How to use rmangal

Overall approach

In order to efficiently retrieve networks from the database, rmangal includes 7 search functions querying the 5 tables described above as well as a table dedicated to the taxonomy backbone.

search_references(): search in the reference table, for instance the user can look for a specific doi;
search_datasets(): search among datasets using a keyword;
search_networks() and search_networks_sf(): search networks based on a keyword or a geographical area;
search_interactions(): list all networks containing a specific interaction type;
search_nodes(): identify nodes based on nodes information;
search_taxonomy(): identify nodes based on taxonomic names and unique identifiers.

All of these functions return specific class objects with the information needed to retrieve the corresponding set of ecological networks with get_collection(). Hence, the user can easily retrieve data in two steps:

networks <- search_*() %>% get_collection()

Note that if there is only one network to be retrieved, get_collection() returns a mgNetwork object, otherwise it returns an object of class mgNetworksCollection which is a collection (a list) of mgNetwork objects. Below, we exemplify how to use the search functions, how to get a collection of networks and how to use other packages to carry out specific analyses.

Search functions

In rmangal, every functions queries a specific table and allow only one query at a time (see section Batch analysis to learn how to perform more than one query). All the functions offer two ways to query the corresponding table:

a keyword: in this case, the entries returned are the partial or full keyword match of any strings contained in the table;
a custom query: in this case, entries returned are exact matches.

Let’s load rmangal as well as two helper packages:

library(rmangal)
library(magrittr) # for the pipe %>%
library(tibble) # to use tibble (enhanced data frames)

Search and list available datasets

Let’s assume we are looking for ecological networks including species living in lagoons. If we have no idea about any existing data set, the best starting point is then to query the dataset table with lagoon as a keyword:

lagoon <- search_datasets(query = "lagoon")
class(lagoon)
#> [1] "tbl_df"           "tbl"              "data.frame"       "mgSearchDatasets"
lagoon
#> # A tibble: 2 × 10
#>      id name        description                   public created_at        updated_at        ref_id user_id references  networks  
#>   <int> <chr>       <chr>                         <lgl>  <chr>             <chr>              <int>   <int> <list>      <list>    
#> 1    22 zetina_2003 Dietary matrix of the Huizac… TRUE   2019-02-23T17:04… 2019-02-23T17:04…     22       3 <df [1 × 1… <df [1 × …
#> 2    52 yanez_1978  Food web of the Guerrero lag… TRUE   2019-02-24T23:42… 2019-02-24T23:42…     53       3 <df [1 × 1… <df [2 × …

If the Mangal reference id containing the lagoon networks was known, we could build a custom query as follow:

lagoon_zetina <- search_datasets(list(ref_id = 22))
lagoon_zetina
#> # A tibble: 1 × 10
#>      id name        description                   public created_at        updated_at        ref_id user_id references  networks  
#>   <int> <chr>       <chr>                         <lgl>  <chr>             <chr>              <int>   <int> <list>      <list>    
#> 1    22 zetina_2003 Dietary matrix of the Huizac… TRUE   2019-02-23T17:04… 2019-02-23T17:04…     22       3 <df [1 × 1… <df [1 × …

Note that if an empty character is passed, i.e. "", all entries are returned. We can use this behavior to list all datasets available:

all_datasets <- search_datasets("", verbose = FALSE)
glimpse(all_datasets)
#> Rows: 175
#> Columns: 10
#> $ id          <int> 2, 7, 9, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,…
#> $ name        <chr> "howking_1968", "lundgren_olesen_2005", "elberling_olesen_1999", "johnston_1956", "havens_1992", "kemp_1977"…
#> $ description <chr> "Insect activity recorded on flower at Lake Hazen, Ellesmere Island, N.W.T., Canada", "Pollnator activity re…
#> $ public      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, …
#> $ created_at  <chr> "2019-02-22T15:39:00.427Z", "2019-02-22T20:04:25.322Z", "2019-02-22T20:09:17.994Z", "2019-02-22T21:10:45.269…
#> $ updated_at  <chr> "2019-02-22T15:39:00.427Z", "2019-02-22T20:04:25.322Z", "2019-02-22T20:09:17.994Z", "2019-02-22T21:10:45.269…
#> $ ref_id      <int> 2, 7, 9, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 38, 39, 40,…
#> $ user_id     <int> 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, …
#> $ references  <list> [<data.frame[1 x 11]>], [<data.frame[1 x 11]>], [<data.frame[1 x 11]>], [<data.frame[1 x 11]>], [<data.fram…
#> $ networks    <list> [<data.frame[1 x 13]>], [<data.frame[1 x 13]>], [<data.frame[1 x 13]>], [<data.frame[1 x 13]>], [<data.fram…

