emodnet.wfs: Access EMODnet Web Feature Service data through R

The goal of emodnet.wfs is to allow interrogation of and access to EMODnet’s, European Marine Observation and Data Network, geographic vector data in R through the EMODnet Web Feature Services. Web Feature services (WFS) represent a change in the way geographic information is created, modified and exchanged on the Internet and offer direct fine-grained access to geographic information at the feature and feature property level. emodnet.wfs aims at offering an user-friendly interface to this rich data.

Installation and setup

You can install the development version of emodnet.wfs from GitHub with:

# install.packages("pak")
pak::pak("EMODnet/emodnet.wfs")

If you want to avoid reading messages from emodnet.wfs such as “WFS client created successfully”, set the "emodnet.wfs.quiet" option to TRUE.

options("emodnet.wfs.quiet" = TRUE)

Available services

All available services are contained in the tibble returned by emodnet_wfs().

#> [1] "data.frame"
#> [1] "service_name" "service_url"
#>  [1] "bathymetry"                                                     
#>  [2] "biology"                                                        
#>  [3] "biology_occurrence_data"                                        
#>  [4] "chemistry_cdi_data_discovery_and_access_service"                
#>  [5] "chemistry_cdi_distribution_observations_per_category_and_region"
#>  [6] "chemistry_contaminants"                                         
#>  [7] "chemistry_marine_litter"                                        
#>  [8] "geology_coastal_behavior"                                       
#>  [9] "geology_events_and_probabilities"                               
#> [10] "geology_marine_minerals"                                        
#> [11] "geology_sea_floor_bedrock"                                      
#> [12] "geology_seabed_substrate_maps"                                  
#> [13] "geology_submerged_landscapes"                                   
#> [14] "human_activities"                                               
#> [15] "physics"                                                        
#> [16] "seabed_habitats_general_datasets_and_products"                  
#> [17] "seabed_habitats_individual_habitat_map_and_model_datasets"

To explore available services you can use View() or your usual way to explore data.frames.

Create Service Client

Specify the service using the service argument.

wfs_bio <- emodnet_init_wfs_client(service = "biology")
#> Loading ISO 19139 XML schemas...
#> Loading ISO 19115 codelists...
#> ✔ WFS client created successfully
#> ℹ Service: "https://geo.vliz.be/geoserver/Emodnetbio/wfs"
#> ℹ Version: "2.0.0"

wfs_bio
#> <WFSClient>
#> ....|-- url: https://geo.vliz.be/geoserver/Emodnetbio/wfs
#> ....|-- version: 2.0.0
#> ....|-- capabilities <WFSCapabilities>

Get WFS Layer info

You can get metadata about the layers available from a service.

emodnet_get_wfs_info(service = "biology")
#> ✔ WFS client created successfully
#> ℹ Service: "https://geo.vliz.be/geoserver/Emodnetbio/wfs"
#> ℹ Version: "2.0.0"
#> # A tibble: 35 × 9
#> # Rowwise: 
#>    data_source service_name service_url   layer_name title abstract class format
#>    <chr>       <chr>        <chr>         <chr>      <chr> <chr>    <chr> <chr> 
#>  1 emodnet_wfs biology      https://geo.… mediseh_c… EMOD… "Coral … WFSF… sf    
#>  2 emodnet_wfs biology      https://geo.… mediseh_c… EMOD… "Coral … WFSF… sf    
#>  3 emodnet_wfs biology      https://geo.… mediseh_c… EMOD… "Cymodo… WFSF… sf    
#>  4 emodnet_wfs biology      https://geo.… Species_g… EMOD… "This d… WFSF… sf    
#>  5 emodnet_wfs biology      https://geo.… Species_g… EMOD… "This d… WFSF… sf    
#>  6 emodnet_wfs biology      https://geo.… Species_g… EMOD… "This d… WFSF… sf    
#>  7 emodnet_wfs biology      https://geo.… mediseh_h… EMOD… "Haloph… WFSF… sf    
#>  8 emodnet_wfs biology      https://geo.… mediseh_m… EMOD… "Maërl … WFSF… sf    
#>  9 emodnet_wfs biology      https://geo.… mediseh_m… EMOD… "Maërl … WFSF… sf    
#> 10 emodnet_wfs biology      https://geo.… mediseh_p… EMOD… "This d… WFSF… sf    
#> # ℹ 25 more rows
#> # ℹ 1 more variable: layer_namespace <chr>

or you can pass a wfs client object.

