Exercise 3 (A) Vibe

In this tutorial, we will be going through the following exercise (found here).

We will go through the exercise problems and evaluate how well Gemini performs on each task. These tasks include importing and merging data, cleaning and filtering observations, and once the final dataset is completed, examining missing data, and displaying the data in a table.

In this part of the tutorial, we will go through the first set of tasks, which includes loading, subsetting the data according to eligibility criteria and necessary variables, creating an analytic dataset, and reporting the number of columns and variable names in this dataset. These tutorials were completed in RStudio (Posit team 2023) using R version 4.3.2 (R Core Team 2023), with the following packages: tidyverse (Wickham et al. 2019), and nhanesA (Ale et al. 2024), tableone (Yoshida and Bartel 2022), and naniar (Tierney and Cook 2023)

For this tutorial, I used Gemini’s free 2.5 Flash model.


Load Required Packages

First, we’ll run the following code to install and load all required packages into our R session.

packages <- c("tidyverse", "nhanesA", "tableone", "naniar")
lapply(packages, function(pkg) {
  if (!require(pkg, character.only = TRUE)) install.packages(pkg, dependencies = TRUE)
  library(pkg, character.only = TRUE)
})


Problem 1: Import and Translate Single-Year Data

For this first problem, we want to download the Demographic (DEMO) data from the 2013-2014 NHANES cycle using the nhanes package. We will also want translate = TRUE to automatically convert coded values into text labels.


Prompt Sent to Gemini:

Note

Download the Demographic (DEMO) data for the 2013–2014 NHANES cycle using the nhanes() function.

  • Set translated = TRUE so that coded values are automatically converted into text labels.

  • Print the first few rows of the variables translated (SEQN, RIAGENDR, RIDAGEYR, and RIDRETH3)

  • Provide the R code to complete this task.


Gemini’s response:

Tip 
# Download the Demographic (DEMO) data for the 2013–2014 NHANES cycle
demo_2013_2014 <- nhanes( 'DEMO_H', translated = TRUE )

# Select the specified variables and print the first few rows
head(demo_2013_2014[, c("SEQN", "RIAGENDR", "RIDAGEYR", "RIDRETH3")])


No issues were observed. Gemini successfully downloaded and translated the 2013–2014 demographic data, and printed the head of the dataset to display the translated values.


Problem 2: Add Body Measures Data for a Single Year

Using our newly created dataset for 2013-2014 Demographic data, we want to download the Body Measures (BMX) data for the same 2013-2014 cycle and merge it to the demographic data from Problem 1.


Prompt Sent to Gemini:

Note

Download the Body Measures (BMX) data for the 2013–2014 NHANES cycle.

  • Merge it with the Demographic (DEMO) data from Problem 1 using the participant identifier (SEQN) and the full_join() function.

  • After merging, check the dimensions of the completed dataset (nhanes_1314_complete). The dimensions should be 10175 rows and 72 columns.

  • Provide the R code to complete this task.


Gemini’s response:

Tip 
# Download the Body Measures (BMX) data for the 2013–2014 NHANES cycle
bmx_2013_2014 <- nhanes( 'BMX_H', translated = TRUE )

# Merge the DEMO and BMX data frames using the participant identifier (SEQN)
nhanes_1314_complete <- full_join(demo_2013_2014, bmx_2013_2014, by = "SEQN")

# Check the dimensions of the merged dataset
dim(nhanes_1314_complete)
#> [1] 10175    72


That successfully performed a full merge. Gemini was able to load the correct Body Measures data for 2013–2014 and successfully performed a full join with the previously loaded Demographic data from the same year.


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

Now that we have both the Demographic (DEMO) and Body Measures (BMX) data for the 2013–2014 cycle, we want to extend this by loading the same data for the 2015–2016 and 2017–2018 cycles. Once all cycles are loaded, we will combine them into a single dataframe named nhanes_raw.


Prompt Sent to Gemini:

Note

Download the Demographic (DEMO) and Body Measures (BMX) data for the three NHANES cycles — 2013–2014 (H), 2015–2016 (I), and 2017–2018 (J) — using translated = TRUE for all downloads.

  • For each cycle, merge the DEMO and BMX datasets by the participant identifier (SEQN) using the full_join() function.

  • Combine the merged datasets from all three cycles into a single dataframe named nhanes_raw.

  • After combining, check the dimensions of nhanes_raw. The dimensions should be 29400 rows and 78 columns.

  • Provide the R code to complete this task.


