Related Experiment Video
Updated: Aug 6, 2026
05:26
PyOKR: A Semi-Automated Method for Quantifying Optokinetic Reflex Tracking Ability
Published on: April 12, 2024
gp3tools: An R Package for Reproducible Analysis and Reporting of Gazepoint GP3 Eye-Tracking Exports
1eGovernment & eCommerce Lab (Innovation & Entrepreneurship), Department of Business Administration, University of Patras, 26504 Patras, Greece.
Journal of Eye Movement Research
|July 24, 2026
Summary
This study introduces gp3tools, an R package that streamlines eye-tracking data analysis from Gazepoint devices. It automates data organization and quality checks, making research more reproducible.
Area of Science:
- Eye-tracking research
- Computational neuroscience
- Psychophysics
Background:
- Reproducible eye-movement research necessitates a clear data pipeline from device output to analysis-ready formats.
- Gazepoint GP3 and Gazepoint Analysis export data in CSV format, which often requires manual organization before analysis.
- Existing R tools for eye-tracking and pupillometry analysis typically assume data are already structured, lacking Gazepoint-specific workflows.
Purpose of the Study:
- To present gp3tools, an open-source R package designed to process Gazepoint eye-tracking data exports.
- To establish a Gazepoint-aware workflow for data import, quality control, preprocessing, and reporting.
- To enhance the reproducibility and efficiency of eye-movement and pupillometry research using Gazepoint hardware.
Main Methods:
- Development of the gp3tools R package (version 4.6.1) to convert Gazepoint CSV exports into structured R objects.
- Implementation of an export-aware workflow including import checking, quality diagnostics, pupil preprocessing, and area-of-interest analysis.
- Evaluation using a synthetic Gazepoint-style dataset and a small, private real-export compatibility check.
Main Results:
- The gp3tools package successfully converts Gazepoint exports into structured R objects, diagnostic summaries, and reporting outputs.
- Demonstration dataset processing yielded expected file pairs, sample-level gaze/pupil tables, fixation tables, and quality summaries.
- A real-export check confirmed the workflow's ability to process an empirical Gazepoint folder without manual restructuring.
Conclusions:
- gp3tools provides a robust scaffold for processing Gazepoint eye-tracking data, improving workflow efficiency and reproducibility.
- The package facilitates data organization, quality control, and preparation for downstream statistical modeling.
- Further validation is needed to establish general robustness, computational scalability, and accuracy across all Gazepoint export variations.
