Lab 1

library(tidyverse)
library(ggridges)

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 into a bullet point list:

• Apples

• Bananas

• Potatoes

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

5. Using code chunk options, make it so this chunk shows the plot but not the source code:

6. Using code chunk options, remove the messages about bandwidth geom_density_ridges() chose to use:

ggplot(data = mpg, 
       mapping = aes(y = manufacturer, x = hwy)) + 
  geom_density_ridges() +
  labs(x = "",
       y = "", 
       title = "Highway Milage (mpg) for Different Car Manufacturers"
       )

7. Using code chunk options, make it so that these plots are printed side-by-side:

ggplot(data = mpg, 
       mapping = aes(y = manufacturer, x = hwy)) + 
  geom_boxplot() +
  labs(x = "",
       y = "", 
       title = "Highway Milage (mpg) for Different Car Manufacturers"
       )
ggplot(data = mpg, 
       mapping = aes(y = manufacturer, x = hwy)) + 
  geom_density_ridges() +
  labs(x = "",
       y = "", 
       title = "Highway Milage (mpg) for Different Car Manufacturers"
       )

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

2 + 2

9. Using code chunk options, make it so the file can still knit even though this chunk has an error

2 + a

10. Using code chunk options, create a descriptive label for each of the code chunks above.

Data Wrangling Review

Since you already seen some ggplots, let’s do a bit of review on data handling. 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.

11. 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 if_else() function to create a new variable (fuel_type) with two levels: petrol (any car using petroleum-based gas) and alternative energy (any car not using petroleum-based gas).

P: premium, R: regular, E: electric, D: diesel, C: Compressed Natural Gas (CNG) (no clear official documentation, this could be incorrect)

mpg <- mpg %>%
  mutate(fuel_type = if_else(fl == "c"|fl=="e", "alternative energy","petrol"))

12. 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 case_when() function to change the drv variable to have the following levels: front, rear, four wheel.

mpg <- mpg %>%
  mutate(drv = case_when(
    drv == "f"~"front",
    drv == "r"~"rear",
    drv == "4"~"four wheel"
  ))

13. 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). Using the str_split() function, create a new variable (trans_type) containing the specific type of transmission of each car. Once you’ve made this new variable, use the rename() function to change the name of the trans column to trans_style.
Hint: You will need to deal with the stray parenthesis! (string split + remove extra “)” )

mpg <- mpg %>% 
  mutate(trans_type = str_split(trans, "\\(", simplify = T)[,2],
         trans_type = str_remove(trans_type,"\\)"),
         trans = str_split(trans, "\\(", simplify = T)[,1]
         ) %>%
  rename(trans_style=trans)

Getting to know your classmates

14. 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.

    Jacob (the GOAT) took STAT 331 with Dr. Theobold.

    Jacob learned more about stringr. I did more work with dplyr and tidyverse as this was the main focus of the course.

15. 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.

    Rachel’s birthday is August 14, 2002. Mine is July 11, 2002.

    start_date <- as.Date("2002-07-11")
    end_date   <- as.Date("2002-08-14")
    num_days <- end_date - start_date
    num_days

    Time difference of 34 days