Introduction

In this assignment, I will analyze airline on-time performance data for two airlines, Alaska and Amwest, across five cities. The original dataset is provided in a wide format, with separate counts for on-time and delayed flights for each airline and destination.

The main goal of this assignment is to recreate the dataset in R, convert it into a tidy format, and compare the performance of the two airlines. I will first look at the overall performance of each airline and then compare their performance within each city. The comparison will focus on percentages instead of raw flight counts.

An important part of the analysis will be to examine what happens if the overall comparison gives a different result from the comparisons within individual cities. I will use the data to explain why this difference can occur.

Data Analysis Approach

Dataset Preparation

First, I will recreate the original dataset programmatically in R using a wide format similar to the source data. Because the original wide-format table contains blank cells used as visual grouping, I will use R code to populate those missing labels before transforming the data into tidy format.

After creating the dataframe, I will export it as a CSV file using write.csv(). The CSV file will then be uploaded to a public GitHub repository. For the analysis, I will read the data directly from the GitHub raw URL so that the work can be reproduced without relying on a local file.

The dataset will contain information for:

  • Airlines: Alaska and Amwest
  • Flight status: On Time and Delayed
  • Destination cities:
    • Los Angeles
    • Phoenix
    • San Diego
    • San Francisco
    • Seattle

Data Loading and Validation

After importing the CSV file into R, I will examine the data before making any transformations.

I will:

  • Check the column names and overall structure.
  • Identify and handle any blank or missing structural values.
  • Make sure that all flight counts are stored as numeric values.
  • Check the totals for each airline.
  • Check the totals for each city.
  • Confirm that the data are complete and consistent.

If any structural values are missing because of the original wide format, I will handle them programmatically before continuing with the analysis.

Data Transformation to Tidy Format

The original data are in a wide format, so I will transform them into a tidy, long format using tidyr and dplyr.

The final tidy dataset will contain one row for each combination of airline, city, and flight status, with the corresponding flight count.

The main variables will be:

  • airline
  • city
  • status
  • count

The status variable will identify whether the flights were on_time or delayed, while count will contain the number of flights.

Using tidy data will make it easier to calculate percentages and perform consistent comparisons across airlines and cities.

Overall Airline Performance Comparison

Next, I will compare the overall performance of Alaska and Amwest.

For each airline, I will:

  1. Calculate the total number of flights.
  2. Calculate the total number of delayed flights.
  3. Calculate the percentage of flights that were delayed.
  4. Calculate the percentage of flights that were on time if needed.
  5. Present the results in a summary table and/or visualization.

The main comparison will use percentages rather than the number of delayed flights. This is important because the two airlines may have different total numbers of flights.

City-Level Airline Performance Comparison

After examining the overall results, I will compare the two airlines separately within each of the five cities.

For each airline and city, I will calculate the percentage of flights that were delayed. I will then compare these percentages across the cities.

The results will be presented using:

  • A summary table of city-level percentages.
  • Grouped bar charts to show differences between the airlines.
  • A short written interpretation of the main patterns.

Looking at the results by city will provide more detail than the overall comparison alone and will help determine whether the overall results are consistent across individual cities.

Analysis of Overall and City-Level Differences

One of the main purposes of this analysis is to determine whether the overall comparison agrees with the city-level comparisons.

If the overall results and the city-level results lead to different conclusions, I will investigate why this occurs. One possible explanation is that the airlines have different distributions of flights across the five cities.

The overall percentage is effectively a weighted result because cities with more flights contribute more to the overall percentage. Therefore, an airline’s overall performance can be affected by where it operates more flights.

For example, an airline may have a higher delay percentage in several individual cities but still have a different overall percentage if it operates a larger share of its flights in a city where its delay rate is lower.

This part of the analysis will help demonstrate why it is important to consider both aggregated results and results within individual groups.

Data Considerations

There are several data-related issues that I expect to address during the analysis:

Reproducible Analysis

I will use the following steps to make the analysis reproducible:

Conclusion

This approach will allow me to recreate the airline delay dataset, clean and organize the data, and compare Alaska and Amwest using percentage-based measures. I will examine both overall airline performance and performance within each city.

The final part of the analysis will focus on explaining any difference between the overall and city-level comparisons. This will help show how the distribution of flights across cities can affect an aggregated result and why examining the data at more than one level is important.