Download example data and select columns to reduce printing.
galapagos_albatrosses <- movebank_download_study(2911040,
attributes = c(
"ground_speed",
"heading",
"height_above_ellipsoid",
"eobs_temperature",
"individual_local_identifier"
)
) %>%
select_track_data(study_site, weight, animal_life_stage)
Finding and filtering duplicated records
When dealing with trajectories frequently duplicated records do occur. There are many reasons these can appear ranging from the way in which data is recorded to duplicated data transmissions and uploads. These data are often stored, but for analysis they need to be removed. A simple definition of a duplicate record would be an observation at exactly the same time of the same individual. However many tracking devices record additional information such as acceleration. These records frequently have the same time as location records meaning not all records with duplicated timestamps can directly be deleted.
Duplicated records can be found in the following way:
galapagos_albatrosses %>%
group_by(mt_time(), mt_track_id()) %>%
filter(n() != 1) %>%
arrange(mt_time())
#> A <move2> with `track_id_column` "individual_local_identifier" and
#> `time_column` "timestamp"
#> Containing 28 tracks lasting on average 3172363 secs in a
#> Simple feature collection with 8092 features and 8 fields (with 4066 geometries empty)
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -91.24518 ymin: -12.79464 xmax: -77.51874 ymax: 0.1821983
#> Geodetic CRS: WGS 84
#> # A tibble: 8,092 × 9
#> # Groups: mt_time(), mt_track_id() [4,046]
#> ground_speed heading height_above_ellipsoid eobs_temperature
#> [m/s] [°] [m] [°C]
#> 1 0.3 14.4 7.9 27
#> 2 NA NA NA NA
#> 3 0.55 330. 1.6 24
#> 4 NA NA NA NA
#> 5 0.15 53.1 11.4 27
#> # ℹ 8,087 more rows
#> # ℹ 5 more variables: individual_local_identifier <fct>, timestamp <dttm>,
#> # geometry <POINT [°]>, `mt_time()` <dttm>, `mt_track_id()` <fct>
#> First 5 track features:
#> # A tibble: 28 × 4
#> study_site weight animal_life_stage individual_local_identifier
#> <chr> [g] <fct> <fct>
#> 1 Isla de la Plata 22 adult unbanded-151
#> 2 Isla de la Plata 22 adult unbanded-153
#> 3 Isla de la Plata 22 adult unbanded-154
#> 4 Isla de la Plata 22 adult unbanded-156
#> 5 Isla de la Plata 22 adult unbanded-159
#> # ℹ 23 more rows
If you are only interested in finding duplicated records where there is a location this can as follows (in this case there are none):
galapagos_albatrosses %>%
filter(!st_is_empty(.)) %>%
group_by(mt_time(), mt_track_id()) %>%
filter(n() != 1) %>%
arrange(mt_time())
#> Warning in mean.default(do.call(c, lapply(lapply(split(mt_time(x),
#> mt_track_id(x), : argument is not numeric or logical: returning NA
#> A <move2> with `track_id_column` "individual_local_identifier" and
#> `time_column` "timestamp"
#> Containing 0 tracks lasting on average NA in a
#> Simple feature collection with 0 features and 8 fields
#> Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
#> Geodetic CRS: WGS 84
#> # A tibble: 0 × 9
#> # Groups: mt_time(), mt_track_id() [0]
#> # ℹ 9 variables: ground_speed [m/s], heading [°], height_above_ellipsoid [m],
#> # eobs_temperature [°C], individual_local_identifier <fct>, timestamp <dttm>,
#> # geometry <GEOMETRY [°]>, mt_time() <dttm>, mt_track_id() <fct>
#> Track features:
#> # A tibble: 0 × 4
#> # ℹ 4 variables: study_site <chr>, weight [g], animal_life_stage <fct>,
#> # individual_local_identifier <fct>
The package also has some build in functions for filtering unique records. Several strategies for omitting duplicated records are build in.
First it is possible to omit all records that are a subset of other records, i.e. records that got added later with more information are retained. This happens with some tracking devices if data gets directly downloaded from the tag. As no information is lost this is the default strategy.
simulated_data <- mt_sim_brownian_motion(1:2)[rep(1:4, 2), ]
simulated_data$temperature <- c(1:3, NA, 1:2, 7:8)
simulated_data
#> A <move2> with `track_id_column` "track" and `time_column` "time"
#> Containing 2 tracks lasting on average 1 in a
#> Simple feature collection with 8 features and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 0 ymin: -1.781979 xmax: 1.132471 ymax: 1.087483
#> CRS: NA
#> First 5 features:
#> time track geometry temperature
#> 1 1 1 POINT (0 0) 1
#> 2 2 1 POINT (1.132471 1.087483) 2
#> 3 1 2 POINT (0 0) 3
#> 4 2 2 POINT (0.1095682 -1.781979) NA
#> 1.1 1 1 POINT (0 0) 1
#> Track features:
#> track
#> 1 1
#> 2 2
simulated_data %>% mt_filter_unique()
#> Warning: After removing all records that are subsets of other records there are
#> still remaining duplicates.
#> A <move2> with `track_id_column` "track" and `time_column` "time"
#> Containing 2 tracks lasting on average 1 in a
#> Simple feature collection with 5 features and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: 0 ymin: -1.781979 xmax: 1.132471 ymax: 1.087483
#> CRS: NA
#> time track geometry temperature
#> 1 1 1 POINT (0 0) 1
#> 2 2 1 POINT (1.132471 1.087483) 2
#> 3 1 2 POINT (0 0) 3
#> 3.1 1 2 POINT (0 0) 7
#> 4.1 2 2 POINT (0.1095682 -1.781979) 8
#> Track features:
#> track
#> 1 1
#> 2 2
This strategy how ever does not guarantee not duplicates are left, as two records might not be subsets from each other.
An alternative is to take a random record from each set of duplicates, this is not advised for formal analysis but might help for a quick inspection of data. This is also a lot quicker then inspecting subsets. How ever care needs to be taken as the example below, for example, results in empty points being retained at the cost of informative locations.
galapagos_albatrosses %>% mt_filter_unique("sample")
#> A <move2> with `track_id_column` "individual_local_identifier" and
#> `time_column` "timestamp"
#> Containing 28 tracks lasting on average 3206656 secs in a
#> Simple feature collection with 110883 features and 6 fields (with 96907 geometries empty)
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -91.3732 ymin: -12.79464 xmax: -77.52837 ymax: 0.1814998
#> Geodetic CRS: WGS 84
#> # A tibble: 110,883 × 7
#> ground_speed heading height_above_ellipsoid eobs_temperature
#> * [m/s] [°] [m] [°C]
#> 1 0.01 21.6 16.5 12
#> 2 0 95.7 12.6 19
#> 3 0.11 13.8 17.4 24
#> 4 0.2 9.83 24.8 18
#> 5 0.24 37.4 19 22
#> # ℹ 110,878 more rows
#> # ℹ 3 more variables: individual_local_identifier <fct>, timestamp <dttm>,
#> # geometry <POINT [°]>
#> First 5 track features:
#> # A tibble: 28 × 4
#> study_site weight animal_life_stage individual_local_identifier
#> <chr> [g] <fct> <fct>
#> 1 Isla de la Plata 22 adult unbanded-151
#> 2 Isla de la Plata 22 adult unbanded-153
#> 3 Isla de la Plata 22 adult unbanded-154
#> 4 Isla de la Plata 22 adult unbanded-156
#> 5 Isla de la Plata 22 adult unbanded-159
#> # ℹ 23 more rows