As shown in the diagram above, a dataset comes from a specific reference and search_references() queries the reference table directly. A handy argument of this function is doi as it allows to pass a Digital Object Identifier and so to retrieve all datasets attached to a specific publication.

zetina_2003 <- search_references(doi = "10.1016/s0272-7714(02)00410-9")

Finding a specific network

We can also search by keyword across all networks.

insect_coll <- search_networks(query="insect%")
glimpse(insect_coll)
#> Rows: 14
#> Error: Input must be a vector, not a <data.frame/mgSearchNetworks> object.

It is also possible to retrieve all networks based on interaction types involved:

# List all interaction types available
avail_type()
#>  [1] "competition"  "amensalism"   "neutralism"   "commensalism" "mutualism"    "parasitism"   "predation"    "herbivory"   
#>  [9] "symbiosis"    "scavenger"    "detritivore"  "unspecified"
comp_interac <- search_interactions(type="competition")
# Number of competition interactions in mangal
nrow(comp_interac)
#> [1] 12

search_networks_sf() handles spatial queries: argument query_sf takes a sf object as input and returns all networks included in the spatial extent of this object. For instance, one can retrieve all Californian networks included in Mangal like so:

library(sf)
library(mapview)
library(USAboundaries)

area <- us_states(state = "california")
in_CA <- search_networks_sf(area, verbose = FALSE)

mapView(st_geometry(area), color = "red", legend = FALSE, col.regions = "#FF000033") + mapView(in_CA, legend = FALSE) 
#> Error in path.expand(path): invalid 'path' argument

Search for a specific taxon

The user can easily identify networks including a specific taxonomic entity with search_taxonomy():

sr_ficus <- search_taxonomy("Ficus")

This function allows to search for a specific taxonomic entity using it’s validated name or unique identifiers, i.e. EOL, TSN, GBIF, COL, BOLD and NCBI IDs. Taxon names of the taxonomy table were validated with TNRS (see https://tnrs.biendata.org/ and/or GNR (see https://resolver.globalnames.org/). The taxon names in this table might not be the taxon name documented in the original publication. In order to identify relevant networks with the original name, use [search_nodes()].

The validation of taxon names was performed by an automated procedure using taxize (Chamberlain et al. 2019) and if there is any doubt, the original names recorded by authors should be regarded as the most reliable information. Please report any issue related to taxonomy at https://github.com/mangal-interactions/contribute/issues/new/choose.

glimpse(search_taxonomy(tsn = 28749))
#> Rows: 1
#> Error: Input must be a vector, not a <data.frame/mgSearchTaxonomy> object.
glimpse(search_taxonomy(eol = 583069))
#> Rows: 1
#> Error: Input must be a vector, not a <data.frame/mgSearchTaxonomy> object.

Note that in some case, one may need to find a dataset based on the original name included in the publication, in such case, search_nodes() must be used:

sr_ficus2 <- search_nodes("Ficus")

Get networks associated with a `search_*` object

Once the search performed, ecological networks are accessible from the object returned with get_collection():

nets_lagoons <- lagoon %>% get_collection
nets_in_CA <- in_CA %>% get_collection
nets_competition <- comp_interac %>% get_collection

nets_lagoons
#> A collection of 3 networks
#> 
#> * Network #86 included in dataset #22
#> * 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 #927 included in dataset #52
#> * 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 #926 included in dataset #52
#> * 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
class(nets_lagoons)
#> [1] "mgNetworksCollection"

