularity of 'Rory' since 2000")

One other name is of note in the previous visualization showing female names leading up to and after 2000. The name ‘Sookie’ did not exist in this dataset prior to she show airing, but appears around the year 2010. Due to the uniqueness of this name, this is interesting to note especially considering the resurgence in popularity of the show. So while the show ended in 2007, it continued to create an evergrowing fanbase along with a reboot in 2016. Therefore, it is very possiblebased on the data that ‘Sookie’ as a baby name could be attributed to ‘Gilmore Girls’.

Next, we will do the same process but for males. This graph uses the filter() function to only look at the males names after the year 1980.

babynames %>% 
  filter(name %in% top8 & sex=="M" & year > 1980) %>% 
  ggplot(aes(year,prop,color=name))+geom_line()+labs(y="Proportion",x="Year",title="Male Names after Gilmore Girls Aired", subtitle = "Air date 2000")

This visualization shows the popularity of male names leading up to and after the show aired. There is one clear name that stands out among the rest as being the most popular, ‘Luke’. While ‘Luke’ has been around for while, it started to gain even more popularity in the 1990s and continued this trend through the mid 2000s. There is a good chance this can be attributed to the ‘Star Wars’ franchise that contiued to put out movies during this time. Again, it would be hard to say with any confidence that ‘Gilmore’ Girls’ had an influence on this name, especially one that is already pretty common and popular.

As for the other male names, there does appear to be any further significant changes to point out. In the show, ‘Luke’, ‘Michel’, and ‘Kirk’ are all male characters, but even still there doesn’t appear to be any significant increase among the male population.For the most part, they remain fairly steady in the uniqueness, save for a minor increase in popularity for the name ‘Rory’ among boys.

Since it appears that the 3 names that most stand out from this analysis are ‘Lorelai’,‘Rory’, and ‘Sookie’, it might be of interest to look at different spellings of these names. It is a common practice to name children after fictional characters, but change the spelling. So, I thought I would look into this further. I also limited these visualizations to only look at females, since previous graphs showed that these names were not very popular among males.

The following code uses filter() to only include to two variations of the name ‘Lorelai’ for females.

babynames %>% 
  filter(name %in%c("Lorelei","Lorelai") & sex=="F" ) %>% 
  ggplot(aes(year,prop,color=name))+geom_line()+labs(y="Proportion",x="Year",title="Female Variations of 'Lorelai'")

‘Lorelei’ appears to be the much more common spelling of the characters name, though this is not how it is spelled in the show. In fact, this variation has been around much longer than the character’s spelling. ‘Lorelai’ actually didnt exist in this dataset, granted the minimum count to be included is 5 names in total, until after 2000, the year the show aired. This is incredibly important to note as this could provide some evidence that the show’s character and spelling may have impacted the popularity of this name. Also, both variations of this name saw an upward spike after 2000, so even ‘Lorelei’ saw an increase in prevlance possibly due to ‘Gilmore Girls’.

This next visualization follows the same process, but filtered to include the variation of the name ‘Sookie’ for females.

babynames %>% 
  filter(name %in%c("Sookie","Sukie") & sex=="F" ) %>% 
  ggplot(aes(year,prop,color=name))+geom_line()+labs(y="Proportion",x="Year",title="Female Variations of 'Sookie'")

‘Sookie’ itself is already a very uncommon name, but the spelling ‘Sukie’ does exist as well. Though both variations didn’t exist until after 2010, 3 years after the show ended. ‘Sukie’ also only showed up in the data for about a year before becoming even more rare. While it’s possible the show is to blame for the addition of ‘Sukie’ to the dataset, there is not enough evidence based on this visualization to say for sure.

This last visualization once again will use the same logic to filter out the two variations of ‘Rory’ for females.

babynames %>% 
  filter(name %in%c("Rory","Rori") & sex=="F" ) %>% 
  ggplot(aes(year,prop,color=name))+geom_line()+labs(y="Proportion",x="Year",title="Female Variations of 'Rory'")

Lastly, this graph shows the variation ‘Rori’ as well as the traditional spelling useed by the character. Again, we see minor spikes in both prior to 2000, but increases for both after the show aired. ‘Rory’ remains the most popular spelling, but it’s also interesting that another variation became more common as well.

All together, this analysis has found that there are both common and uncommon names among the characters in the show ‘Gilmore Girls’. Due to the uniqueness of some of these names, some were found to have increased in popularity among the U.S. population after the first episode aired in 2000. Others were found to be too common to conclusively find a realtionship or have other factors impacting their popularity. For example, ‘Emily’ and ‘Richard’ were simply too common to be able to say anything about the influence of the show. The same can be said for ‘Luke’, an already common name with other pop culture referneces that could have impacted its popularity. Other names simply remained in their status of being rare, such as ‘Michel’ or ‘Lane’. Three names were the excpetion to this observation. These would be two main characters, ‘Lorelai’and ’Rory’, and ‘Sookie’, along with different variations of the spelling of these names.The popularity of these names saw an increase after 2000 and continued to do so in the years that followed. Therefore, the only names that could possibly be said to have been influenced in prevalence in the U.S., based on this dataset and analysis, by the show’Gilmore Girls’ are the names ‘Lorelai’, ‘Rory’, and ‘Sookie’.