6: Exploratory analysis

Data visualization, part 1. Code for Quiz 7.

  1. Load the R package we will use.
  1. Quiz questions
  1. Pick one of your plots to save as your preview plot. Use the ggsave command at the end of the chunk of the plot that you want to preview.

Question: modify slide 34

ggplot(faithful) + 
  geom_point(aes(x = eruptions, y = waiting, colour = waiting > 60))

Question: modify slide 35

ggplot(faithful) + 
  geom_point(aes(x = eruptions, y = waiting),
             colour = 'blue')
ggsave(filename = here::here("_posts/2021-03-26-exploratory-analysis/preview.png"))

Question: modify slide 36

ggplot(faithful) + 
  geom_histogram(aes(x = eruptions))

Question: modify geom-ex-1

ggplot(faithful) + 
  geom_point(aes(x = eruptions, y = waiting),
             shape ="diamond", size = 5, alpha = 0.9)

Question: modify geom-ex-2

ggplot(faithful) + 
  geom_histogram(aes(x = eruptions, fill = eruptions > 3.2))

Question: stat-slide-40

data("mpg")

# variable definitions
# ?mpg

# mpg  %>% glimpse()


ggplot(mpg) + 
  geom_bar(aes(x = manufacturer))

Question: stat-slide-41

mpg_counted <- mpg %>% 
  count(manufacturer, name = 'count')
ggplot(mpg_counted) + 
  geom_bar(aes(x = manufacturer, y = count), stat = 'identity')

Question: stat-slide-43

ggplot(mpg) + 
  geom_bar(aes(x = manufacturer, y = after_stat(100 * count / sum(count))))

Question: answer to stat-ex-2

ggplot(mpg) + 
  geom_jitter(aes(x = class, y = hwy), width = 0.2) +
  stat_summary(aes(x = class, y = hwy), geom = "point",
               fun = "median", color = "blueviolet",
               shape = "cross", size = 9)