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Comparison · Analytics

rfm vs washdata

A side-by-side editorial comparison of rfm and washdata — release velocity, themes, recent moves, and the top alternatives to consider.

rfm vs washdata: at a glance

Featurerfmwashdata
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr-package, customer-analytics, segmentation, dependenciesopen data, wash surveys, data package, maintenance
Last editorial update2h ago42m ago
WebsiteVisit →Visit →

What is rfm?

A customer segmentation package that went quiet for six years and returned with dependency hygiene

rfm computes recency, frequency and monetary segmentation for customer analytics in R. The feature surface was set early: 0.1.0 shipped a Shiny app and customer-level input, 0.2.0 added default segments and median statistics, 0.2.1 added user-specified score thresholds and returnable plot objects. Then nothing for nearly six years. Version 0.4.0 in April 2026 fixes a missing-column error and a customer id fault, and moves plotly and gganimate from Suggests to Imports.

Read the full rfm trajectory →

What is washdata?

washdata is a fixed survey dataset; eight years of releases have changed only its packaging.

A data package distributing the Urban Water and Sanitation Survey, on CRAN since January 2018. No release has altered the data. The 2018 pair added survey country, year and aim to DESCRIPTION and fixed a README link; everything since — 2020, 2024 and the January 2026 release — is documentation, formatting, badges, repository refreshes and updates for a new rhub version.

Read the full washdata trajectory →

rfm vs washdata: editorial side-by-side

R
rfm
ANALYTICS
0.0

A customer segmentation package that went quiet for six years and returned with dependency hygiene

◆ Current state

rfm computes recency, frequency and monetary segmentation for customer analytics in R. The feature surface was set early: 0.1.0 shipped a Shiny app and customer-level input, 0.2.0 added default segments and median statistics, 0.2.1 added user-specified score thresholds and returnable plot objects. Then nothing for nearly six years. Version 0.4.0 in April 2026 fixes a missing-column error and a customer id fault, and moves plotly and gganimate from Suggests to Imports.

◆ Where it's heading

The 0.4.0 release says more about maintenance posture than about product direction — the version jump past 0.3.x with only two bug fixes and a dependency reshuffle suggests a package being brought back to a releasable state rather than resuming development. Promoting plotly and gganimate to Imports makes the visualization stack mandatory, which is a heavier install in exchange for a simpler code path. The core RFM computation itself has not changed in this window.

◆ Prediction

The entries show a package returning from dormancy rather than pursuing a roadmap, so further small fixes are more likely than new segmentation capability.

W
washdata
ANALYTICS
0.0

washdata is a fixed survey dataset; eight years of releases have changed only its packaging.

◆ Current state

A data package distributing the Urban Water and Sanitation Survey, on CRAN since January 2018. No release has altered the data. The 2018 pair added survey country, year and aim to DESCRIPTION and fixed a README link; everything since — 2020, 2024 and the January 2026 release — is documentation, formatting, badges, repository refreshes and updates for a new rhub version.

◆ Where it's heading

Nothing is heading anywhere, and for a dataset package that is the point: the value is a citable, unchanging artifact, and the release history exists to keep it installable as R's toolchain moves. The maintenance cadence matches the maintainer's other nutrition packages, which received the same repository-refresh treatment in the same period. Note also that the tags are backfilled out of order — v0.1.0 carries a later stamp than v0.1.2.

◆ Prediction

Expect further releases only when CRAN checks or infrastructure require them; there is no indication the survey data itself will be extended.

Alternatives to rfm and washdata

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 rfm or washdata.

See all rfm alternatives → · See all washdata alternatives →

Recent activity from rfm and washdata

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 3mo agorfmrfm 0.4.0
  2. 6mo agowashdataRepository and toolchain maintenance
  3. 2y agowashdataDocumentation and formatting updates
  4. 5y agowashdataSecond release: documentation and formatting
  5. 6y agorfmrfm 0.2.2
  6. 6y agorfmrfm 0.2.1
  7. 7y agorfmrfm 0.2.0
  8. 8y agorfmrfm 0.1.1
  9. 8y agorfmrfm 0.1.0
  10. 8y agowashdataPre-release of the survey dataset
  11. 8y agowashdataFirst CRAN release of the Urban Water and Sanitation Survey

Frequently asked questions

What is the difference between rfm and washdata?

They serve adjacent needs but don't currently overlap on shipped themes. rfm and washdata 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 rfm better than washdata?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. rfm and washdata 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.

What are the best alternatives to rfm?

Top rfm alternatives in Analytics are ranked by recent ship velocity. Browse the "rfm alternatives" section above for the current picks, or visit /alternatives/rfm for the full list with editorial commentary on each.

What are the best alternatives to washdata?

Top washdata alternatives in Analytics are ranked by recent ship velocity. Browse the "washdata alternatives" section above for the current picks, or visit /alternatives/washdata for the full list with editorial commentary on each.