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title cmapr Roadmap

cmapr Roadmap

This document outlines planned features and improvements for the cmapr package. Contributions and feedback are welcome!

Implemented Features (Current Release)

Career Path Analysis

  • find_career_paths() - Find all promotion paths between two job titles
  • career_ladder() - Discover common progression routes from a starting title

Graph/Network Integration

  • as_igraph() - Convert promotion edges to igraph objects for network analysis
  • as_tidygraph() - Convert promotion edges to tidygraph objects for tidy network analysis

Planned Features

Title Search & Matching

  • search_titles() - Fuzzy search across 123k+ unique job titles
  • similar_titles() - Find similar titles based on text similarity or network proximity
  • cross_sector_titles() - Find equivalent titles that exist across multiple sectors
  • lookup_title_si() - Quick lookup of specialization index for any title

Comparative Analysis

  • compare_sectors() - Side-by-side comparison of career metrics across sectors
  • sector_mobility_matrix() - Analyze cross-sector career movement patterns
  • title_comparison() - Compare career trajectories for similar titles across regions

Data Exploration

  • cmap_summary() - One-function overview of loaded dataset structure and statistics
  • validate_cmap_data() - Check data integrity and completeness
  • available_sectors() - List all sectors with summary statistics

Visualization Helpers

  • plot_career_ladder() - Visualize promotion hierarchies as directed graphs
  • plot_sector_comparison() - Comparative bar/radar charts for sectors
  • plot_title_network() - Interactive network visualization of career transitions

Advanced Analytics

  • promotion_probability() - Calculate transition probabilities between titles
  • career_velocity() - Analyze speed of career progression by sector/region
  • bottleneck_titles() - Identify titles that serve as career progression bottlenecks
  • emerging_titles() - Detect titles with growing specialization or frequency

Contributing

We welcome contributions! If you'd like to implement any of these features:

  1. Open an issue to discuss the approach
  2. Fork the repository
  3. Create a feature branch
  4. Submit a pull request

For questions or suggestions, please open an issue on GitHub.

Research Applications

The CMap dataset enables research in:

  • Labor Economics: Wage progression, skill premiums, sector dynamics
  • Organizational Behavior: Career development patterns, promotion structures
  • HR Analytics: Talent pipeline analysis, succession planning
  • Education Policy: Skills-to-jobs mapping, credential value analysis
  • Urban Economics: Regional labor market differences, geographic mobility

See the CMap paper for methodology details.