ecodive is an R package for calculating ecological diversity metrics in a
parallelized and memory-efficient manner. It is designed to handle large
datasets, such as those common in microbiome research.
Analyzing ecological diversity is often a computational bottleneck, especially
with large datasets. ecodive addresses this by providing:
-
High Performance:
ecodiveis written in C and parallelized using pthreads, making it dramatically faster than other R packages. Benchmarks show it can be up to 10,000x faster and use up to 90,000x less memory. -
Zero Dependencies: The package has no external R dependencies, making it lightweight, stable, and easy to install. This also makes it an ideal and secure backend for other R packages.
-
Comprehensive Metrics: It implements a wide range of common alpha and beta diversity metrics, including classic and phylogenetic-aware methods like Faith's PD and the complete UniFrac family.
-
Ease of Use: The API is simple and integrates seamlessly with popular bioinformatics packages like
phyloseqandrbiom.
The latest stable version can be installed from CRAN.
install.packages('ecodive')The development version is available on GitHub.
install.packages('pak')
pak::pak('cmmr/ecodive')ecodive functions are straightforward to use. Here are a few examples.
The easiest way to use ecodive is with a phyloseq or rbiom object. These
objects conveniently bundle the count data and phylogenetic tree.
library(ecodive)
data(esophagus, package = 'phyloseq')
data(hmp50, package = 'rbiom')
# Calculate weighted UniFrac distance
w_unifrac <- weighted_unifrac(esophagus)
print(w_unifrac)
#> B C
#> C 0.1050480
#> D 0.1401124 0.1422409
# Calculate Faith's Phylogenetic Diversity
faith_pd <- faith(hmp50)
print(faith_pd[1:4])
#> HMP01 HMP02 HMP03 HMP04
#> 6.22296 8.59432 8.93375 9.86597 You can also provide the count data and phylogenetic tree as separate objects.
The ex_counts and ex_tree objects are included with ecodive.
## Example Data ----------------------
counts <- rarefy(ex_counts)
counts[,1:4]
#> Streptococcus Bacteroides Corynebacterium Haemophilus
#> Saliva 162 2 0 180
#> Gums 309 2 0 34
#> Nose 6 0 171 0
#> Stool 1 341 1 1
## Alpha Diversity -------------------
shannon(counts)
#> Saliva Gums Nose Stool
#> 0.74119910 0.35692121 1.10615349 0.07927797
faith(counts, tree = ex_tree)
#> Saliva Gums Nose Stool
#> 180 155 101 202
## Beta Diversity --------------------
bray(counts)
#> Saliva Gums Nose
#> Gums 0.4260870
#> Nose 0.9797101 0.9826087
#> Stool 0.9884058 0.9884058 0.9913043
weighted_unifrac(counts, tree = ex_tree)
#> Saliva Gums Nose
#> Gums 36.97681
#> Nose 67.23768 55.97101
#> Stool 109.77971 109.44058 110.00870Use list_metrics() to browse the metrics available for calculating diversity.
# Alpha Diversity
list_metrics('alpha', 'id')
#> [1] "ace" "berger" "brillouin" "chao1" "faith"
#> [6] "fisher" "simpson" "inv_simpson" "margalef" "mcintosh"
#> [11] "menhinick" "observed" "shannon" "squares"
# Beta Diversity
list_metrics('beta', 'id')
#> [1] "aitchison" "bhattacharyya" "bray"
#> [4] "canberra" "chebyshev" "chord"
#> [7] "clark" "sorensen" "divergence"
#> [10] "euclidean" "generalized_unifrac" "gower"
#> [13] "hamming" "hellinger" "horn"
#> [16] "jaccard" "jensen" "jsd"
#> [19] "lorentzian" "manhattan" "matusita"
#> [22] "minkowski" "morisita" "motyka"
#> [25] "normalized_unifrac" "ochiai" "psym_chisq"
#> [28] "soergel" "squared_chisq" "squared_chord"
#> [31] "squared_euclidean" "topsoe" "unweighted_unifrac"
#> [34] "variance_adjusted_unifrac" "wave_hedges" "weighted_unifrac" The online manual for ecodive is available at https://cmmr.github.io/ecodive/. It includes a getting started guide, articles on alpha/beta diversity, and reference pages for each function.
The following commands will check if ecodive passes the bundled testing suite.
install.packages('testthat')
testthat::test_check('ecodive')Bug reports, feature requests, and general questions can be submitted at https://github.com/cmmr/ecodive/issues.
Pull requests are welcome. Please ensure contributed code is covered by tests and documentation (add additional tests and documentation as needed) and passes all automated tests.
New functions must leverage C and pthreads to minimize memory and CPU time.
Please note that the ecodive project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
MIT License © 2025 ecodive authors