Important
This version ofgfwr
gives access to Global Fishing Watch API version 3. Starting April 30th, 2024, this is the official API version. For latest API releases, please check our API release notes
A Python package to communicate with Global Fishing Watch APIs was released in April 2025. Check the gfw-api-python-client repository.
The gfwr
R package is a simple wrapper for the Global Fishing Watch (GFW) APIs. It provides convenient functions to freely pull GFW data directly into R in tidy formats.
The package currently works with the following APIs:
Note: See the Terms of Use page for Global Fishing Watch APIs for information on our API licenses and rate limits.
You can install the most recent version of gfwr
using:
# Check/install remotes
if (!require("remotes"))
install.packages("remotes")
remotes::install_github("GlobalFishingWatch/gfwr",
dependencies = TRUE)
gfwr
is also in the rOpenSci R-universe, and can be installed like this:
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)
The use of gfwr
requires a GFW API token, which users can request from the GFW API Portal. 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:
In case you need to specify the key you can use gfw_auth()
to save an object
key <- gfw_auth()
or fetch the key directly from the .Renviron
file
key <- Sys.getenv("GFW_TOKEN")
The examples in the package documentation will omit an explicit call to key.
The get_vessel_info()
function allows you to get vessel identity details from the 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”(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
:
get_vessel_info(query = 224224000,
search_type = "search")
#> 1 total vessels
#> $dataset
#> # A tibble: 1 × 1
#> dataset
#> <chr>
#> 1 public-global-vessel-identity:v3.0
#>
#> $registryInfoTotalRecords
#> # A tibble: 1 × 1
#> registryInfoTotalRecords
#> <int>
#> 1 1
#>
#> $registryInfo
#> # A tibble: 2 × 16
#> index recordId sourceCode ssvid flag shipname nShipname callsign imo
#> <dbl> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 e0c9823749264a… <chr [7]> 2242… ESP AGURTZA… AGURTZAB… EBSJ 8733…
#> 2 1 e0c9823749264a… <chr [7]> 2242… ESP AGURTZA… AGURTZAB… EBSJ 8733…
#> # ℹ 7 more variables: transmissionDateFrom <chr>, transmissionDateTo <chr>,
#> # geartypes <chr>, lengthM <dbl>, tonnageGt <dbl>, vesselInfoReference <chr>,
#> # extraFields <list>
#>
#> $registryOwners
#> # A tibble: 0 × 2
#> # ℹ 2 variables: index <dbl>, <list> <list>
#>
#> $registryPublicAuthorizations
#> # A tibble: 3 × 5
#> index dateFrom dateTo ssvid sourceCode
#> <dbl> <chr> <chr> <chr> <list>
#> 1 1 2019-01-01T00:00:00Z 2019-10-01T00:00:00Z 224224000 <chr [1]>
#> 2 1 2012-01-01T00:00:00Z 2019-01-01T00:00:00Z 224224000 <chr [1]>
#> 3 1 2019-10-15T00:00:00Z 2023-02-01T00:00:00Z 306118000 <chr [1]>
#>
#> $combinedSourcesInfo
#> # A tibble: 2 × 10
#> index vesselId geartypes_name geartypes_source geartypes_yearFrom
#> <dbl> <chr> <chr> <chr> <int>
#> 1 1 6632c9eb8-8009-abdb-… PURSE_SEINE_S… GFW_VESSEL_LIST 2019
#> 2 1 3c99c326d-dd2e-175d-… PURSE_SEINE_S… GFW_VESSEL_LIST 2015
#> # ℹ 5 more variables: geartypes_yearTo <int>, shiptypes_name <chr>,
#> # shiptypes_source <chr>, shiptypes_yearFrom <int>, shiptypes_yearTo <int>
#>
#> $selfReportedInfo
#> # A tibble: 2 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 6632c9eb8… 3061… AGURTZA… AGURTZAB… BES PJBL 8733… 418581
#> 2 1 3c99c326d… 2242… AGURTZA… AGURTZAB… ESP EBSJ 8733… 135057
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>
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
:
get_vessel_info(where = "shipname LIKE '%GABU REEFE%' OR imo = '8300949'",
search_type = "search")
