tqdm is a fast, extensible progress bar library for Python and CLI. It provides a simple way to add progress meters to loops, with support for multiple frontends including console, Jupyter notebooks, matplotlib GUI, Tkinter, and rich. The core implementation in `tqdm/std.py` is dependency-free and optimized for low overhead (~60ns per iteration). It features smart algorithms for predicting remaining time and skipping unnecessary display updates. The library integrates with pandas, keras, dask, asyncio, and concurrent.futures. The CLI mode allows piping data through tqdm for progress monitoring. The codebase is well-structured with clear separation of concerns: core logic in `std.py`, frontends in separate modules (`notebook.py`, `gui.py`, `tk.py`, `rich.py`), and integrations in `contrib/`. Extensive test suite covers performance, correctness, and edge cases. The project is actively maintained with regular releases and CI.
The progressr package provides a minimal, unifying API for reporting progress updates in R. It separates progress signaling (developer-controlled) from progress presentation (end-user controlled) using R's condition system. Developers create progressor functions to signal updates, while end users configure handlers to determine how progress is displayed (terminal, GUI, audio, notifications, or file-based). The architecture centers around a handler plugin system where `with_progress()` manages context and handler state. Key features include integration with parallel frameworks (future, furrr, plyr), Shiny compatibility via `withProgressShiny()`, and a global progression handler for R ≥ 4.0.0. The package emphasizes flexibility—handlers can be mixed and matched, progress reporting can be suppressed, and updates work seamlessly in iterative and parallel contexts. Software engineering rigor is evident through an extensive test suite (50+ test scripts in `inst/testme/`), continuous integration, semantic versioning, and comprehensive vignettes. The design avoids tight coupling—core components like `progressor()`, `with_progress()`, and `progress_aggregator()` are isolated and reusable. While dependencies on optional packages (cli, progress, beepr, etc.) introduce some maintenance complexity, the core remains stable and well-maintained with recent activity.