sf::st_coordinates(): returns the coordinates from the the events in the track(s)
sf::st_crs(): returns the projection of the tracks(s)
mt_time(): returns the timestamps for each event in the track
mt_track_data(): returns the table containing the information associated to the tracks
mt_track_id(): returns a vector of the track id associated to each event
unique(mt_track_id()): returns the names of the tracks
mt_n_tracks(): returns the number of the tracks
nrow()/table(mt_track_id()): returns the number of events in total and per track respectively
mt_time_column(): returns the name of the column containing the timestamps used by the
mt_track_id_column(): returns the name of the column containing the track ids used by the
mt_as_move2(): creates a
move2object from objects of class
telemetry listfrom ctmm,
track_xytfrom amt or
move2object into other classes
to_move(): converts to a object of class
mt_track_id(x) <- NULL: to remove
move2 class from the object, it will be recognized as a object of class
mt_as_event_attribute(x, names(mt_track_data(x))): join all attributes (event and track) into one table. If additionally
mt_track_id(x) <- NULL is applied, the resulting
sf object should behave as any
mt_read(): read in data downloaded from movebank, by just stating the path to the file
mt_read(mt_example()): example dataset
dplyr::filter(x, !sf::st_is_empty(x)): exclude all empty locations
filter_track_data(x, .track_id = c("nameTrack1", "nameTrack3"): subset to one or more tracks
split(x, mt_track_id(x)): split a
move2 object into a list of single objects per track. Alternatively see
group_by_track_data() to apply calculations to tracks separately
mt_stack(): combine multiple
move2 objects into one
mt_as_event_attribute(): move columns between track and event attributes (and vv)
mt_set_track_id(): replace track ids with new values, set new column to define tracks or rename track id column
mutate_track_data(): add or modify attributes in the track data
sf::st_transform(): to reproject the
move2 into a different projection
mt_aeqd_crs(): create a AEQD coordinate reference system
mt_track_lines(): convert a trajectory into lines for plotting with e.g.
use the option
options(sf_max.plot=1) to display a single plot of the track. The attribute that should be used to color the tracks can be specified, e.g.
plot(x["individual_local_identifier"]). Here is more information on how to do simple plots.
All functions of the
move2 package are described here.
it is based on the
sf objects and compatible with a lot of
tidyverse based functionality
information of non location data (other sensors as e.g. acceleration, magnetometer,etc) are associated to an empty locations.
track attributes and event attributes are distinguished. event attributes are attributes associated to each recorded event (location or non location), these will at least have a time and track id associated to them. track attributes are attributes associated to each track (e.g. individual, species, sex, etc), these will at least contain the track id, and can be retrieved with the function
To be able to expand and use the object in
move2 it is important to understand how the objects is structured. Here we explain some of the choices and explain the requirements.
A move object in
move2 uses the
S3 class system, this is less rigors then the
S4 system that was used in the original
move package. The objects are based on the
sf objects from the
sf package. This change is inspired by several factors, first by basing on
sf we are able to profit from the speed and improvements that went into that package, second it makes it directly compatible with a lot of
tidyverse based functionality. To ensure information specific to movement is retrained we use attributes. This is in a fairly similar style to
To facilitate working with the associated sensor data we store other records with an empty point. This means, for example, acceleration and activity measurements can be part of the same
sf package and
sf in general allow to store coordinates as three dimensional records. As the altitude of tracking devices is typically much less accurate, few functions actually support this functionality we do not use it at this time.
move package we implemented separate objects for one single individuals (
Move) and multiple individuals (
MoveStack). Here we choose to not do this. This reduces complexity. If functions require single individuals to work it is easy enough to split these of.
Tracking data generally consists of a time series of observations from a range of “sensors”. Each of these observation or events at least have a time and a sensor associated with them. Some have a location recorded by, for example, a gps sensor other have non locations data like acceleration or gyroscope measurements. All events are combined in one large dataset, this facilitates combined analysis between them (e.g. interpolation to the position of an acceleration measurement). However for some analysis specific sensors or data types will be needed therefore filtering functions are available that subset the data to, for example, all location data.
