The credentials of the user are stored using the keyring
package. With the following command a user can be added to the keyring.
Run this line once, it will store your credentials in keyring. After
that every time you load move2 and execute a download
function from movebank, these functions will retrieve your credentials
from keyring.
The keyring package can use several mechanisms to store
credentials, these are called backends. Some of these backends are
operating system dependent, others are more general. Some of the
operating systems dependent backends have the advantage that they do not
require providing credentials when opening a new R session.
The move2 package uses the default backend as is
returned by keyring::default_backend(), this function thus
shows the backend move2 is using. If you want to change the
default you can use the keyring_backend option, for more
details see the documentation in the keyring package.
macOS and Windows generally do not
require entering an extra password for keyring. The default in
Linux is often the file backend which can
be confusing as it creates an encrypted file with credentials that need
a password to unlock. In this case a separate password for the keyring
file has to be entered for each new R session before the movebank
password can be accessed. To avoid having to enter each time a keyring
password the Secret Service API can be used by installing the
libsecret library. (Debian/Ubuntu:
libsecret-1-dev; Recent RedHat, Fedora and CentOS systems:
libsecret-devel)
key_nameIf you have multiple user accounts on movebank, the easiest way is to
give each of them a key name with the argument key_name.
For the most used account also the default option can be used. The
movebank_store_credentials() only has to be executed once
for each account. After that the credentials will be retrieved from
keyring.
## store credentials for the most used account.
movebank_store_credentials("myUserName", "myPassword")
## store credentials for another movebank account
movebank_store_credentials("myUserName_2", "myPassword_2", key_name = "myOtherAccount")When you want to download from Movebank using your default movebank account, nothing has to be specified before the download functions. If you want to download from Movebank with another account, than you should execute the line below, specifying the key name of the account to use, before the download functions are executed.
If in one script/Rsession you are using several accounts, to use the credentials of the default account execute the line below:
To check which accounts are stored in keyring:
The service column corresponds to the names provided in
key_name. The account entered without a key name (the
default) will be called movebank. Note that the key names
have to be unique, if there are several usernames with the same key name
(service), it will cause an error.
To deleted credentials from keyring:
## for the default account
movebank_remove_credentials()
#> There is 1 key removed from the keyring.
## for an account with a key name
movebank_remove_credentials(key_name = "myOtherAccount")
#> There is 1 key removed from the keyring.Next we can check if the keys are successfully removed:
Here you can check if the movebank service is
successfully removed.
Using the movebank_retrieve function it is possible to
directly access the API, here all studies with a creative commons 0
license are returned. These are a good candidate for exploration and
testing
movebank_retrieve(entity_type = "study", license_type = "CC_0") |>
select(id, name, number_of_deployed_locations) |>
filter(!is.na(number_of_deployed_locations))
#> # A tibble: 301 × 3
#> id name number_of_deployed_l…¹
#> <int64> <fct> [count]
#> 1 1169957016 spectacledEider_USGS_ASC_argos 61299
#> 2 1199929756 Spatial ecology of urban copperheads 2031
#> 3 1605798640 O_BALGZAND - Eurasian oystercatchers (Haem… 165891
#> 4 1605803389 O_AMELAND - Eurasian oystercatchers (Haema… 216108
#> 5 1605797471 O_ASSEN - Eurasian oystercatchers (Haemato… 20152
#> 6 1605799506 O_SCHIERMONNIKOOG - Eurasian oystercatcher… 602380
#> 7 1605802367 O_VLIELAND - Eurasian oystercatchers (Haem… 4908942
#> 8 1402467516 Black kites of different age and sex show … 231193
#> 9 7249090 Peregrine Falcon, High Arctic Institute, n… 3004
#> 10 920008781 Ringed seals Igloolik 9519
#> # ℹ 291 more rows
