Exercises

ggplot(mpg, aes(x = cty, y = hwy)) + 
  geom_point()

• directly correlated
• issues
• discrete measurements
• overplotting
• scale

ggplot(mpg, aes(model, manufacturer)) + geom_point()

• This plot maps model (specific car models) to the x-axis and manufacturer to the y-axis. Each point represents one observation in the dataset.

• usefulness

• both variables are categorical

• no natural order

• cluttered

• informative

• aggregate

• reorder

• facet

ggplot(mpg, aes(cty, hwy)) + geom_point()

ggplot(diamonds, aes(carat, price)) + geom_point()

ggplot(economics, aes(date, unemploy)) + geom_line()

ggplot(mpg, aes(cty)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.