This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter.

plot(cars)

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Ctrl+Shift+K to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

library(babynames)
view(babynames)
babynames %>%                             
  filter(year == 1992, sex == "F") %>%    
  mutate(rank = row_number()) %>%         
  mutate(percent = round(prop * 100, 1)) %>% 
  filter(name == "Joelle")

In 1992, there were 214 “Joelle’s”, with the name ranking #915 most popular names.

babynames %>%
  filter(year == 1992) %>%     # 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)

The most popular names in 1992 for females were “Jessica” and “Ashley”.

babynames %>%
  filter(name == "Joelle") %>% 
  ggplot(aes(x = year, y = n)) +
  geom_line()

babynames %>%
  filter(name == "Joelle") %>% 
  filter(sex == "F") %>%
  ggplot(aes(x = year, y = n)) +
  geom_line()

The name “Joelle” seems to spike up in the 1970’s and then drop off a little bit with some stability over the years until a recent spike in late 2010’s.

babynames %>%
  filter(name == "Joelle") %>% 
  filter(sex == "F") %>% 
  filter(year > 1960) %>%
  ggplot(aes(x = year, y = n, color = name)) +
  geom_line()

babynames %>%                                     # Start with the dataset
  filter(name == "Joelle", sex == "F") %>%       # only look at the name you want
  top_n(1, prop)                                  # get the year with the top number for that name

The name “Joelle” was most popular in 2017 with 435 individuals.

babynames %>%
  filter(name == "Joelle" | name == "Tabitha" | name == "Ashlynn") %>% 
  filter(sex == "F") %>% 
  ggplot(aes(x = year, y = n, color = name)) +
  geom_line()

babynames %>%
  filter(name == "Joelle" | name == "Tabitha" | name == "Ashlynn") %>% 
  filter(sex == "F") %>% 
  filter(year > 1960) %>%
  ggplot(aes(x = year, y = n, color = name)) +
  geom_line()

I compared the popularity of my name, “Joelle” with two females I know that were also born in 1992, “Tabitha” and “Ashlynn”. It appears Tabitha has been the more popular name consistently over time. Ashlynn’s name peaked most after the year 2000. Compared to “Tabitha” and “Ashlynn”, “Joelle” is the least popular name but has stayed pretty steadily consistent between the years of 1960 to 2010.

babynames %>%
  filter(name == "Joelle" | name == "Tabitha" | name == "Ashlynn") %>% 
  filter(sex == "F") %>% 
  filter(year > 2010) %>%
  ggplot(aes(x = year, y = n, color = name)) +
  geom_line()

I wanted to check for the years after 2010 to get a better picture. This graph shows that Ashlynn was the most popular name between the years 2010 to 2017. “Tabitha” decreased quite considerably since the previous graph. “Joelle” surprisingly surpasses “Tabitha” in 2016.

library(knitr)
?knit
knit(input)
Error in knit(input) : object 'input' not found

knit(input, output = NULL, tangle = FALSE, text = NULL, quiet = FALSE, 
    envir = parent.frame(), encoding = getOption("encoding"))
Error in knit(input, output = NULL, tangle = FALSE, text = NULL, quiet = FALSE,  : 
  object 'input' not found
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