airline_delay: Airline Delays for December 2019 and 2020.

In usdata: Data on the States and Counties of the United States

Airline Delays for December 2019 and 2020.

Description

Summary Data counts for airline per carrier per US City.

Usage

airline_delay

Format

A data frame with 3351 rows and 21 variables.

year

Year data collected

month

Numeric representation of the month

carrier

Carrier.

carrier_name

Carrier Name.

airport

Airport code.

airport_name

Name of airport.

arr_flights

Number of flights arriving at airport

arr_del15

Number of flights more than 15 minutes late

carrier_ct

Number of flights delayed due to air carrier. (e.g. no crew)

weather_ct

Number of flights due to weather.

nas_ct

Number of flights delayed due to National Aviation System (e.g. heavy air traffic).

security_ct

Number of flights canceled due to a security breach.

late_aircraft_ct

Number of flights delayed as a result of another flight on the same aircraft delayed

arr_cancelled

Number of cancelled flights

arr_diverted

Number of flights that were diverted

arr_delay

Total time (minutes) of delayed flight.

carrier_delay

Total time (minutes) of delay due to air carrier

weather_delay

Total time (minutes) of delay due to inclement weather.

nas_delay

Total time (minutes) of delay due to National Aviation System.

security_delay

Total time (minutes) of delay as a result of a security issue .

late_aircraft_delay

Total time (minutes) of delay flights as a result of a previous flight on the same airplane being late.

Source

Bureau of Transportation Statistics

Examples

library(ggplot2)
ggplot(airline_delay, aes(arr_flights, arr_del15, color = as.factor(year))) +
  geom_point(alpha = 0.3) +
  labs(
    x = "Total Number of inbound flights",
    y = "Number of flights delayed by more than 15 mins",
    title = "Inbound vs delayed flights by year",
    color = "Year"
  )

usdata documentation built on June 22, 2024, 9:57 a.m.