Gemini’s response:

Tip 
# --- Cycle 2013-2014 (H) ---
# Download DEMO and BMX data
demo_H <- nhanes('DEMO_H', translated = TRUE)
bmx_H <- nhanes('BMX_H', translated = TRUE)
# Merge the datasets for this cycle
nhanes_H <- full_join(demo_H, bmx_H, by = "SEQN")

# --- Cycle 2015-2016 (I) ---
# Download DEMO and BMX data
demo_I <- nhanes('DEMO_I', translated = TRUE)
bmx_I <- nhanes('BMX_I', translated = TRUE)
# Merge the datasets for this cycle
nhanes_I <- full_join(demo_I, bmx_I, by = "SEQN")

# --- Cycle 2017-2018 (J) ---
# Download DEMO and BMX data
demo_J <- nhanes('DEMO_J', translated = TRUE)
bmx_J <- nhanes('BMX_J', translated = TRUE)
# Merge the datasets for this cycle
nhanes_J <- full_join(demo_J, bmx_J, by = "SEQN")

# --- Combine all three cycles ---
# Use bind_rows to combine the merged datasets into a single dataframe
nhanes_raw <- bind_rows(nhanes_H, nhanes_I, nhanes_J)

# Check the dimensions of the final combined dataset
dim(nhanes_raw)
#> [1] 29400    78


Based on the dimensions of Gemini’s final combined dataset, the code worked as intended. Gemini successfully loaded and translated the data for each specified cycle and combined them into a single dataframe.


Problem 4: Data Cleaning and Filtering

We now have a dataframe containing demographic and body measure data for the 2013–2014, 2015–2016, and 2017–2018 NHANES cycles. Using this combined dataset (nhanes_raw), we will begin the process of cleaning and preparing the data for analysis. This includes filtering the population to include only adults and creating new variables based on existing columns.

The steps include:

  • Filtering for adults aged 20 years or older

  • Renaming key variables for clarity (RIAGENDR to Sex, RIDAGEYR to Age, RIDRETH3 to RaceEthnicity, and BMXBMI to BMI)

  • Grouping race/ethnicity by combining “Mexican American” and “Other Hispanic” into a single “Hispanic” category

  • Creating an AgeGroup variable with categories: “20–39”, “40–59”, and “60+”

  • Creating a BMICat variable to classify BMI as “Underweight”, “Normal weight”, “Overweight”, or “Obese”


Prompt Sent to Gemini:

Note

Using the nhanes_raw dataset, create a cleaned dataset suitable for analysis:

  • Filter for adults: keep only participants aged 20 years or older.

  • Rename variables:

    • RIAGENDR → Sex

    • RIDAGEYR → Age

    • RIDRETH3 → RaceEthnicity

    • BMXBMI → BMI

  • Group Race/Ethnicity:

    • Combine "Mexican American" and "Other Hispanic" into "Hispanic"

    • Keep "Non-Hispanic White", "Non-Hispanic Black", and "Non-Hispanic Asian" as they are

    • Recode all other categories (including NA) as "Other"

  • Create AgeGroup: categorize Age into "20-39", "40-59", and "60+".

  • Create BMICat: categorize BMI into "Underweight", "Normal weight", "Overweight", and "Obese".

Use tidyverse syntax and provide the R code to complete this task.


Gemini’s response:

Tip 
# Create the cleaned dataset suitable for analysis
nhanes_adult <- nhanes_raw %>%
  # Filter for adults aged 20 years or older
  filter(RIDAGEYR >= 20) %>%
  # Rename variables for clarity
  rename(
    Sex = RIAGENDR,
    Age = RIDAGEYR,
    RaceEthnicity = RIDRETH3,
    BMI = BMXBMI
  ) %>%
  # Group Mexican American and Other Hispanic into a single Hispanic category
  # and recode all other categories (including NA) as "Other"
  mutate(RaceEthnicity = case_when(
    RaceEthnicity %in% c("Mexican American", "Other Hispanic") ~ "Hispanic",
    RaceEthnicity == "Non-Hispanic White" ~ "Non-Hispanic White",
    RaceEthnicity == "Non-Hispanic Black" ~ "Non-Hispanic Black",
    RaceEthnicity == "Non-Hispanic Asian" ~ "Non-Hispanic Asian",
    TRUE ~ "Other"
  )) %>%
  # Create AgeGroup categories
  mutate(AgeGroup = case_when(
    Age >= 60 ~ "60+",
    Age >= 40 ~ "40-59",
    Age >= 20 ~ "20-39"
  )) %>%
  # Create BMI categories
  mutate(BMICat = case_when(
    BMI < 18.5 ~ "Underweight",
    BMI >= 18.5 & BMI < 25 ~ "Normal weight",
    BMI >= 25 & BMI < 30 ~ "Overweight",
    BMI >= 30 ~ "Obese"
  ))

str(nhanes_adult[, c("SEQN", "Sex", "Age", "AgeGroup", "RaceEthnicity", "BMI")])
#> 'data.frame':    17057 obs. of  6 variables:
#>  $ SEQN         : num  73557 73558 73559 73561 73562 ...
#>  $ Sex          : Factor w/ 2 levels "Male","Female": 1 1 1 2 1 2 1 2 1 2 ...
#>  $ Age          : num  69 54 72 73 56 61 42 56 65 26 ...
#>  $ AgeGroup     : chr  "60+" "40-59" "60+" "60+" ...
#>  $ RaceEthnicity: chr  "Non-Hispanic Black" "Non-Hispanic White" "Non-Hispanic White" "Non-Hispanic White" ...
#>  $ BMI          : num  26.7 28.6 28.9 19.7 41.7 35.7 NA 26.5 22 20.3 ...


