PROGRAM 15

Author

JAGADISH J M

Program 9

Objective

Create multiple histograms using ggplot2::facet_wrap() to visualize how a variable (e.g., Sepal.Length) is distributed across different groups (e.g., Species) in a built-in R dataset.

Requirements

Before proceeding, make sure you have the ggplot2 package installed. You can install it using: install.packages(“ggplot2”) Then, load the package:

# Load the ggplot2 package
library(ggplot2)

Step 1: Load and Explore the Dataset

We’ll use the built-in iris dataset. This dataset contains:

  • 150 rows (observations)

  • 4 numeric columns: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width

  • 1 categorical column: Species (Setosa, Versicolor, Virginica)

Let’s view the first few rows.

# Load the iris dataset
data(iris)

# View the first few rows of the dataset
head(iris)
  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 2: Create Grouped Histograms Using facet_wrap

Let’s now create histograms of Sepal.Length for each Species using ggplot2 and facet_wrap().

# Create histograms using facet_wrap for grouped data
ggplot(iris, aes(x = Sepal.Length)) +
  geom_histogram(binwidth = 0.3, fill = "skyblue", color = "black") +
  facet_wrap(~ Species) +
  labs(title = "Distribution of Sepal Length by Species",
       x = "Sepal Length (cm)",
       y = "Frequency") +
  theme_minimal()

Step 3: Explanation of Each Line

Code Line Description
ggplot(iris, aes(x = Sepal.Length)) Initializes a plot using the iris dataset and maps Sepal.Length to the x-axis.
geom_histogram(binwidth = 0.3, ...) Adds a histogram layer with a bin width of 0.3.
fill = "skyblue" Sets the fill color of the bars.
color = "black" Sets the border color of the bars.
facet_wrap(~ Species) Creates separate histograms for each species in a grid layout.
labs(...) Adds a title and axis labels.
theme_minimal() Applies a minimal theme for better visualization.

Output Description

The output will be three side-by-side histograms, each showing the distribution of Sepal Length for one of the following species:

  • setosa

  • versicolor

  • virginica

Each histogram allows us to visually compare the distribution of Sepal Length across the species.

Bonus Tip: Try with Different Variables

You can replace Sepal.Length in the aes(x = ...) part with:

  • Sepal.Width

  • Petal.Length

  • Petal.Width

This lets you explore how other features vary across species!

Summary

This exercise demonstrates:

  • How to create grouped visualizations using facet_wrap().

  • How to analyze and compare distributions across categories using histograms.

  • Use of ggplot2, one of the most powerful R libraries for data visualization.

Program 10

Program

  1. Develop an R function to draw a density curve representing the probability density function of a continuous variable, with separate curves for each group, using ggplot2.

Step 1: Load Required Library

We need the ggplot2 package to create density plots.

# Load ggplot2 for plotting
library(ggplot2)

Step 2: Define the Function

We will create a function called plot_density_by_group() which: - Accepts a data frame, the name of a continuous variable, and a grouping variable - Draws density curves by group - Allows optional custom color schemes

plot_density_by_group <- function(data, continuous_var, group_var, fill_colors = NULL) {
  # Check if the specified columns exist
  if (!(continuous_var %in% names(data)) || !(group_var %in% names(data))) {
    stop("Invalid column names. Make sure both variables exist in the dataset.")
  }

  # Create the ggplot object
  p <- ggplot(data, aes_string(x = continuous_var, color = group_var, fill = group_var)) +
    geom_density(alpha = 0.4) +
    labs(title = paste("Density Plot of", continuous_var, "by", group_var),
         x = continuous_var,
         y = "Density") +
    theme_minimal()

  # Apply custom fill colors if provided
  if (!is.null(fill_colors)) {
    p <- p + scale_fill_manual(values = fill_colors) +
             scale_color_manual(values = fill_colors)
  }

  # Return the plot
  return(p)
}

Step 3: Explanation of Function Components

Code Description
data The dataset (e.g., iris)
continuous_var Name of the continuous variable (e.g., "Sepal.Length")
group_var Grouping variable (e.g., "Species")
aes_string() Maps the variables using string names (for flexibility)
geom_density(alpha = 0.4) Draws smoothed density curves with transparency
facet_wrap(~ group_var) Not used here; instead we overlay curves in one plot
theme_minimal() Clean layout with minimal gridlines
scale_fill_manual() Applies custom fill colors if provided

Step 4: Example with Built-in iris Dataset

Let’s draw density plots for Sepal.Length across different Species in the iris dataset.

