# 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)

## Study information

Using the function `movebank_download_study_info` it is possible to download information for all studies, for all studies that have certain property or for a single study. Any column of the table can be used to download only the information of the studies that comply with the selected property. This table contains all the information that can be seen on the “Study page” on the Movebank webpage, plus additional information about download rights and ownership.

NOTE: due to incorrect timestamps in some Movebank studies, the function `movebank_download_study_info()` sometimes returns a Warning message as the one in the example below. You can ignore this (see issue #17).

- For all studies

    movebank_download_study_info()
    #> Warning: `vroom()` finds reading problems with the movebank specification.
    #> ℹ This might relate to the returned data not fitting the expectation of the
    #>   movebank data format specified in the package.
    #> ℹ For retrieving the specific problem you can enable `global_entrace` using
    #>   `rlang::global_entrace()` then run the command and use
    #>   `rlang::last_warnings()[[1]]$problems` to retrieve the problems.
    #> ℹ The requested url can then be retrieved with: `rlang::last_warnings()[[1]]$url`
    #> ℹ Alternatively in some cases you might be able to retrieve the problems calling
    #>   `vroom::problems()` on the result of the function call that produced the warning.
    #> # A tibble: 8,064 × 31
    #>    acknowledgements    citation go_public_date grants_used      has_quota i_am_owner
    #>    <chr>               <chr>    <dttm>         <chr>            <lgl>     <lgl>
    #>  1 thesis              <NA>     NA             master           TRUE      FALSE
    #>  2 <NA>                <NA>     NA             <NA>             TRUE      FALSE
    #>  3 <NA>                Bonelli… NA             <NA>             TRUE      FALSE
    #>  4 Universidad Estata… <NA>     NA             Comisión Federa… TRUE      FALSE
    #>  5 Kidd Biological In… Kidd et… NA             None             TRUE      FALSE
    #>  6 <NA>                <NA>     NA             <NA>             TRUE      FALSE
    #>  7 <NA>                <NA>     NA             <NA>             TRUE      FALSE
    #>  8 <NA>                <NA>     NA             <NA>             TRUE      FALSE
    #>  9 Lilongwe Wildlife … <NA>     NA             <NA>             TRUE      FALSE
    #> 10 <NA>                <NA>     NA             <NA>             TRUE      FALSE
    #> # ℹ 8,054 more rows
    #> # ℹ 25 more variables: id <int64>, is_test <lgl>, license_terms <chr>,
    #> #   license_type <fct>, name <fct>, …

This code took: 1.63 [s]

- All studies where you have access to download the data

    movebank_download_study_info(i_have_download_access = TRUE)

- All studies where you are owner of the data

    movebank_download_study_info(i_am_owner = TRUE)

- All studies with a creative commons zero license are returned. These are a good candidate for exploration and testing

    movebank_download_study_info(license_type = "CC_0")

- For a specific study

    movebank_download_study_info(id = 2911040)

## Individual, tag and deployment information

The function `movebank_download_deployment` downloads a table with the associated information to individuals, tags and deployments. This table reassembles the “Reference Data” table that can be downloaded from the Movebank webpage.

    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       9472204 2911118       2911079 adult             tape
    #>  8       9472205 2911110       2911075 adult             tape
    #>  9       9472206 2911122       2911087 adult             tape
    #> 10       9472207 2911131       2911064 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>, …

This code took: 4.98 [s]

## Location & non-location data (Event data)

With the function `movebank_download_study` the complete study from Movebank can be downloaded. There are many options to download a subset of the complete study. The `study_id` can either be specified either as an `integer` or `character` with respectively the id or name of the study.