Everything looks correct. Gemini successfully filtered the dataset, renamed the variables, and created the new grouped and categorized variables as expected.


Problem 5: Create Final Analytic Dataset

Now that the data has been correctly cleaned and filtered, the next step is to create a final, analysis-ready dataset named nhanes_analysis that includes only the key variables needed for analysis.


Prompt Sent to Gemini:

Note

From the cleaned dataset, create a final analysis-ready dataset named nhanes_analysis.

  • Keep only the key variables needed for analysis:

    • SEQN

    • Sex

    • Age

    • AgeGroup

    • RaceEthnicity

    • BMI

    • BMICat

  • Ensure the dataset is tidy, with variables properly labeled and ready for statistical analysis.

  • Provide the R code to complete this task.


Gemini’s response:

Tip 
# Create the final analysis-ready dataset by selecting key variables
nhanes_analysis <- nhanes_adult %>%
  select(
    SEQN,
    Sex,
    Age,
    AgeGroup,
    RaceEthnicity,
    BMI,
    BMICat
  )

# Print the internal structure of the final dataset
str(nhanes_analysis)
#> 'data.frame':    17057 obs. of  7 variables:
#>  $ SEQN         : num  73557 73558 73559 73561 73562 ...
#>  $ Sex          : Factor w/ 2 levels "Male","Female": 1 1 1 2 1 2 1 2 1 2 ...
#>  $ Age          : num  69 54 72 73 56 61 42 56 65 26 ...
#>  $ AgeGroup     : chr  "60+" "40-59" "60+" "60+" ...
#>  $ RaceEthnicity: chr  "Non-Hispanic Black" "Non-Hispanic White" "Non-Hispanic White" "Non-Hispanic White" ...
#>  $ BMI          : num  26.7 28.6 28.9 19.7 41.7 35.7 NA 26.5 22 20.3 ...
#>  $ BMICat       : chr  "Overweight" "Overweight" "Overweight" "Normal weight" ...

# Print the first few rows of the final dataset to verify
print(head(nhanes_analysis))
#>    SEQN    Sex Age AgeGroup      RaceEthnicity  BMI        BMICat
#> 1 73557   Male  69      60+ Non-Hispanic Black 26.7    Overweight
#> 2 73558   Male  54    40-59 Non-Hispanic White 28.6    Overweight
#> 3 73559   Male  72      60+ Non-Hispanic White 28.9    Overweight
#> 4 73561 Female  73      60+ Non-Hispanic White 19.7 Normal weight
#> 5 73562   Male  56    40-59           Hispanic 41.7         Obese
#> 6 73564 Female  61      60+ Non-Hispanic White 35.7         Obese

# Print the dimensions of the final dataset
print(dim(nhanes_analysis))
#> [1] 17057     7


The final dataset has been successfully created. nhanes_analysis now contains the cleaned and formatted variables ready for analysis.


Problem 6: Investigating Missing Data

Now that our data has been properly filtered and processed, we can re-examine patterns of missingness. We’ll start by checking the number of missing values in each variable, then use the naniar package to visualize the extent of missing data in the nhanes_analysis dataset.


Prompt Sent to Gemini:

Note

Now that the data has been filtered and processed, re-examine patterns of missingness in the nhanes_analysis dataset.

  • Check the number of missing values in each variable.

  • Use the naniar package and gg_miss_var function to visualize the extent of missing data across the dataset. Title the plot “Missing Data in NHANES Adult Participants (2013-2018)”

  • Provide the R code to complete this task.


Gemini’s response:

Tip 
# Check the number of missing values in each variable
colSums(is.na(nhanes_analysis))
#>          SEQN           Sex           Age      AgeGroup RaceEthnicity 
#>             0             0             0             0             0 
#>           BMI        BMICat 
#>           956           956

# Create a bar plot of missing data by variable, showing percentages
gg_miss_var(nhanes_analysis, show_pct = TRUE) +
  labs(title = "Missing Data in NHANES Adult Participants (2013-2018)")


Gemini was able to perform the missingness check successfully. It provided the correct code using the gg_miss_var function to visualize missing values, and the output clearly showed that missingness is limited to the BMI-related variables.


Problem 7: Investigating Missing Data

Finally, with the data correctly loaded and cleaned for our adult population, we’ll create a summary table of sample characteristics, stratified by Sex, using the tableone package.


