A waffle chart provides a clear visual representation of how individual components contribute to a whole. It’s particularly useful for tracking progress toward a goal or for showing parts-to-whole relationships. Unlike pie charts, waffle charts use a grid of equal-sized squares to represent data without distorting the proportions.
Implementation in R
We’ll use ggplot2 for its versatile and elegant plotting capabilities along with the waffle package, an extension that simplifies the creation of waffle charts.
Installing Required Packages
To install the necessary packages in R Studio, run:
The Pareto chart combines a bar chart and a line chart: the left vertical axis shows the frequency of occurrences for different categories (sorted in descending order), and the right vertical axis displays the cumulative percentage. This visualization follows the Pareto principle, which states that roughly 80% of effects come from 20% of the causes.
x: A vector of values. The names attached to x are used for labeling the bars.
ylab: A string specifying the label for the primary y-axis (left side).
ylab2: A string specifying the label for the secondary y-axis (right side) showing the cumulative percentage.
xlab: A string specifying the label for the x-axis.
cumperc: A vector of percentage values to be used as tick marks for the secondary y-axis.
ylim: A numeric vector specifying the limits for the primary y-axis.
main: A string specifying the main title for the plot.
col: A value for the color, a vector of colors, or a palette for the bars.
Steps for Plotting a Pareto Chart in R
Create a vector that contains the frequency counts of different categories.
Assign names to the vector elements to label each category.
Plot the vector using the pareto.chart() function.
Example:
# Install and load the qcc package
install.packages("qcc")
library(qcc)
# Frequency counts for various customer issues
issues <- c(35, 850, 15, 50, 20, 120, 40, 10, 55, 500)
# Labels for the issues
names(issues) <- c("Late Delivery", "Damaged Item", "Incorrect Order",
"Poor Packaging", "Missing Item", "Wrong Product",
"Customer Service", "Billing Error", "Return Issues", "Other")
# Generate the Pareto chart
pareto.chart(issues,
xlab = "Issue Categories", # Label for x-axis
ylab = "Frequency", # Label for left y-axis
col = heat.colors(length(issues)),# Colors for the bars
cumperc = seq(0, 100, by = 20), # Tick marks for the cumulative percentage
ylab2 = "Cumulative Percentage", # Label for right y-axis
main = "Customer Complaints") # Chart title
Output:
Example 2: Product Defects
# Frequency counts for product defects
defects <- c(6000, 3500, 4800, 2500, 900)
# Labels for the product categories
names(defects) <- c("Type X", "Type Y", "Type Z", "Type W", "Type V")
# Generate the Pareto chart
pareto.chart(defects,
xlab = "Product Categories", # Label for x-axis
ylab = "Frequency", # Label for left y-axis
col = heat.colors(length(defects)),# Colors for the bars
cumperc = seq(0, 100, by = 10), # Tick marks for the cumulative percentage
ylab2 = "Cumulative Percentage", # Label for right y-axis
main = "Product Defects") # Chart title
A boxplot is a graphical summary that represents groups of numerical data using their quartiles. Since boxplots are non-parametric, they display the variation in samples from a statistical population without assuming any specific underlying distribution. The spacing within the box indicates the degree of dispersion and skewness in the data, while also highlighting outliers. Boxplots can be oriented vertically or horizontally, and they get their name from the rectangular “box” in the center.
Stratified boxplots are used to examine the relationship between a categorical variable and a numeric variable, or to compare multiple groups defined by an additional categorical variable. They are especially useful for comparing the distributions of a numeric variable across different categories.
