tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of daiquiri and datasetjson — release velocity, themes, recent moves, and the top alternatives to consider.
A data-quality report generator that finished its API rewrite and has been coasting on small features since.
daiquiri turns a raw clinical or administrative dataset into an HTML report of time-series data-quality plots, driven by a field-type specification the user writes. The public API settled in 2022 after a wholesale rename for rOpenSci acceptance, and releases since then have added specification conveniences rather than new report content. The 1.2.0 release is the first in nearly two years.
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.
daiquiri turns a raw clinical or administrative dataset into an HTML report of time-series data-quality plots, driven by a field-type specification the user writes. The public API settled in 2022 after a wholesale rename for rOpenSci acceptance, and releases since then have added specification conveniences rather than new report content. The 1.2.0 release is the first in nearly two years.
Development has shifted from restructuring the interface to lowering the cost of using it — field_types_advanced() lets users name only the columns they care about and default the rest, which is the kind of change that matters when a dataset has hundreds of fields. Plot rendering is getting incremental attention (heatmap scaling) rather than new visualisation types. Cadence is roughly annual.
Expect the next release to continue trimming specification boilerplate for wide datasets rather than adding report sections; the entries give no indication of a new plot type or output format in progress.
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.
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 daiquiri or datasetjson.
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 daiquiri alternatives → · See all datasetjson alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. daiquiri and datasetjson 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. daiquiri and datasetjson 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 daiquiri alternatives in Analytics are ranked by recent ship velocity. Browse the "daiquiri alternatives" section above for the current picks, or visit /alternatives/daiquiri for the full list with editorial commentary on each.
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.