#> # ℹ abbreviated name: ¹number_of_deployed_locationsA more quick way to retrieve the information is the following (the selection is performed on movebank and not all data is downloaded):
By default all attributes are downloaded:
movebank_download_study(2911040, sensor_type_id = "gps")
#> A <move2> with `track_id_column` "individual_local_identifier" and
#> `time_column` "timestamp"
#> Containing 28 tracks lasting on average 3202499 secs in a
#> Simple feature collection with 16414 features and 18 fields (with 386 geometries empty)
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -91.3732 ymin: -12.79464 xmax: -77.51874 ymax: 0.1821983
#> Geodetic CRS: WGS 84
#> # A tibble: 16,414 × 19
#> sensor_type_id individual_local_identifier eobs_battery_voltage
#> <int64> <fct> [mV]
#> 1 653 4264-84830852 3686
#> 2 653 4264-84830852 3701
#> 3 653 4264-84830852 3701
#> 4 653 4264-84830852 3691
#> 5 653 4264-84830852 3691
#> # ℹ 16,409 more rows
#> # ℹ 16 more variables: eobs_fix_battery_voltage [mV],
#> # eobs_horizontal_accuracy_estimate [m], eobs_key_bin_checksum <int64>,
#> # eobs_speed_accuracy_estimate [m/s], eobs_start_timestamp <dttm>,
#> # eobs_status <ord>, eobs_temperature [°C], eobs_type_of_fix <fct>,
#> # eobs_used_time_to_get_fix [s], ground_speed [m/s], heading [°],
#> # height_above_ellipsoid [m], timestamp <dttm>, visible <lgl>, …
#> First 5 track features:
#> # A tibble: 28 × 52
#> deployment_id tag_id individual_id animal_life_stage attachment_type
#> <int64> <int64> <int64> <fct> <fct>
#> 1 2911170 2911124 2911090 adult tape
#> 2 2911150 2911126 2911091 adult tape
#> 3 2911167 2911127 2911092 adult tape
#> 4 2911168 2911129 2911093 adult tape
#> 5 2911178 2911132 2911094 adult tape
#> # ℹ 23 more rows
#> # ℹ 47 more variables: deployment_comments <chr>, deploy_on_timestamp <dttm>,
#> # duty_cycle <chr>, deployment_local_identifier <fct>,
#> # manipulation_type <fct>, study_site <chr>, tag_readout_method <fct>,
#> # sensor_type_ids <chr>, capture_location <POINT [°]>,
#> # deploy_on_location <POINT [°]>, deploy_off_location <POINT [°]>,
#> # individual_comments <chr>, individual_local_identifier <fct>, …For speed of download you might want to add the argument
attributes = NULL as it reduces the columns to download to
the bare minimum. Note still all individual attributes are downloaded as
this does not take much time.
movebank_download_study(1259686571, sensor_type_id = "gps", attributes = NULL)
#> ℹ In total 293323 records were omitted as they were not deployed (the
#> `deployment_id` was `NA`).
#> A <move2> with `track_id_column` "deployment_id" and `time_column` "timestamp"
#> Containing 92 tracks lasting on average 11637519 secs in a
#> Simple feature collection with 809090 features and 2 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -9.097052 ymin: 34.82506 xmax: 10.34339 ymax: 52.88891
#> Geodetic CRS: WGS 84
#> # A tibble: 809,090 × 3
#> deployment_id timestamp geometry
#> <int64> <dttm> <POINT [°]>
#> 1 3029108353 2021-08-19 21:16:35 (2.84631 51.19662)
#> 2 3029108353 2021-08-20 09:16:35 (2.846492 51.19654)
#> 3 3029108353 2021-08-20 21:16:29 (2.847637 51.20317)
#> 4 3029108353 2021-08-21 09:16:35 (2.849055 51.20314)
#> 5 3029108353 2021-08-21 21:16:35 (2.846533 51.2034)
#> # ℹ 809,085 more rows
#> First 5 track features:
#> # A tibble: 92 × 56
#> deployment_id tag_id individual_id alt_project_id animal_life_stage
#> <int64> <int64> <int64> <fct> <fct>
#> 1 3029108356 3029107937 3029107890 LBBG_JUVENILE juvenile
#> 2 3029108353 3029107972 3029107816 LBBG_JUVENILE juvenile
#> 3 3029108347 3029107959 3029107819 LBBG_JUVENILE juvenile
#> 4 3029108346 3029107996 3029107822 LBBG_JUVENILE juvenile
#> 5 3029108345 3029107925 3029107891 LBBG_JUVENILE juvenile
#> # ℹ 87 more rows
#> # ℹ 51 more variables: animal_mass [g], attachment_type <fct>,
#> # deployment_comments <chr>, deploy_off_timestamp <dttm>,
#> # deploy_on_timestamp <dttm>, deployment_end_type <fct>,
#> # manipulation_type <fct>, study_site <chr>, tag_readout_method <fct>,
#> # sensor_type_ids <chr>, capture_location <POINT [°]>,
#> # deploy_on_location <POINT [°]>, deploy_off_location <POINT [°]>, …If only specific attributes want to be download you can state them in
the argument attributes. The available attributes vary
between studies and sensors. You can retrieve the list of available
attributes for a specific sensor in given study. Note that only one
sensor at a time can be stated.
