← Back to home
Comparison · Analytics

ggmapinset vs TidyDensity

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

ggmapinset vs TidyDensity: at a glance

FeatureggmapinsetTidyDensity
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesggplot2, geospatial, inset-maps, extension-apistatistical-distributions, random-generation, parameter-estimation, tidyverse
Last editorial update3h ago1h ago
WebsiteVisit →Visit →

What is ggmapinset?

A ggplot2 inset-map extension that is now infrastructure for other packages

ggmapinset adds magnified inset panels to ggplot2 sf maps, handling the coordinate transformation, the inset frame and the sf-related stat layers that have to follow it. The 0.5.0 release is aimed less at end users than at extension authors: coerce_centre() is a new extension point required by sibling package ggautomap, and the inset parameter drops NA in favour of waiver() as its default. It comes from cidm-ph, alongside nswgeo.

Read the full ggmapinset trajectory →

What is TidyDensity?

A distribution catalogue that grows by one family at a time, and rarely breaks anything.

TidyDensity generates tidy-format random data from statistical distributions, with parameter estimation, AIC calculation, summary tables and automatic plotting for each one. Its releases follow a fixed template — breaking changes, new features, minor fixes — and the breaking section is usually empty. Growth comes distribution by distribution: Bernoulli, Burr, triangular, chi-square, zero-truncated negative binomial and others each arrive with a matching set of param_estimate, aic and stats_tbl helpers.

Read the full TidyDensity trajectory →

ggmapinset vs TidyDensity: editorial side-by-side

G
ggmapinset
ANALYTICS
0.0

A ggplot2 inset-map extension that is now infrastructure for other packages

◆ Current state

ggmapinset adds magnified inset panels to ggplot2 sf maps, handling the coordinate transformation, the inset frame and the sf-related stat layers that have to follow it. The 0.5.0 release is aimed less at end users than at extension authors: coerce_centre() is a new extension point required by sibling package ggautomap, and the inset parameter drops NA in favour of waiver() as its default. It comes from cidm-ph, alongside nswgeo.

◆ Where it's heading

The package has moved steadily from feature to foundation. 0.3.0 replaced confusing parameter names and rebuilt everything on stat_sf_inset() so coordinate limits stayed correct, then exposed transform_to_inset() explicitly for extension developers. 0.4.0 generalised inset shapes beyond circles to rectangles and arbitrary sf geometries. 0.5.0 continues in that direction, changing defaults in ways that require downstream extensions to adapt — the cost of being depended upon.

◆ Prediction

Expect further extension points driven by what ggautomap and the other cidm-ph mapping packages need, with the user-facing inset API staying largely settled after the shape generalisation.

T
TidyDensity
ANALYTICS
0.0

A distribution catalogue that grows by one family at a time, and rarely breaks anything.

◆ Current state

TidyDensity generates tidy-format random data from statistical distributions, with parameter estimation, AIC calculation, summary tables and automatic plotting for each one. Its releases follow a fixed template — breaking changes, new features, minor fixes — and the breaking section is usually empty. Growth comes distribution by distribution: Bernoulli, Burr, triangular, chi-square, zero-truncated negative binomial and others each arrive with a matching set of param_estimate, aic and stats_tbl helpers.

◆ Where it's heading

The package is filling out a matrix rather than changing shape — every new distribution gets the same four or five companion functions, so the surface grows predictably and the design does not. What variation exists comes from utilities that work across distributions: MCMC sampling, bootstrap helpers, time series conversion, distribution comparison. The two genuine breaking changes in this window were both internal reworks, moving generation onto data.table and rewriting quantile normalization for speed.

◆ Prediction

The established pattern of adding a distribution with its full helper set is the most likely continuation. Recent releases have been small, suggesting the catalogue is approaching the distributions its author considers worth covering.

Alternatives to ggmapinset and TidyDensity

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 ggmapinset or TidyDensity.

See all ggmapinset alternatives → · See all TidyDensity alternatives →

Recent activity from ggmapinset and TidyDensity

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

  1. 4mo agoggmapinsetNew extension point for ggautomap; waiver() replaces NA
  2. 11mo agoTidyDensityquantile_normalize rewritten, changing its output
  3. 1y agoTidyDensityDocumentation corrections for two distribution functions
  4. 1y agoggmapinsetRectangular and arbitrary sf inset shapes
  5. 2y agoTidyDensityZero-truncated distributions and AIC helpers added in bulk
  6. 2y agoTidyDensityMCMC sampling and quantile normalization join the utilities
  7. 2y agoTidyDensityGeneration moves to data.table; native pipe raises the R floor
  8. 2y agoTidyDensityDistributions convertible to time series objects
  9. 3y agoggmapinsetRebuilt on stat_sf_inset() with corrected coordinate limits

Frequently asked questions

What is the difference between ggmapinset and TidyDensity?

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

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

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

What are the best alternatives to TidyDensity?

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