Q&A 11 How do you display individual data points by category using a swarm plot?

11.1 Explanation

A swarm plot displays individual data points while intelligently spacing them to avoid overlap. Unlike strip plots (which may stack points randomly), swarm plots use a repulsion algorithm to spread points for better visibility.

They are especially helpful when:

• The dataset is small to medium-sized
• You want to show raw observations
• Identifying clusters, gaps, or outliers is important

Combining swarm plots with color (hue) and category grouping enhances clarity and storytelling.

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11.2 Python Code

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
iris = pd.read_csv("data/iris.csv")

# Set style
sns.set(style="whitegrid")

# Swarm plot
plt.figure(figsize=(8, 6))
sns.swarmplot(data=iris, x="species", y="sepal_length", hue="species", palette="Set2", dodge=False, size=6)
plt.title("Swarm Plot: Sepal Length by Species", fontsize=14)
plt.xlabel("Species")
plt.ylabel("Sepal Length")
plt.tight_layout()
plt.show()

11.3 R Code

library(readr)
library(ggplot2)
library(ggbeeswarm)

# Load dataset
iris <- read_csv("data/iris.csv")

# Swarm plot using ggbeeswarm::geom_quasirandom
ggplot(iris, aes(x = species, y = sepal_length, color = species)) +
  geom_quasirandom(size = 2.5, width = 0.25) +
  scale_color_brewer(palette = "Set2") +
  theme_minimal() +
  labs(title = "Swarm Plot: Sepal Length by Species",
       x = "Species", y = "Sepal Length")

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✅ Swarm plots reveal individual data points without overlap, making them ideal for exploring real observations, spotting outliers, and understanding group patterns in moderate-sized datasets.