The rscorecard package is an R wrapper for the U.S. Department of Education College Scorecard API. It allows users to select and filter Scorecard variables with piped commands using dplyr syntax, making it easy to access comprehensive college and university data for higher education research.

The econocharts package allows creating microeconomics or macroeconomics charts in R with simple functions. This package is inspired by reconPlots by Andrew Heiss and provides an easy way to generate professional economic visualizations including supply and demand curves, indifference curves, production possibility frontiers, and tax impact analysis.

The blsAPI package allows R users to request data for one or multiple series through the U.S. Bureau of Labor Statistics (BLS) Application Programming Interface (API). The BLS API provides public access to economic data from all BLS programs including employment, unemployment, and labor market statistics.

The bea.R package provides programmatic access to U.S. Bureau of Economic Analysis data through their API. This package enables researchers to easily retrieve and analyze national and regional economic statistics including GDP, national accounts, and comprehensive economic indicators.

The urbnmapr package provides state and county maps for the United States with Alaska and Hawaii repositioned for better visualization. This package makes it easy to create appealing and informative choropleth maps with pre-formatted spatial data optimized for data visualization.

The education-data-package-r provides R users with access to the Urban Institute's Education Data Portal API, containing comprehensive U.S. education datasets. Essential for education researchers, policy analysts, and anyone studying trends in American education systems.

A comprehensive online textbook covering causal inference methods for social scientists. Includes practical applications using both R, Python and Stata, covering topics like regression discontinuity, instrumental variables, difference-in-differences, and synthetic controls. Essential reading for applied economists working with observational data.

A foundational text for statistical learning and machine learning methods, available in both R and Python versions. Covers regression, classification, resampling methods, tree-based methods, and more. Particularly useful for economists interested in modern data science techniques and predictive modeling.

A comprehensive guide to Python's data science ecosystem, covering NumPy, Pandas, Matplotlib, and Scikit-Learn. Excellent resource for economists looking to expand their computational toolkit beyond R, with practical examples and applications relevant to economic data analysis.

A collection of concise reference sheets for popular R packages including ggplot2, dplyr, tidyr, and many others. Invaluable for quick syntax lookup and discovering new functions. Available in multiple languages and regularly updated to reflect the latest package versions.