Performs an WFS getFeature request for layers from a wfs object or
specified EMODnet Service. Filtering
of layer features can also be handled via ECQL language filters.
Usage
emodnet_get_layers(
wfs = NULL,
service = NULL,
service_version = NULL,
layers,
crs = NULL,
cql_filter = NULL,
reduce_layers = FALSE,
...
)Arguments
- wfs
A
WFSClientR6 object with methods for interfacing an OGC Web Feature Service. Fromemodnet_init_wfs_client().- service
the EMODnet OGC WFS service name. For available services, see
emodnet_wfs().- service_version
![[Deprecated]](figures/lifecycle-deprecated.svg)
the WFS service version. Now always "2.0.0".- layers
a character vector of layer names. To get info on layers, including
layer_nameuseemodnet_get_wfs_info().- crs
integer. EPSG code for the output crs. If
NULL(default), layers are returned with original crs.- cql_filter
character. Features returned can be filtered using valid Extended Common Query Language (ECQL) filtering statements (https://docs.geoserver.org/stable/en/user/filter/ecql_reference.html). Should be one of:
character string or character vector of length 1. Filter will be recycled across all layers requested.
character vector of length equal to the length of layers. Filter will be matched to layers sequentially. Elements containing
NAare ignorednamed character vector. Each filter will be applied to the layer corresponding to the filter name. Filters with names that do not correspond to any layers are ignored. Layers without corresponding filters are returned whole.
- reduce_layers
whether to reduce output layers to a single
sfobject.- ...
additional vendor parameter arguments passed to
ows4R::GetFeature(). For example, includingcount = 1returns the first available feature. OroutputFormat = "CSV"(oroutputFormat = "JSON") might help downloading bigger datasets.
Value
If reduce_layers = FALSE (default), a list of sf
objects, one element for each layer. Any layers for which download was
unsuccessful will be NULL. If reduce_layers = TRUE, all layers are
reduced (if possible) to a single sf containing data for all layers.
NULL layers are ignored. reduce_layers = TRUE can also be used to return
an sf out of a single layer request instead of a list of length 1.
Big downloads
If a layer is really big (like "abiotic_observations" of the
"biology_occurrence_data" service),
you might consider a combination of these ideas:
using
outputFormat = "CSV";filtering using
cql_filtersor bounding boxes (possibly splitting the area of interests into several requests);Using EMODnet's download toolbox.
Examples
# Layers as character vector
emodnet_get_layers(
service = "biology",
layers = c("mediseh_zostera_m_pnt", "mediseh_posidonia_nodata")
)
#> 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"
#> $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....
#>
# Usage of cql_filter
emodnet_get_layers(
service = "biology",
layers = "mediseh_zostera_m_pnt",
cql_filter = "country = 'Francia'"
)
#> ✔ 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 1 feature and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 4.84864 ymin: 43.37637 xmax: 4.84864 ymax: 43.37637
#> Geodetic CRS: WGS 84
#> gml_id id country the_geom
#> 1 mediseh_zostera_m_pnt.8 0 Francia POINT (4.84864 43.37637)
#>
# Usage of vendor parameter
emodnet_get_layers(
service = "biology",
layers = "mediseh_zostera_m_pnt",
count = 1
)
#> ✔ 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 1 feature and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -2.61314 ymin: 36.71681 xmax: -2.61314 ymax: 36.71681
#> Geodetic CRS: WGS 84
#> gml_id id country the_geom
#> 1 mediseh_zostera_m_pnt.1 0 Spagna POINT (-2.61314 36.71681)
#>
# Usage of csv output
data <- emodnet_get_layers(
service = "biology_occurrence_data",
layers = "abiotic_observations",
outputFormat = "CSV"
)
#> ✔ WFS client created successfully
#> ℹ Service: "https://geo.vliz.be/geoserver/Dataportal/wfs"
#> ℹ Version: "2.0.0"
#> Rows: 1000000 Columns: 37
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (13): FID, datatype, season, parametername, parameterunit, dataprovider...
#> dbl (11): id, latitude, longitude, value, standardparameterid, dataprovider...
#> lgl (12): aphiaid, depth, lod, loq, class, classunit, classcode, dateprecis...
#> dttm (1): datetime
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
str(data[["abiotic_observations"]])
#> spc_tbl_ [1,000,000 × 37] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#> $ FID : chr [1:1000000] "abiotic_observations.fid-33b119e7_19323f87fbb_1b93" "abiotic_observations.fid-33b119e7_19323f87fbb_1b94" "abiotic_observations.fid-33b119e7_19323f87fbb_1b95" "abiotic_observations.fid-33b119e7_19323f87fbb_1b96" ...
#> $ id : num [1:1000000] 1.64e+11 1.64e+11 1.64e+11 1.64e+11 1.64e+11 ...
#> $ aphiaid : logi [1:1000000] NA NA NA NA NA NA ...
#> $ latitude : num [1:1000000] 51.1 51.1 51.1 51.1 51.1 ...
#> $ longitude : num [1:1000000] 4.22 4.22 4.22 4.22 4.22 ...
#> $ depth : logi [1:1000000] NA NA NA NA NA NA ...
#> $ datetime : POSIXct[1:1000000], format: "2019-08-30 05:10:00" "2019-08-30 05:20:00" ...
#> $ value : num [1:1000000] 3.69 3.51 3.33 3.16 2.99 2.83 2.67 2.51 2.36 2.21 ...
