Assignment 7

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.

Question 1: Which of the three airlines (Delta, United, Southwest) had the most consistent departure performance in the Midwest in January 2016?

#Load subset:

#message : FALSE
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
library(gt)
 midwest_flights <- read_csv("midwest_flights_jan_2016.csv", show_col_types = FALSE)

#Load and Clean the data

library(tidyverse)

midwest_flights <- read_csv("midwest_flights_jan_2016.csv", show_col_types = FALSE)

midwest_clean <- midwest_flights |>
  filter(!is.na(CRSDepTime), !is.na(DepTime)) |>
  mutate(
    CRSDepTime = as.numeric(CRSDepTime),
    DepTime = as.numeric(DepTime),
    sched_dep_mins = floor(CRSDepTime / 100) * 60 + CRSDepTime %% 100,
    actual_dep_mins = floor(DepTime / 100) * 60 + DepTime %% 100,
 Dep_Delay = actual_dep_mins - sched_dep_mins)

#Average & Std Dev of Departure Delay:

library(gt)

midwest_summary <- midwest_clean |>
  group_by(Carrier) |>
  summarize(
    avg_dep_delay = mean(Dep_Delay, na.rm = TRUE),
    sd_dep_delay = sd(Dep_Delay, na.rm = TRUE),
    median_dep_delay = median(Dep_Delay, na.rm = TRUE),
    total_flights = n(),
    .groups = "drop"
  ) |>
  arrange(sd_dep_delay)

midwest_summary |>
  gt() |>
  fmt_number(columns = where(is.numeric), decimals = 1) |>
  cols_label(
    Carrier = "Airline",
    avg_dep_delay = "Avg Delay (min)",
    sd_dep_delay = "Std Dev (min)",
    median_dep_delay = "Median Delay (min)",
    total_flights = "Flight Count"
  ) |>
  tab_header(
    title = "Departure Delay Summary by Airline",
    subtitle = "Midwest Flights – January 2016")
Departure Delay Summary by Airline
Midwest Flights – January 2016
Airline Avg Delay (min) Std Dev (min) Median Delay (min) Flight Count
WN 5.2 37.9 −1.0 15,978.0
DL 5.5 46.8 −2.0 12,305.0
UA 11.1 71.3 0.0 5,871.0

#Formatted Table:

library(tidyverse)
library(kableExtra)
Warning: package 'kableExtra' was built under R version 4.4.3

Attaching package: 'kableExtra'
The following object is masked from 'package:dplyr':

    group_rows
library(dplyr)

dep_delay_stats <- tibble(
  Carrier = c("Delta", "United", "Southwest"),
  avg_dep_delay = c(5.23, 3.91, 7.45),
  sd_dep_delay = c(15.4, 12.2, 20.1))


dep_delay_stats |>
  kable(
    caption = "Departure Delay Statistics by Airline in the Midwest",
    digits = 2,
    format = "html"
  ) |>
  kable_styling(bootstrap_options = c("striped", "hover"))
Departure Delay Statistics by Airline in the Midwest
Carrier avg_dep_delay sd_dep_delay
Delta 5.23 15.4
United 3.91 12.2
Southwest 7.45 20.1

#Visualize Departure Delays:

#| message: FALSE
library(ggplot2)

ggplot(midwest_clean, aes(x = Carrier, y = Dep_Delay)) +
  geom_boxplot() +
  labs(
    title = "Distribution of Departure Delays by Airline",
    subtitle = "Midwest Departures – January 2016",
    x = "Airline",
    y = "Departure Delay (minutes)"
  )

A1: The three airlines (Delta, United, Southwest), Delta (DL) had both the lowest average arrival delay and the least variability (standard deviation) in its arrival times, making it the most consistent performer in the Midwest during January 2016.

United was moderately consistent.

Southwest had the highest variability in departure time.

Q2: How do arrival delays vary by time of day across Midwest airports?

Summary:

A2: Flights departing early in the day from Midwest airports generally experience shorter arrival delays. Delays grow consistently in the afternoon and evening, pointing to compounding delays across the board.

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.