Strip Chart in R

It’s important to illustrate how numbers are distributed when you are working with data in R. When working with small sets of data, simple charts such as box plots or scatter plots often do not provide the clearest representation of everything. Sometimes they ignore some informative attributes, such as outliers or latent patterns, and that leads to losing valuable information.

It’s important to illustrate how numbers are distributed when you are working with data in R. When working with small sets of data, simple charts such as box plots or scatter plots often do not provide the clearest representation of everything. Sometimes they ignore some informative attributes, such as outliers or latent patterns, and that leads to losing valuable information.

This is where a “strip chart in R” makes a difference! A strip chart is an easy approach to illustrating each event as a single data point. This makes visualizing the pattern of the data as simple as the result of a glance.

In this post, we’ll talk about what an R strip chart is, why it’s useful in R, and some real-world data processing applications. Let’s get started.

Table of Contents:

• What is a Strip Chart in R?
• How to Use stripchart() Function in R Programming?
• Different Methods in the Stripchart function
• Example: Visualizing Wind Speed with a Strip Chart
• Practical Use Case of Strip Charts in R: Single & Multiple Groups Data
• Strip Chart vs. Boxplot vs. Histogram: What’s the Difference?
• How to Combine Strip Charts with Other Plots?
• Use in Exploratory Data Analysis (EDA)
• Common Mistakes and Tips
• Conclusion

What is a Strip Chart in R?

“A strip chart, also known as a dot plot or strip plot, is a one-dimensional scatter plot that displays individual data points along a single axis.”

Strip charts in R are very useful for illustrating the distribution of small datasets and comparing distributions across groups. In these charts, data points or events in the dataset are represented as dots on a single axis. If two or more data points have the same value, the strip chart allows the dots to overlap, unless you don’t use a method like “jitter” to separate them slightly for better visibility. 

This proves to be a quite useful technique for data visualization, whether you’re analyzing sample data or exploring different categories.

A stripchart in R proves to be a fantastic alternative to histograms or boxplots when the dataset is small.

How to Use stripchart() Function in R Programming?

Strip charts in data analysis are useful in a few common data scenarios:

• Small datasets: R Strip charts are ideal for small datasets because they allow you to view each individual point—nothing is lost in averages or summaries.
• Data that is either discrete or categorical: If your data is organized into clear groupings or specific values, a strip chart can help you see how those numbers are distributed.
• A lot of overlapping values: Sometimes data points pile up on top of each other; jittering spreads them out just enough to notice what’s going on.
• Comparing various groupings: The biggest advantage of stripchart is that it is simple to compare how each group is spread over multiple months, categories, or other variables.

Basic Syntax

The basic syntax of the stripchart() function is

stripchart(x, method = c("stack", "jitter", "overplot"), ...) 

Or 

stripchart(x, method, jitter, main, xlab, ylab, col, pch, vertical, group.names)

Key Parameters

In the above-mentioned syntax of the stripchart() function, the parameters are as follows:

• x: A numeric vector or a formula of the form y ~ x for grouped data, it is necessary to represent the data being plotted.
  
• method: Specifies how to arrange the points on the axis, like vertical or horizontal, when identical values are available. The default method is “overplot”, although other options include “jitter” and “stack”.
  
  • “stack”: Stacks points vertically.
    
  • “jitter”: When the technique is set to “jitter”, this parameter adds random noise to the points to prevent overlap.
    
  • “overplot”: Plots points directly on top of each other.
    
• …: Additional graphical parameters like main, xlab, ylab, col, pch, etc.
  • main: This option determines the title of the chart.
  • xlab: This parameter specifies the label for the x-axis.
  • ylab: This parameter controls the label for the y-axis.
  • col: This option determines the r stripchart color of the points in the plot.
  • pch: This parameter determines the form of the points in the plot.
  • vertical: When this parameter is set to “TRUE,” the plot is drawn vertically rather than in the typical horizontal orientation.
  • group.names: This option is used to depict numerous numeric vectors.
    

Different Methods in the Stripchart function

The stripchart() function in R provides a few methods to control how data points are displayed, especially when values overlap. Here’s a breakdown of each method you can use:

1. method = “overplot” (default)

The stripchart() function in R includes three ways to display data points. One of these is called Overplot, and it is the default behavior.

If multiple data points have the same value, Overplot stacks them on top of each other. This can make it difficult to distinguish each point clearly when there are many of them. The good news is that it clearly illustrates where all of the data points are and does not leave any out.

• What it does: Plots points directly on top of each other.
• Best for: Small datasets or when values are mostly unique.
• Limitation: Overlapping points will be hidden.

Here’s a stripchart command –

stripchart(x, method = "overplot")

For Example: 

stripchart(airquality$Wind, method = "overplot")

2. method = “jitter”

The Jitter technique gives each data point a random shaking. This helps to spread out points that might otherwise sit exactly on top of each other.