movebank_retrieve(
entity_type = "study_attribute",
study_id = 2911040,
sensor_type_id = "gps"
)$short_name
#> [1] "eobs_battery_voltage" "eobs_fix_battery_voltage"
#> [3] "eobs_horizontal_accuracy_estimate" "eobs_key_bin_checksum"
#> [5] "eobs_speed_accuracy_estimate" "eobs_start_timestamp"
#> [7] "eobs_status" "eobs_temperature"
#> [9] "eobs_type_of_fix" "eobs_used_time_to_get_fix"
#> [11] "ground_speed" "heading"
#> [13] "height_above_ellipsoid" "location_lat"
#> [15] "location_long" "timestamp"
#> [17] "update_ts" "visible"
movebank_download_study(
study_id = 2911040,
sensor_type_id = "gps",
attributes = c(
"height_above_ellipsoid",
"eobs_temperature"
)
)
#> A <move2> with `track_id_column` "deployment_id" and `time_column` "timestamp"
#> Containing 28 tracks lasting on average 3202499 secs in a
#> Simple feature collection with 16414 features and 4 fields (with 386 geometries empty)
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -91.3732 ymin: -12.79464 xmax: -77.51874 ymax: 0.1821983
#> Geodetic CRS: WGS 84
#> # A tibble: 16,414 × 5
#> height_above_ellipsoid eobs_temperature deployment_id timestamp
#> [m] [°C] <int64> <dttm>
#> 1 16.5 12 9472219 2008-05-31 13:30:02
#> 2 12.6 19 9472219 2008-05-31 15:00:44
#> 3 17.4 24 9472219 2008-05-31 16:30:39
#> 4 24.8 18 9472219 2008-05-31 18:00:49
#> 5 19 22 9472219 2008-05-31 19:30:18
#> # ℹ 16,409 more rows
#> # ℹ 1 more variable: geometry <POINT [°]>
#> First 5 track features:
#> # A tibble: 28 × 52
#> deployment_id tag_id individual_id animal_life_stage attachment_type
#> <int64> <int64> <int64> <fct> <fct>
#> 1 2911170 2911124 2911090 adult tape
#> 2 2911150 2911126 2911091 adult tape
#> 3 2911167 2911127 2911092 adult tape
#> 4 2911168 2911129 2911093 adult tape
#> 5 2911178 2911132 2911094 adult tape
#> # ℹ 23 more rows
#> # ℹ 47 more variables: deployment_comments <chr>, deploy_on_timestamp <dttm>,
#> # duty_cycle <chr>, deployment_local_identifier <fct>,
#> # manipulation_type <fct>, study_site <chr>, tag_readout_method <fct>,
#> # sensor_type_ids <chr>, capture_location <POINT [°]>,
#> # deploy_on_location <POINT [°]>, deploy_off_location <POINT [°]>,
#> # individual_comments <chr>, individual_local_identifier <fct>, …Only load gps records:
movebank_download_study(1259686571, sensor_type_id = 653)
#> ℹ In total 293323 records were omitted as they were not deployed (the
#> `deployment_id` was `NA`).
