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Week 8 Plots in R - ggplot2

1. Introduction to Data

We’ll use the NHANES dataset, which contains demographic, health, and lifestyle data from a U.S. population survey.

library("NHANES")
library(tidyverse)
library(ggplot2)
library(haven)
data("NHANES")

data<-data.frame(NHANES)
glimpse(data)
## Rows: 10,000
## Columns: 76
## $ ID               <int> 51624, 51624, 51624, 51625, 51630, 51638, 51646, 51647, 51647, 51647, 516…
## $ SurveyYr         <fct> 2009_10, 2009_10, 2009_10, 2009_10, 2009_10, 2009_10, 2009_10, 2009_10, 2…
## $ Gender           <fct> male, male, male, male, female, male, male, female, female, female, male,…
## $ Age              <int> 34, 34, 34, 4, 49, 9, 8, 45, 45, 45, 66, 58, 54, 10, 58, 50, 9, 33, 60, 1…
## $ AgeDecade        <fct>  30-39,  30-39,  30-39,  0-9,  40-49,  0-9,  0-9,  40-49,  40-49,  40-49,…
## $ AgeMonths        <int> 409, 409, 409, 49, 596, 115, 101, 541, 541, 541, 795, 707, 654, 123, 700,…
## $ Race1            <fct> White, White, White, Other, White, White, White, White, White, White, Whi…
## $ Race3            <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Education        <fct> High School, High School, High School, NA, Some College, NA, NA, College …
## $ MaritalStatus    <fct> Married, Married, Married, NA, LivePartner, NA, NA, Married, Married, Mar…
## $ HHIncome         <fct> 25000-34999, 25000-34999, 25000-34999, 20000-24999, 35000-44999, 75000-99…
## $ HHIncomeMid      <int> 30000, 30000, 30000, 22500, 40000, 87500, 60000, 87500, 87500, 87500, 300…
## $ Poverty          <dbl> 1.36, 1.36, 1.36, 1.07, 1.91, 1.84, 2.33, 5.00, 5.00, 5.00, 2.20, 5.00, 2…
## $ HomeRooms        <int> 6, 6, 6, 9, 5, 6, 7, 6, 6, 6, 5, 10, 6, 10, 10, 4, 3, 11, 5, 7, 10, 10, 9…
## $ HomeOwn          <fct> Own, Own, Own, Own, Rent, Rent, Own, Own, Own, Own, Own, Rent, Rent, Own,…
## $ Work             <fct> NotWorking, NotWorking, NotWorking, NA, NotWorking, NA, NA, Working, Work…
## $ Weight           <dbl> 87.4, 87.4, 87.4, 17.0, 86.7, 29.8, 35.2, 75.7, 75.7, 75.7, 68.0, 78.4, 7…
## $ Length           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ HeadCirc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Height           <dbl> 164.7, 164.7, 164.7, 105.4, 168.4, 133.1, 130.6, 166.7, 166.7, 166.7, 169…
## $ BMI              <dbl> 32.22, 32.22, 32.22, 15.30, 30.57, 16.82, 20.64, 27.24, 27.24, 27.24, 23.…
## $ BMICatUnder20yrs <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ BMI_WHO          <fct> 30.0_plus, 30.0_plus, 30.0_plus, 12.0_18.5, 30.0_plus, 12.0_18.5, 18.5_to…
## $ Pulse            <int> 70, 70, 70, NA, 86, 82, 72, 62, 62, 62, 60, 62, 76, 80, 94, 74, 92, 96, 8…
## $ BPSysAve         <int> 113, 113, 113, NA, 112, 86, 107, 118, 118, 118, 111, 104, 134, 104, 127, …
## $ BPDiaAve         <int> 85, 85, 85, NA, 75, 47, 37, 64, 64, 64, 63, 74, 85, 68, 83, 68, 63, 74, 1…
## $ BPSys1           <int> 114, 114, 114, NA, 118, 84, 114, 106, 106, 106, 124, 108, 136, 102, NA, 1…
## $ BPDia1           <int> 88, 88, 88, NA, 82, 50, 46, 62, 62, 62, 64, 76, 86, 66, NA, 66, 56, 80, 9…
## $ BPSys2           <int> 114, 114, 114, NA, 108, 84, 108, 118, 118, 118, 108, 104, 132, 102, 134, …
## $ BPDia2           <int> 88, 88, 88, NA, 74, 50, 36, 68, 68, 68, 62, 72, 88, 66, 82, 74, 64, 74, 9…
## $ BPSys3           <int> 112, 112, 112, NA, 116, 88, 106, 118, 118, 118, 114, 104, 136, 106, 120, …
## $ BPDia3           <int> 82, 82, 82, NA, 76, 44, 38, 60, 60, 60, 64, 76, 82, 70, 84, 62, 62, NA, 1…
## $ Testosterone     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ DirectChol       <dbl> 1.29, 1.29, 1.29, NA, 1.16, 1.34, 1.55, 2.12, 2.12, 2.12, 0.67, 0.96, 1.1…
## $ TotChol          <dbl> 3.49, 3.49, 3.49, NA, 6.70, 4.86, 4.09, 5.82, 5.82, 5.82, 4.99, 4.24, 6.4…
## $ UrineVol1        <int> 352, 352, 352, NA, 77, 123, 238, 106, 106, 106, 113, 163, 215, 7, 29, 64,…
## $ UrineFlow1       <dbl> NA, NA, NA, NA, 0.094, 1.538, 1.322, 1.116, 1.116, 1.116, 0.489, NA, 0.90…
## $ UrineVol2        <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ UrineFlow2       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Diabetes         <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, N…
## $ DiabetesAge      <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ HealthGen        <fct> Good, Good, Good, NA, Good, NA, NA, Vgood, Vgood, Vgood, Vgood, Vgood, Fa…
## $ DaysPhysHlthBad  <int> 0, 0, 0, NA, 0, NA, NA, 0, 0, 0, 10, 0, 4, NA, NA, 0, NA, 3, 7, 0, 3, 3, …
## $ DaysMentHlthBad  <int> 15, 15, 15, NA, 10, NA, NA, 3, 3, 3, 0, 0, 0, NA, NA, 0, NA, 7, 0, 20, 0,…
## $ LittleInterest   <fct> Most, Most, Most, NA, Several, NA, NA, None, None, None, None, None, None…
## $ Depressed        <fct> Several, Several, Several, NA, Several, NA, NA, None, None, None, None, N…
## $ nPregnancies     <int> NA, NA, NA, NA, 2, NA, NA, 1, 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ nBabies          <int> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ Age1stBaby       <int> NA, NA, NA, NA, 27, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ SleepHrsNight    <int> 4, 4, 4, NA, 8, NA, NA, 8, 8, 8, 7, 5, 4, NA, 5, 7, NA, 6, 6, 6, 7, 7, 8,…
## $ SleepTrouble     <fct> Yes, Yes, Yes, NA, Yes, NA, NA, No, No, No, No, No, Yes, NA, No, No, NA, …
## $ PhysActive       <fct> No, No, No, NA, No, NA, NA, Yes, Yes, Yes, Yes, Yes, Yes, NA, Yes, Yes, N…
## $ PhysActiveDays   <int> NA, NA, NA, NA, NA, NA, NA, 5, 5, 5, 7, 5, 1, NA, 2, 7, NA, NA, NA, 3, 7,…
## $ TVHrsDay         <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ CompHrsDay       <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ TVHrsDayChild    <int> NA, NA, NA, 4, NA, 5, 1, NA, NA, NA, NA, NA, NA, 4, NA, NA, 0, NA, NA, NA…
## $ CompHrsDayChild  <int> NA, NA, NA, 1, NA, 0, 6, NA, NA, NA, NA, NA, NA, 3, NA, NA, 1, NA, NA, NA…
## $ Alcohol12PlusYr  <fct> Yes, Yes, Yes, NA, Yes, NA, NA, Yes, Yes, Yes, Yes, Yes, Yes, NA, NA, No,…
## $ AlcoholDay       <int> NA, NA, NA, NA, 2, NA, NA, 3, 3, 3, 1, 2, 6, NA, NA, NA, NA, 3, 6, NA, 1,…
## $ AlcoholYear      <int> 0, 0, 0, NA, 20, NA, NA, 52, 52, 52, 100, 104, 364, NA, NA, 0, NA, 104, 3…
## $ SmokeNow         <fct> No, No, No, NA, Yes, NA, NA, NA, NA, NA, No, NA, NA, NA, Yes, NA, NA, No,…
## $ Smoke100         <fct> Yes, Yes, Yes, NA, Yes, NA, NA, No, No, No, Yes, No, No, NA, Yes, No, NA,…
## $ Smoke100n        <fct> Smoker, Smoker, Smoker, NA, Smoker, NA, NA, Non-Smoker, Non-Smoker, Non-S…
## $ SmokeAge         <int> 18, 18, 18, NA, 38, NA, NA, NA, NA, NA, 13, NA, NA, NA, 17, NA, NA, NA, 1…
## $ Marijuana        <fct> Yes, Yes, Yes, NA, Yes, NA, NA, Yes, Yes, Yes, NA, Yes, Yes, NA, NA, No, …
## $ AgeFirstMarij    <int> 17, 17, 17, NA, 18, NA, NA, 13, 13, 13, NA, 19, 15, NA, NA, NA, NA, NA, N…
## $ RegularMarij     <fct> No, No, No, NA, No, NA, NA, No, No, No, NA, Yes, Yes, NA, NA, No, NA, No,…
## $ AgeRegMarij      <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20, 15, NA, NA, NA, NA, NA, N…
## $ HardDrugs        <fct> Yes, Yes, Yes, NA, Yes, NA, NA, No, No, No, No, Yes, Yes, NA, NA, No, NA,…
## $ SexEver          <fct> Yes, Yes, Yes, NA, Yes, NA, NA, Yes, Yes, Yes, Yes, Yes, Yes, NA, NA, Yes…
## $ SexAge           <int> 16, 16, 16, NA, 12, NA, NA, 13, 13, 13, 17, 22, 12, NA, NA, NA, NA, 27, 2…
## $ SexNumPartnLife  <int> 8, 8, 8, NA, 10, NA, NA, 20, 20, 20, 15, 7, 100, NA, NA, 9, NA, 1, 1, NA,…
## $ SexNumPartYear   <int> 1, 1, 1, NA, 1, NA, NA, 0, 0, 0, NA, 1, 1, NA, NA, 1, NA, 1, NA, NA, 1, 1…
## $ SameSex          <fct> No, No, No, NA, Yes, NA, NA, Yes, Yes, Yes, No, No, No, NA, NA, No, NA, N…
## $ SexOrientation   <fct> Heterosexual, Heterosexual, Heterosexual, NA, Heterosexual, NA, NA, Bisex…
## $ PregnantNow      <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
summary(data %>% select(BMI,MaritalStatus,Depressed,HHIncomeMid,Gender,Age, SmokeNow,PhysActive))
##       BMI             MaritalStatus    Depressed     HHIncomeMid        Gender          Age       
##  Min.   :12.88   Divorced    : 707   None   :5246   Min.   :  2500   female:5020   Min.   : 0.00  
##  1st Qu.:21.58   LivePartner : 560   Several:1009   1st Qu.: 30000   male  :4980   1st Qu.:17.00  
##  Median :25.98   Married     :3945   Most   : 418   Median : 50000                 Median :36.00  
##  Mean   :26.66   NeverMarried:1380   NA's   :3327   Mean   : 57206                 Mean   :36.74  
##  3rd Qu.:30.89   Separated   : 183                  3rd Qu.: 87500                 3rd Qu.:54.00  
##  Max.   :81.25   Widowed     : 456                  Max.   :100000                 Max.   :80.00  
##  NA's   :366     NA's        :2769                  NA's   :811                                   
##  SmokeNow    PhysActive 
##  No  :1745   No  :3677  
##  Yes :1466   Yes :4649  
##  NA's:6789   NA's:1674  
##                         
##                         
##                         
## 
data_clean <- data %>%
  drop_na(
          BMI,
          MaritalStatus,
          Depressed,
          Poverty,
          Gender,
          Age, 
          SmokeNow,
          PhysActive,
          HHIncomeMid)

