emodnet.wfs

Access EMODnet Web Feature Service data through R

https://github.com/emodnet/emodnet.wfs

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Keywords

biology dataproducts emodnet marine-data rstats wfs

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Repository

Access EMODnet Web Feature Service data through R

Basic Info

Host: GitHub
Owner: EMODnet
License: other
Language: R
Default Branch: main
Homepage: https://docs.ropensci.org/emodnet.wfs/
Size: 35 MB

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Open Issues: 6
Releases: 4

Topics

biology dataproducts emodnet marine-data rstats wfs

Created over 5 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Citation Codemeta

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
options(timeout = 2000)
```

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


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The goal of emodnet.wfs is to allow interrogation of and access to [EMODnet's, European Marine Observation and Data Network, geographic vector data](https://emodnet.ec.europa.eu/en/emodnet-web-service-documentation#inline-nav-3) in R through the [EMODnet Web Feature Services](https://emodnet.ec.europa.eu/en/data-0). 
[Web Feature services (WFS)](https://www.ogc.org/standards/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. 
Features are representation of geographic entities, such as a coastlines, marine protected areas, offshore platforms, or fishing areas. In WFS, features have geometry (spatial information) and attributes (descriptive data).
emodnet.wfs aims at offering an user-friendly interface to this rich data.

## Installation and setup

You can install emodnet.wfs from CRAN:

``` r
install.packages("emodnet.wfs")
```


Alternatively, you can install the development version of emodnet.wfs from the rOpenSci R-universe with:

``` r
install.packages("emodnet.wfs", repos = c("https://ropensci.r-universe.dev", "https://cloud.r-project.org"))
```

Or from GitHub with:

``` r
# 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`.

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

The use of the EMODnet Web Feature Services is not subjet to rate limiting at the moment.

## Pre-requisites

The emodnet.wfs is designed to be compatible with the modern R geospatial stack, in particular output geospatial objects are [`sf`](https://r-spatial.github.io/sf/) objects, that is to say, a tibble with a geometry list-column.

For users not familiar yet with geospatial data in R, we recommend the following resources:

- [Spatial Data Science With Applications in R](https://r-spatial.org/book/) by Edzer Pebesma and Roger Bivand.

- [Geocomputation with R](https://r.geocompx.org/) by Robin Lovelace, Jakub Nowosad and Jannes Muenchow.

In the documentation we assume a basic familiarity with spatial data: knowing about coordinates and about projections / [coordinate reference systems (CRS)](https://r.geocompx.org/spatial-class#crs-intro).

## Available data sources (services)

All available data sources, called services, are contained in the [tibble](https://tibble.tidyverse.org/) returned by `emodnet_wfs()`. 
```{r, echo=TRUE}
library(emodnet.wfs)
services <- emodnet_wfs()
class(services)
names(services)
services[, c("emodnet_thematic_lot", "service_name")]
```

EMODnet data covers several disciplines organized in 7 thematic lots: bathymetry, biology, chemistry, geology, human activities, physics, seabed habitats. Some thematic lots organize their data in more than one data source or service.

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

## Initialise a WFS Service Client

A WFS service client is responsible for sending requests to a WFS server and processing the responses to retrieve, display, or analyze geospatial features. As such, initialising a client is the first step to interacting with an EMODnet Web Feature Services.

Specify the service using the `service` argument. 

```{r}
wfs_bio <- emodnet_init_wfs_client(service = "biology")

wfs_bio
```

## List contents of a WFS: Get layer information from a service client

In the context of a Web Feature Service (WFS), a layer refers to a logical grouping of geographic features that share the same schema (i.e., the same feature type, geometry, and attributes). Layers are the units of data that clients can query, retrieve, and manipulate through a WFS.

You can access information (metadata) about each layer available from an EMODnet WFS with `emodnet_get_wfs_info()`

```{r}
emodnet_get_wfs_info(service = "biology")
```

or you can pass a wfs client object.

```{r}
emodnet_get_wfs_info(wfs_bio)
```

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

```{r}
layers <- c("mediseh_zostera_m_pnt", "mediseh_posidonia_nodata")

emodnet_get_layer_info(wfs = wfs_bio, layers = layers)
```


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

```{r eval=FALSE}
emodnet_get_all_wfs_info()
```


## Get data from a data source: get 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.
The argument `simplify = TRUE` stack all the layers in one single tibble, if possible (for instance if all column names are the same, otherwise it fails).

