mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
Comparison · Analytics
datawizard vs workflows
A side-by-side editorial comparison of datawizard and workflows — release velocity, themes, recent moves, and the top alternatives to consider.
datawizard vs workflows: at a glance
What is datawizard?
datawizard is turning easystats' data layer into a general-purpose I/O and reshaping tool
datawizard handles the data preparation half of the easystats stack — reshaping, recoding, describing, and reading and writing files. The 1.x releases have pushed hardest on I/O: parquet via nanoparquet, then password-protected R formats, alongside a run of breaking cleanups in data_to_wide(), data_modify() and describe_distribution().
What is workflows?
The tidymodels pipeline grew a third stage, and it happens after the model runs.
workflows bundles a preprocessor and a model into one object that tidymodels can fit, tune and extract from. Version 1.3.0 added a post stage backed by the tailor package, wired through every generic a workflow supports — augment, tidy, tunable, tune_args, required_pkgs and parameter extraction. Version 1.2.0 added sparse data support so fit() and predict() accept dgCMatrix and sparse tibbles. Earlier releases in view are boundary tightening: erroring on unknown model modes, on trained recipes, and on silently ignored formula offsets.
datawizard vs workflows: editorial side-by-side
datawizard
0.0ANALYTICS
datawizard is turning easystats' data layer into a general-purpose I/O and reshaping tool
◆ Current state
datawizard handles the data preparation half of the easystats stack — reshaping, recoding, describing, and reading and writing files. The 1.x releases have pushed hardest on I/O: parquet via nanoparquet, then password-protected R formats, alongside a run of breaking cleanups in data_to_wide(), data_modify() and describe_distribution().
◆ Where it's heading
The package is willing to break its own interfaces to reach behavior users expect from tidyr and friends — data_to_wide() explicitly moved toward pivot_wider() semantics, and data_modify() stopped guessing whether a string was an expression. Output formatting is consolidating behind insight's display() and tinytable. The direction is fewer surprises and more file formats, not more statistics.
◆ Prediction
Expect encryption and format support to extend past R-native files if it continues, and further alignment of print and display behavior with the shared insight infrastructure.
workflows
0.0ANALYTICS
The tidymodels pipeline grew a third stage, and it happens after the model runs.
◆ Current state
workflows bundles a preprocessor and a model into one object that tidymodels can fit, tune and extract from. Version 1.3.0 added a post stage backed by the tailor package, wired through every generic a workflow supports — augment, tidy, tunable, tune_args, required_pkgs and parameter extraction. Version 1.2.0 added sparse data support so fit() and predict() accept dgCMatrix and sparse tibbles. Earlier releases in view are boundary tightening: erroring on unknown model modes, on trained recipes, and on silently ignored formula offsets.
◆ Where it's heading
The object is filling out into a complete pipeline description rather than a preprocessing-plus-model pair. Postprocessing is the structural addition — calibration and threshold selection were previously done by hand after prediction, outside anything tidymodels could tune or record — and the fact that it arrived integrated with tunable() and tune_args() rather than as a standalone step is the point. The rest of the arc is the steady tidymodels habit of converting silent guesses into errors.
◆ Prediction
Expect tailor postprocessors to spread through tune and workflowsets next, since the parameter and tuning generics were wired up first, and expect sparse support to extend to more engines after lightgbm.
Alternatives to datawizard and workflows
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 datawizard or workflows.
mlr3viz
mlr3viz keeps the ecosystem's plots working while the plots themselves move out
mlr3tuning
mlr3tuning is rebuilding its async machinery under a stable public surface
timetk
timetk swallowed anomalize whole, then went quiet for two years
modelbased
modelbased is turning marginal effects into a full contrast grammar
parameters
easystats' parameters package absorbs one more model class every few weeks
See all datawizard alternatives → · See all workflows alternatives →
Recent activity from datawizard and workflows
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agodatawizardEncrypted data files via a password argument on read/write
- 10mo agodatawizarddata_to_wide() moves toward pivot_wider() semantics
- 11mo agoworkflowsWorkflows gain a postprocessing stage via tailor ⚡
- 1y agodatawizardParquet read and write support via nanoparquet
- 1y agodatawizarddata_modify() stops inferring expressions from strings
- 1y agodatawizarddatawizard 1.0.2
- 1y agodatawizarddata_arrange() preserves single-column data frames
- 1y agoworkflowsSparse matrices work through fit() and predict()
- 2y agoworkflowsaugment() aligns with parsnip; censored regression supported
- 3y agoworkflowsRegister tuning generics unconditionally
- 3y agoworkflowsMissing parsnip extensions now error early; unsupervised specs supported
- 3y agoworkflowsMode guessing removed; silent offset handling now errors
Frequently asked questions
What is the difference between datawizard and workflows?
They serve adjacent needs but don't currently overlap on shipped themes. datawizard and workflows 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 datawizard better than workflows?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. datawizard and workflows 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 datawizard?
Top datawizard alternatives in Analytics are ranked by recent ship velocity. Browse the "datawizard alternatives" section above for the current picks, or visit /alternatives/datawizard for the full list with editorial commentary on each.
What are the best alternatives to workflows?
Top workflows alternatives in Analytics are ranked by recent ship velocity. Browse the "workflows alternatives" section above for the current picks, or visit /alternatives/workflows-r for the full list with editorial commentary on each.