Implementation in R
Stratified boxplots in R can be created using the boxplot() function from the R Graphics Package. Here is the syntax and a brief description of key parameters:
formula: A formula describing the relationship between the numeric and categorical variables.
data: A data frame or list containing the variables specified in the formula.
subset: An optional vector specifying a subset of observations.
na.action: A function indicating what should be done with missing values.
xlab, ylab: Labels for the x- and y-axes; can be suppressed with ann = FALSE.
add: Logical flag to add the boxplot to an existing plot.
horizontal: Logical flag; if TRUE, boxplots are drawn horizontally.
range: Determines how far the whiskers extend from the box.
width: A vector specifying the relative widths of the boxes.
varwidth: If TRUE, the widths of the boxes are proportional to the square roots of the number of observations in each group.
notch: If TRUE, a notch is drawn in each side of the boxes.
outline: If FALSE, outliers are not plotted.
names: Group labels displayed under each boxplot.
border: Colors for the outlines of the boxplots.
col: Colors for the bodies of the boxplots.
log: A character string indicating whether the x or y (or both) should be on a log scale.
pars: A list of additional graphical parameters.
at: Numeric vector specifying the locations for drawing the boxplots when adding to an existing plot.
Example
In this example, we will use the iris dataset to create a stratified boxplot that compares the petal lengths across the three iris species.
# Load the iris dataset
data(iris)
# Create a stratified boxplot of Petal.Length by Species
boxplot(Petal.Length ~ Species, data = iris,
main = "Boxplot of Petal Length by Iris Species",
xlab = "Iris Species",
ylab = "Petal Length (cm)",
col = c("lightblue", "lightgreen", "lightpink"),
border = "darkblue")
Output:
Example:
Below is an example where we compare lung capacity by gender rather than smoking status. In this revised example, we:
Load the same dataset.
Categorize ages into new groups.
Create three boxplots:
Boxplot 1: Compares lung capacity between males and females.
Boxplot 2: Compares lung capacity between males and females for subjects aged 20 and above.
Boxplot 3: Displays stratified boxplots of lung capacity by gender within the defined age groups.
# Load the dataset
LungCapData <- read.csv("LungCapData.csv", header = TRUE)
LungCapData <- data.frame(LungCapData)
attach(LungCapData)
# Categorise Age into groups with new breakpoints
AgeGroups <- cut(LungCapData$Age,
breaks = c(0, 15, 20, 30),
labels = c("Under 15", "15-20", "Over 20"))
# BoxPlot 1: Lung Capacity by Gender
boxplot(LungCapData$LungCap ~ LungCapData$Gender,
ylab = "Lung Capacity",
main = "Lung Capacity: Males vs Females",
col = c("skyblue", "salmon"),
las = 1)
# BoxPlot 2: Lung Capacity by Gender for subjects aged 20 and above
boxplot(LungCapData$LungCap[LungCapData$Age >= 20] ~ LungCapData$Gender[LungCapData$Age >= 20],
ylab = "Lung Capacity",
main = "Lung Capacity (Age >= 20): Males vs Females",
col = c("skyblue", "salmon"),
las = 1)
# BoxPlot 3: Stratified Lung Capacity by Gender across Age Groups
boxplot(LungCapData$LungCap ~ LungCapData$Gender * AgeGroups,
ylab = "Lung Capacity",
xlab = "Gender and Age Group",
main = "Stratified Lung Capacity by Gender and Age Groups",
col = c("skyblue", "salmon"),
las = 2)
A boxplot is a graphical summary that represents groups of numerical data using their quartiles. Since boxplots are non-parametric, they display the variation in samples from a statistical population without assuming any specific underlying distribution. The spacing within the box indicates the degree of dispersion and skewness in the data, while also highlighting outliers. Boxplots can be oriented vertically or horizontally, and they get their name from the rectangular “box” in the center.
Stratified boxplots are used to examine the relationship between a categorical variable and a numeric variable, or to compare multiple groups defined by an additional categorical variable. They are especially useful for comparing the distributions of a numeric variable across different categories.
