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.

Some advice:

• Google can be your friend! If an error is confusing, copy it into Google and see what other people are saying. BUT only Google if you and your partner cannot determine the solution. Google is the last resort.

• Just because there is no error message doesn’t mean everything went smoothly! Use the console (lower left pane) to check each step and make sure you have accomplished what you wanted to accomplish.

Groupwork Protocols

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

When you are the Computer, 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 Computer 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 Computer to debug the code you specified. Once the code runs, you are expected to collaborate with the Computer to write code comments that describe the actions taken by your code.

Here are more details of the Pair Programming Protocols

Group Norms

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

1. Be curious. Don’t correct.
2. Be open minded.
3. Ask questions rather than contribute.
4. Respect each other.
5. Allow each teammate to contribute to the activity through their role.
6. Do not divide the work.
7. No cross talk with other groups.
8. Communicate with each other!

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 Quarto doc
  
• 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 2015. 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.

Note

The partner who most recently watched an Avengers movies is the Computer (typing and listening to instructions from the Coder) and should set up the project on their account.

Setting up your Project

Your project should have the following components:

1. data-clean that will eventually hold your cleaned up data
   
2. completed pa-4-import-tidy-data-activity.qmd
3. rendered file as .html

Step 1: Attempt to Read in the Data

Go ahead and read in the data, a .csv, calling it youth. Note that the original data file is quite large, so we will read it in from GitHub where it is stored. What do you notice?

Describe some of the issues you notice in the data.

Step 2: Skip the First Row

Using either the help options or the readr cheatsheet, 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 = _________)

Here is some code structure to help get you started:

list(`AgeCat` = ,
     `Grade` = ,
     `Perception of weight` = ,
     `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` = ,
     .default = )

Step 5: Clean the Variable Names

Warning

You may need to install janitor

Use the janitor package to clean the variable names to snake_case. You may need to install it first.

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.

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

Insert Answer Here

Read the clean data back into your environment (Quarto document), calling it youth_clean.

Step 7: Explore Missingness

Warning

You may need to install naniar

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 (If Time)

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. You will submit the html separately from the zipped project file.