summary(data_clean %>% select(BMI,MaritalStatus,Depressed,HHIncomeMid,Gender,Age, SmokeNow,PhysActive))
##       BMI             MaritalStatus    Depressed     HHIncomeMid        Gender          Age       
##  Min.   :15.02   Divorced    : 325   None   :1965   Min.   :  2500   female:1127   Min.   :20.00  
##  1st Qu.:24.07   LivePartner : 305   Several: 473   1st Qu.: 30000   male  :1503   1st Qu.:35.00  
##  Median :27.71   Married     :1345   Most   : 192   Median : 50000                 Median :49.00  
##  Mean   :28.64   NeverMarried: 440                  Mean   : 54744                 Mean   :49.07  
##  3rd Qu.:32.00   Separated   :  56                  3rd Qu.: 87500                 3rd Qu.:62.00  
##  Max.   :67.83   Widowed     : 159                  Max.   :100000                 Max.   :80.00  
##  SmokeNow   PhysActive
##  No :1445   No :1355  
##  Yes:1185   Yes:1275  
##                       
##                       
##                       
##

2. Basic Graphs in Base R

Advantage: quick

Limitation: Hard to customize further or overlay additional elements.

2.1. Histogram of Age

hist(data_clean$BMI, 
     main = "Histogram of BMI", 
     xlab = "BMI", 
     col = "lightblue", 
     border = "black")

2.2. Boxplot of BMI by Gender

boxplot(BMI ~ Gender, data = data_clean, 
        main = "Boxplot of BMI by Gender", 
        xlab = "Gender", ylab = "BMI")

2.3 Scatter Plot

plot(BMI ~ Age, data = data_clean, 
     main = "Scatter Plot of BMI vs Age",
     xlab = "Age", ylab = "BMI",
     col = "blue", pch = 16)

3. Why Use ggplot2?

  • Layering: ggplot2 allows adding multiple layers (points, lines, etc.).

  • Faceting: Easily split data across categories.

  • Customization: Control over themes, labels, and scales.

  • Aesthetics: Consistent look and feel with better visualization options.

4. Step-by-Step Plotting with ggplot2

4.1. Basic Scatter Plot: Age vs BMI

# Basic scatter plot
ggplot(data_clean, aes(x = Age, y = BMI))+ 
  geom_point()

4.2. Adding Color and Titles

  • Color helps differentiate genders, and titles make the plot easier to interpret.
# Scatter plot with color and labels
ggplot(data_clean, aes(x = Age, y = BMI, color = Gender)) +
  geom_point() +
  labs(title = "Scatter Plot of BMI vs Age by Gender", 
       x = "Age", y = "BMI")

ggplot(data_clean, aes(x = Age, y = BMI, color = Gender))+
  geom_point()

4.3. Adding a Trend Line

  • geom_smooth() helps visualize trends in data.

  • Alpha transparency avoids overplotting by making points semi-transparent.

# Scatter plot with a smooth trend line
ggplot(data_clean, aes(x = Age, y = BMI, color = Gender)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "loess", se = T) +
  labs(title = "BMI vs Age with Trend Line", x = "Age", y = "BMI")+
  theme_classic()

Locally estimated scatterplot smoothing (loess)

Locally estimated scatterplot smoothing, or LOESS, is a nonparametric method for smoothing a series of data in which no assumptions are made about the underlying structure of the data.

4.4. Faceted Plots

  • Faceting splits the data into smaller plots by categories (like marital status), making patterns easier to spot.
# Facet by Marital Status
ggplot(data_clean, aes(x = Age, y = BMI, color = Gender)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "lm", se = FALSE) +
  facet_wrap(~MaritalStatus) +
  labs(title = "BMI vs Age by Marital Status", x = "Age", y = "BMI")+
  theme_classic()

4.5. Bar Plot: Gender Distribution

# Bar plot of gender distribution
ggplot(data_clean, aes(x = Gender)) +
  geom_bar(fill = "blue") +
  labs(title = "Gender Distribution", x = "Gender", y = "Count")+
  theme_bw()

geom_bar() automatically counts observations for categorical variables.

4.6. Boxplot: BMI by Depression Levels

# Boxplot of BMI by Depression Levels
ggplot(data_clean, aes(x = Depressed, y = BMI, fill = Gender)) +
  geom_boxplot() +
  labs(title = "BMI by Depression Levels", x = "Depression Level", y = "BMI")+theme_classic()

Exercise

Exercise 1: Inspect the Age Distribution of Our Sample

  • Create a histogram of Age using ggplot2.

  • Customize it by changing the binwidth and color.

Exercise 2: Scatter Plot with Additional Aesthetic Mappings

  1. Create a scatter plot of BMI vs HHIncomeMid.

  2. Color the points by Gender and use alpha transparency to avoid overplotting.

# Scatter plot of BMI vs Income
ggplot(data_clean, aes(x = HHIncomeMid, y = BMI, color = Gender)) +
  geom_jitter(alpha = 0.6) +
  labs(title = "Scatter Plot of BMI vs Household Income Midpoint", 
       x = "Household Income Midpoint", y = "BMI")

Exercise 3: Adding Trend Lines

  1. Using the scatter plot from Exercise 2, add a trend line with geom_smooth().

  2. Use method = “lm” to fit a linear trend and disable confidence intervals.

# Scatter plot with a linear trend line
ggplot(data_clean, aes(x = HHIncomeMid, y = BMI, color = Gender)) +
  geom_jitter(alpha = 0.6) +
  geom_smooth(method = "loess",)+
  labs(title = "BMI vs Income with Linear Trend", 
       x = "Household Income Midpoint", y = "BMI")

Exercise 4: Faceting with Multiple Categories

  1. Create a scatter plot of Age vs BMI and facet it by Smoking Status (SmokeNow).

  2. Use different colors for Gender and add a smooth trend line.

# Faceted scatter plot by Smoking Status
ggplot(data_clean, aes(x = Age, y = BMI, color = Gender)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "loess", se = FALSE) +
  facet_wrap(~SmokeNow) +
  labs(title = "BMI vs Age by Smoking Status", x = "Age", y = "BMI")