HARplus is an R package designed to process and analyze .HAR and .SL4 files, making it easier for GEMPACK users and GTAP model researchers to handle large economic datasets. It simplifies the management of multiple experiment results, enabling faster and more efficient comparisons without complexity.

With HARplus, users can extract, restructure, and merge data seamlessly, ensuring compatibility across different tools. The processed data can be exported and used in R, Stata, Python, Julia, or any software that supports .txt, CSV, or Excel formats.

• Efficient Data Extraction – Supports selective header loading and optimized memory usage for handling large .HAR and .SL4 files.
• Flexible Data Structuring – Extract variables by name or dimension patterns while ensuring consistency across multiple inputs.
• Customizable Aggregation & Merging – Manage subtotals, merge datasets, and structure data dynamically.
• Multiple Export Options – Output extracted data in CSV, Stata, RDS, and Excel formats with structured formatting.
• Designed for GEMPACK – Ensures smooth integration with .HAR and .SL4 files while offering additional flexibility.
• Ideal for GTAP Model Users – Built specifically to process and analyze GTAP model results efficiently.

HARplus simplifies .HAR and .SL4 file processing. You can:

• Load files and selectively extract headers.
• Extract data by variable name or dimension patterns.
• Group, merge, and restructure data with ease.
• Pivot and export data into structured formats.
• Filter subtotals and rename dimensions for clarity.

HARplus is currently under CRAN review and will be available there soon. In the meantime, install it directly from GitHub using the following command:

devtools::install_github("Bodysbobb/HARplus")

All commands in this package have several options that allow users to play around with the data more freely and efficiently, not just import and get the data. For a complete guide on HARplus functions, check out the Vignette or GitHub Vignette

Below is a categorized reference of the main functions in HARplus:

• load_harx() – Loads .HAR files with selective header extraction and structured metadata.
• load_sl4x() – Loads .SL4 files, extracting variable names and dimension structures.

• get_data_by_var() – Extracts specific variables from .HAR or .SL4 datasets, supporting subtotal filtering and merging.
• get_data_by_dims() – Extracts data based on dimension patterns, with options for merging and subtotal filtering.

• get_dim_elements() – Lists unique dimension elements (e.g., REG, COMM).
• get_dim_patterns() – Extracts unique dimension structures (e.g., REG*COMM*ACTS).
• get_var_structure() – Summarizes variable names, dimensions, and data structure.
• compare_var_structure() – Compares variable structures across multiple datasets for compatibility.

• group_data_by_dims() – Groups extracted data by dimension priority, with support for automatic renaming and subtotal handling.
• rename_dims() – Renames dimension names for consistency.

• pivot_data() – Converts long-format data into wide format.
• pivot_data_hierarchy() – Creates hierarchical pivot tables for structured reporting.

• export_data() – Exports extracted data to CSV, Stata, TXT, RDS, or XLSX, with support for multi-sheet exports.

HARplus is released under the MIT License. See the full license.

Author:
Pattawee Puangchit
Ph.D. Candidate, Agricultural Economics
Purdue University
Research Assistant at GTAP

Acknowledgement is due to Maros Ivanic for his work on the HARr package, which served as the foundation for HARplus. This package would not have been possible without his contributions.

I have developed another package specifically for visualization, particularly for GTAP users: GTAPViz

Sample data used in this vignette is obtained from the GTAPv7 model and utilizes publicly available data from the GTAP 9 database. For more details about the GTAP database and model, refer to the GTAP Database.