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WPD: World Panel Data

R-CMD-check Website Lifecycle: maturing License: AGPL v3

Overview

WPD (World Panel Data) is an R package that simplifies access to and visualization of global economic and social panel data from major international organizations and academic researchers. It aims to serve as a bridge between publicly available datasets and researchers, students, and the general public, making cross-country panel data easier to work with.

The package includes a large database (approximately 50MB) of world panel data that is loaded on demand through the ‘wp_data’ function. Features include customizable time series plots, cross-sectional analysis tools, scatter plots with trend analysis, bar charts for comparative analysis, built-in seasonal adjustment capabilities, and support for both annual and quarterly frequencies. The package focuses on macroeconomic indicators such as GDP, trade, prices, employment, and financial and balance-of-payments data across multiple countries and regions.

Key Features

  • Unified Data Access: Load and manipulate data from multiple sources through a single interface
  • Data Harmonization: Standardized country names and ISO3 codes across all sources
  • Advanced Data Processing:
    • Frequency matching (quarterly to yearly or vice versa)
    • Multiple interpolation methods
    • Seasonal adjustments for quarterly data
    • Group aggregation with various methods
    • Automatic data validation and cleaning
  • Publication-Ready Visualizations:
    • Time series plots with multiple customization options
    • Scatter plots with statistical annotations
    • Bar plots with flexible grouping
    • Support for multi-panel layouts
    • Export in various formats (PNG, PDF)

Getting Started

Visit our Documentation Website for comprehensive guides and tutorials. We recommend starting with:

  1. R Basics: Getting Ready for WPD
  2. Introduction to WPD
  3. Loading data with wp_data()
  4. Introduction
  5. Getting Started

Click here for complete function reference.

Installation 

# Install from GitHub
devtools::install_github("benjaminpeeters/WPD")

Technical Requirements

  • R version ≥ 3.5.0
  • Disk space: ~100MB for installation
  • Runtime memory: 15-50MB (depending on data loaded)
  • Dependencies: ggplot2 (≥3.4.0, <3.5.0), scales, patchwork, zoo, seasonal

Basic Example 

library(WPD)

# Load data for G7 countries
data <- wp_data(
  ISO = "G7",
  formula = c("GDP_C", "CU_C/GDP_C*100"),
  variable = c("GDP", "Current Account"),
  years = c(2000, 2023)
)

# Create a time series plot
wp_plot_series(data, 
               y_axis = c("Billion USD", "% of GDP"),
               right_axis = "Current Account",
               title = "GDP and Current Account in G7 Countries")

Available Data

Browse available data using the wp_info() function:

# View all available variables
wp_info()

# View only quarterly data
wp_info("Q")

# View only yearly data
wp_info("Y")

Advanced Features

Data Processing 

# Complex data manipulation example
data <- wp_data(
  ISO = c("EU", "BRICS"),  # Multiple country groups
  formula = "100*CU_C/GDP_C",  # Custom formula
  years = c(2000, 2023),
  adjust_seasonal = TRUE,  # Apply seasonal adjustment
  aggregate_iso = "Mean",  # Average across countries
  aggregate_period = "CAGR"  # Calculate compound growth
)

Visualization Examples

Time Series Plots 

# Multi-panel time series with events
events <- data.frame(
  Event = c("Global Financial Crisis", "Covid-19 Pandemic"),
  Date = as.Date(c("2008-09-15", "2020-03-11"))
)

wp_plot_series(data,
               key_dates = events,
               area = TRUE,  # Create area plot
               reference = TRUE)  # Add data sources

Scatter Plots 

# Scatter plot with regression and grouping
wp_plot_scatter(data,
                color = "Region",
                interpolation = "Spline",
                r_squared = 3,  # Show full equation
                ISO = "Both")  # Show both points and labels

Citation and Data Attribution

When using WPD, please cite both the package and the original data sources:

# Generate citation dynamically
citation("WPD")

To cite package 'WPD' in publications use:

  Benjamin Peeters (2024). WPD: World Panel Data: A Macroeconomic Data Visualization and Analysis Toolkit. R package version 0.0.45.
  https://benjaminpeeters.github.io/WPD/

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {WPD: World Panel Data: A Macroeconomic Data Visualization and Analysis Toolkit},
    author = {Benjamin Peeters},
    year = {2024},
    note = {R package version 0.0.45},
    url = {https://benjaminpeeters.github.io/WPD/},
  }

Data Sources and References

International Organizations

  1. World Bank Development Indicators
    Comprehensive collection of development indicators.
    https://data.worldbank.org/indicator

  2. International Monetary Fund

    • International Financial Statistics (IFS)
      Global financial and economic indicators.
    • Balance of Payments Statistics
      International transactions data.

  3. Bank for International Settlements (BIS)
    Credit aggregates, property prices, and exchange rates.

  4. OECD Main Economic Indicators
    Economic indicators for OECD members and partners.

Research Databases

  1. Jordà-Schularick-Taylor Macrohistory Database

    • Jordà, Ò., Schularick, M., & Taylor, A. M. (2017). “Macrofinancial History and the New Business Cycle Facts.” NBER Macroeconomics Annual, 31(1), 213-263.
    • Jordà, Ò., et al. (2019). “The Rate of Return on Everything, 1870–2015.” The Quarterly Journal of Economics, 134(3), 1225-1298.
    • Jordà, Ò., et al. (2021). “Bank Capital Redux: Solvency, Liquidity, and Crisis.” Review of Financial Studies, 34(7), 3062-3107.

  2. KOF Globalisation Index
    Gygli, S., Haelg, F., Potrafke, N., & Sturm, J. E. (2019). “The KOF Globalisation Index – Revisited.” Review of International Organizations, 14(3), 543-574.

  3. Emergency Events Database (EM-DAT)
    Centre for Research on the Epidemiology of Disasters (CRED). (2024). EM-DAT: The International Disaster Database.

  4. Sovereign Default Database
    Beers, D., Ndukwe, C. I., & Charron, S. (2024). BoC–BoE Sovereign Default Database. Bank of Canada, Bank of England.

  5. Capital Account Openness
    Quinn, D., & Toyoda, A. M. (2008). “Does Capital Account Liberalization Lead to Economic Growth?” Review of Financial Studies, 21(3), 1403-1449.

  6. Maddison Project Database
    Maddison Project Database 2023.

Licensing

This package is available under a dual-licensing model:

  1. AGPL-3.0 License (Open Source)
    • Free for open-source projects, academic research, and personal use
    • Full terms in LICENSE.md
  2. Commercial License
    • For proprietary/commercial applications
    • Allows closed-source usage
    • Details in LICENSE_COMM.md

For commercial licensing inquiries: - Email: - Subject: “Commercial License - R Package WPD - [Your Use Case]”

Development and Contributions

We welcome contributions! Please read our Contributing Guide for details on how to submit pull requests, report issues, and contribute to documentation.

⚠️ Important Note: Currently, there are known issues with legend rendering when using ggplot2 3.5.x. For optimal visualization, we recommend using ggplot2 3.4.x. You can install the recommended version using:

# Install specific ggplot2 version
remotes::install_version("ggplot2", version = "3.4.3")

Future Developments

We are actively working on extending the package’s functionality. Here are our planned features:

  • Expand database coverage:
    • Include World Inequality Database (WID)
    • Incorporate Aizenman’s research data
    • Add datasets from additional researchers
  • Enhance country identification flexibility:
  • Improve user experience:
    • Develop wp_quick() for streamlined data visualization (e.g., balance of payments graphs)
    • Enhance wp_info() to display timespan and country coverage for each indicator