bbotk
bbotk is generalizing from an optimizer toolkit into an evaluation framework.
Comparison · Analytics
scales vs tune
A side-by-side editorial comparison of scales and tune — release velocity, themes, recent moves, and the top alternatives to consider.
scales vs tune: at a glance
What is scales?
scales keeps widening what ggplot2 can put on an axis.
scales supplies the breaks, labels and transformations behind ggplot2's axes and legends. Unlike much of the tidyverse infrastructure around it, it still ships genuine feature work each release: native timespan handling in 1.3.0, then custom range-training classes and label_glue() in 1.4.0.
What is tune?
tune extends tuning past the model itself to postprocessors, and adds a second parallel backend
tune runs hyperparameter search for tidymodels. Version 2.0.0 rewrote tune_grid() to make postprocessing tunable alongside preprocessing and the model, changed the .config naming scheme to match, and added mirai as a parallel backend next to future. Version 2.1.0 followed with quantile regression support and a replacement Gaussian process engine.
scales vs tune: editorial side-by-side
scales
0.0ANALYTICS
scales keeps widening what ggplot2 can put on an axis.
◆ Current state
scales supplies the breaks, labels and transformations behind ggplot2's axes and legends. Unlike much of the tidyverse infrastructure around it, it still ships genuine feature work each release: native timespan handling in 1.3.0, then custom range-training classes and label_glue() in 1.4.0.
◆ Where it's heading
The arc runs toward extensibility and type coverage. First came built-in support for awkward types like difftime and hms; 1.4.0 inverts that by letting any third-party class participate in range training simply by implementing range() or levels(). Labelling is getting more expressive rather than merely more numerous.
◆ Prediction
Expect continued type-support and labelling work, with extension points that let downstream packages plug in their own classes instead of scales enumerating every one.
tune
0.0ANALYTICS
tune extends tuning past the model itself to postprocessors, and adds a second parallel backend
◆ Current state
tune runs hyperparameter search for tidymodels. Version 2.0.0 rewrote tune_grid() to make postprocessing tunable alongside preprocessing and the model, changed the .config naming scheme to match, and added mirai as a parallel backend next to future. Version 2.1.0 followed with quantile regression support and a replacement Gaussian process engine.
◆ Where it's heading
Two migrations run through this timeline. The tunable surface keeps widening - first censored regression as a mode, then postprocessors via tailor - so that a candidate is now a preprocessor, model and postprocessor triple rather than just a model. The parallel story has moved from foreach to future and now to mirai, each step deprecating the last. Neither is finished.
◆ Prediction
Expect the foreach path to be removed outright, and the postprocessing surface to grow as tailor gains more steps; the GauPro switch will likely need follow-up as its behavior differs from the old engine.
Alternatives to scales and tune
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 scales or tune.
bayesplot
bayesplot keeps widening its posterior-check catalogue while absorbing each ggplot2 break.
animation
Contribution-driven maintenance on a package whose last structural change was magick support.
pins
pins keeps adding a storage backend per release while retiring its original API
tsibble
tsibble shipped one release in five and a half years - the data structure is finished
yardstick
yardstick made fairness metrics a first-class part of tidymodels evaluation
Recent activity from scales and tune
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agotuneQuantile regression tuning; Bayesian search moves to GauPro
- 9mo agotuneFixes int_pctl() with future parallelism on last_fit()
- 11mo agotunePostprocessors become tunable; mirai joins future as a backend ⚡
- 11mo agotuneDevelopment snapshot re-enabling skipped tests
- 1y agoscalesscales 1.4.0 opens range training to custom classes
- 1y agotuneWarns on foreach parallelism; space-filling grids by default
- 2y agotuneFixes parallel tuning errors under multisession plans
- 2y agoscalesscales 1.3.0 makes timespans first-class on axes
- 3y agoscalesscales 1.2.1 re-documents to fix .Rd HTML issues
- 4y agoscalesscales 1.2.0 fixes currency sign order and adds scale_cut
- 6y agoscalesscales 1.1.1 fixes palette inversion and adds oob_keep()
- 6y agoscalesscales 1.1.0 reorganises breaks and labels into a naming scheme
Frequently asked questions
What is the difference between scales and tune?
They serve adjacent needs but don't currently overlap on shipped themes. scales and tune are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is scales better than tune?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. scales and tune are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to scales?
Top scales alternatives in Analytics are ranked by recent ship velocity. Browse the "scales alternatives" section above for the current picks, or visit /alternatives/scales-r for the full list with editorial commentary on each.
What are the best alternatives to tune?
Top tune alternatives in Analytics are ranked by recent ship velocity. Browse the "tune alternatives" section above for the current picks, or visit /alternatives/tune for the full list with editorial commentary on each.