tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of geotargets and ijtiff — release velocity, themes, recent moves, and the top alternatives to consider.
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.
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.
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.
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 geotargets 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 geotargets 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. geotargets 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. geotargets 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 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.
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.