emodnet_get_wfs_info(wfs_bio)
#> # A tibble: 35 × 9
#> # Rowwise: 
#>    data_source service_name service_url   layer_name title abstract class format
#>    <chr>       <chr>        <chr>         <chr>      <chr> <chr>    <chr> <chr> 
#>  1 emodnet_wfs biology      https://geo.… mediseh_c… EMOD… "Coral … WFSF… sf    
#>  2 emodnet_wfs biology      https://geo.… mediseh_c… EMOD… "Coral … WFSF… sf    
#>  3 emodnet_wfs biology      https://geo.… mediseh_c… EMOD… "Cymodo… WFSF… sf    
#>  4 emodnet_wfs biology      https://geo.… Species_g… EMOD… "This d… WFSF… sf    
#>  5 emodnet_wfs biology      https://geo.… Species_g… EMOD… "This d… WFSF… sf    
#>  6 emodnet_wfs biology      https://geo.… Species_g… EMOD… "This d… WFSF… sf    
#>  7 emodnet_wfs biology      https://geo.… mediseh_h… EMOD… "Haloph… WFSF… sf    
#>  8 emodnet_wfs biology      https://geo.… mediseh_m… EMOD… "Maërl … WFSF… sf    
#>  9 emodnet_wfs biology      https://geo.… mediseh_m… EMOD… "Maërl … WFSF… sf    
#> 10 emodnet_wfs biology      https://geo.… mediseh_p… EMOD… "This d… WFSF… sf    
#> # ℹ 25 more rows
#> # ℹ 1 more variable: layer_namespace <chr>

You can also get info for specific layers from wfs object:

layers <- c("mediseh_zostera_m_pnt", "mediseh_posidonia_nodata")

emodnet_get_layer_info(wfs = wfs_bio, layers = layers)
#> # A tibble: 2 × 9
#> # Rowwise: 
#>   data_source service_name    service_url layer_name title abstract class format
#>   <chr>       <chr>           <chr>       <chr>      <chr> <chr>    <chr> <chr> 
#> 1 emodnet_wfs https://geo.vl… biology     mediseh_p… EMOD… "Coastl… WFSF… sf    
#> 2 emodnet_wfs https://geo.vl… biology     mediseh_z… EMOD… "Zoster… WFSF… sf    
#> # ℹ 1 more variable: layer_namespace <chr>

Finally, you can get details on all available services and layers from the server

emodnet_get_all_wfs_info()

Get WFS layers

You can extract layers directly from a wfs object using layer names. All layers are downloaded as sf objects and output as a list with a named element for each layer requested.

emodnet_get_layers(wfs = wfs_bio, layers = layers)
#> $mediseh_zostera_m_pnt
#> Simple feature collection with 54 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -4.167154 ymin: 33.07783 xmax: 15.35766 ymax: 45.72451
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>                      gml_id id country                   the_geom
#> 1   mediseh_zostera_m_pnt.1  0  Spagna  POINT (-2.61314 36.71681)
#> 2   mediseh_zostera_m_pnt.2  0  Spagna POINT (-3.846598 36.75127)
#> 3   mediseh_zostera_m_pnt.3  0  Spagna POINT (-3.957785 36.72266)
#> 4   mediseh_zostera_m_pnt.4  0  Spagna POINT (-4.039712 36.74217)
#> 5   mediseh_zostera_m_pnt.5  0  Spagna POINT (-4.100182 36.72331)
#> 6   mediseh_zostera_m_pnt.6  0  Spagna POINT (-4.167154 36.71226)
#> 7   mediseh_zostera_m_pnt.7  0  Spagna POINT (-1.268366 37.55796)
#> 8   mediseh_zostera_m_pnt.8  0 Francia   POINT (4.84864 43.37637)
#> 9   mediseh_zostera_m_pnt.9  0  Italia  POINT (13.71831 45.70017)
#> 10 mediseh_zostera_m_pnt.10  0  Italia  POINT (13.16378 45.72451)
#> 
#> $mediseh_posidonia_nodata
#> Simple feature collection with 465 features and 3 fields
#> Geometry type: MULTICURVE
#> Dimension:     XY
#> Bounding box:  xmin: -2.1798 ymin: 30.26623 xmax: 34.60767 ymax: 45.47668
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>                         gml_id id         km                       the_geom
#> 1   mediseh_posidonia_nodata.1  0 291.503233 MULTICURVE (LINESTRING (27....
#> 2   mediseh_posidonia_nodata.2  0  75.379502 MULTICURVE (LINESTRING (23....
#> 3   mediseh_posidonia_nodata.3  0  38.627764 MULTICURVE (LINESTRING (22....
#> 4   mediseh_posidonia_nodata.4  0 110.344802 MULTICURVE (LINESTRING (19....
#> 5  mediseh_posidonia_nodata.13  0  66.997461 MULTICURVE (LINESTRING (9.1...
#> 6  mediseh_posidonia_nodata.14  0  18.090640 MULTICURVE (LINESTRING (9.7...
#> 7  mediseh_posidonia_nodata.15  0  16.618978 MULTICURVE (LINESTRING (9.8...
#> 8  mediseh_posidonia_nodata.16  0   1.913773 MULTICURVE (LINESTRING (10....
#> 9  mediseh_posidonia_nodata.83  0   2.173447 MULTICURVE (LINESTRING (15....
#> 10 mediseh_posidonia_nodata.84  0   2.817453 MULTICURVE (LINESTRING (15....

