Filtering and plotting

Histogram of air time of all flights

Plot a histogram of the air time of all flights. Exclude Honolulu International Airport in Hawaii to get rid of the peak at the right-hand side. Zoom into the flights that have an air time between 400 and 500 minutes.

Hint: Start with flights %>% ggplot() + ...

flights %>% 
  ggplot(___) +
    ___()

flights %>% 
  filter(___) %>%
  ggplot(___) +
    ___()

flights %>% 
  filter(___) %>%
  filter(___) %>%
  ___

► Solution:

flights %>%
  ggplot() +
  geom_histogram(
    aes(x = air_time),
    na.rm = TRUE,
    binwidth = 15
  )

flights %>%
  filter(dest != "HNL") %>%
  ggplot() +
  geom_histogram(
    aes(x = air_time),
    na.rm = TRUE,
    binwidth = 15
  )

flights %>%
  filter(dest != "HNL") %>%
  filter(between(air_time, 400, 500)) %>%
  ggplot() +
  geom_histogram(
    aes(x = air_time),
    na.rm = TRUE,
    binwidth = 10
  )

All very close relations

Plot a heat map for all relations with an air time shorter than one hour.

Hint: Use geom_bin2d().

flights %>% 
  filter(___) %>%
  ggplot(___) +
    ___()

► Solution:

flights %>%
  filter(air_time < 60) %>%
  ggplot() +
  geom_bin2d(aes(origin, dest))

More plotting after filtering

Think of other plots of the flights data that would not work if applied on the full dataset but are useful when applying a filter beforehand.