Important
The current version ofgfwrgives access to Global Fishing Watch API version 2. This version is in Maintenance mode, it will be operational and available but no new functionalities will be added. This version will be Deprecated on April 30 2024. A new version fetching data from version 3 is being prepared.
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
- Events API: encounters, loitering, port visits and fishing events based on AIS data
- Map Visualization (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.
Installation
You can install the development version of gfwr like so:
# Check/install remotes
if (!require("remotes"))
install.packages("remotes")
remotes::install_github("GlobalFishingWatch/gfwr")Once everything is installed, you can load and use gfwr in your scripts with library(gfwr)
Authorization
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()) by 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 save the information to an object in your R workspace every time you need to extract the token 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.
Vessels API
The get_vessel_info() function allows you to get vessel identity details from the GFW Vessels API. There are three search types: basic, advanced, and id.
-
basicsearch takes features like MMSI, IMO, callsign, shipname as inputs and identifies all vessels in the specified dataset that match -
advancedsearch allows for the use of fuzzy matching with terms such as LIKE. Theidsearch allows the user to search using a GFW vessel -
idallows the user to specify thevessel id(generated by GFW)
Note:
vessel idis an internal ID generated by GFW to connect data accross APIs and involves a combination of vessel and tracking data information
The user can also specify which identity databases to use: carrier_vessel, support_vessel, fishing_vessel, or all. With the latter, all databases are used for the search. This is generally recommended and is the option set by default.
Examples
To get information of a vessel with MMSI = 224224000 using all datasets:
get_vessel_info(query = 224224000,
search_type = "basic",
dataset = "all",
key = key)
#> # A tibble: 1 × 17
#> name callsign firstTransmissionDate flag geartype id imo
#> <int> <chr> <chr> <chr> <lgl> <chr> <chr>
#> 1 1 EBSJ 2015-10-13T15:47:16Z ESP NA 3c99c326d-dd2e-175d… 8733…
#> # ℹ 10 more variables: lastTransmissionDate <chr>, mmsi <chr>, msgCount <int>,
#> # posCount <int>, shipname <chr>, source <chr>, vesselType <chr>,
#> # years <list>, dataset <chr>, score <dbl>To combine different fields and do fuzzy matching to search the carrier vessel dataset:
get_vessel_info(query = "shipname LIKE '%GABU REEFE%' OR imo = '8300949'",
search_type = "advanced", dataset = "carrier_vessel", key = key)
#> # A tibble: 3 × 17
#> name callsign firstTransmissionDate flag geartype id imo
#> <int> <chr> <chr> <chr> <lgl> <chr> <chr>
#> 1 1 ER2732 2019-02-22T21:46:13Z MDA NA 0b7047cb5-58c8-6e63… 8300…
#> 2 2 TJMC996 2022-01-24T09:13:48Z CMR NA 1da8dbc23-3c48-d5ce… 8300…
#> 3 3 D6FJ2 2012-01-02T16:50:42Z COM NA 58cf536b1-1fca-dac3… 8300…
#> # ℹ 10 more variables: lastTransmissionDate <chr>, mmsi <chr>, msgCount <int>,
#> # posCount <int>, shipname <chr>, source <chr>, vesselType <chr>,
#> # years <list>, dataset <chr>, score <dbl>To specify a vessel id:
get_vessel_info(query = "8c7304226-6c71-edbe-0b63-c246734b3c01",
search_type = "id",
dataset = "carrier_vessel",
key = key)
#> # A tibble: 1 × 16
#> name callsign firstTransmissionDate flag geartype id imo
#> <int> <chr> <chr> <chr> <lgl> <chr> <chr>
#> 1 1 5BWC3 2013-05-15T20:18:31Z CYP NA 8c7304226-6c71-edbe… 9076…
#> # ℹ 9 more variables: lastTransmissionDate <chr>, mmsi <chr>, msgCount <int>,
#> # posCount <int>, shipname <chr>, source <chr>, vesselType <chr>,
#> # years <list>, dataset <chr>To specify more than one vessel id:
Note:
No spaces or newlines are permitted between thevessel ids
get_vessel_info(query =
"8c7304226-6c71-edbe-0b63-c246734b3c01,6583c51e3-3626-5638-866a-f47c3bc7ef7c,71e7da672-2451-17da-b239-857831602eca",
search_type = "id", key = key)
#> # A tibble: 3 × 16
#> name callsign firstTransmissionDate flag geartype id imo
#> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 5BWC3 2013-05-15T20:18:31Z CYP <NA> 8c7304226-… 9076…
#> 2 2 DTBY3 2013-09-02T03:59:51Z KOR tuna_purse_seines 6583c51e3-… 8919…
#> 3 3 DUQA-7 2017-02-15T05:54:53Z PHL tuna_purse_seines 71e7da672-… 8118…
#> # ℹ 9 more variables: lastTransmissionDate <chr>, mmsi <chr>, msgCount <int>,
#> # posCount <int>, shipname <chr>, source <chr>, vesselType <chr>,
#> # years <list>, dataset <chr>Events API
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), and port visits. Find more information in our caveat documentation.
