Busiest month

Which month is busiest in terms of miles flown, per carrier?

Hint: Compute the share of yearly miles flown of each airline in each month.

monthly_shares <-
  flights %>%
  group_by(___, ___) %>%
  summarize(total_distance_by_carrier = sum(distance)) %>%
  mutate(total_distance = sum(___)) %>%
  ungroup() %>%
  mutate(month_share_by_carrier = ___ / ___)

monthly_shares %>%
  arrange(month_share_by_carrier) %>% 
  group_by(___) %>%
  slice(1) %>%
  ungroup()

► Solution:

monthly_shares <-
  flights %>%
  group_by(carrier, month) %>%
  summarize(total_distance_by_carrier = sum(distance)) %>%
  mutate(total_distance = sum(total_distance_by_carrier)) %>%
  ungroup() %>%
  mutate(month_share_by_carrier = total_distance_by_carrier / total_distance)

monthly_shares %>%
  arrange(month_share_by_carrier) %>% 
  group_by(carrier) %>%
  slice(1) %>%
  ungroup()
## # A tibble: 16 x 5
##    carrier month total_distance_by_carrier total_distance month_share_by_carrier
##    <chr>   <int>                     <dbl>          <dbl>                  <dbl>
##  1 9E          2                    682656        9788152                 0.0697
##  2 AA          2                   3398633       43864584                 0.0775
##  3 AS         11                    124904        1715028                 0.0728
##  4 B6          2                   4336422       58384137                 0.0743
##  5 DL          2                   4225774       59507317                 0.0710
##  6 EV          2                   2009426       30498951                 0.0659
##  7 F9          2                     79380        1109700                 0.0715
##  8 FL         11                    133849        2167344                 0.0618
##  9 HA         10                    104643        1704186                 0.0614
## 10 MQ          2                   1154956       15033955                 0.0768
## 11 OO          1                       733          16026                 0.0457
## 12 UA          2                   6239683       89705524                 0.0696
## 13 US          2                    818288       11365778                 0.0720
## 14 VX          2                    675525       12902327                 0.0524
## 15 WN          2                    865202       12229203                 0.0707
## 16 YV          3                      4122         225395                 0.0183

Heat map of miles flown

Draw a heat map of miles flown per month per airline to see if this pattern holds across all airlines.

monthly_shares <-
  _____

monthly_shares %>%
  ggplot(aes(factor(month), ___, fill = ___)) +
  geom_tile() +
  scale_fill_continuous(trans = "log10")

► Solution:

monthly_shares %>% 
  filter(carrier != "OO") %>% 
  ggplot(aes(factor(month), carrier, fill = month_share_by_carrier)) +
  geom_tile() +
  scale_fill_continuous(trans = "log10")

Busiest month

Which month is busiest in terms of miles flown, over all carriers?

flights %>%
  group_by(___) %>%
  mutate(total_distance = sum(___)) %>%
  mutate(month_share = ___ / ___) %>% 
  arrange(desc(month_share)) %>%
  slice(1)

Busiest month, visualized

Visualize the number of flights in the busiest month with a bar chart.

More exercises

Find more exercises in Section 5.7.1 of r4ds.

Copyright © 2019 Kirill Müller. Licensed under CC BY-NC 4.0.