Homework 1

Your first major assignment is a set of exercises based around a single dataset called rail_trail, which will provide you with practice in creating visualizations using R and ggplot2.

Due: Month Day, Year @ Time

• Instructions
• The rail trail dataset
• How to describe your visualizations
• Questions
• How to submit

Instructions

Use the R Markdown file homework_1.Rmd to do your work and write-up when completing the questions below. Remember to fill in your name at the top of the R Markdown document and be sure to save, commit, and push (upload) frequently to Github so that you have incremental snapshots of your work. When you’re done, follow the How to submit section below to setup a Pull Request, which will be used for feedback.

• Remember that the point of us using RMarkdown documents is to combine code and writeups! Each block of R code should have some sort of explanation or justification using full sentences.

• Your grade will take into account your code, your explanations, and whether your document looks nice when “knitted” to PDF.

The rail trail dataset

For this homework assignment, you will be working though a set of visualization problems based on the rail_trail dataset. The rail_trail dataset was collected by the Pioneer Valley Planning Commission (PVPC) and counts the number of people that walked through a sensor on a rail trail during a ninety day period. A rail trail is a retired or abandoned railway that was converted into a walking trail. The data was collected from April 5, 2005 to November 15, 2005 using a laser sensor placed at a location north of Chestnut Street in Florence, MA.

The dataset contains the following variables:

Variable	Description
hightemp	daily high temperature (in degrees Fahrenheit)
lowtemp	daily low temperature (in degrees Fahrenheit)
avgtemp	average of daily low and daily high temperature (in degrees Fahrenheit)
season	indicates whether the season was Spring, Summer, or Fall
cloudcover	measure of cloud cover (in oktas)
precip	measure of precipitation (in inches)
volume	estimated number of trail users that day (number of breaks recorded)
weekday	indicator of whether the day was a non-holiday weekday

How to describe your visualizations

When describing the contents of a visualization, follow the ideas discussed in these resources:

• Describing univariate and bivariate data

• Data visualization - Examining numerical data slides

Questions

1. In the rail_trail dataset, how many rows are there? How many columns? Which variables in the dataset are continuous/numerical and which are categorical?

2. Create a histogram of the variable volume using the following code:

   ggplot(data = rail_trail) +
     geom_histogram(mapping = aes(x = volume))

   Describe the shape and center of the distribution. Afterward, try adjusting the size of the histogram bins by adding the binwidth input. To start with, use binwidth = 21. If you need help with where to place binwidth, read the documentation by running ?geom_histogram in your Console window. Then, find a binwidth that’s too narrow and another one that’s too wide to produce a meaningful histogram.

3. Choosing a proper bin width for a histogram can be tricky, and for that reason it’s preferable to use visualizations that avoid using bin widths when possible. An easy-to-use alternative to the histogram is geom_density, which creates a density plot. Use geom_density to create a density plot of the variable volume.

4. Create a density plot for each of the remaining numerical variables, and describe the shape and center of each distribution. Are there any distributions that are similar in shape to each other?

5. Use geom_point() to create a scatterplot that plots weekday versus season. Why is this plot not useful?

6. Create a geom_count() plot (an alternative to a mosaic plot) using the same variables you considered in question 5:

   ggplot(data = rail_trail) +
     geom_count(mapping = aes(x = season, y = weekday))

   Which circle in the plot takes up the most area? Explain the meaning of the different size circles in the plot and what information it contains that is missing in the previous scatter plot.

7. Run ?geom_bar in the Console window and read the documentation for geom_bar(), and then look at the entry for it on the ggplot2 cheatsheet Use geom_bar() to reproduce the following bar chart:

   

   After reproducing the plot, explain what the height of each bar means.

8. Starting from the code snippet you deduced in question 7, create two more bar charts:

   • Create a bar chart by supplying the input position = "dodge" to geom_bar()

   • Create a bar chart by supplying the input position = "fill" to geom_bar().

   After creating the visualizations, describe the feature that position controls.

9. Create a bar chart that maps its aesthetic aes() to precip > 0. Interpret what this bar chart means.

10. Create a scatter plot of volume versus hightemp using geom_point(). Describe any trends that you see.

11. Take the code snippet you wrote for question 10 and map the weekday variable to color. Then create a second plot where, instead of mapping weekday to color, you facet over weekday using either facet_wrap() or facet_grid(). Discuss the advantages and disadvantages to faceting instead of mapping to the color aesthetic. How might the balance change if you had a larger dataset?

12. Take the code snippet that you wrote down in question 11 that faceted over weekday and create a model for each facet panel using geom_smooth(). Discuss the trends in the number of rail trail users that geom_smooth() picks up.

13. Copy the code snippet you deduced in question 12 and use the input se = FALSE for geom_smooth(). What does the se input option for geom_smooth() control?

How to submit

When you are ready to submit, be sure to save, commit, and push your final result so that everything is synchronized to Github. Then, navigate to your copy of the Github repository you used for this assignment. You should see your repository, along with the updated files that you just synchronized to Github. Confirm that your files are up-to-date, and then do the following steps:

1. Click the Pull Requests tab near the top of the page.

2. Click the green button that says “New pull request”.

3. Click the dropdown menu button labeled “base:”, and select the option grading.

4. Confirm that the dropdown menu button labeled “compare:” is set to master.

5. Click the green button that says “Create pull request”.

6. Give the pull request the following title: Submission: Homework 1, FirstName LastName, replacing FirstName and LastName with your actual first and last name.

7. In the messagebox, write: My homework submission is ready for grading @instructor_username.

8. Click “Create pull request” to lock in your submission.

Cheatsheets

You are encouraged to review and keep the following cheatsheets handy while working on this assignment:

• RStudio cheatsheet
• RMarkdown cheatsheet
• RMarkdown reference
• ggplot2 cheatsheet