Examples
Let’s say that you don’t know the vessel id but you have the MMSI (or other identity information). You can use get_vessel_info() function first to extract vessel id and then use it in the get_event() function:
vessel_id <- get_vessel_info(query = 224224000, search_type = "basic", key = key)$idTo get a list of port visits for that vessel:
get_event(event_type = "port_visit",
vessel = vessel_id,
confidences = "4",
key = key
)
#> [1] "Downloading 35 events from GFW"
#> # A tibble: 35 × 11
#> id type start end lat lon regions
#> <chr> <chr> <dttm> <dttm> <dbl> <dbl> <list>
#> 1 b725… port… 2015-11-04 05:22:13 2015-11-07 10:46:28 5.23 -4.00 <named list>
#> 2 f03f… port… 2015-12-06 11:48:38 2015-12-10 16:19:37 5.24 -4.08 <named list>
#> 3 cbd7… port… 2016-01-09 06:47:57 2016-01-13 14:30:33 5.24 -4.00 <named list>
#> 4 6265… port… 2016-02-25 14:26:38 2016-03-01 13:21:21 5.25 -4.00 <named list>
#> 5 4a7f… port… 2016-03-03 05:47:02 2016-03-03 11:46:33 5.20 -4.02 <named list>
#> 6 617d… port… 2016-03-31 04:43:41 2016-04-02 09:07:10 5.23 -4.00 <named list>
#> 7 3c26… port… 2016-04-20 06:50:58 2016-04-20 19:47:10 14.7 -17.4 <named list>
#> 8 104e… port… 2016-04-24 07:14:33 2016-04-24 11:54:59 14.7 -17.4 <named list>
#> 9 8f19… port… 2016-05-18 19:31:04 2016-05-22 14:20:05 5.20 -4.01 <named list>
#> 10 bf64… port… 2016-06-26 15:08:16 2016-06-30 10:39:03 5.20 -4.07 <named list>
#> # ℹ 25 more rows
#> # ℹ 4 more variables: boundingBox <list>, distances <list>, vessel <list>,
#> # event_info <list>We can also use more than one vessel id:
get_event(event_type = "port_visit",
vessel = "8c7304226-6c71-edbe-0b63-c246734b3c01,6583c51e3-3626-5638-866a-f47c3bc7ef7c",
confidences = 4,
start_date = "2020-01-01",
end_date = "2020-02-01",
key = key
)
#> [1] "Downloading 3 events from GFW"
#> # A tibble: 3 × 11
#> id type start end lat lon regions
#> <chr> <chr> <dttm> <dttm> <dbl> <dbl> <list>
#> 1 7cd1e3… port… 2019-12-19 23:05:31 2020-01-24 19:05:18 28.1 -15.4 <named list>
#> 2 c2f096… port… 2020-01-26 05:52:47 2020-01-29 14:39:33 20.8 -17.0 <named list>
#> 3 7c06e4… port… 2020-01-31 02:20:08 2020-02-03 15:56:31 28.1 -15.4 <named list>
#> # ℹ 4 more variables: boundingBox <list>, distances <list>, vessel <list>,
#> # event_info <list>Or get encounters for all vessels in a given date range:
get_event(event_type = "encounter",
start_date = "2020-01-01",
end_date = "2020-01-03",
key = key
)
#> [1] "Downloading 70 events from GFW"
#> # A tibble: 70 × 11
#> id type start end lat lon
#> <chr> <chr> <dttm> <dttm> <dbl> <dbl>
#> 1 a3cff76a070a919f… enco… 2019-12-31 08:40:00 2020-01-01 07:40:00 57.5 157.
#> 2 a3cff76a070a919f… enco… 2019-12-31 08:40:00 2020-01-01 07:40:00 57.5 157.
