# A tibble: 9 × 2
name n
<chr> <int>
1 Danny 207
2 Gia 62
3 Jesse 276
4 Joey 200
5 Kimmy 85
6 Michelle 165
7 Rebecca 228
8 Stephanie 187
9 Steve 212
clean |>filter(tv =='fullhouse', year %in%1982:1987) |>count(name) |>head(10) -> fullhouseb4clean |>filter(tv =='fullhouse', year %in%1995:2000) |>count(name) |>head(10) -> fullhouseafterclean |>filter(tv =='fullhouse', year %in%1987:1995) |>count(name) |>head(10) -> fullhouseduringclean |>filter(tv =='freshprince', year %in%1985:1990) |>count(name) |>head(10) -> freshprinceb4clean |>filter(tv =='freshprince', year %in%1996:2001) |>count(name) |>head(10) -> freshprinceafterclean |>filter(tv =='freshprince', year %in%1990:1996) |>count(name) |>head(10) -> freshprinceduringclean |>filter(tv =='fullhouse') -> fullhousefullhouse |>filter(year %in%1982:1987) -> before
Fresh Prince Hypothesis: I think that there will be an increase in names such as Will and Ashley, during and after the show air time, not directly due to the show’s popularity, but because those names are already commonly used in the US, and would have gotten more popular or stayed as popular, had the show not been on the air at the time.
Visualization 1: Process: I wanted to see how many people were given specific female names 5 years before ‘Fresh Prince’ premiered in 1990. So I created an Excel spreadsheet table with the names of the female main characters of the show ‘Fresh Prince’: Ashley, Hillary, Jackie, Lisa, and Vivian. I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1985 to 1990. Then I searched for how many times those female names were given each year, between 1985 and 1990.
library(ggplot2)babynames |>filter(year %in% (1985:1990)) %>%filter(name %in%c("Ashley", "Hilary", "Jackie", "Lisa", "Vivian")) |>filter(sex =="F") |>mutate(percent = (prop *100000)) |>ggplot(aes(year,percent, color = name)) +geom_line() +ggtitle('Female Fresh Prince Names, 5 Years Before Shows Premire') +xlab('Year') +ylab('Females Born')
Observations:
There were more women born between the years 1985-1990
The least common male name between 1985-1990 was ‘Nicky’
Visualization 1.1: I also wanted to see how many people were given specific male names 5 years before ‘Fresh Prince’ premiered in 1990. So I created an Excel spreadsheet table with the male names of the main characters of the show ‘Fresh Prince’: Carlton, Philip, Will, Nicky, and Geoffrey. I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1985 to 1990. Then I searched for how many times those male names were given each year, between 1985 and 1990.
babynames |>filter(year %in% (1985:1990)) %>%filter(name %in%c("Carlton", "Geoffrey", "Will", "Philip", "Nicky")) |>filter(sex =="M") |>mutate(percent = (prop *100000)) |>ggplot(aes(year,percent, color = name)) +geom_line() +ggtitle('Male Fresh Prince Names, 5 Years Before Shows Premire') +xlab('Year') +ylab('Males Born')
Visualization 2: I wanted to see how many people were given specific female names while ‘Fresh Prince’ was on the air. So I created an Excel spreadsheet table with the names of the female main characters of the show ‘Fresh Prince’: Ashley, Hillary, Jackie, Lisa, and Vivian. I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1990-1995. Then I searched for how many times those female names were given each year, between 1990 and 1995.
The most common female name between 1990-1995 was ‘Ashley’
The least common male name between 1990-1995 was ‘Jackie’
Visualization 2.1: I also wanted to see how many people were given specific male names while ’ Fresh Prince’ was on the air. So I created an Excel spreadsheet table with the male names of the main characters of the show ‘Fresh Prince’: Carlton, Philip, Will, Nicky, and Geoffrey. I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1990-1995. Then I searched for how many times those male names were given each year, between 1990 and 1995.
Visualization 3: Process: I wanted to see how many people were given specific female names while ‘Fresh Prince’ was on the air. So I created an Excel spreadsheet table with the female names of the main characters of the show ‘Fresh Prince’: Ashley, Hillary, Jackie, Lisa, and Vivian. I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years between 1996 and 2001. I created an Excel spreadsheet that listed the names, uploaded that spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1996 to 2001.
babynames |>filter(year %in% (1996:2001)) %>%filter(name %in%c("Ashley", "Hilary", "Jackie", "Lisa", "Vivian")) |>filter(sex =="F") |>mutate(percent = (prop *100000)) |>ggplot(aes(year,percent, color = name)) +geom_line() +ggtitle('Female Fresh Prince Names, 5 Years After Shows Finale') +xlab('Year') +ylab('Females Born')
Observations:
There were over 50 males each year from 1996-2001 named ‘Philip’
There were over 700 females each year from 1996-2001 named ‘Ashley’
Visualization 3.1: I also wanted to see how many people were given specific male names for the 5 years after’ Fresh Prince’ was on the air. So I created an Excel spreadsheet table with the male names of the main characters of the show ‘Fresh Prince’: Carlton, Philip, Will, Nicky, and Geoffrey. I then uploaded that Excel spreadsheet to R and cross-referenced those names with the ‘babynames’ program, only 1996 and 2001. I created an Excel spreadsheet that listed the names, uploaded that spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1996 to 2001.
babynames |>filter(year %in% (1996:2001)) %>%filter(name %in%c("Carlton", "Geoffrey", "Will", "Philip", "Nicky")) |>filter(sex =="M") |>mutate(percent = (prop *100000)) |>ggplot(aes(year,percent, color = name)) +geom_line() +ggtitle('Male Fresh Prince Names, 5 Years After Shows Finale') +xlab('Year') +ylab('Males Born')
Conclusions for Fresh Prince: There was not an increase in the name ‘Will’ after the show’s finale, nor was there an increase in the name ‘Ashley’ after the show’s finale either.
