Q&A 17 How do you visualize two categorical variables with a grouped bar plot?

17.1 Explanation

A grouped bar plot allows you to compare two categorical variables simultaneously by showing side-by-side bars within each group.

This is ideal when:

• You want to analyze proportions or counts across two categorical dimensions
  
• You need a clean comparison without stacking
  
• Each group has a manageable number of levels

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17.2 Python Code

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

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

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

# Grouped bar plot: count of cut across clarity levels
plt.figure(figsize=(10, 6))
sns.countplot(data=diamonds, x="clarity", hue="cut", palette="Set2")
plt.title("Grouped Bar Plot: Diamond Cut by Clarity", fontsize=14)
plt.xlabel("Clarity")
plt.ylabel("Count")
plt.tight_layout()
plt.show()

17.3 R Code

library(readr)
library(ggplot2)

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

# Grouped bar plot
ggplot(diamonds, aes(x = clarity, fill = cut)) +
  geom_bar(position = "dodge") +
  scale_fill_brewer(palette = "Set2") +
  theme_minimal() +
  labs(title = "Grouped Bar Plot: Diamond Cut by Clarity",
       x = "Clarity", y = "Count")

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✅ Grouped bar plots allow clean comparison across two categorical dimensions. They’re especially useful for understanding distribution patterns in grouped data.