Implementation in R
Stratified boxplots in R can be created using the boxplot() function from the R Graphics Package. Here is the syntax and a brief description of key parameters:
formula: A formula describing the relationship between the numeric and categorical variables.
data: A data frame or list containing the variables specified in the formula.
subset: An optional vector specifying a subset of observations.
na.action: A function indicating what should be done with missing values.
xlab, ylab: Labels for the x- and y-axes; can be suppressed with ann = FALSE.
add: Logical flag to add the boxplot to an existing plot.
horizontal: Logical flag; if TRUE, boxplots are drawn horizontally.
range: Determines how far the whiskers extend from the box.
width: A vector specifying the relative widths of the boxes.
varwidth: If TRUE, the widths of the boxes are proportional to the square roots of the number of observations in each group.
notch: If TRUE, a notch is drawn in each side of the boxes.
outline: If FALSE, outliers are not plotted.
names: Group labels displayed under each boxplot.
border: Colors for the outlines of the boxplots.
col: Colors for the bodies of the boxplots.
log: A character string indicating whether the x or y (or both) should be on a log scale.
pars: A list of additional graphical parameters.
at: Numeric vector specifying the locations for drawing the boxplots when adding to an existing plot.
Example
In this example, we will use the iris dataset to create a stratified boxplot that compares the petal lengths across the three iris species.
# Load the iris dataset
data(iris)
# Create a stratified boxplot of Petal.Length by Species
boxplot(Petal.Length ~ Species, data = iris,
main = "Boxplot of Petal Length by Iris Species",
xlab = "Iris Species",
ylab = "Petal Length (cm)",
col = c("lightblue", "lightgreen", "lightpink"),
border = "darkblue")
Output:
Example:
Below is an example where we compare lung capacity by gender rather than smoking status. In this revised example, we:
Load the same dataset.
Categorize ages into new groups.
Create three boxplots:
Boxplot 1: Compares lung capacity between males and females.
Boxplot 2: Compares lung capacity between males and females for subjects aged 20 and above.
Boxplot 3: Displays stratified boxplots of lung capacity by gender within the defined age groups.
# Load the dataset
LungCapData <- read.csv("LungCapData.csv", header = TRUE)
LungCapData <- data.frame(LungCapData)
attach(LungCapData)
# Categorise Age into groups with new breakpoints
AgeGroups <- cut(LungCapData$Age,
breaks = c(0, 15, 20, 30),
labels = c("Under 15", "15-20", "Over 20"))
# BoxPlot 1: Lung Capacity by Gender
boxplot(LungCapData$LungCap ~ LungCapData$Gender,
ylab = "Lung Capacity",
main = "Lung Capacity: Males vs Females",
col = c("skyblue", "salmon"),
las = 1)
# BoxPlot 2: Lung Capacity by Gender for subjects aged 20 and above
boxplot(LungCapData$LungCap[LungCapData$Age >= 20] ~ LungCapData$Gender[LungCapData$Age >= 20],
ylab = "Lung Capacity",
main = "Lung Capacity (Age >= 20): Males vs Females",
col = c("skyblue", "salmon"),
las = 1)
# BoxPlot 3: Stratified Lung Capacity by Gender across Age Groups
boxplot(LungCapData$LungCap ~ LungCapData$Gender * AgeGroups,
ylab = "Lung Capacity",
xlab = "Gender and Age Group",
main = "Stratified Lung Capacity by Gender and Age Groups",
col = c("skyblue", "salmon"),
las = 2)
A box graph (or boxplot) is used to display the distribution of data based on a five-number summary: the minimum, first quartile, median, third quartile, and maximum. In R, you can create boxplots using the boxplot() function.
Syntax:
boxplot(x, data, notch, varwidth, names, main)
x: A vector or a formula.
data: A data frame containing the variables.
notch: Logical value indicating whether to display a notch (useful for comparing medians).
varwidth: Logical value; if TRUE, the box width is proportional to the square root of the sample size.
names: Group labels to be shown under each box.
main: The main title of the chart.
