Economist-Style Distribution Plots - Professional Data Visualization
Creating elegant distribution charts with The Economist’s distinctive visual style
ClinicoPath
2025-07-13
Source:vignettes/jjstatsplot-12-economistplots.Rmd
jjstatsplot-12-economistplots.RmdIntroduction to Economist-Style Distribution Plots
The Economist magazine has revolutionized data visualization in journalism with their distinctive, elegant approach to presenting statistical distributions. Their style emphasizes clarity, sophistication, and the effective communication of complex statistical information in a compact, visually appealing format.
What Makes Economist-Style Visualization Special?
Design Philosophy
- Diminutive Excellence: Maximum information in minimal space
- Statistical Rigor: Emphasizes key percentiles and distribution characteristics
- Professional Typography: Clean, readable font choices with proper hierarchy
- Elegant Simplicity: Sophisticated appearance without unnecessary complexity
- Editorial Quality: Publication-ready formatting for professional reports
Visual Standards
- Typography: ITC Officina Sans family with systematic size hierarchy
- Color Palette: Carefully selected colors highlighting key percentiles
- Distribution Elements: Strategic emphasis on 10th, 50th, and 90th percentiles
- Layout: Efficient use of space with clear visual hierarchy
- Statistical Integration: Built-in statistical tests and effect size calculations
When to Use Economist-Style Distribution Plots
Perfect for: - Clinical research publications requiring elegant distribution comparisons - Medical journal articles showing treatment outcome distributions - Healthcare cost analysis and resource utilization studies - Biomarker distribution analysis across patient populations - Quality improvement reports comparing institutional performance - Academic presentations requiring publication-quality visualizations
Key Advantages: - Instant professional credibility and sophistication - Effective communication of distribution characteristics - Space-efficient design ideal for publications - Built-in statistical analysis and interpretation - Proven readability across diverse audiences
Getting Started
Installation Requirements
The Economist-style plots require the ggeconodist
package for optimal functionality:
# Install from CRAN
install.packages("ggeconodist")
# Or install from GitHub for latest features
remotes::install_github("hrbrmstr/ggeconodist")
# Alternative repository
install.packages("ggeconodist", repos = "https://cinc.rud.is")Clinical Research Applications
Understanding Distribution Characteristics
Economist-style distribution plots excel at revealing:
Treatment Response Patterns
- Comparison of outcome distributions across treatment groups
- Identification of treatment response heterogeneity
- Visualization of therapeutic windows and optimal dosing ranges
Biomarker Analysis
- Distribution of biomarker values across patient subpopulations
- Identification of diagnostic thresholds and reference ranges
- Comparison of biomarker performance across different assays
Practical Examples
Example 1: Treatment Outcome Distribution Analysis
This example demonstrates comparing treatment outcomes between groups using the histopathology dataset:
library(ClinicoPath)
# Load the histopathology dataset
data("histopathology")
# Basic Economist-style distribution plot
# Comparing overall survival time by treatment group
economistplots(
data = histopathology,
y_var = "OverallTime",
x_var = "Group",
plot_title = "Treatment Response Distribution Analysis",
x_title = "Treatment Group",
y_title = "Overall Survival Time (months)",
caption_text = "Source: Histopathology study (n=250)",
economist_theme = TRUE,
show_legend = TRUE,
add_statistics = TRUE,
stat_method = "anova",
effect_size = TRUE
)Clinical Interpretation: - The 10th, 50th, and 90th percentiles are clearly highlighted - Statistical comparison automatically included - Effect size calculation provides clinical significance context - Compact design suitable for publication
Example 2: Biomarker Distribution by Disease Status
Analyzing biomarker distributions across disease groups:
# Compare measurement distributions by disease status
economistplots(
data = histopathology,
y_var = "MeasurementA",
x_var = "Disease Status",
facet_var = "Sex",
plot_title = "Biomarker A Distribution by Disease Status",
x_title = "Disease Status",
y_title = "Biomarker A Level",
caption_text = "Faceted by patient sex",
distribution_style = "enhanced",
show_points = TRUE,
point_jitter = 0.15,
add_statistics = TRUE,
stat_method = "kruskal",
summary_statistics = TRUE
)Key Features: - Faceting allows subgroup analysis by sex - Non-parametric statistical testing for non-normal distributions - Individual points overlay for detailed data exploration - Comprehensive summary statistics included
Example 3: Multi-Group Age Distribution Analysis
Examining age distributions across multiple clinical categories:
# Age distribution across mortality groups
economistplots(
data = histopathology,
y_var = "Age",
x_var = "Mortality5yr",
color_var = "Grade_Level",
plot_title = "Age Distribution by 5-Year Mortality",
x_title = "5-Year Mortality Status",
y_title = "Patient Age (years)",
caption_text = "Colored by tumor grade level",
distribution_style = "publication",
percentile_colors = TRUE,
tenth_color = "#c7254e",
median_color = "#2c3e50",
ninetieth_color = "#18bc9c",
alpha_level = 0.8,
comparison_annotations = TRUE
)Advanced Features: - Custom percentile coloring for enhanced visualization - Color grouping by clinical variables - Publication-ready styling - Pairwise comparison annotations
Statistical Integration
Built-in Statistical Tests
The module provides comprehensive statistical analysis:
Parametric Tests
- ANOVA: For normally distributed continuous variables
- t-test: For two-group comparisons with normal distributions
Customization Options
Visual Styling
Distribution Styles
- Classic Economist: Traditional Economist magazine styling
- Minimal: Clean, understated appearance
- Enhanced: Additional visual elements for exploration
- Publication: Optimized for academic journals
Best Practices for Clinical Research
Design Recommendations
Variable Selection
- Continuous Variables: Choose clinically meaningful measures
- Grouping Variables: Use clear, mutually exclusive categories
- Sample Sizes: Ensure adequate power for statistical comparisons
Troubleshooting and Support
Common Issues
Font-Related Problems
- Font Not Detected: Restart jamovi after font installation
- Rendering Issues: Verify system-wide font installation
- Fallback Fonts: Module automatically uses best available options
Advanced Features
Reproducible Research
R Code Generation
The module automatically generates reproducible R code:
# Example of generated code
library(ggeconodist)
library(ggplot2)
# Create the plot
plot <- ggplot(data, aes(x = Group, y = OverallTime)) +
geom_econodist() +
theme_econodist()
# Add Economist legend
if (show_legend) {
plot <- add_econodist_legend(plot)
}
# Display the plot
print(plot)Conclusion
Economist-style distribution plots represent a sophisticated approach to statistical visualization that combines aesthetic excellence with analytical rigor. For clinical researchers, these plots offer:
- Professional Quality: Publication-ready visualizations that enhance research impact
- Statistical Integration: Comprehensive analysis with appropriate test selection
- Clinical Relevance: Focus on distribution characteristics important for medical interpretation
- Accessibility: Clear communication suitable for diverse audiences
The integration of The Economist’s design principles with clinical research needs creates a powerful tool for advancing evidence-based medicine through superior data visualization.
Key Takeaways
- Visual Excellence: Economist-style plots provide professional, sophisticated appearance
- Statistical Rigor: Built-in analysis ensures appropriate statistical testing
- Clinical Utility: Designed specifically for healthcare research applications
- Reproducibility: Automated code generation supports open science practices
- Accessibility: Clear design principles enhance communication effectiveness
For optimal results, combine careful variable selection, appropriate statistical testing, and thoughtful design customization to create visualizations that effectively communicate your research findings to clinical and academic audiences.