Strip Charts in R

Introduction

A strip chart (also called a dot plot) is a simple graphical technique used to display individual observations of a dataset. Each data value is represented by a dot placed along an axis.

Strip charts are particularly useful for:

• Small datasets.
• Visualizing the distribution of data.
• Comparing groups.
• Detecting clusters and outliers.
• Displaying individual observations without grouping them into bins.

Unlike histograms, strip charts display every data point, making them ideal when the number of observations is relatively small.

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Syntax

stripchart(x,
           method,
           pch,
           col,
           main,
           xlab,
           ylab,
           vertical)

where

• x : vector or formula.
• method : arrangement of points.
• pch : plotting symbol.
• col : color.
• main : title.
• xlab : x-axis label.
• ylab : y-axis label.
• vertical : vertical or horizontal display.

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Example 1: Simple Strip Chart

Suppose marks of students are

marks <- c(45,50,55,60,65,70,75,80,85,90)

stripchart(marks)

Output:

Dots are plotted horizontally.

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Example 2: Add Title and Labels

stripchart(marks,
           main="Student Marks",
           xlab="Marks")

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Example 3: Change Plotting Symbol

stripchart(marks,
           pch=19)

Common Values of pch

pch	Symbol
1	Circle
2	Triangle
3	Plus
15	Square
16	Filled Circle
17	Filled Triangle
18	Diamond
19	Solid Circle

Example:

stripchart(marks,
           pch=18)

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Example 4: Change Color

stripchart(marks,
           pch=19,
           col="blue")

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Example 5: Vertical Strip Chart

stripchart(marks,
           vertical=TRUE,
           pch=19,
           col="red")

This displays the data vertically.

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Example 6: Stack Method

Suppose data contain repeated values:

marks <- c(60,65,65,70,70,70,75,80,80,85)

stripchart(marks,
           method="stack",
           pch=19,
           col="blue")

Here identical values are stacked.

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Example 7: Jitter Method

stripchart(marks,
           method="jitter",
           pch=19,
           col="green")

The points are slightly displaced to avoid overlap.

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Example 8: Overplot Method

stripchart(marks,
           method="overplot",
           pch=19)

Repeated values overlap.

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Example 9: Student Heights

height <- c(150,155,160,162,165,
            170,172,175,178,180)

stripchart(height,
           pch=16,
           col="purple",
           main="Student Heights",
           xlab="Height (cm)")

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Example 10: Comparing Two Groups

male <- c(165,170,172,175,180)

female <- c(155,160,162,165,168)

stripchart(list(Male=male,
                Female=female),
           pch=19,
           col=c("blue","red"),
           method="jitter")

This creates two strip charts side by side.

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Example 11: Using Formula Interface

marks <- c(80,85,75,90,70,
           88,95,78,65,72)

gender <- factor(c("Male","Male","Male","Male","Male",
                   "Female","Female","Female","Female","Female"))

stripchart(marks~gender,
           method="jitter",
           pch=19,
           col=c("blue","pink"))

The notation

marks ~ gender

means:

Plot marks grouped by gender.

 

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Example 12: Using Data Frames

student <- data.frame(
Gender=c("Male","Male","Female",
         "Female","Male"),
Marks=c(80,85,90,75,88)
)

stripchart(Marks~Gender,
           data=student,
           pch=19,
           method="jitter",
           col=c("blue","red"))

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Example 13: Multiple Departments

cse <- c(80,85,90,88,92)
ece <- c(70,75,78,82,85)
me <- c(65,68,72,76,80)

stripchart(list(CSE=cse,
                ECE=ece,
                ME=me),
           method="jitter",
           pch=19,
           col=rainbow(3))

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Example 14: Strip Chart with Box Plot

marks <- c(45,50,55,60,65,70,75,80,85,90)

boxplot(marks,
        horizontal=TRUE)

stripchart(marks,
           method="jitter",
           pch=19,
           col="red",
           add=TRUE)

This overlays individual observations on the box plot.

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Example 15: Random Data

x <- rnorm(100)

stripchart(x,
           method="jitter",
           pch=19,
           col="green")

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Methods Available

Method	Description
"overplot"	Points overlap
"stack"	Repeated values stacked
"jitter"	Points randomly displaced

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Important Parameters

Parameter	Purpose
method	Arrangement of points
pch	Plotting symbol
col	Color
main	Title
xlab	X-axis label
ylab	Y-axis label
vertical	Vertical plot
add	Add to existing graph

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Applications

• Displaying individual observations.
• Comparing groups.
• Detecting outliers.
• Supplementing box plots.
• Small sample data analysis.
• Exploratory Data Analysis (EDA).

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Difference Between Strip Chart and Histogram

Strip Chart	Histogram
Shows every observation	Shows frequencies
Suitable for small datasets	Suitable for large datasets
Uses dots	Uses bars
No binning required	Requires bins
Created using stripchart()	Created using hist()

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Difference Between Strip Chart and Box Plot

Strip Chart	Box Plot
Shows all observations	Shows summary statistics
Individual values visible	Values summarized by quartiles
Better for small datasets	Better for comparing distributions
Uses dots	Uses box and whiskers

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Summary of Useful Functions

Function	Purpose
stripchart()	Create strip chart
boxplot()	Create box plot
jitter()	Random displacement
list()	Multiple groups
factor()	Grouping variable
rainbow()	Generate colors

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Conclusion

A strip chart is a one-dimensional scatter plot used to display individual data values. It is especially useful for small datasets and for visualizing the distribution and spread of observations. In R, the stripchart() function provides various methods such as overplot, stack, and jitter, making it an effective tool for exploratory data analysis and comparison of groups.