PA 4: Preparing the Youth Risk Behavior Analysis

This task is complex. It requires many different types of abilities. Everyone will be good at some of these abilities but nobody will be good at all of them. In order to solve this puzzle, you will need to use the skills of each member of your group.

Groupwork Protocols

During the Practice Activity, you and your partner will alternate between two roles—Developer and Coder.

When you are the Developer, you will type into the Quarto document in RStudio. However, you do not type your own ideas. Instead, you type what the Coder tells you to type. You are permitted to ask the Coder clarifying questions, and, if both of you have a question, you are permitted to ask the professor. You are expected to run the code provided by the Coder and, if necessary, to work with the Coder to debug the code. Once the code runs, you are expected to collaborate with the Coder to write code comments that describe the actions taken by your code.

When you are the Coder, you are responsible for reading the instructions / prompts and directing the Developer what to type in the Quarto document. You are responsible for managing the resources your group has available to you (e.g., cheatsheet, textbook). If necessary, you should work with the Developer to debug the code you specified. Once the code runs, you are expected to collaborate with the Developer to write code comments that describe the actions taken by your code.

Group Norms

Remember, your group is expected to adhere to the following norms:

  1. Think and work together. Do not divide the work.
  2. You are smarter together.
  3. No cross-talk with other groups.

Goals for the Activity

  • Solve issues in your data as you read it in by parsing out text from numbers, specifying NA values, and cleaning up variable names

THROUGHOUT the Activity be sure to follow the Style Guide by doing the following:

  • load the appropriate packages at the beginning of the Rmarkdown
  • use proper spacing
  • name all code chunks
  • comment at least once in each code chunk to describe why you made your coding decisions
  • add appropriate labels to all graphic axes

Youth Risk Behavior Surveillance Systems (YRBSS)

This data is from a survey given every other year called the Youth Risk Behavior Surveillance System administered by the Center for Disease Control and Prevention. The survey monitors health risks among youth, such as violence, sexually transmitted diseases, tobacco, and alcohol use.

The data set contains over 100,000 rows from 2023. A code book is also provided that gives extensive information on the survey. The data can be stored in an Excel or spreadsheet program, but it cannot be manipulated in those programs because of its size - this why we need R.

Setting up your Project

Your project should have the following components:

  1. data-raw folder that contains the provided data set
  2. data-clean that will eventually hold your cleaned up data
  3. completed .qmd and rendered file

Step 1: Attempt to Read in the Data

Go ahead and read in the data, calling it youth. What do you notice?

youth <- read_csv("data-raw/YRBS2015.csv")

Describe some of the issues you notice in the data:

Insert answer here

Step 2: Skip the First Row

Using either the help options or the readr add an argument to the code above that will skip the first row of the data file so that the ‘headers’ (column titles) read in correctly.

Step 3: Fix the Missing Values

Take note of how missing values are designated in the data. Add an argument to the code above that identifies and fixes the data so that each missing value type is treated as the same NA.

Step 4: Clean A few of the Variables

Add additional arguments to the code above that does the following:

  • AgeCat: remove the text “years old” from the numeric values
  • Grade: remove the text “th grade” from the numeric values
  • Perception of weight: read as a factor with the following
    • levels = c("Very underweight","Slightly underweight", "About the right weight", "Slightly overweight", "Very overweight")
  • Set the following variables to the right data type:
    • Gender
    • Height in meters
    • Weight in kilograms
    • Body Mass Index
    • BMI percentile
    • Fruit eating
    • Salad eating
    • Other vegetable eating
    • Soda drinking
    • Breakfast eating
    • Physical activity >= 5 days
    • Television watching
    • Sports team participation
  • Skip the rest of the variables (hint set .default = _________)

Step 5: Clean the Variable Names

Use the janitor package to clean the variable names to snake_case.

Step 6: Write the Clean Data

Write the clean data to the data-clean folder.

Now that the data is clean, you can add eval: false to the prior code chunks and read in the clean data below.

youth_clean <-

Why do we keep the code for the data cleaning but set the code chunks to eval: false?

Insert Answer Here

Step 7: Explore Missingness

Look at the following graphs

youth_clean |> 
  naniar::gg_miss_var()
youth_clean |> 
    naniar::gg_miss_upset()

What do the two graphs tell you about the missingness in the data (i.e., which variables are missing the most values and which combinations of variables are missing the most together)?

Insert Answer Here

How does the missingness of the data impact what variables you might choose to analyze? Why?

Insert Answer Here

Step 8: Make a Visualization

youth_clean |>  #instead of data = youth_clean in ggplot we can "pipe" our data  
  drop_na() |>  #drops missing values
  ggplot()

Complete the following steps in your visualization in the code chunk above:

  • Plot the height on the x-axis
  • Plot the weight on the y-axis
  • Color the points based on student perception of weight
  • Add a linear model (use method = "lm" and geom_smooth())
  • Make the points more transparent
  • Facet by grade and gender
  • Add descriptive titles and labels
  • Export the graph as the file weight-perception.png

Challenge Modifications

  • Make the points a color-blind friendly color scale
  • Change the general theme of the graph
  • Move the legend to the bottom of the graph
  • Adjust the figure height and width to 10x10

Canvas Quiz

Working together as a team, the above analysis to answer the following questions on canvas:

  1. Which variable has the most missing values?

  2. Which variable combination has the most missing values?

Be ready to submit the complete project, with files rendered and created data and image files.