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Comparison · Infra & APIs

ggpointless vs mice

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

ggpointless vs mice: at a glance

Featureggpointlessmice
SectorInfra & APIsInfra & APIs
Velocity score0.00.0
Sparks · 30d00
Top themesggplot2-extensions, data-visualization, pictogram-charts, alpha-gradientsmissing-data, multiple-imputation, statistics, r-package
Last editorial update2h ago48m ago
WebsiteVisit →Visit →

What is ggpointless?

ggpointless keeps adding the ggplot2 layers nobody else bothered to write.

ggpointless is a small ggplot2 extension collecting geoms that sit outside the standard set — Lexis diagrams, Chaikin-smoothed paths, hanging chains, Fourier reconstructions — and it has grown steadily rather than changed shape. The May release is the largest yet: isotype and pictogram bar charts as stacks of discrete unit cells, a family of geoms that fade paths, segments, curves and reference lines along their length, and geom_gridline(), which draws grid lines as a layer on top of the data rather than beneath it.

Read the full ggpointless trajectory →

What is mice?

mice can finally predict, not just estimate, from multiply imputed data.

mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.

Read the full mice trajectory →

ggpointless vs mice: editorial side-by-side

G
ggpointless
INFRA · APIS
0.0

ggpointless keeps adding the ggplot2 layers nobody else bothered to write.

◆ Current state

ggpointless is a small ggplot2 extension collecting geoms that sit outside the standard set — Lexis diagrams, Chaikin-smoothed paths, hanging chains, Fourier reconstructions — and it has grown steadily rather than changed shape. The May release is the largest yet: isotype and pictogram bar charts as stacks of discrete unit cells, a family of geoms that fade paths, segments, curves and reference lines along their length, and geom_gridline(), which draws grid lines as a layer on top of the data rather than beneath it.

◆ Where it's heading

Two patterns are visible. Ideas get generalized rather than left as one-offs: geom_area_fade() in the previous release established alpha gradients via grid::linearGradient(), and the recent release spreads that treatment across paths, lines, steps, segments, curves and the three reference-line geoms, each with the same fade_direction and alpha_fade_to arguments. And each new geom is expected to survive real plots — the unit charts work under coord_equal, coord_polar, coord_radial, coord_flip and faceting, and geom_gridline reads positions from trained scales and inherits styling from the theme's panel grid. The package also tracks ggplot2 closely, requiring 4.0.0 and using make_constructor() and gg_par() internally, and it dropped its bundled datasets outright rather than maintain stale copies.

◆ Prediction

The fade treatment now covers most path-like geoms but not the area and ribbon family beyond geom_area_fade(), which is where the pattern has room left to run. The unit-cell charts arrive with a label helper and no fill or grouping variants, so those are the plausible next additions.

M
mice
INFRA · APIS
0.0

mice can finally predict, not just estimate, from multiply imputed data.

◆ Current state

mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.

◆ Where it's heading

Two things are happening. The imputation method catalogue keeps widening — lasso variants, multivariate PMM, categorical PMM via canonical correlation — while the pooling side is being extended past its original purpose, first to synthetic data, now to predictions on held-out sets. That second thread points at predictive modelling workflows rather than the inferential ones mice was built for. Meanwhile the maintainers keep finding consequential old bugs: the augment() ordered-factor defect in 3.18.0 had been silently degrading ordinal imputations for years.

◆ Prediction

predict_mi() is framed around evaluating predictive performance on test sets, and the ignore argument added in 3.12.0 already exists to hold out rows from the imputation model. Expect the next work to join those up into a fuller train/test story for imputed data, since the pieces are now in place but not yet connected.

Alternatives to ggpointless and mice

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 ggpointless or mice.

See all ggpointless alternatives → · See all mice alternatives →

Recent activity from ggpointless and mice

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

  1. 3mo agoggpointlessPictogram unit charts, gridline layers and a family of fading geoms
  2. 5mo agoggpointlessFourier and arch geoms, area fades and glowing points
  3. 8mo agomicemice 3.19.0
  4. 1y agomicemice 3.18.0
  5. 1y agomicemice 3.17.0
  6. 2y agoggpointlessgeom_catenary() draws a hanging chain
  7. 3y agomicemice 3.16.0
  8. 3y agomicemice 3.15.0
  9. 3y agoggpointlessgeom_chaikin() adds corner-cutting path smoothing
  10. 4y agoggpointlessgeom_lexis() and the female_leaders dataset
  11. 4y agomicemice 3.14.0

Frequently asked questions

What is the difference between ggpointless and mice?

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

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggpointless and mice 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 ggpointless?

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

What are the best alternatives to mice?

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