You can change the output crs through the argument crs.

emodnet_get_layers(wfs = wfs_bio, layers = layers, crs = 3857)
#> ℹ crs transformed to 3857.
#> ℹ crs transformed to 3857.
#> $mediseh_zostera_m_pnt
#> Simple feature collection with 54 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -463885.4 ymin: 3905639 xmax: 1709607 ymax: 5736311
#> Projected CRS: WGS 84 / Pseudo-Mercator
#> First 10 features:
#>                      gml_id id country                  the_geom
#> 1   mediseh_zostera_m_pnt.1  0  Spagna POINT (-290893.4 4399707)
#> 2   mediseh_zostera_m_pnt.2  0  Spagna POINT (-428201.3 4404494)
#> 3   mediseh_zostera_m_pnt.3  0  Spagna POINT (-440578.6 4400520)
#> 4   mediseh_zostera_m_pnt.4  0  Spagna POINT (-449698.6 4403229)
#> 5   mediseh_zostera_m_pnt.5  0  Spagna POINT (-456430.1 4400610)
#> 6   mediseh_zostera_m_pnt.6  0  Spagna POINT (-463885.4 4399075)
#> 7   mediseh_zostera_m_pnt.7  0  Spagna POINT (-141193.9 4517168)
#> 8   mediseh_zostera_m_pnt.8  0 Francia  POINT (539748.1 5369436)
#> 9   mediseh_zostera_m_pnt.9  0  Italia   POINT (1527115 5732431)
#> 10 mediseh_zostera_m_pnt.10  0  Italia   POINT (1465385 5736311)
#> 
#> $mediseh_posidonia_nodata
#> Simple feature collection with 465 features and 3 fields
#> Geometry type: MULTICURVE
#> Dimension:     XY
#> Bounding box:  xmin: -242654.3 ymin: 3537818 xmax: 3852508 ymax: 5696879
#> Projected CRS: WGS 84 / Pseudo-Mercator
#> First 10 features:
#>                         gml_id id         km                       the_geom
#> 1   mediseh_posidonia_nodata.1  0 291.503233 MULTICURVE (LINESTRING (302...
#> 2   mediseh_posidonia_nodata.2  0  75.379502 MULTICURVE (LINESTRING (257...
#> 3   mediseh_posidonia_nodata.3  0  38.627764 MULTICURVE (LINESTRING (246...
#> 4   mediseh_posidonia_nodata.4  0 110.344802 MULTICURVE (LINESTRING (221...
#> 5  mediseh_posidonia_nodata.13  0  66.997461 MULTICURVE (LINESTRING (101...
#> 6  mediseh_posidonia_nodata.14  0  18.090640 MULTICURVE (LINESTRING (108...
#> 7  mediseh_posidonia_nodata.15  0  16.618978 MULTICURVE (LINESTRING (110...
#> 8  mediseh_posidonia_nodata.16  0   1.913773 MULTICURVE (LINESTRING (121...
#> 9  mediseh_posidonia_nodata.83  0   2.173447 MULTICURVE (LINESTRING (169...
#> 10 mediseh_posidonia_nodata.84  0   2.817453 MULTICURVE (LINESTRING (169...