Creating a Dataset Example
For illustration, we’ll use the iris dataset. First, let’s inspect a few rows of the data focusing on Sepal.Length and Species:
# Extract the relevant columns from the iris dataset
input <- iris[, c("Sepal.Length", "Species")]
head(input)
Now, let’s create a simple boxplot to compare the sepal length across different species:
# Load the iris dataset
data(iris)
# Create the boxplot for Sepal Length grouped by Species
boxplot(Sepal.Length ~ Species, data = iris,
main = "Sepal Length by Species",
xlab = "Species",
ylab = "Sepal Length")
Output:
Boxplot with Notch
Notches can be added to boxplots to provide a rough guide for comparing medians between groups. Here’s how to create a notch boxplot with customized colors:
# Load the iris dataset
data(iris)
# Define custom colors for the boxes
custom_colors <- c("#FF6347", "#3CB371", "#4682B4")
# Create the notch boxplot with custom aesthetics
boxplot(Sepal.Length ~ Species, data = iris,
main = "Sepal Length by Species",
xlab = "Species",
ylab = "Sepal Length",
col = custom_colors, border = "black",
notch = TRUE, notchwidth = 0.5,
medcol = "white", whiskcol = "black",
boxwex = 0.5, outpch = 19, outcol = "black")
# Add a legend to the plot
legend("topright", legend = unique(iris$Species),
fill = custom_colors, border = "black", title = "Species")
Output:
Multiple Boxplots
Let’s create multiple boxplots for different variables from the iris dataset. We will compare the distributions of Sepal.Length, Sepal.Width, Petal.Length, and Petal.Width across the species. The plotting area will be divided into multiple panels.
# Load the iris dataset
data(iris)
# List the variables for which we want to create boxplots
variables <- c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
# Set up the plotting layout: one row and one column per variable
par(mfrow = c(1, length(variables)))
# Create boxplots for each variable grouped by Species
for (var in variables) {
boxplot(get(var) ~ Species, data = iris,
main = paste("Boxplot of", var),
xlab = "Species",
ylab = var,
col = "lightblue", border = "black",
notch = TRUE, notchwidth = 0.5,
medcol = "white", whiskcol = "black",
boxwex = 0.5, outpch = 19, outcol = "black")
}
# Reset the plotting layout to default
par(mfrow = c(1, 1))
The dotchart() function in R is used to create a Cleveland dot plot, where individual data points are represented as dots. This type of plot is particularly useful for comparing a set of numerical values along an axis, with optional grouping and labeling for enhanced clarity.
Syntax
dotchart(x, labels = NULL, groups = NULL, gcolor = par("fg"), color = par("fg"), ...)
x: A numeric vector or matrix.
labels: A vector of labels for each point.
groups: A grouping variable to indicate how the elements of x are categorized.
gcolor: Color(s) used for group labels and group values.
color: Color(s) used for the individual points and their labels.
Example 1: Dot Chart of a Single Numeric Vector
This example demonstrates how to create a dot chart for a numeric vector representing exam scores, with each score labeled by a student’s name.
# Example 1: Dot Chart for Exam Scores
# Create a numeric vector for exam scores
scores <- c(85, 90, 78, 92, 88, 75, 95)
# Create labels for each student
student_names <- c("Alice", "Bob", "Charlie", "David", "Eva", "Frank", "Grace")
# Generate the dot chart
dotchart(scores, labels = student_names,
cex = 0.9, xlab = "Exam Score",
main = "Dot Chart of Exam Scores")
Output:
Example 2: Dot Chart with Grouping
In this example, a numeric vector representing monthly sales is grouped into two categories (e.g., “First Half” and “Second Half” of the year). Different colors are used to distinguish the groups.
# Example 2: Dot Chart Grouped by Period
# Create a numeric vector for monthly sales figures
sales <- c(120, 150, 180, 130, 160, 170, 140, 190, 200, 155)
# Create labels for each month
months <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct")
# Define a grouping variable: "First Half" for the first 5 months and "Second Half" for the rest
group <- factor(ifelse(1:10 <= 5, "First Half", "Second Half"))
# Define colors for the two groups
group_colors <- c("purple", "teal")
# Generate the dot chart with grouping
dotchart(sales, labels = months, groups = group,
gcolor = group_colors,
color = group_colors[as.numeric(group)],
cex = 0.9, pch = 21, xlab = "Monthly Sales",
main = "Monthly Sales Dot Chart by Group")
Output:
Example 3: Dot Chart with Custom Labels and Titles
This example shows how to create a dot chart for a series of measurements with custom point labels and axis titles.
