Program 15

Author

1NT23IS080 - Section B - Harsh Deep B Nair

Develop a report on all 7 previous programs.

Program 9

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.

Step 1: Load the necessary libraries

# Load the ggplot2 package
library(ggplot2)

Step 2: Load and explore the dataset

# 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 3: Create grouped histograms using 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 4: 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.

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

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 the required library

# Load the ggplot2 package
library(ggplot2)

Step 2: Define the function

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 data set

# 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)

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: 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.

Program 11

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 the required library

# Load the ggplot2 package
library(ggplot2)

Step 2: Load and explore 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 ...

Step 3: Create a Notched Box Plot Grouped by Species

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.

Program 12

Write a program 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 the required library

library(ggplot2)

Step 2: Use an in-built 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 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.

Program 13

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.

Step 1: Load the required libraries

# Load the ggplot2 package
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

Step 2: Create multiple dot plots

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',   #  The axis to bin along y
    stackdir = 'center', # Which direction to stack the dots
    position = position_dodge(width = 0.8),
    dotsize = 0.6,  # Diameter of the dots relative to binwidth
    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 along the y-axis.

Program 14

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.

Step 1: Load the required libraries

library(ggplot2)
library(tidyr)
library(dplyr)

Step 2: Use built-in dataset

We use the built-in mtcars dataset.

# 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 3: 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`	Tilts x-axis labels for better readability.