Skip to content

A foremost aviation industry player with a significant presence in New York City has launched an in-depth data analysis project focused on identifying trends in flight durations in air travel. This initiative aims to delve into a wealth of data related to flight schedules and operational patterns, with the objective of optimizing flight times and enhancing the overall travel experience for passengers. As the head data analyst, you have access to rich datasets, sourced from the 'nycflights2022' collection produced by the ModernDive team. These datasets include records of flights departing from major New York City airports, including JFK (John F. Kennedy International Airport), LGA (LaGuardia Airport), and EWR (Newark Liberty International Airport), during the second half of 2022. They offer a comprehensive view of flight operations, covering various aspects such as departure and arrival times, flight paths, and airline specifics:

  • flights2022-h2.csv contains information about each flight including
VariableDescription
carrierAirline carrier code
originOrigin airport (IATA code)
destDestination airport (IATA code)
air_timeDuration of the flight in air, in minutes
  • airlines.csv contains information about each airline:
VariableDescription
carrierAirline carrier code
nameFull name of the airline
  • airports.csv provides details of airports:
VariableDescription
faaFAA code of the airport
nameFull name of the airport
# Load necessary libraries
library(dplyr)
library(tidyr)

# Read the data files
flights <- read.csv("flights2022-h2.csv")
airlines <- read.csv("airlines.csv")
airports <- read.csv("airports.csv")

# 1. Find the airline and airport pair that receives the most flights from NYC
frequent_pair <- flights %>%
  filter(origin %in% c("JFK", "LGA", "EWR")) %>%
  group_by(carrier, dest) %>%
  summarise(flight_count = n(), .groups = "drop") %>%
  arrange(desc(flight_count)) %>%
  slice(1)

# Add names
frequent <- frequent_pair %>%
  left_join(airlines, by = "carrier") %>%
  left_join(airports, by = c("dest" = "faa")) %>%
  select(carrier, dest, airline_name = name.x, airport_name = name.y)

# Calculate average duration for the most frequent pair
avg_duration <- flights %>%
  filter(carrier == frequent$carrier[1], dest == frequent$dest[1]) %>%
  summarise(avg_duration = mean(air_time, na.rm = TRUE) / 60)  # FIX: missing closing parenthesis

frequent$avg_duration_hours <- avg_duration$avg_duration

# 2. Find the airport with longest average flight duration from NYC
# Step-by-step to find the airport with the longest average flight duration from NYC

# Step 1: Join airline and airport names to flights
joined_data <- flights %>%
  left_join(airlines, by = "carrier") %>%
  left_join(airports, by = c("dest" = "faa"))

# Step 2: Filter to only NYC airports
nyc_flights <- joined_data %>%
  filter(origin %in% c("JFK", "LGA", "EWR"))

# Step 3: Group by destination and airline
summary_data <- nyc_flights %>%
  group_by(dest, name.x, name.y) %>%
  summarise(
    flight_count = n(),
    avg_duration = mean(air_time, na.rm = TRUE) / 60,
    .groups = "drop"
  )

# Step 4: Filter only destinations with valid duration
longest <- summary_data %>%
  arrange(desc(avg_duration)) %>%
  slice(1) %>%
  rename(
    airline_name = name.x,
    airport_name = name.y
  )

# 3. Find the least frequented destination for flights departing from JFK
least <- flights %>%
  filter(origin == "JFK") %>%
  group_by(dest) %>%
  summarise(flight_count = n(), .groups = "drop") %>%
  arrange(flight_count) %>%
  slice(1) %>%
  left_join(airports, by = c("dest" = "faa")) %>%
  pull(name)

# Results
print("Most frequent airline and airport pair from NYC:")
print(frequent)

print("Airport with longest average flight duration from NYC:")
print(longest)

print("Least frequented destination from JFK:")
print(least)