# Example 3: Dot Chart for Measurements
# Create a numeric vector of measurement values
measurements <- c(5.2, 7.8, 6.1, 8.5, 7.0, 6.8, 9.2)
# Create custom labels for each measurement
labels <- c("P1", "P2", "P3", "P4", "P5", "P6", "P7")
# Generate the dot chart
dotchart(measurements, labels = labels,
main = "Dot Chart of Measurements",
xlab = "Measurement Value", ylab = "Points",
cex = 0.9)
A scatter plot is a set of dotted points representing individual data pieces on the horizontal and vertical axis. In a graph in which the values of two variables are plotted along the X-axis and Y-axis, the pattern of the resulting points reveals a correlation between them.
R – Scatter Plots
We can create a scatter plot in R Programming Language using the plot() function.
Syntax:
plot(x, y, main, xlab, ylab, xlim, ylim, axes)
Parameters:
x: This parameter sets the horizontal coordinates.
y: This parameter sets the vertical coordinates.
xlab: This parameter is the label for the horizontal axis.
ylab: This parameter is the label for the vertical axis.
main: This parameter is the title of the chart.
xlim: This parameter is used for plotting values of x.
ylim: This parameter is used for plotting values of y.
axes: This parameter indicates whether both axes should be drawn on the plot.
Simple Scatterplot Chart
To create a Scatterplot Chart:
We use the dataset iris.
Use the columns Sepal.Length and Petal.Length in iris.
Example:
# Get the input values.
input <- iris[, c('Sepal.Length', 'Petal.Length')]
# Print the first few rows
print(head(input))
We use the plot() function to generate the scatterplot.
The xlab parameter describes the X-axis and ylab describes the Y-axis.
Example:
# Get the input values.
input <- iris[, c('Sepal.Length', 'Petal.Length')]
# Plot the chart for Sepal.Length and Petal.Length.
plot(x = input$Sepal.Length, y = input$Petal.Length,
xlab = "Sepal Length",
ylab = "Petal Length",
xlim = c(4, 8),
ylim = c(1, 7),
main = "Sepal Length vs Petal Length"
)
Output:
Scatterplot Matrices
When we have two or more variables and we want to correlate between one variable and others, we use an R scatterplot matrix.
The pairs() function is used to create matrices of scatterplots.
Syntax:
pairs(formula, data)
Parameters:
formula: This parameter represents the series of variables used in pairs.
data: This parameter represents the dataset from which the variables will be taken.
Example:
# Load the built-in iris dataset
data(iris)
# Create the scatterplot matrix
pairs(~Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
data = iris,
main = "Scatterplot Matrix")
Output:
Scatterplot with Fitted Values
Creating a Scatterplot in R
To create a scatterplot in R, we use the ggplot2 package, which provides the ggplot() and geom_point() functions for visualization.
In this example, we use the mtcars dataset and plot the relationship between the logarithm of mpg (miles per gallon) and drat (rear axle ratio). The stat_smooth() function is used to add a fitted linear regression line.
Example:
# Loading ggplot2 package
library(ggplot2)
# Creating scatterplot with fitted values.
ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +
geom_point(aes(color = factor(gear))) +
stat_smooth(method = "lm", col = "#C42126", se = FALSE, size = 1)
Output:
Adding Titles Dynamically
To enhance the visualization, we add a title, subtitle, and caption using the labs() function.