Full House Hypothesis: I think that there will be an increase in names such as Rebecca and Stephanie for females, and for males, names such as Steve during and after the show’s finale.
Visualization 1: Process: I wanted to see how many people were given specific male names 5 years before ‘Full House premiered in 1987. So I created an Excel spreadsheet table with the male names of the main characters of the show ‘Full House’: Danny, Jesse, Joey, and Steve. I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1982 to 1987. Then I searched for how many times those names were given each year, between 1982 to 1987.
babynames |>filter(year %in% (1982:1987)) %>%filter(name %in%c("Danny", "Jesse", "Joey", "Steve")) |>filter(sex =="M") |>mutate(percent = (prop *100000)) |>ggplot(aes(year,percent, color = name)) +geom_line() +ggtitle('Male Full House Names, 5 Years Before Shows Premire') +xlab('Year') +ylab('Males Born')
Observations:
There were more females born between 1982-1897 than males
There were no females named ‘Kimmy’ between the 1982-1987
Visualization 1.1: I also wanted to see how many people were given specific female names 5 years before ‘Full House premiered in 1987. So I created an Excel spreadsheet table with the male names of the main characters of the show ‘Full House’: Gia, Stephanie, Kimmy, Michelle, and Rebecca, I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1982 to 1987. Then I searched for how many times those names were given each year, between 1982 to 1987.
babynames |>filter(year %in% (1982:1987)) %>%filter(name %in%c("Gia", "Kimmy", "Michelle", "Rebecca", "Stephanie", "DJ")) |>filter(sex =="F") |>mutate(percent = (prop *100000)) |>ggplot(aes(year,percent, color = name)) +geom_line() +ggtitle('Female Full House Names, 5 Years Before Shows Premire') +xlab('Year') +ylab('Females Born')
Visualization 2: Process: I wanted to see how many people were given specific male names when ‘Full House was on the air. So I created an Excel spreadsheet table with the 10 names of the main characters of the show ‘Full House’: Danny, Jesse, Joey, and Steve. I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1988 to 1994. Then I searched for how many times those names were given each year, between 1988 and 1994.
babynames |>filter(year %in% (1987:1995)) %>%filter(name %in%c("Danny", "Jesse", "Joey", "Steve")) |>filter(sex =="M") |>mutate(percent = (prop *100000)) |>ggplot(aes(year,percent, color = name)) +geom_line() +ggtitle('Male Full House Names, During Shows Air') +xlab('Year') +ylab('Males Born')
Observations:
The most popular male name from 1988-1994 was ‘Jesse’
The least popular male name from 1988-1994 was ‘Joey’
Visualization 2.1: I also wanted to see how many people were given specific female names for when ‘Full House was on the air. So I created an Excel spreadsheet table with the female names of the main characters of the show ‘Full House’: Gia, Stephanie, Kimmy, Michelle, and Rebecca. I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1988 to 1994. Then I searched for how many times those names were given each year, between 1988 and 1994.
babynames |>filter(year %in% (1987:1995)) %>%filter(name %in%c("Gia", "Kimmy", "Michelle", "Rebecca", "Stephanie", "DJ")) |>filter(sex =="F") |>mutate(percent = (prop *100000)) |>ggplot(aes(year,percent, color = name)) +geom_line() +ggtitle('Female Full House Names, During Shows Air') +xlab('Year') +ylab('Females Born')
Visualization 3: Process: I wanted to see how many people were given specific male names 5 years after ‘Full House’s finale. So I created an Excel spreadsheet table with the male names of the main characters of the show ‘Full House’: Danny, Jesse, Joey, and Steve. I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1996 to 2001. Then I searched for how many times those male names were given each year, between 1996 to 2001.
babynames |>filter(year %in% (1996:2001)) %>%filter(name %in%c("Danny", "Jesse", "Joey", "Steve")) |>filter(sex =="M") |>mutate(percent = (prop *100000)) |>ggplot(aes(year,percent, color = name)) +geom_line() +ggtitle('Male Full House Names, 5 Years After Shows Finale') +xlab('Year') +ylab('Males Born')
Observations:
The female name ‘Stephanie’ was less popular in the 5 years after the finale of ‘Full House’
The male name ‘Jessie’ was less popular in the 5 years after the season finale of ‘Full House’
Visualization 3.1: I also wanted to see how many people were given specific female names 5 years after ‘Full House premiered in 1987. So I created an Excel spreadsheet table with the male names of the main characters of the show ‘Full House’: Gia, Stephanie, Kimmy, Michelle, and Rebecca. I then uploaded that Excel spreadsheet to R, and cross-referenced those names with the ‘babynames’ program, only looking at the years 1996 to 2001. Then I searched for how many times those male names were given each year, between 1996 to 2001.
babynames |>filter(year %in% (1996:2001)) %>%filter(name %in%c("Gia", "Kimmy", "Michelle", "Rebecca", "Stephanie", "DJ")) |>filter(sex =="F") |>mutate(percent = (prop *100000)) |>ggplot(aes(year,percent, color = name)) +geom_line() +ggtitle('Female Full House Names, 5 Years After Shows Finale') +xlab('Year') +ylab('Females Born')
Full House Conclusion: The name ‘Steve’ did not get more popular after the show’s finale, the name ‘Stephanie’ did not get more popular after the show’s finale, nor did ‘Rebecca’.
