name analysis

1. Determine its rank the year you were born.

This will determine the rank in popularity of the name Kinsey among girls in the year 2000.

babynames %>%                             
  filter(year == 2000, sex == "F") %>%    
  mutate(rank = row_number()) %>%         
  mutate(percent = round(prop * 100, 1)) %>% 
  filter(name == "Kinsey")               

NA

Kinsey was the 877th most popular in the year 2000.

2. Create a word cloud of the names of your sex and the year you were born.

Here is the word cloud of the popular names of Females in the year I was born.

babynames %>%
  filter(year == 2000) %>%     # 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()                 # generate the word cloud

Emily was the most popular name in 2000.

3. Graph its popularity over time.

This will show the popularity of the name Kinsey over time.

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

The name Kinsey was most popular in the mid 2000’s.

4. Create a table showing which years it was most popular.

This will show what years the name Kinsey was most popular.

babynames %>%                                  # Start with the dataset
  filter(name == "Kinsey", 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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This shows that the name Kinsey was most popular in the year 2011.

5. Graph its popularity in comparison to another name or two (e.g., a friend, family member, etc.). To keep it simple, use other names of the same sex.

This will show a comparison of poularity between the names Kinsey, Calli, and Emily.

babynames %>%
  filter(name == "Kinsey" | name == "Calli" | name == "Emily") %>% 
  filter(sex == "F") %>% 
  filter(year>2000) %>%
  ggplot(aes(x = year, y = n, color = name)) +
  geom_line()

This shows that the name Emliy is far more popular than the names Kinsey and Calli.