Note that mgNetworksCollection objects are lists of mgNetwork object which are a list of five datasets reflecting the 5 tables presented in the diagram in the first section:

names(nets_lagoons[[1]])
#> [1] "network"      "nodes"        "interactions" "dataset"      "reference"
glimpse(nets_lagoons[[1]]$network)
#> Rows: 1
#> Columns: 13
#> $ network_id       <int> 86
#> $ name             <chr> "zetina_2003_20030101_86"
#> $ date             <chr> "2003-01-01T00:00:00.000Z"
#> $ description      <chr> "Dietary matrix of the Huizache–Caimanero lagoon"
#> $ public           <lgl> TRUE
#> $ all_interactions <lgl> FALSE
#> $ created_at       <chr> "2019-02-23T17:04:34.046Z"
#> $ updated_at       <chr> "2019-02-23T17:04:34.046Z"
#> $ dataset_id       <int> 22
#> $ user_id          <int> 3
#> $ geom_type        <chr> "Point"
#> $ geom_lon         <list> -106.1099
#> $ geom_lat         <list> 22.98531
glimpse(nets_lagoons[[1]]$nodes)
#> Rows: 26
#> Columns: 19
#> $ node_id             <int> 4904, 4905, 4906, 4907, 4908, 4909, 4910, 4911, 4912, 4913, 4914, 4915, 4916, 4917, 4918, 4919, 4920…
#> $ original_name       <chr> "Scianids", "Elopids", "Lutjanids", "Carangids", "Centropomids", "Ariids", "Haemulids", "Pleuronecto…
#> $ node_level          <chr> "taxon", "taxon", "taxon", "taxon", "taxon", "taxon", "taxon", "taxon", "taxon", "taxon", "taxon", "…
#> $ network_id          <int> 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, …
#> $ taxonomy_id         <int> 4363, 4364, 4365, 4366, 4367, 4368, 4369, 4355, 3823, 4370, 4371, 4372, 4373, 4374, 4279, 4375, 4376…
#> $ created_at          <chr> "2019-02-23T17:04:42.505Z", "2019-02-23T17:04:42.571Z", "2019-02-23T17:04:42.622Z", "2019-02-23T17:0…
#> $ updated_at          <chr> "2019-02-23T17:04:42.505Z", "2019-02-23T17:04:42.571Z", "2019-02-23T17:04:42.622Z", "2019-02-23T17:0…
#> $ taxonomy.id         <int> 4363, 4364, 4365, 4366, 4367, 4368, 4369, 4355, 3823, 4370, 4371, 4372, 4373, 4374, 4279, 4375, 4376…
#> $ taxonomy.name       <chr> "Sciaenidae", "Elops", "Lutjanidae", "Carangidae", "Centropomidae", "Ariidae", "Haemulidae", "Pleuro…
#> $ taxonomy.ncbi       <int> 30870, 7927, 30850, 8157, 8184, 31017, 30840, 8256, 6762, 94935, 55118, 274463, 8079, 8219, 8189, 66…
#> $ taxonomy.tsn        <int> 169237, 28630, 168845, 168584, 167642, 43998, 169055, 172859, 13951, 165546, NA, 169013, 165876, 171…
#> $ taxonomy.eol        <int> 5211, 46561210, 5294, 5361, 5355, 5115, 5317, 5173, 46508442, 8246, 6893, 5321, 5517, 46575119, 5287…
#> $ taxonomy.bold       <int> 1856, 4061, 1858, 1851, 586, 1313, 1855, 1126, 4985, 1326, 1252, 422, 1259, NA, 1863, 1504, 28100, 7…
#> $ taxonomy.gbif       <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ taxonomy.col        <chr> "81a86c329909d507edb5c296906ef3f4", "94532a14786adeb25bcec244a53aadc1", "7150078b7dd31a5f7575240f1b7…
#> $ taxonomy.rank       <chr> "family", "genus", "family", "family", "family", "family", "family", "family", "genus", "family", "f…
#> $ taxonomy.created_at <chr> "2019-02-23T17:04:35.620Z", "2019-02-23T17:04:35.744Z", "2019-02-23T17:04:35.870Z", "2019-02-23T17:0…
#> $ taxonomy.updated_at <chr> "2019-06-14T15:25:46.438Z", "2019-06-14T15:25:46.492Z", "2019-06-14T15:25:46.546Z", "2019-06-14T15:2…
#> $ taxonomy            <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
glimpse(nets_lagoons[[1]]$interactions)
