Assignment 5A: Airline Delays

library(tidyverse)

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library(knitr)
library(ggplot2)

Reading in the data

data_url <- "https://raw.githubusercontent.com/Jeovany97/Data-607/refs/heads/main/Assignment%205a/airline_delays.csv"

raw_data <- read_csv(data_url, na = c("", "NA"))

Rows: 5 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): Airline, Status
dbl (5): Los Angeles, Phoenix, San Diego, San Francisco, Seattle

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

clean_data <- raw_data %>%
  #Removing empty row & filling in missing Airline names
  filter(!if_all(everything(),is.na)) %>% 
  fill(Airline, .direction = "down")

Tidying the data

tidy_flights <- clean_data %>%
  pivot_longer(
    cols = c("Los Angeles", "Phoenix", "San Diego", "San Francisco", "Seattle"),
    names_to = "City",
    values_to = "Flights"
  )

Calculations

Total number of delayed and on time flights

airline_summary <- tidy_flights %>%
  group_by(Airline, Status) %>%
  summarise(total_flights = sum(Flights), .groups = "drop")

kable(airline_summary, caption = "Summary of Airline Delays")

Summary of Airline Delays

Airline	Status	total_flights
ALASKA	delayed	501
ALASKA	on time	3274
AM WEST	delayed	787
AM WEST	on time	6438

Comparing the number of delayed and on time for each Airline

delay_comparison <- tidy_flights %>%
  group_by(Airline, Status) %>%
  summarise(total = sum(Flights), .groups = "drop") %>%
  pivot_wider(names_from = Status, values_from = total) %>%
  mutate(
    total_flights = `on time` + delayed,
    delay_rate = delayed / total_flights
  )

kable(delay_comparison, caption = "Comparsion of delays between each airline")

Comparsion of delays between each airline

Airline	delayed	on time	total_flights	delay_rate
ALASKA	501	3274	3775	0.1327152
AM WEST	787	6438	7225	0.1089273

Based on the data given we can see the AM West currently has a much larger operation nearly double the total flights. Despite this AM WEST manages to maintain a slightly lower percentage of delays. Rounding both delay rates to the hundredth that mean a ALASKA customer has 22% increase chance to experience a delay.

Comparing the the delay and arrive time for each City

city_delay_rates <- tidy_flights %>%
  group_by(Airline, City, Status) %>%
  summarise(total = sum(Flights), .groups = "drop") %>%
  pivot_wider(names_from = Status, values_from = total) %>%
  mutate(
    total_flights = `on time` + delayed,
    delay_rate = delayed / total_flights
  )
kable(city_delay_rates, caption = "Comparsion of delays between each city")

Comparsion of delays between each city

Airline	City	delayed	on time	total_flights	delay_rate
ALASKA	Los Angeles	62	497	559	0.1109123
ALASKA	Phoenix	12	221	233	0.0515021
ALASKA	San Diego	20	212	232	0.0862069
ALASKA	San Francisco	102	503	605	0.1685950
ALASKA	Seattle	305	1841	2146	0.1421249
AM WEST	Los Angeles	117	694	811	0.1442663
AM WEST	Phoenix	415	4840	5255	0.0789724
AM WEST	San Diego	65	383	448	0.1450893
AM WEST	San Francisco	129	320	449	0.2873051
AM WEST	Seattle	61	201	262	0.2328244

While previous data suggested AM WEST to be the better airline this city specific data reveals that ALASKA has the lower data. ALASKA beats AM WEST in every city. The reason AM WEST appeared better is because the concentration in phoenix. AM WEST concentrated 73% of all their flights in the city that has lowest average. This volume bias poisoned the previous data.

Creating graph to show difference between city flights

# Creating graph
ggplot(city_delay_rates, aes(x = City, y = total_flights, fill = Airline)) +
  geom_col(position = "dodge") +
  # Titles and labels
  labs(
    title = "Flight Volume by City and Airline",
    subtitle = "Comparing the number of flights for each city",
    x = "Destination City",
    y = "Total Number of Flights",
    fill = "Airline"
  ) +
  # Clean up the theme and rotate city names for better readability
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))