Due: 23 November by 11:00 pm
Weight: This assignment is worth 4% of your final grade.
Purpose, Skills, & Knowledge: The purposes of this assignment are:
- To practice exploring and wrangling data frames in R using the dplyr library
Assessment: Each question indicates the % of the assignment grade, summing to 100%. The credit for each question will be assigned as follows:
- 0% for not attempting a response.
- 50% for attempting the question but with major errors.
- 75% for attempting the question but with minor errors.
- 100% for correctly answering the question.
Rules:
- Problems marked SOLO may not be worked on with other classmates, though you may consult instructors for help.
- For problems marked COLLABORATIVE, you may work in groups of up to 3 students who are in this course this semester. You may not split up the work – everyone must work on every problem. And you may not simply copy any code but rather truly work together.
- Even though you work collaboratively, you still must submit your own solutions.
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.
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.
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 dataWrite 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):
Use the data frames in the nycflights13 library 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
nrow(flights)## [1] 336776# Answer: There are 336,776 observations in the flights data frameYou do not have to use the dplyr library functions (i.e. filter(), arrange(), mutate(), etc.) to answer these questions, but they generally make it easier.
How many flights out of NYC airports in 2013 had an arrival delay of two or more hours? Hint: use filter()
How many flights out of NYC airports in 2013 departed in fall semester (i.e. the months August - December, inclusive)? Hint: use filter()
How many flights out of NYC airports in 2013 were operated by United, American, or Delta airlines? Hint: use filter()
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()
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 flightsSave 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:
Create a zip file of all files in your R project folder for this assignment and submit the zip file on Blackboard by the due deadline.
How many flights have a missing departure time? What might these rows represent?
Which flights (i.e. carrier + flight) departed every day of the year, and which airports did they fly to?
Use the flights data frame to create a season variable. The seasons are defined as the following:
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!