#> Rows: 189
#> Columns: 20
#> $ interaction_id        <int> 48376, 48377, 48378, 48379, 48380, 48381, 48382, 48383, 48384, 48385, 48388, 48389, 48390, 48391, …
#> $ node_from             <int> 4912, 4912, 4912, 4912, 4912, 4912, 4912, 4912, 4912, 4912, 4913, 4913, 4913, 4913, 4913, 4913, 49…
#> $ node_to               <int> 4912, 4914, 4915, 4918, 4919, 4920, 4921, 4922, 4925, 4926, 4914, 4916, 4917, 4919, 4920, 4922, 49…
#> $ date                  <chr> "2003-01-01T00:00:00.000Z", "2003-01-01T00:00:00.000Z", "2003-01-01T00:00:00.000Z", "2003-01-01T00…
#> $ direction             <chr> "directed", "directed", "directed", "directed", "directed", "directed", "directed", "directed", "d…
#> $ type                  <chr> "predation", "predation", "predation", "predation", "predation", "predation", "predation", "predat…
#> $ method                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ attr_id               <int> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12…
#> $ value                 <dbl> 0.026, 0.025, 0.003, 0.009, 0.009, 0.016, 0.284, 0.231, 0.079, 0.090, 0.100, 0.002, 0.004, 0.006, …
#> $ geom                  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ public                <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…
#> $ network_id            <int> 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86…
#> $ created_at            <chr> "2019-02-23T17:05:45.061Z", "2019-02-23T17:05:45.131Z", "2019-02-23T17:05:45.193Z", "2019-02-23T17…
#> $ updated_at            <chr> "2019-02-23T17:05:45.061Z", "2019-02-23T17:05:45.131Z", "2019-02-23T17:05:45.193Z", "2019-02-23T17…
#> $ attribute.id          <int> 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12…
#> $ attribute.name        <chr> "dietary matrix", "dietary matrix", "dietary matrix", "dietary matrix", "dietary matrix", "dietary…
#> $ attribute.description <chr> "Proportions of the consumer diets made up by the prey.", "Proportions of the consumer diets made …
#> $ attribute.unit        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ attribute.created_at  <chr> "2019-02-23T17:04:25.350Z", "2019-02-23T17:04:25.350Z", "2019-02-23T17:04:25.350Z", "2019-02-23T17…
#> $ attribute.updated_at  <chr> "2019-02-23T17:04:25.350Z", "2019-02-23T17:04:25.350Z", "2019-02-23T17:04:25.350Z", "2019-02-23T17…
glimpse(nets_lagoons[[1]]$dataset)
#> Rows: 1
#> Columns: 8
#> $ dataset_id  <int> 22
#> $ name        <chr> "zetina_2003"
#> $ description <chr> "Dietary matrix of the Huizache–Caimanero lagoon"
#> $ public      <lgl> TRUE
#> $ created_at  <chr> "2019-02-23T17:04:32.017Z"
#> $ updated_at  <chr> "2019-02-23T17:04:32.017Z"
#> $ ref_id      <int> 22
#> $ user_id     <int> 3
glimpse(nets_lagoons[[1]]$reference)
#> Rows: 1
#> Columns: 11
#> $ ref_id       <int> 22
#> $ doi          <chr> "10.1016/s0272-7714(02)00410-9"
#> $ first_author <chr> "manuel j. zetina-rejon"
#> $ year         <chr> "2003"
#> $ jstor        <lgl> NA
#> $ pmid         <lgl> NA
#> $ bibtex       <chr> "@article{Zetina_Rej_n_2003, doi = {10.1016/s0272-7714(02)00410-9}, url = {https://doi.org/10.1016%2Fs0272-…
#> $ paper_url    <chr> "https://doi.org/10.1016%2Fs0272-7714%2802%2900410-9"
#> $ data_url     <chr> "https://globalwebdb.com/"
#> $ created_at   <chr> "2019-02-23T17:04:28.307Z"
#> $ updated_at   <chr> "2019-02-23T17:04:28.307Z"