To get the study ID of a Movebank study use `movebank_get_study_id`

    movebank_get_study_id(study_id = "Galapagos Albatrosses")
    #> integer64
    #> [1] 2911040

This code took: 1.19 [s]

- Download an entire study (all data of all sensors)

    movebank_download_study_info(study_id = 2911040)$sensor_type_ids
    #> [1] "GPS,Acceleration"
    movebank_download_study(
      study_id = 2911040,
      sensor_type_id = c("gps", "acceleration")
    )
    #> A <move2> with `track_id_column` "individual_local_identifier" and `time_column`
    #> "timestamp"
    #> Containing 28 tracks lasting on average 37.1 days in a
    #> Simple feature collection with 114929 features and 21 fields (with 98901 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: 114,929 × 22
    #>   sensor_type_id individual_local_iden…¹ eobs_battery_voltage eobs_fix_battery_vol…²
    #>          <int64> <fct>                                   [mV]                   [mV]
    #> 1            653 4264-84830852                           3686                   3437
    #> 2            653 4264-84830852                           3701                   3452
    #> 3            653 4264-84830852                           3701                   3482
    #> 4            653 4264-84830852                           3691                   3476
    #> 5            653 4264-84830852                           3691                   3541
    #> # ℹ 114,924 more rows
    #> # ℹ abbreviated names: ¹​individual_local_identifier, ²​eobs_fix_battery_voltage
    #> # ℹ 18 more variables: eobs_horizontal_accuracy_estimate [m],
    #> #   eobs_key_bin_checksum <int64>, eobs_speed_accuracy_estimate [m/s],
    #> #   eobs_start_timestamp <dttm>, eobs_status <ord>, …
    #> 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>, …

This code took: 10.4 [s]

- Download gps data of one individual

    movebank_download_study(
      study_id = "Galapagos Albatrosses",
      sensor_type_id = "gps",
      individual_local_identifier = "unbanded-160"
    )
    #> A <move2> with `track_id_column` "individual_local_identifier" and `time_column`
    #> "timestamp"
    #> Containing 1 track lasting 13.3 days in a
    #> Simple feature collection with 213 features and 18 fields (with 4 geometries empty)
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: -81.20167 ymin: -1.829105 xmax: -80.93177 ymax: -1.11206
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 213 × 19
    #>   sensor_type_id individual_local_iden…¹ eobs_battery_voltage eobs_fix_battery_vol…²
    #>          <int64> <fct>                                   [mV]                   [mV]
    #> 1            653 unbanded-160                            3754                   3496
    #> 2            653 unbanded-160                            3701                   3530
    #> 3            653 unbanded-160                            3754                   3505
    #> 4            653 unbanded-160                            3759                   3535
    #> 5            653 unbanded-160                            3732                   3515
    #> # ℹ 208 more rows
    #> # ℹ abbreviated names: ¹​individual_local_identifier, ²​eobs_fix_battery_voltage
    #> # ℹ 15 more variables: eobs_horizontal_accuracy_estimate [m],
    #> #   eobs_key_bin_checksum <int64>, eobs_speed_accuracy_estimate [m/s],
    #> #   eobs_start_timestamp <dttm>, eobs_status <ord>, …
    #> Track features:
    #> # A tibble: 1 × 52
    #>   deployment_id  tag_id individual_id animal_life_stage attachment_type
    #>         <int64> <int64>       <int64> <fct>             <fct>
    #> 1       2911163 2911133       2911095 adult             tape
    #> # ℹ 47 more variables: deployment_comments <chr>, deploy_on_timestamp <dttm>,
    #> #   duty_cycle <chr>, deployment_local_identifier <fct>, manipulation_type <fct>, …

This code took: 5.93 [s]