#> 1 total vessels
#> $dataset
#> # A tibble: 1 × 1
#> dataset
#> <chr>
#> 1 public-global-vessel-identity:v3.0
#>
#> $registryInfoTotalRecords
#> # A tibble: 1 × 1
#> registryInfoTotalRecords
#> <int>
#> 1 1
#>
#> $registryInfo
#> # A tibble: 1 × 17
#> index recordId sourceCode ssvid flag shipname nShipname callsign imo
#> <dbl> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 b16ca93ea690fc… <chr [3]> 6290… GMB GABU RE… GABUREEF… C5J278 8300…
#> # ℹ 8 more variables: latestVesselInfo <lgl>, transmissionDateFrom <chr>,
#> # transmissionDateTo <chr>, geartypes <chr>, lengthM <dbl>, tonnageGt <int>,
#> # vesselInfoReference <chr>, extraFields <list>
#>
#> $registryOwners
#> # A tibble: 4 × 7
#> index name flag ssvid sourceCode dateFrom dateTo
#> <dbl> <chr> <chr> <chr> <list> <chr> <chr>
#> 1 1 FISHING CARGO SERVICES PAN 629009266 <chr [2]> 2024-08-07T10:… 2025-…
#> 2 1 FISHING CARGO SERVICES PAN 613590000 <chr [2]> 2022-01-24T09:… 2024-…
#> 3 1 FISHING CARGO SERVICES PAN 214182732 <chr [2]> 2019-02-23T11:… 2022-…
#> 4 1 FISHING CARGO SERVICES PAN 616852000 <chr [2]> 2012-01-08T19:… 2019-…
#>
#> $registryPublicAuthorizations
#> # A tibble: 0 × 2
#> # ℹ 2 variables: index <dbl>, <list> <list>
#>
#> $combinedSourcesInfo
#> # A tibble: 4 × 10
#> index vesselId geartypes_name geartypes_source geartypes_yearFrom
#> <dbl> <chr> <chr> <chr> <int>
#> 1 1 58cf536b1-1fca-dac3-… CARRIER GFW_VESSEL_LIST 2012
#> 2 1 9827ea1ea-a120-f374-… CARRIER GFW_VESSEL_LIST 2024
#> 3 1 1da8dbc23-3c48-d5ce-… CARRIER GFW_VESSEL_LIST 2022
#> 4 1 0b7047cb5-58c8-6e63-… CARRIER GFW_VESSEL_LIST 2019
#> # ℹ 5 more variables: geartypes_yearTo <int>, shiptypes_name <chr>,
#> # shiptypes_source <chr>, shiptypes_yearFrom <int>, shiptypes_yearTo <int>
#>
#> $selfReportedInfo
#> # A tibble: 4 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 9827ea1ea… 6290… GABU RE… GABUREEF… GMB C5J278 8300… 515219
#> 2 1 1da8dbc23… 6135… GABU RE… GABUREEF… CMR TJMC996 8300… 973251
#> 3 1 0b7047cb5… 2141… GABU RE… GABUREEF… MDA ER2732 8300… 642750
#> 4 1 58cf536b1… 6168… GABU RE… GABUREEF… COM D6FJ2 8300… 469834
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>
To search by vesselId
, use parameter ids
and specify search_type = "id"
.
Note:
vesselId
is an internal ID generated by Global Fishing Watch to connect data accross APIs and involves a combination of vessel and tracking data information. It can be retrieved usingget_vessel_info()
and fetching the vector of responses inside$selfReportedInfo$vesselId
. See the identity vignette for more information.
get_vessel_info(ids = "8c7304226-6c71-edbe-0b63-c246734b3c01",
search_type = "id")
#> 1 total vessels
#> $dataset
#> # A tibble: 1 × 1
#> dataset
#> <chr>
#> 1 public-global-vessel-identity:v3.0
#>
#> $registryInfoTotalRecords
#> # A tibble: 1 × 1
#> registryInfoTotalRecords
#> <int>
#> 1 5
#>
#> $registryInfo
#> # A tibble: 5 × 17
#> index recordId sourceCode ssvid flag shipname nShipname callsign imo
#> <dbl> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 a8d00ce54b37ad… <chr [4]> 2733… RUS FRIO FO… FRIOFORW… UCRZ 9076…
#> 2 1 a8d00ce54b37ad… <chr [3]> 5111… PLW FRIO FO… FRIOFORW… T8A4891 9076…
#> 3 1 a8d00ce54b37ad… <chr [7]> 2106… CYP FRIO FO… FRIOFORW… 5BWC3 9076…
#> 4 1 a8d00ce54b37ad… <chr [2]> 3413… KNA FRIO FO… FRIOFORW… V4JQ3 9076…
#> 5 1 a8d00ce54b37ad… <chr [3]> 3546… PAN FRIOAEG… FRIOAEGE… 3FGY4 9076…
#> # ℹ 8 more variables: latestVesselInfo <lgl>, transmissionDateFrom <chr>,
#> # transmissionDateTo <chr>, geartypes <chr>, lengthM <dbl>, tonnageGt <dbl>,
#> # vesselInfoReference <chr>, extraFields <list>
#>
#> $registryOwners
#> # A tibble: 3 × 7
#> index name flag ssvid sourceCode dateFrom dateTo
#> <dbl> <chr> <chr> <chr> <list> <chr> <chr>
#> 1 1 COLINER RUS 273379740 <chr [2]> 2015-02-27T10:59:43Z 2025-02-27T11:2…
#> 2 1 COLINER CYP 511101495 <chr [1]> 2024-07-04T14:27:04Z 2024-07-24T14:2…
#> 3 1 COLINER CYP 210631000 <chr [1]> 2013-05-15T20:19:43Z 2024-07-04T14:1…
#>
#> $registryPublicAuthorizations
#> # A tibble: 3 × 5
#> index dateFrom dateTo ssvid sourceCode
#> <dbl> <chr> <chr> <chr> <list>
#> 1 1 2023-01-01T00:00:00Z 2024-12-31T00:00:00Z 210631000 <chr [1]>
#> 2 1 2020-01-01T00:00:00Z 2024-12-01T00:00:00Z 210631000 <chr [1]>
#> 3 1 2024-08-09T00:00:00Z 2025-03-01T00:00:00Z 273379740 <chr [1]>
#>
#> $combinedSourcesInfo
#> # A tibble: 8 × 10
#> index vesselId geartypes_name geartypes_source geartypes_yearFrom
#> <dbl> <chr> <chr> <chr> <int>
#> 1 1 0cb77880e-ee49-2ce4-… CARRIER GFW_VESSEL_LIST 2012
#> 2 1 da1cd7e1b-b8d0-539c-… CARRIER GFW_VESSEL_LIST 2019
#> 3 1 da1cd7e1b-b8d0-539c-… CARRIER GFW_VESSEL_LIST 2019
#> 4 1 da1cd7e1b-b8d0-539c-… CARRIER GFW_VESSEL_LIST 2015
#> 5 1 da1cd7e1b-b8d0-539c-… CARRIER GFW_VESSEL_LIST 2015
#> 6 1 0edad163f-f53d-9ddb-… CARRIER GFW_VESSEL_LIST 2024
#> 7 1 8c7304226-6c71-edbe-… CARRIER GFW_VESSEL_LIST 2013
#> 8 1 3c81a942b-bf0a-f476-… CARRIER GFW_VESSEL_LIST 2015
#> # ℹ 5 more variables: geartypes_yearTo <int>, shiptypes_name <chr>,
#> # shiptypes_source <chr>, shiptypes_yearFrom <int>, shiptypes_yearTo <int>
#>
#> $selfReportedInfo
#> # A tibble: 1 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 8c7304226… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 3369802
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>
To specify more than one vesselId
, you can submit a vector:
get_vessel_info(ids = c("8c7304226-6c71-edbe-0b63-c246734b3c01",
"6583c51e3-3626-5638-866a-f47c3bc7ef7c",
"71e7da672-2451-17da-b239-857831602eca"),
search_type = "id")
#> 3 total vessels
#> $dataset
#> # A tibble: 3 × 1
#> dataset
#> <chr>
#> 1 public-global-vessel-identity:v3.0
#> 2 public-global-vessel-identity:v3.0
#> 3 public-global-vessel-identity:v3.0
#>
#> $registryInfoTotalRecords
#> # A tibble: 3 × 1
#> registryInfoTotalRecords
#> <int>
#> 1 1
#> 2 5
#> 3 2
#>
#> $registryInfo
#> # A tibble: 8 × 17
#> index recordId sourceCode ssvid flag shipname nShipname callsign imo
#> <dbl> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 685862e0626f62… <chr [6]> 5480… PHL JOHNREY… JOHNREYN… DUQA7 8118…
#> 2 2 a8d00ce54b37ad… <chr [4]> 2733… RUS FRIO FO… FRIOFORW… UCRZ 9076…
#> 3 2 a8d00ce54b37ad… <chr [3]> 5111… PLW FRIO FO… FRIOFORW… T8A4891 9076…
#> 4 2 a8d00ce54b37ad… <chr [7]> 2106… CYP FRIO FO… FRIOFORW… 5BWC3 9076…
#> 5 2 a8d00ce54b37ad… <chr [2]> 3413… KNA FRIO FO… FRIOFORW… V4JQ3 9076…
#> 6 2 a8d00ce54b37ad… <chr [3]> 3546… PAN FRIOAEG… FRIOAEGE… 3FGY4 9076…
#> 7 3 b82d02e5c2c11e… <chr [6]> 4417… KOR ADRIA ADRIA DTBY3 8919…
#> 8 3 b82d02e5c2c11e… <chr [5]> 4417… KOR PREMIER PREMIER DTBY3 8919…
#> # ℹ 8 more variables: latestVesselInfo <lgl>, transmissionDateFrom <chr>,
#> # transmissionDateTo <chr>, geartypes <chr>, lengthM <dbl>, tonnageGt <dbl>,
#> # vesselInfoReference <chr>, extraFields <list>
#>
#> $registryOwners
#> # A tibble: 3 × 7
#> index name flag ssvid sourceCode dateFrom dateTo
#> <dbl> <chr> <chr> <chr> <list> <chr> <chr>
#> 1 2 COLINER RUS 273379740 <chr [2]> 2015-02-27T10:59:43Z 2025-02-27T11:2…
#> 2 2 COLINER CYP 511101495 <chr [1]> 2024-07-04T14:27:04Z 2024-07-24T14:2…
#> 3 2 COLINER CYP 210631000 <chr [1]> 2013-05-15T20:19:43Z 2024-07-04T14:1…
#>
#> $registryPublicAuthorizations
#> # A tibble: 8 × 5
#> index dateFrom dateTo ssvid sourceCode
#> <dbl> <chr> <chr> <chr> <list>
#> 1 1 2012-01-01T00:00:00Z 2017-10-25T00:00:00Z 548012100 <chr [1]>
#> 2 1 2019-02-10T18:02:49Z 2025-03-01T00:00:00Z 548012100 <chr [1]>
#> 3 2 2023-01-01T00:00:00Z 2024-12-31T00:00:00Z 210631000 <chr [1]>
#> 4 2 2020-01-01T00:00:00Z 2024-12-01T00:00:00Z 210631000 <chr [1]>
#> 5 2 2024-08-09T00:00:00Z 2025-03-01T00:00:00Z 273379740 <chr [1]>
#> 6 3 2015-10-08T00:00:00Z 2020-07-21T00:00:00Z 441734000 <chr [1]>
#> 7 3 2012-01-01T00:00:00Z 2013-09-19T00:00:00Z 441734000 <chr [1]>
#> 8 3 2013-09-20T00:00:00Z 2025-01-01T00:00:00Z 441734000 <chr [1]>
#>
#> $combinedSourcesInfo
#> # A tibble: 12 × 10
#> index vesselId geartypes_name geartypes_source geartypes_yearFrom
#> <dbl> <chr> <chr> <chr> <int>
#> 1 1 71e7da672-2451-17da… TUNA_PURSE_SE… COMBINATION_OF_… 2017
#> 2 1 55889aefb-bef9-224c… TUNA_PURSE_SE… COMBINATION_OF_… 2017
#> 3 2 0cb77880e-ee49-2ce4… CARRIER GFW_VESSEL_LIST 2012
#> 4 2 da1cd7e1b-b8d0-539c… CARRIER GFW_VESSEL_LIST 2019
#> 5 2 da1cd7e1b-b8d0-539c… CARRIER GFW_VESSEL_LIST 2019
#> 6 2 da1cd7e1b-b8d0-539c… CARRIER GFW_VESSEL_LIST 2015
#> 7 2 da1cd7e1b-b8d0-539c… CARRIER GFW_VESSEL_LIST 2015
#> 8 2 0edad163f-f53d-9ddb… CARRIER GFW_VESSEL_LIST 2024
#> 9 2 8c7304226-6c71-edbe… CARRIER GFW_VESSEL_LIST 2013
#> 10 2 3c81a942b-bf0a-f476… CARRIER GFW_VESSEL_LIST 2015
#> 11 3 aca119c29-95dd-f5c4… TUNA_PURSE_SE… COMBINATION_OF_… 2012
#> 12 3 6583c51e3-3626-5638… TUNA_PURSE_SE… COMBINATION_OF_… 2013
#> # ℹ 5 more variables: geartypes_yearTo <int>, shiptypes_name <chr>,
#> # shiptypes_source <chr>, shiptypes_yearFrom <int>, shiptypes_yearTo <int>
#>
#> $selfReportedInfo
#> # A tibble: 3 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 71e7da672… 5480… JOHN RE… JOHNREYN… PHL DUQA-7 8118… 133081
#> 2 2 8c7304226… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 3369802
#> 3 3 6583c51e3… 4417… ADRIA ADRIA KOR DTBY3 <NA> 360249
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>
Check the function documentation for examples with the other function arguments and our dedicated vignette for more information about vessel identity markers and the outputs retrieved.
The get_event()
function allows you to get data on specific vessel activities from the 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.
You can get events in a given date range. By not specifying vessels
, the response will return results for all vessels.
get_event(event_type = "ENCOUNTER",
start_date = "2020-01-01",
end_date = "2020-01-02")
#> [1] "Downloading 290 events from GFW"
#> # A tibble: 290 × 16
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2020-01-01 14:10:00 2020-01-01 23:30:00 5acdf7e969b84… encounter 22.8 117.
#> 2 2019-12-31 14:50:00 2020-01-01 20:20:00 4831292899e8c… encounter -17.6 -79.4
#> 3 2020-01-01 00:40:00 2020-01-01 08:20:00 3de48259ac99d… encounter 26.6 120.
#> 4 2020-01-01 00:00:00 2020-01-01 02:30:00 be45e3a9d4ec6… encounter 5.97 156.
#> 5 2019-12-31 08:40:00 2020-01-01 07:40:00 3dabdaf7b79f4… encounter 57.5 157.
#> 6 2019-12-31 08:40:00 2020-01-01 07:40:00 3dabdaf7b79f4… encounter 57.5 157.
#> 7 2019-12-31 12:00:00 2020-01-01 13:50:00 c11e047615243… encounter -17.6 -79.3
#> 8 2020-01-01 15:00:00 2020-01-01 18:20:00 8cc49cd667e74… encounter 9.49 -99.1
#> 9 2020-01-01 10:40:00 2020-01-01 20:40:00 e175bb77a0427… encounter 38.5 121.
#> 10 2020-01-01 16:10:00 2020-01-02 08:20:00 c4be9e59586cf… encounter -17.5 -79.5
#> # ℹ 280 more rows
#> # ℹ 10 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vesselId <chr>, vessel_name <chr>, vessel_ssvid <chr>, vessel_flag <chr>,
#> # vessel_type <chr>, vessel_publicAuthorizations <list>, event_info <list>
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.
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
# 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")
#> [1] "Downloading 59 events from GFW"
#> # A tibble: 59 × 16
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2020-10-25 03:42:50 2020-10-25 04:15:33 950559aadb34… fishing 0.183 -47.8
#> 2 2020-10-20 06:07:54 2020-10-20 08:04:08 31faad518071… fishing 0.398 -47.8
#> 3 2020-10-08 23:45:15 2020-10-09 02:27:47 e622d1ce0a78… fishing 0.0269 -47.9
#> 4 2020-10-18 23:15:26 2020-10-19 07:52:48 b40e1caf208b… fishing 0.474 -47.8
#> 5 2020-10-23 07:53:51 2020-10-23 14:08:00 f2b566146edb… fishing 4.81 -51.5
#> 6 2020-10-01 12:54:31 2020-10-01 21:26:31 083f87bff859… fishing 4.75 -51.6
#> 7 2020-10-03 21:08:06 2020-10-04 03:31:11 3ce13bbe2752… fishing 4.75 -51.6
#> 8 2020-10-07 22:56:40 2020-10-08 01:48:45 238db9546e86… fishing -0.0045 -47.8
#> 9 2020-10-07 12:06:36 2020-10-07 14:11:11 462184ec19c5… fishing 0.207 -47.9
#> 10 2020-10-24 15:44:03 2020-10-24 18:56:19 fbf1567e527d… fishing 0.259 -47.9
#> # ℹ 49 more rows
#> # ℹ 10 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vesselId <chr>, vessel_name <chr>, vessel_ssvid <chr>, vessel_flag <chr>,
#> # vessel_type <chr>, vessel_publicAuthorizations <list>, event_info <list>
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 $selfReportedInfo
in the response.
vessel_info <- get_vessel_info(query = 224224000)
#> 1 total vessels
vessel_info$selfReportedInfo
#> # A tibble: 2 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 6632c9eb8… 3061… AGURTZA… AGURTZAB… BES PJBL 8733… 418581
#> 2 1 3c99c326d… 2242… AGURTZA… AGURTZAB… ESP EBSJ 8733… 135057
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>
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:
id <- vessel_info$selfReportedInfo$vesselId
id
#> [1] "6632c9eb8-8009-abdb-baf9-b67d65f20510"
#> [2] "3c99c326d-dd2e-175d-626f-a3c488a4342b"
get_event(event_type = "PORT_VISIT",
vessels = id[1],
confidences = 4
)
#> [1] "Downloading 25 events from GFW"
#> # A tibble: 25 × 15
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2021-08-01 12:58:44 2021-08-16 16:00:15 a26f4940e189c… port_vis… 5.20 -4.02
#> 2 2021-06-17 13:49:26 2021-06-21 17:10:23 8abe85865ca20… port_vis… 5.20 -4.05
#> 3 2021-11-11 18:41:10 2021-11-20 18:43:26 af0cb5d7ee288… port_vis… 5.20 -4.04
#> 4 2020-08-08 06:40:40 2020-08-10 08:13:39 acd48bf28e6b3… port_vis… 14.6 -17.4
#> 5 2021-10-17 09:52:51 2021-10-17 16:06:40 d133e151d9edd… port_vis… 14.6 -17.4
#> 6 2020-11-01 14:17:48 2020-11-06 12:25:53 f39043169c3c4… port_vis… 5.20 -4.02
#> 7 2020-08-19 09:44:55 2020-08-19 18:39:59 724b8c1b2fb6d… port_vis… 16.9 -25.0
#> 8 2020-06-20 12:33:45 2020-06-20 19:43:10 a8f5401a3bbec… port_vis… 14.6 -17.4
#> 9 2019-11-15 14:15:11 2019-11-19 07:49:20 bbeed3f884a6f… port_vis… 5.20 -4.02
#> 10 2020-01-11 11:18:49 2020-01-15 11:54:49 889beb4fc4bfb… port_vis… 5.23 -4.02
#> # ℹ 15 more rows
#> # ℹ 9 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vesselId <chr>, vessel_name <chr>, vessel_ssvid <chr>, vessel_flag <chr>,
#> # vessel_type <chr>, event_info <list>
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:
get_event(event_type = "PORT_VISIT",
vessels = id, #using the whole vector of vesselIds
confidences = 4
)
#> [1] "Downloading 74 events from GFW"
#> # A tibble: 74 × 15
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2021-03-25 06:49:59 2021-03-28 21:20:36 c56caaedee80f… port_vis… 5.23 -4.02
#> 2 2020-12-14 09:46:52 2020-12-22 16:54:09 9205a53a7b91d… port_vis… 5.20 -4.04
#> 3 2016-04-20 06:50:58 2016-04-20 19:47:10 3c267cf9e13f5… port_vis… 14.7 -17.4
#> 4 2016-12-15 16:12:20 2016-12-22 11:06:48 47948c8bf239e… port_vis… 5.23 -3.97
#> 5 2017-03-09 17:19:17 2017-03-15 09:00:37 6e1a4cdb4b899… port_vis… 5.23 -4.02
#> 6 2021-05-19 22:46:40 2021-06-08 08:54:49 ed0ffc8600077… port_vis… 14.7 -17.4
#> 7 2021-08-01 12:58:44 2021-08-16 16:00:15 a26f4940e189c… port_vis… 5.20 -4.02
#> 8 2016-10-12 10:42:03 2016-10-12 14:31:08 24cc9e1a9c843… port_vis… 5.20 -4.03
#> 9 2018-08-11 06:32:24 2018-08-14 11:09:41 4de1a24bd0d04… port_vis… 5.23 -3.97
#> 10 2017-05-22 08:09:28 2017-06-08 20:10:06 65c42e2c6c7ff… port_vis… 16.9 -25.0
#> # ℹ 64 more rows
#> # ℹ 9 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vesselId <chr>, vessel_name <chr>, vessel_ssvid <chr>, vessel_flag <chr>,
#> # vessel_type <chr>, event_info <list>
Note: Try narrowing your search using
start_date
andend_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, theid
field for encounter events includes an additional suffix (1 or 2) separated by a period. Thevessel
field will also contain different information specific to each vessel.
As another example, let’s combine the Vessels and Events APIs to get fishing events for a list of USA-flagged trawlers:
# 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 6258 vesselIds
belonging to 4008 vessels.
usa_trawlers$selfReportedInfo
#> # A tibble: 6,392 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 d32af732… 3680… SUPERMA… SUPERMAN… USA WDJ8890 <NA> 267397
#> 2 1 5446e7cd… 3680… SUPERMA… SUPERMAN… USA <NA> <NA> 6184
#> 3 2 c698dfcc… 3677… TREMONT TREMONT USA WDJ5556 <NA> 71116
#> 4 3 35eb371c… 3677… STARBRI… STARBRITE USA WDI5354 <NA> 1215905
#> 5 4 454a28f8… 3662… BERNADE… BERNADET… USA WBB6685 <NA> 315835
#> 6 4 131a18c5… 3662… BERNADE… BERNADET… USA <NA> <NA> 37093
#> 7 4 0a595b92… 3662… <NA> <NA> USA WBB6685 <NA> 2772
#> 8 5 23f8d2c6… 3673… COHO COHO USA WDE2935 <NA> 731293
#> 9 6 eb8f2306… 3682… STORMI STORMI USA WDM4977 <NA> 690507
#> 10 6 fb8fc988… 3682… STORMI STORMI USA <NA> <NA> 53
#> # ℹ 6,382 more rows
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>
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 $selfReportedInfo
dataset.
Note:
get_event()
can receive severalvesselIds
at a time but will fail when the character length of the whole request is too long (~100,000 characters). This means it will fail with error HTTP 422: Unprocessable entity when too manyvesselIds
are requested, this value can be around 2,800vesselIds
depending on the other parameters of the search.
For this example, we will send the vesselIds
corresponding to the first twenty vessels in the response:
each_USA_trawler <- usa_trawlers$selfReportedInfo[, c("index", "vesselId")]
# how many vessels correspond to the first twenty vessels.
(twenty_usa_trawlers <- each_USA_trawler %>% filter(index <= 20))
#> # A tibble: 52 × 2
#> index vesselId
#> <dbl> <chr>
#> 1 1 d32af7320-0748-9a63-abd7-48ad721e63b8
#> 2 1 5446e7cd1-1f75-4672-d859-01211df72fba
#> 3 2 c698dfcc5-5c85-9329-b1ac-8b3656ea9233
#> 4 3 35eb371c0-088a-1382-098a-c7fea019d959
#> 5 4 454a28f85-56e4-93cb-efa6-ff786439e8da
#> 6 4 131a18c5e-e5a2-d512-2370-5f74ec044ef8
#> 7 4 0a595b92a-a8c4-81c7-277b-52a3ffb49a65
#> 8 5 23f8d2c62-2a1e-d203-5be1-7fcc324f1c9b
#> 9 6 eb8f2306e-ea4d-1927-a657-df59a075fce1
#> 10 6 fb8fc9883-32e3-783e-f17e-217e1388e348
#> # ℹ 42 more rows
There are 52 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:
fishing_events <- get_event(event_type = "FISHING",
vessels = twenty_usa_trawlers$vesselId,
start_date = "2020-01-01",
end_date = "2020-02-01")
#> [1] "Downloading 65 events from GFW"
fishing_events
#> # A tibble: 65 × 16
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2020-01-25 12:32:16 2020-01-25 13:32:16 0b0e500f2c7e3… fishing 39.9 -73.0
#> 2 2020-01-16 15:07:04 2020-01-16 17:16:20 db046d9ebb664… fishing 41.4 -69.3
#> 3 2020-01-17 09:10:03 2020-01-17 17:57:00 e5d2760a2cd5e… fishing 41.5 -70.1
#> 4 2020-01-05 15:00:22 2020-01-06 00:35:34 873cf3ee8755c… fishing 41.4 -68.7
#> 5 2020-01-15 21:35:14 2020-01-16 11:01:12 2c309e235a0d1… fishing 41.4 -68.7
#> 6 2020-01-27 11:56:45 2020-01-29 13:11:20 fcec3129ef5e2… fishing 40.0 -72.6
#> 7 2020-01-31 07:58:03 2020-01-31 13:32:38 ce97b3eedf575… fishing 42.8 -125.
#> 8 2020-01-13 20:43:47 2020-01-14 04:08:49 ea4db5be49f44… fishing 41.4 -68.7
#> 9 2020-01-14 19:50:50 2020-01-14 22:03:49 b3cebaf7afe78… fishing 25.1 -158.
#> 10 2019-12-31 01:38:26 2020-01-02 05:06:56 b630325a08b11… fishing 41.4 -68.7
#> # ℹ 55 more rows
#> # ℹ 10 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vesselId <chr>, vessel_name <chr>, vessel_ssvid <chr>, vessel_flag <chr>,
#> # vessel_type <chr>, vessel_publicAuthorizations <list>, event_info <list>
The columns starting by vessel
hold the vessel-related information for each event: vesselId
, vessel_name
, ssvid
(MMSI), flag
, vessel type
and public authorizations.
fishing_events %>%
dplyr::select(starts_with("vessel"))
#> # A tibble: 65 × 6
#> vesselId vessel_name vessel_ssvid vessel_flag vessel_type
#> <chr> <chr> <chr> <chr> <chr>
#> 1 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 2 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 3 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 4 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 5 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 6 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 7 454a28f85-56e4-93cb-efa6-ff… BERNADETTE 366233570 USA fishing
#> 8 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> 9 730395c31-185e-5b04-2530-d6… LADY PAULI… 367645140 USA fishing
#> 10 35eb371c0-088a-1382-098a-c7… STARBRITE 367707710 USA fishing
#> # ℹ 55 more rows
#> # ℹ 1 more variable: vessel_publicAuthorizations <list>
When no events are available, the get_event()
function returns nothing.
get_event(event_type = "FISHING",
vessels = twenty_usa_trawlers$vesselId[2],
start_date = "2020-01-01",
end_date = "2020-01-01"
)
#> [1] "Your request returned zero results"
#> NULL
The get_raster()
function gets a raster from the 4Wings API and converts the response to a data frame. In order to use it, you should specify:
LOW
(0.1 degree) or HIGH
(0.01 degree)HOURLY
, DAILY
, MONTHLY
, YEARLY
or ENTIRE
.FLAG
, GEARTYPE
, FLAGANDGEARTYPE
, MMSI
or VESSEL_ID
note: this must be 366 days or less
sf
format or the region code (such as an EEZ code) to filter the rasterEEZ
, MPA
, RFMO
or USER_SHAPEFILE
(for sf
shapefiles).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:
data("test_shape")
test_shape
#> Simple feature collection with 1 feature and 0 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 56.74815 ymin: 0 xmax: 70 ymax: 21.79799
#> Geodetic CRS: WGS 84
#> geometry
#> 1 MULTIPOLYGON (((70 15.20471...
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
)
#> # A tibble: 2,618 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 15.2 61.4 2021 CHN 3 13.3
#> 2 15.1 60.4 2021 CHN 2 15.6
#> 3 15.6 62.7 2021 CHN 2 35.7
#> 4 15.7 63.7 2021 CHN 2 14.6
#> 5 4.6 65.7 2021 TWN 1 1.82
#> 6 5.8 67.2 2021 TWN 1 10.2
#> 7 15.4 60.6 2021 CHN 1 7.81
#> 8 12.5 62.2 2021 CHN 1 6.18
#> 9 6 68.8 2021 <NA> 1 5.46
#> 10 1.5 68.2 2021 TWN 1 1.04
#> # ℹ 2,608 more rows
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"
):
# 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")
#> # A tibble: 577 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 1.7 -5.6 2021 BLZ 1 0.39
#> 2 4 -3.6 2021 BES 1 2.99
#> 3 3.1 -4 2021 FRA 1 5.1
#> 4 4.5 -3.8 2021 PAN 1 2.24
#> 5 4.7 -5.8 2021 CHN 1 3.62
#> 6 4.6 -4 2021 ESP 1 1.07
#> 7 4.7 -3.9 2021 SLV 1 1.04
#> 8 4.8 -4.5 2021 GHA 1 4.19
#> 9 2.3 -5 2021 ESP 1 0.15
#> 10 1.6 -6.7 2021 GHA 1 1.51
#> # ℹ 567 more rows
You could search for just one word in the name of the EEZ and then decide which one you want:
(get_region_id(region_name = "France", region_source = "EEZ"))
#> # A tibble: 3 × 3
#> id label iso3
#> <int> <chr> <chr>
#> 1 5677 France FRA
#> 2 48966 Joint regime area Spain / France FRA
#> 3 48976 Joint regime area Italy / France FRA
From the results above, let’s say we’re interested in the French Exclusive Economic Zone, 5677
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"
)
#> # A tibble: 5,430 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 45.2 -3 2021 FRA 12 234.
#> 2 45.1 -3.3 2021 ESP 22 52.5
#> 3 41.3 8.9 2021 ITA 1 1.04
#> 4 43.4 4.5 2021 FRA 12 87.8
#> 5 44.4 -1.6 2021 FRA 4 64.8
#> 6 46.2 -3.8 2021 FRA 11 76.5
#> 7 46.1 -3.5 2021 ESP 5 13.3
#> 8 49.9 -1.2 2021 FRA 9 76.0
#> 9 43.3 4.4 2021 FRA 21 328.
#> 10 50 0.2 2021 GBR 3 175.
#> # ℹ 5,420 more rows
A similar approach can be used to search for a specific Marine Protected Area, in this case the Phoenix Island Protected Area (PIPA)
# 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
#> # A tibble: 2 × 3
#> id label NAME
#> <chr> <chr> <chr>
#> 1 309888 Phoenix Islands Protected Area - Protected Area Phoe…
#> 2 555512002 Phoenix Islands Protected Area - World Heritage Site (natural… Phoe…
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")
#> # A tibble: 38 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 -3.6 -176. 2015 KOR 1 1.98
#> 2 -3.6 -176. 2015 KIR 1 6.07
#> 3 -2.8 -176. 2015 KOR 1 10.4
#> 4 -3.5 -176. 2015 KOR 1 3.11
#> 5 -1 -170. 2015 KOR 1 2.39
#> 6 -4.1 -176. 2015 KOR 1 1.57
#> 7 -2.9 -176. 2015 FSM 1 5.09
#> 8 -4.7 -176. 2015 KOR 2 13.7
#> 9 -2.9 -176. 2015 FSM 1 2.77
#> 10 -2.6 -176. 2015 KOR 1 13.9
#> # ℹ 28 more rows
It is also possible to filter rasters to regional fisheries management organizations (RFMO) like "ICCAT"
, "IATTC"
, "IOTC"
, "CCSBT"
and "WCPFC"
.
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")
#> # A tibble: 16,424 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <date> <chr> <dbl> <dbl>
#> 1 38.9 26.8 2021-01-02 TUR 2 3
#> 2 59 0.1 2021-01-03 GBR 2 13.9
#> 3 59.9 25.1 2021-01-03 FIN 2 3
#> 4 59.9 -2.8 2021-01-03 GBR 1 3.32
#> 5 47.6 -3 2021-01-02 FRA 1 2.41
#> 6 51.4 -8.9 2021-01-02 IRL 1 0.79
#> 7 58.9 10.6 2021-01-03 SWE 1 0.58
#> 8 38.7 26.7 2021-01-03 TUR 1 13.4
#> 9 15.7 -29.7 2021-01-03 JPN 1 1.09
#> 10 11.8 -16.8 2021-01-03 KOR 1 0.88
#> # ℹ 16,414 more rows
Note: For a complete list of MPAs, RFMOs and EEZ, check the function
get_regions()
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 apis@globalfishingwatch.org
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 documentation on the Global Fishing Watch API page.
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.
fishing_events <- get_event(event_type = "FISHING",
vessels = twenty_usa_trawlers$vesselId,
start_date = "2020-01-01",
end_date = "2020-02-01") %>%
# extract EEZ id code
dplyr::mutate(eez = as.character(
purrr::map(purrr::map(regions, purrr::pluck, "eez"),
paste0, collapse = ","))) %>%
dplyr::select(eez, eventId, eventType, start, end, lat, lon)
#> [1] "Downloading 65 events from GFW"
fishing_events
#> # A tibble: 65 × 7
#> eez eventId eventType start end lat lon
#> <chr> <chr> <chr> <dttm> <dttm> <dbl> <dbl>
#> 1 8456 0b0e500… fishing 2020-01-25 12:32:16 2020-01-25 13:32:16 39.9 -73.0
#> 2 8456 db046d9… fishing 2020-01-16 15:07:04 2020-01-16 17:16:20 41.4 -69.3
#> 3 8456 e5d2760… fishing 2020-01-17 09:10:03 2020-01-17 17:57:00 41.5 -70.1
#> 4 8456 873cf3e… fishing 2020-01-05 15:00:22 2020-01-06 00:35:34 41.4 -68.7
#> 5 8456 2c309e2… fishing 2020-01-15 21:35:14 2020-01-16 11:01:12 41.4 -68.7
#> 6 8456 fcec312… fishing 2020-01-27 11:56:45 2020-01-29 13:11:20 40.0 -72.6
#> 7 8456 ce97b3e… fishing 2020-01-31 07:58:03 2020-01-31 13:32:38 42.8 -125.
#> 8 8456 ea4db5b… fishing 2020-01-13 20:43:47 2020-01-14 04:08:49 41.4 -68.7
#> 9 8453 b3cebaf… fishing 2020-01-14 19:50:50 2020-01-14 22:03:49 25.1 -158.
#> 10 8456 b630325… fishing 2019-12-31 01:38:26 2020-01-02 05:06:56 41.4 -68.7
#> # ℹ 55 more rows
We can apply get_region_id()
to the numeric vector to extract the labels:
fishing_events %>%
mutate(eez_name = purrr::map_df(as.numeric(fishing_events$eez),
~get_region_id(region_name = .x,
region_source = "EEZ"))$label) %>%
dplyr::select(-start, -end)
#> # A tibble: 65 × 6
#> eez eventId eventType lat lon eez_name
#> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 8456 0b0e500f2c7e3b52361b7572a7f47763 fishing 39.9 -73.0 United States
#> 2 8456 db046d9ebb6646aadd5be1d197afa726 fishing 41.4 -69.3 United States
#> 3 8456 e5d2760a2cd5ed8c96372a822aa4dd48 fishing 41.5 -70.1 United States
#> 4 8456 873cf3ee8755c9c201383734758c982a fishing 41.4 -68.7 United States
#> 5 8456 2c309e235a0d16a0cafcd5895936c0f3 fishing 41.4 -68.7 United States
#> 6 8456 fcec3129ef5e248a6723d4794bb2d04d fishing 40.0 -72.6 United States
#> 7 8456 ce97b3eedf575b960a40fe9437ef8477 fishing 42.8 -125. United States
#> 8 8456 ea4db5be49f44b7d7277acc076decf4e fishing 41.4 -68.7 United States
#> 9 8453 b3cebaf7afe78efc08fa5404792d046e fishing 25.1 -158. Hawaii
#> 10 8456 b630325a08b1101ca4d9c3336c79474b fishing 41.4 -68.7 United States
#> # ℹ 55 more rows