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library(tidyverse)
library(babynames)
library(wordcloud2)

these will load the tables to be used for the rest of the data set manipulation

babynames %>%                             
  filter(year == 1989, sex == "M") %>%    
  mutate(rank = row_number()) %>%         
  mutate(percent = round(prop * 100, 1)) %>% 
  filter(name == "Chad")

this will show the popularity of the name “chad” for the year 1989

babynames %>%
  filter(year == 1989) %>%     # use only one year
  filter(sex == "M") %>%       # use only one sex
  select(name, n) %>%          # select the two relevant variables: the name and how often it occurs
  top_n(100, n) %>%            # use only the top names or it could get too big
  wordcloud2(size = .5)        # generate the word cloud at a font size of .5

This shows the most occuring names for the year 1989 (the year I was born)

babynames %>%
  filter(name == "Chad", sex == "M") %>% 
  mutate(percent = round(prop * 100, 1)) %>%
  ggplot(aes(x = year, y = percent)) +
  geom_line()

This graph shows the peak of the name Chad around 1975 and slowly reducing

babynames %>%
  filter(name == "Chad" | name == "Joseph", sex == "M") %>%  
  mutate(percent = round(prop * 100, 1)) %>%  
  ggplot(aes(x = year, y = percent, color = name)) +
  geom_line()

this graph shows the popularity of the names Chad compared to Joseph throughout the years.

babynames %>%                                  # Start with the dataset
  filter(name == "Chad", sex == "M") %>%    # only look at the name and sex you want
  top_n(10, prop) %>%                          # get the top 10 names
  arrange(-prop)

This table shows the top 10 years the name Chad was used

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