I hypothesize that these three names will all see a massive increase in popularity after the year 2000.

library(tidyverse)
library(babynames)

Filtered babynames to include “Roger”, “Rafael”, and “Novak” as well as years after 2000.

maleNames <- c("Rafael", "Roger", "Novak")

filtered_male <- babynames %>% 
  filter(name %in% maleNames & year >= 2000)

These three names were most popular between the years of 2005-2007. Followed by a severe drop in popularity. Early in these players careers, the popularity of their names saw a small spike followed by a rapid decline

filtered_male %>% 
  group_by(year) %>% 
  summarize(n = sum(n)) %>% 
  ggplot(aes(x = year, y = n)) + geom_line()

Rafael was the most popular name out of the group, followed by Roger. Novak’s was significantly less than the other two.

filtered_male %>% 
  group_by(name) %>% 
  summarize(n = sum(n)) %>% 
  ggplot(aes(x = n, y = name)) + geom_col()

Rafael and Rogers names have been slowly declining since 2000, but have remained significantly more popular than Novak. What was interesting was Novak’s name did not even show up on the graph until around the same time his career started. While still low in popularity it was interesting to see his name show up around the same time as his career was gaining popularity.

filtered_male %>% 
  group_by(year, name) %>% 
  summarize(n = sum(n)) %>% 
  ggplot(aes(x = year, y = n, color = name)) + geom_line()
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

When comparing the popularity of the names between genders, it was almost non existent for the female population as a birth name.

filtered_male %>% 
  group_by(year, name, sex) %>% 
  summarize(n = sum(n)) %>% 
  ggplot(aes(x = year, y = n, color = name)) + geom_line() + facet_wrap(~sex)
## `summarise()` has grouped output by 'year', 'name'. You can override using the
## `.groups` argument.

The results yielded mixed results. Two of the names saw a slight decrease over the time period, while the other name actually showed up on the graph for the first time. It is possible that there were prominent public figures that also shared the same names and could have resulted in the decrease.