By default, `emodnet_get_layers()` returns a list of sf objects, one per layer.


```{r}
emodnet_get_layers(wfs = wfs_bio, layers = layers)
```

You can change the output Coordinate Reference System (CRS), which defines how geographic data is mapped to the Earth's surface,  through the argument `crs`.

```{r}
emodnet_get_layers(wfs = wfs_bio, layers = layers, crs = 3857)
```

You can also extract layers using a WFS service name.

```{r}
emodnet_get_layers(
  service = "biology",
  layers = c("mediseh_zostera_m_pnt", "mediseh_posidonia_nodata")
)
```


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

If attempting to reduce fails, it will error:
 
```{r, error=TRUE}
emodnet_get_layers(
  wfs = wfs_bio,
  layers = layers,
  simplify = TRUE
)
```

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

```{r}
emodnet_get_layers(
  service = "biology",
  layers = c("mediseh_posidonia_nodata"),
  simplify = TRUE
)
```

## Help needed?

If you get an unexpected error,

- Look up the [EMODnet monitor](https://monitor.emodnet.eu/resources?lang=en&resource_type=OGC:WFS);
- Open an issue in this [repository](https://github.com/EMODnet/emodnet.wfs/issues).

## Unlock the Full Potential of the EMODnet Web Services: Access Raster and Gridded datasets.

EMODnet hosts a wealth of marine and maritime data distributed through three complementary web services: WFS, WCS, and ERDDAP. Web services allow users to retrieve data programmatically from remote servers, eliminating the need for manual downloads. This is particularly useful for handling large datasets or conducting dynamic analyses. These services are tailored to different data types and research needs, but together, they ensure seamless access to all EMODnet vector, raster, and gridded datasets. Vector data, such as shipwrecks or boundaries, are accessible through `emodnet.wfs` via Web Feature Services (WFS). Complementary, raster and gridded datasets are available through Web Coverage Services (WCS) and ERDDAP respectively.

### Access EMODnet raster data through Web Coverage Services with `emodnet.wcs` in R

EMODnet raster datasets, such as habitat maps or bathymetry, are available through [Web Coverage Services (WCS)](https://en.wikipedia.org/wiki/Web_Coverage_Service). These data are continuous, gridded, and often used for spatial visualization or environmental modeling. The emodnet.wcs R package provides tools to retrieve and process these raser datasets, in a similar fashion as `emodnet.wfs`. Extensive documentation is available at the [emodnet.wcs website](https://emodnet.github.io/emodnet.wcs/).


### Access EMODnet gridded and tabular datasets through the ERDDAP Server and `rerddap` in R

Both WFS and WCS EMODnet services are based on a federated system: each EMODnet thematic lot manages their servers and data, ensuring that their data are exposed both via WFS and WCS. The twin R packages `emodnet.wfs` and `emodnet.wcs` simplify the access to all the entry points by collecting them in single places, which are the packages themselves. 

In contrast, the [EMODnet ERDDAP Server](https://erddap.emodnet.eu) is centrally managed by the EMODnet Central Portal, offering a single access point to all gridded and tabular datasets. ERDDAP simplifies access to datasets such as digital terrain models, vessel density or environmental data. It is particularly suited for large-scale, multidimensional data analysis. In R, the `rerddap` package allows users to query and subset ERDDAP data programmatically, enabling efficient analysis and integration into workflows. For example, researchers can retrieve datasets on vessel density. 

```{r rerddap}
# install.packages("rerrdap")
library(rerddap)

# This is the url where the EMODnet ERDDAP server is located
erddap_url <- "https://erddap.emodnet.eu/erddap/"

# Inspect all available datasets
ed_datasets(url = erddap_url)

# Find datasets with the key words "vessel density"
ed_search(query = "vessel density", url = erddap_url)

# Inspect more info about the vessel density dataset, using its identifier
human_activities_data_info <- info(
  datasetid = "humanactivities_9f8a_3389_f08a",
  url = erddap_url
)
human_activities_data_info

# Retrieve the vessel density at a particular time period
year_2020_gridded_data <- griddap(
  datasetx = human_activities_data_info,
  time = c("2020-03-18", "2020-03-19")
)
head(year_2020_gridded_data$data)
```

More functionalities are available through `rerddap`. Feel free to explore the [rerddap website](https://docs.ropensci.org/rerddap/) to find out what else can you do with the EMODnet datasets in ERDDAP.

## Citation

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

```{r}
citation(package = "emodnet.wfs")
```

## Acknowledgements

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

Owner

Name: EMODnet
Login: EMODnet
Kind: organization
Email: info@emodnet.eu
Location: Belgium

Website: https://emodnet.ec.europa.eu
Twitter: EMODnet
Repositories: 19
Profile: https://github.com/EMODnet

Your gateway to marine data in Europe

Citation (CITATION.cff)

# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------

cff-version: 1.2.0
message: 'To cite package "emodnet.wfs" in publications use:'
type: software
license: MIT
title: 'emodnet.wfs: Access EMODnet Web Feature Service data through R'
version: 2.0.2.9000
doi: 10.14284/679
abstract: Access and interrogate EMODnet (European Marine Observation and Data Network)
  Web Feature Service data through R.
authors:
- family-names: Krystalli
  given-names: Anna
  email: annakrystalli@googlemail.com
  orcid: https://orcid.org/0000-0002-2378-4915
- family-names: Fernández-Bejarano
  given-names: Salvador
  email: salvador.fernandez@vliz.be
  orcid: https://orcid.org/0000-0003-0535-7677
- family-names: Salmon
  given-names: Maëlle
  email: msmaellesalmon@gmail.com
  orcid: https://orcid.org/0000-0002-2815-0399
preferred-citation:
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  title: 'emodnet.wfs: Access EMODnet Web Feature Service data through R'
  authors:
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    given-names: Anna
    email: annakrystalli@googlemail.com
    orcid: https://orcid.org/0000-0002-2378-4915
  - family-names: Fernández-Bejarano
    given-names: Salvador
    email: salvador.fernandez@vliz.be
    orcid: https://orcid.org/0000-0003-0535-7677
  - family-names: Salmon
    given-names: Maëlle
    email: msmaellesalmon@gmail.com
    orcid: https://orcid.org/0000-0002-2815-0399
  notes: 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
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keywords:
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- dataproducts
- emodnet
- marine-data
- rstats
- wfs
references:
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  abstract: 'readr: Read Rectangular Text Data'
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  repository: https://CRAN.R-project.org/package=readr
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    given-names: Hadley
    email: hadley@posit.co
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  year: '2024'
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  abstract: 'rmarkdown: Dynamic Documents for R'
  notes: Suggests
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}

GitHub Events

Total

Create event: 9
Commit comment event: 2
Release event: 1
Issues event: 13
Delete event: 1
Issue comment event: 57
Push event: 114
Pull request review event: 6
Pull request review comment event: 9
Pull request event: 16

Last Year

Create event: 9
Commit comment event: 2
Release event: 1
Issues event: 13
Delete event: 1
Issue comment event: 57
Push event: 114
Pull request review event: 6
Pull request review comment event: 9
Pull request event: 16

Committers

Last synced: 7 months ago

All Time

Total Commits: 404
Total Committers: 4
Avg Commits per committer: 101.0
Development Distribution Score (DDS): 0.389

Past Year

Commits: 107
Committers: 2
Avg Commits per committer: 53.5
Development Distribution Score (DDS): 0.037

Top Committers

Name	Email	Commits
Maëlle Salmon	m**n@y**e	247
Anna Krystalli	a**i@g**m	118
salvafern	s**z@v**e	38
Emmanuel Blondel	e**1@g**m	1

Committer Domains (Top 20 + Academic)

vliz.be: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 43
Total pull requests: 39
Average time to close issues: about 1 month
Average time to close pull requests: 8 days
Total issue authors: 10
Total pull request authors: 3
Average comments per issue: 4.19
Average comments per pull request: 3.44
Merged pull requests: 35
Bot issues: 1
Bot pull requests: 0

Past Year

Issues: 10
Pull requests: 13
Average time to close issues: 23 days
Average time to close pull requests: 3 days
Issue authors: 4
Pull request authors: 2
Average comments per issue: 2.5
Average comments per pull request: 1.69
Merged pull requests: 12
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

maelle (24)
annakrystalli (6)
salvafern (5)
JanJaapPoos (4)
LennertSchepers (1)
brittlnv (1)
teuzindahuz (1)
tomjwebb (1)
github-actions[bot] (1)
ndgallo (1)

Pull Request Authors

maelle (27)
annakrystalli (11)
salvafern (8)

Top Labels

Issue Labels

documentation (3) bug (2) enhancement (1) services (1) comms (1) CI (1)

Pull Request Labels

enhancement (2) services (1) CI (1)

Dependencies

DESCRIPTION cran

R >= 3.6.0 depends
checkmate * imports
curl * imports
httr * imports
janitor * imports
magrittr * imports
memoise * imports
ows4R >= 0.3 imports
purrr * imports
readr * imports
rlang * imports
sf * imports
tibble * imports
tidyr * imports
usethis * imports
utils * imports
covr * suggests
dplyr * suggests
httptest * suggests
knitr * suggests
mapview * suggests
rmarkdown * suggests
skimr * suggests
testthat >= 3.1.2 suggests
testthis * suggests
webmockr * suggests
withr * suggests

.github/workflows/R-CMD-check.yaml actions

actions/checkout v2 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/Test-Services.yaml actions

actions/cache v2 composite
actions/checkout v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite

.github/workflows/pkgcheck.yaml actions

ropensci-review-tools/pkgcheck-action main composite

.github/workflows/pkgdown.yaml actions

JamesIves/github-pages-deploy-action 4.1.4 composite
actions/checkout v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/pr-commands.yaml actions

actions/checkout v2 composite
r-lib/actions/pr-fetch v1 composite
r-lib/actions/pr-push v1 composite
r-lib/actions/setup-r v1 composite
r-lib/actions/setup-r-dependencies v1 composite

.github/workflows/test-coverage.yaml actions

actions/checkout v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/test-no-fixtures.yaml actions

actions/checkout v2 composite
actions/upload-artifact main composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Open Source Science

emodnet.wfs

Science Score: 67.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.Rmd

Owner

Citation (CITATION.cff)

CodeMeta (codemeta.json)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Dependencies