Q&A 13 How do you show group means and variability using a bar plot?

13.1 Explanation

A bar plot with error bars summarizes numerical data by showing:

• Mean value for each category as a bar
  
• Error bars representing variability (e.g., standard deviation or standard error)

This is ideal for comparing central tendencies between groups, especially when:

• You’ve already summarized your data
  
• You want to highlight differences in means
  
• Distribution details (e.g., skewness, modality) are less important

Adding colors using hue and modern palettes enhances readability and prevents warnings.

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13.2 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")

# Bar plot with error bars (standard deviation), hue to avoid warnings
plt.figure(figsize=(8, 6))
sns.barplot(
    data=iris,
    x="species",
    y="sepal_length",
    hue="species",
    palette="Set2",
    ci="sd",
    errorbar="sd",
    capsize=0.1
)
plt.title("Bar Plot with Error Bars: Sepal Length by Species", fontsize=14)
plt.xlabel("Species")
plt.ylabel("Mean Sepal Length ± SD")
plt.legend([],[], frameon=False)  # Hides duplicate legend
plt.tight_layout()
plt.show()

/var/folders/m1/0dxpqygn2ds41kxkjgwtftr00000gn/T/ipykernel_75563/2750927964.py:13: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar='sd'` for the same effect.

  sns.barplot(

13.3 R Code

library(readr)
library(ggplot2)
library(dplyr)

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

# Summarize mean and standard deviation
summary_df <- iris %>%
  group_by(species) %>%
  summarise(
    mean_val = mean(sepal_length),
    sd_val = sd(sepal_length),
    .groups = "drop"
  )

# Bar plot with error bars
ggplot(summary_df, aes(x = species, y = mean_val, fill = species)) +
  geom_bar(stat = "identity", width = 0.6) +
  geom_errorbar(aes(ymin = mean_val - sd_val, ymax = mean_val + sd_val), width = 0.2) +
  scale_fill_brewer(palette = "Set2") +
  theme_minimal() +
  labs(title = "Bar Plot with Error Bars: Sepal Length by Species",
       x = "Species", y = "Mean Sepal Length ± SD")

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✅ Bar plots with error bars are great for summarizing group-level differences in means. Using hue or fill ensures correct color mapping and avoids warnings in modern plotting libraries.