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Piping Operator %>% in dplyr
# %>% OPERATOR ----------------------------------------------------------------------
# with %>% operator
hflights %>%
mutate(diff = TaxiOut - TaxiIn) %>%
filter(!is.na(diff)) %>%
summarise(avg = mean(diff))
# without %>% operator
# arguments get further and further apart
summarize(filter(mutate(hflights, diff = TaxiOut - TaxiIn),!is.na(diff)),
avg = mean(diff))
# with %>% operator
d <- hflights %>%
select(Dest, UniqueCarrier, Distance, ActualElapsedTime) %>%
mutate(RealTime = ActualElapsedTime + 100, mph = Distance/RealTime*60)
# without %>% operator
d <- mutate(select(hflights, Dest, UniqueCarrier, Distance, ActualElapsedTime),
RealTime = ActualElapsedTime + 100, mph = Distance/RealTime*60)
# Filter and summarise d
d %>%
filter(!is.na(mph), mph < 70) %>%
summarise(n_less = n(), n_dest = n_distinct(Dest),
min_dist = min(Distance), max_dist = max(Distance))
# Let's define preferable flights as flights that are 150% faster than driving,
# i.e. that travel 105 mph or greater in real time. Also, assume that cancelled or
# diverted flights are less preferable than driving.
# ADVANCED PIPING EXERCISES
# Use one single piped call to print a summary with the following variables:
# n_non - the number of non-preferable flights in hflights,
# p_non - the percentage of non-preferable flights in hflights,
# n_dest - the number of destinations that non-preferable flights traveled to,
# min_dist - the minimum distance that non-preferable flights traveled,
# max_dist - the maximum distance that non-preferable flights traveled
hflights %>%
mutate(RealTime = ActualElapsedTime + 100, mph = Distance/RealTime*60) %>%
filter(mph < 105 | Cancelled == 1 | Diverted == 1) %>%
summarise(n_non = n(), p_non = 100*n_non/nrow(hflights), n_dest = n_distinct(Dest),
min_dist = min(Distance), max_dist = max(Distance))
# Use summarise() to create a summary of hflights with a single variable, n,
# that counts the number of overnight flights. These flights have an arrival
# time that is earlier than their departure time. Only include flights that have
# no NA values for both DepTime and ArrTime in your count.
hflights %>%
mutate(overnight = (ArrTime < DepTime)) %>%
filter(overnight == TRUE) %>%
summarise(n = n())
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