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https://github.com/ropensci/rmangal

Retrieve and explore data from the ecological interactions database MANGAL
https://github.com/ropensci/rmangal

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Retrieve and explore data from the ecological interactions database MANGAL

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# rmangal :package: - an R Client for the Mangal database 



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## Context



[Mangal](https://mangal.io/#/) -- a global ecological interactions database --

serializes ecological interaction matrices into nodes (e.g. taxon, individuals

or population) and interactions (i.e. edges). For each network, Mangal offers

the opportunity to store study context such as the location, sampling

environment, inventory date and informations pertaining to the original

publication. For every nodes involved in the ecological networks, Mangal

references unique taxonomic identifiers such as Encyclopedia of Life (EOL),

Catalogue of Life (COL), Global Biodiversity Information Facility (GBIF) etc.

and can extend nodes informations to individual traits.



**rmangal** is an R client to the Mangal database and provides various functions

to explore his content through search functions. It offers methods to retrieve

networks structured as `mgNetwork` or `mgNetworksCollection` S3 objects and

methods to convert `mgNetwork` to other class objects in order to analyze and

visualize networks properties: [`igraph`](https://igraph.org/r/),

[`tidygraph`](https://github.com/thomasp85/tidygraph), and

[`ggraph`](https://github.com/thomasp85/ggraph).



## Installation



So far, only the development version is available and can be installed via the [remotes](https://CRAN.R-project.org/package=remotes) :package:



```r

R> remotes::install_github("ropensci/rmangal")

R> library("rmangal")

```



## How to use `rmangal`



There are [seven `search_*()` functions](https://docs.ropensci.org/rmangal/reference/index.html#section-explore-database) to explore the content of Mangal, for

instance `search_datasets()`:



```r

R> mgs <- search_datasets("lagoon")

Found 2 datasets

```



Once this first step achieved, networks found can be retrieved with the `get_collection()` function.



```r

R> mgn <- get_collection(mgs)

```



`get_collection()` returns an object `mgNetwork` if there is one network

returned, otherwise an object `mgNetworkCollection`, which is a list of

`mgNetwork` objects.



```r

R> class(mgn)

[1] "mgNetworksCollection"

R> mgn

A collection of 3 networks



* Network # from data set #

* Description: Dietary matrix of the Huizache–Caimanero lagoon

* Includes 189 edges and 26 nodes

* Current taxonomic IDs coverage for nodes of this network:

  --> ITIS: 81%, BOLD: 81%, EOL: 85%, COL: 81%, GBIF: 0%, NCBI: 85%

* Published in ref # DOI:10.1016/s0272-7714(02)00410-9



* Network # from data set #

* Description: Food web of the Brackish lagoon

* Includes 27 edges and 11 nodes

* Current taxonomic IDs coverage for nodes of this network:

  --> ITIS: 45%, BOLD: 45%, EOL: 45%, COL: 45%, GBIF: 18%, NCBI: 45%

* Published in ref # DOI:NA



* Network # from data set #

* Description: Food web of the Costal lagoon

* Includes 34 edges and 13 nodes

* Current taxonomic IDs coverage for nodes of this network:

  --> ITIS: 54%, BOLD: 54%, EOL: 54%, COL: 54%, GBIF: 15%, NCBI: 54%

* Published in ref # DOI:NA

```



[`igraph`](https://igraph.org/r/) and

[`tidygraph`](https://github.com/thomasp85/tidygraph) offer powerful features to

analyze networks and **rmangal** provides functions to convert `mgNetwork` to

`igraph` and `tbl_graph` so that the user can easily benefit from those

packages.



```r

R> ig <- as.igraph(mgn[[1]])

R> class(ig)

[1] "igraph"

R> library(tidygraph)

R> tg <- as_tbl_graph(mgn[[1]])

R> class(tg)

[1] "tbl_graph" "igraph"

```



:book: Note that the vignette ["Get started with

rmangal"](https://docs.ropensci.org/rmangal/articles/rmangal.html) will guide

the reader through several examples and provide further details about **rmangal** features.



## How to publish ecological networks



We are working on that part. The networks publication process will be

facilitated with structured objects and tests suite to maintain data integrity

and quality.Comments and suggestions are welcome, feel free to open issues.



## `rmangal` vs `rglobi`



Those interested only in pairwise interactions among taxons may consider using

`rglobi`, an R package that provides an interface to the [GloBi

infrastructure](https://www.globalbioticinteractions.org/about.html). GloBi

provides open access to aggregated interactions from heterogeneous sources. In

contrast, Mangal gives access to the original networks and open the gate to

study ecological networks properties (i.e. connectance, degree etc.) along large

environmental gradients, which wasn't possible using the GloBi infrastructure.



## Older versions 



* See https://github.com/mangal-interactions/rmangal-v1 for the first version of the client.

* Note that due to changes in the RESTful API, there is no backward compatibility.



## Acknowledgment



We are grateful to [Noam Ross](https://github.com/noamross) for acting as an editor during the review process. We also thank [Anna Willoughby](https://github.com/arw36) and [Thomas Lin Petersen](https://github.com/thomasp85) for reviewing the package. Their comments strongly contributed to improve the quality of rmangal.



## Code of conduct



Please note that the `rmangal` project is released with a [Contributor Code of Conduct](https://mangal.io/doc/r/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.



## Meta



* Get citation information for `rmangal` in R doing `citation(package = 'rmangal')`

* Please [report any issues or bugs](https://github.com/ropensci/rmangal/issues).



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