—# Part 1: Setting Up My Workspace
# Load required packages
library(tidyverse) # Data manipulation (dplyr, ggplot2, etc.)
library(NHANES) # NHANES dataset
library(knitr) # For professional table output
library(kableExtra) # Enhanced tablesTroubleshooting: If you see an error, run this once:
install.packages("NHANES")Then reload: library(NHANES)
# Load the NHANES data
data(NHANES)
# Examine the first few rows
head(NHANES, n = 10)## # A tibble: 10 × 76
## ID SurveyYr Gender Age AgeDecade AgeMonths Race1 Race3 Education MaritalStatus HHIncome
## <int> <fct> <fct> <int> <fct> <int> <fct> <fct> <fct> <fct> <fct>
## 1 51624 2009_10 male 34 " 30-39" 409 White <NA> High School Married 25000-349…
## 2 51624 2009_10 male 34 " 30-39" 409 White <NA> High School Married 25000-349…
## 3 51624 2009_10 male 34 " 30-39" 409 White <NA> High School Married 25000-349…
## 4 51625 2009_10 male 4 " 0-9" 49 Other <NA> <NA> <NA> 20000-249…
## 5 51630 2009_10 female 49 " 40-49" 596 White <NA> Some College LivePartner 35000-449…
## 6 51638 2009_10 male 9 " 0-9" 115 White <NA> <NA> <NA> 75000-999…
## 7 51646 2009_10 male 8 " 0-9" 101 White <NA> <NA> <NA> 55000-649…
## 8 51647 2009_10 female 45 " 40-49" 541 White <NA> College Grad Married 75000-999…
## 9 51647 2009_10 female 45 " 40-49" 541 White <NA> College Grad Married 75000-999…
## 10 51647 2009_10 female 45 " 40-49" 541 White <NA> College Grad Married 75000-999…
## # ℹ 65 more variables: HHIncomeMid <int>, Poverty <dbl>, HomeRooms <int>, HomeOwn <fct>,
## # Work <fct>, Weight <dbl>, Length <dbl>, HeadCirc <dbl>, Height <dbl>, BMI <dbl>,
## # BMICatUnder20yrs <fct>, BMI_WHO <fct>, Pulse <int>, BPSysAve <int>, BPDiaAve <int>,
## # BPSys1 <int>, BPDia1 <int>, BPSys2 <int>, BPDia2 <int>, BPSys3 <int>, BPDia3 <int>,
## # Testosterone <dbl>, DirectChol <dbl>, TotChol <dbl>, UrineVol1 <int>, UrineFlow1 <dbl>,
## # UrineVol2 <int>, UrineFlow2 <dbl>, Diabetes <fct>, DiabetesAge <int>, HealthGen <fct>,
## # DaysPhysHlthBad <int>, DaysMentHlthBad <int>, LittleInterest <fct>, Depressed <fct>, …# View data structure
str(NHANES)## tibble [10,000 × 76] (S3: tbl_df/tbl/data.frame)