#> $ lod : logi [1:1000000] NA NA NA NA NA NA ...
#> $ loq : logi [1:1000000] NA NA NA NA NA NA ...
#> $ standardparameterid: num [1:1000000] 13568 13568 13568 13568 13568 ...
#> $ dataproviderid : num [1:1000000] 7 7 7 7 7 7 7 7 7 7 ...
#> $ imisdatasetid : num [1:1000000] 971 971 971 971 971 971 971 971 971 971 ...
#> $ dataficheid : num [1:1000000] 34 34 34 34 34 34 34 34 34 34 ...
#> $ seriesid : num [1:1000000] 17037 17037 17037 17037 17037 ...
#> $ datatype : chr [1:1000000] "NC" "NC" "NC" "NC" ...
#> $ class : logi [1:1000000] NA NA NA NA NA NA ...
#> $ classunit : logi [1:1000000] NA NA NA NA NA NA ...
#> $ classcode : logi [1:1000000] NA NA NA NA NA NA ...
#> $ season : chr [1:1000000] "summer" "summer" "summer" "summer" ...
#> $ parametername : chr [1:1000000] "10 minuten gemiddelde waterhoogte in m TAW" "10 minuten gemiddelde waterhoogte in m TAW" "10 minuten gemiddelde waterhoogte in m TAW" "10 minuten gemiddelde waterhoogte in m TAW" ...
#> $ parameterunit : chr [1:1000000] "m TAW" "m TAW" "m TAW" "m TAW" ...
#> $ dataprovider : chr [1:1000000] "MOW WL - Waterbouwkundig Laboratorium" "MOW WL - Waterbouwkundig Laboratorium" "MOW WL - Waterbouwkundig Laboratorium" "MOW WL - Waterbouwkundig Laboratorium" ...
#> $ datasettitle : chr [1:1000000] "Flanders Hydraulics Research: Continuous monitoring of water levels in the Scheldt estuary" "Flanders Hydraulics Research: Continuous monitoring of water levels in the Scheldt estuary" "Flanders Hydraulics Research: Continuous monitoring of water levels in the Scheldt estuary" "Flanders Hydraulics Research: Continuous monitoring of water levels in the Scheldt estuary" ...
#> $ datafichetitle : chr [1:1000000] "S-HD-V-001 - Waterstanden - Getij" "S-HD-V-001 - Waterstanden - Getij" "S-HD-V-001 - Waterstanden - Getij" "S-HD-V-001 - Waterstanden - Getij" ...
#> $ stationname : chr [1:1000000] "Temse tij/Zeeschelde" "Temse tij/Zeeschelde" "Temse tij/Zeeschelde" "Temse tij/Zeeschelde" ...
#> $ gid : num [1:1000000] 8 8 8 8 8 8 8 8 8 8 ...
#> $ seasonid : num [1:1000000] 3 3 3 3 3 3 3 3 3 3 ...
#> $ unit : chr [1:1000000] "m TAW" "m TAW" "m TAW" "m TAW" ...
#> $ category : chr [1:1000000] "waterstand, golven en stroming" "waterstand, golven en stroming" "waterstand, golven en stroming" "waterstand, golven en stroming" ...
#> $ the_geom : chr [1:1000000] "POINT (51.1228087597397 4.21867338754705)" "POINT (51.1228087597397 4.21867338754705)" "POINT (51.1228087597397 4.21867338754705)" "POINT (51.1228087597397 4.21867338754705)" ...
#> $ valuesign : chr [1:1000000] "=" "=" "=" "=" ...
#> $ dateprecision : logi [1:1000000] NA NA NA NA NA NA ...
#> $ standard_name : logi [1:1000000] NA NA NA NA NA NA ...
#> $ externalid : logi [1:1000000] NA NA NA NA NA NA ...
#> $ external_name : logi [1:1000000] NA NA NA NA NA NA ...
#> $ scientificname : logi [1:1000000] NA NA NA NA NA NA ...
#> - attr(*, "spec")=
#> .. cols(
#> .. FID = col_character(),
#> .. id = col_double(),
#> .. aphiaid = col_logical(),
#> .. latitude = col_double(),
#> .. longitude = col_double(),
#> .. depth = col_logical(),
#> .. datetime = col_datetime(format = ""),
#> .. value = col_double(),
#> .. lod = col_logical(),
#> .. loq = col_logical(),
#> .. standardparameterid = col_double(),
#> .. dataproviderid = col_double(),
#> .. imisdatasetid = col_double(),
#> .. dataficheid = col_double(),
#> .. seriesid = col_double(),
#> .. datatype = col_character(),
#> .. class = col_logical(),
#> .. classunit = col_logical(),
#> .. classcode = col_logical(),
#> .. season = col_character(),
#> .. parametername = col_character(),
#> .. parameterunit = col_character(),
#> .. dataprovider = col_character(),
#> .. datasettitle = col_character(),
#> .. datafichetitle = col_character(),
#> .. stationname = col_character(),
#> .. gid = col_double(),
#> .. seasonid = col_double(),
#> .. unit = col_character(),
#> .. category = col_character(),
#> .. the_geom = col_character(),
#> .. valuesign = col_character(),
#> .. dateprecision = col_logical(),
#> .. standard_name = col_logical(),
#> .. externalid = col_logical(),
#> .. external_name = col_logical(),
#> .. scientificname = col_logical()
#> .. )
#> - attr(*, "problems")=<externalptr>