You can also extract layers using a WFS service name.

emodnet_get_layers(
  service = "biology",
  layers = c("mediseh_zostera_m_pnt", "mediseh_posidonia_nodata")
)
#> ✔ WFS client created successfully
#> ℹ Service: "https://geo.vliz.be/geoserver/Emodnetbio/wfs"
#> ℹ Version: "2.0.0"
#> $mediseh_zostera_m_pnt
#> Simple feature collection with 54 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -4.167154 ymin: 33.07783 xmax: 15.35766 ymax: 45.72451
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>                      gml_id id country                   the_geom
#> 1   mediseh_zostera_m_pnt.1  0  Spagna  POINT (-2.61314 36.71681)
#> 2   mediseh_zostera_m_pnt.2  0  Spagna POINT (-3.846598 36.75127)
#> 3   mediseh_zostera_m_pnt.3  0  Spagna POINT (-3.957785 36.72266)
#> 4   mediseh_zostera_m_pnt.4  0  Spagna POINT (-4.039712 36.74217)
#> 5   mediseh_zostera_m_pnt.5  0  Spagna POINT (-4.100182 36.72331)
#> 6   mediseh_zostera_m_pnt.6  0  Spagna POINT (-4.167154 36.71226)
#> 7   mediseh_zostera_m_pnt.7  0  Spagna POINT (-1.268366 37.55796)
#> 8   mediseh_zostera_m_pnt.8  0 Francia   POINT (4.84864 43.37637)
#> 9   mediseh_zostera_m_pnt.9  0  Italia  POINT (13.71831 45.70017)
#> 10 mediseh_zostera_m_pnt.10  0  Italia  POINT (13.16378 45.72451)
#> 
#> $mediseh_posidonia_nodata
#> Simple feature collection with 465 features and 3 fields
#> Geometry type: MULTICURVE
#> Dimension:     XY
#> Bounding box:  xmin: -2.1798 ymin: 30.26623 xmax: 34.60767 ymax: 45.47668
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>                         gml_id id         km                       the_geom
#> 1   mediseh_posidonia_nodata.1  0 291.503233 MULTICURVE (LINESTRING (27....
#> 2   mediseh_posidonia_nodata.2  0  75.379502 MULTICURVE (LINESTRING (23....
#> 3   mediseh_posidonia_nodata.3  0  38.627764 MULTICURVE (LINESTRING (22....
#> 4   mediseh_posidonia_nodata.4  0 110.344802 MULTICURVE (LINESTRING (19....
#> 5  mediseh_posidonia_nodata.13  0  66.997461 MULTICURVE (LINESTRING (9.1...
#> 6  mediseh_posidonia_nodata.14  0  18.090640 MULTICURVE (LINESTRING (9.7...
#> 7  mediseh_posidonia_nodata.15  0  16.618978 MULTICURVE (LINESTRING (9.8...
#> 8  mediseh_posidonia_nodata.16  0   1.913773 MULTICURVE (LINESTRING (10....
#> 9  mediseh_posidonia_nodata.83  0   2.173447 MULTICURVE (LINESTRING (15....
#> 10 mediseh_posidonia_nodata.84  0   2.817453 MULTICURVE (LINESTRING (15....

Layers can also be returned to a single sf object through argument reduce_layers.
If TRUE the function will try to reduce all layers into a single sf.

If attempting to reduce fails, it will error:

emodnet_get_layers(
  wfs = wfs_bio,
  layers = layers,
  reduce_layers = TRUE
)
#> Error in `value[[3L]]()`:
#> ! Cannot reduce layers.
#> ℹ Try again with `reduce_layers = FALSE`

Using reduce_layers = TRUE is also useful for returning an sf object rather than a list in single layer request.

