Kendall Name

library(tidyverse)

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library(gt)
library(wordcloud2)
library(babynames)

babynames |>                             
  filter(year == 1991, sex == "M") |>    
  mutate(rank = row_number()) |>         
  mutate(percent = round(prop * 100, 1)) |> 
  filter(name == "Kendall") |> 
  gt()

year	sex	name	n	prop	rank	percent
1991	M	Kendall	843	0.00039779	305	0

This shows that in the year 1991, the name Kendall for males was rank 305. Not very many males named Kendall were born that year.

babynames |>
  filter(year == 1991) |>     # use only one year
  filter(sex == "M") |>       # use only one sex
  slice_max(prop, n=100) |>   # use the top 100 names
  select(name, n) |>          # select the two relevant variables: the name and how often it occurs
  wordcloud2(size = .5)

This word cloud shows the most popular male names as the largest names, during the year 1991.

babynames |>                                    # start with the data
  filter(name == "Kendall", sex == "M") |>      # choose the name and sex
  ggplot(aes(x = year, y = prop)) +              # put year on the x-axis and prop (proportion) on y
  geom_line()

It looks like males named Kendall got popular around 1963 then got a lot more popular in 1990. Which is around the time I was born.

babynames |>                                  # Start with the dataset
  filter(name == "Kendall", sex == "M") |>    # only look at the name and sex you want
  slice_max(prop, n = 10)

# A tibble: 10 × 5
    year sex   name        n     prop
   <dbl> <chr> <chr>   <int>    <dbl>
 1  1993 M     Kendall   965 0.000467
 2  1994 M     Kendall   949 0.000466
 3  1990 M     Kendall   867 0.000403
 4  1991 M     Kendall   843 0.000398
 5  1992 M     Kendall   787 0.000375
 6  1995 M     Kendall   720 0.000358
 7  1997 M     Kendall   703 0.000352
 8  2000 M     Kendall   733 0.000351
 9  1998 M     Kendall   697 0.000344
10  1996 M     Kendall   676 0.000337

I’m not sure why the table is small, but it looks like the name Kendall was most popular from the mid 1990’s to early 2000’s.

babynames |>
  filter(name == "Darin" | name == "Kendall" | name == "Adam") |> 
  filter(sex == "F") |> 
  ggplot(aes(x = year, y = n, color = name)) +
  geom_line()

One thing I would not with this graph is that the name Kendall is more gender neutral and that may be why it is so large when comparing it to only male Kendall. However, it appears that Adam was most popular around the 1980’s and Darin was more popular in the 1960’s while Kendall is overwhelmingly more popular throughout, especially during the early 2000’s.

babynames |>
  filter(name == "Marilyn" | name == "Reese" | name == "Scarlett") |> 
  filter(sex == "F") |> 
  ggplot(aes(x = year, y = n, color = name)) +
  geom_line()

I chose Marilyn Monroe, Reese Witherspoon, and Scarlett Johansson. Marilyn was very popular around 1930 to 1955. And it appears to be a spike in names around the time Marilyn was famous, which was from the 1950’s to 1960’s. Reese Witherspoon was most popular around the year 2001 and it looks like the name become much more popular around the early 2000’s. Scarlett Johansson was most popluar from around the 2010’s and it looks like it coincides with the graph as well.