# Basic usage
plot_density_by_group(iris, "Sepal.Length", "Species")
Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.

Step 5: Example with Custom Colors

You can customize colors to improve visual appeal or match your theme.

# Define custom colors
custom_colors <- c("setosa" = "steelblue",
                   "versicolor" = "forestgreen",
                   "virginica" = "darkorange")

# Plot with custom colors
plot_density_by_group(iris, "Petal.Length", "Species", fill_colors = custom_colors)

Step 6: Output Description

  • The X-axis shows the continuous variable (e.g., Sepal.Length)

  • The Y-axis shows the probability density

  • Each group (e.g., Species) is represented by a separate curve

  • The alpha = 0.4 setting allows curves to overlap transparently

Summary

This function is: - Reusable: Works for any dataset with a numeric and a categorical variable - Customizable: Supports color schemes - Effective: Helps visualize distribution patterns across groups

Use it for exploratory data analysis to compare how different categories behave in terms of continuous measurements

Program 11

Objective

To generate a basic box plot using ggplot2, enhanced with notches and outliers, and grouped by a categorical variable using an in-built dataset in R.

Step 1: Load Required Package.

# install.packages("ggplot2") # Uncomment if needed
library(ggplot2)

Step 2: Use an Inbuilt Dataset

We will use the built-in iris dataset. This dataset contains measurements of sepal and petal dimensions for three species of iris flowers:

  • setosa

  • versicolor

  • virginica

    # Load and preview the dataset
data(iris)
head(iris)
      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
str(iris)
'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 Species column is categorical, making it suitable for grouping, while Sepal.Length is a numeric variable we’ll analyze.

Step 3: Create a Notched Box Plot Grouped by Species

We now create a box plot for Sepal.Length, grouped by Species. We’ll enhance the plot using: - Notches to show the confidence interval around the median - Outlier highlighting using color and shape - Aesthetic enhancements like fill color and theme

ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot(
    notch = TRUE,
    notchwidth = 0.6,
    outlier.color = "red",
    outlier.shape = 16,
    fill = "skyblue",
    alpha = 0.7
  ) +
  labs(
    title = "Sepal Length Distribution by Iris Species",
    subtitle = "Box Plot with Notches and Outlier Highlighting",
    x = "Species",
    y = "Sepal Length (cm)"
  ) +
  theme_minimal()

  • Box Plot: Each box summarizes the distribution of Sepal.Length for a species — showing the interquartile range (IQR), median, and potential outliers.

  • Notches: The notches give a rough 95% confidence interval around the median. If notches of two boxes do not overlap, the medians are significantly different.

  • Outliers: Points that fall outside 1.5 × IQR from the quartiles are considered outliers and shown in red.

  • Grouping: The plot groups values based on the categorical variable Species, helping compare between groups.

  • Aesthetics: theme_minimal() provides a clean background, while colors and transparency make the plot readable.

p=ggplot(iris, aes(x = Species, y = Sepal.Length))
p

p=p+ geom_boxplot(

)
p

p=p+ geom_boxplot(
  notch = TRUE,
    notchwidth = 0.6,
    outlier.color = "red",
    outlier.shape = 16,
    fill = "skyblue",
    alpha = 0.7
)
p

Program 12

Objective

To create a violin plot using ggplot2 in R that displays the distribution of a continuous variable with separate violins for each group using an in-built dataset.

Step 1: Load Required Package

We use the ggplot2 package for plotting. Install it if not already available.

# install.packages("ggplot2") # Uncomment if not installed
library(ggplot2)

Step 2: Use an Inbuilt Dataset

We’ll use the built-in iris dataset. This dataset includes measurements of sepal and petal lengths/widths for three species of iris flowers.

data(iris)
head(iris)
  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
str(iris)
'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 ...

Step 3: Create the Violin Plot

We’ll use geom_violin() from ggplot2, with custom fill and transparency.

ggplot(iris, aes(x = Species, y = Petal.Length, fill = Species)) +
  geom_violin(trim = FALSE, alpha = 0.6, color = "black") +
  labs(
    title = "Distribution of Petal Length by Iris Species",
    x = "Species",
    y = "Petal Length (cm)"
  ) +
  theme_minimal(base_size = 14)

  • Violin Plot: Combines box plot features with a kernel density plot on each side.