Integrated workflow with rmangal

Batch analysis

So far, the search functions of rmangal allow the user to perform only a single search at a time. The simplest way to do more than one search is to loop over a vector or a list of queries. Below we exemplify how to do so using lapply():

tsn <- c(837855, 169237)
mgn <- lapply(tsn, function(x) search_taxonomy(tsn = x)) %>%
  lapply(get_collection) %>%
  combine_mgNetworks
mgn
#> A collection of 3 networks
#> 
#> * Network #948 included in dataset #66
#> * Description: Flower and anthophilous insect interactions in the primary cool-temperate subalpine forests and meadows at Mt. Kushigata, Yamanashi Prefecture, Japan
#> * Includes 871 edges and 456 nodes 
#> * Current taxonomic IDs coverage for nodes of this network: 
#>   --> ITIS: 20%, BOLD: 33%, EOL: 46%, COL: 43%, GBIF: 35%, NCBI: 38%
#> * Published in ref # DOI:NA
#> 
#> * Network #86 included in dataset #22
#> * 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 #1101 included in dataset #77
#> * Description: Food web of the Angolan fishery landings
#> * Includes 127 edges and 28 nodes 
#> * Current taxonomic IDs coverage for nodes of this network: 
#>   --> ITIS: 61%, BOLD: 50%, EOL: 61%, COL: 54%, GBIF: 4%, NCBI: 57%
#> * Published in ref # DOI:10.3989/scimar.2011.75n2309

Geolocalize Mangal networks with `sf`

The function get_collection() has an argument as_sf than converts network metadata of mgNetwork objects to sf objects, which requires sf to be installed. This allows the user to easily geolocalize the networks retrieved from Mangal.

# assuming sf and mapview are is loaded (as we did above)
mg_lag_sf <- search_datasets(query = 'lagoon') %>% get_collection(as_sf = TRUE)
class(mg_lag_sf[[1]]$network)
#> [1] "sf"         "data.frame"

# let's combine all these sf object into a single one
mapView(mg_lag_sf[[1]]$network) + mapView(mg_lag_sf[[2]]$network)

plot of chunk unnamed-chunk-18 ## Taxonomic analysis with taxize

As Mangal includes taxonomic identifiers, rmangal can readily be combined with taxize (see taxize for more details about this package):

library(taxize)
tsn_acer <- search_taxonomy("Acer")$taxonomy.tsn
classification(tsn_acer, db = "itis")
#> $`28749`
#>               name          rank     id
#> 1          Plantae       kingdom 202422
#> 2    Viridiplantae    subkingdom 954898
#> 3     Streptophyta  infrakingdom 846494
#> 4      Embryophyta superdivision 954900
#> 5     Tracheophyta      division 846496
#> 6  Spermatophytina   subdivision 846504
#> 7    Magnoliopsida         class  18063
#> 8          Rosanae    superorder 846548
#> 9       Sapindales         order  28643
#> 10     Sapindaceae        family  28657
#> 11            Acer         genus  28727
#> 12    Acer negundo       species  28749
#> 
#> $`28757`
#>                name          rank     id
#> 1           Plantae       kingdom 202422
#> 2     Viridiplantae    subkingdom 954898
#> 3      Streptophyta  infrakingdom 846494
#> 4       Embryophyta superdivision 954900
#> 5      Tracheophyta      division 846496
#> 6   Spermatophytina   subdivision 846504
#> 7     Magnoliopsida         class  18063
#> 8           Rosanae    superorder 846548
#> 9        Sapindales         order  28643
#> 10      Sapindaceae        family  28657
#> 11             Acer         genus  28727
#> 12 Acer saccharinum       species  28757
#> 
#> $<NA>
#> [1] NA
#> 
#> $<NA>
#> [1] NA
#> 
#> $`837855`
#>               name          rank     id
#> 1          Plantae       kingdom 202422
#> 2    Viridiplantae    subkingdom 954898
#> 3     Streptophyta  infrakingdom 846494
#> 4      Embryophyta superdivision 954900
#> 5     Tracheophyta      division 846496
#> 6  Spermatophytina   subdivision 846504
#> 7    Magnoliopsida         class  18063
#> 8          Rosanae    superorder 846548
#> 9       Sapindales         order  28643
#> 10     Sapindaceae        family  28657
#> 11            Acer         genus  28727
#> 12  Acer japonicum       species 837855
#> 
#> attr(,"class")
#> [1] "classification"
#> attr(,"db")
#> [1] "itis"

