How to Customize Colors in ggplot for Raster Data with Two Values in R

Описание: Learn how to efficiently choose and customize colors for raster data visualizations in R using `ggplot2`, focusing on a scenario with two distinct values.
---
This video is based on the question https://stackoverflow.com/q/65922911/ asked by the user 'JerryN' ( https://stackoverflow.com/u/5943539/ ) and on the answer https://stackoverflow.com/a/65923069/ provided by the user 'Leonardo' ( https://stackoverflow.com/u/14976580/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: R ggplot How do I choose the colors of a raster with 2 values

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Customizing Colors in ggplot for Raster Data with Two Values in R

Visualizing raster data can sometimes pose challenges, particularly when it comes to choosing the right colors for your data points. In this guide, we will explore how to customize the colors for a raster consisting of just two values – 0 and 1 – using the powerful ggplot2 package in R. Follow along as we break down the process into easy-to-follow steps.

The Problem: How to Choose Colors for a Raster with Two Values

You have a raster data set that you want to visualize with specific colors. The steps involved are straightforward:

Create a raster using the terra package.

Convert the raster to a data frame.

Plot the raster using ggplot, selecting colors for the discrete values.

However, many users encounter issues when attempting to set custom colors, leading to error messages such as:

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

Let's dive into the solution for effectively setting the colors of the two values in your raster.

Step-by-Step Solution

To change the default colors of your raster visualization, we will utilize the scale_fill_gradient function within ggplot2. Here's how to execute this process clearly.

Step 1: Create Your Raster

Using the terra package, we will first create a raster with 10 rows and 10 columns containing random 0s and 1s.

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

Step 2: Convert to a Data Frame

Next, we convert our raster object into a data frame. This is essential for ggplot to be able to visualize the data effectively.

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

Step 3: Create the Plot with Custom Colors

Now, we are ready to visualize the data with custom colors. The key here is to use geom_tile along with scale_fill_gradient to choose your colors. Instead of directly specifying colors in the aes mapping, we set it in the scale_fill_gradient function.

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

Explanation of the Code

ggplot(data = x_df, aes(x, y)): This initializes the ggplot object with the specified data frame and maps the x and y coordinates.

geom_tile(aes(fill = value)): This adds the tiles for each cell's value, where value determines the fill color.

scale_fill_gradient(low = "red", high = "green"): This function defines a gradient scale where 0 (the lowest value) will be colored red and 1 (the highest value) will be colored green.

Conclusion

By following these steps, you can successfully choose and customize colors for a raster with two distinct values in R using ggplot2. This method allows for clear representation and better understanding of your data.

Feel free to experiment with different color combinations to match your project's theme or to enhance visual clarity!

Now you can create stunning visualizations that communicate your data effectively!