## $ ID : int [1:10000] 51624 51624 51624 51625 51630 51638 51646 51647 51647 51647 ...
## $ SurveyYr : Factor w/ 2 levels "2009_10","2011_12": 1 1 1 1 1 1 1 1 1 1 ...
## $ Gender : Factor w/ 2 levels "female","male": 2 2 2 2 1 2 2 1 1 1 ...
## $ Age : int [1:10000] 34 34 34 4 49 9 8 45 45 45 ...
## $ AgeDecade : Factor w/ 8 levels " 0-9"," 10-19",..: 4 4 4 1 5 1 1 5 5 5 ...
## $ AgeMonths : int [1:10000] 409 409 409 49 596 115 101 541 541 541 ...
## $ Race1 : Factor w/ 5 levels "Black","Hispanic",..: 4 4 4 5 4 4 4 4 4 4 ...
## $ Race3 : Factor w/ 6 levels "Asian","Black",..: NA NA NA NA NA NA NA NA NA NA ...
## $ Education : Factor w/ 5 levels "8th Grade","9 - 11th Grade",..: 3 3 3 NA 4 NA NA 5 5 5 ...
## $ MaritalStatus : Factor w/ 6 levels "Divorced","LivePartner",..: 3 3 3 NA 2 NA NA 3 3 3 ...
## $ HHIncome : Factor w/ 12 levels " 0-4999"," 5000-9999",..: 6 6 6 5 7 11 9 11 11 11 ...
## $ HHIncomeMid : int [1:10000] 30000 30000 30000 22500 40000 87500 60000 87500 87500 87500 ...
## $ Poverty : num [1:10000] 1.36 1.36 1.36 1.07 1.91 1.84 2.33 5 5 5 ...
## $ HomeRooms : int [1:10000] 6 6 6 9 5 6 7 6 6 6 ...
## $ HomeOwn : Factor w/ 3 levels "Own","Rent","Other": 1 1 1 1 2 2 1 1 1 1 ...
## $ Work : Factor w/ 3 levels "Looking","NotWorking",..: 2 2 2 NA 2 NA NA 3 3 3 ...
## $ Weight : num [1:10000] 87.4 87.4 87.4 17 86.7 29.8 35.2 75.7 75.7 75.7 ...
## $ Length : num [1:10000] NA NA NA NA NA NA NA NA NA NA ...
## $ HeadCirc : num [1:10000] NA NA NA NA NA NA NA NA NA NA ...
## $ Height : num [1:10000] 165 165 165 105 168 ...
## $ BMI : num [1:10000] 32.2 32.2 32.2 15.3 30.6 ...
## $ BMICatUnder20yrs: Factor w/ 4 levels "UnderWeight",..: NA NA NA NA NA NA NA NA NA NA ...
## $ BMI_WHO : Factor w/ 4 levels "12.0_18.5","18.5_to_24.9",..: 4 4 4 1 4 1 2 3 3 3 ...
## $ Pulse : int [1:10000] 70 70 70 NA 86 82 72 62 62 62 ...
## $ BPSysAve : int [1:10000] 113 113 113 NA 112 86 107 118 118 118 ...
## $ BPDiaAve : int [1:10000] 85 85 85 NA 75 47 37 64 64 64 ...
## $ BPSys1 : int [1:10000] 114 114 114 NA 118 84 114 106 106 106 ...
## $ BPDia1 : int [1:10000] 88 88 88 NA 82 50 46 62 62 62 ...
## $ BPSys2 : int [1:10000] 114 114 114 NA 108 84 108 118 118 118 ...
## $ BPDia2 : int [1:10000] 88 88 88 NA 74 50 36 68 68 68 ...
## $ BPSys3 : int [1:10000] 112 112 112 NA 116 88 106 118 118 118 ...
## $ BPDia3 : int [1:10000] 82 82 82 NA 76 44 38 60 60 60 ...
## $ Testosterone : num [1:10000] NA NA NA NA NA NA NA NA NA NA ...
## $ DirectChol : num [1:10000] 1.29 1.29 1.29 NA 1.16 1.34 1.55 2.12 2.12 2.12 ...
## $ TotChol : num [1:10000] 3.49 3.49 3.49 NA 6.7 4.86 4.09 5.82 5.82 5.82 ...
## $ UrineVol1 : int [1:10000] 352 352 352 NA 77 123 238 106 106 106 ...
## $ UrineFlow1 : num [1:10000] NA NA NA NA 0.094 ...
## $ UrineVol2 : int [1:10000] NA NA NA NA NA NA NA NA NA NA ...
## $ UrineFlow2 : num [1:10000] NA NA NA NA NA NA NA NA NA NA ...
## $ Diabetes : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ DiabetesAge : int [1:10000] NA NA NA NA NA NA NA NA NA NA ...
