easystats
The easystats meta-package is install tooling wrapped around a relicensed ecosystem.
A side-by-side editorial comparison of ggpubr and sparklyr — release velocity, themes, recent moves, and the top alternatives to consider.
ggpubr reached 1.0.0 with p-value formatting presets for specific journals
ggpubr adds publication-ready statistics and annotation to ggplot2. Two releases define its capability: 0.5.0 introduced the stat_*_test family and geom_pwc() for pairwise comparison brackets, and 1.0.0 added p-value formatting presets matching named journal house styles. In between, most releases are ggplot2 and dplyr deprecation chasing.
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.
ggpubr adds publication-ready statistics and annotation to ggplot2. Two releases define its capability: 0.5.0 introduced the stat_*_test family and geom_pwc() for pairwise comparison brackets, and 1.0.0 added p-value formatting presets matching named journal house styles. In between, most releases are ggplot2 and dplyr deprecation chasing.
The package is moving from drawing statistics to matching the conventions of where they get published - style presets are a different kind of feature from a new test. That sits on a persistent maintenance load: after_stat migrations, linewidth parameters, R-devel changing how the Wilcoxon test handles ties. ggpubr absorbs upstream deprecations so that figure code written years ago keeps rendering.
Expect the preset list to grow as users request their own journals' conventions, and the deprecation-chasing to continue with each ggplot2 release; the statistical test coverage looks complete enough that additions there would be surprising.
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.
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.
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.
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 ggpubr or sparklyr.
The easystats meta-package is install tooling wrapped around a relicensed ecosystem.
discrim settled into a thin engine shim after handing its model definitions to parsnip.
dbparser shed its database and CSV writers to become just a DrugBank parser.
desirability2 is making multi-metric model selection a first-class tidymodels step.
crosstalk is frozen infrastructure: four releases in five years, mostly CRAN upkeep.
cmdstanr keeps adding fast approximations beside full HMC, and fighting Windows toolchains.
See all ggpubr alternatives → · See all sparklyr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. ggpubr 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggpubr 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.
Top ggpubr alternatives in Analytics are ranked by recent ship velocity. Browse the "ggpubr alternatives" section above for the current picks, or visit /alternatives/ggpubr for the full list with editorial commentary on each.
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.