Q&A 12 How do you show raw observations by group using a strip plot?

12.1 Explanation

A strip plot is a simple yet powerful way to show every individual data point for a numerical variable grouped by a categorical variable. Unlike boxplots or violin plots, which summarize data, strip plots highlight raw measurements.

They are best used when:

• You want complete visibility of individual observations
  
• Your dataset is small or moderate in size
  
• You want to explore variation and outliers without summary overlays

Adding jitter (slight random displacement) and using vibrant palettes makes the visualization more readable and visually engaging.

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12.2 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")

# Warning-free strip plot with hue and palette
plt.figure(figsize=(8, 6))
sns.stripplot(
    data=iris,
    x="species",
    y="sepal_length",
    hue="species",
    jitter=True,
    palette="Set2",
    dodge=False,
    size=6,
    alpha=0.8
)
plt.title("Strip Plot: Sepal Length by Species", fontsize=14)
plt.xlabel("Species")
plt.ylabel("Sepal Length")
plt.legend([],[], frameon=False)  # Hides duplicate legend
plt.tight_layout()
plt.show()

12.3 R Code

library(readr)
library(ggplot2)

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

# Strip plot with jitter and color
ggplot(iris, aes(x = species, y = sepal_length, color = species)) +
  geom_jitter(width = 0.2, size = 2.5, alpha = 0.8) +
  scale_color_brewer(palette = "Set2") +
  theme_minimal() +
  labs(title = "Strip Plot: Sepal Length by Species",
       x = "Species", y = "Sepal Length")

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✅ Strip plots offer a direct view of all data points in each category. They are perfect for spotting data spread, clusters, or outliers—especially when combined with color and jitter for clarity.