Scatter Plots in R

Introduction

A scatter plot is a graph used to display the relationship between two numerical variables. Each observation is represented as a point whose coordinates correspond to the values of the two variables.

Scatter plots are widely used to:

Study relationships between variables.
Detect trends and patterns.
Identify clusters and outliers.
Examine correlations.
Visualize experimental and statistical data.

For example:

Height vs Weight
Age vs Salary
Study Hours vs Marks
Temperature vs Ice Cream Sales

In R, scatter plots are created using the plot() function.

Syntax


plot(x, y,
     main,
     xlab,
     ylab,
     col,
     pch,
     cex)

where

x : values on x-axis.
y : values on y-axis.
main : title.
xlab : x-axis label.
ylab : y-axis label.
col : point color.
pch : plotting symbol.
cex : point size.

Example 1: Simple Scatter Plot

Suppose the study hours and marks of students are:

Hours	Marks
2	45
4	55
5	60
7	75
9	90


hours <- c(2,4,5,7,9)

marks <- c(45,55,60,75,90)

plot(hours, marks)

Example 2: Adding Title and Labels


plot(hours,
     marks,
     main="Study Hours vs Marks",
     xlab="Hours Studied",
     ylab="Marks")

Example 3: Changing Color


plot(hours,
     marks,
     col="blue")

Available colors:

red
blue
green
black
orange
purple

Example 4: Changing Symbols


plot(hours,
     marks,
     pch=16)

Common Values of `pch`

pch	Symbol
1	Circle
2	Triangle
3	Plus
4	Cross
15	Square
16	Filled Circle
17	Filled Triangle
18	Diamond

Example:


plot(hours,
     marks,
     pch=17,
     col="red")

Example 5: Increasing Point Size


plot(hours,
     marks,
     pch=16,
     cex=2)

cex=2 doubles the size of the points.

Example 6: Adding Grid


plot(hours,
     marks,
     pch=19)

grid()

Example 7: Adding Labels to Points


names <- c("A","B","C","D","E")

plot(hours,
     marks,
     pch=19)

text(hours,
     marks,
     labels=names,
     pos=3)

The labels appear above the points.

Example 8: Height vs Weight


height <- c(150,160,165,170,180)

weight <- c(50,60,65,70,85)

plot(height,
     weight,
     main="Height vs Weight",
     xlab="Height",
     ylab="Weight",
     pch=16,
     col="blue")

Example 9: Age vs Salary


age <- c(22,25,30,35,40)

salary <- c(25000,35000,50000,70000,90000)

plot(age,
     salary,
     pch=19,
     col="green")

Example 10: Adding a Regression Line


hours <- c(2,4,5,7,9)
marks <- c(45,55,60,75,90)

plot(hours,
     marks,
     pch=16)

model <- lm(marks ~ hours)

abline(model,
       col="red",
       lwd=2)

This draws the best-fitting straight line.

Example 11: Customizing the Plot


plot(hours,
     marks,
     main="Study Hours vs Marks",
     xlab="Hours",
     ylab="Marks",
     col="blue",
     pch=18,
     cex=1.5)

Example 12: Using Data Frames


student <- data.frame(
    Hours=c(2,4,5,7,9),
    Marks=c(45,55,60,75,90)
)

plot(student$Hours,
     student$Marks,
     pch=19,
     col="red")

Example 13: Multiple Colors


hours <- c(2,4,5,7,9)

marks <- c(45,55,60,75,90)

plot(hours,
     marks,
     col=c("red","blue","green","orange","purple"),
     pch=19,
     cex=2)

Example 14: Specifying Axis Limits


plot(hours,
     marks,
     xlim=c(0,10),
     ylim=c(0,100))

Example 15: Plotting Random Data


x <- rnorm(100)

y <- rnorm(100)

plot(x,
     y,
     pch=16,
     col="blue")

This is often used to study distributions and clusters.

Correlation Patterns

Positive Correlation

As x increases, y increases.

Example:

Height vs Weight
Study Hours vs Marks

Negative Correlation

As x increases, y decreases.

Example:

Speed vs Travel Time

No Correlation

Randomly scattered points.

Important Parameters

Parameter	Purpose
x	x-axis values
y	y-axis values
main	Title
xlab	x-axis label
ylab	y-axis label
col	Color
pch	Point shape
cex	Point size
xlim	x-axis limits
ylim	y-axis limits

Additional Functions

Add Text


text(x,y,labels)

Add Grid


grid()

Add Regression Line


abline(model)

Fit Linear Model


lm(y~x)

Applications of Scatter Plots

Correlation analysis
Machine learning datasets
Regression analysis
Height vs Weight studies
Stock market analysis
Experimental measurements
Population studies
Age vs Salary analysis
Scientific research

Difference Between Line Graph and Scatter Plot

Scatter Plot	Line Graph
Shows relationship between variables	Shows trends over time
Data represented as points	Points connected by lines
Order not important	Order important
Created using `plot(x,y)`	Created using `plot(x,y,type="l")`
Used for correlation analysis	Used for trend analysis

Conclusion

A scatter plot is one of the most important graphical tools in R for visualizing the relationship between two numerical variables. By customizing parameters such as col, pch, and cex, and adding regression lines using abline(), scatter plots become powerful tools for correlation analysis, statistical modeling, and machine learning.