group_by illustrative examples

group_by.R

# group_by() -------------------------------------------------------------------------

# Generate a per-carrier summary of hflights with the following variables: n_flights, 
# the number of flights flown by the carrier; n_canc, the number of cancelled flights; 
# p_canc, the percentage of cancelled flights; avg_delay, the average arrival delay of 
# flights whose delay does not equal NA. Next, order the carriers in the summary from 
# low to high by their average arrival delay. Use percentage of flights cancelled to 
# break any ties. Which airline scores best based on these statistics?

hflights %>% 
  group_by(UniqueCarrier) %>% 
  summarise(n_flights = n(), n_canc = sum(Cancelled), p_canc = 100*n_canc/n_flights, 
            avg_delay = mean(ArrDelay, na.rm = TRUE)) %>% arrange(avg_delay)

# Generate a per-day-of-week summary of hflights with the variable avg_taxi, 
# the average total taxiing time. Pipe this summary into an arrange() call such 
# that the day with the highest avg_taxi comes first.

hflights %>% 
  group_by(DayOfWeek) %>% 
  summarize(avg_taxi = mean(TaxiIn + TaxiOut, na.rm = TRUE)) %>% 
  arrange(desc(avg_taxi))