Can You Use a Scatter Plot for Categorical Data? Practical Applications and Visualizations

Scatter plots are primarily designed for visualizing the relationship between two continuous numerical variables. Despite their original purpose, there are creative ways to adapt scatter plots for categorical data. This article will explore techniques to effectively use scatter plots with categorical variables, the benefits and limitations, and real-world applications in healthcare.

Visualizing Categorical Data with Scatter Plots

A scatter plot visually displays the relationship between two continuous numerical variables by plotting one variable on the x-axis and the other on the y-axis. Here are some useful techniques for visualizing categorical data in a scatter plot:

Categorical Variables on One Axis

One common method is to place a categorical variable on one axis, typically the x-axis, and a continuous variable on the y-axis. This layout allows you to observe how the continuous variable varies across different categories. Each category is represented by a cluster of points along the categorical axis.

Jittering

When you have multiple data points at the same category, you can employ a technique called jittering to spread out the points. Jittering adds a small amount of random variation to the positions of the points, making it easier to distinguish individual points from each other.

Color-Coding

If you have a second categorical variable, you can use color or shape to differentiate points based on this variable. This not only enhances the visualization but also helps in understanding the data more intuitively.

Example Use Case: Patient Data Visualization

For instance, in healthcare, a scatter plot can be used to visualize patient data related to vital statistics. In such a scenario, the age in decimal years can be placed on the x-axis, while various parameters like blood pressure, heart rate, or respiratory rate can be plotted on the y-axis.

To effectively represent categorical data, you can use color scales or different symbols. This is particularly useful when the scatter plot needs to be printed in black and white. Symbolic lines or connectors in color can enhance the visibility of trends, even on a monochrome printer.

Limitations and Practical Considerations

While scatter plots can be used for categorical data, there are certain limitations to consider. For example, it's essential to have only one data point per patient encounter and parameter. Placing multiple data points at the same time for a given parameter would not be feasible.

In the example given, we used color scales and symbols to represent different parameters. The x-axis always showed a range from 0 to 21 years of age, and the y-axis plotted up to four parameters simultaneously. This format was chosen to ensure an information-dense yet readable visualization, especially for comparing treatment effectiveness against medication dosage.

Such a visualization can be adapted to compare different patient data or various types of information within the limits of the x-axis, which always represented age. However, the practical number of colors to represent categories was limited to six, with a common limitation of four colors as stated.

Conclusion

Scatter plots may not be the traditional choice for categorical data, but they can be effectively used in specific contexts, particularly when combined with continuous variables. For purely categorical data, other visualizations such as bar charts or box plots may be more appropriate.

By applying the techniques discussed above, you can create insightful scatter plots that effectively communicate the relationship between categorical and continuous variables, providing a powerful tool for data analysis and presentation.