Comparison with other packages

This vignette compares eulerr with other R packages that fit area-proportional Euler and Venn diagrams. It compares their accuracy, speed, features, and scope.

What counts as a competitor

In an area-proportional diagram, each region’s area is proportional to the quantity it represents. Few R packages solve this geometric fitting problem. Most packages for Venn and Euler diagrams draw fixed, schematic shapes and encode quantities through labels or color instead of area.

On CRAN and Bioconductor, the packages that fit area-proportional diagrams are:

eulerr fits circles, ellipses, or axis-aligned rectangles or squares through numerical optimization and reports stress and diagError goodness-of-fit statistics.
venneuler fits only circles, using Wilkinson’s algorithm (Wilkinson 2012), and depends on Java through rJava.
nVennR uses the nVenn algorithm (Pérez-Silva et al. 2018) to build quasi-proportional, n-dimensional diagrams from irregular polygons. It is distributed via GitHub.
BioVenn generates accurate 2–3 circle diagrams from identifier lists (Hulsen 2021).
vennplot fits 2D circles and 3D spheres for 2–3 sets (Xu et al. 2017). It is currently dormant on CRAN.
VennDiagram is a drawing package whose scaled mode is area-proportional for two circles only (Chen and Boutros 2011).

The Excluded packages section lists packages that are not area-proportional fitters and explains why they are excluded.

How accuracy is measured

Fitters optimize different objectives, which makes fair comparisons difficult. A package that minimizes overall squared error will score poorly if judged by its single worst region, and vice versa. We therefore compare packages by objective: for each competitor, we configure eulerr to optimize the same objective and score both packages on the same metric.

To apply each metric consistently across packages, we compute fit statistics from each package’s realized geometry rather than use its self-reported diagnostics. We turn the fitted shapes into polygons, compute the area of every disjoint region with polyclip, and calculate eulerr’s statistics from those areas:

stress: the normalized residual sum of squares from venneuler, $\sum_i (A_i - \beta\omega_i)^2 \big / \sum_i A_i^2$ , with $\beta = \sum_i A_i\omega_i \big / \sum_i \omega_i^2$ .
diagError: the largest absolute difference between a region’s realized and target proportion, $\max_i |A_i/\sum_k A_k - \omega_i/\sum_k \omega_k|$ (from eulerAPE, (Micallef and Rodgers 2014)).

Here, $\omega_i$ is the input size of region $i$ , and $A_i$ is its realized area. Both statistics are scale-invariant, so packages that work in different coordinate systems remain comparable.

We use eight three- to six-set cases from the Python comparison corpus. The four three-set cases cover symmetric overlaps, an example from the eulerAPE article, a large triple intersection with small pairwise regions, and singleton regions that differ by two orders of magnitude. Both four-set cases populate all 15 regions, and one has a large four-way intersection. The five-set kinase data and Wilkinson’s six-set example add sparse overlaps among larger numbers of sets. The region sizes are copied unchanged from the Python corpus.

venneuler and nVennR run on all eight cases. BioVenn runs on the four three-set cases. Plot labels include the number of sets in parentheses.

The three comparisons are:

Comparison	Competitor optimizes	eulerr setting	Scored on
A	overall fit (stress)	`loss = "stress"`, circles	stress
B	region proportionality	`loss = "diag_error"`, circles	diagError
C	overlap areas (3 circles)	`loss = "stress"`, circles	stress

Each comparison includes eulerr’s default ellipse fit as a reference (eulerr (ellipse)). Ellipses have more degrees of freedom than circles and typically fit better. For the direct comparison with each competitor that fits circles, however, we use eulerr’s circle mode.

Accuracy

Competitors benchmarked: venneuler, nVennR, BioVenn (generated 2026-11-11 10:52:59).

Fit error by dataset and package. Lower is better. Each competitor is matched against eulerr configured for the same objective.

Speed

Runtimes are median wall-clock times for repeated fits on the machine that generated the results (see sessionInfo in the stored meta). They indicate broad differences in speed, but venneuler incurs a fixed JVM cost, and absolute times depend on the hardware.

Median fit time (ms, log scale) by dataset and package.

Feature comparison

The packages also differ in scope and capabilities, as the table below shows.

