Exercise 3 (A)

Exercise: Phased Multi-Year NHANES Data Wrangling

Instructions: Use the R programming language and functions from the tidyverse, nhanesA, tableone, and naniar packages to complete this exercise. We will build up our dataset in phases, starting with a single year and expanding to multiple survey cycles.

Please knit your final R Markdown file and submit the knitted HTML or PDF document ONLY.

Setup: Load Packages

First, run the following code block to ensure the required packages are installed and loaded into your R session.

# Load required packages

Problem 1: Import and Translate Single-Year Data

Download the Demographic (DEMO) data for the 2013-2014 NHANES cycle, using translated = TRUE to automatically convert coded values into text labels.

# Download and translate the 2013-2014 demographic data

# Check the first few rows to see the translated values

Problem 2: Add Body Measures Data for a Single Year

Download the Body Measures (BMX) data for the same 2013-2014 cycle and merge it with the demographic data from Problem 1.

# Download the 2013-2014 body measures data

# Merge the BMX data with the DEMO data from Problem 1

# Check the dimensions of the merged dataset

Problem 3: Import and Merge Multi-Cycle Data with Translation

Expand to multiple years, using translated = TRUE for all downloads. Merge both the Demographic (DEMO) and Body Measures (BMX) data for all three NHANES cycles: 2013-2014 (H), 2015-2016 (I), and 2017-2018 (J). Combine them into a single dataframe named nhanes_raw.

# Define the cycles to download

# Create a list to store data from each cycle

# Loop through each cycle, download with translation, and merge the data

# Combine the data from all cycles into one dataframe

# Check the dimensions of the final raw dataset

Problem 4: Data Cleaning and Filtering

Using the nhanes_raw dataset, we will now create our clean dataset. This involves filtering our population to adults and then creating our analysis variables.

  1. Filter for Adults: Keep only participants aged 20 years or older.
  2. Rename Variables: RIAGENDR to Sex, RIDAGEYR to Age, RIDRETH3 to RaceEthnicity, BMXBMI to BMI.
  3. Group RaceEthnicity: Combine “Mexican American” and “Other Hispanic” into a single “Hispanic” category.
  4. Create AgeGroup: Categorize Age into “20-39”, “40-59”, and “60+”.
  5. Create BMICat: Categorize BMI into “Underweight”, “Normal weight”, “Overweight”, and “Obese”.
# nhanes_clean <- ..
  # Step 1: Filter the data to include only adults
   
  # Step 2: Rename variables
   
  # Create new variables
   
    # Convert the new character RaceEthnicity to a factor with the desired level order
     
    
    # Step 4: Create AgeGroup (now without NAs because we filtered)
     
                   
    # Step 5: Create BMICat
     

# Check the structure to confirm variables are correct

Problem 5: Create Final Analytic Dataset

Create a final, analysis-ready dataset named nhanes_analysis that includes only the key variables.


# Display the structure of the final analytic dataset

Problem 6: Investigate Missing Data

Now that our data is correctly filtered and processed, let’s re-examine the missing data patterns. The missingness should be much lower.

# 1. Count missing values for each column

# 2. Visualize the missing data

Problem 7: Create a Descriptive Table

Finally, with the data correctly loaded and cleaned for our adult population, create the summary table of sample characteristics, stratified by Sex.

# Define the variables for the table

# Create the table, stratified by Sex

# Print the table, showing all levels and missing data counts