R Notebook

library(babynames)
mydata <- babynames
str(mydata)

## Classes 'tbl_df', 'tbl' and 'data.frame':    1858689 obs. of  5 variables:
##  $ year: num  1880 1880 1880 1880 1880 1880 1880 1880 1880 1880 ...
##  $ sex : chr  "F" "F" "F" "F" ...
##  $ name: chr  "Mary" "Anna" "Emma" "Elizabeth" ...
##  $ n   : int  7065 2604 2003 1939 1746 1578 1472 1414 1320 1288 ...
##  $ prop: num  0.0724 0.0267 0.0205 0.0199 0.0179 ...

library(tidyverse)

## -- Attaching packages ------------------------------------------------------------------------------- tidyverse 1.2.1 --

## v ggplot2 3.1.0     v purrr   0.2.5
## v tibble  1.4.2     v dplyr   0.7.8
## v tidyr   0.8.1     v stringr 1.3.1
## v readr   1.1.1     v forcats 0.3.0

## -- Conflicts ---------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

OK, let’s select Elleana.

elleana <- mydata %>%
          filter(name =="Elleana")
tail(elleana)

Let’s compare it to Emma

emma <- mydata %>%
          filter(name =="Emma" & sex == "F")
tail(emma)

In 2015, there were 20,355 female Emmas. Compare that with 15 - 20 Elleanas. For every 1000 Emmas, there is only one Elleana.