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plot(cars)

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babynames %>%                             
  filter(year == 1993, sex == "F") %>%    
  mutate(rank = row_number()) %>%         
  mutate(percent = round(prop * 100, 1)) %>% 
  filter(name == "Breanna")

babynames %>%
  filter(year == 1993) %>%     # use only one year
  filter(sex == "F") %>%       # 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) + WCtheme(2)    # generate the word cloud and add theme 1, 2, or 3

babynames %>%                                    # start with the data
  filter(name == "Breanna", sex == "F") %>%      # choose the name and sex
  mutate(percent = round(prop * 100, 1)) %>%     # create a new variable called percent
  ggplot(aes(x = year, y = percent)) +           # put year on the x-axis and prop (proportion) on y
  geom_line(color = "pink")                      # make it a line graph and give the line a color

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

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

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

babynames %>%                                  # Start with the dataset
  filter(name == "Breanna", sex == "F") %>%    # only look at the name and sex you want
  top_n(10, prop) %>%                          # get the top 10 names
  arrange(-prop)                               # sort in descending order

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