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Comparison · Infra & APIs
tealeaves vs testdat
A side-by-side editorial comparison of tealeaves and testdat — release velocity, themes, recent moves, and the top alternatives to consider.
tealeaves vs testdat: at a glance
What is tealeaves?
A leaf-temperature model that finished its job in 2020 and has stayed finished
tealeaves solves for leaf temperature from an energy balance, using explicit units to keep parameters consistent and modelling lower and upper leaf surfaces separately so sensible and latent heat loss are computed for each. The package reached its current form in 2020 across versions 1.0.2 to 1.0.5, which added direct or functional sky temperature, dplyr 1.0.0 compatibility, and fixes to a parameter-crossing bug that the new sky temperature function had introduced. The only release since, v1.0.6 in July 2022, corrects a name in the citation file, stops parallel evaluation in a vignette and fixes README links.
What is testdat?
Unit testing for datasets, built on testthat and now bending to its next release.
testdat applies the testthat idiom to data rather than code: expectations that assert properties of a data frame, run as a suite, with results exportable to Excel. Recent releases have been about correctness and upstream compatibility. Expectations are now constructed via new_expectation() ahead of testthat 3.3.0, and expect_base() errors on a missing variable instead of silently passing.
tealeaves vs testdat: editorial side-by-side
tealeaves
0.0INFRA · APIS
A leaf-temperature model that finished its job in 2020 and has stayed finished
◆ Current state
tealeaves solves for leaf temperature from an energy balance, using explicit units to keep parameters consistent and modelling lower and upper leaf surfaces separately so sensible and latent heat loss are computed for each. The package reached its current form in 2020 across versions 1.0.2 to 1.0.5, which added direct or functional sky temperature, dplyr 1.0.0 compatibility, and fixes to a parameter-crossing bug that the new sky temperature function had introduced. The only release since, v1.0.6 in July 2022, corrects a name in the citation file, stops parallel evaluation in a vignette and fixes README links.
◆ Where it's heading
This is finished scientific software. The arc runs from a 1.0.0 that already described the full model, through a usability decision in 1.0.1 to accept unitless values and assign units rather than demand them, to a 2020 cluster of compatibility and correctness work around publication. Nothing since has touched the model, and the 2022 release is pure paperwork. Its most instructive entry remains 1.0.5, where a new feature silently produced incorrect parameter crossing and the fix arrived with tests to pin the behaviour.
◆ Prediction
Expect nothing unless a dependency or CRAN check forces a release; on this record any such release will be documentation and packaging rather than a change to the energy balance.
testdat
0.0INFRA · APIS
Unit testing for datasets, built on testthat and now bending to its next release.
◆ Current state
testdat applies the testthat idiom to data rather than code: expectations that assert properties of a data frame, run as a suite, with results exportable to Excel. Recent releases have been about correctness and upstream compatibility. Expectations are now constructed via new_expectation() ahead of testthat 3.3.0, and expect_base() errors on a missing variable instead of silently passing.
◆ Where it's heading
The package reached its shape early and has spent the years since sanding it. The design decisions worth noting are all in the past: the move to tidyselect at 0.3.0, the test data pipe at 0.4.0, and the failure messages at 0.4.1 that name which variable failed rather than just reporting a count. Since then activity is sparse and reactive, tracking testthat and R-devel. The two 0.4.3 and 0.4.4 tags arriving ninety minutes apart on the same day is the signature of a release caught by an upstream deadline.
◆ Prediction
The immediate work is finishing the testthat 3.3.0 adaptation. Beyond that the notes give no evidence of new expectation families; the package looks maintained rather than developed.
Alternatives to tealeaves and testdat
Other Infra & APIs 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 tealeaves or testdat.
Infisical
A credential platform assembled two or three pull requests at a time, never a headline
SigNoz
Search without knowing the field — SigNoz keeps lowering the cost of not knowing your schema
nmfspalette
A NOAA Fisheries colour palette that ships when the branding guide changes
onemap
A genetic-mapping mainstay that now points new users toward MAPpoly at load time
logbin
Relative-risk regression that converges where glm fails, under an unreadable tag order
See all tealeaves alternatives → · See all testdat alternatives →
Recent activity from tealeaves and testdat
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 9mo agotestdatExpectations rebuilt on new_expectation() for testthat 3.3.0
- 9mo agotestdatexpect_base() errors on a missing variable instead of passing
- 2y agotestdatGrouped data frames ungrouped before testing
- 3y agotestdatFailure messages name the failing variable; expect_depends() added
- 4y agotealeavesCitation file, vignette and README fixes
- 4y agotestdatTest data pipe lets expectations sit inline in a chain
- 4y agotestdatCRAN release moves variable selection to tidyselect ⚡
- 6y agotealeavesParameter-crossing bug fixed with tests; coverage added
- 6y agotealeavesFix for custom sky temperature function being overwritten
- 6y agotealeavesSky temperature as value or function; dplyr 1.0.0 compatibility
- 7y agotealeavesUnitless parameter values now accepted and assigned units
- 7y agotealeavesFirst release: leaf energy balance with per-surface conductances
Frequently asked questions
What is the difference between tealeaves and testdat?
They serve adjacent needs but don't currently overlap on shipped themes. tealeaves and testdat 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.
Is tealeaves better than testdat?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tealeaves and testdat 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 Infra & APIs products to evaluate alongside.
What are the best alternatives to tealeaves?
Top tealeaves alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "tealeaves alternatives" section above for the current picks, or visit /alternatives/tealeaves for the full list with editorial commentary on each.
What are the best alternatives to testdat?
Top testdat alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "testdat alternatives" section above for the current picks, or visit /alternatives/testdat for the full list with editorial commentary on each.