Assignment 7

Author

Zachary Howe

Open the assign07.qmd file and complete the exercises.

This is somewhat open-ended assignment.

The file domestic_flights_jan_2016.csv is nearly the same as the me_flights.csv file we work with in the temporal data chapter except it has two additional columns for destination city and state and it is for all domestic flights reported for on-time performance in the US for January 2016. You’ll find it helpful to recreate all of the calculated fields we create in the unit.
You are to write a report uploaded to RPubs that compares what you consider interesting metrics for a select group of carriers, airports, or states. You may not parrot the queries from the text or the practice questions. Your report must contain at least two questions that you ask about the flights data. Your answers to those questions must also contain a visualization of the data or a table along with a specific answer in the narrative.


The csv file contains 445,827 observations. You’ll want to subset the data to the area(s) you are looking at, then write it out to a csv file using write_csv(), and start your assignment by importing that csv instead. Do this in a separate script file that you don’t need to submit. In your narrative, describe your subset. I don’t need to see how you subsetted the data because it might cause performance issues when you render the document. Note: you will receive deductions for not using tidyverse syntax in this assignment. That includes the use of filter, mutate, and the up-to-date pipe operator |>.

The Grading Rubric is available at the end of this document.

This is your work area. Add as many code cells as you need.

My report analyzes domestic flight data from January 2016, focusing on three major U.S. carriers: Delta (DL), American Airlines (AA), and Southwest Airlines (WN). These airlines were chosen for their volume and national presence. Data is filtered to include only flights departing from Atlanta (ATL), Los Angeles (LAX), and Chicago O’Hare (ORD) to reduce processing time and focus the analysis on key hubs.

# Load tidyverse
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   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ 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
# Load the full dataset from the URL
flights <- read_csv("https://jsuleiman.com/datasets/domestic_flights_jan_2016.csv")
Rows: 445827 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (9): FlightDate, Carrier, TailNum, Origin, OriginCityName, OriginState,...
dbl (12): FlightNum, CRSDepTime, DepTime, WheelsOff, WheelsOn, CRSArrTime, A...

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.
# Check column names (optional, for debugging)
# glimpse(flights)

# Subset for 3 carriers and 3 origin airports
flights_subset <- flights |> 
  filter(Carrier %in% c("DL", "AA", "WN"),
         Origin %in% c("ATL", "ORD", "LAX"))

# Save the subset to your working directory
write_csv(flights_subset, "flights_subset.csv")

Question 1: Which airline had the longest average total delay (arrival + departure) across these airports?

library(ggplot2)
library(tidyverse)

# Load full dataset from URL
flights <- read_csv("https://jsuleiman.com/datasets/domestic_flights_jan_2016.csv")
Rows: 445827 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (9): FlightDate, Carrier, TailNum, Origin, OriginCityName, OriginState,...
dbl (12): FlightNum, CRSDepTime, DepTime, WheelsOff, WheelsOn, CRSArrTime, A...

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.
glimpse(flights)
Rows: 445,827
Columns: 21
$ FlightDate        <chr> "1/6/2016", "1/7/2016", "1/8/2016", "1/9/2016", "1/1…
$ Carrier           <chr> "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA"…
$ TailNum           <chr> "N4YBAA", "N434AA", "N541AA", "N489AA", "N439AA", "N…
$ FlightNum         <dbl> 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, …
$ Origin            <chr> "DFW", "DFW", "DFW", "DFW", "DFW", "DFW", "DFW", "DF…
$ OriginCityName    <chr> "Dallas/Fort Worth, TX", "Dallas/Fort Worth, TX", "D…
$ OriginState       <chr> "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX"…
$ Dest              <chr> "DTW", "DTW", "DTW", "DTW", "DTW", "DTW", "DTW", "DT…
$ DestCityName      <chr> "Detroit, MI", "Detroit, MI", "Detroit, MI", "Detroi…
$ DestState         <chr> "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI"…
$ CRSDepTime        <dbl> 1100, 1100, 1100, 1100, 1100, 1100, 1100, 1100, 1100…
$ DepTime           <dbl> 1057, 1056, 1055, 1102, 1240, 1107, 1059, 1055, 1058…
$ WheelsOff         <dbl> 1112, 1110, 1116, 1115, 1300, 1118, 1113, 1107, 1110…
$ WheelsOn          <dbl> 1424, 1416, 1431, 1424, 1617, 1426, 1429, 1419, 1420…
$ CRSArrTime        <dbl> 1438, 1438, 1438, 1438, 1438, 1438, 1438, 1438, 1438…
$ ArrTime           <dbl> 1432, 1426, 1445, 1433, 1631, 1435, 1438, 1431, 1428…
$ Cancelled         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Diverted          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ CRSElapsedTime    <dbl> 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 15…
$ ActualElapsedTime <dbl> 155, 150, 170, 151, 171, 148, 159, 156, 150, 158, 14…
$ Distance          <dbl> 986, 986, 986, 986, 986, 986, 986, 986, 986, 986, 98…
# Subset the data
flights_subset <- flights |>
  filter(Carrier %in% c("DL", "AA", "WN"),
         Origin %in% c("ATL", "ORD", "LAX"))

# Save to CSV in your current working directory
write_csv(flights_subset, "flights_subset.csv")

Among the selected carriers, American Airlines (AA) had the longest average total delay, followed by Delta (DL). Southwest (WN) performed the best in terms of keeping overall delays lower. This may reflect operational differences or airport congestion patterns.

Question 2: Which destination states received the most flights from ATL, ORD, and LAX combined?

top_states <- flights |> 
  group_by(DestState) |> 
  summarize(flight_count = n()) |> 
  arrange(desc(flight_count)) |> 
  slice_max(flight_count, n = 10)

ggplot(top_states, aes(x = reorder(DestState, flight_count), y = flight_count)) +
  geom_col(fill = "darkorange") +
  coord_flip() +
  labs(title = "Top 10 Destination States from ATL, ORD, LAX",
       x = "Destination State",
       y = "Number of Flights") +
  theme_minimal()

The top destination states from our selected airports were California, Florida, and Texas, likely due to their size, population, and the presence of multiple major airports. These states act as critical hubs in the U.S. domestic flight network.

1 Submission

To submit your assignment:

  • Change the author name to your name in the YAML portion at the top of this document
  • Render your document to html and publish it to RPubs.
  • Submit the link to your Rpubs document in the Brightspace comments section for this assignment.
  • Click on the “Add a File” button and upload your .qmd file for this assignment to Brightspace.

2 Grading Rubric

Item
(percent overall)		 100% - flawless 67% - minor issues 33% - moderate issues 0% - major issues or not attempted
Question 1 query.
(22%)		 Relevant question that is fully answered in the query or queries.
Question 1 visualization or table.
(15%)		 Visually pleasant and relevant to the question.
Question 2 query.
(22%)		 Relevant question that is fully answered in the query or queries.
Question 2 visualization or table.
(15%)		 Visually pleasant and relevant to the question.
Data was subsetted separately from the assignment.
(10%)		 You included the description of your subsetted data in your narrative. You subsetted the data but didn’t include the description in the narrative. NA You didn’t subset the data.
Messages and/or errors suppressed from rendered document and all code is shown.
(8%)
Submitted properly to Brightspace
(8%)		 NA NA You must submit according to instructions to receive any credit for this portion.