  • trim = FALSE: Ensures full density is shown rather than clipped at extreme values.

  • fill = Species: Automatically assigns different fill colors to each group.

  • alpha: Adjusts the transparency.

  • theme_minimal(): Applies a clean layout for the plot.

The violin plot gives a detailed visualization of the distribution and density of the Petal.Length across different Iris species. It’s a powerful alternative to box plots when you want to see the full shape of the distribution.

Dot Plot for Grouped Data using ggplot2

Write an R program to create multiple dot plots for grouped data, comparing the distributions of variables across different categories, using ggplot2’s position_dodge function

# Load required library
library(ggplot2)
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Objective

Create multiple dot plots to compare the distribution of tooth length (len) across different supplement types (supp) and dosages (dose), using position_dodge for group-wise separation.

# Convert dose to a factor for grouping
ToothGrowth$dose <- as.factor(ToothGrowth$dose)

# Plot with defined binwidth
ggplot(ToothGrowth, aes(x = dose, y = len, color = supp)) +
  geom_dotplot(
    binaxis = 'y',
    stackdir = 'center',
    position = position_dodge(width = 0.8),
    dotsize = 0.6,
    binwidth = 1.5  # Controls spacing of dots on y-axis
  ) +
  labs(
    title = "Dot Plot of Tooth Length by Dose and Supplement Type",
    x = "Dose (mg/day)",
    y = "Tooth Length",
    color = "Supplement Type"
  ) +
  theme_minimal()

Explanation

  • Dataset: ToothGrowth contains observations of tooth length in guinea pigs under different doses of Vitamin C and supplement types (VC or OJ)

  • Factor Conversion: dose is converted to a factor for categorical grouping.

  • position_dodge(): This separates dots by supp within each dose level for clearer group comparison.

  • geom_dotplot(): Plots individual data points as dots, stacked alon

Correlation Matrix Visualization using ggplot2

Develop a script in R to calculate and visualize a correlation matrix for a given dataset, with color-coded cells indicating the strength and direction of correlations, using ggplot2’s geom_tile function.

# Load required libraries
library(ggplot2)
library(tidyr)
library(dplyr)

Dataset

We use the built-in mtcars dataset.

# Preview the dataset
head(mtcars)
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Cundefined

# Use built-in mtcars dataset
data(mtcars)

# Compute correlation matrix
cor_matrix <- cor(mtcars)

# Convert matrix to a data frame for plotting
cor_df <- as.data.frame(as.table(cor_matrix))
head(cor_df)
  Var1 Var2       Freq
1  mpg  mpg  1.0000000
2  cyl  mpg -0.8521620
3 disp  mpg -0.8475514
4   hp  mpg -0.7761684
5 drat  mpg  0.6811719
6   wt  mpg -0.8676594

Explanation:

  • cor(mtcars) computes pairwise correlation.

  • as.table() flattens the matrix into a long-format table.

  • The result has 3 columns: Var1, Var2, and the correlation value (Freq).

Step 2: Visualize Using ggplot2::geom_tile

ggplot(cor_df, aes(x = Var1, y = Var2, fill = Freq)) +
  geom_tile(color = "white") +  # Draw tile borders
  scale_fill_gradient2(
    low = "blue", mid = "white", high = "red", 
    midpoint = 0, limit = c(-1, 1),
    name = "Correlation"
  ) +
  geom_text(aes(label = round(Freq, 2)), size = 3) +  # Show values
  theme_minimal() +
  labs(
    title = "Correlation Matrix (mtcars)",
    x = "", y = ""
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Step Description
cor() Computes correlation values between numeric variables.
as.table() + as.data.frame() Converts matrix into a long format suitable for plotting.
ggplot() Initializes the plot using long-form data.
geom_tile() Creates color-coded tiles based on correlation values.
scale_fill_gradient2() Applies a diverging color scale: red (strong +ve), blue (strong -ve), white (neutral).
geom_text() Adds correlation values as text in each cell.
theme_minimal() Cleans up the plot visually.
axis.text.x rotation Tilts x-axis labels for better readability.