Assignment 7

Author

Matthew Albano

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.

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(lubridate)
library(skimr)
library(gt)
flights <- read.csv("https://jsuleiman.com/datasets/domestic_flights_jan_2016.csv")

Question 1: Which flight had the shortest actual elapsed time?

shortest_flight <- flights |>
  arrange(ActualElapsedTime) |>
  slice(1)

print(shortest_flight)
  FlightDate Carrier TailNum FlightNum Origin OriginCityName OriginState Dest
1   1/6/2016      AS  N795AS        65    WRG   Wrangell, AK          AK  PSG
    DestCityName DestState CRSDepTime DepTime WheelsOff WheelsOn CRSArrTime
1 Petersburg, AK        AK       1106    1047      1050     1058       1127
  ArrTime Cancelled Diverted CRSElapsedTime ActualElapsedTime Distance
1    1102         0        0             21                15       31

Answer 1: The flight with the shortest actual elapsed time occurred on 1/6/2016, with the tail number being “N795AS”, and the flight number being “65”. The origin city was Wrangell, AK, and the destination city was Petersburg, AK. The flight time was only 15 minutes.

Question 2: Which date had the most flights?

busiest_date <- flights |>
  count(FlightDate, name = "num_flights") |>
  arrange (desc(num_flights)) |>
  slice(1)

print(busiest_date)
  FlightDate num_flights
1   1/3/2016       15878

Answer 2: The date where the most flights took place was on 1/3/2016. On this date, 15,878 took place.

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.