DAS522/DAS241 Midterm Exam Part 2 - Spring 2026

Rules

1. Submission: Complete your solutions in the provided R Markdown file, named midterm-<your-name>.Rmd (replace <your-name>). Submit both the .Rmd and the HTML/PDF by the deadline.

2. Permitted Resources: You may use R help, class materials, your own previous code, and the following packages: tidyverse, openintro, nycflights13 (and dependencies). No Internet search, AI tools, or outside communication (except with the instructor) allowed.

3. Late Policy: No late submissions.

4. Code Requirements: Use only the allowed packages. For each question, include relevant code and concise written answers. Use graphs/tables as instructed.

5. Graph Formatting: Polish your graphs.

Grading (100% + up to 20% bonus)

• Each question (including bonus) is worth 10%.
• Answer any 10 of 12 questions for full credit (100%). Correctly answering more can earn up to 120 points.
• Partial credit is based on completeness and correctness.

Exam Questions

You should use codes, graphs or results to answer each question unless noted otherwise.

1. The mpg data set

After loading tidyverse library, a data set named mpg should be ready to explore. The following questions are based on this data set.

Task list

1. Create a new variable mpg_overall which is the average of city and highway fuel consumption in miles per gallon. Then create a histogram of this new variable with each group covering values of 20-22, 22-24 etc.

2. Create a graph to study the relationship between drive train types and mpg_overall.

3. Create a table to find out which car class has the highest mean mpg_overall.

4. Create a proper graph to study the composite effect of year and cyl to mpg_overall. You shall treat year and cyl as categorical variables in your graph.

2. The flights data set

For the following tasks, use data set flights of the nycflights13 package.

Task list

1. For JFK airport, which day in November 2013 has the biggest average arrival delay? Create a table to answer the question.

2. Create a new variable cancel_flight which is Cancelled if the departure time or arrival time is NA, otherwise Not Cancelled.

3. Create a density graph that compares the distribution of distance between cancelled flights and non-cancelled flights.

4. How many unique flight routes are there in the data set? That is, each unique combination of an origin airport and a destination airport (such as from EWR to ORD) is considered as a route. Create a table to answer the question.

5. Add distance as a column to the table you created in d). Hint: You should go back to the original flights data set and reconstruct the table with distance included. Create a histogram of distance for the route table.

6. Which route has the highest rate of flight cancellation? Create a table to answer the question.

Bonus Question for flights data set

The following questions are also from flights data set. Each question is worth 5% bonus points if answered correctly.

Task list

1. Create a proper graph to show the rate of cancellation flights for each airline. Answer which airline has the lowest rate of cancellation.

2. If multiple airlines run the same route, they can be considered as competitors. Which route is most competitive (has the most number of carriers)? List all of them in a table.