Getting Started with Strip Charts in R: A Beginner's Guide

 Data visualization is a cornerstone of effective data analysis, especially when working with small datasets. One of the simplest yet often overlooked tools in R’s visualization toolkit is the strip chart. In this beginner’s guide, we’ll walk through what a strip chart is, why you should use it, and how to create and customize it with real examples.



What is a Strip Chart?

A strip chart (also called a stripplot) is a basic scatter plot used to display the distribution of a small numeric dataset, often grouped by a categorical variable. It’s ideal when the number of observations is low, making plots like histograms or boxplots less informative.

Strip charts help reveal the actual data points, allowing analysts to spot clusters, outliers, or gaps in the distribution that might be hidden in a summary plot.

How to Create a Basic Strip Chart in R

R makes it easy to create strip charts using the built-in stripchart() function. Here's how you can get started:

Example 1: Basic Strip Chart


# Using the built-in mtcars dataset
data(mtcars)

# Strip chart for miles per gallon (mpg)
stripchart(mtcars$mpg,
           main = "Basic Strip Chart of mpg",
           xlab = "Miles Per Gallon",
           method = "overplot")

This basic chart plots the mpg values along a single axis.

Grouped Strip Charts

To compare distributions across groups (e.g., number of cylinders in cars), we can use a formula interface:

Example 2: Grouped Strip Chart with Jitter

stripchart(mpg ~ cyl,
           data = mtcars,
           main = "MPG by Number of Cylinders",
           xlab = "Cylinders",
           ylab = "Miles Per Gallon",
           method = "jitter",    # Adds jitter to prevent overlap
           pch = 19,             # Solid circles
           col = "blue",
           vertical = TRUE)      # Vertical layout

Here, each point represents a car, and the jitter helps prevent overlapping values.

Customizing Strip Charts

Strip charts can be customized in many ways to improve readability and aesthetics.

Common Parameters

  • method: "overplot" (default), "jitter", or "stack"

  • pch: Point character (e.g., 1 for circle, 19 for filled circle)

  • col: Color of points

  • vertical: Set to TRUE for vertical plots

Example 3: Horizontal Stacked Strip Chart


stripchart(mpg ~ cyl,
           data = mtcars,
           method = "stack",
           pch = 16,
           col = "darkgreen",
           main = "Horizontal Strip Chart - Stacked",
           xlab = "Miles Per Gallon")

Stacked method is useful when values are discrete and repeated.

When to Use a Strip Chart

Use a strip chart when:

  • You’re working with small sample sizes

  • You want to see the raw data instead of just summaries

  • You're comparing distributions across categories

Avoid using strip charts for very large datasets — they get cluttered and unreadable. In those cases, consider boxplots or violin plots instead.

Want More Examples?

If you're looking to go deeper, check out our detailed post on R Strip chart with examples for advanced use cases, comparisons with boxplots, and interactive versions using ggplot2 and plotly.

Summary

Strip charts are a simple but powerful way to visualize numeric data, especially when paired with grouping factors. By showing each individual data point, they provide transparency and clarity that other plot types may obscure.

Whether you're analyzing car performance, student test scores, or experimental results, strip charts can help you uncover patterns quickly and effectively.

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