Example:
# Loading ggplot2 package
library(ggplot2)
# Creating scatterplot with fitted values.
graph <- ggplot(mtcars, aes(x = log(mpg), y = log(drat))) +
geom_point(aes(color = factor(gear))) +
stat_smooth(method = "lm", col = "#C42126", se = FALSE, size = 1)
# Adding title, subtitle, and caption
graph + labs(
title = "Relationship between Mileage and Drat",
subtitle = "Data categorized by gear count",
caption = "Computed using mtcars dataset"
)
Output:
3D Scatterplots
For 3D scatterplots, we use the plotly package, which enables interactive visualizations.
Example:
# Loading required library
library(plotly)
# Attaching mtcars dataset
attach(mtcars)
# Creating a 3D scatterplot
plot_ly(data = mtcars, x = ~mpg, y = ~hp, z = ~cyl, color = ~gear)
Output:
Scatter Plot
A scatter plot visually represents the relationship between two numerical variables. The x-axis represents one data vector, while the y-axis represents another.
Syntax:
plot(x, y, type, xlab, ylab, main)
Parameters:
x: Data vector for the x-axis
y: Data vector for the y-axis
type: Type of plot (e.g., “l” for lines, “p” for points, “s” for steps)
xlab: Label for the x-axis
ylab: Label for the y-axis
main: Title of the graph
help("plot")
Example:
# Creating a dataset
data_set <- data.frame(Height = c(150, 160, 165, 170, 175, 180, 185, 190),
Weight = c(50, 55, 60, 65, 70, 75, 80, 85))
# Output as a PNG file
png(file = "scatterplot_output.png")
# Creating the scatter plot
plot(x = data_set$Height, y = data_set$Weight,
xlab = "Height (cm)", ylab = "Weight (kg)",
main = "Height vs. Weight", col = "red", pch = 19)
# Saving the file
dev.off()
A pie chart is a circular statistical graphic divided into slices to illustrate numerical proportions. Each sector (or slice) represents the relative sizes of data. It is also known as a circle graph, where a circular chart is cut into segments to describe relative frequencies or magnitudes.
The R programming language provides the pie() function to create pie charts. It takes positive numbers as a vector input.
Syntax:
pie(x, labels, radius, main, col, clockwise)
Parameters:
x: A vector containing numeric values used in the pie chart.
labels: Descriptions for the slices in the pie chart.
radius: Defines the radius of the circle (value between -1 and +1).
main: Title of the pie chart.
clockwise: Logical value indicating whether slices are drawn clockwise or counterclockwise.
col: Specifies colors for the pie slices.
Creating a Simple Pie Chart
By using the above parameters, we can create a basic pie chart with labels.
Example:
# Create data for the graph
values <- c(30, 50, 40, 60)
labels <- c("Apple", "Banana", "Grapes", "Mango")
# Plot the chart
pie(values, labels)
Output:
Pie Chart with Title and Colors
We can enhance the pie chart by adding a title and colors using the col parameter.
Example:
# Create data for the graph
values <- c(25, 45, 35, 55)
labels <- c("New York", "London", "Tokyo", "Sydney")
# Plot the chart with title and rainbow color palette
pie(values, labels, main = "City Pie Chart",
col = rainbow(length(values)))
Output:
Pie Chart with Color Palettes
Using the RColorBrewer package to add colors to a pie chart.
# Load necessary library
library(RColorBrewer)
# Create data for the graph
sales <- c(40, 60, 30, 50)
cities <- c("New York", "Los Angeles", "Chicago", "Houston")
# Assign colors using brewer.pal
colors <- brewer.pal(length(sales), "Set2")
# Plot the pie chart
pie(sales, labels = cities, col = colors)
Output:
Modify Border Line Type
Using the lty argument to change the border style.
# Load necessary library
library(RColorBrewer)
# Create data for the graph
sales <- c(40, 60, 30, 50)
cities <- c("New York", "Los Angeles", "Chicago", "Houston")
# Assign colors using brewer.pal
colors <- brewer.pal(length(sales), "Set2")
# Plot the pie chart with modified border type
pie(sales, labels = cities, col = colors, lty = 2)
Output:
Add Shading Lines
Using the density and angle arguments to add shading.
