How to use ggplot2's geom_dotplot() with both fill and group?

In data visualization, dot plots are effective for displaying distributions of data, especially when dealing with categorical variables and their relationships with numeric values. geom_dotplot() in the ggplot2 package for R allows us to create such plots, providing insights into how variables are distributed across categories. This article explores how to use geom_dotplot() specifically with both fill and group aesthetics in R Programming Language.

What is a Dot Plot?

A dot plot is a statistical chart that displays data using dots plotted on a simple scale. Each dot represents an observation or data point. Dot plots are useful for visualizing distributions, comparing groups, and identifying patterns or outliers in data.

Using geom_dotplot() in ggplot2

geom_dotplot() is a function in the ggplot2 package that creates dot plots. It arranges dots horizontally or vertically to represent the frequency or density of observations in each group or category. It supports various aesthetics (aes()) to map data variables to visual properties like position, color, and grouping.

Let's start with a practical example to demonstrate how to use geom_dotplot() with both fill and group aesthetics.

# Load libraries
library(ggplot2)

# Create a sample dataset
set.seed(123)
n <- 100
data <- data.frame(
  group_var = factor(rep(letters[1:3], each = n)),
  fill_var = factor(rep(letters[1:2], each = n * 3)),
  value = rnorm(n * 3, mean = 0, sd = 1)
)

• group_var represents three groups (a, b, c).
• fill_var represents two categories (a and b).
• value is a numeric variable that we will visualize with dot plots.

Plotting with geom_dotplot()

Now we will plot the dot plot using with geom_dotplot().

# Plot using geom_dotplot with fill and group aesthetics
ggplot(data, aes(x = group_var, y = value, fill = fill_var, group = fill_var)) +
  geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.5) +
  scale_fill_manual(values = c("#0072B2", "#D55E00")) +  # Custom fill colors
  theme_minimal() +
  labs(
    title = "Dot Plot with Fill and Group Aesthetics",
    x = "Group Variable",
    y = "Value"
  )

Output:

• ggplot(): Initializes the plot and specifies the data (data) and aesthetics (aes()).
• geom_dotplot(): Creates the dot plot. Parameters like binaxis, stackdir, and dotsize control the appearance of the dots.
• binaxis = "y": Dots are placed along the y-axis.
• stackdir = "center": Dots are stacked in the center of each group.
• dotsize = 0.5: Adjusts the size of the dots for better visibility.
• scale_fill_manual(): Sets custom fill colors for the categories in fill_var.
• theme_minimal(): Applies a minimal theme to the plot for clarity.
• labs(): Sets the plot title, x-axis label (Group Variable), and y-axis label (Value).

Visualizing Distribution of Exam Scores

In this example, we visualize the distribution of exam scores (score) across different groups (group) and fill the dots based on pass (pass) or fail status.

# Sample dataset
set.seed(567)
n <- 120
data <- data.frame(
  group = factor(rep(letters[1:2], each = n)),
  pass = factor(sample(c("Pass", "Fail"), size = n * 2, replace = TRUE)),
  score = rnorm(n * 2, mean = 70, sd = 10)
)

# Plotting
ggplot(data, aes(x = group, y = score, fill = pass, group = pass)) +
  geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.5) +
  scale_fill_manual(values = c("#0072B2", "#D55E00")) +
  theme_minimal() +
  labs(
    title = "Dot Plot: Distribution of Exam Scores by Pass/Fail",
    x = "Group",
    y = "Score"
  )

Output:

Key Concepts and Features

Here are the main Key Concepts and Features of the geom_dotplot().

• fill Aesthetic: The fill aesthetic allows you to color elements (like dots) based on categorical variables. It groups data points by the fill_var variable in our example, assigning different colors (#0072B2 and #D55E00) to each category (a and b).
• group Aesthetic: The group aesthetic ensures that dots are grouped by fill_var within each group_var. This means that within each group (a, b, c), dots are organized based on their fill_var category (a or b), allowing for clear comparisons across categories.

Customization Options

Using ggplot2 we have multiple Customization Options so lets discuss some of them.

• Colors: Use scale_fill_manual() to define custom colors for categorical variables.
• Orientation: Adjust binaxis (x or y) and stackdir to control dot placement and stacking direction.
• Themes: Apply different themes (theme_minimal(), theme_light(), etc.) to change the plot's appearance.

Benefits of Using Dot Plots

• Clarity: Dot plots simplify complex data distributions into intuitive visual representations.
• Comparison: Facilitate comparisons between groups and categories effectively.
• Insight Generation: Identify patterns, trends, and outliers within categorical data.

Conclusion

Using geom_dotplot() with both fill and group aesthetics in ggplot2 enhances the visualization of categorical and numeric relationships. By mapping variables to visual properties like color and grouping, you can create informative dot plots that reveal insights into your data. This article has provided a practical guide with examples, covering essential concepts and customization options to effectively use dot plots for data exploration and communication in R. Experiment with different datasets and settings to harness the full potential of geom_dotplot() for your visual analytics needs.