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Comparison · Analytics
dplyr vs iris
A side-by-side editorial comparison of dplyr and iris — release velocity, themes, recent moves, and the top alternatives to consider.
dplyr vs iris: at a glance
What is dplyr?
After two quiet years dplyr widened its verb vocabulary in one release
dplyr sat on patch releases from late 2023 until 1.2.0 landed in February 2026, and that release did a lot at once: a filter_out() counterpart to filter(), elementwise when_any() and when_all(), and three new recoding verbs alongside case_when(). It also rewrote if_else(), case_when() and coalesce() in C via vctrs, and promoted .by and reframe() from experimental to stable. The follow-up 1.2.1 is a compliance patch.
What is iris?
Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.
Iris tags a release candidate roughly every four to five months — 3.13 through 3.16 over the past year — and the cadence is the only thing the feed actually reports. Every entry is the same seven-line template: a line saying this is a release candidate, conda-forge and PyPI install commands, and a link to a 'What's New' page held elsewhere. No release notes reach the feed at all.
dplyr vs iris: editorial side-by-side
dplyr
0.0ANALYTICS
After two quiet years dplyr widened its verb vocabulary in one release
◆ Current state
dplyr sat on patch releases from late 2023 until 1.2.0 landed in February 2026, and that release did a lot at once: a filter_out() counterpart to filter(), elementwise when_any() and when_all(), and three new recoding verbs alongside case_when(). It also rewrote if_else(), case_when() and coalesce() in C via vctrs, and promoted .by and reframe() from experimental to stable. The follow-up 1.2.1 is a compliance patch.
◆ Where it's heading
The package is expanding its verb set deliberately, through published Tidyup design proposals rather than ad-hoc additions, and each new verb targets a case where the old idiom was error-prone - most obviously NA handling in negated filters. Underneath, hot paths keep moving from R into C, so the API grows while the runtime cost falls.
◆ Prediction
Expect the remaining experimental surface to follow .by and reframe() toward stable, and further hot paths to be rewritten in C via vctrs. The two Tidyup proposals referenced here suggest more of the filter and recode families is still being designed.
iris
2.5ANALYTICS
Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.
◆ Current state
Iris tags a release candidate roughly every four to five months — 3.13 through 3.16 over the past year — and the cadence is the only thing the feed actually reports. Every entry is the same seven-line template: a line saying this is a release candidate, conda-forge and PyPI install commands, and a link to a 'What's New' page held elsewhere. No release notes reach the feed at all.
◆ Where it's heading
The version numbers say a mature Met Office library is being maintained on a predictable schedule; nothing in the published entries says what is being maintained. Until the project puts release content in the tag body, its public trail will read as cadence without substance, and readers have to leave the feed to learn anything. The pattern has been identical across four consecutive releases, so it is a deliberate publishing choice rather than an oversight.
◆ Prediction
Expect v3.17.0rc0 around late 2026 on the same schedule, carrying the same boilerplate — the notes will again live on the documentation site rather than in the release entry.
Alternatives to dplyr and iris
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 dplyr or iris.
OpenHouse
OpenHouse is hardening the seams where table policies and jobs quietly fail.
silx
silx 3.0 moved its default Qt binding to PySide6 — a migration for everyone embedding it.
statsmodels
statsmodels ships only what the ecosystem breaks — six releases, no new statistics.
StatsBase.jl
StatsBase.jl is in caretaker mode — correctness fixes in, dependency bumps out.
shiny
Shiny made reactive apps observable, then gave them a way to tear themselves down
Recent activity from dplyr and iris
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 13d agoirisv3.16.0rc0
- 4mo agodplyrFull compliance with the R C API
- 4mo agoirisv3.15.0rc0
- 6mo agodplyrfilter_out(), when_any() and three recoding verbs land in 1.2.0 ⚡
- 9mo agoirisv3.14.0rc0
- 1y agoirisv3.13.0rc0
- 2y agodplyrNamespaced join_by() helpers and refreshed bundled datasets
- 2y agodplyrDeprecation message and setequal() consistency fixes
- 3y agodplyrAll-NA join key fix and count() documentation
- 3y agodplyrJoins gain a relationship argument and warn far less often
Frequently asked questions
What is the difference between dplyr and iris?
They serve adjacent needs but don't currently overlap on shipped themes. iris is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is dplyr better than iris?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. iris is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to dplyr?
Top dplyr alternatives in Analytics are ranked by recent ship velocity. Browse the "dplyr alternatives" section above for the current picks, or visit /alternatives/dplyr for the full list with editorial commentary on each.
What are the best alternatives to iris?
Top iris alternatives in Analytics are ranked by recent ship velocity. Browse the "iris alternatives" section above for the current picks, or visit /alternatives/scitools-iris for the full list with editorial commentary on each.