gfwr

R package for accessing data from Global Fishing Watch APIs.
https://github.com/GlobalFishingWatch/gfwr

Category: Biosphere
Sub Category: Marine Life and Fishery

Keywords

ais ais-data api-wrapper globalfishingwatch mapping r

Last synced: about 6 hours ago
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Repository metadata

R package for accessing data from Global Fishing Watch APIs

• Host: GitHub
• URL: https://github.com/GlobalFishingWatch/gfwr
• Owner: GlobalFishingWatch
• License: apache-2.0
• Created: 2022-01-21T20:46:18.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2025-04-12T00:37:48.000Z (16 days ago)
• Last Synced: 2025-04-20T06:29:58.716Z (8 days ago)
• Topics: ais, ais-data, api-wrapper, globalfishingwatch, mapping, r
• Language: HTML
• Homepage: https://globalfishingwatch.github.io/gfwr/
• Size: 13.1 MB
• Stars: 67
• Watchers: 18
• Forks: 11
• Open Issues: 17
• Releases: 4
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • Contributing: Contributing.md
  • License: LICENSE.md

README.Rmd

          ---
output: github_document
---



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

# `gfwr`: Access data from Global Fishing Watch APIs  


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> **Important**  
> This version of `gfwr` gives access to Global Fishing Watch API [version 3](https://globalfishingwatch.org/our-apis/documentation#version-3-api). Starting
April 30th, 2024, this is the official API version. For latest API releases, 
please check our [API release notes](https://globalfishingwatch.org/our-apis/documentation#api-release-notes)

> A __Python package__ to communicate with Global Fishing Watch APIs was released in April 2025. Check the [gfw-api-python-client](https://github.com/GlobalFishingWatch/gfw-api-python-client) repository.


The `gfwr` R package is a simple wrapper for the Global Fishing Watch (GFW) [APIs](https://globalfishingwatch.org/our-apis/documentation#introduction). It 
provides convenient functions to freely pull GFW data directly into R in tidy formats.

The package currently works with the following APIs:

* [Vessels API](https://globalfishingwatch.org/our-apis/documentation#vessels-api): 
vessel search and identity based on AIS self reported data and public registry 
information
* [Events API](https://globalfishingwatch.org/our-apis/documentation#events-api):
encounters, loitering, port visits, AIS-disabling events and fishing events 
based on AIS data
* [Gridded apparent fishing effort (4Wings API)](https://globalfishingwatch.org/our-apis/documentation#map-visualization-4wings-api):
apparent fishing effort based on AIS data


> **Note**:
> See the [Terms of Use](https://globalfishingwatch.org/our-apis/documentation#reference-data) 
page for Global Fishing Watch APIs for information on our API licenses and rate limits.


## Installation

You can install the most recent version of `gfwr` using:

```{r install_instructions, eval = FALSE}
# Check/install remotes
if (!require("remotes"))
  install.packages("remotes")

remotes::install_github("GlobalFishingWatch/gfwr",
                        dependencies = TRUE)
```

`gfwr` is also in the rOpenSci 
[R-universe](https://globalfishingwatch.r-universe.dev/gfwr#), and can be 
installed like this: 

```{r r-universe, eval = FALSE}
install.packages("gfwr", 
                 repos = c("https://globalfishingwatch.r-universe.dev",
                           "https://cran.r-project.org"))
```

Once everything is installed, you can load and use `gfwr` in your scripts with 
`library(gfwr)`

```{r load, eval = FALSE}
library(gfwr)
```

```{r load_all, eval = TRUE, echo = FALSE, message = FALSE}
devtools::load_all()
```

## Authorization

The use of `gfwr` requires a GFW API token, which users can request from
the [GFW API Portal](https://globalfishingwatch.org/our-apis/tokens). Save
this token to your `.Renviron` file using `usethis::edit_r_environ()` and adding
a variable named `GFW_TOKEN` to the file (`GFW_TOKEN="PASTE_YOUR_TOKEN_HERE"`).
Save the `.Renviron` file and restart the R session to make the edit effective.


`gfwr` functions are set to use `key = gfw_auth()` by default so in general you shouldn't need to refer to the key in your function calls. 

