Unleash the Power of Strip Charts in R: Visualize Time Series Data with Ease

Unlock the Power of Visual Data Analysis with Strip Charts in R

Getting Started with Strip Charts

Imagine being able to visualize dozens of time series at once, effortlessly comparing and analyzing numerical data. That’s exactly what strip charts offer. In this tutorial, we’ll dive into the world of strip charts in R, using the built-in airquality dataset to create stunning visualizations.

Meet the Airquality Dataset

Before we begin, let’s take a peek at the first six rows of our dataset:

[Output]

This dataset will serve as the foundation for our strip chart creations.

Crafting a Basic Strip Chart

To create a strip chart in R, we’ll utilize the stripchart() function. Here’s a simple example:

[Output]

In this example, we’ve employed the stripchart() function and the $ operator to generate a strip chart of the Ozone reading from the airquality dataset. But that’s just the beginning – we can customize our plot to perfection.

Elevating Your Strip Chart

Want to add a title, labels, and a pop of color to your strip chart? We’ve got you covered! By passing additional parameters, we can refine our plot:

[Output]

Here, we’ve added a title, labels for the axes, and changed the color of the strip to orange using the following parameters:

• main: adds the title “Mean ozone in parts per billion at Roosevelt Island”
• xlab: adds the label “Parts Per Billion” for the x-axis
• ylab: adds the label “Ozone” for the y-axis
• col = "Orange": changes the color of the strip to orange

Jitter Plots: A Variant of Strip Charts

When dealing with large clusters of data points, jitter plots come to the rescue. This variant of the strip plot provides a clearer view of overlapping data points. To create a jitter plot, we simply pass method = "Jitter" inside the stripchart() method:

[Output]

In this example, we’ve used the method parameter to create a jitter plot, ensuring that coincident points are plotted like stacked or jittered, without any overlap.

Multiple Strip Charts in One

Why settle for just one strip chart when you can create multiple ones in a single plot? By passing a list of numeric vectors, we can generate multiple strips:

[Output]

Here, we’ve passed a list named list1 with two vectors – Ozone and Solar Radiation from the airquality dataset – inside stripchart() to create multiple strips. We’ve also assigned two colors to represent the two different strip charts: “orange” for Ozone readings and “brown” for Solar.R readings.