Highlight Minimum and Maximum Points in Faceted ggplot2 Graph in R

In data visualization, highlighting specific points such as the minimum and maximum values can provide additional insights and make the graph more informative. In this article, we will explore how to highlight the minimum and maximum points in a faceted ggplot2 graph in R. Faceting allows you to create multiple plots based on a categorical variable, and highlighting key points in these plots can help in better data interpretation.

Prerequisites

To follow along, you need to have the following R packages installed:

• ggplot2: For creating the plots.
• dplyr: For data manipulation.

You can install these packages using the following commands:

install.packages("ggplot2")
install.packages("dplyr")

Let's start by creating an example dataset. We'll simulate a dataset with two groups and create a scatter plot where we will highlight the minimum and maximum points.

# Load necessary libraries
library(ggplot2)
library(dplyr)
library(tidyr)

# Create a sample dataset
set.seed(123)
data <- data.frame(
  group = rep(c("Group 1", "Group 2"), each = 100),
  x = rep(1:100, 2),
  y = c(rnorm(100, mean = 50, sd = 10), rnorm(100, mean = 60, sd = 15))
)

Finding Minimum and Maximum Points

To highlight the minimum and maximum points, we first need to identify these points within each group. We can do this using the dplyr package.

# Find minimum and maximum points in each group
highlight_points <- data %>%
  group_by(group) %>%
  summarise(min_y = min(y),
            max_y = max(y)) %>%
  pivot_longer(cols = c(min_y, max_y), names_to = "type", values_to = "y") %>%
  left_join(data, by = c("group", "y"))

• group_by(group): Groups the data by the group variable.
• summarise(min_y = min(y), max_y = max(y)): Calculates the minimum and maximum values of y for each group.
• pivot_longer(cols = c(min_y, max_y), names_to = "type", values_to = "y"): Reshapes the data to have a single y column with both minimum and maximum values.
• left_join(data, by = c("group", "y")): Merges the original data to retain the x values for the highlighted points.

Creating the Faceted ggplot2 Graph

Now that we have identified the minimum and maximum points, we can proceed to create the faceted graph and highlight these points.

# Create a faceted scatter plot with highlighted points
ggplot(data, aes(x = x, y = y)) +
  geom_point(color = "blue", alpha = 0.6) +  # Regular points
  geom_point(data = highlight_points, aes(color = type), size = 3) +  # Highlighted points
  scale_color_manual(values = c("min_y" = "red", "max_y" = "green")) +  # Custom colors for min and max
  facet_wrap(~ group) +
  labs(title = "Faceted Scatter Plot with Highlighted Min and Max Points",
       x = "X-axis",
       y = "Y-axis",
       color = "Highlight") +
  theme_minimal()

Output:

• geom_point(color = "blue", alpha = 0.6): Plots the regular data points in blue with some transparency.
• geom_point(data = highlight_points, aes(color = type), size = 3): Adds the highlighted points using a larger size and colors them based on whether they are minimum or maximum points.
• scale_color_manual(values = c("min_y" = "red", "max_y" = "green")): Assigns custom colors (red for minimum and green for maximum).
• facet_wrap(~ group): Creates faceted plots based on the group variable.

Conclusion

Highlighting minimum and maximum points in a faceted ggplot2 graph is a powerful way to enhance your data visualization and provide clear insights into the distribution of your data. By combining the data manipulation capabilities of dplyr with the versatile plotting functions of ggplot2, you can create informative and visually appealing graphs that draw attention to key data points.