Network analysis with `igraph`

Once the data are retrieved and a mgNetwork or a mgNetworkCollection objects obtained, it is straightforward to convert it as a igraph (see the dedicated website) object and then to carry out network analysis:

library(igraph)
mg_lagoons <- search_datasets(query = 'lagoon') %>% get_collection
# NB the line below returns a list of igraph objects
ig_lagoons <- as.igraph(mg_lagoons)
## Modularity analysis for the first network
modularity(ig_lagoons[[1]], membership(cluster_walktrap(ig_lagoons[[1]])))
#> [1] 0.04824893
## Degree values for all networks
lapply(ig_lagoons, degree)
#> [[1]]
#> 4904 4905 4906 4907 4908 4909 4910 4911 4912 4913 4914 4915 4916 4917 4918 4919 4920 4921 4922 4924 4925 4926 4927 4923 4929 4928 
#>   17   11   14   13   18   20   14   10   18   14   12   15    7   15   14   12   14   11   26    7   22   15   21   16    5   17 
#> 
#> [[2]]
#> 6459 6460 6461 6463 6464 6465 6458 6462 6466 6456 6457 
#>    4    7    9    3    3    7    4    6    3    4    4 
#> 
#> [[3]]
#> 6445 6447 6448 6449 6450 6452 6453 6454 6446 6451 6455 6443 6444 
#>    6    4    5    5   11    2    5    8    3    5    4    5    5

Network manipulation and visualization with `tidygraph` and `ggraph`

The package tidygraph treats networks as two tidy tables (one for the edges and one for the nodes) that can be modified using the grammar of data manipulation developed in the tidyverse. Moreover, tidygraph wraps over most of the igraph functions so that the user can call a vast variety of algorithms to properly analysis networks. Fortunately, objects of class mgNetwork can readily be converted into tbl_graph objects which allows the user to benefit from all the tools included in tidygraph:

library(tidygraph)
# NB the line below would not work with a mgNetworksCollection (use lapply)
tg_lagoons <-  as_tbl_graph(mg_lagoons[[1]]) %>%
  mutate(centrality_dg = centrality_degree(mode = 'in'))
tg_lagoons %E>% as_tibble
#> # A tibble: 189 × 19
#>     from    to interaction_id date    direction type   method attr_id value public network_id created_at  updated_at  attribute.id
#>    <int> <int>          <int> <chr>   <chr>     <chr>  <lgl>    <int> <dbl> <lgl>       <int> <chr>       <chr>              <int>
#>  1     9     9          48376 2003-0… directed  preda… NA          12 0.026 TRUE           86 2019-02-23… 2019-02-23…           12
#>  2     9    11          48377 2003-0… directed  preda… NA          12 0.025 TRUE           86 2019-02-23… 2019-02-23…           12
#>  3     9    12          48378 2003-0… directed  preda… NA          12 0.003 TRUE           86 2019-02-23… 2019-02-23…           12
#>  4     9    15          48379 2003-0… directed  preda… NA          12 0.009 TRUE           86 2019-02-23… 2019-02-23…           12
#>  5     9    16          48380 2003-0… directed  preda… NA          12 0.009 TRUE           86 2019-02-23… 2019-02-23…           12
#>  6     9    17          48381 2003-0… directed  preda… NA          12 0.016 TRUE           86 2019-02-23… 2019-02-23…           12
#>  7     9    18          48382 2003-0… directed  preda… NA          12 0.284 TRUE           86 2019-02-23… 2019-02-23…           12
#>  8     9    19          48383 2003-0… directed  preda… NA          12 0.231 TRUE           86 2019-02-23… 2019-02-23…           12
#>  9     9    21          48384 2003-0… directed  preda… NA          12 0.079 TRUE           86 2019-02-23… 2019-02-23…           12
#> 10     9    22          48385 2003-0… directed  preda… NA          12 0.09  TRUE           86 2019-02-23… 2019-02-23…           12
#> # … with 179 more rows, and 5 more variables: attribute.name <chr>, attribute.description <chr>, attribute.unit <lgl>,
#> #   attribute.created_at <chr>, attribute.updated_at <chr>
tg_lagoons %N>% as_tibble %>%
  select(original_name, taxonomy.tsn, centrality_dg)
#> # A tibble: 26 × 3
#>    original_name  taxonomy.tsn centrality_dg
#>    <chr>                 <int>         <dbl>
#>  1 Scianids             169237             1
#>  2 Elopids               28630             0
#>  3 Lutjanids            168845             1
#>  4 Carangids            168584             2
#>  5 Centropomids         167642             2
#>  6 Ariids                43998             1
#>  7 Haemulids            169055             4
#>  8 Pleuronectoids       172859             3
#>  9 Callinectes           13951             6
#> 10 Belonoids            165546             4
#> # … with 16 more rows

