Practice Activity Week 1

Accessing the Practice Activity

Download the template Practice Activity Quarto file here: pa-1.qmd

Important

Be sure to save the file inside your Week 2 folder of your STAT 431 (or 541) folder!

Quarto

First, let’s make sure you know how to use Markdown formatting to style a Quarto document.

  1. Make this text bold.

  2. Make this text italicized.

  3. Make these items into a bullet point list:

Apples Bananas Potatoes

  1. Edit the YAML to remove warning messages from being output in the rendered HTML file

  2. Using code chunk options, make it so this chunk shows the code but not the output:

2 + 2
[1] 4
  1. Using code chunk options, make it so the file can still render even though this chunk has an error
2 + a
Error: object 'a' not found

Data Visualization Review

In this class, we will exclusively make visualizations using ggplot2 and packages developed to be compatible with ggplot2. If you are less familiar with plotting in ggplot2 or it’s been some time since you used this package, I would strongly recommend referencing resources from the data visualization section of this week’s coursework!

The questions below (Questions 7-11) utilize the mpg data from the tidyverse package.

  1. Use side-by-side boxplots to visualize the distribution of hwy miles per gallon for each manufacturer. The names of the manufacturers should be easily readable, and your axes should include any necessary information regarding the variables and their units!
  1. Use faceted scatterplots to visualize how the relationship between hwy mileage and cty mileage has changed over time. Your axes should include any necessary information regarding the variables and their units!

Data Wrangling Review

In this class, we will exclusively make use of tools from the tidyverse suite of packages to perform our data cleaning and wrangling operations. If you are less familiar with these packages or it’s been some time since you used them, I would strongly recommend referencing the function documentation!

For these problems, we will continue to work with the mpg data frame, making various changes to the data to clean it up.

  1. The fl variable describes the type of fuel for each car, with levels: p, r, e, d, and c. Do some research into what each of these labels mean! Then, use the mutate() and if_else() functions to create a new variable (fuel_type) with two levels: petrol (any car using petrolium-based gas) and alternative energy (any car not using petrolium-based gas).
  1. The drv variable describes if the car has front drive (f), rear drive (r), or four wheel drive (4). Let’s make better labels for these values! Specifically, use the mutate() and recode_values() to change the drv variable to have the following descriptive levels: front, rear, four wheel.

Hint: The recode_values() function was added to dplyr version 1.2.0. If you don’t see this function you need to update your version of dplyr!

  1. The trans variable contains two pieces of information, (1) the transmission style (auto or manual) and the specific type of transmission (e.g., l5, m5, av). Using the separate_wider_delim() function, create two new variables from trans, (1) trans_type containing the specific type of transmission of each car (e.g., l5), and (2) trans_style the style of the transmission (e.g., auto). Hint: You will need to deal with the stray parenthesis!

Getting to know your classmates

  1. Find someone who took Stat 331 from a different professor than you. Compare your experiences. Tell me their name and professor. List one or two things that you think you learned more about, and one or two things that they learned more about.

  2. Find someone in the class who does not share your birth month. Tell me their name and birthday, and use R to find out how many days apart your birthdays are.