- Download gps data for multiple individuals

    movebank_download_study(
      study_id = 2911040,
      sensor_type_id = "gps",
      individual_local_identifier = c("1094-1094", "1103-1103")
    )
    #> A <move2> with `track_id_column` "individual_local_identifier" and `time_column`
    #> "timestamp"
    #> Containing 2 tracks lasting on average 10.3 days in a
    #> Simple feature collection with 289 features and 18 fields (with 59 geometries empty)
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: -89.96382 ymin: -1.502011 xmax: -89.59216 ymax: -0.7981921
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 289 × 19
    #>   sensor_type_id individual_local_iden…¹ eobs_battery_voltage eobs_fix_battery_vol…²
    #>          <int64> <fct>                                   [mV]                   [mV]
    #> 1            653 1103-1103                               3671                   3444
    #> 2            653 1103-1103                               3615                   3437
    #> 3            653 1103-1103                               3662                   3476
    #> 4            653 1103-1103                               3662                   3471
    #> 5            653 1103-1103                               3662                   3452
    #> # ℹ 284 more rows
    #> # ℹ abbreviated names: ¹​individual_local_identifier, ²​eobs_fix_battery_voltage
    #> # ℹ 15 more variables: eobs_horizontal_accuracy_estimate [m],
    #> #   eobs_key_bin_checksum <int64>, eobs_speed_accuracy_estimate [m/s],
    #> #   eobs_start_timestamp <dttm>, eobs_status <ord>, …
    #> Track features:
    #> # A tibble: 2 × 52
    #>   deployment_id  tag_id individual_id animal_life_stage attachment_type
    #>         <int64> <int64>       <int64> <fct>             <fct>
    #> 1       9472212 2911119       2911080 adult             tape
    #> 2       9472225 2911114       2911061 adult             tape
    #> # ℹ 47 more variables: deployment_comments <chr>, deploy_on_timestamp <dttm>,
    #> #   duty_cycle <chr>, deployment_local_identifier <fct>, manipulation_type <fct>, …

This code took: 4.57 [s]

    ## it is also possible to use the numerical identifiers
    movebank_download_study(
      study_id = 2911040,
      sensor_type_id = "gps",
      individual_id = c(2911086, 2911065)
    )

- Download acceleration data of one or several individuals

    movebank_download_study(2911040,
      sensor_type_id = "acceleration",
      individual_local_identifier = "1094-1094"
    )
    #> A <move2> with `track_id_column` "individual_local_identifier" and `time_column`
    #> "timestamp"
    #> Containing 1 track lasting 3.05 days in a
    #> Simple feature collection with 291 features and 10 fields (with 291 geometries empty)
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 291 × 11
    #>   sensor_type_id individual_local_identifier eobs_acceleration_axes
    #>          <int64> <fct>                       <fct>
    #> 1        2365683 1094-1094                   XY
    #> 2        2365683 1094-1094                   XY
    #> 3        2365683 1094-1094                   XY
    #> 4        2365683 1094-1094                   XY
    #> 5        2365683 1094-1094                   XY
    #> # ℹ 286 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>, …
    #> Track features:
    #> # A tibble: 1 × 52
    #>   deployment_id  tag_id individual_id animal_life_stage attachment_type
    #>         <int64> <int64>       <int64> <fct>             <fct>
    #> 1       9472212 2911119       2911080 adult             tape
    #> # ℹ 47 more variables: deployment_comments <chr>, deploy_on_timestamp <dttm>,
    #> #   duty_cycle <chr>, deployment_local_identifier <fct>, manipulation_type <fct>, …

This code took: 4.88 [s]

Note that the `sensor_type_id` can either be specified either as an `integer` or `character` with respectively the ‘id’ or ‘external_id’ of the sensor. Here is how you get the correspondence table of sensor name and id:

    movebank_retrieve(entity_type = "tag_type")
    #> # A tibble: 23 × 5
    #>    description external_id                  id is_location_sensor name
    #>    <chr>       <chr>                   <int64> <lgl>              <fct>
    #>  1 <NA>        bird-ring                   397 TRUE               Bird Ring
    #>  2 <NA>        gps                         653 TRUE               GPS
    #>  3 <NA>        radio-transmitter           673 TRUE               Radio Transmitter
    #>  4 <NA>        argos-doppler-shift       82798 TRUE               Argos Doppler Shi…
    #>  5 <NA>        natural-mark            2365682 TRUE               Natural Mark
    #>  6 <NA>        acceleration            2365683 FALSE              Acceleration
    #>  7 <NA>        solar-geolocator        3886361 TRUE               Solar Geolocator
    #>  8 <NA>        accessory-measurements  7842954 FALSE              Accessory Measure…
    #>  9 <NA>        solar-geolocator-raw    9301403 FALSE              Solar Geolocator …
    #> 10 <NA>        barometer              77740391 FALSE              Barometer
    #> # ℹ 13 more rows