emodnet_get_layers(
  service = "biology",
  layers = c("mediseh_posidonia_nodata"),
  reduce_layers = TRUE
)
#> ✔ WFS client created successfully
#> ℹ Service: "https://geo.vliz.be/geoserver/Emodnetbio/wfs"
#> ℹ Version: "2.0.0"
#> Simple feature collection with 465 features and 3 fields
#> Geometry type: MULTICURVE
#> Dimension:     XY
#> Bounding box:  xmin: -2.1798 ymin: 30.26623 xmax: 34.60767 ymax: 45.47668
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>                         gml_id id         km                       the_geom
#> 1   mediseh_posidonia_nodata.1  0 291.503233 MULTICURVE (LINESTRING (27....
#> 2   mediseh_posidonia_nodata.2  0  75.379502 MULTICURVE (LINESTRING (23....
#> 3   mediseh_posidonia_nodata.3  0  38.627764 MULTICURVE (LINESTRING (22....
#> 4   mediseh_posidonia_nodata.4  0 110.344802 MULTICURVE (LINESTRING (19....
#> 5  mediseh_posidonia_nodata.13  0  66.997461 MULTICURVE (LINESTRING (9.1...
#> 6  mediseh_posidonia_nodata.14  0  18.090640 MULTICURVE (LINESTRING (9.7...
#> 7  mediseh_posidonia_nodata.15  0  16.618978 MULTICURVE (LINESTRING (9.8...
#> 8  mediseh_posidonia_nodata.16  0   1.913773 MULTICURVE (LINESTRING (10....
#> 9  mediseh_posidonia_nodata.83  0   2.173447 MULTICURVE (LINESTRING (15....
#> 10 mediseh_posidonia_nodata.84  0   2.817453 MULTICURVE (LINESTRING (15....

Help needed?

If you get an unexpected error,

Look up the EMODnet monitor;
Open an issue in this repository.

Other web services

There are three ways to access EMODnet data at the moment, that complement each other.

EMODnet ERDDAP server

Some EMODnet data are also published in an ERDDAP server. You can access these data in R using the rerddap R package:

# install.packages("rerrdap")
library(rerddap)
#> Registered S3 method overwritten by 'hoardr':
#>   method           from
#>   print.cache_info httr

erddap_url <- "https://erddap.emodnet.eu/erddap/"

rerddap::ed_datasets(url = erddap_url)
#> # A tibble: 8 × 16
#>   griddap Subset tabledap Make.A.Graph wms   files Title Summary FGDC  ISO.19115
#>   <chr>   <chr>  <chr>    <chr>        <chr> <chr> <chr> <chr>   <chr> <chr>    
#> 1 ""      "/erd… /erddap… /erddap/tab… ""    ""    * Th… "This … ""    ""       
#> 2 ""      ""     /erddap… /erddap/tab… ""    "/er… EMOD… "The d… ""    ""       
#> 3 ""      ""     /erddap… /erddap/tab… ""    "/er… EMOD… "The d… ""    ""       
#> 4 ""      "/erd… /erddap… /erddap/tab… ""    "/er… EMOD… "The d… "/er… "/erddap…
#> 5 ""      ""     /erddap… /erddap/tab… ""    "/er… Pres… "The p… "/er… "/erddap…
#> 6 ""      ""     /erddap… /erddap/tab… ""    ""    PSMS… "Perma… ""    ""       
#> 7 ""      ""     /erddap… /erddap/tab… ""    "/er… PSMS… "Perma… ""    ""       
#> 8 ""      "/erd… /erddap… /erddap/tab… ""    "/er… TAO/… "This … "/er… "/erddap…
#> # ℹ 6 more variables: Info <chr>, Background.Info <chr>, RSS <chr>,
#> #   Email <chr>, Institution <chr>, Dataset.ID <chr>

rerddap::ed_search(query = "vessel density", url = erddap_url)
#> # A tibble: 16 × 2
#>    title                                                     dataset_id         
#>    <chr>                                                     <chr>              
#>  1 Vessel Density                                            humanactivities_9f…
#>  2 Vessel Density                                            humanactivities_e9…
#>  3 Vessel traffic density, 2019, All                         EMODPACE_VD_All    
#>  4 Vessel traffic density, 2019, Cargo                       EMODPACE_VD_09_Car…
#>  5 Vessel traffic density, 2019, Dredging or underwater ops  EMODPACE_VD_03_Dre…
#>  6 Vessel traffic density, 2019, Fishing                     EMODPACE_VD_01_Fis…
#>  7 Vessel traffic density, 2019, High Speed craft            EMODPACE_VD_06_High
#>  8 Vessel traffic density, 2019, Miliary and law enforcement EMODPACE_VD_11_Mil…
#>  9 Vessel traffic density, 2019, Other                       EMODPACE_VD_00_Oth…
#> 10 Vessel traffic density, 2019, Passenger                   EMODPACE_VD_08_Pas…
#> 11 Vessel traffic density, 2019, Pleasure craft              EMODPACE_VD_05_Ple…
#> 12 Vessel traffic density, 2019, Sailing                     EMODPACE_VD_04_Sai…
#> 13 Vessel traffic density, 2019, Service                     EMODPACE_VD_02_Ser…
#> 14 Vessel traffic density, 2019, Tanker                      EMODPACE_VD_10_Tan…
#> 15 Vessel traffic density, 2019, Tug and Towing              EMODPACE_VD_07_Tug 
#> 16 Vessel traffic density, 2019, Unknown                     EMODPACE_VD_12_Unk…

