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, replacingnetIDwith 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:
- To practice exploring and wrangling data frames in R using the dplyr package
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.
The reflection portion is always worth 10% and graded for completion.
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 and 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.
Using good style
For this assignment, you must use good style to receive full credit. Follow the best practices described in this style guide.
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 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
nrow(flights)#> [1] 336776# Answer: There are 336,776 observations in the flights data frameYou 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.
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:
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.
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!