How to Easily Create Strip Charts in R

Understanding the Strip Chart

A strip chart is a fundamental yet highly effective tool in exploratory data analysis. It serves to display numerical data along a single linear axis, providing a clear visualization of the data’s distribution. This chart type is particularly useful for observing the density, spread, and individual values within a dataset.

While similar in function to density plots or histograms, strip charts offer distinct advantages, especially when dealing with small to medium sample sizes. They act as a strong alternative to boxplots because they explicitly show every single data point. When data volumes are low, seeing these individual points can provide deeper insight into the distribution structure than summarized representations.

This tutorial will explain precisely how to leverage R’s built-in stripchart() function to generate these visualizations, covering scenarios for single variables, multiple variables, and grouped data.

The stripchart() Function: Core Syntax

The stripchart() function is the central tool for generating these visualizations in R. Its basic structure is highly intuitive, requiring only the data input to produce a plot. However, its true power lies in the array of optional arguments available for fine-tuning the output.

The general syntax required to construct a strip chart in R is detailed below:

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

Understanding these parameters is essential for moving from a default plot to a presentation-ready figure. The only mandatory element is the data input itself, represented by x.

• x: This is the required argument and represents the data to be plotted. It must be provided as a numeric vector or a list containing multiple numeric vectors.
• method: This critical argument dictates how points with identical values are handled. The default setting, "overplot", causes points to overlap, often obscuring data density. Alternatives include "jitter", which slightly offsets overlapping points, or "stack", which arranges identical points in layers.
• jitter: Applicable only when method = "jitter" is selected, this parameter controls the degree or amount of random displacement applied to the points, helping prevent visual overlap.
• main: Used to define the primary title displayed at the top of the chart.
• xlab: Defines the label for the x-axis (horizontal axis).
• ylab: Defines the label for the y-axis (vertical axis).
• col: Specifies the color used for the plotted data points.
• pch: Specifies the plotting character or shape used for the data points (e.g., circles, squares, triangles).
• vertical: A logical argument (TRUE or FALSE). If set to TRUE, the plot is drawn vertically, overriding the default horizontal orientation.
• group.names: Used when plotting multiple numeric vectors simultaneously. This argument allows you to provide custom labels for the groups displayed along the axis.

Creating a Strip Chart for a Single Vector (Basic Implementation)

To demonstrate the utility of the strip chart, we will utilize the renowned built-in R dataset, the iris dataset. This dataset contains measurements for 150 instances of three species of iris flowers.

First, we inspect the structure of the data to identify the variable we wish to plot. We will focus on the continuous variable Sepal.Length.

#view first six rows of iris dataset
head(iris)

#  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#1          5.1         3.5          1.4         0.2  setosa
#2          4.9         3.0          1.4         0.2  setosa
#3          4.7         3.2          1.3         0.2  setosa
#4          4.6         3.1          1.5         0.2  setosa
#5          5.0         3.6          1.4         0.2  setosa
#6          5.4         3.9          1.7         0.4  setosa

The simplest implementation involves passing the column vector iris$Sepal.Length directly into the function. This generates a default, horizontal strip chart, primarily showing the raw distribution of sepal lengths across all observed flowers.

stripchart(iris$Sepal.Length)

As observed in the basic plot above, many points overlap due to the default "overplot" method. This necessitates further customization to accurately represent the data density.

Advanced Customization: Jitter, Stacking, and Orientation

To produce a chart that is both informative and visually appealing, we introduce specific arguments to control appearance and handling of point overlap. We can define a title, label the axes, specify a color (col), select a point shape (pch), and importantly, implement the "jitter" method to separate points that share identical measurement values.

stripchart(iris$Sepal.Length,
           main = 'Sepal Length Distribution',
           xlab = 'Sepal Length',
           col = 'red',
           pch = 1,
           method = 'jitter')

Alternatively, if random jittering is not desired, the "stack" method provides a cleaner, structured way to visualize point frequency. The stack method places points vertically on top of one another, forming clear piles where data density is highest.

stripchart(iris$Sepal.Length,
           main = 'Sepal Length Distribution',
           xlab = 'Sepal Length',
           col = 'red',
           pch = 1,
           method = 'stack')

Finally, the orientation of the chart can be inverted using the vertical = TRUE argument. When plotting vertically, remember to adjust your axis labels, shifting the measurement label to the y-axis.

stripchart(iris$Sepal.Length,
           main = 'Sepal Length Distribution',
           ylab = 'Sepal Length',
           col = 'red',
           pch = 1,
           method = 'jitter',
           vertical = TRUE)

Visualizing Multiple Variables using a List

The stripchart() function is not limited to displaying a single variable. By passing a list of numeric vectors, you can easily plot the distributions of multiple variables side-by-side within a single visualization, facilitating easy comparison.

In this example, we compare the distributions of Sepal.Length and Sepal.Width from the iris dataset. We first create a list object containing these two variables, assigning descriptive names to each vector, which R will use as default group labels.

#create list of variables
x <- list('Sepal Length' = iris$Sepal.Length, 'Sepal Width' = iris$Sepal.Width)

#create plot that contains one strip chart per variable
stripchart(x,
           main = 'Sepal Width & Length Distributions',
           xlab = 'Measurement', 
           ylab = 'Variable',
           col = c('steelblue', 'coral2'),
           pch = 16,
           method = 'jitter')

Just as with the single-variable plots, the orientation can be easily switched to vertical by setting vertical = TRUE, which often provides a more natural comparison axis when dealing with multiple categories or variables.

stripchart(x, main = 'Sepal Width & Length Distributions',
           xlab = 'Measurement', 
           ylab = 'Variable',
           col = c('steelblue', 'coral2'),
           pch = 16,
           method = 'jitter',
           vertical = TRUE)

Grouping Data with the Formula Interface

A powerful feature of the stripchart() function is its ability to accept a formula interface, typically in the structure y ~ x. In this format, y represents a numeric vector, and x represents a grouping variable (often a factor or categorical variable) that dictates how the data points in y should be separated and plotted.

Using the iris dataset, we can investigate how Sepal.Length varies across the different Species of iris. Since Species has three distinct categorical values (“setosa”, “versicolor”, and “virginica”), R will automatically generate three distinct strip charts, one for each group, displayed vertically for optimal comparison.

stripchart(Sepal.Length ~ Species,
           data = iris,
           main = 'Sepal Length by Species',
           xlab = 'Species', 
           ylab = 'Sepal Length',
           col = c('steelblue', 'coral2', 'purple'),
           pch = 16,
           method = 'jitter',
           vertical = TRUE)

Conclusion and Further Resources

The stripchart() function provides a flexible and visually explicit method for exploring data distribution in R. Whether you are analyzing a single variable, comparing multiple measurements, or examining grouped data distributions, the ability to customize point separation (jitter/stack), orientation, and aesthetics makes it an indispensable tool in the initial phases of data visualization.

For a comprehensive understanding of all available arguments and their advanced usage, it is recommended to consult the official R documentation.

To view the full documentation on the stripchart() function in R, simply type the following command into your R console:

?stripchart