Assignment 6
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
.Rmdfile on time. It must be named exactly as specified, and it must knit without errors to produce a.htmlfile.The
.htmlfile 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
.Rmdfile must be clear, readable, and follow the coding standards.The text in your
.Rmdfile 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. Self-Reported Heights
The data set heights in package dslabs contains self-reported heights for a number of female and male students. You can load the data set with
data(heights, package = "dslabs")Construct a density plot showing the densities of the height distributions for males and for females. Also construct an eCDF plot showing the empirical cumulative distributions for the heights of the two groups. (You can do this using stat_ecdf and mapping x to height and color to sex).
Comment on what features are easier to see in one plot or the other.
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 2022 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 and the airport name from the airportstable. [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.]
5. High Altitude Destinations
For the nycflights13 data identify the destination airports at an altitude of more than 6,000 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