# Load necessary library
library(RColorBrewer)
# Create data for the graph
sales <- c(40, 60, 30, 50)
cities <- c("New York", "Los Angeles", "Chicago", "Houston")
# Assign colors using brewer.pal
colors <- brewer.pal(length(sales), "Set2")
# Plot the pie chart with shading lines
pie(sales, labels = cities, col = colors, density = 50, angle = 45)
Output:
3D Pie Chart
Using the plotrix package to create a 3D pie chart.
# Load necessary library
library(plotrix)
# Create data for the graph
sales <- c(40, 60, 30, 50)
cities <- c("New York", "Los Angeles", "Chicago", "Houston")
# Calculate percentages
sales_percent <- round(100 * sales / sum(sales), 1)
# Plot the 3D pie chart
pie3D(sales, labels = sales_percent,
main = "Sales Distribution", col = rainbow(length(sales)))
# Add a legend
legend("topright", cities, cex = 0.5, fill = rainbow(length(sales)))
A histogram is a graphical representation of statistical data that groups data points into specified ranges. The rectangular bars in a histogram represent frequencies, with their heights proportional to the frequency of values in each range. Unlike bar graphs, histograms do not have gaps between bars.
Creating Histograms in R
Histograms in R can be created using the hist() function.
The lines() function in R is used to add lines of different types, colors, and widths to an existing plot.
Syntax:
lines(x, y, col, lwd, lty)
Parameters:
x, y: Vectors of coordinates
col: Color of the line
lwd: Width of the line
lty: Type of line
Adding Lines to a Plot using lines() Function
Example 1: Adding a Line to a Scatter Plot
This example demonstrates how to create a scatter plot and add a line to it.
# Creating coordinate vectors
x <- c(2.1, 4.2, 1.5, -2.8, 6.3,
3.1, 4.0, 2.8, 2.6, 2.2, 2.0, 2.8)
y <- c(3.2, 6.5, 2.8, -2.5, 10.5, 4.8,
5.9, 5.1, 3.9, 3.2, 3.4, 4.8)
# Plotting the scatter plot
plot(x, y, cex = 1, pch = 3, xlab = "X-axis",
ylab = "Y-axis", col = "black")
# Creating another set of coordinates for the line
x2 <- c(3.5, 1.0, -1.8, 0.2)
y2 <- c(4.0, 5.2, 3.0, 3.5)
# Adding a red line to the plot
lines(x2, y2, col = "red", lwd = 2, lty = 1)
Output:
Example 2: Connecting Points with lines()
This example shows how to plot a scatter plot and connect the points using lines().
# Creating coordinate vectors
x <- c(2.1, 4.2, 1.5, -2.8, 6.3, 3.1,
4.0, 2.8, 2.6, 2.2, 2.0, 2.8)
y <- c(3.2, 6.5, 2.8, -2.5, 10.5, 4.8,
5.9, 5.1, 3.9, 3.2, 3.4, 4.8)
# Plotting the scatter plot
plot(x, y, cex = 1, pch = 3, xlab = "X-axis",
ylab = "Y-axis", col = "black")
# Connecting points with a red line
lines(x, y, col = "red")
Output:
Example: Adding Lines to a Plot in R using lines()
# Create sample data
x <- seq(-5, 5, length.out = 10)
y <- x^3
# Create a plot of the data
plot(x, y, main = "Adding Lines to a Plot", col = "blue")
# Add a vertical line at x = 0
abline(v = 0, col = "green", lwd = 2)
# Add a horizontal line at y = 0
abline(h = 0, col = "purple", lwd = 2)
# Add a diagonal line with slope -2 and intercept 3
abline(a = 3, b = -2, col = "orange", lty = 2, lwd = 2)
# Add a custom line using lines() function
x2 <- seq(-5, 5, length.out = 10)
y2 <- -x2^2 + 4
lines(x2, y2, col = "red", lty = 2, lwd = 2)
