Program 6

Write an R script to construct a box plot showcasing the distribution of a continuous variable, grouped by a categorical variable, using ggplot2’s fill aesthetic.

Step 1: Load the required library

# Load ggplot2 package for visualization
library(ggplot2)

Step 2: Explore the inbuilt dataset

# Use the built-in 'iris' dataset
# 'Petal.Width' is a continuous variable
# 'Species' is a categorical grouping variable

str(iris)  # View structure of the dataset

'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

head(iris) # View sample data

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

Step 3: Construct Box Plot with Grouping

Step 3.1: Initialize the ggplot with aesthetic mappings

# Initialize ggplot with data and aesthetic mappings

p <- ggplot(data = iris, aes(x = Species, y = Petal.Width, fill = Species))

Explanation:

• x = Species: Grouping variable (categorical)

• y = Petal.Width: Continuous variable to show distribution

• fill = Species: Fill box colors by species

Step 3.2: Add box plot layer

# Add the box plot layer

p <- p + geom_boxplot()

Explanation:

• geom_boxplot() creates box plots for each group

• Automatically shows median, quartiles, and outliers

Step 3.3: Add labels and theme

# Add title and labels and use a minimal theme

p <- p + labs(title = "Box Plot of Petal Width by Species",
              x = "Species",
              y = "Petal Width") +
         theme_minimal()

Explanation:

• labs() adds a descriptive title and axis labels

• theme_minimal() gives a clean, modern look

Step 3.4: Display the plot

# Render the final plot
p

Summary

• Used the iris dataset

• Visualized Petal.Width as a box plot

• Grouped by Species

• Used fill = Species for colorful grouping

• Each box represents the distribution of values for one species