## $ HealthGen : Factor w/ 5 levels "Excellent","Vgood",..: 3 3 3 NA 3 NA NA 2 2 2 ...
## $ DaysPhysHlthBad : int [1:10000] 0 0 0 NA 0 NA NA 0 0 0 ...
## $ DaysMentHlthBad : int [1:10000] 15 15 15 NA 10 NA NA 3 3 3 ...
## $ LittleInterest : Factor w/ 3 levels "None","Several",..: 3 3 3 NA 2 NA NA 1 1 1 ...
## $ Depressed : Factor w/ 3 levels "None","Several",..: 2 2 2 NA 2 NA NA 1 1 1 ...
## $ nPregnancies : int [1:10000] NA NA NA NA 2 NA NA 1 1 1 ...
## $ nBabies : int [1:10000] NA NA NA NA 2 NA NA NA NA NA ...
## $ Age1stBaby : int [1:10000] NA NA NA NA 27 NA NA NA NA NA ...
## $ SleepHrsNight : int [1:10000] 4 4 4 NA 8 NA NA 8 8 8 ...
## $ SleepTrouble : Factor w/ 2 levels "No","Yes": 2 2 2 NA 2 NA NA 1 1 1 ...
## $ PhysActive : Factor w/ 2 levels "No","Yes": 1 1 1 NA 1 NA NA 2 2 2 ...
## $ PhysActiveDays : int [1:10000] NA NA NA NA NA NA NA 5 5 5 ...
## $ TVHrsDay : Factor w/ 7 levels "0_hrs","0_to_1_hr",..: NA NA NA NA NA NA NA NA NA NA ...
## $ CompHrsDay : Factor w/ 7 levels "0_hrs","0_to_1_hr",..: NA NA NA NA NA NA NA NA NA NA ...
## $ TVHrsDayChild : int [1:10000] NA NA NA 4 NA 5 1 NA NA NA ...
## $ CompHrsDayChild : int [1:10000] NA NA NA 1 NA 0 6 NA NA NA ...
## $ Alcohol12PlusYr : Factor w/ 2 levels "No","Yes": 2 2 2 NA 2 NA NA 2 2 2 ...
## $ AlcoholDay : int [1:10000] NA NA NA NA 2 NA NA 3 3 3 ...
## $ AlcoholYear : int [1:10000] 0 0 0 NA 20 NA NA 52 52 52 ...
## $ SmokeNow : Factor w/ 2 levels "No","Yes": 1 1 1 NA 2 NA NA NA NA NA ...
## $ Smoke100 : Factor w/ 2 levels "No","Yes": 2 2 2 NA 2 NA NA 1 1 1 ...
## $ Smoke100n : Factor w/ 2 levels "Non-Smoker","Smoker": 2 2 2 NA 2 NA NA 1 1 1 ...
## $ SmokeAge : int [1:10000] 18 18 18 NA 38 NA NA NA NA NA ...
## $ Marijuana : Factor w/ 2 levels "No","Yes": 2 2 2 NA 2 NA NA 2 2 2 ...
## $ AgeFirstMarij : int [1:10000] 17 17 17 NA 18 NA NA 13 13 13 ...
## $ RegularMarij : Factor w/ 2 levels "No","Yes": 1 1 1 NA 1 NA NA 1 1 1 ...
## $ AgeRegMarij : int [1:10000] NA NA NA NA NA NA NA NA NA NA ...
## $ HardDrugs : Factor w/ 2 levels "No","Yes": 2 2 2 NA 2 NA NA 1 1 1 ...
## $ SexEver : Factor w/ 2 levels "No","Yes": 2 2 2 NA 2 NA NA 2 2 2 ...
## $ SexAge : int [1:10000] 16 16 16 NA 12 NA NA 13 13 13 ...
## $ SexNumPartnLife : int [1:10000] 8 8 8 NA 10 NA NA 20 20 20 ...
## $ SexNumPartYear : int [1:10000] 1 1 1 NA 1 NA NA 0 0 0 ...
## $ SameSex : Factor w/ 2 levels "No","Yes": 1 1 1 NA 2 NA NA 2 2 2 ...
## $ SexOrientation : Factor w/ 3 levels "Bisexual","Heterosexual",..: 2 2 2 NA 2 NA NA 1 1 1 ...
## $ PregnantNow : Factor w/ 3 levels "Yes","No","Unknown": NA NA NA NA NA NA NA NA NA NA ...# Dimensions: rows (observations) and columns (variables)
dim(NHANES)## [1] 10000 76# What variables do we have?
