tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
ggraph vs mlr3cluster
A side-by-side editorial comparison of ggraph and mlr3cluster — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-stats
ggraph vs mlr3cluster: 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 mlr3cluster?
mlr3cluster went from a handful of clusterers to covering the field
mlr3cluster supplies clustering learners to the mlr3 framework. Over three releases it added roughly a dozen learners — CLARA, k-prototypes, spectral, then a batch of nine covering finite mixtures, spherical and directional families, self-organising maps, spatio-temporal DBSCAN and robust trimmed clustering. The newest release fixes predict-time behaviour across the hierarchical learners.
ggraph vs mlr3cluster: 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.
mlr3cluster
0.0ANALYTICS
mlr3cluster went from a handful of clusterers to covering the field
◆ Current state
mlr3cluster supplies clustering learners to the mlr3 framework. Over three releases it added roughly a dozen learners — CLARA, k-prototypes, spectral, then a batch of nine covering finite mixtures, spherical and directional families, self-organising maps, spatio-temporal DBSCAN and robust trimmed clustering. The newest release fixes predict-time behaviour across the hierarchical learners.
◆ Where it's heading
The package is at the tail end of a coverage push, and the emphasis has shifted from adding algorithms to making the ones it has behave correctly at prediction time — cutting trees at the current k, reclustering coresets, failing informatively on unsupported metric combinations. That is the normal sequence after a rapid expansion.
◆ Prediction
Expect further predict-path corrections and parameter-set alignment across the newly added learners before any more algorithms arrive.
Alternatives to ggraph and mlr3cluster
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 mlr3cluster.
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 mlr3cluster alternatives →
Recent activity from ggraph and mlr3cluster
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomlr3clusterHierarchical learners now honour k at prediction time
- 2mo agomlr3clusterNine new clustering learners in one release ⚡
- 5mo agomlr3clusterCLARA, k-prototypes and spectral clustering learners added
- 6mo agomlr3clusterTyped error classes and probabilistic EM assignments
- 8mo agomlr3clusterHDBSCAN gains cluster_selection_epsilon
- 11mo agoggraphget_edges() collapse fix and ggplot2 v4 upkeep
- 1y agomlr3clusterMclust learner brought in line with paradox conventions
- 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 mlr3cluster?
Both compete on the same themes — r-stats — within Analytics. ggraph and mlr3cluster 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 mlr3cluster?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggraph and mlr3cluster 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 mlr3cluster?
Top mlr3cluster alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3cluster alternatives" section above for the current picks, or visit /alternatives/mlr3cluster for the full list with editorial commentary on each.