If the token configuration was not done properly you will see the following error: 

```r
Error in `httr2::req_perform()`:
! HTTP 401 Unauthorized.
```

 
In case you need to specify the key you can use `gfw_auth()` to save an object

```r
key <- gfw_auth()
```

or fetch the key directly from the `.Renviron` file 

```r
key <- Sys.getenv("GFW_TOKEN")
```

The examples in the package documentation will omit an explicit call to key.


## Vessels API

The `get_vessel_info()` function allows you to get vessel identity details from
the [Vessels API](https://globalfishingwatch.org/our-apis/documentation#introduction-vessels-api).

There are two search types: `search`, and `id`.

* `search` is performed by using parameters `query` for basic searches and
`where` for advanced searchers using SQL expressions
  + `query` takes a single identifier that can be the MMSI, IMO, callsign, or
  shipname as input and identifies all vessels that match.
  + `where` search allows for the use of complex search with logical clauses 
  (AND, OR) and fuzzy matching with terms such as LIKE, using SQL syntax (see 
  examples in the function) 
  + `includes` adds information from public registries. Options are
  "MATCH_CRITERIA", "OWNERSHIP" and "AUTHORIZATIONS"

### Basic search by identity markers `(search_type = "search")`

To get information of a vessel using its MMSI, IMO number, callsign or name, the 
search can be done directly using the number or the string. For example, to look 
for a vessel with `MMSI = 224224000`:

```{r example_vessel_info_1, eval = TRUE}
get_vessel_info(query = 224224000,
                search_type = "search")
```

### Complex searches using `where`

To do more specific searches (e.g. `"imo = '8300949'"`), combine different fields 
(`"imo = '8300949' AND ssvid = '214182732'"`) and do fuzzy matching 
(`"shipname LIKE '%GABU REEFE%' OR imo = '8300949'"`), use parameter `where` 
instead of `query`:

```{r example_vessel_info_2, eval = TRUE}
get_vessel_info(where = "shipname LIKE '%GABU REEFE%' OR imo = '8300949'",
                search_type = "search")
```

### Search by vessel ID

To search by `vesselId`, use parameter `ids` and specify `search_type = "id"`.

> **Note**:
> `vesselId` is an internal ID generated by Global Fishing Watch to connect data accross APIs
and involves a combination of vessel and tracking data information. It can be
retrieved using `get_vessel_info()` and fetching the vector of responses inside
`$selfReportedInfo$vesselId`. See the
[identity vignette](https://globalfishingwatch.github.io/gfwr/articles/identity)
for more information.


#### Single vessel IDs

```{r example_vessel_info_3, eval = TRUE}
get_vessel_info(ids = "8c7304226-6c71-edbe-0b63-c246734b3c01",
                search_type = "id")
```

#### Multiple vessel IDs
To specify more than one `vesselId`, you can submit a vector:

```{r example_vessel_info_4, eval = TRUE}
get_vessel_info(ids = c("8c7304226-6c71-edbe-0b63-c246734b3c01",
                        "6583c51e3-3626-5638-866a-f47c3bc7ef7c",
                        "71e7da672-2451-17da-b239-857831602eca"),
                search_type = "id")
```

__Check the function documentation for examples with the other function arguments and
[our dedicated vignette](https://globalfishingwatch.github.io/gfwr/articles/identity)
for more information about vessel identity markers and the outputs retrieved.__


## Events API

The `get_event()` function allows you to get data on specific vessel activities 
from the 
[Events API](https://globalfishingwatch.org/our-apis/documentation#events-api).
Event types include apparent fishing events, potential transshipment events 
(two-vessel encounters and loitering by refrigerated carrier vessels), port 
visits, and AIS-disabling events ("gaps").
Find more information about events in our 
[caveat documentation](https://globalfishingwatch.org/our-apis/documentation#data-caveat).

### Events in a given time range

You can get events in a given date range. By not specifying `vessels`, the 
response will return results for all vessels. 


```{r example_event_type_3, eval = TRUE}
get_event(event_type = "ENCOUNTER",
          start_date = "2020-01-01",
          end_date = "2020-01-02")
```

> *Note*: We do not recommend trying too large downloads, such as all 
encounters for all vessels over a long period of time. This will possibly 
return time out (524) errors. Our API team is working on another API specific 
for large downloads in the future.


