Assignment 6

Guidelines
1. Highway Fuel Economy Over the Years, Revisited
2. Top Fleet Highway Gas Mileages
3. Top Destinations
4. Summer-Only Destinations
5. High Altitude Destinations
Create an HTML File and Commit Your Work

Guidelines

You will submit your homework as an R Markdown (.Rmd) file by committing to your git repository and pushing to GitLab. We will knit this file to produce the .html output file (you do not need to submit the .html, but you should make sure that it can be produced successfully).

We will review both your .Rmd file and the .html file. To receive full credit:

You must submit your .Rmd file on time. It must be named exactly as specified, and it must knit without errors to produce a .html file.
The .html file should read as a well written report, with all results and graphs supported by text explaining what they are and, when appropriate, what conclusions can be drawn. Your report should not contain any extraneous material, such as leftovers from a template.
The R code in your .Rmd file must be clear, readable, and follow the coding standards.
The text in your .Rmd file must be readable and use R markdown properly, as shown in the class template file.

Create a new folder called HW6 in your repository. Use exactly this spelling with upper case letters. You can do this in the RStudio IDE, with R’s dir.create function, or using a shell.

In this folder, create a new Rmarkdown file called hw6.Rmd. Again use exactly this spelling. RStudio will give you a template, or you can use the one available here. Commit your new file to your repository. (If you are using git in a shell you will need to use git add before git commit).

In this file present your answers to the following problems. Your presentation should follow the pattern and guidelines in the class template file.

1. Highway Fuel Economy Over the Years, Revisited

In Problem 4 of Assignment 4 you created a strip plot showing highway fuel economy values for each of the years from 2000 through 2025. Compare your result to three other options:

box plots;
violin plots;
ridgeline plots.

Comment on the advantages and disadvanteges of each approach in this case.

2. Top Fleet Highway Gas Mileages

For the EPA data used in the last two assignments compute the average highway gas mileage and average city gas mileage for each manufacturer’s vehicles for the year 2025 in the data set. Select the manufacturers with the top five average highway gas mileage values and show the results as a nicely formatted table. The rows should be arranged in descending order of the average highway gas mileage value. [You can compute the results using filter, grouped summarize, slice_max and arrange steps.]

Do not commit the vehicles.csv.zip file to your repository as it is quite large. Use the approach shown in Assignment 4 instead.

3. Top Destinations

For the nycflights13 data identify the top four destinations with the most flights to them from New York City in 2013. For each of these four destinations find the proportion of flights that originate from each of the three New York City airports. Show the results as a faceted bar chart, with one panel for each of the four destinations. [After computing the top destinations with count and slice_max you can use filter or semi_join to select the flights to those destinations, and then find the proportions with a count followed by a grouped mutate.]

4. Summer-Only Destinations

For the nycflights13 data find the destinations for which there are only flights in the months June, July, and August from the three New York City ariports. Present the result in a nicely formatted table that shows the three-letter airport code, the airport name from the airportstable, and the number of flights to each of these destinations. [One approach: After using filter to select the summer flights and another filter to select the non-summer flights you can use anti_join to find the summer flights with destinations only flown to in summer and then semi_join to find the corresponding entries in the airports table. You can then bring in counts from a counts table with a left_join.]

5. High Altitude Destinations

For the nycflights13 data identify the destination airports at an altitude of more than 5,000 feet and compute how many flights there were to each from New York City in 2013. Present the results as a nicely formatted table.

Create an HTML File and Commit Your Work

You can create an HTML file in RStudio using the Knit tab on the editor window. You can also use the R command

rmarkdown::render("hw6.Rmd")

with your working directory set to HW6.

Commit your changes to your hw6.Rmd file to your local git repository. You do not heed to commit your HTML file.

Submit your work by pushing your local repository changes to your remote repository on the UI GitLab site. After doing this, it is a good idea to check your repository on the UI GitLab site to make sure everything has been submitted successfully