#> A <move2> with `track_id_column` "individual_local_identifier" and
#> `time_column` "timestamp"
#> Containing 92 tracks lasting on average 11637519 secs in a
#> Simple feature collection with 809090 features and 25 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -9.097052 ymin: 34.82506 xmax: 10.34339 ymax: 52.88891
#> Geodetic CRS: WGS 84
#> # A tibble: 809,090 × 26
#> sensor_type_id individual_local_identi…¹ acceleration_raw_x acceleration_raw_y
#> <int64> <fct> <dbl> <dbl>
#> 1 653 H911406 177 60
#> 2 653 H911406 283 -262
#> 3 653 H911406 278 574
#> 4 653 H911406 506 -32
#> 5 653 H911406 467 -222
#> # ℹ 809,085 more rows
#> # ℹ abbreviated name: ¹individual_local_identifier
#> # ℹ 22 more variables: acceleration_raw_z <dbl>, barometric_height [m],
#> # battery_charge_percent [%], battery_charging_current [mA],
#> # external_temperature [°C], gps_hdop [1], gps_satellite_count [count],
#> # gps_time_to_fix [s], ground_speed [m/s], heading [°], height_above_msl [m],
#> # import_marked_outlier <lgl>, light_level <dbl>, …
#> First 5 track features:
#> # A tibble: 92 × 56
#> deployment_id tag_id individual_id alt_project_id animal_life_stage
#> <int64> <int64> <int64> <fct> <fct>
#> 1 3029108356 3029107937 3029107890 LBBG_JUVENILE juvenile
#> 2 3029108353 3029107972 3029107816 LBBG_JUVENILE juvenile
#> 3 3029108347 3029107959 3029107819 LBBG_JUVENILE juvenile
#> 4 3029108346 3029107996 3029107822 LBBG_JUVENILE juvenile
#> 5 3029108345 3029107925 3029107891 LBBG_JUVENILE juvenile
#> # ℹ 87 more rows
#> # ℹ 51 more variables: animal_mass [g], attachment_type <fct>,
#> # deployment_comments <chr>, deploy_off_timestamp <dttm>,
#> # deploy_on_timestamp <dttm>, deployment_end_type <fct>,
#> # manipulation_type <fct>, study_site <chr>, tag_readout_method <fct>,
#> # sensor_type_ids <chr>, capture_location <POINT [°]>,
#> # deploy_on_location <POINT [°]>, deploy_off_location <POINT [°]>, …Note that the sensor_type_id can either be specified
either of an integer or character with
respectively the id or name of the sensor. In some cases additional data
is added is downloaded if a specific sensor is selected. For example the
column eobs_acceleration_raw:
movebank_download_study(2911040, sensor_type_id = "acceleration")
#> A <move2> with `track_id_column` "individual_local_identifier" and
#> `time_column` "timestamp"
#> Containing 28 tracks lasting on average 3206632 secs in a
#> Simple feature collection with 98515 features and 10 fields (with 98515 geometries empty)
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
#> Geodetic CRS: WGS 84
#> # A tibble: 98,515 × 11
#> sensor_type_id individual_local_identifier eobs_acceleration_axes
#> <int64> <fct> <fct>
#> 1 2365683 4264-84830852 XY
#> 2 2365683 4264-84830852 XY
#> 3 2365683 4264-84830852 XY
#> 4 2365683 4264-84830852 XY
#> 5 2365683 4264-84830852 XY
#> # ℹ 98,510 more rows
#> # ℹ 8 more variables: eobs_acceleration_sampling_frequency_per_axis [Hz],
#> # eobs_accelerations_raw <chr>, eobs_key_bin_checksum <int64>,
#> # eobs_start_timestamp <dttm>, timestamp <dttm>, visible <lgl>,
#> # event_id <int64>, geometry <POINT [°]>
#> First 5 track features:
#> # A tibble: 28 × 52
#> deployment_id tag_id individual_id animal_life_stage attachment_type
#> <int64> <int64> <int64> <fct> <fct>
#> 1 2911170 2911124 2911090 adult tape
#> 2 2911150 2911126 2911091 adult tape
#> 3 2911167 2911127 2911092 adult tape
#> 4 2911168 2911129 2911093 adult tape
#> 5 2911178 2911132 2911094 adult tape
#> # ℹ 23 more rows
#> # ℹ 47 more variables: deployment_comments <chr>, deploy_on_timestamp <dttm>,
#> # duty_cycle <chr>, deployment_local_identifier <fct>,
#> # manipulation_type <fct>, study_site <chr>, tag_readout_method <fct>,
#> # sensor_type_ids <chr>, capture_location <POINT [°]>,
#> # deploy_on_location <POINT [°]>, deploy_off_location <POINT [°]>,
#> # individual_comments <chr>, individual_local_identifier <fct>, …The following list of sensors is available:
movebank_retrieve(
entity_type = "tag_type",
attributes = c("external_id", "id")
)
#> # A tibble: 21 × 2
#> external_id id
#> <chr> <int64>
#> 1 bird-ring 397
#> 2 gps 653
#> 3 radio-transmitter 673
#> 4 argos-doppler-shift 82798
#> 5 natural-mark 2365682
#> 6 acceleration 2365683
#> 7 solar-geolocator 3886361
#> 8 accessory-measurements 7842954
#> 9 solar-geolocator-raw 9301403
#> 10 barometer 77740391
#> # ℹ 11 more rowsAlternatively more informative names can be used for some arguments.
For example you can use a character string to identify a
study or a timestamp as a POSIXct:
movebank_download_study("LBBG_JUVENILE",
sensor_type_id = "gps",
timestamp_start = as.POSIXct("2021-02-03 00:00:00"),
timestamp_end = as.POSIXct("2021-03-03 00:00:00")
)
#> ℹ In total 7001 records were omitted as they were not deployed (the
#> `deployment_id` was `NA`).
#> A <move2> with `track_id_column` "individual_local_identifier" and
#> `time_column` "timestamp"
#> Containing 6 tracks lasting on average 20.3 days in a
#> Simple feature collection with 8763 features and 25 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -7.169092 ymin: 35.18931 xmax: 3.229445 ymax: 49.06081
#> Geodetic CRS: WGS 84
#> # A tibble: 8,763 × 26
#> sensor_type_id individual_local_identi…¹ acceleration_raw_x acceleration_raw_y
#> <int64> <fct> <dbl> <dbl>
#> 1 653 L930074 313 -18
#> 2 653 L930074 308 -18
#> 3 653 L930074 310 -18
#> 4 653 L930074 314 -17
#> 5 653 L930074 312 -18
#> # ℹ 8,758 more rows
#> # ℹ abbreviated name: ¹individual_local_identifier
#> # ℹ 22 more variables: acceleration_raw_z <dbl>, barometric_height [m],
#> # battery_charge_percent [%], battery_charging_current [mA],
#> # external_temperature [°C], gps_hdop [1], gps_satellite_count [count],
#> # gps_time_to_fix [s], ground_speed [m/s], heading [°], height_above_msl [m],
#> # import_marked_outlier <lgl>, light_level <dbl>, …
#> First 5 track features:
#> # A tibble: 6 × 56
#> deployment_id tag_id individual_id alt_project_id animal_life_stage
#> <int64> <int64> <int64> <fct> <fct>
#> 1 3029108271 3029107922 3029107866 LBBG_JUVENILE juvenile
#> 2 3029108241 3029107929 3029107889 LBBG_JUVENILE juvenile
#> 3 3029108205 3029107957 3029107883 LBBG_JUVENILE juvenile
#> 4 3029108176 3029107945 3029107876 LBBG_JUVENILE juvenile
#> 5 3029108161 3029107941 3029107863 LBBG_JUVENILE juvenile
#> # ℹ 1 more row
#> # ℹ 51 more variables: animal_mass [g], attachment_type <fct>,
#> # deployment_comments <chr>, deploy_off_timestamp <dttm>,
#> # deploy_on_timestamp <dttm>, deployment_end_type <fct>,
#> # manipulation_type <fct>, study_site <chr>, tag_readout_method <fct>,
#> # sensor_type_ids <chr>, capture_location <POINT [°]>,
#> # deploy_on_location <POINT [°]>, deploy_off_location <POINT [°]>, …If you are interested in the deployment information you can use the
movebank_download_deployment function.