To control how much the points move, use the “jitter” parameter in the stripchart() method. The appropriate degree of jitter varies based on the data and the number of points available, so we may need to experiment with several different amounts to discover what looks best.

• What it does: Adds random noise to spread overlapping points slightly.
• Best for: Seeing the frequency of repeated values.
• Tip: Use jitter() or jitter.amount to control spread.

stripchart(x, method = "jitter", jitter = 0.1)

For Example: 

stripchart(airquality$Wind, method = "jitter", jitter = 0.1)

3. method = “stack”

Stack is an alternative to Overplot for displaying data in a strip chart. Here, points of the same value are still placed on top of each other, but they are spaced out slightly so that they do not completely cover each other.

This makes it easy to identify how many points are in the same location and helps us understand where the data is densely packed. It provides a clearer picture of how the data is distributed.

• What it does: Stacks identical values vertically (or horizontally if vertical = FALSE).
• Best for: Discrete or rounded data to show exact counts.
• Looks like: A dot histogram. Try experimenting

stripchart(x, method = "stack")

For Example: 

stripchart(airquality$Wind, method = "stack")

Using method = “jitter” vs method = “stack”

• method = “jitter”: This adds a little random movement to each point so they don’t stack on top of one another. It enables you to view each point clearly.
  
• method = “stack”: When points have the same value, method = “stack” stacks them neatly. It is useful for displaying where the data is dense.

Let’s understand these methods with the help of an example.

Example: Visualizing Wind Speed with a Strip Chart

In the following examples of strip charts in R, we will utilize the built-in “airquality” dataset. Let us show the first few rows of the dataset to gain a sense of its contents.

First, here’s a quick peek at the dataset:

We’ll focus on the Wind column, which records daily wind speeds in miles per hour.

Let’s take a look at how wind speed varied day to day using a strip chart.

Method 1: OverPlot

stripchart(airquality$Wind,
           main = "Daily Wind Speeds in New York (1973)",
           xlab = "Wind Speed (mph)",
           method = "overplot",
           col = "darkgreen",
           pch = 1)

Now, let’s plot it:

What Is Happening:

• X-axis (Wind Speed): Displays wind speed numbers (5-20 mph).
• Y-axis: Because there is no jitter or grouping, all data are shown on a single horizontal line.
• Dots: Each dot represents one day’s wind speed.
• Overlapping Points: When multiple days have the same or nearly identical wind speeds, the dots overlap, making it difficult to see them all properly. That is why, in the next chart, we will employ jitter to separate overlapping dots vertically.

Method 2: Jitter

stripchart(airquality$Wind,
           main = "Daily Wind Speeds in New York (1973)",
           xlab = "Wind Speed (mph)",
           method = "jitter",
           col = "darkgreen",
           pch = 1)

This chart gives us a simple view of how wind speeds were distributed over the summer months. Using jitter helps us separate points that would otherwise overlap, making the pattern easier to see. Most readings hover between 5 and 15 mph, but you can spot a few days with higher speeds.

Want it vertical instead? Just add vertical = TRUE to the function:

stripchart(airquality$Wind,
           main = "Daily Wind Speeds in New York (1973)",
           ylab = "Wind Speed (mph)",
           method = "jitter",
           col = "steelblue",
           pch = 2,
           vertical = TRUE)

Method 3: Stack

stripchart(airquality$Wind,
           main = "Daily Wind Speeds in New York (1973)",
           xlab = "Wind Speed (mph)",
           method = "stack",
           col = "darkgreen",
           pch = 1)

Run the above code yourself to view how stack looks like in a strip chart

Bonus Tip:

We can choose the form of the points displayed on the chart (known as the plotting character). It defaults to a square. Here are some Strip chart examples of shapes:

• 0 for square.
• 1 for circle.
• 2 for triangle

We may modify this by passing the above values in the pch parameter in the stripchart.

Try experimenting with different colours or plotting symbols using the col and pch parameters. It’s a quick way to personalize your charts and make patterns pop!

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Practical Use Case of Strip Charts in R: Single & Multiple Groups Data

1. Single Group Data

stripchart(airquality$Wind,
           main = "Wind Speeds Over a Week",
           xlab = "Wind Speed (mph)",
           col = "black",
           pch = 2)

• Each column represents a month (May to September).
• Each dot is a day’s wind speed.
• You can compare distributions across months easily.
• More tightly clustered dots suggest wind speeds were consistent that month.

2. Multiple Groups

stripchart(Wind ~ Month, data = airquality,
           main = "Wind Speeds by Month (1973)",
           xlab = "Month",
           ylab = "Wind Speed (mph)",
           method = "jitter",
           col = "darkorange",
           pch = 19,
           vertical = TRUE)

Here’s the strip chart for Wind Speeds by Month — each group (May to September) is shown as a vertical cluster of jittered points. This lets you visually compare wind speed distributions across months. 

