tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
ggraph vs mlr3learners
A side-by-side editorial comparison of ggraph and mlr3learners — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-stats
ggraph vs mlr3learners: at a glance
What is ggraph?
ggraph has settled into ggplot2 compatibility duty
ggraph provides the grammar of graphics for network and tree data. Its recent releases are compatibility and bug-fix work — a collapse fix in get_edges() alongside ggplot2 v4.0.0 upkeep, and before that a pipe rollback. The last release with real content was 2.2.0, a long list of layout and edge-geom corrections.
What is mlr3learners?
mlr3learners spends its releases absorbing upstream churn
mlr3learners wraps the standard model packages — ranger, xgboost, glmnet, kknn — for mlr3. Its recent history is dominated by upstream events rather than its own plans: kknn was pulled from CRAN and its learners removed in 0.11.0, then restored in 0.12.0 when the package returned. The newest release absorbs glmnet 5.0 while adding a predict_raw flag across all learners and probit support to logistic regression.
ggraph vs mlr3learners: editorial side-by-side
ggraph
0.0ANALYTICS
ggraph has settled into ggplot2 compatibility duty
◆ Current state
ggraph provides the grammar of graphics for network and tree data. Its recent releases are compatibility and bug-fix work — a collapse fix in get_edges() alongside ggplot2 v4.0.0 upkeep, and before that a pipe rollback. The last release with real content was 2.2.0, a long list of layout and edge-geom corrections.
◆ Where it's heading
The feature surface looks finished and the maintenance is about keeping it working under a moving ggplot2. The one architectural move in this window — pushing dendrogram layout into compiled code to escape R's recursion limits, back in 2.1.0 — was about scaling existing features, not adding new ones.
◆ Prediction
Expect the next release to follow ggplot2 4.0.0 rather than introduce layouts or edge geoms.
mlr3learners
0.0ANALYTICS
mlr3learners spends its releases absorbing upstream churn
◆ Current state
mlr3learners wraps the standard model packages — ranger, xgboost, glmnet, kknn — for mlr3. Its recent history is dominated by upstream events rather than its own plans: kknn was pulled from CRAN and its learners removed in 0.11.0, then restored in 0.12.0 when the package returned. The newest release absorbs glmnet 5.0 while adding a predict_raw flag across all learners and probit support to logistic regression.
◆ Where it's heading
The package's job is insulation, and the changelog shows what that costs — compatibility-only releases interleaved with small capability additions that expose more of each upstream model. The direction of travel is toward giving users access to the raw upstream objects rather than hiding them.
◆ Prediction
Expect the next release to track another upstream version bump, with incremental exposure of learner-specific fields continuing alongside.
Alternatives to ggraph and mlr3learners
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 ggraph or mlr3learners.
textrecipes
Text features finally stay sparse all the way to the model.
probably
The package that made calibration a step instead of an afterthought.
workflowsets
workflowsets keeps widening what counts as a model worth comparing.
workflows
The tidymodels pipeline grew a third stage, and it happens after the model runs.
vetiver
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
See all ggraph alternatives → · See all mlr3learners alternatives →
Recent activity from ggraph and mlr3learners
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agomlr3learnerspredict_raw across all learners, plus probit and ranger internals
- 8mo agomlr3learnersxgboost 3.1.2.1 compatibility
- 9mo agomlr3learnersUncertainty estimation methods for ranger regression
- 10mo agomlr3learnersDevelopment snapshot: LDA test adjustment
- 11mo agoggraphget_edges() collapse fix and ggplot2 v4 upkeep
- 1y agomlr3learnerskknn learners restored after returning to CRAN
- 1y agomlr3learnerskknn learners removed after CRAN archival
- 2y agoggraphNative pipe usage rolled back
- 2y agoggraphLayout precision and edge geom fixes across the package
- 3y agoggraphBinned edge scales and compiled dendrogram layouts
- 5y agoggraphC++11 pinned to fix std::random_shuffle deprecation
- 5y agoggraphFaceting and edge geom bug fixes
Frequently asked questions
What is the difference between ggraph and mlr3learners?
Both compete on the same themes — r-stats — within Analytics. ggraph and mlr3learners 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 ggraph better than mlr3learners?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggraph and mlr3learners 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 ggraph?
Top ggraph alternatives in Analytics are ranked by recent ship velocity. Browse the "ggraph alternatives" section above for the current picks, or visit /alternatives/ggraph for the full list with editorial commentary on each.
What are the best alternatives to mlr3learners?
Top mlr3learners alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3learners alternatives" section above for the current picks, or visit /alternatives/mlr3learners for the full list with editorial commentary on each.