# Load the ggplot2 package
library(ggplot2)Program-15
Develop Report of program 9 to 14 using R Quartro
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.
Loading ggplot2
Step 1: 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 setosaStep 2: 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()
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 Required Library
# Load ggplot2 for plotting
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: Example with Built-in 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 4: Example with Custom Colors
# 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)
Program 11:
To generate a basic box plot usin ggplot2, enhanced with notches and outliers, and grouped by a categorical variable using an in-build dataset in R.
Step 1: Load Required Library
library(ggplot2)
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 setosaStep 2: Creating a Boxplot
# Create the boxplot
ggplot(iris, aes(x = Species, y=Sepal.Length)) +
geom_boxplot(notch = TRUE, outlier.colour = "red", outlier.shape = 16, outlier.size = 2, fill="skyblue") +
labs(title = "Distribution of Sepal Length ", x = "Species", y = "Sepal Length") +
theme_minimal()
Program 12:
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
library(ggplot2)Step 2: Use an Inbuilt 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 setosastr(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
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)
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 required library
library(ggplot2)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionStep 2: Plotting the dot plot
# 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 for bin along y
stackdir = 'center',
position = position_dodge(width = 0.8),
dotsize = 0.6, #defines the size of dots
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()
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 required library
library(ggplot2)
library(tidyr)
library(dplyr)Step 2: Finding head of mtcars
# 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 1Step 3: Converting matrix to Data Frame
# 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 -0.8676594Step 4: 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))