Due: April 06 by 11:59pm

Submit: To submit this assignment, create a zip file of all the files in your R project folder for this assignment. Name the zip file hw10-netID.zip, replacing netID with your netID (e.g., hw10-jph.zip). Use this link to submit your file.

Weight: This assignment is worth 5% of your final grade.

Purpose: The purposes of this assignment are:

Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:

The reflection portion is always worth 10% and graded for completion.


1) Staying organized [SOLO, 5%]

Download and use this template for your assignment. Inside the “hw10” folder, open and edit the R script called “hw10.R” and fill out your name, GW Net ID, and the names of anyone you worked with on this assignment.

Using good style

For this assignment, you must use good style to receive full credit. Follow the best practices described in this style guide.

2) Load the data [SOLO, 5%]

For this assignment, we will work with data on flights from New York City airports during 2013. The data are accessible from the nycflights13 package. Write R code to install and then load the package.

3) Inspect the data [SOLO, 5%]

Look at the datasets that are included in this package:

data(package = "nycflights13")
Data sets in package 'nycflights13':

airlines                Airline names.
airports                Airport metadata
flights                 Flights data
planes                  Plane metadata.
weather                 Hourly weather data

Write some code to preview and summarize each of these data frames using some of the methods we’ve used in class. You should be able to quickly get an understanding of what variables are included in each data frame and their nature. For each dataset, consider the following questions in your exploration (you don’t have to write out answers to these questions - just write code to help you answer them by previewing the data in different ways):

Answer questions about the data

Use the data frames in the nycflights13 package to answer the following questions. For each question, write R code to find the solution. Leave comments where appropriate to explain what you are doing, and then write your final answer as a comment at the end.

For example, if the question was “how many observations are in the flights data frame?”, here is an acceptable answer:

# Find the number of rows in the flights data frame
#> [1] 336776
# Answer: There are 336,776 observations in the flights data frame

You do not have to use the dplyr package functions (i.e. filter(), arrange(), mutate(), etc.) to answer these questions, but they generally make it easier.

4) [SOLO, 5%]

How many flights out of NYC airports in 2013 had an arrival delay of two or more hours? Hint: use filter()

5) [SOLO, 5%]

How many flights out of NYC airports in 2013 departed in fall semester (i.e. the months August - December, inclusive)? Hint: use filter()

6) [SOLO, 5%]

How many flights out of NYC airports in 2013 were operated by United, American, or Delta airlines? Hint: use filter()

7) [SOLO, 5%]

List the top 3 airlines (by name, not carrier code) that had the highest delay time of any one flight leaving a NYC airport in 2013. Hint: use arrange()

8) [SOLO, 5%]

How many flights out of NYC airports in 2013 flew to the 3 major DC-area airports: Reagan National, Dulles, or BWI? Hint: use filter()


What is the year manufactured and tail number of the oldest airplane that any one airline used in 2013 to fly out of NYC airports, and which airline operated that plane? Hint: use arrange() and filter()


Using the flights data frame, compute a new variable speed (in miles per hour) using the air_time and distance variables. For the fastest flight in the dataset, what was its speed and what were the origin and destination airport codes? Hint: use mutate() and arrange()


Of all the flights in 2013 departing from NYC airports, list the top 3 destinations (airport names, not airport codes) with the highest mean arrival delay. Hint: Use a “pipeline” of group_by(), summarise(), and arrange(). Don’t forget to filter out any NA values before summarizing!


Use the flights data frame to create a new summary data frame called dailyDelaySummary that contains the following variables for each day in 2013:

  • meanDepDelay: the mean departure delay (in minutes)
  • numDelayedFlights: the total number of delayed flights

Save this file in your “data” folder as “dailyDelaySummary.csv” Hint: Use a “pipeline” of group_by() and summarise(), and don’t forget to filter out any NA values before summarizing!


Using the dailyDelaySummary data frame that you created in part i), answer the following two questions:

  • Find the day in 2013 with the highest number of delayed flights. On that day, how many flights were delayed, and what was the mean delay time (in minutes)?
  • Find the day in 2013 with the highest mean departure delay (in minutes). On that day, how many flights were delayed and what was the mean delay time (in minutes)?

14) Read and reflect [SOLO, 10%]

Read and reflect on next week’s readings on data visualization. Afterwards, in a comment (#) in your R file, write a short reflection on what you’ve learned and any questions or points of confusion you have about what we’ve covered thus far. This can just few a few sentences related to this assignment, next week’s readings, things going on in the world that remind you something from class, etc. If there’s anything that jumped out at you, write it down.

Bonus 1) [SOLO, 1%]

How many flights have a missing departure time? What might these rows represent?

Bonus 2) [SOLO, 1%]

Which flights (i.e. carrier + flight) departed every day of the year, and which airports did they fly to?

Bonus 3) [SOLO, 2%]

Use the flights data frame to create a season variable. The seasons are defined as the following:

  • Spring: March, April, May
  • Summer: June, July, August
  • Fall: September, October, November
  • Winter: December, January, February

What season experiences the largest mean delay, and why might that be? Hint: Use a “pipeline” of mutate, group_by() and summarise(). Don’t forget to filter out any NA values before summarizing!

EMSE 4571: Intro to Programming for Analytics (Spring 2022)
Thursdays | 12:45 - 3:15 PM EST | Tompkins 208 | Dr. John Paul Helveston | jph@gwu.edu