### Events in a specific area

You can provide a polygon in `sf` format or the region code (such as an EEZ 
code) to filter the raster. Check the function documentation for more 
information about parameters `region` and `region_source`

```{r events_shapefile}
 # fishing events in user shapefile
test_polygon <- sf::st_bbox(c(xmin = -70,
                              xmax = -40,
                              ymin = -10,
                              ymax = 5),
  crs = 4326) |>
  sf::st_as_sfc() |>
  sf::st_as_sf()
get_event(event_type = "FISHING",
               start_date = "2020-10-01",
               end_date = "2020-10-31",
               region = test_polygon,
               region_source = "USER_SHAPEFILE")
```


### Events for specific vessels

To extract events for specific vessels, the Events API needs `vesselId` as 
input, so you always need to use `get_vessel_info()` first to extract 
`vesselId` from `$selfReportedInfo` in the response. 


#### Single vessel events


```{r example_id_event, eval = TRUE}
vessel_info <- get_vessel_info(query = 224224000)
vessel_info$selfReportedInfo
```

The results show this vessel's story is grouped in two `vesselIds`.

To get a list of port visits for that vessel, you can use a single `vesselId`
of your interest:

```{r event_single_vesselid, eval = TRUE}
id <- vessel_info$selfReportedInfo$vesselId
id

get_event(event_type = "PORT_VISIT",
          vessels = id[1],
          confidences = 4
          )
```

But to get the whole event history, it's better to use the whole vector of 
`vesselId` for that vessel. Notice how the following request provides more results than the previous one: 


```{r event_onevessel_allvesselids, eval = TRUE}
get_event(event_type = "PORT_VISIT",
          vessels = id, #using the whole vector of vesselIds
          confidences = 4
          )
```

> *Note*: Try narrowing your search using `start_date` and `end_date` if the 
request is too large and returns a time out error (524)



When a date range is provided to `get_event()` using both `start_date` and 
`end_date`, any event overlapping that range will be returned, including events 
that start prior to `start_date` or end after `end_date`. If just `start_date` 
or `end_date` are provided, results will include all events that end after 
`start_date` or begin prior to `end_date`, respectively.

> **Note**:  
> Because encounter events are events between two vessels, a single event will 
be represented twice in the data, once for each vessel. To capture this 
information and link the related data rows, the `id` field for encounter events 
includes an additional suffix (1 or 2) separated by a period. The `vessel` field
will also contain different information specific to each vessel.



#### Events for multiple vessels

As another example, let's combine the Vessels and Events APIs to get fishing 
events for a list of USA-flagged trawlers:

```{r example_event_type_4a}
# Download the list of USA trawlers
usa_trawlers <- get_vessel_info(
  where = "flag='USA' AND geartypes='TRAWLERS'",
  search_type = "search",
  quiet = TRUE 
  )
# Set quiet = TRUE if you want the output to return silently
```

This list returns `r length(unique(usa_trawlers$selfReportedInfo$vesselId))` `vesselIds` belonging
to `r nrow(usa_trawlers$dataset)` vessels. 

```{r usa_trawlers_id}
usa_trawlers$selfReportedInfo
```

To fetch events for this list of vessels, we will use the `vesselId` column and 
send it to the `vessels` parameter in `get_event()` function. 

For clarity, we should try to send groups of `vesselIds` that belong to the same
vessels. For this, we can check the `index` column in the `$selfReportedInfo`
dataset. 

> *Note*: `get_event()` can receive several `vesselIds` at a time but will fail 
when the character length of the whole request is too long (~100,000 characters).
This means it will fail with error __HTTP 422: Unprocessable entity__ when too 
many `vesselIds` are requested, this value can be around 2,800 `vesselIds` depending 
on the other parameters of the search.

For this example, we will send the `vesselIds` corresponding to the first twenty 
vessels in the response: 

```{r usa_ten}
each_USA_trawler <- usa_trawlers$selfReportedInfo[, c("index", "vesselId")] 
# how many vessels correspond to the first twenty vessels. 
(twenty_usa_trawlers <- each_USA_trawler %>% filter(index <= 20))
```

There are `r length(unique(twenty_usa_trawlers$vesselId))` 
`vesselIds` corresponding to those 20 vessels.

