Code
tips_by_day_hour <- taxi_data_2019 %>% filter(total_amount > 0) %>% mutate(
tip_pct = 100 * tip_amount / total_amount, dn = wday(pickup_datetime), hr = hour(
pickup_datetime)) %>% summarise(avg_tip_pct = mean(tip_pct), n = n(), .by = c(
dn, hr)) %>% arrange(desc(avg_tip_pct))
tips_by_day_hour
Output
[1] dn hr avg_tip_pct n
<0 rows> (or 0-length row.names)
Code
tips_by_passenger <- taxi_data_2019 %>% filter(total_amount > 0) %>% mutate(
tip_pct = 100 * tip_amount / total_amount) %>% summarise(avg_tip_pct = median(
tip_pct), n = n(), .by = passenger_count) %>% arrange(desc(passenger_count))
tips_by_passenger
Output
[1] passenger_count avg_tip_pct n
<0 rows> (or 0-length row.names)
Code
popular_manhattan_cab_rides <- taxi_data_2019 %>% filter(total_amount > 0) %>%
inner_join(zone_map, by = join_by(pickup_location_id == LocationID)) %>%
inner_join(zone_map, by = join_by(dropoff_location_id == LocationID)) %>%
filter(Borough.x == "Manhattan", Borough.y == "Manhattan") %>% select(
start_neighborhood = Zone.x, end_neighborhood = Zone.y) %>% summarise(
num_trips = n(), .by = c(start_neighborhood, end_neighborhood), ) %>% arrange(
desc(num_trips))
popular_manhattan_cab_rides
Output
[1] start_neighborhood end_neighborhood num_trips
<0 rows> (or 0-length row.names)
Code
num_trips_per_borough <- taxi_data_2019 %>% filter(total_amount > 0) %>%
inner_join(zone_map, by = join_by(pickup_location_id == LocationID)) %>%
inner_join(zone_map, by = join_by(dropoff_location_id == LocationID)) %>%
mutate(pickup_borough = Borough.x, dropoff_borough = Borough.y) %>% select(
pickup_borough, dropoff_borough, tip_amount) %>% summarise(num_trips = n(),
.by = c(pickup_borough, dropoff_borough))
num_trips_per_borough_no_tip <- taxi_data_2019 %>% filter(total_amount > 0,
tip_amount == 0) %>% inner_join(zone_map, by = join_by(pickup_location_id ==
LocationID)) %>% inner_join(zone_map, by = join_by(dropoff_location_id ==
LocationID)) %>% mutate(pickup_borough = Borough.x, dropoff_borough = Borough.y,
tip_amount) %>% summarise(num_zero_tip_trips = n(), .by = c(pickup_borough,
dropoff_borough))
num_zero_percent_trips <- num_trips_per_borough %>% inner_join(
num_trips_per_borough_no_tip, by = join_by(pickup_borough, dropoff_borough)) %>%
mutate(num_trips = num_trips, percent_zero_tips_trips = 100 *
num_zero_tip_trips / num_trips) %>% select(pickup_borough, dropoff_borough,
num_trips, percent_zero_tips_trips) %>% arrange(desc(percent_zero_tips_trips))
num_zero_percent_trips
Output
[1] pickup_borough dropoff_borough num_trips
[4] percent_zero_tips_trips
<0 rows> (or 0-length row.names)
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