Program 06

Author

Manjunath

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 required library.

# Load ggplot2 package for visualization
library(ggplot2)

Step 2: Explore the InBuilt Dataset.

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

str(iris) # Shows the 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 ...

The echo: false option disables the printing of code (only output is displayed).

head(iris) # View the first few rows of the 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: Create Histogram with group-wise density curves.

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:

This initializes the plot and tells ggplot to map:

Petal.Length (continuous variable) to the x-axis

Species (categorical) to fill aesthetic to distinguish groups.

Step 3.2: Add the Box plot Layer.

# Add histogram with density scaling

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.

# Finally, render the 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