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Life and Fishery","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r setup, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  eval = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# `gfwr`: Access data from Global Fishing Watch APIs  \u003cimg src=\"man/figures/gfwr_hex_rgb.png\" align=\"right\" width=\"200px\"/\u003e\n\n\u003c!-- badges: start --\u003e\n[![DOI](https://zenodo.org/badge/450635054.svg)](https://zenodo.org/badge/latestdoi/450635054)\n[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n[![Licence](https://img.shields.io/badge/license-Apache%202-blue)](https://opensource.org/licenses/Apache-2.0)\n[![:registry status badge](https://globalfishingwatch.r-universe.dev/badges/:registry)](https://github.com/r-universe/globalfishingwatch/actions/workflows/sync.yml)\n\u003c!-- badges: end --\u003e\n\n\u003e **Important**  \n\u003e This is a major release of the package, with several breaking changes in \nfunction names and two new endpoints: AIS vessel presence and SAR vessel detections.\nPlease take a look at the [changelog](https://globalfishingwatch.github.io/gfwr/news/index.html#gfwr-300).\n\u003e Several improvements are on the way in subsequent minor releases, please log any feedback in the issues.\n\n\n\u003e For latest API releases, please check our [API release notes](https://globalfishingwatch.org/our-apis/documentation#api-release-notes)\n\n\u003e 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.\n\n\nThe `gfwr` R package is a simple wrapper for the Global Fishing Watch (GFW) [APIs](https://globalfishingwatch.org/our-apis/documentation#introduction). It \nprovides convenient functions to freely pull GFW data directly into R in tidy formats.\n\nThe package currently works with the following APIs:\n\n* [Vessels API](https://globalfishingwatch.org/our-apis/documentation#vessels-api): \nvessel search and identity based on AIS self reported data and public registry \ninformation\n* [Events API](https://globalfishingwatch.org/our-apis/documentation#events-api):\nencounters, loitering, port visits, AIS-disabling events and fishing events \nbased on AIS data\n* [Vessel presence based on AIS](https://globalfishingwatch.org/our-apis/documentation#map-visualization-4wings-api) for all vessel types\n* [Apparent fishing effort in hours, based on AIS](https://globalfishingwatch.org/our-apis/documentation#map-visualization-4wings-api) for all vessels classified as fishing vessels\n* [SAR vessel detections](https://globalfishingwatch.org/our-apis/documentation#map-visualization-4wings-api), indicating which vessels were matched or unmatched with AIS\n\n\n\u003e **Note**:\n\u003e See the [Terms of Use](https://globalfishingwatch.org/our-apis/documentation#reference-data) \npage for Global Fishing Watch APIs for information on our API licenses and rate limits.\n\n\n## Installation\n\nYou can install the most recent version of `gfwr` using:\n\n```{r install_instructions, eval = FALSE}\n# Check/install remotes\nif (!require(\"remotes\"))\n  install.packages(\"remotes\")\n\nremotes::install_github(\"GlobalFishingWatch/gfwr\",\n                        dependencies = TRUE)\n```\n\n`gfwr` is also in the rOpenSci \n[R-universe](https://globalfishingwatch.r-universe.dev/gfwr#), and can be \ninstalled like this: \n\n```{r r-universe, eval = FALSE}\ninstall.packages(\"gfwr\", \n                 repos = c(\"https://globalfishingwatch.r-universe.dev\",\n                           \"https://cran.r-project.org\"))\n```\n\nOnce everything is installed, you can load and use `gfwr` in your scripts with \n`library(gfwr)`\n\n```{r load, eval = FALSE}\nlibrary(gfwr)\n```\n\n```{r load_all, eval = TRUE, echo = FALSE, message = FALSE}\ndevtools::load_all()\n```\n\n## Authorization\n\nThe use of `gfwr` requires a GFW API token, which users can request from\nthe [GFW API Portal](https://globalfishingwatch.org/our-apis/tokens). Save\nthis token to your `.Renviron` file using `usethis::edit_r_environ()` and adding\na variable named `GFW_TOKEN` to the file (`GFW_TOKEN=\"PASTE_YOUR_TOKEN_HERE\"`).\nSave the `.Renviron` file and restart the R session to make the edit effective.\n\n\n`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. \n\nIf the token configuration was not done properly you will see the following error: \n\n```r\nError in `httr2::req_perform()`:\n! HTTP 401 Unauthorized.\n```\n\n \nIn case you need to specify the key you can use `gfw_auth()` to save an object\n\n```r\nkey \u003c- gfw_auth()\n```\n\nor fetch the key directly from the `.Renviron` file \n\n```r\nkey \u003c- Sys.getenv(\"GFW_TOKEN\")\n```\n\nThe examples in the package documentation will omit an explicit call to key.\n\n\n## Vessels API\n\nThe `gfw_vessel_info()` function allows you to get vessel identity details from\nthe [Vessels API](https://globalfishingwatch.org/our-apis/documentation#introduction-vessels-api).\n\nThere are two search types: `search`, and `id`.\n\n* `search` is performed by using parameters `query` for basic searches and\n`where` for advanced searchers using SQL expressions\n  + `query` takes a single identifier that can be the MMSI, IMO, callsign, or\n  shipname as input and identifies all vessels that match.\n  + `where` search allows for the use of complex search with logical clauses \n  (AND, OR) and fuzzy matching with terms such as LIKE, using SQL syntax (see \n  examples in the function) \n  + `includes` adds information from public registries. Options are\n  \"MATCH_CRITERIA\", \"OWNERSHIP\" and \"AUTHORIZATIONS\"\n\n### Basic search by identity markers `(search_type = \"search\")`\n\nTo get information of a vessel using its MMSI, IMO number, callsign or name, the \nsearch can be done directly using the number or the string. For example, to look \nfor a vessel with `MMSI = 224224000`:\n\n```{r example_vessel_info_1, eval = TRUE}\ngfw_vessel_info(query = 224224000,\n                search_type = \"search\")\n```\n\n### Complex searches using `where`\n\nTo do more specific searches (e.g. `\"imo = '8300949'\"`), combine different fields \n(`\"imo = '8300949' AND ssvid = '214182732'\"`) and do fuzzy matching \n(`\"shipname LIKE '%GABU REEFE%' OR imo = '8300949'\"`), use parameter `where` \ninstead of `query`:\n\n```{r example_vessel_info_2, eval = TRUE}\ngfw_vessel_info(where = \"shipname LIKE '%GABU REEFE%' OR imo = '8300949'\",\n                search_type = \"search\")\n```\n\n### Search by vessel ID\n\nTo search by `vesselId`, use parameter `ids` and specify `search_type = \"id\"`.\n\n\u003e **Note**:\n\u003e `vesselId` is an internal ID generated by Global Fishing Watch to connect data accross APIs\nand involves a combination of vessel and tracking data information. It can be\nretrieved using `gfw_vessel_info()` and fetching the vector of responses inside\n`$selfReportedInfo$vesselId`. See the\n[identity vignette](https://globalfishingwatch.github.io/gfwr/articles/identity)\nfor more information.\n\n\n#### Single vessel IDs\n\n```{r example_vessel_info_3, eval = TRUE}\ngfw_vessel_info(ids = \"8c7304226-6c71-edbe-0b63-c246734b3c01\",\n                search_type = \"id\")\n```\n\n#### Multiple vessel IDs\nTo specify more than one `vesselId`, you can submit a vector:\n\n```{r example_vessel_info_4, eval = TRUE}\ngfw_vessel_info(ids = c(\"8c7304226-6c71-edbe-0b63-c246734b3c01\",\n                        \"6583c51e3-3626-5638-866a-f47c3bc7ef7c\",\n                        \"71e7da672-2451-17da-b239-857831602eca\"),\n                search_type = \"id\")\n```\n\n__Check the function documentation for examples with the other function arguments and\n[our dedicated vignette](https://globalfishingwatch.github.io/gfwr/articles/identity)\nfor more information about vessel identity markers and the outputs retrieved.__\n\n\n## Events API\n\nThe `gfw_event()` function allows you to get data on specific vessel activities \nfrom the \n[Events API](https://globalfishingwatch.org/our-apis/documentation#events-api).\nEvent types include apparent fishing events, potential transshipment events \n(two-vessel encounters and loitering by refrigerated carrier vessels), port \nvisits, and AIS-disabling events (\"gaps\").\nFind more information about events in our \n[caveat documentation](https://globalfishingwatch.org/our-apis/documentation#data-caveat).\n\n### Events in a given time range\n\nYou can get events in a given date range. By not specifying `vessels`, the \nresponse will return results for all vessels. \n\n\n```{r example_event_type_3, eval = TRUE}\ngfw_event(event_type = \"ENCOUNTER\",\n          start_date = \"2020-01-01\",\n          end_date = \"2020-01-02\")\n```\n\n\u003e *Note*: We do not recommend trying too large downloads, such as all \nencounters for all vessels over a long period of time. This will possibly \nreturn time out (524) errors. Our API team is working on another API specific \nfor large downloads in the future.\n\n\n### Events in a specific area\n\nYou can provide a polygon in `sf` format or the region code (such as an EEZ \ncode) to filter the raster. Check the function documentation for more \ninformation about parameters `region` and `region_source`\n\n```{r events_shapefile}\n # fishing events in user shapefile\ntest_polygon \u003c- sf::st_bbox(c(xmin = -70,\n                              xmax = -40,\n                              ymin = -10,\n                              ymax = 5),\n  crs = 4326) |\u003e\n  sf::st_as_sfc() |\u003e\n  sf::st_as_sf()\ngfw_event(event_type = \"FISHING\",\n               start_date = \"2020-10-01\",\n               end_date = \"2020-10-31\",\n               region = test_polygon,\n               region_source = \"USER_SHAPEFILE\")\n```\n\n\n### Events for specific vessels\n\nTo extract events for specific vessels, the Events API needs `vesselId` as \ninput, so you always need to use `gfw_vessel_info()` first to extract \n`vesselId` from `$selfReportedInfo` in the response. \n\n\n#### Single vessel events\n\n\n```{r example_id_event, eval = TRUE}\nvessel_info \u003c- gfw_vessel_info(query = 224224000)\nvessel_info$selfReportedInfo\n```\n\nThe results show this vessel's story is grouped in two `vesselIds`.\n\nTo get a list of port visits for that vessel, you can use a single `vesselId`\nof your interest:\n\n```{r event_single_vesselid, eval = TRUE}\nid \u003c- vessel_info$selfReportedInfo$vesselId\nid\n\ngfw_event(event_type = \"PORT_VISIT\",\n          vessels = id[1],\n          confidences = 4\n          )\n```\n\nBut to get the whole event history, it's better to use the whole vector of \n`vesselId` for that vessel. Notice how the following request provides more results than the previous one: \n\n\n```{r event_onevessel_allvesselids, eval = TRUE}\ngfw_event(event_type = \"PORT_VISIT\",\n          vessels = id, #using the whole vector of vesselIds\n          confidences = 4\n          )\n```\n\n\u003e *Note*: Try narrowing your search using `start_date` and `end_date` if the \nrequest is too large and returns a time out error (524)\n\n\n\nWhen a date range is provided to `gfw_event()` using both `start_date` and \n`end_date`, any event overlapping that range will be returned, including events \nthat start prior to `start_date` or end after `end_date`. If just `start_date` \nor `end_date` are provided, results will include all events that end after \n`start_date` or begin prior to `end_date`, respectively.\n\n\u003e **Note**:  \n\u003e Because encounter events are events between two vessels, a single event will \nbe represented twice in the data, once for each vessel. To capture this \ninformation and link the related data rows, the `id` field for encounter events \nincludes an additional suffix (1 or 2) separated by a period. The `vessel` field\nwill also contain different information specific to each vessel.\n\n\n\n#### Events for multiple vessels\n\nAs another example, let's combine the Vessels and Events APIs to get fishing \nevents for a list of USA-flagged trawlers:\n\n```{r example_event_type_4a}\n# Download the list of USA trawlers\nusa_trawlers \u003c- gfw_vessel_info(\n  where = \"flag='USA' AND geartypes='TRAWLERS'\",\n  search_type = \"search\",\n  quiet = TRUE \n  )\n# Set quiet = TRUE if you want the output to return silently\n```\n\nThis list returns `r length(unique(usa_trawlers$selfReportedInfo$vesselId))` `vesselIds` belonging\nto `r nrow(usa_trawlers$dataset)` vessels. \n\n```{r usa_trawlers_id}\nusa_trawlers$selfReportedInfo\n```\n\nTo fetch events for this list of vessels, we will use the `vesselId` column and \nsend it to the `vessels` parameter in `gfw_event()` function. \n\nFor clarity, we should try to send groups of `vesselIds` that belong to the same\nvessels. For this, we can check the `index` column in the `$selfReportedInfo`\ndataset. \n\n\u003e *Note*: `gfw_event()` can receive several `vesselIds` at a time but will fail \nwhen the character length of the whole request is too long (~100,000 characters).\nThis means it will fail with error __HTTP 422: Unprocessable entity__ when too \nmany `vesselIds` are requested, this value can be around 2,800 `vesselIds` depending \non the other parameters of the search.\n\nFor this example, we will send the `vesselIds` corresponding to the first twenty \nvessels in the response: \n\n```{r usa_ten}\neach_USA_trawler \u003c- usa_trawlers$selfReportedInfo[, c(\"index\", \"vesselId\")] \n# how many vessels correspond to the first twenty vessels. \n(twenty_usa_trawlers \u003c- each_USA_trawler %\u003e% filter(index \u003c= 20))\n```\n\nThere are `r length(unique(twenty_usa_trawlers$vesselId))` \n`vesselIds` corresponding to those 20 vessels.\n\nLet's pass the vector of `vesselIds` to Events API. Now get the list of fishing\nevents for these trawlers in January, 2020:\n\n```{r example_event_type_4b, eval=T}\nfishing_events \u003c- gfw_event(event_type = \"FISHING\",\n                            vessels = twenty_usa_trawlers$vesselId,\n                            start_date = \"2020-01-01\",\n                            end_date = \"2020-02-01\")\nfishing_events\n```\n\nThe columns starting by `vessel` hold the vessel-related information for each \nevent: `vesselId`, `vessel_name`, `ssvid` (MMSI), `flag`, `vessel type` and \npublic authorizations.\n\n```{r unnest_vessel}\nfishing_events %\u003e% \n  dplyr::select(starts_with(\"vessel\"))\n```\n\nWhen no events are available, the `gfw_event()` function returns nothing.\n\n```{r example_event_type_4c, eval=T}\ngfw_event(event_type = \"FISHING\",\n          vessels = twenty_usa_trawlers$vesselId[2],\n          start_date = \"2020-01-01\",\n          end_date = \"2020-01-01\")\n```\n\n\n## Apparent fishing hours\n\nThe `gfw_ais_fishing_hours()` function gets a raster from the [4Wings API](https://globalfishingwatch.org/our-apis/documentation#map-visualization-4wings-api)\nand converts the response to a data frame. In order to use it, you should specify:\n\n* The spatial resolution, which can be `LOW` (0.1 degree) or `HIGH` (0.01 \ndegree)\n* The temporal resolution, which can be `HOURLY`, `DAILY`, `MONTHLY`, `YEARLY` \nor `ENTIRE`.\n* The variable to group by: `FLAG`, `GEARTYPE`, `FLAGANDGEARTYPE`, `MMSI` or \n`VESSEL_ID`\n* The date range `note: this must be 366 days or less`\n* The region polygon in `sf` format or the region code (such as an EEZ code) to\nfilter the raster\n* The source for the specified region. Currently, `EEZ`, `MPA`, `RFMO` or \n`USER_SHAPEFILE` (for `sf` shapefiles).\n\n\n### User-defined shapefile\n\nYou can load an `sf` shapefile with your area of interest and fetch apparent the\ndata of interest.\nfor this area using `region_source = \"USER_SHAPEFILE\"` and `region = [YOUR_SHAPE]`.\nWe added a sample shapefile inside `gfwr` to show how `\"USER_SHAPEFILE\"` works:\n\n```{r example_map_1}\ndata(\"gfw_test_shape\")\n\ngfw_test_shape\n\ngfw_ais_fishing_hours(spatial_resolution = \"LOW\",\n               temporal_resolution = \"YEARLY\",\n               group_by = \"FLAG\",\n               start_date = \"2021-01-01\",\n               end_date = \"2021-02-01\",\n               region_source = \"USER_SHAPEFILE\",\n               region = gfw_test_shape)\n```\n\n### Apparent fishing hours in preloaded EEZ, RFMOs and MPAs\n\n#### EEZ\n\nIf you want raster data from a particular EEZ, you can use the `gfw_region_id()`\nfunction to get the EEZ id, and enter that code in the `region` argument\nof `gfw_ais_fishing_hours()` instead of the region shapefile (with `region_source = \"EEZ\"`):\n\n```{r example_map_2, eval= TRUE}\n# use EEZ function to get EEZ code of Cote d'Ivoire\ncode_eez \u003c- gfw_region_id(region = \"CIV\", region_source = \"EEZ\")\n\ngfw_ais_fishing_hours(spatial_resolution = \"LOW\",\n                      temporal_resolution = \"YEARLY\",\n                      group_by = \"FLAG\",\n                      start_date = \"2021-01-01\",\n                      end_date = \"2021-10-01\",\n                      region = code_eez$id,\n                      region_source = \"EEZ\")\n```\n\nYou could search for just one word in the name of the EEZ and then decide which\none you want:\n\n```{r example_map_3, eval = TRUE}\n(gfw_region_id(region = \"France\", region_source = \"EEZ\"))\n```\n\n\nFrom the results above, let's say we're interested in the French Exclusive \nEconomic Zone, `5677`\n\n```{r fr_eez, eval = TRUE}\ngfw_ais_fishing_hours(spatial_resolution = \"LOW\",\n                      temporal_resolution = \"YEARLY\",\n                      group_by = \"FLAG\",\n                      start_date = \"2021-01-01\",\n                      end_date = \"2021-10-01\",\n                      region = 5677,\n                      region_source = \"EEZ\")\n```\n\n#### Marine Protected Areas (MPAs)\n\nA similar approach can be used to search for a specific Marine Protected Area,\nin this case the Phoenix Island Protected Area (PIPA)\n\n```{r example_map_4, eval= TRUE}\n# use region id function to get MPA code of Phoenix Island Protected Area\ncode_mpa \u003c- gfw_region_id(region = \"Phoenix\",\n                          region_source = \"MPA\")\ncode_mpa\ngfw_ais_fishing_hours(spatial_resolution = \"LOW\",\n                      temporal_resolution = \"YEARLY\",\n                      group_by = \"FLAG\",\n                      start_date = \"2015-01-01\",\n                      end_date = \"2015-06-01\",\n                      region = code_mpa$id[1],\n                      region_source = \"MPA\")\n```\n\n#### Regional Fisheries Management Organizations (RFMOs)\n\nIt is also possible to filter rasters to regional fisheries management \norganizations (RFMO) like `\"ICCAT\"`, `\"IATTC\"`, `\"IOTC\"`, `\"CCSBT\"` and\n`\"WCPFC\"`.\n\n\n```{r example_map_5, eval=T}\ngfw_ais_fishing_hours(spatial_resolution = \"LOW\",\n               temporal_resolution = \"DAILY\",\n               group_by = \"FLAG\",\n               start_date = \"2021-01-01\",\n               end_date = \"2021-01-04\",\n               region = \"ICCAT\",\n               region_source = \"RFMO\")\n```\n\n\u003e *Note*: For a complete list of MPAs, RFMOs and EEZ, check the function `gfw_regions()`\n\n\n### When your API request times out\n\nFor API performance reasons, the `gfw_ais_fishing_hours()` function restricts individual \nqueries to a single year of data. However, even with this restriction, it is \npossible for API request to time out before it completes. When this occurs, the\ninitial `gfw_ais_fishing_hours()` call will return an `HTTP 524 error`, and subsequent API \nrequests using any `gfwr` `gfw_` function will return an `HTTP 429 error` until \nthe original request completes:\n\n\u003e\nError in `httr2::req_perform()`:\n! HTTP 429 Too Many Requests.\n• Your application token is not currently enabled to perform more than one \nconcurrent report. If you need to generate more than one report concurrently,\ncontact us at apis@globalfishingwatch.org\n\nAlthough no data was received, the request is still being processed by the APIs\n and will become available when it completes. To account for this, `gfwr` \n includes the `gfw_last_report()` function, which lets users request the \n results of their last API request with `gfw_ais_fishing_hours()`.\n\nThe `gfw_last_report()` function will tell you if the APIs are still \nprocessing your request and will download the results if the request has \nfinished successfully. You will receive an error message if the request \nfinished but resulted in an error or if it's been \u003e30 minutes since the last\n report was generated using `gfw_ais_fishing_hours()`. For more information, see the \n [Get last report generated endpoint](https://globalfishingwatch.org/our-apis/documentation#get-last-report-generated) \n documentation on the Global Fishing Watch API page.\n\n## Reverse region id search \n\nThe `gfw_region_id()` function also works in reverse. If a region id is passed as\na `numeric` to the function as the `region`, the corresponding region label\nor iso3 code can be returned. This is especially useful when events are \nreturned with regions.\n\nUsing the same example with twenty trawlers fishing events, `fishing_events`, \nyou can see the `eez` information is returned as the numeric code in the `\"eez\"` \ncolumn.\n\n```{r example_region_id}\nfishing_events \u003c- gfw_event(event_type = \"FISHING\",\n                            vessels = twenty_usa_trawlers$vesselId,\n                            start_date = \"2020-01-01\",\n                            end_date = \"2020-02-01\") %\u003e%\n  # extract EEZ id code\n  dplyr::mutate(eez = as.character(\n    purrr::map(purrr::map(regions, purrr::pluck, \"eez\"),\n               paste0, collapse = \",\"))) %\u003e%\n  dplyr::select(eez, eventId, eventType, start, end, lat, lon) \n\nfishing_events\n```\n\n\nWe can apply `gfw_region_id()` to the numeric vector to extract the labels:\n\n```{r example_region_id_cont}\nfishing_events %\u003e% \n  mutate(eez_name = purrr::map_df(as.numeric(fishing_events$eez),\n                                  ~gfw_region_id(region = .x,\n                                                 region_source = \"EEZ\"))$label) %\u003e% \n  dplyr::relocate(eez, eez_name)\n  \n```\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/28471","html_url":"https://ost.ecosyste.ms/projects/28471"}