tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of datasetjson and ijtiff — 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.
A TIFF reader for scientific imaging that spent its recent releases shedding weight and fixing memory bugs.
ijtiff reads and writes TIFF files the way ImageJ writes them, which ordinary R TIFF readers get wrong — multi-channel, multi-frame, and unusual bit depths. The 3.1.x line is dominated by memory correctness in the C tag-handling layer, alongside dropping the large imager dependency from the display path. Cadence is sporadic, with multi-year gaps.
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.
ijtiff reads and writes TIFF files the way ImageJ writes them, which ordinary R TIFF readers get wrong — multi-channel, multi-frame, and unusual bit depths. The 3.1.x line is dominated by memory correctness in the C tag-handling layer, alongside dropping the large imager dependency from the display path. Cadence is sporadic, with multi-year gaps.
Two threads run through the window. The C layer is being hardened — memory leaks in tag handling, buffer cleanup, PROTECT errors, validation of malformed files — which is the kind of work that surfaces when a package gets run against real-world files at volume. Separately, the R layer is shedding dependencies, with base graphics replacing imager for display. Both make the package cheaper and safer to depend on rather than more capable.
Expect continued C-level correctness work rather than format features, since three of the last three substantive releases were memory or compiler fixes.
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 ijtiff.
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 ijtiff alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. datasetjson and ijtiff 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 ijtiff 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 ijtiff alternatives in Analytics are ranked by recent ship velocity. Browse the "ijtiff alternatives" section above for the current picks, or visit /alternatives/ijtiff for the full list with editorial commentary on each.