Let's pass the vector of `vesselIds` to Events API. Now get the list of fishing
events for these trawlers in January, 2020:

```{r example_event_type_4b, eval=T}
fishing_events <- get_event(event_type = "FISHING",
                            vessels = twenty_usa_trawlers$vesselId,
                            start_date = "2020-01-01",
                            end_date = "2020-02-01")
fishing_events
```

The columns starting by `vessel` hold the vessel-related information for each 
event: `vesselId`, `vessel_name`, `ssvid` (MMSI), `flag`, `vessel type` and 
public authorizations.

```{r unnest_vessel}
fishing_events %>% 
  dplyr::select(starts_with("vessel"))
```

When no events are available, the `get_event()` function returns nothing.

```{r example_event_type_4c, eval=T}
get_event(event_type = "FISHING",
          vessels = twenty_usa_trawlers$vesselId[2],
          start_date = "2020-01-01",
          end_date = "2020-01-01"
          )
```


## Apparent fishing effort API

The `get_raster()` function gets a raster from the [4Wings API](https://globalfishingwatch.org/our-apis/documentation#map-visualization-4wings-api)
and converts the response to a data frame. In order to use it, you should specify:

* The spatial resolution, which can be `LOW` (0.1 degree) or `HIGH` (0.01 
degree)
* The temporal resolution, which can be `HOURLY`, `DAILY`, `MONTHLY`, `YEARLY` 
or `ENTIRE`.
* The variable to group by: `FLAG`, `GEARTYPE`, `FLAGANDGEARTYPE`, `MMSI` or 
`VESSEL_ID`
* The date range `note: this must be 366 days or less`
* The region polygon in `sf` format or the region code (such as an EEZ code) to
filter the raster
* The source for the specified region. Currently, `EEZ`, `MPA`, `RFMO` or 
`USER_SHAPEFILE` (for `sf` shapefiles).


### User-defined shapefile

You can load an `sf` shapefile with your area of interest and fetch apparent fishing effort 
for this area using `region_source = "USER_SHAPEFILE"` and `region = [YOUR_SHAPE]`.
We added a sample shapefile inside `gfwr` to show how `"USER_SHAPEFILE"` works:

```{r example_map_1}
data("test_shape")

test_shape

get_raster(
  spatial_resolution = "LOW",
  temporal_resolution = "YEARLY",
  group_by = "FLAG",
  start_date = "2021-01-01",
  end_date = "2021-02-01",
  region_source = "USER_SHAPEFILE",
  region = test_shape
  )
```

### Apparent fishing effort in preloaded EEZ, RFMOs and MPAs

#### EEZ

If you want raster data from a particular EEZ, you can use the `get_region_id()`
function to get the EEZ id, and enter that code in the `region_name` argument
of `get_raster()` instead of the region shapefile (with `region_source = "EEZ"`):

```{r example_map_2, eval= TRUE}
# use EEZ function to get EEZ code of Cote d'Ivoire
code_eez <- get_region_id(region_name = "CIV", region_source = "EEZ")

get_raster(spatial_resolution = "LOW",
           temporal_resolution = "YEARLY",
           group_by = "FLAG",
           start_date = "2021-01-01",
           end_date = "2021-10-01",
           region = code_eez$id,
           region_source = "EEZ")
```

You could search for just one word in the name of the EEZ and then decide which
one you want:

```{r example_map_3, eval = TRUE}
(get_region_id(region_name = "France", region_source = "EEZ"))
```


From the results above, let's say we're interested in the French Exclusive 
Economic Zone, `5677`

```{r fr_eez, eval = TRUE}
get_raster(spatial_resolution = "LOW",
           temporal_resolution = "YEARLY",
           group_by = "FLAG",
           start_date = "2021-01-01",
           end_date = "2021-10-01",
           region = 5677,
           region_source = "EEZ"
           )
```

#### Marine Protected Areas (MPAs)

A similar approach can be used to search for a specific Marine Protected Area,
in this case the Phoenix Island Protected Area (PIPA)

```{r example_map_4, eval= TRUE}
# use region id function to get MPA code of Phoenix Island Protected Area
code_mpa <- get_region_id(region_name = "Phoenix",
                          region_source = "MPA")
code_mpa
get_raster(spatial_resolution = "LOW",
           temporal_resolution = "YEARLY",
           group_by = "FLAG",
           start_date = "2015-01-01",
           end_date = "2015-06-01",
           region = code_mpa$id[1],
           region_source = "MPA")
```

#### Regional Fisheries Management Organizations (RFMOs)

It is also possible to filter rasters to regional fisheries management 
organizations (RFMO) like `"ICCAT"`, `"IATTC"`, `"IOTC"`, `"CCSBT"` and
`"WCPFC"`.


```{r example_map_5, eval=T}
get_raster(spatial_resolution = "LOW",
           temporal_resolution = "DAILY",
           group_by = "FLAG",
           start_date = "2021-01-01",
           end_date = "2021-01-04",
           region = "ICCAT",
           region_source = "RFMO")
```

> *Note*: For a complete list of MPAs, RFMOs and EEZ, check the function `get_regions()`


### When your API request times out

For API performance reasons, the `get_raster()` function restricts individual 
queries to a single year of data. However, even with this restriction, it is 
possible for API request to time out before it completes. When this occurs, the
initial `get_raster()` call will return an `HTTP 524 error`, and subsequent API 
requests using any `gfwr` `get_` function will return an `HTTP 429 error` until 
the original request completes:

>
Error in `httr2::req_perform()`:
! HTTP 429 Too Many Requests.
• Your application token is not currently enabled to perform more than one 
concurrent report. If you need to generate more than one report concurrently,
contact us at [email protected]

Although no data was received, the request is still being processed by the APIs
 and will become available when it completes. To account for this, `gfwr` 
 includes the `get_last_report()` function, which lets users request the 
 results of their last API request with `get_raster()`.

The `get_last_report()` function will tell you if the APIs are still 
processing your request and will download the results if the request has 
finished successfully. You will receive an error message if the request 
finished but resulted in an error or if it's been >30 minutes since the last
 report was generated using `get_raster()`. For more information, see the 
 [Get last report generated endpoint](https://globalfishingwatch.org/our-apis/documentation#get-last-report-generated) 
 documentation on the Global Fishing Watch API page.

## Reverse region id search 

The `get_region_id()` function also works in reverse. If a region id is passed as
a `numeric` to the function as the `region_name`, the corresponding region label
or iso3 code can be returned. This is especially useful when events are 
returned with regions.

Using the same example with twenty trawlers fishing events, `fishing_events`, 
you can see the `eez` information is returned as the code `5696`, as characters.

```{r example_region_id}
fishing_events <- get_event(event_type = "FISHING",
                            vessels = twenty_usa_trawlers$vesselId,
                            start_date = "2020-01-01",
                            end_date = "2020-02-01") %>%
  # extract EEZ id code
  dplyr::mutate(eez = as.character(
    purrr::map(purrr::map(regions, purrr::pluck, "eez"),
               paste0, collapse = ","))) %>%
  dplyr::select(eez, eventId, eventType, start, end, lat, lon) 

fishing_events
```


We can apply `get_region_id()` to the numeric vector to extract the labels:

```{r example_region_id_cont}
fishing_events %>% 
  mutate(eez_name = purrr::map_df(as.numeric(fishing_events$eez),
                                  ~get_region_id(region_name = .x,
                                                 region_source = "EEZ"))$label) %>% 
  dplyr::select(-start, -end)
  
```

        

---

Owner metadata

• Name: Global Fishing Watch
• Login: GlobalFishingWatch
• Email: [email protected]
• Kind: organization
• Description: Technology Illuminating the World's Fishing Fleet
• Website: http://globalfishingwatch.org
• Location: 0,0
• Twitter:
• Company:
• Icon url: https://avatars.githubusercontent.com/u/9467707?v=4
• Repositories: 144
• Last ynced at: 2024-04-12T10:02:18.769Z
• Profile URL: https://github.com/GlobalFishingWatch

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Last Year

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---

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---

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