Overview:

I am working to transform this data on Alasaka and AM West airline flight delays to more easily compare the airlines based on their on-time and delayed flights.

Load Data

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Make all blank cells “NA”

x <- read.csv("607_airline_dirty.csv", na.strings = c("", "NA"))

Consolidated destinations into a single destination column

y <- x %>% 
  pivot_longer(
    cols = c(Los.Angeles, Phoenix, San.Diego, San.Francisco, Seattle),
    names_to = "Destination",
    values_to = "Flights",
    values_drop_na =  TRUE
  ) %>%
  rename(Airline = X, Delay_status = X.1) %>% 
  fill(Airline, .direction = c("down"))

I did not need to do this, but created a df of on time-flights

on_time <- y %>% 
  filter(Delay_status == "on time")

I tried to evaluate the data based on on-time flights, but realized there was a big difference in overall flight totals between both airlines

ggplot(data = on_time, aes(x = Destination, y = Flights, fill = Airline)) +
  geom_bar(position = "dodge", stat = "identity")

delayed <- y %>% 
  filter(Delay_status == "delayed")

ggplot(data = delayed, aes(x = Destination, y = Flights, fill = Airline)) +
  geom_bar(position = "dodge", stat = "identity")

I decided to try to evaluate the data based on flight delay status percentages, I grouped by Airline and Delay status to get a total flight count, and then calculated the flight delay percentages based on those totals.

z <- y %>% 
  group_by(Airline, Delay_status) %>% 
  summarise(Flights_count = sum(Flights)) %>% 
  ungroup() %>% 
  group_by(Airline) %>% 
  mutate(Total_flights = sum(Flights_count)) %>% 
  mutate(Percentage = Flights_count/Total_flights)

## `summarise()` has grouped output by 'Airline'. You can override using the
## `.groups` argument.

I created a bar graph to help visualize those percentages of on-time and delayed flights between both airlines

ggplot(data = z, aes(x = Airline, y = Percentage, fill = Delay_status)) +
  geom_bar(position = "dodge", stat = "identity")

wider_y <- y %>% 
  pivot_wider(names_from = Delay_status, values_from = Flights) %>% 
   rename(on_time = `on time`) %>% 
  group_by(Airline, Destination) %>% 
  mutate(total_flights = sum(on_time, delayed)) %>% 
  mutate(on_time_rate = on_time / total_flights) %>% 
  mutate(delay_rate = delayed / total_flights)

ggplot(data = wider_y, aes(x = Destination, y = on_time_rate, fill = Airline )) +
  geom_bar(position = "dodge", stat = "identity")

ggplot(data = wider_y, aes(x = Destination, y = delay_rate, fill = Airline )) +
  geom_bar(position = "dodge", stat = "identity")

607 Week 5

Lu Beyer

Overview:

Conclusion: