Important
This version ofgfwrgives 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
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:
- Vessels API: vessel search and identity based on AIS self reported data and public registry information
- Events API: encounters, loitering, port visits, AIS-disabling events and fishing events based on AIS data
- Gridded fishing effort (4Wings API): apparent fishing effort based on AIS data
Note: See the Terms of Use page for GFW 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")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)
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.
Then use the gfw_auth() helper function to inform the key on your
function calls. You can use gfw_auth() directly or save the
information to an object in your R workspace every time and pass it to
subsequent gfwr functions.
So you can do:
key <- gfw_auth()or this
key <- Sys.getenv("GFW_TOKEN")Note:
gfwrfunctions are set to usekey = gfw_auth()by default.
The get_vessel_info() function allows you to get vessel identity
details from the GFW Vessels
API.
There are two search types: search, and id.
searchis performed by using parametersqueryfor basic searches andwherefor advanced searchers using SQL expressionsquerytakes a single identifier that can be the MMSI, IMO, callsign, or shipname as input and identifies all vessels that match.wheresearch 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)includesadds information from public registries. Options are “MATCH_CRITERIA”, “OWNERSHIP” and “AUTHORIZATIONS”
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",
key = key)
#> 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 × 16
#> index recordId sourceCode ssvid flag shipname nShipname callsign imo
#> <dbl> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 e0c9823749264a… <chr [6]> 2242… ESP AGURTZA… AGURTZAB… EBSJ 8733…
#> # ℹ 7 more variables: transmissionDateFrom <chr>, transmissionDateTo <chr>,
#> # geartypes <chr>, lengthM <dbl>, tonnageGt <int>, 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>To do more specific searches (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",
key = key)
#> 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 [2]> 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 [1]> 2024-08-07T10:… 2024-…
#> 2 1 FISHING CARGO SERVICES PAN 613590000 <chr [1]> 2022-01-24T09:… 2024-…
#> 3 1 FISHING CARGO SERVICES PAN 214182732 <chr [1]> 2019-02-23T11:… 2022-…
#> 4 1 FISHING CARGO SERVICES PAN 616852000 <chr [1]> 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 0b7047cb5-58c8-6e63-… CARRIER GFW_VESSEL_LIST 2019
#> 3 1 1da8dbc23-3c48-d5ce-… CARRIER GFW_VESSEL_LIST 2022
#> 4 1 9827ea1ea-a120-f374-… CARRIER GFW_VESSEL_LIST 2024
#> # ℹ 5 more variables: geartypes_yearTo <int>, shiptypes_name <chr>,
#> # shiptypes_source <chr>, shiptypes_yearFrom <int>, shiptypes_yearTo <int>
#>
#> $selfReportedInfo
#> # A tibble: 4 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 9827ea1ea… 6290… GABU RE… GABUREEF… GMB C5J278 8300… 235095
#> 2 1 1da8dbc23… 6135… GABU RE… GABUREEF… CMR TJMC996 8300… 973251
#> 3 1 0b7047cb5… 2141… GABU RE… GABUREEF… MDA ER2732 8300… 642750
#> 4 1 58cf536b1… 6168… GABU RE… GABUREEF… COM D6FJ2 8300… 469834
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>- The
idsearch allows the user to specify a vector ofvesselIds
Note:
vesselIdis an internal ID generated by GFW to connect data accross APIs and involves a combination of vessel and tracking data information. It can be retrieved usingget_vessel_info()and fetching the vector of responses inside$selfReportedInfo$vesselId. See the identity vignette for more information.
To search by vesselId, use parameter ids and specify
search_type = "id":
get_vessel_info(ids = "8c7304226-6c71-edbe-0b63-c246734b3c01",
search_type = "id",
key = key)
#> 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 [3]> 2733… RUS FRIO FO… FRIOFORW… UCRZ 9076…
#> 2 1 a8d00ce54b37ad… <chr [2]> 5111… PLW FRIO FO… FRIOFORW… T8A4891 9076…
#> 3 1 a8d00ce54b37ad… <chr [6]> 2106… CYP FRIO FO… FRIOFORW… 5BWC3 9076…
#> 4 1 a8d00ce54b37ad… <chr [2]> 3413… KNA FRIO FO… FRIOFORW… V4JQ3 9076…
#> 5 1 a8d00ce54b37ad… <chr [2]> 3546… PAN FRIO AE… FRIOAEGE… 3FGY4 9076…
#> # ℹ 8 more variables: latestVesselInfo <lgl>, transmissionDateFrom <chr>,
#> # transmissionDateTo <chr>, geartypes <chr>, lengthM <int>, tonnageGt <int>,
#> # 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 [1]> 2015-02-27T10:59:43Z 2024-11-30T23:3…
#> 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 2024-12-01T00:00:00Z 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 2024-12-01T00:00:00Z 273379740 <chr [1]>
#>
#> $combinedSourcesInfo
#> # A tibble: 5 × 10
#> index vesselId geartypes_name geartypes_source geartypes_yearFrom
#> <dbl> <chr> <chr> <chr> <int>
#> 1 1 3c81a942b-bf0a-f476-… CARRIER GFW_VESSEL_LIST 2015
#> 2 1 8c7304226-6c71-edbe-… CARRIER GFW_VESSEL_LIST 2013
#> 3 1 da1cd7e1b-b8d0-539c-… CARRIER GFW_VESSEL_LIST 2015
#> 4 1 0cb77880e-ee49-2ce4-… CARRIER GFW_VESSEL_LIST 2012
#> 5 1 0edad163f-f53d-9ddb-… CARRIER GFW_VESSEL_LIST 2024
#> # 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',
key = key)
#> 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 2
#> 2 1
#> 3 5
#>
#> $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 b82d02e5c2c11e… <chr [5]> 4417… KOR ADRIA ADRIA DTBY3 8919…
#> 2 1 b82d02e5c2c11e… <chr [4]> 4417… KOR PREMIER PREMIER DTBY3 8919…
#> 3 2 685862e0626f62… <chr [5]> 5480… PHL JOHNREY… JOHNREYN… DUQA7 8118…
#> 4 3 a8d00ce54b37ad… <chr [3]> 2733… RUS FRIO FO… FRIOFORW… UCRZ 9076…
#> 5 3 a8d00ce54b37ad… <chr [2]> 5111… PLW FRIO FO… FRIOFORW… T8A4891 9076…
#> 6 3 a8d00ce54b37ad… <chr [6]> 2106… CYP FRIO FO… FRIOFORW… 5BWC3 9076…
#> 7 3 a8d00ce54b37ad… <chr [2]> 3413… KNA FRIO FO… FRIOFORW… V4JQ3 9076…
#> 8 3 a8d00ce54b37ad… <chr [2]> 3546… PAN FRIO AE… 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: 5 × 7
#> index name flag ssvid sourceCode dateFrom dateTo
#> <dbl> <chr> <chr> <chr> <list> <chr> <chr>
#> 1 1 DONGWON INDUSTRIES KOR 441734000 <chr [2]> 2013-09… 2024-…
#> 2 2 TRANS PACIFIC JOURNEY FISHING PHL 548012100 <chr [4]> 2017-02… 2019-…
#> 3 3 COLINER RUS 273379740 <chr [1]> 2015-02… 2024-…
#> 4 3 COLINER CYP 511101495 <chr [1]> 2024-07… 2024-…
#> 5 3 COLINER CYP 210631000 <chr [1]> 2013-05… 2024-…
#>
#> $registryPublicAuthorizations
#> # A tibble: 8 × 5
#> index dateFrom dateTo ssvid sourceCode
#> <dbl> <chr> <chr> <chr> <list>
#> 1 1 2015-10-08T00:00:00Z 2020-07-21T00:00:00Z 441734000 <chr [1]>
#> 2 1 2012-01-01T00:00:00Z 2013-09-19T00:00:00Z 441734000 <chr [1]>
#> 3 1 2013-09-20T00:00:00Z 2024-12-01T00:00:00Z 441734000 <chr [1]>
#> 4 2 2012-01-01T00:00:00Z 2017-10-25T00:00:00Z 548012100 <chr [1]>
#> 5 2 2019-02-10T18:02:49Z 2024-12-01T00:00:00Z 548012100 <chr [1]>
#> 6 3 2023-01-01T00:00:00Z 2024-12-01T00:00:00Z 210631000 <chr [1]>
#> 7 3 2020-01-01T00:00:00Z 2024-12-01T00:00:00Z 210631000 <chr [1]>
#> 8 3 2024-08-09T00:00:00Z 2024-12-01T00:00:00Z 273379740 <chr [1]>
#>
#> $combinedSourcesInfo
#> # A tibble: 9 × 10
#> index vesselId geartypes_name geartypes_source geartypes_yearFrom
#> <dbl> <chr> <chr> <chr> <int>
#> 1 1 aca119c29-95dd-f5c4-… TUNA_PURSE_SE… COMBINATION_OF_… 2012
#> 2 1 6583c51e3-3626-5638-… TUNA_PURSE_SE… COMBINATION_OF_… 2013
#> 3 2 55889aefb-bef9-224c-… TUNA_PURSE_SE… COMBINATION_OF_… 2017
#> 4 2 71e7da672-2451-17da-… TUNA_PURSE_SE… COMBINATION_OF_… 2017
#> 5 3 3c81a942b-bf0a-f476-… CARRIER GFW_VESSEL_LIST 2015
#> 6 3 8c7304226-6c71-edbe-… CARRIER GFW_VESSEL_LIST 2013
#> 7 3 da1cd7e1b-b8d0-539c-… CARRIER GFW_VESSEL_LIST 2015
#> 8 3 0cb77880e-ee49-2ce4-… CARRIER GFW_VESSEL_LIST 2012
#> 9 3 0edad163f-f53d-9ddb-… CARRIER GFW_VESSEL_LIST 2024
#> # 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 6583c51e3… 4417… ADRIA ADRIA KOR DTBY3 <NA> 360249
#> 2 2 71e7da672… 5480… JOHN RE… JOHNREYN… PHL DUQA-7 8118… 133081
#> 3 3 8c7304226… 2106… FRIO FO… FRIOFORW… CYP 5BWC3 9076… 3369802
#> # ℹ 5 more variables: positionsCounter <int>, sourceCode <list>,
#> # matchFields <chr>, transmissionDateFrom <chr>, transmissionDateTo <chr>This is useful especially because a vessel can have different
vesselIds in time.
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 GFW 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",
key = key
)
#> [1] "Downloading 286 events from GFW"
#> # A tibble: 286 × 16
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2020-01-01 00:00:00 2020-01-02 15:40:00 2c90a8d0717c0… encounter -17.5 -79.4
#> 2 2020-01-01 08:30:00 2020-01-02 23:50:00 18937559dfefb… encounter 38.8 121.
#> 3 2020-01-01 13:30:00 2020-01-01 21:40:00 9ce327a81b0c3… encounter 38.5 121.
#> 4 2020-01-01 16:10:00 2020-01-01 18:30:00 bfffbb6b67ec7… encounter -19.5 -106.
#> 5 2020-01-01 21:30:00 2020-01-02 03:50:00 bac8b5eed8e33… encounter 43.4 135.
#> 6 2020-01-01 18:00:00 2020-01-01 21:20:00 e5fc17eae3613… encounter 38.5 121.
#> 7 2020-01-01 21:30:00 2020-01-02 03:50:00 bac8b5eed8e33… encounter 43.4 135.
#> 8 2020-01-01 16:30:00 2020-01-01 19:30:00 0aa8a30f0c491… encounter 24.7 122.
#> 9 2020-01-01 12:00:00 2020-01-01 15:50:00 5c3ed91fa38aa… encounter -18.1 -79.3
#> 10 2020-01-01 00:00:00 2020-01-01 11:20:00 255d53a814907… encounter 44.5 136.
#> # ℹ 276 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 a bulk download solution for 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',
key = gfw_auth())
#> [1] "Downloading 59 events from GFW"
#> # A tibble: 59 × 16
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2020-10-01 12:09:55 2020-10-02 22:03:36 d8ed35e85829… fishing 4.73 -51.5
#> 2 2020-10-18 11:48:46 2020-10-18 19:40:20 d49e316d616e… fishing 0.753 -47.8
#> 3 2020-10-07 22:56:40 2020-10-08 01:48:45 238db9546e86… fishing -0.0045 -47.8
#> 4 2020-10-20 14:14:15 2020-10-20 19:47:10 aae6ff441593… fishing 0.252 -47.9
#> 5 2020-10-19 11:28:05 2020-10-19 15:56:47 52b41baac7ea… fishing 0.376 -47.8
#> 6 2020-10-08 05:13:22 2020-10-08 08:43:30 b8f2d96e040a… fishing -0.0841 -47.7
#> 7 2020-10-08 23:45:15 2020-10-09 02:27:47 e622d1ce0a78… fishing 0.0269 -47.9
#> 8 2020-10-17 17:59:45 2020-10-17 19:45:10 b08ba788b149… fishing 0.222 -47.7
#> 9 2020-10-18 21:29:13 2020-10-18 22:28:02 eb82a3cc27a9… fishing 4.95 -51.6
#> 10 2020-10-22 11:17:28 2020-10-22 18:02:00 5c3819505497… fishing 4.71 -51.5
#> # 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 $selfReportedInfo in the response.
vessel_info <- get_vessel_info(query = 224224000, key = key)
#> 1 total vessels
vessel_info$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,
key = key
)
#> [1] "Downloading 25 events from GFW"
#> # A tibble: 25 × 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 2020-08-08 06:40:40 2020-08-10 08:13:39 acd48bf28e6b3… port_vis… 14.6 -17.4
#> 3 2020-08-11 11:43:45 2020-08-11 19:34:16 c6042c5da685f… port_vis… 14.7 -17.4
#> 4 2020-02-23 12:44:03 2020-02-24 10:35:02 672bc20417b3c… port_vis… 16.9 -25.0
#> 5 2021-05-19 22:46:40 2021-06-08 08:54:49 ed0ffc8600077… port_vis… 14.7 -17.4
#> 6 2020-09-26 16:17:55 2020-10-01 18:59:15 80f2a666bf03b… port_vis… 5.20 -4.02
#> 7 2020-01-27 08:04:38 2020-02-23 10:18:02 abed2e0c06e4e… port_vis… 16.9 -25.0
#> 8 2020-04-19 06:16:46 2020-04-21 14:02:19 5ad5c93c5448d… port_vis… 28.1 -15.4
#> 9 2021-11-11 18:41:10 2021-11-20 18:43:26 af0cb5d7ee288… port_vis… 5.20 -4.04
#> 10 2019-11-15 14:15:11 2019-11-19 07:49:20 bbeed3f884a6f… port_vis… 5.20 -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, you can also use the whole vector of
vesselId for that vessel:
get_event(event_type = 'PORT_VISIT',
vessels = id, #using the whole vector of vesselIds
confidences = 4,
key = key
)
#> [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 c56caaedee80… port_vis… 5.23 -4.02
#> 2 2020-08-08 06:40:40 2020-08-10 08:13:39 acd48bf28e6b… port_vis… 14.6 -17.4
#> 3 2020-08-11 11:43:45 2020-08-11 19:34:16 c6042c5da685… port_vis… 14.7 -17.4
#> 4 2018-07-09 06:50:58 2018-07-10 19:13:31 c13d6051d577… port_vis… 0.351 6.76
#> 5 2018-01-29 06:46:19 2018-02-01 15:22:03 fb5a55ad2159… port_vis… 14.6 -17.4
#> 6 2021-05-19 22:46:40 2021-06-08 08:54:49 ed0ffc860007… port_vis… 14.7 -17.4
#> 7 2020-09-26 16:17:55 2020-10-01 18:59:15 80f2a666bf03… port_vis… 5.20 -4.02
#> 8 2017-03-09 17:19:17 2017-03-15 09:00:37 6e1a4cdb4b89… port_vis… 5.23 -4.02
#> 9 2016-02-25 14:26:38 2016-03-01 13:21:21 6265aa07f2c1… port_vis… 5.23 -3.97
#> 10 2020-12-14 09:46:52 2020-12-22 16:54:09 9205a53a7b91… port_vis… 5.20 -4.04
#> # 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_dateandend_dateif 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, theidfield for encounter events includes an additional suffix (1 or 2) separated by a period. Thevesselfield 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",
key = key,
quiet = TRUE
)
# Set quiet = FALSE if you want an estimate progress of the downloadThis list returns 6441 vesselIds belonging to 4168 vessels.
usa_trawlers$selfReportedInfo
#> # A tibble: 6,441 × 14
#> index vesselId ssvid shipname nShipname flag callsign imo messagesCounter
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1 64907178… 3662… SUSAN L SUSANL USA WQZ4631 <NA> 1659566
#> 2 2 c698dfcc… 3677… TREMONT TREMONT USA WDJ5556 <NA> 71116
#> 3 3 47b94476… 3668… ORION ORION USA <NA> <NA> 23007
#> 4 4 9f555214… 3680… PELICAN PELICAN USA WDJ8253 <NA> 319425
#> 5 4 15cea26f… 3680… PELICAN PELICAN USA <NA> <NA> 407
#> 6 5 242fa3fb… 3670… TAUNY A… TAUNYANN USA WDC4097 <NA> 2131617
#> 7 6 0dddd2a8… 3673… SHAMROCK SHAMROCK USA WDD8722 <NA> 2720
#> 8 6 695b254f… 3673… SHAMROCK SHAMROCK USA <NA> <NA> 477
#> 9 6 ac994bda… 3673… <NA> <NA> USA WDD8722 <NA> 3179
#> 10 7 bc29946f… 3667… ALEX ALEX USA WDA2216 <NA> 663521
#> # ℹ 6,431 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
$selfReportedInfo dataset.
Note:
get_event()can receive severalvesselIdsat a time but will fail when the character length of the whole request is too long (~100,000 characters). This means it will fail with error HTTP 422: Unprocessable entity when too manyvesselIdsare requested, this value can be around 2,800vesselIdsdepending on the other parameters of the search.
For this example, we will send the vesselIds corresponding to the
first twenty vessels in the response:
each_USA_trawler <- usa_trawlers$selfReportedInfo[, c("index", "vesselId")]
# how many vessels correspond to the first twenty vessels.
(twenty_usa_trawlers <- each_USA_trawler %>% filter(index <= 20))
#> # A tibble: 47 × 2
#> index vesselId
#> <dbl> <chr>
#> 1 1 64907178b-b02a-f401-afa1-b3a099d7a142
#> 2 2 c698dfcc5-5c85-9329-b1ac-8b3656ea9233
#> 3 3 47b944765-5819-b2ab-8c2e-cfc82bd2e82c
#> 4 4 9f5552145-50ed-92f4-4514-5177b1a6511d
#> 5 4 15cea26f5-57ad-acac-4cbf-b45cefb7ab04
#> 6 5 242fa3fbf-fa03-eb47-5855-f0880b8e7acf
#> 7 6 0dddd2a83-3626-24f1-0fe6-3c4d45bbb409
#> 8 6 695b254f7-7e6c-ff50-dc63-55139d9e0101
#> 9 6 ac994bdab-b59c-9fcc-659e-40179e5dddfb
#> 10 7 bc29946f2-2b0b-9613-054a-cd59327226d9
#> # ℹ 37 more rowsThere are 47 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:
events <- get_event(event_type = 'FISHING',
vessels = twenty_usa_trawlers$vesselId,
start_date = "2020-01-01",
end_date = "2020-02-01",
key = key)
#> [1] "Downloading 38 events from GFW"
events
#> # A tibble: 38 × 16
#> start end eventId eventType lat lon
#> <dttm> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 2020-01-31 07:58:03 2020-01-31 13:32:38 ce97b3eedf575… fishing 42.8 -125.
#> 2 2020-01-03 07:38:49 2020-01-03 18:22:45 e67a584f28389… fishing 37.9 -123.
#> 3 2020-01-07 05:10:47 2020-01-07 08:57:13 72d1e4f6bf30b… fishing 28.1 -93.9
#> 4 2020-01-06 07:35:06 2020-01-06 10:20:02 47bbfb7d2f0ee… fishing 45.9 -124.
#> 5 2020-01-24 00:18:48 2020-01-24 11:42:20 1b55f469ff119… fishing 38.1 -73.9
#> 6 2020-01-14 12:21:55 2020-01-14 15:39:38 ab0541c79191f… fishing 37.5 -74.4
#> 7 2020-01-30 23:49:26 2020-01-31 04:34:08 90c53f1ca7571… fishing 38 -73.9
#> 8 2020-01-29 10:01:51 2020-01-29 12:21:51 25faed9edb8e8… fishing 42.8 -125.
#> 9 2020-01-31 15:05:36 2020-01-31 21:32:05 32c27b8be6040… fishing 43.0 -125.
#> 10 2020-01-31 05:25:15 2020-01-31 06:43:40 ea857a15cef2e… fishing 42.8 -125.
#> # ℹ 28 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.
events %>%
dplyr::select(starts_with("vessel"))
#> # A tibble: 38 × 6
#> vesselId vessel_name vessel_ssvid vessel_flag vessel_type
#> <chr> <chr> <chr> <chr> <chr>
#> 1 454a28f85-56e4-93cb-efa6-ff… BERNADETTE 366233570 USA fishing
#> 2 47a337946-6ed2-b5b7-adf8-b8… MISTASEA 338200347 USA fishing
#> 3 1d9aff16a-a1fc-b65a-3bf0-be… JOYA CRUZ 367192020 USA fishing
#> 4 0bc8f5c22-27d2-92d9-a979-2a… GRANADA 367156340 USA fishing
#> 5 64907178b-b02a-f401-afa1-b3… SUSAN L 366211560 USA fishing
#> 6 64907178b-b02a-f401-afa1-b3… SUSAN L 366211560 USA fishing
#> 7 64907178b-b02a-f401-afa1-b3… SUSAN L 366211560 USA fishing
#> 8 454a28f85-56e4-93cb-efa6-ff… BERNADETTE 366233570 USA fishing
#> 9 454a28f85-56e4-93cb-efa6-ff… BERNADETTE 366233570 USA fishing
#> 10 454a28f85-56e4-93cb-efa6-ff… BERNADETTE 366233570 USA fishing
#> # ℹ 28 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",
key = key
)
#> [1] "Your request returned zero results"
#> NULLThe 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:
- The spatial resolution, which can be
LOW(0.1 degree) orHIGH(0.01 degree) - The temporal resolution, which can be
HOURLY,DAILY,MONTHLY,YEARLYorENTIRE. - The variable to group by:
FLAG,GEARTYPE,FLAGANDGEARTYPE,MMSIorVESSEL_ID - The date range
note: this must be 366 days or less - The region polygon in
sfformat or the region code (such as an EEZ code) to filter the raster - The source for the specified region. Currently,
EEZ,MPA,RFMOorUSER_SHAPEFILE(forsfshapefiles).
You can load an sf shapefile with your area of interest and fetch
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,
key = key
)
#> Rows: 2618 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): flag
#> dbl (5): Lat, Lon, Time Range, Vessel IDs, Apparent Fishing Hours
#>
#> 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.
#> # A tibble: 2,618 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 12.5 62.9 2021 CHN 1 3.75
#> 2 15.9 62.6 2021 CHN 1 7.66
#> 3 0.7 68 2021 LKA 1 3.08
#> 4 15.2 63.8 2021 CHN 3 25.8
#> 5 15.1 62.8 2021 CHN 1 4.82
#> 6 4.6 65.9 2021 LKA 1 11.8
#> 7 12 62.3 2021 CHN 1 5.89
#> 8 15.6 62.4 2021 CHN 1 3.37
#> 9 15.7 63.4 2021 CHN 2 13.0
#> 10 4.4 67.5 2021 LKA 1 3.39
#> # ℹ 2,608 more rowsIf 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 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', key = key)
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',
key = key)
#> Rows: 577 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): flag
#> dbl (5): Lat, Lon, Time Range, Vessel IDs, Apparent Fishing Hours
#>
#> 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.
#> # A tibble: 577 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 4.7 -5.8 2021 CHN 1 3.62
#> 2 4.5 -3.8 2021 PAN 1 2.24
#> 3 1.7 -5.6 2021 BLZ 1 0.39
#> 4 4 -3.6 2021 BES 1 2.99
#> 5 3.1 -4 2021 FRA 1 5.1
#> 6 2.6 -4.3 2021 FRA 1 0.09
#> 7 4.6 -4 2021 ESP 1 1.07
#> 8 4.7 -3.9 2021 SLV 1 1.04
#> 9 4.8 -4.5 2021 GHA 1 4.19
#> 10 2.3 -5 2021 ESP 1 0.15
#> # ℹ 567 more rowsYou 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', key = key))
#> # A tibble: 3 × 3
#> id label iso3
#> <dbl> <chr> <chr>
#> 1 5677 France FRA
#> 2 48966 Joint regime area Spain / France FRA
#> 3 48976 Joint regime area Italy / France FRAFrom 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',
key = key)
#> Rows: 5430 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): flag
#> dbl (5): Lat, Lon, Time Range, Vessel IDs, Apparent Fishing Hours
#>
#> 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.
#> # 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 rowsA 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',
key = key)
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',
key = key)
#> Rows: 38 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): flag
#> dbl (5): Lat, Lon, Time Range, Vessel IDs, Apparent Fishing Hours
#>
#> 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.
#> # 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 -2.9 -176. 2015 FSM 1 2.77
#> 3 -2.6 -176. 2015 KOR 1 13.9
#> 4 -3 -176. 2015 FSM 1 2.16
#> 5 -2.8 -176. 2015 KOR 1 10.4
#> 6 -3.6 -176. 2015 KIR 1 6.07
#> 7 -3.5 -176. 2015 KOR 1 3.11
#> 8 -1 -170. 2015 KOR 1 2.39
#> 9 -4.1 -176. 2015 KOR 1 2.67
#> 10 -2.9 -176. 2015 FSM 1 5.09
#> # ℹ 28 more rowsIt is also possible to filter rasters to one of the five regional
fisheries management organizations (RFMO) that manage tuna and tuna-like
species. These include "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',
key = key)
#> Rows: 16424 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): flag
#> dbl (4): Lat, Lon, Vessel IDs, Apparent Fishing Hours
#> date (1): Time Range
#>
#> 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.
#> # 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 rowsThe 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 same example as above
get_event(event_type = 'FISHING',
vessels = twenty_usa_trawlers$vesselId,
start_date = "2020-01-01",
end_date = "2020-02-01",
key = key
) %>%
# extract EEZ id code
dplyr::mutate(eez = as.character(
purrr::map(purrr::map(regions, purrr::pluck, 'eez'),
paste0, collapse = ','))) %>%
dplyr::select(eventId, eventType, start, end, lat, lon, eez) %>%
dplyr::rowwise() %>%
dplyr::mutate(eez_name = get_region_id(region_name = as.numeric(eez),
region_source = 'EEZ',
key = key)$label) %>%
dplyr::select(-start, -end)
#> [1] "Downloading 38 events from GFW"
#> # A tibble: 38 × 6
#> # Rowwise:
#> eventId eventType lat lon eez eez_name
#> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 ce97b3eedf575b960a40fe9437ef8477 fishing 42.8 -125. 8456 United States
#> 2 e67a584f28389a843e98d17b063570c7 fishing 37.9 -123. 8456 United States
#> 3 72d1e4f6bf30b438f60876b0361ce75c fishing 28.1 -93.9 8456 United States
#> 4 47bbfb7d2f0ee76f1f6789a163639ec3 fishing 45.9 -124. 8456 United States
#> 5 1b55f469ff119d7f4bf9b3b4f3ecd09f fishing 38.1 -73.9 8456 United States
#> 6 ab0541c79191fa4379ff181db3034191 fishing 37.5 -74.4 8456 United States
#> 7 90c53f1ca75710bdc234d58b8027fc05 fishing 38 -73.9 8456 United States
#> 8 25faed9edb8e8e2b7098fb6d55d5fbb4 fishing 42.8 -125. 8456 United States
#> 9 32c27b8be60405dd20d344325b298d04 fishing 43.0 -125. 8456 United States
#> 10 ea857a15cef2ef00df6b4645d4e7f699 fishing 42.8 -125. 8456 United States
#> # ℹ 28 more rowsFor 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
documentation on the GFW API page.
We welcome all contributions to improve the package! Please read our Contribution Guide and reach out!