- Download data of a specific time window and sensor. The `timestamp_*` arguments can either be formatted as a `POSIXct` timestamp, `Date` or a character string (e.g. `"20080604133046000"`(yyyyMMddHHmmssSSS)). The `timestamp_*` arguments can also be used separately.

    movebank_download_study(2911040,
      sensor_type_id = "gps",
      timestamp_start = as.POSIXct("2008-08-01 00:00:00"),
      timestamp_end = as.POSIXct("2008-08-02 00:00:00")
    )
    #> A <move2> with `track_id_column` "individual_local_identifier" and `time_column`
    #> "timestamp"
    #> Containing 9 tracks lasting on average 22.5 hours in a
    #> Simple feature collection with 144 features and 18 fields (with 6 geometries empty)
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: -90.0103 ymin: -5.571548 xmax: -80.94916 ymax: -0.6785461
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 144 × 19
    #>   sensor_type_id individual_local_iden…¹ eobs_battery_voltage eobs_fix_battery_vol…²
    #>          <int64> <fct>                                   [mV]                   [mV]
    #> 1            653 4266-84831108                           3754                   3505
    #> 2            653 4266-84831108                           3754                   3508
    #> 3            653 4266-84831108                           3754                   3576
    #> 4            653 4266-84831108                           3759                   3520
    #> 5            653 4266-84831108                           3759                   3593
    #> # ℹ 139 more rows
    #> # ℹ abbreviated names: ¹​individual_local_identifier, ²​eobs_fix_battery_voltage
    #> # ℹ 15 more variables: eobs_horizontal_accuracy_estimate [m],
    #> #   eobs_key_bin_checksum <int64>, eobs_speed_accuracy_estimate [m/s],
    #> #   eobs_start_timestamp <dttm>, eobs_status <ord>, …
    #> First 5 track features:
    #> # A tibble: 9 × 52
    #>   deployment_id  tag_id individual_id animal_life_stage attachment_type
    #>         <int64> <int64>       <int64> <fct>             <fct>
    #> 1       2911150 2911126       2911091 adult             tape
    #> 2       2911167 2911127       2911092 adult             tape
    #> 3       2911168 2911129       2911093 adult             tape
    #> 4       2911178 2911132       2911094 adult             tape
    #> 5       9472208 2911108       2911074 adult             tape
    #> # ℹ 4 more rows
    #> # ℹ 47 more variables: deployment_comments <chr>, deploy_on_timestamp <dttm>,
    #> #   duty_cycle <chr>, deployment_local_identifier <fct>, manipulation_type <fct>, …

This code took: 4.88 [s]

- Reduce columns downloaded to a minimal set (only for location data). By default all attributes are downloaded, but to speed up download, the argument `attributes = NULL` can be used as it reduces the columns to download to the bare minimum. All individual attributes are downloaded as this does not take much time. Note that this option should only be used when downloading location data (by specifying the sensor), as only timestamps, location and track id is downloaded.

