1 A Layered Grammar of Graphics: ggplot2 methods

Author

Nguyen Kim Hau

Published

April 16, 2026

Modified

April 28, 2026

1.1 Getting Started

After reading this page, you can draw this chart by yourself!

1.1.1 Install and launching R packages

The code chunk below uses p_load() of pacman package to check if tidyverse packages are installed in the computer. If they are, then they will be launched into R.

pacman::p_load(tidyverse)

1.1.2 Importing the data

exam_data <- read_csv("data/Exam_data.csv")

head(exam_data, n = 10)

# A tibble: 10 × 7
   ID         CLASS GENDER RACE    ENGLISH MATHS SCIENCE
   <chr>      <chr> <chr>  <chr>     <dbl> <dbl>   <dbl>
 1 Student321 3I    Male   Malay        21     9      15
 2 Student305 3I    Female Malay        24    22      16
 3 Student289 3H    Male   Chinese      26    16      16
 4 Student227 3F    Male   Chinese      27    77      31
 5 Student318 3I    Male   Malay        27    11      25
 6 Student306 3I    Female Malay        31    16      16
 7 Student313 3I    Male   Chinese      31    21      25
 8 Student316 3I    Male   Malay        31    18      27
 9 Student312 3I    Male   Malay        33    19      15
10 Student297 3H    Male   Indian       34    49      37

1.2 Plotting different chart types

1.2.1 Bar chart

p1 <- ggplot(data=exam_data, 
       aes(x=RACE)) +
  geom_bar() +
  ggtitle("Number of students by Nationalities")
p1

p1 + coord_flip()

p1 + theme_linedraw()

1.2.3 Histogram chart

ggplot(data=exam_data, aes(x = MATHS)) +
  geom_histogram(bins=10, 
                 boundary = 100,
                 color="black", 
                 fill="light blue") +
  ggtitle("Distribution of Maths scores")

ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot(binwidth=2.5,         
               dotsize = 0.5) +      
  scale_y_continuous(NULL,           
                     breaks = NULL)  +
  ggtitle("Distribution of Maths scores")

ggplot(data=exam_data, 
       aes(x= MATHS, 
           fill = GENDER)) +
  geom_histogram(bins=20, 
                 color="grey30") +
  ggtitle("Stacked Distribution of Maths scores by Gender")

ggplot(data=exam_data, 
       aes(x = MATHS, 
           colour = GENDER)) +
  geom_density() +
  ggtitle("Density of Maths scores by Gender")

1.2.4 Box plot

ggplot(data=exam_data, 
       aes(y = MATHS, 
           x= GENDER)) +
  geom_boxplot(notch=TRUE) +
  ggtitle("Boxplot of Maths scores by Gender")

1.2.5 Violin plot

ggplot(data=exam_data, 
       aes(y = MATHS, 
           x= GENDER)) +
  geom_violin() +
  ggtitle("Violin plot of Math scores by Gender")

1.2.6 Scatter plot

ggplot(data=exam_data, 
       aes(x= MATHS, 
           y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              linewidth=0.5) +
  ggtitle("Scatter plot between Math and English scores")

ggplot(data=exam_data, 
       aes(x= MATHS, y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              size=0.5) +  
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100)) +
  ggtitle("Scatter plot between Math and English scores, equal axis")

1.2.7 Combination chart types

ggplot(data=exam_data, 
       aes(y = MATHS, 
           x= GENDER)) +
  geom_boxplot() +                    
  geom_point(position="jitter", 
             size = 0.5) +
  ggtitle("Scatter and box plots of Math scores by Gender")

ggplot(data=exam_data, 
       aes(y = MATHS, x= GENDER)) +
  geom_boxplot() +
  stat_summary(geom = "point",       
               fun = "mean",         
               colour ="red",        
               size=4)

ggplot(data=exam_data, 
       aes(x= MATHS)) +
  geom_histogram(bins=20) +
  facet_wrap(~ CLASS) +
  ggtitle("facet_wrap()")

ggplot(data=exam_data, 
       aes(x= MATHS)) +
  geom_histogram(bins=20) +
  facet_grid(~ CLASS) +
  ggtitle("facet_grid()")