movebank_download_deployment("Galapagos Albatrosses")
#> # A tibble: 28 × 26
#> deployment_id tag_id individual_id animal_life_stage attachment_type
#> <int64> <int64> <int64> <fct> <fct>
#> 1 2911170 2911124 2911090 adult tape
#> 2 2911150 2911126 2911091 adult tape
#> 3 2911167 2911127 2911092 adult tape
#> 4 2911168 2911129 2911093 adult tape
#> 5 2911178 2911132 2911094 adult tape
#> 6 2911163 2911133 2911095 adult tape
#> 7 9472225 2911114 2911061 adult tape
#> 8 9472224 2911120 2911062 adult tape
#> 9 9472223 2911121 2911086 adult tape
#> 10 9472222 2911134 2911065 adult tape
#> # ℹ 18 more rows
#> # ℹ 21 more variables: deployment_comments <chr>, deploy_on_timestamp <dttm>,
#> # duty_cycle <chr>, deployment_local_identifier <fct>,
#> # manipulation_type <fct>, study_site <chr>, tag_readout_method <fct>,
#> # sensor_type_ids <chr>, capture_location <POINT [°]>,
#> # deploy_on_location <POINT [°]>, deploy_off_location <POINT [°]>,
#> # individual_comments <chr>, individual_local_identifier <fct>, …For specific request it might be useful to directly retrieve
information from the movebank api. The movebank_retrieve
function provides this functionality. The first argument is the entity
type you would like to retrieve information for (e.g. tag
or event). Other arguments make it possible to select, a
study id is always required. For more details how to use the api see the
documentation.
One common reason to use this options is to retrieve undeployed
locations. In some cases a set of locations is collected before the tag
attached to the animal for quality control or error measurements. The
example below shows how all records for a specific tag can be retrieved.
Filtering for locations where the deployment_id is
NA, returns those locations that were collected while the
tag was not deployed. The timestamp_start and
timestamp_end might be good argument to filter down the
data even more in the call to movebank_retrieve.
movebank_retrieve("event",
study_id = 1259686571,
tag_local_identifier = "193967", attributes = "all"
) %>%
filter(is.na(deployment_id))
#> # A tibble: 57 × 33
#> individual_id deployment_id tag_id study_id sensor_type_id
#> <int64> <int64> <int64> <int64> <int64>
#> 1 NA NA 3029107920 1259686571 653
#> 2 NA NA 3029107920 1259686571 653
#> 3 NA NA 3029107920 1259686571 653
#> 4 NA NA 3029107920 1259686571 653
#> 5 NA NA 3029107920 1259686571 653
#> 6 NA NA 3029107920 1259686571 653
#> 7 NA NA 3029107920 1259686571 653
#> 8 NA NA 3029107920 1259686571 653
#> 9 NA NA 3029107920 1259686571 653
#> 10 NA NA 3029107920 1259686571 653
#> # ℹ 47 more rows
#> # ℹ 28 more variables: individual_local_identifier <fct>,
#> # tag_local_identifier <fct>, individual_taxon_canonical_name <fct>,
#> # acceleration_raw_x <dbl>, acceleration_raw_y <dbl>,
#> # acceleration_raw_z <dbl>, barometric_height [m],
#> # battery_charge_percent [%], battery_charging_current [mA],
#> # external_temperature [°C], gps_hdop [1], gps_satellite_count [count], …