#> 3 b059d20534c7fd5f… enco… 2019-12-31 12:00:00 2020-01-01 13:50:00 -17.6 -79.3
#> 4 b059d20534c7fd5f… enco… 2019-12-31 12:00:00 2020-01-01 13:50:00 -17.6 -79.3
#> 5 cd07d7e5d65e81b3… enco… 2019-12-31 12:50:00 2020-01-01 09:50:00 -17.7 -79.2
#> 6 cd07d7e5d65e81b3… enco… 2019-12-31 12:50:00 2020-01-01 09:50:00 -17.7 -79.2
#> 7 13dac0526c993292… enco… 2019-12-31 14:50:00 2020-01-01 20:20:00 -17.6 -79.4
#> 8 13dac0526c993292… enco… 2019-12-31 14:50:00 2020-01-01 20:20:00 -17.6 -79.4
#> 9 2e8b8040d87ad0ae… enco… 2019-12-31 16:00:00 2020-01-01 08:50:00 -3.44 -147.
#> 10 2e8b8040d87ad0ae… enco… 2019-12-31 16:00:00 2020-01-01 08:50:00 -3.44 -147.
#> # ℹ 60 more rows
#> # ℹ 5 more variables: regions <list>, boundingBox <list>, distances <list>,
#> # vessel <list>, event_info <list>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 100 USA-flagged trawlers:
# Download the list of USA trawlers
usa_trawlers <- get_vessel_info(
query = "flag = 'USA' AND geartype = 'trawlers'",
search_type = "advanced",
dataset = "fishing_vessel",
key = key
)
# Collapse vessel ids into a commas separated list to pass to Events API
usa_trawler_ids <- paste0(usa_trawlers$id[1:100], collapse = ",")Now get the list of fishing events for these trawlers in January, 2020:
get_event(event_type = "fishing",
vessel = usa_trawler_ids,
start_date = "2020-01-01",
end_date = "2020-02-01",
key = key
)
#> [1] "Downloading 106 events from GFW"
#> # A tibble: 106 × 11
#> id type start end lat lon regions
#> <chr> <chr> <dttm> <dttm> <dbl> <dbl> <list>
#> 1 0678… fish… 2020-01-01 15:56:25 2020-01-02 00:41:57 35.1 -76.0 <named list>
#> 2 4891… fish… 2020-01-02 01:55:51 2020-01-03 00:05:57 35.0 -76.0 <named list>
#> 3 d75a… fish… 2020-01-02 23:31:48 2020-01-03 04:37:19 41.1 -71.4 <named list>
#> 4 8dda… fish… 2020-01-03 00:39:08 2020-01-03 02:49:08 35.0 -76.0 <named list>
#> 5 c85b… fish… 2020-01-03 15:51:15 2020-01-03 18:24:44 39.8 -73.9 <named list>
#> 6 1bee… fish… 2020-01-05 00:35:43 2020-01-05 06:11:43 39.7 -73.9 <named list>
#> 7 379d… fish… 2020-01-05 04:58:45 2020-01-05 06:31:45 43.7 -124. <named list>
#> 8 0b45… fish… 2020-01-06 06:20:19 2020-01-08 02:46:19 39.6 -73.9 <named list>
#> 9 04d2… fish… 2020-01-06 21:12:01 2020-01-07 02:35:11 34.6 -76.6 <named list>
#> 10 2ad0… fish… 2020-01-07 13:37:54 2020-01-07 15:16:54 34.7 -76.8 <named list>
#> # ℹ 96 more rows
#> # ℹ 4 more variables: boundingBox <list>, distances <list>, vessel <list>,
#> # event_info <list>When no events are available, the get_event() function returns nothing.
get_event(event_type = "fishing",
vessel = usa_trawler_ids[2],
start_date = "2020-01-01",
end_date = "2020-01-01",
key = key
)
#> [1] "Your request returned zero results"
#> NULLMap Visualization API
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:
- The spatial resolution, which can be
low(0.1 degree) orhigh(0.01 degree) - The temporal resolution, which can be
daily,monthly, oryearly. - The variable to group by:
vessel_id,flag,gearType, orflagAndGearType - The date range
note: this must be one (1) year or less - The
geojsonregion or region code (such as an EEZ code) to filter the raster - The source for the specified region (currently,
eez,mpa, oruser_json)
Examples
Here’s an example where we enter the geojson data manually:
Note:
Ingwfr, the geojson needs to be enclosed by a{"geojson": ...}tag. If you have ageojsonsf::sf_geojson()object, you can obtain the geojson object with a simple concatenation:paste0('{"geojson":', your_geojson,'}')
region_json = '{"geojson":{"type":"Polygon","coordinates":[[[-76.11328125,-26.273714024406416],[-76.201171875,-26.980828590472093],[-76.376953125,-27.527758206861883],[-76.81640625,-28.30438068296276],[-77.255859375,-28.767659105691244],[-77.87109375,-29.152161283318918],[-78.486328125,-29.45873118535532],[-79.189453125,-29.61167011519739],[-79.892578125,-29.6880527498568],[-80.595703125,-29.61167011519739],[-81.5625,-29.382175075145277],[-82.177734375,-29.07537517955835],[-82.705078125,-28.6905876542507],[-83.232421875,-28.071980301779845],[-83.49609375,-27.683528083787756],[-83.759765625,-26.980828590472093],[-83.84765625,-26.35249785815401],[-83.759765625,-25.64152637306576],[-83.583984375,-25.16517336866393],[-83.232421875,-24.447149589730827],[-82.705078125,-23.966175871265037],[-82.177734375,-23.483400654325635],[-81.5625,-23.241346102386117],[-80.859375,-22.998851594142906],[-80.15625,-22.917922936146027],[-79.453125,-22.998851594142906],[-78.662109375,-23.1605633090483],[-78.134765625,-23.40276490540795],[-77.431640625,-23.885837699861995],[-76.9921875,-24.28702686537642],[-76.552734375,-24.846565348219727],[-76.2890625,-25.48295117535531],[-76.11328125,-26.273714024406416]]]}}'
get_raster(
spatial_resolution = "low",
temporal_resolution = "yearly",
group_by = "flag",
date_range = "2021-01-01,2021-12-31",
region = region_json,
region_source = "user_json",
key = key
)
#> Rows: 5 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 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 -24.2 -77.8 2021 ESP 1 0.42
#> 2 -24.6 -78.4 2021 ESP 2 0.28
#> 3 -27.3 -82 2021 ESP 1 0.43
#> 4 -24.7 -78.5 2021 ESP 1 0.03
#> 5 -24.7 -78.6 2021 ESP 2 0.96If you want raster data from a particular EEZ, you can use the get_region_id() function to get the EEZ id, enter that code in the region argument of get_raster() instead of the geojson data (ensuring you specify the region_source as "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",
date_range = "2021-01-01,2021-10-01",
region = code_eez$id,
region_source = "eez",
key = key)
#> Rows: 573 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: 573 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 1.4 -6.6 2021 CPV 1 1.27
#> 2 2.4 -4 2021 FRA 1 1.04
#> 3 4.3 -4.1 2021 FRA 2 3.51
#> 4 5 -5.3 2021 CHN 2 38.4
#> 5 5.3 -4 2021 SLV 2 17.0
#> 6 4 -4.3 2021 BLZ 1 4.13
#> 7 5.1 -4.2 2021 BLZ 1 1.99
#> 8 2 -6 2021 BLZ 1 4.52
#> 9 1.2 -6.8 2021 BLZ 1 2.46
#> 10 1.3 -6.7 2021 BLZ 1 3.46
#> # ℹ 563 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 iso3 label
#> <dbl> <chr> <chr>
#> 1 5677 FRA France
#> 2 48976 FRA Joint regime area Italy / France
#> 3 48966 FRA Joint regime area Spain / France
# Let's say we're interested in the French Exclusive Economic Zone, 5677
get_raster(spatial_resolution = "low",
temporal_resolution = "yearly",
group_by = "flag",
date_range = "2021-01-01,2021-10-01",
region = 5677,
region_source = "eez",
key = key)
#> Rows: 5611 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: 5,611 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 49.1 -5.8 2021 FRA 26 295.
#> 2 49.1 -5.9 2021 FRA 21 180.
#> 3 49 -5.9 2021 FRA 19 244.
#> 4 49 -6.1 2021 FRA 21 239.
#> 5 49.1 -5.6 2021 FRA 18 470.
#> 6 51 1.6 2021 FRA 24 317.
#> 7 50 0 2021 FRA 28 145.
#> 8 49.8 0 2021 FRA 62 1188.