names(NHANES)## [1] "ID" "SurveyYr" "Gender" "Age" "AgeDecade"
## [6] "AgeMonths" "Race1" "Race3" "Education" "MaritalStatus"
## [11] "HHIncome" "HHIncomeMid" "Poverty" "HomeRooms" "HomeOwn"
## [16] "Work" "Weight" "Length" "HeadCirc" "Height"
## [21] "BMI" "BMICatUnder20yrs" "BMI_WHO" "Pulse" "BPSysAve"
## [26] "BPDiaAve" "BPSys1" "BPDia1" "BPSys2" "BPDia2"
## [31] "BPSys3" "BPDia3" "Testosterone" "DirectChol" "TotChol"
## [36] "UrineVol1" "UrineFlow1" "UrineVol2" "UrineFlow2" "Diabetes"
## [41] "DiabetesAge" "HealthGen" "DaysPhysHlthBad" "DaysMentHlthBad" "LittleInterest"
## [46] "Depressed" "nPregnancies" "nBabies" "Age1stBaby" "SleepHrsNight"
## [51] "SleepTrouble" "PhysActive" "PhysActiveDays" "TVHrsDay" "CompHrsDay"
## [56] "TVHrsDayChild" "CompHrsDayChild" "Alcohol12PlusYr" "AlcoholDay" "AlcoholYear"
## [61] "SmokeNow" "Smoke100" "Smoke100n" "SmokeAge" "Marijuana"
## [66] "AgeFirstMarij" "RegularMarij" "AgeRegMarij" "HardDrugs" "SexEver"
## [71] "SexAge" "SexNumPartnLife" "SexNumPartYear" "SameSex" "SexOrientation"
## [76] "PregnantNow"# Count missing values in each column
missing_summary <- data.frame(
Variable = names(NHANES),
Missing_Count = colSums(is.na(NHANES)),
Missing_Percent = round(colSums(is.na(NHANES)) / nrow(NHANES) * 100, 2)
)
# Show variables with the most missing data
print(missing_summary[order(-missing_summary$Missing_Count), ][1:15, ])## Variable Missing_Count Missing_Percent
## HeadCirc HeadCirc 9912 99.12
## Length Length 9457 94.57
## DiabetesAge DiabetesAge 9371 93.71
## TVHrsDayChild TVHrsDayChild 9347 93.47
## CompHrsDayChild CompHrsDayChild 9347 93.47
## BMICatUnder20yrs BMICatUnder20yrs 8726 87.26
## AgeRegMarij AgeRegMarij 8634 86.34
## UrineFlow2 UrineFlow2 8524 85.24
## UrineVol2 UrineVol2 8522 85.22
## PregnantNow PregnantNow 8304 83.04
## Age1stBaby Age1stBaby 8116 81.16
## nBabies nBabies 7584 75.84
## nPregnancies nPregnancies 7396 73.96
## AgeFirstMarij AgeFirstMarij 7109 71.09
## SmokeAge SmokeAge 6920 69.20Epidemiological Note: Always use
na.rm = TRUE in functions like sum() and
mean() to exclude missing values, but report how
many were excluded.
