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.
.HAR
and .SL4
files. .HAR
and .SL4
files while offering additional flexibility. 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.
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