broom.helpers
After eleven quiet months, the tidier under gtsummary is back to absorbing model classes.
A side-by-side editorial comparison of ggquiver and Power BI — release velocity, themes, recent moves, and the top alternatives to consider.
ggquiver is awake again, fixing arrow scaling that quietly misread irregular data.
A small ggplot2 extension for quiver and vector-field plots. The 0.3.x line in late 2021 made arrows behave outside plain Cartesian coordinates; after a four-year gap, 0.4.0 made them honour scale transformations and exposed grid::arrow() styling. 0.5.0 continues in correctness: automatic vecsize grid detection no longer rescales arrows wrongly on irregularly spaced data like GPS coordinates, an undetectable grid warns rather than errors, and legend keys now show an arrowhead.
Power BI's monthly grind: authoring defaults, DAX documentation, and mobile finally catching up.
Power BI ships on a monthly cadence where each release is a long list of small, independently useful changes rather than a headline feature. The current batch runs from report-wide theme customization and DAX measure descriptions written as triple-slash comments, through matrix expand and collapse reaching general availability, to the mobile apps gaining Excel export and one-tap layout rotation. Nothing here redirects the product; all of it removes a specific piece of manual work.
A small ggplot2 extension for quiver and vector-field plots. The 0.3.x line in late 2021 made arrows behave outside plain Cartesian coordinates; after a four-year gap, 0.4.0 made them honour scale transformations and exposed grid::arrow() styling. 0.5.0 continues in correctness: automatic vecsize grid detection no longer rescales arrows wrongly on irregularly spaced data like GPS coordinates, an undetectable grid warns rather than errors, and legend keys now show an arrowhead.
The consistent theme across both eras is deferring to ggplot2 rather than drawing on top of it — coordinate systems first, then scale transformations, then arrow styling handed to grid, and now the automatic sizing heuristic itself. The two 0.4.0/0.5.0 releases inside seven months suggest the four-year gap was dormancy rather than abandonment, and the work has shifted from integration gaps to the package's own inference: 0.5.0 is the first release to treat automatic grid detection as something that can be wrong rather than merely absent. The changelog remains entirely correctness and integration; there is still no sign of new plot types.
The entries support a narrow read: further releases will likely keep hardening vecsize inference and closing ggplot2 integration gaps as users report them. Two releases in seven months hint the cadence has resumed, but one gap of four years makes that weak evidence.
Power BI ships on a monthly cadence where each release is a long list of small, independently useful changes rather than a headline feature. The current batch runs from report-wide theme customization and DAX measure descriptions written as triple-slash comments, through matrix expand and collapse reaching general availability, to the mobile apps gaining Excel export and one-tap layout rotation. Nothing here redirects the product; all of it removes a specific piece of manual work.
The through-line is moving decisions from repetition to defaults. Theme customization sets report-wide visual defaults and exports them for reuse or for organizational themes; matrix row-header freeze becomes a saved authoring choice rather than a per-session right-click; measure documentation lives inside the DAX rather than in a separate step. A second, quieter thread is mobile parity — exporting to Excel with filters, slicers, drill state and row-level security intact is the kind of gap that kept people on the desktop. The formatting long tail continues in parallel, mostly axis, padding, and slicer styling controls.
Expect the preview features in this window — modern visual defaults and theme customization — to move toward general availability, and the formatting pane to keep absorbing controls that were previously theme-file edits.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either ggquiver or Power BI.
After eleven quiet months, the tidier under gtsummary is back to absorbing model classes.
OpenHouse starts adding per-column defaults while still closing silent-failure holes.
Julia's distribution library keeps filing down the edges where sampling meets array types
A lazy vector container keeps closing the gaps where it quietly materialised anyway.
Mesh interpolation drops its custom fork dependency and sheds weight.
A mesh renderer grows a scene graph and an exit route into glTF.
See all ggquiver alternatives → · See all Power BI alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — data-visualization — within Analytics. Power BI is currently shipping more aggressively (velocity 5.0 vs 2.5), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Power BI is currently shipping more aggressively (velocity 5.0 vs 2.5), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top ggquiver alternatives in Analytics are ranked by recent ship velocity. Browse the "ggquiver alternatives" section above for the current picks, or visit /alternatives/ggquiver for the full list with editorial commentary on each.
Top Power BI alternatives in Analytics are ranked by recent ship velocity. Browse the "Power BI alternatives" section above for the current picks, or visit /alternatives/power-bi for the full list with editorial commentary on each.