# Select key variables for analysis
nhanes_analysis <- NHANES %>%
dplyr::select(
ID,
Gender, # Sex (Male/Female)
Age, # Age in years
Race1, # Race/ethnicity
Education, # Education level
BMI, # Body Mass Index
Pulse, # Resting heart rate
BPSys1, # Systolic blood pressure (1st reading)
BPDia1, # Diastolic blood pressure (1st reading)
PhysActive, # Physically active (Yes/No)
SmokeNow, # Current smoking status
Diabetes, # Diabetes diagnosis (Yes/No)
HealthGen # General health rating
) %>%
# Create a binary hypertension indicator (BPSys1 >= 140 OR BPDia1 >= 90)
mutate(
Hypertension = factor(ifelse(BPSys1 >= 140 | BPDia1 >= 90, "Yes", "No"))
)
# Remove rows with missing values for key variables
nhanes_analysis2 <- nhanes_analysis %>%
filter(complete.cases(.)) # Complete cases only
# View the processed dataset
head(nhanes_analysis, 10)## # A tibble: 10 × 14
## ID Gender Age Race1 Education BMI Pulse BPSys1 BPDia1 PhysActive SmokeNow Diabetes
## <int> <fct> <int> <fct> <fct> <dbl> <int> <int> <int> <fct> <fct> <fct>
## 1 51624 male 34 White High School 32.2 70 114 88 No No No
## 2 51624 male 34 White High School 32.2 70 114 88 No No No
## 3 51624 male 34 White High School 32.2 70 114 88 No No No
## 4 51625 male 4 Other <NA> 15.3 NA NA NA <NA> <NA> No
## 5 51630 female 49 White Some College 30.6 86 118 82 No Yes No
## 6 51638 male 9 White <NA> 16.8 82 84 50 <NA> <NA> No
## 7 51646 male 8 White <NA> 20.6 72 114 46 <NA> <NA> No
## 8 51647 female 45 White College Grad 27.2 62 106 62 Yes <NA> No
## 9 51647 female 45 White College Grad 27.2 62 106 62 Yes <NA> No
## 10 51647 female 45 White College Grad 27.2 62 106 62 Yes <NA> No
## # ℹ 2 more variables: HealthGen <fct>, Hypertension <fct># Check dimensions
dim(nhanes_analysis)## [1] 10000 14Using the nhanes_analysis data,We are exploring:
“How does hypertension prevalence vary by education level?”
Following is the code to:
# code here:
health_by_education <- nhanes_analysis %>%
group_by(Education) %>%
summarise(
N = n(),
Mean_SysBP = round(mean(BPSys1, na.rm = TRUE), 2),
Pct_Hypertension = round(
sum(Hypertension == "Yes", na.rm = TRUE) / sum(!is.na(Hypertension)) * 100, 2)
)
print(health_by_education)Creating a bar chart showing hypertension by education level:
# visualization here:
health_by_education %>%
filter(!is.na(Education)) %>%
ggplot(aes(x = Education, y = Pct_Hypertension)) +
geom_col(fill = "steelblue", alpha = 0.7) +
geom_text(aes(label = paste0(Pct_Hypertension, "%")),
vjust = -0.5, size = 3) +
labs(
title = "Hypertension Prevalence by Education Level",
x = "Education Level",
y = "Percent with Hypertension (%)",
caption = "Source: NHANES"
) +
ylim(0, 50) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))Write 2-3 sentences:
“What does this pattern tell us about health disparities and social determinants?”
Consider: - Which education groups have highest/lowest hypertension?
8th Grade (28.3%) has highest hypertension and college graduate has the lowest hypertension (13.1%).
What might explain these differences? The differences could be explained due to the higher education leading to better health literacy, access to care, healthier habits.
Why does this matter for public health? This matters for public health because it helps to target public health efforts, reduce disparities, and improve outcomes through education-based interventions.
Lab Activity 1 Complete!
Last updated: January 29, 2026