Package	Shapes	Max sets	Proportional	Input	Fit reported	Key dependency	Source
eulerr	circle, ellipse, rectangle, square	many	approximate	vectors, data frames, matrices, tables, lists	stress, diagError, regionError	none (Rust)	CRAN
venneuler	circle	many	approximate	named vector, data frame	stress	rJava / Java	CRAN
nVennR	irregular polygon	many	quasi	lists	none	C++	GitHub
BioVenn	circle	2–3	accurate (2–3)	ID lists	none	none	CRAN
vennplot	circle (2D), sphere (3D)	2–3	approximate	counts, lists	none	Rcpp, rgl	CRAN (dormant)
VennDiagram	circle	4 (2 scaled)	2 sets only	counts	none	none	CRAN

The main differences are:

eulerr is the only package that fits ellipses (and rectangles or squares). Ellipses can exactly represent many three-set configurations that circles cannot. The results above show where this extra flexibility improves the fit.
eulerr and venneuler are the only packages that report a numeric measure of goodness of fit. Without such a measure, readers cannot tell how much a diagram distorts the data. Area-proportional diagrams with three or more sets almost always distort the data to some degree.
eulerr has no system dependencies beyond a Rust toolchain at build time, whereas venneuler requires a working Java installation through rJava, which is a common source of installation trouble.
nVennR achieves closer proportionality only by replacing smooth shapes with irregular polygons. The central trade-off is between smooth, interpretable shapes (eulerr and venneuler) and exact topology with jagged regions (nVennR).

Excluded packages

Many widely cited packages for Venn and Euler diagrams are not area-proportional fitters and therefore fall outside the scope of this comparison.

Package	Reason
venn, ggVennDiagram, ggvenn, RVenn, gplots	Draw fixed, schematic shapes; quantity shown via labels or color, not area
UpSetR	Not a Venn/Euler diagram at all (UpSet matrix/bar charts)
colorfulVennPlot	Archived from CRAN; only a 2-set helper, never a general fitter
VennMaster	Area-proportional, but a standalone Java application and not an R package
Vennerable	Area-weighted, but hosted on R-Forge/GitHub, not CRAN or Bioconductor

Caveats

“Area-proportional” is a spectrum. For three or more sets, exact solutions with circles or ellipses are often geometrically impossible, so eulerr and venneuler produce approximate diagrams. nVennR achieves closer proportionality only by replacing smooth shapes with irregular polygons. Comparisons should report fit error, as the accuracy section above does, rather than merely whether a package produced a diagram.

The BioVenn documentation claims it is “the only R package that can automatically generate an accurate area-proportional Venn diagram,” but eulerr, venneuler, and vennplot also produce accurate area-proportional circle diagrams.

The numbers here come from one machine and one set of package versions. To regenerate them, enter the development environment with devenv shell and run Rscript data-raw/benchmarks.R from the package root.

References

Chen, Hanbo, and Paul C. Boutros. 2011. “VennDiagram: A Package for the Generation of Highly-Customizable Venn and Euler Diagrams in R.” BMC Bioinformatics 12 (1): 35. https://doi.org/10.1186/1471-2105-12-35.

Hulsen, Tim. 2021. “BioVenn – an R and Python Package for the Comparison and Visualization of Biological Lists Using Area-Proportional Venn Diagrams.” Data Science 4 (1): 51–61. https://doi.org/10.3233/DS-210032.

Micallef, Luana, and Peter Rodgers. 2014. “eulerForce: Force-Directed Layout for Euler Diagrams.” Journal of Visual Languages and Computing 25 (6): 924–34. https://doi.org/10.1016/j.jvlc.2014.09.002.

Pérez-Silva, José G., Miguel Araujo-Voces, and Víctor Quesada. 2018. “nVenn: Generalized, Quasi-Proportional Venn and Euler Diagrams.” Bioinformatics 34 (13): 2322–24. https://doi.org/10.1093/bioinformatics/bty109.

Wilkinson, L. 2012. “Exact and Approximate Area-Proportional Circular Venn and Euler Diagrams.” IEEE Transactions on Visualization and Computer Graphics 18 (2): 321–31. https://doi.org/10.1109/TVCG.2011.56.

Xu, Zehao, R. Wayne Oldford, and Martin Lysy. 2017. vennplot: Venn Diagrams in 2D and 3D. https://CRAN.R-project.org/package=vennplot.

Johan Larsson

2026-11-11