tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of datasetjson and geotargets — release velocity, themes, recent moves, and the top alternatives to consider.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
datasetjson reads and writes CDISC Dataset-JSON, the JSON replacement for SAS transport files in clinical-trial submissions. The package went from a thin reader in 2023 to a redesigned interface in 0.3.0 that targets the 1.1.0 schema, uses yyjsonr as its JSON backend, and exposes column metadata as first-class arguments. Development is contributor-driven inside the Atorus and pharmaverse orbit.
Geospatial targets grew from two raster helpers into a tiling and multi-backend pipeline layer.
geotargets extends the targets pipeline framework with target factories that know how to serialise geospatial objects — terra rasters and vectors, stars arrays, raster collections, and VRT references. It completed rOpenSci review and transferred ownership during 0.3.0. Writing behaviour is now configurable through per-target arguments and package-level options.
datasetjson reads and writes CDISC Dataset-JSON, the JSON replacement for SAS transport files in clinical-trial submissions. The package went from a thin reader in 2023 to a redesigned interface in 0.3.0 that targets the 1.1.0 schema, uses yyjsonr as its JSON backend, and exposes column metadata as first-class arguments. Development is contributor-driven inside the Atorus and pharmaverse orbit.
The package's roadmap is not its own — it tracks a CDISC standard that is still moving, and 0.3.0 is what happens when the standard revises: object model, read and write paths, and JSON backend all changed together. Performance was addressed in the same pass, which matters because submission datasets are large enough that a slow serialiser is a real constraint.
The next significant release will most likely follow the next Dataset-JSON schema revision rather than an internal roadmap, given that 0.3.0 was driven entirely by the 1.1.0 update.
geotargets extends the targets pipeline framework with target factories that know how to serialise geospatial objects — terra rasters and vectors, stars arrays, raster collections, and VRT references. It completed rOpenSci review and transferred ownership during 0.3.0. Writing behaviour is now configurable through per-target arguments and package-level options.
The arc runs from 'targets can hold a SpatRaster' to 'targets can hold a tiled, dynamically branched raster workflow with controlled datatype and driver.' Recent work is about giving users control over how objects hit disk — datatype, driver, metadata sidecars, pass-through arguments to the underlying writers — which is where correctness problems in geospatial pipelines actually live. External contributors are driving a visible share of it.
Expect continued work on write-path fidelity and format coverage rather than new target types, since the last two releases both resolved metadata and driver defaults that were silently losing information.
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 datasetjson or geotargets.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
See all datasetjson alternatives → · See all geotargets alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. datasetjson and geotargets 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. datasetjson and geotargets 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 datasetjson alternatives in Analytics are ranked by recent ship velocity. Browse the "datasetjson alternatives" section above for the current picks, or visit /alternatives/datasetjson for the full list with editorial commentary on each.
Top geotargets alternatives in Analytics are ranked by recent ship velocity. Browse the "geotargets alternatives" section above for the current picks, or visit /alternatives/geotargets for the full list with editorial commentary on each.