human_activities_data_info <- rerddap::info(datasetid = "humanactivities_9f8a_3389_f08a", url = erddap_url)
human_activities_data_info
#> <ERDDAP info> humanactivities_9f8a_3389_f08a 
#>  Base URL: https://erddap.emodnet.eu/erddap 
#>  Dataset Type: griddap 
#>  Dimensions (range):  
#>      time: (2017-01-01T00:00:00Z, 2021-12-01T00:00:00Z) 
#>      y: (604500.0, 7034500.0) 
#>      x: (-622500.0, 6884500.0) 
#>  Variables:  
#>      vd: 
#>          Units: seconds

year_2020_gridded_data <- griddap(datasetx = human_activities_data_info, time = c("2020-03-18", "2020-03-19"))
#> info() output passed to x; setting base url to: https://erddap.emodnet.eu/erddap
head(year_2020_gridded_data$data)
#>         x       y                 time vd
#> 1 -622500 7034500 2020-04-01T00:00:00Z NA
#> 2 -621500 7034500 2020-04-01T00:00:00Z NA
#> 3 -620500 7034500 2020-04-01T00:00:00Z NA
#> 4 -619500 7034500 2020-04-01T00:00:00Z NA
#> 5 -618500 7034500 2020-04-01T00:00:00Z NA
#> 6 -617500 7034500 2020-04-01T00:00:00Z NA

EMODnetWCS: Access EMODnet Web Coverage Service data

This package emodnet.wfs uses Web Feature Services, hence it is limited to getting vector data. EMODnet also hosts raster data that can be accessed via Web Coverage Services (WCS). The R package EMODnetWCS makes these data available in R.

Citation

To cite emodnet.wfs, please use the output from citation(package = "emodnet.wfs").

citation(package = "emodnet.wfs")
#> To cite package 'emodnet.wfs' in publications use:
#> 
#>   Krystalli A, Fernández-Bejarano S, Salmon M (????). _emodnet.wfs:
#>   Access EMODnet Web Feature Service data through R_. doi:10.14284/679
#>   <https://doi.org/10.14284/679>, R package version 2.0.2.9000.
#>   Integrated data products created under the European Marine
#>   Observation Data Network (EMODnet) Biology project
#>   (EASME/EMFF/2017/1.3.1.2/02/SI2.789013), funded by the by the
#>   European Union under Regulation (EU) No 508/2014 of the European
#>   Parliament and of the Council of 15 May 2014 on the European Maritime
#>   and Fisheries Fund, <https://github.com/EMODnet/emodnet.wfs>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {{emodnet.wfs}: Access EMODnet Web Feature Service data through R},
#>     author = {Anna Krystalli and Salvador Fernández-Bejarano and Maëlle Salmon},
#>     note = {R package version 2.0.2.9000. Integrated data products created under the European Marine Observation Data Network (EMODnet) Biology project (EASME/EMFF/2017/1.3.1.2/02/SI2.789013), funded by the by the European Union under Regulation (EU) No 508/2014 of the European Parliament and of the Council of 15 May 2014 on the European Maritime and Fisheries Fund},
#>     url = {https://github.com/EMODnet/emodnet.wfs},
#>     doi = {10.14284/679},
#>   }

Acknowledgements

This package was started by the Sheffield University during the EMODnet Biology WP4 data products workshop in June 2020.

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README.md

README.md

emodnet.wfs: Access EMODnet Web Feature Service data through R

Installation and setup

Available services

Create Service Client

Get WFS Layer info

Get WFS layers

Help needed?

Other web services

EMODnet ERDDAP server

EMODnetWCS: Access EMODnet Web Coverage Service data

Citation

Acknowledgements

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README.md

Latest commit

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README.md

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emodnet.wfs: Access EMODnet Web Feature Service data through R

Installation and setup

Available services

Create Service Client

Get WFS Layer info

Get WFS layers

Help needed?

Other web services

EMODnet ERDDAP server

EMODnetWCS: Access EMODnet Web Coverage Service data

Citation

Acknowledgements