How to Change the Color of Points for ggplot2 Scatterplot Using ColorBrewer in R

When visualizing data using scatter plots, coloring the points can help distinguish between categories or highlight certain patterns in the data. In this article, we will explore how to change the color of points in a ggplot2 scatterplot using RColorBrewer in R.

Introduction to ColorBrewer

RColorBrewer is an R package that provides predefined color palettes for sequential, diverging, and qualitative data. The colors are designed to be visually appealing and colorblind-friendly, which is why they are often preferred in data visualizations. These palettes are especially useful for categorical and continuous variables, ensuring that your visualizations are both effective and accessible.

Why Use ColorBrewer with ggplot2?

ColorBrewer palettes offer several advantages:

• Easy-to-read colors: They are designed to work well even in grayscale print, and many are colorblind-friendly.
• Wide range of color options: It provides different types of palettes suitable for various data types (categorical, continuous, or diverging).
• Aesthetic appeal: The colors are well-balanced and look professional in visualizations.

Now we will discuss step by step How to Change the Color of Points for ggplot2 Scatterplot Using ColorBrewer in R Programming Language.

Step 1: Loading Required Packages and Create a Sample Dataset

For this example, we will create a simple dataset consisting of three variables: x, y, and category. The category variable will be used to differentiate the points in the scatter plot.

# Create a sample dataset
set.seed(123)
data <- data.frame(
  x = rnorm(100),
  y = rnorm(100),
  category = sample(letters[1:3], 100, replace = TRUE)
)

# Print the first few rows of the dataset
head(data)

Output:

            x           y category
1 -0.56047565 -0.71040656        c
2 -0.23017749  0.25688371        c
3  1.55870831 -0.24669188        c
4  0.07050839 -0.34754260        a
5  0.12928774 -0.95161857        a
6  1.71506499 -0.04502772        c

• x: Random values generated from a normal distribution.
• y: Random values generated from a normal distribution.
• category: A factor variable with three levels (a, b, and c), randomly assigned to each point.

Step 2: Basic Scatter Plot with ggplot2

Let's first create a basic scatter plot using ggplot2 to visualize the relationship between x and y, with points colored by category.

# Basic scatter plot
ggplot(data, aes(x = x, y = y, color = category)) +
  geom_point(size = 3) +
  labs(title = "Scatter Plot with ggplot2", x = "X-axis", y = "Y-axis")

Output:

By default, ggplot2 will assign a color scheme to the points based on the category variable. However, we can customize these colors using RColorBrewer.

Step 3: Using RColorBrewer to Customize Colors

RColorBrewer provides a variety of color palettes specifically designed for different types of data visualization. The three main types of palettes are:

• Sequential: For ordered data that progresses from low to high (e.g., Blues, Reds).
• Diverging: For data with a critical midpoint (e.g., RdBu, PiYG).
• Qualitative: For categorical data (e.g., Set1, Paired).

Since our category variable is a categorical variable, we will use a qualitative palette. To view the available palettes in RColorBrewer, you can use the following command:

display.brewer.all()

Let's apply the Set1 qualitative palette to color the points in our scatter plot.

# Scatter plot using ColorBrewer palette 'Set1'
ggplot(data, aes(x = x, y = y, color = category)) +
  geom_point(size = 3) +
  scale_color_brewer(palette = "Set1") +
  labs(title = "Scatter Plot with ColorBrewer (Set1)", x = "X-axis", y = "Y-axis")

Output:

In this plot, the colors assigned to the category levels are taken from the Set1 palette, which is designed for categorical data.

Step 4: Using Other ColorBrewer Palettes

You can experiment with different palettes from RColorBrewer to find the one that best suits your data visualization needs. For example, let's try using the Dark2 palette.

# Scatter plot using ColorBrewer palette 'Dark2'
ggplot(data, aes(x = x, y = y, color = category)) +
  geom_point(size = 3) +
  scale_color_brewer(palette = "Dark2") +
  labs(title = "Scatter Plot with ColorBrewer (Dark2)", x = "X-axis", y = "Y-axis")

Output:

This applies the Dark2 palette to the points, giving the plot a different look.

Conclusion

Using RColorBrewer with ggplot2 allows you to customize the colors of points in a scatter plot based on categorical variables. With various qualitative color palettes, you can easily enhance the visual appeal of your plots and make your data more interpretable. By combining RColorBrewer palettes with ggplot2 customization options like legend labels, point size, and transparency, you can create highly effective and visually attractive scatter plots in R.