#> 9 42.6 3.2 2021 ESP 9 27.8
#> 10 42.9 3.3 2021 FRA 18 547.
#> # ℹ 5,601 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",
date_range = "2015-01-01,2015-06-01",
region = code_mpa$id[1],
region_source = "mpa",
key = key)
#> Rows: 93 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: 93 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 -3.9 -176. 2015 KOR 1 4.88
#> 2 -4 -176. 2015 KOR 1 1.37
#> 3 -4.1 -176. 2015 KOR 1 1.57
#> 4 -2.9 -176. 2015 FSM 1 2.77
#> 5 -3.3 -176. 2015 <NA> 1 1.45
#> 6 -2.8 -176. 2015 KOR 1 9.29
#> 7 -3.5 -176. 2015 KOR 2 12.3
#> 8 -3.4 -176. 2015 KOR 1 1.37
#> 9 -3.5 -176. 2015 KOR 1 10.8
#> 10 -3.6 -176. 2015 KOR 1 1.08
#> # ℹ 83 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",
date_range = "2021-01-01,2021-01-15",
region = "ICCAT",
region_source = "rfmo",
key = key)
#> Rows: 114979 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: 114,979 × 6
#> Lat Lon `Time Range` flag `Vessel IDs` `Apparent Fishing Hours`
#> <dbl> <dbl> <date> <chr> <dbl> <dbl>
#> 1 68.7 -51.4 2021-01-08 GRL 1 0.25
#> 2 68.8 -51.2 2021-01-05 GRL 1 4.78
#> 3 68.8 -51.2 2021-01-04 GRL 2 0.73
#> 4 66.9 -24.7 2021-01-03 ISL 11 14.7
#> 5 66.9 -24.6 2021-01-03 ISL 12 34.5
#> 6 66.9 -24.5 2021-01-03 ISL 13 40.2
#> 7 66.8 -24.3 2021-01-04 ISL 15 42.2
#> 8 67 -24 2021-01-04 ISL 3 6.13
#> 9 66.9 -23.9 2021-01-04 ISL 2 1.43
#> 10 66.9 -24.1 2021-01-03 ISL 3 3.4
#> # ℹ 114,969 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 can be returned. This is especially useful when events are returned with regions.
# using same example as above
get_event(event_type = "fishing",
vessel = usa_trawler_ids,
start_date = "2020-01-01",
end_date = "2020-02-01",
include_regions = TRUE,
key = key
) %>%
# extract EEZ id code
dplyr::mutate(eez = as.character(purrr::map(purrr::map(regions, pluck, "eez"),
paste0, collapse = ","))) %>%
dplyr::select(id, type, 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)
#> [1] "Downloading 106 events from GFW"
#> # A tibble: 106 × 8
#> # Rowwise:
#> id type start end lat lon eez
#> <chr> <chr> <dttm> <dttm> <dbl> <dbl> <chr>
#> 1 06783a15944… fish… 2020-01-01 15:56:25 2020-01-02 00:41:57 35.1 -76.0 8456
#> 2 4891aab6703… fish… 2020-01-02 01:55:51 2020-01-03 00:05:57 35.0 -76.0 8456
#> 3 d75af335992… fish… 2020-01-02 23:31:48 2020-01-03 04:37:19 41.1 -71.4 8456
#> 4 8ddaf495862… fish… 2020-01-03 00:39:08 2020-01-03 02:49:08 35.0 -76.0 8456
#> 5 c85b3f8c738… fish… 2020-01-03 15:51:15 2020-01-03 18:24:44 39.8 -73.9 8456
#> 6 1bee4c2bbe2… fish… 2020-01-05 00:35:43 2020-01-05 06:11:43 39.7 -73.9 8456
#> 7 379d452b49e… fish… 2020-01-05 04:58:45 2020-01-05 06:31:45 43.7 -124. 8456
#> 8 0b45ad5daf1… fish… 2020-01-06 06:20:19 2020-01-08 02:46:19 39.6 -73.9 8456
#> 9 04d20daaf37… fish… 2020-01-06 21:12:01 2020-01-07 02:35:11 34.6 -76.6 8456
#> 10 2ad00a03bf5… fish… 2020-01-07 13:37:54 2020-01-07 15:16:54 34.7 -76.8 8456
#> # ℹ 96 more rows
#> # ℹ 1 more variable: eez_name <chr>Contributing
We welcome all contributions to improve the package! Please read our Contribution Guide and reach out!
