# Downloading data from movebank

    library(move2)

# User credentials

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.

    movebank_store_credentials("myUserName", "myPassword")

    movebank_remove_credentials()
    #> There is 1 key removed from the 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`)

### Handling multiple Movebank accounts - use `key_name`

If 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.

    options("move2_movebank_key_name" = "myOtherAccount")

If in one script/Rsession you are using several accounts, to use the credentials of the default account execute the line below:

    options("move2_movebank_key_name" = "movebank")

To check which accounts are stored in keyring:

    keyring::key_list()
    #   service           username
    # 1 movebank          myUserName
    # 2 myOtherAccount    myUserName_2

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.

### Removing user credentials from keyring

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:

    keyring::key_list()

Here you can check if the `movebank` service is successfully removed.

# Downloading data

    library(dplyr, quietly = TRUE)

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: ¹​number_of_deployed_locations

This code took: 0.78 [s]

A more quick way to retrieve the information is the following (the selection is performed on movebank and not all data is downloaded):

    movebank_download_study_info(license_type = "CC_0")

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>, …

This code took: 4.07 [s]

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 [°]>, …

This code took: 30.4 [s]

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>, …

This code took: 4.32 [s]

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: ¹​individual_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 [°]>, …

This code took: 53.6 [s]

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>, …

This code took: 11.2 [s]

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 rows

Alternatively 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: ¹​individual_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 [°]>, …

This code took: 6.2 [s]

# Deployments

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>, …

This code took: 3.97 [s]

# Advanced usage

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], …

This code took: 1.38 [s]