Package Review Checklist

General Information & Metadata

• Package Name: Is it clear, concise, and indicative of the data's content?
• Description: Is the Description field in the DESCRIPTION file informative and accurate?
• License: Is a license specified (e.g., MIT, GPL-3)?
• Author/Maintainer: Are the author(s) and maintainer clearly identified with contact information?
• Website: Is the data package published on GitHub Pages?

Data Content & Quality

• Data Completeness: Are there any known missing values. If so, are they handled appropriately (e.g., NA values)?
• Data Accuracy: Are there data entry errors or inconsistencies?
• Data Size: Is the data size appropriate for an R package (i.e., not excessively large, potentially requiring external download)?
• Data Format: Is the data in a tidy format?
• Variable Types: Are variable types appropriate (e.g., factors for categorical, numeric for quantitative, Date for dates)?
• Unique Identifiers: Are there any unique identifiers, and are they unique where expected?

Data Processing

• Remove code that is commented out
• No blank spaces in any of the file names
• Are there obsolete files?
• Do all variables names have sensible names? No unexplained acronyms? No weird numbers?

Documentation

• README.md: Does the README.md provide a clear overview, installation instructions, and basic usage examples?
  • Axes and title legible on the plots?
• Data Documentation (.Rd files):
  • Are all datasets documented with .Rd files?
  • Do the .Rd files include a clear title, description, usage examples, and details on each variable?
  • Are the data structures (e.g., number of rows/columns) and types clearly described?
• Codebook/Data Dictionary: Is there a detailed codebook or data dictionary explaining variables, units, and categories?