easystats
The easystats meta-package is install tooling wrapped around a relicensed ecosystem.
Comparison · Analytics
desirability2 vs sparklyr
A side-by-side editorial comparison of desirability2 and sparklyr — release velocity, themes, recent moves, and the top alternatives to consider.
desirability2 vs sparklyr: at a glance
What is desirability2?
desirability2 is making multi-metric model selection a first-class tidymodels step.
desirability2 implements desirability functions, which map several metrics onto a common 0-1 scale so they can be combined into a single objective. The package is young: three releases, the first of which only added a NEWS file. Its substance arrived in 0.1.0 with hooks into tidymodels' tune package.
What is sparklyr?
sparklyr now spends its releases absorbing dbplyr changes and feeding pysparklyr
sparklyr connects R to Spark, and almost nothing in this window originates inside the package. Releases restore compatibility after dbplyr changes its SQL generation, adapt to Spark 4.0 and to R 4.4's version-comparison changes, and convert functions into S3 methods so pysparklyr can supply its own implementations.
desirability2 vs sparklyr: editorial side-by-side
desirability2
0.0ANALYTICS
desirability2 is making multi-metric model selection a first-class tidymodels step.
◆ Current state
desirability2 implements desirability functions, which map several metrics onto a common 0-1 scale so they can be combined into a single objective. The package is young: three releases, the first of which only added a NEWS file. Its substance arrived in 0.1.0 with hooks into tidymodels' tune package.
◆ Where it's heading
The direction is integration rather than standalone use. Version 0.1.0 added select_best_desirability() and show_best_desirability() to resolve a tuning run against several metrics at once; 0.2.0 exported make_desirability_cols() so other packages can build on it and made data-driven limits the default, removing the need to state ranges by hand. Both releases move work from the user into the package.
◆ Prediction
The exported helper and the developer-facing desirability() API point to adoption by other tidymodels packages as the next step rather than new functionality here.
sparklyr
0.0ANALYTICS
sparklyr now spends its releases absorbing dbplyr changes and feeding pysparklyr
◆ Current state
sparklyr connects R to Spark, and almost nothing in this window originates inside the package. Releases restore compatibility after dbplyr changes its SQL generation, adapt to Spark 4.0 and to R 4.4's version-comparison changes, and convert functions into S3 methods so pysparklyr can supply its own implementations.
◆ Where it's heading
Two dependencies set the agenda. dbplyr repeatedly changes identifier quoting and lazy-table internals, and each change costs sparklyr a release. Meanwhile the package is being hollowed into a backend: ml_fit(), spark_apply(), spark_write_delta() and now tune_grid_spark() exist as methods so that pysparklyr, the Databricks Connect path, can override them. Dependency removal - tibble, rappdirs, digest - runs alongside as the package slims down.
◆ Prediction
Expect the next releases to continue tracking dbplyr and Spark versions, and more functions to be converted to methods as functionality shifts toward pysparklyr; new capability arriving in sparklyr itself looks unlikely.
Alternatives to desirability2 and sparklyr
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 desirability2 or sparklyr.
discrim
discrim settled into a thin engine shim after handing its model definitions to parsnip.
dbparser
dbparser shed its database and CSV writers to become just a DrugBank parser.
crosstalk
crosstalk is frozen infrastructure: four releases in five years, mostly CRAN upkeep.
cmdstanr
cmdstanr keeps adding fast approximations beside full HMC, and fighting Windows toolchains.
comtradr
comtradr's 1.0 line is a long tail of patches against a brittle UN trade API.
See all desirability2 alternatives → · See all sparklyr alternatives →
Recent activity from desirability2 and sparklyr
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agosparklyrRestores compatibility after dbplyr changed Hive quoting
- 3mo agosparklyrAdds tune_grid_spark() for pysparklyr to implement
- 10mo agosparklyrFixes lazy-table field lookup and a name collision
- 11mo agodesirability2make_desirability_cols() exported; data-driven limits on by default
- 1y agosparklyrCatches up with released Spark 4.0; ml_load() reads via Spark
- 1y agodesirability2Desirability-based model selection added for tune ⚡
- 2y agosparklyrDatabricks autoloader streaming ingestion; R 4.4 fixes
- 2y agosparklyrDrops tibble and rappdirs; retires Spark 2.3 JARs
- 3y agodesirability2NEWS.md added to track package changes
Frequently asked questions
What is the difference between desirability2 and sparklyr?
They serve adjacent needs but don't currently overlap on shipped themes. desirability2 and sparklyr 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 desirability2 better than sparklyr?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. desirability2 and sparklyr 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 desirability2?
Top desirability2 alternatives in Analytics are ranked by recent ship velocity. Browse the "desirability2 alternatives" section above for the current picks, or visit /alternatives/desirability2 for the full list with editorial commentary on each.
What are the best alternatives to sparklyr?
Top sparklyr alternatives in Analytics are ranked by recent ship velocity. Browse the "sparklyr alternatives" section above for the current picks, or visit /alternatives/sparklyr for the full list with editorial commentary on each.