Enhance Your ggplot2 Heatmap by Using Multiple Colours

Discover how to change the scale of your `ggplot2` heatmap to display more colours and achieve better visual representation of your data.
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This video is based on the question https://stackoverflow.com/q/68930467/ asked by the user 'AW27' ( https://stackoverflow.com/u/12250206/ ) and on the answer https://stackoverflow.com/a/68930748/ provided by the user 'Marek Fiołka' ( https://stackoverflow.com/u/16671736/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Enhance Your ggplot2 Heatmap by Using Multiple Colours

Creating effective visualizations is essential for data analysis, and heatmaps are a powerful tool to identify patterns. However, it can be frustrating when the heatmap does not adequately represent the range of your data values. A common issue occurs when the scale of the heatmap is set too narrow, resulting in a lack of visible distinction between different values.

In this guide, we will explore how to change the scale of a heatmap using the ggplot2 package in R, enabling it to display a broader range of colours for your data values. This ensures that even the smallest values are visible and interpretable.

The Problem

If you're working with a dataset where values fluctuate significantly — for instance, values between 1e-2 and 1e-5 with an outlier at 1e-13 — you may find that the default colour scale results in minimal differentiation. The heatmap may appear predominantly as one colour, obscuring essential data points.

Example of the Data Structure

Let’s consider an example where:

[[See Video to Reveal this Text or Code Snippet]]

With these sequences, we intend to create a heatmap. However, due to the variance in z, the visual output mightn't align with our expectations.

The Solution: Applying a Finer Scale

To make the grid finer and add more colours to your heatmap, follow the steps in the example below:

Step-by-Step Guide

Setup Your Data: Generate the combinations of x, y, and apply the values of z.

[[See Video to Reveal this Text or Code Snippet]]

Create the Heatmap: Use the geom_contour_filled function to create a filled contour heatmap, which better represents the varying values of z.

[[See Video to Reveal this Text or Code Snippet]]

Explanation of Code

geom_contour_filled(): This function creates filled contours where different densities of z correspond to different colours. It offers a visually distinguishable representation of various value ranges.

Highlighting Critical Values: Adding geom_point() allows you to emphasize specific data points — in this case, where z is less than 1e-6 — making them visible regardless of the overall colour scheme.

Conclusion

By refining the colour scale of your ggplot2 heatmap, you can significantly improve the visibility of your data across a broader spectrum. This enhanced visual clarity will not only aid in data interpretation but also ensure that crucial outlier values are easily noticed.

Give this approach a try on your datasets, and enhance your data visualization skills today!