layered plotting

Data Visualization in R Using ggplot2 - Module 4

library(ggplot2)

Warning: package 'ggplot2' was built under R version 4.5.2

data(ToothGrowth) 
str(ToothGrowth)

'data.frame':   60 obs. of  3 variables:
 $ len : num  4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ...
 $ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
 $ dose: num  0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...

data(airquality)

data(ToothGrowth)                                         # Load example data
 
ggplot(data = ToothGrowth,                                # Basic Boxplot
       aes(x = supp,
           y = len)) +
  geom_boxplot()

Add a layer of data points to the boxplot layer.

ggplot(data = ToothGrowth,                                # Overlay jittered points
       aes(x = supp,
           y = len)) +
  geom_boxplot() +
  geom_jitter(width = 0.2,
              color = "#1b98e0")

my_ggp1 <- ggplot(data = ToothGrowth,                     # Save plot in data object
                  aes(x = supp,
                      y = len)) +
  geom_boxplot()
my_ggp1                                                   # Draw plot in data object

Add a layer to the plot object…

my_ggp1 +                                                 # Add layer to data object
  geom_jitter(width = 0.2,
              color = "#1b98e0")

Save as new object…

my_ggp2 <- my_ggp1 +                                      # Create new data object
  geom_jitter(width = 0.2,
              color = "#1b98e0")
my_ggp2   

Adding different layer types

my_ggp2 +                                                 # Different layer types
  stat_summary(fun = mean, 
               geom = "point", 
               color = "red", 
               size = 10,
               shape = 18) +
  annotate(geom = "text",
           x = 1, 
           y = 34,
           label = "Plot with different layer types!",
           size = 5) +
  theme_void()

Exercises

In this module, we will build on the airquality data set introduced earlier. To prepare, we will install and load the ggplot2 package, convert the Month column to a factor class to use it as a categorical variable, and set a random seed for reproducibility.

airquality$Month <- as.factor(airquality$Month)           # Convert Month to factor

Create a boxplot mapping the Month column to the x-axis and the Temp column to the y-axis. Save the plot as a data object named my_ggp3 and display the plot.

my_ggp3 <- ggplot(airquality,
  aes(x = Month,
      y = Temp)) +
    geom_boxplot()
my_ggp3

Enhance the plot stored in my_ggp3 by adding a jitter layer with a width of 0.1. Save the enhanced plot as my_ggp4 and display it.

my_ggp4 <- my_ggp3 +                                      # Create new data object
  geom_jitter(width = 0.1,
              color = "#1b98e0")
my_ggp4

Modify the plot stored in my_ggp4 by applying the theme_classic(). Save the modified plot as my_ggp5 and display it.

my_ggp5 <- my_ggp4 +
  theme_classic()
my_ggp5