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 36.7 days 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 days 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>, …
#> 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 secs in a
#> Simple feature collection with 8 features and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -0.8744358 ymin: -0.7084858 xmax: 0.2385534 ymax: 1.56553
#> CRS: NA
#> First 5 features:
#> time track geometry temperature
#> 1 1 1 POINT (0 0) 1
#> 2 2 1 POINT (0.2385534 1.56553) 2
#> 3 1 2 POINT (0 0) 3
#> 4 2 2 POINT (-0.8744358 -0.7084858) 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 secs in a
#> Simple feature collection with 5 features and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -0.8744358 ymin: -0.7084858 xmax: 0.2385534 ymax: 1.56553
#> CRS: NA
#> time track geometry temperature
#> 1 1 1 POINT (0 0) 1
#> 2 2 1 POINT (0.2385534 1.56553) 2
#> 3 1 2 POINT (0 0) 3
#> 3.1 1 2 POINT (0 0) 7
#> 4.1 2 2 POINT (-0.8744358 -0.7084858) 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 37.1 days in a
#> Simple feature collection with 110883 features and 6 fields (with 96860 geometries empty)
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -91.3732 ymin: -12.79464 xmax: -77.52837 ymax: 0.1821983
#> 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