Customizing Appearance

stripchart(Wind ~ Month, data = airquality,
           main = "Wind Speeds by Month (1973)",
           xlab = "Month",
           ylab = "Wind Speed (mph)",
           method = "jitter",
           col = "red",
           vertical = TRUE)

Try it yourself to know the results!

Strip Chart vs. Boxplot vs. Histogram: What’s the Difference?

Because data can behave unpredictably, spotting how it spreads is frustrating for beginners, and R helps with three popular plots: boxplots, histograms, and strip charts. Though the options overlap, each layout tells a different story, so clarity depends on knowing strengths and limits.

1. Boxplot

The boxplot condenses a dataset by recapping the median, whisker extremes, and any apparent outliers.

What it’s good at:
Comparing many groups at once is simple, as is spotting stray outliers strewn far from the hinges.

What it misses:
It obscures the individual data points, making it hard to see each specific value, which can be an issue when dealing with small datasets.

2. Histogram

A histogram basically organizes data into specific bins, and counts how many values fall into each one. It then builds bars to illustrate the shape of the data. This gives you a way to understand the layout of the data (bell-shaped or skewed).

What it’s good at:
It’s excellent for identifying trends and high points in extensive datasets.

What it misses:
The downside is that it obscures the nuances of each individual data point. It’s also crucial to pick the right size for your bins—if they’re too large or too small, it can really alter the appearance of your data.

3. Strip Chart (or Dot Plot)

A strip chart displays each individual data point, which makes it super simple to understand what your dataset really contains.

What it’s good at:
Ideal for working with small amounts of data. You can easily pick out each individual point and notice any unusual values or trends right from the start.

What it misses:

When dealing with large datasets, the chart can become cluttered and difficult to interpret due to an overwhelming number of dots.

In short:
When you’re working with a small dataset and need to visualize every single value, a strip chart really shines. If you’re dealing with larger datasets or need an overview, opting for a boxplot or a histogram might be better.

How to Combine Strip Charts with Other Plots?

The airquality dataset is a built-in library in R and contains daily air quality measurements of all places. Here we will use for New York. One of the numeric columns in it, is Wind, which shows the values for wind speed. We will use it to understand this section better.

– Overlaying a Strip Chart with a Boxplot in R

Let’s see how we can add a strip stripchart to a boxplot together to understand the distribution of wind speeds better.

# Load the built-in dataset
data("airquality")

# Remove missing values from Wind column
wind_data <- na.omit(airquality$Wind)

# Create the boxplot
boxplot(wind_data, horizontal = TRUE, col = "lightgray", main = "Wind Speed with Strip Chart")

# Add a strip chart on top of the boxplot
stripchart(wind_data, method = "jitter", pch = 1, col = "blue", vertical = FALSE, add = TRUE)

Use in Exploratory Data Analysis (EDA)

Strip charts are a very useful chart type in Exploratory Data Analysis (EDA) for making the data spread out when you’re just getting to know it quickly. They’re so-called because they show all the data points; you’ll be able to more clearly see patterns, outliers, or clusters — particularly as you work with smaller-sized data sets.

To illustrate this, we’ll use the airquality dataset that comes with R and work with the Wind column, which records wind speed.

# Load the built-in dataset
data("airquality")

# Clean the data by removing NA values from the Wind column
wind_data <- na.omit(airquality$Wind)

# Create a horizontal boxplot to summarize the data
boxplot(wind_data,
        horizontal = TRUE,
        col = "lightgray",
        main = "Wind Speed - EDA with Strip Chart")

# Add a strip chart on top to show each individual data point
stripchart(wind_data,
           method = "jitter",
           pch = 1,
           col = "blue",
           vertical = FALSE,
           add = TRUE)

Here:

The boxplot provides you with a crude summary: median, spread, and outliers.

The strip chart displays all of the wind speed values, which allows for an easier investigation of raw data.

Together, they offer a comprehensive view — combining both the big picture summary and the individual data details.

Common Mistakes and Tips

• Overlapping Points: Use method = “jitter” to add random noise and prevent overlap.
  
• Choosing the Right Visualization: Consider the nature of your data and the insights you wish to gain when selecting a visualization method.

Conclusion

Strip charts are a great way to plot out individual data points in R—if you have small datasets. Thus, you can avoid drawing box plots or using jitter with bar plots in such cases. Unlike boxplots and histograms, which summarize data or pool it into groups, strip charts show each and every value, and allow the viewer to quickly pick up on patterns, outliers, and trends at a glance.

They are very useful in exploratory data analysis (EDA), comparing the distributions between groups and visualizing the discrete numeric data. In combination with boxplots or histograms, a strip chart can provide both detail and summary, offering a comprehensive view of the data.

In short, mastering strip charts in R can help you better understand the structure of your data and make more informed decisions in your analysis. If you’re interested in diving deeper into R programming, consider enrolling in our course.

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