Prompt Sent to Gemini:

Note

With the data correctly loaded and cleaned for the adult population, create a summary table of sample characteristics using the tableone package.

  • Use the nhanes_analysis dataset.

  • Stratify the table by Sex.

  • Include the key variables: Age, AgeGroup, RaceEthnicity, BMI, and BMICat.

  • Report means and standard deviations for continuous variables, and counts and percentages for categorical variables.

  • Provide the R code to complete this task.


Gemini’s response:

Tip 
# Define the variables to be included in the table
vars <- c("Age", "AgeGroup", "RaceEthnicity", "BMI", "BMICat")

# Define the variables to be stratified by (Sex)
strata <- "Sex"

# Create the summary table
table_one <- CreateTableOne(
  vars = vars,
  strata = strata,
  data = nhanes_analysis,
  factorVars = c("AgeGroup", "RaceEthnicity", "BMICat"), addOverall = TRUE, test = FALSE
)

# Print the table with both mean/SD for continuous variables
# and counts/percentages for categorical variables.
print(table_one, , smd = FALSE, showAllLevels = TRUE, missing = TRUE)
#>                    Stratified by Sex
#>                     level              Overall       Male         
#>   n                                    17057          8207        
#>   Age (mean (SD))                      50.03 (17.74) 50.23 (17.83)
#>   AgeGroup (%)      20-39               5594 (32.8)   2687 (32.7) 
#>                     40-59               5571 (32.7)   2612 (31.8) 
#>                     60+                 5892 (34.5)   2908 (35.4) 
#>   RaceEthnicity (%) Hispanic            4290 (25.2)   1978 (24.1) 
#>                     Non-Hispanic Asian  2168 (12.7)   1034 (12.6) 
#>                     Non-Hispanic Black  3673 (21.5)   1759 (21.4) 
#>                     Non-Hispanic White  6270 (36.8)   3094 (37.7) 
#>                     Other                656 ( 3.8)    342 ( 4.2) 
#>   BMI (mean (SD))                      29.49 (7.21)  28.93 (6.33) 
#>   BMICat (%)        Normal weight       4244 (26.4)   1966 (25.5) 
#>                     Obese               6442 (40.0)   2790 (36.2) 
#>                     Overweight          5168 (32.1)   2853 (37.0) 
#>                     Underweight          247 ( 1.5)    105 ( 1.4) 
#>                    Stratified by Sex
#>                     Female        Missing
#>   n                  8850                
#>   Age (mean (SD))   49.86 (17.66) 0.0    
#>   AgeGroup (%)       2907 (32.8)  0.0    
#>                      2959 (33.4)         
#>                      2984 (33.7)         
#>   RaceEthnicity (%)  2312 (26.1)  0.0    
#>                      1134 (12.8)         
#>                      1914 (21.6)         
#>                      3176 (35.9)         
#>                       314 ( 3.5)         
#>   BMI (mean (SD))   30.01 (7.90)  5.6    
#>   BMICat (%)         2278 (27.2)  5.6    
#>                      3652 (43.5)         
#>                      2315 (27.6)         
#>                       142 ( 1.7)


Gemini successfully generated the summary table using the tableone package. The table is correctly stratified by Sex and includes the key variables, providing a clear overview of the sample characteristics.


Summary

Throughout this tutorial, Gemini consistently performed well in executing each step of the data processing workflow. It correctly loaded and translated NHANES data across multiple cycles, successfully merged datasets, and applied the necessary cleaning and filtering steps. Gemini also handled variable recoding, missingness checks, and descriptive summarization with accuracy and appropriate use of R packages.


What Worked Well

  • Accurately loaded, translated, and merged NHANES data across multiple cycles

  • Correctly filtered for the adult population and applied exclusion criteria

  • Successfully renamed and recoded variables to match analysis specifications

  • Appropriately checked for missingness using the naniar package

  • Generated a clear and complete summary table using the tableone package, stratified by Sex

References

Ale, Laha, Robert Gentleman, Teresa Filshtein Sonmez, Deepayan Sarkar, and Christopher Endres. 2024. “nhanesA: Achieving Transparency and Reproducibility in NHANES Research.” Database Apr 15. https://doi.org/10.1093/database/baae028.
Posit team. 2023. RStudio: Integrated Development Environment for r. Boston, MA: Posit Software, PBC. http://www.posit.co/.
R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Tierney, Nicholas, and Dianne Cook. 2023. “Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations.” Journal of Statistical Software 105 (7): 1–31. https://doi.org/10.18637/jss.v105.i07.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.
Yoshida, Kazuki, and Alexander Bartel. 2022. Tableone: Create ’Table 1’ to Describe Baseline Characteristics with or Without Propensity Score Weights. https://CRAN.R-project.org/package=tableone.