Another strong advantage of tbl_graph objects is that there are the objects used by the package ggraph that that offers various functions (theme, geoms, etc.) to efficiently visualize networks:

library(ggraph)
ggraph(tg_lagoons, layout = "stress") +
  geom_edge_parallel(end_cap = circle(.5), start_cap = circle(.5),
        arrow = arrow(length = unit(1, 'mm'), type = 'closed')) +
  geom_node_point(aes(colour = taxonomy.rank), size = 8) +
  theme_graph(background = "grey40", foreground = NA, text_colour = 'white')

plot of chunk ggraph

Creating a list references for a set of networks

We can easily print the BibTeX of all publications involved in the networks collection.

   search_datasets(query = 'lagoon') %>%
   get_collection %>% get_citation %>% cat(sep = "\n\n")
#> @article{Zetina_Rej_n_2003, doi = {10.1016/s0272-7714(02)00410-9}, url = {https://doi.org/10.1016%2Fs0272-7714%2802%2900410-9}, year = 2003, month = {aug}, publisher = {Elsevier {BV}}, volume = {57}, number = {5-6}, pages = {803--815}, author = {Manuel J. Zetina-Rejón and Francisco Arreguí-Sánchez and Ernesto A. Chávez}, title = {Trophic structure and flows of energy in the Huizache{   extendash}Caimanero lagoon complex on the Pacific coast of Mexico},journal = {Estuarine, Coastal and Shelf Science}}
#> 
#> @book{yanez_1978, Author = {Yáñez-Arancibia, Alejandro}, Editor = {Universidad Nacional Autónoma de México, Centro de Ciencias del Mar y Limnología. Ciudad Universitaria, México, D.F. -- 1a ed.},Title = {Taxonomía, ecología y estructura de las comunidades de peces en lagunas costeras con bocas efímeras del Pacífico de México}, Year = {1978}}

References

Chamberlain, Scott, Eduard Szoecs, Zachary Foster, Zebulun Arendsee, Carl Boettiger, Karthik Ram, Ignasi Bartomeus, et al. 2019. Taxize: Taxonomic Information from Around the Web. https://github.com/ropensci/taxize.

Poisot, Timothée, Benjamin Baiser, Jennifer A. Dunne, Sonia Kéfi, François Massol, Nicolas Mouquet, Tamara N. Romanuk, Daniel B. Stouffer, Spencer A. Wood, and Dominique Gravel. 2016. “Mangal - Making Ecological Network Analysis Simple.” Ecography 39 (4): 384–90. https://doi.org/10.1111/ecog.00976.

Get started with rmangal

Steve Vissault & Kevin Cazelles

2021-11-09

Context

The Mangal project

Data structure

Authentication

How to use rmangal

Overall approach

Search functions

Search and list available datasets

Finding a specific network

Search for a specific taxon

Get networks associated with a `search_*` object

Integrated workflow with rmangal

Batch analysis

Geolocalize Mangal networks with `sf`

Network analysis with `igraph`

Network manipulation and visualization with `tidygraph` and `ggraph`

Creating a list references for a set of networks

References

Get started with rmangal

Steve Vissault & Kevin Cazelles

2021-11-09

Context

The Mangal project

Data structure

Authentication

How to use rmangal

Overall approach

Search functions

Search and list available datasets

Finding a specific network

Search for a specific taxon

Get networks associated with a search_* object

Integrated workflow with rmangal

Batch analysis

Geolocalize Mangal networks with sf

Network analysis with igraph

Network manipulation and visualization with tidygraph and ggraph

Creating a list references for a set of networks

References

Get networks associated with a `search_*` object

Geolocalize Mangal networks with `sf`

Network analysis with `igraph`

Network manipulation and visualization with `tidygraph` and `ggraph`