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Comparison · Analytics
ggspatial vs mlr3learners
A side-by-side editorial comparison of ggspatial and mlr3learners — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-stats
ggspatial vs mlr3learners: at a glance
What is ggspatial?
ggspatial finishes its move off raster and onto terra
ggspatial puts spatial data into ggplot2. Its recent history is a single multi-year migration: terra support arrived alongside raster in 1.1.6, and by 1.1.10 terra is the default in the bundled data loaders while raster is described as deprecated. Everything between those two releases is ggplot2 compatibility patching.
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.
ggspatial vs mlr3learners: editorial side-by-side
ggspatial
0.0ANALYTICS
ggspatial finishes its move off raster and onto terra
◆ Current state
ggspatial puts spatial data into ggplot2. Its recent history is a single multi-year migration: terra support arrived alongside raster in 1.1.6, and by 1.1.10 terra is the default in the bundled data loaders while raster is described as deprecated. Everything between those two releases is ggplot2 compatibility patching.
◆ Where it's heading
The package tracks the R spatial stack's own generational shift rather than setting direction itself — sf and stars support, then terra, then preparing S3 methods for the next ggplot2. Feature work is rare; the value it delivers is staying current with the layers underneath it.
◆ Prediction
The likely next step is completing the ggplot2 S3 method preparation that 1.1.10 started, with raster support eventually dropped rather than maintained in parallel.
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 ggspatial 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 ggspatial 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 ggspatial alternatives → · See all mlr3learners alternatives →
Recent activity from ggspatial 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 agoggspatialterra becomes the default raster backend
- 1y agomlr3learnerskknn learners restored after returning to CRAN
- 1y agomlr3learnerskknn learners removed after CRAN archival
- 2y agoggspatialExample fixes for the updated raster/terra stack
- 3y agoggspatialannotation_spatial() fix for the latest ggplot2
- 3y agoggspatialFix for behaviour deprecated in ggplot2
- 3y agoggspatialterra support, categorical rasters and better stars handling
- 8y agoggspatialPackage size and CRAN check time reduced
Frequently asked questions
What is the difference between ggspatial and mlr3learners?
Both compete on the same themes — r-stats — within Analytics. ggspatial 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 ggspatial better than mlr3learners?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggspatial 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 ggspatial?
Top ggspatial alternatives in Analytics are ranked by recent ship velocity. Browse the "ggspatial alternatives" section above for the current picks, or visit /alternatives/ggspatial 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.