program5

Implement an R program to create a histogram illustrating the distribution of a continuous variable, with overlays of density curves for each group, using ggplot2.

Step1 : Load Required Library

#Load ggplot2 package for visualisation
library(ggplot2)

Warning: package ‘ggplot2’ was built under R version 4.1.3

Step2 : 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 ...

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

Step3 : Create Histogram With Group-wise Density Curves

Step 3.1: Initialize the ggplot2 with aesthetic mappings

#Start ggplot2 with iris dataset
#Map Petal.length to x-axis and fill by Species (grouping variable)

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

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 Histogram Layer

#Add histogram with density scaling
p <- p + geom_histogram(aes(y = ..density..),
                        alpha = 0.4,#Set transparency
                        position = "identity",#Overlap histograms
                      bins = 30)#Number of bins
p

Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.

Explanation:

aes(y = ..density..) normalizes the histogram to density

alpha = 0.4 makes bars semi-transparent so overlaps are visible

position = "identity" lets different group histograms stack on top

bins = 30 controls histogram resolution

Step 3.3: Add Density Curve Layer

#Overlay density curves for each group
p <- p + 
  geom_density(aes(color = Species),#Line color by group
               size= 1.2)#Line thickness

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

p

Explanation: This overlays smooth density curves for each species using color. The aes(color = Species) ensures each curve is colored by group.

Step 3.4: Add labels and theme

p <- p + labs(
  title = "Distribution of Petal Length with Group-wise Density Curves",
  x= "Petal Length",
  y= "Density")+
  theme_minimal()
p

Explanation:

labs() adds a title and axis labels

theme_minimal() applies a clean, modern plot style

Step 3.5: Display the Plot

# Finally, render the plot
p

Summary

Used built-in iris dataset

Visualized Petal.Length as histogram

Grouped and color-coded by Species

Overlaid group-wise density curves for better interpretation