    movebank_download_study(1259686571, sensor_type_id = 653, attributes = NULL)
    #> ℹ In total 302875 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 183 days in a
    #> Simple feature collection with 985419 features and 3 fields
    #> Geometry type: POINT
    #> Dimension:     XY
    #> Bounding box:  xmin: -9.097052 ymin: 34.82506 xmax: 10.34339 ymax: 53.40649
    #> Geodetic CRS:  WGS 84
    #> # A tibble: 985,419 × 4
    #>   deployment_id timestamp           visible            geometry
    #>         <int64> <dttm>              <lgl>           <POINT [°]>
    #> 1    3029108353 2021-08-19 21:16:35 TRUE     (2.84631 51.19662)
    #> 2    3029108353 2021-08-20 09:16:35 TRUE    (2.846492 51.19654)
    #> 3    3029108353 2021-08-20 21:16:29 TRUE    (2.847637 51.20317)
    #> 4    3029108353 2021-08-21 09:16:35 TRUE    (2.849055 51.20314)
    #> 5    3029108353 2021-08-21 21:16:35 TRUE     (2.846533 51.2034)
    #> # ℹ 985,414 more rows
    #> First 5 track features:
    #> # A tibble: 92 × 56
    #>   deployment_id    tag_id individual_id alt_project_id animal_life_stage animal_mass
    #>         <int64>   <int64>       <int64> <fct>          <fct>                     [g]
    #> 1    3029108308       3e9    3029107877 LBBG_JUVENILE  juvenile                  951
    #> 2    3029108309       3e9    3029107843 LBBG_JUVENILE  juvenile                  648
    #> 3    3029108311       3e9    3029107905 LBBG_JUVENILE  juvenile                   NA
    #> 4    3029108313       3e9    3029107899 LBBG_JUVENILE  juvenile                  772
    #> 5    3029108315       3e9    3029107824 LBBG_JUVENILE  juvenile                  763
    #> # ℹ 87 more rows
    #> # ℹ 50 more variables: attachment_type <fct>, deployment_comments <chr>,
    #> #   deploy_off_timestamp <dttm>, deploy_on_timestamp <dttm>,
    #> #   deployment_end_type <fct>, …

This code took: 29.2 [s]

- Download only specific attributes. 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.

    ## get all attributes available for a specific study and sensor
    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 37.1 days in a
    #> Simple feature collection with 16414 features and 5 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 × 6
    #>   height_above_ellipsoid eobs_temperature deployment_id timestamp           visible
    #>                      [m]             [°C]       <int64> <dttm>              <lgl>
    #> 1                   16.5               12       9472219 2008-05-31 13:30:02 TRUE
    #> 2                   12.6               19       9472219 2008-05-31 15:00:44 TRUE
    #> 3                   17.4               24       9472219 2008-05-31 16:30:39 TRUE
    #> 4                   24.8               18       9472219 2008-05-31 18:00:49 TRUE
    #> 5                   19                 22       9472219 2008-05-31 19:30:18 TRUE
    #> # 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>, …

This code took: 6.73 [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`). A study id is always required and other arguments make it possible to select. For more details how to use the api see the documentation.

## Downloading undeployed data

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`. By omitting the argument `tag_local_identifier` the entire study can downloaded. With the argument `sensor_type_id` the sensors can be specified.

    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 individual_local_ide…¹
    #>          <int64>       <int64> <int6>  <int64>        <int64> <fct>
    #>  1            NA            NA    3e9      1e9            653 <NA>
    #>  2            NA            NA    3e9      1e9            653 <NA>
    #>  3            NA            NA    3e9      1e9            653 <NA>
    #>  4            NA            NA    3e9      1e9            653 <NA>
    #>  5            NA            NA    3e9      1e9            653 <NA>
    #>  6            NA            NA    3e9      1e9            653 <NA>
    #>  7            NA            NA    3e9      1e9            653 <NA>
    #>  8            NA            NA    3e9      1e9            653 <NA>
    #>  9            NA            NA    3e9      1e9            653 <NA>
    #> 10            NA            NA    3e9      1e9            653 <NA>
    #> # ℹ 47 more rows
    #> # ℹ abbreviated name: ¹​individual_local_identifier
    #> # ℹ 27 more variables: tag_local_identifier <fct>,
    #> #   individual_taxon_canonical_name <fct>, acceleration_raw_x <dbl>,
    #> #   acceleration_raw_y <dbl>, acceleration_raw_z <dbl>, …

This code took: 1.58 [s]