mathworks/arrow
Feb 2026 – Feb 2026
1 Month active
Languages Used
R
Technical Skills
R programmingdata manipulationdplyr
larry77
PROFILE
Larry77
Lorenzo Isella developed support for dplyr::filter_out() in the mathworks/arrow repository, enabling R users to exclude rows from Arrow-backed datasets using predicate logic. He extended the existing filter() infrastructure by adding an exclude flag to set_filters(), aligning the backend’s behavior with dplyr semantics. The implementation ensured compatibility with arrow_table(), RecordBatchReader, and open_dataset(), while preserving lazy evaluation for efficient handling of large datasets. Comprehensive tests addressed basic functionality, NA handling, and multi-predicate scenarios. Using R programming and dplyr for data manipulation, Lorenzo’s work improved expressive filtering and advanced feature parity between Arrow and native dplyr workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
178
Activity Months1
Your Network
149 peopleWork History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026: Delivered dplyr::filter_out() support in the Arrow R backend for mathworks/arrow, enabling exclusion of rows via predicates in Arrow-backed workflows. The change reuses the existing filter() path and extends set_filters() with an exclude flag to implement dplyr semantics (drop rows where predicate is TRUE, keep FALSE/NA). It works with arrow_table(), RecordBatchReader, and open_dataset(), and preserves lazy evaluation for large datasets. Tests cover basic behavior, NA handling, and multi-predicate scenarios. This feature reduces data wrangling friction for R users, improves parity with dplyr, and lays groundwork for broader predicate-based filtering across Arrow datasets. Commit: 111495870686ef269254232b876de3aee2f919b6 (GH-49186; PR #49256)
1 Commits • 1 Features
Feb 1, 2026February 2026: Delivered dplyr::filter_out() support in the Arrow R backend for mathworks/arrow, enabling exclusion of rows via predicates in Arrow-backed workflows. The change reuses the existing filter() path and extends set_filters() with an exclude flag to implement dplyr semantics (drop rows where predicate is TRUE, keep FALSE/NA). It works with arrow_table(), RecordBatchReader, and open_dataset(), and preserves lazy evaluation for large datasets. Tests cover basic behavior, NA handling, and multi-predicate scenarios. This feature reduces data wrangling friction for R users, improves parity with dplyr, and lays groundwork for broader predicate-based filtering across Arrow datasets. Commit: 111495870686ef269254232b876de3aee2f919b6 (GH-49186; PR #49256)
February 2026
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
R
Technical Skills
R programmingdata manipulationdplyr
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