To facilitate working with the trajectories we distinguish between track attributes and event attributes. Track level data could be individual and species names, sex and age. This can furthermore greatly facilitate object sizes as that is not duplicated. Keeping track attributes separate also contributes to data integrity as ensures track level attributes are consistent within a track.
In this section we go through the attributes that
This attributes should contain a string with a length of
1. This string indicates in which column the timestamp information of the locations in it. The string should thus be an existing column. The time column in most cases will contain timestamps in the
POSIXct format. In some cases timestamps will not be referring to an exact time point. For example when simulating movement data or analysis from a video. In these cases times can also be stored as
This attribute should contain a string of length
1. A column with this name should be contained both in the
track_data attribute and in the main dataset. This column also functions as the link between the
track_data and the main data, linking the individual attributes to the individual data.
This dataset contains the track level data. Properties of the individual follows (e.g. sex, age and name) can be stored here. Additionally other deployment level information can be contained. As the move2 package does not separate individuals, tags and deployments. All information from these 3 entities in movebank are combined here.
time_column attribute this column can be identified, for quick retrieval there is the
mt_time function. Values should be either timestamps (e.g.
numeric. Numeric values are facilitated as it can be useful for simulation, videos and laboratory experiments were absolute time reference is not available or relevant.
This column is identified by the
track_id_column attributes, values can either be a
integer like values. For retrieval there is the
move relatively stringent quality checking was done on the object. This enforced certain attributes for a trajectory that are sensible but in practice are not always adhered to. Some of these properties are:
Every record had a valid location (except for
unUsedRecords but those were rarely used)
Records were time ordered within individual
All individuals were ordered
Timestamps could not be duplicated.
Even though these are some useful properties for subsequent work when reading not all data adheres to these standards. To solve this there were options to remove duplicated records but these simply took the first record. Here we take a more permissive approach where less stringent checking is done on the input side. This means functions working with
move2 need to ensure input data adheres to their expectations. To facilitate that several assertion functions are provided that can quickly check data. Taking this approach gives the users more flexibility in resolving inconsistencies within R. We provide several functions to make this work quick. For specific use cases more informed functions can be developed.
If you are writing functions based on the
move2 package and your function assumes a specific data structure this can best be checked with
assert_that in combination with one of the assertion functions. This construct results in informative error messages:
mt_sim_brownian_motion(1:3)[c(1, 3, 2, 6, 4, 5), ] data <-assert_that(mt_is_time_ordered(data)) #> Error: Not all timestamps in `data` are ordered within track. #> ℹ It is required that all subsequent records have an equal or later timestamps. #> ℹ The first offending record is of track: 1 at time: 3 (record: 2), the next #> record has an earlier timestamp.
To facilitate finding functions and assist in recognizably we use a prefix. For functions relating to movement trajectories we use
mt_, similar to how the
sf package uses
st_ for spatial type. This prefix has the advantage of being short compared to
move_. Functions for accessing data from movebank use the prefix
movebank_. Furthermore do all assertions functions start with either
When analyzing trajectories frequently metrics are calculated that are properties of the time period in between two observations. Prime examples are the distance and speed between locations. This means that for each track with a length of \(n\) locations there are \(n-1\) measurements. To facilitate storing and processing this data we pad each track with a
NA value at the end. This ensured that return vectors from functions like
mt_azimuth return vectors with the same length of as the number of rows in the
move2 object. If the return values from these kind of functions are assigned to the
move2 object the properties stored in the first row reflect the value for the interval between the first and second row.
Some metrics are calculated as a function of the segment before and after a segment (e.g. turn angles). In these cases the return vectors still have the same length however they are padded by a
NA value at the beginning and end of each track so that the metric is stored with the location it is representative for.
Data sets have been growing considerably over the past decade since
move was written. The ambition with
move2 is to facilitate this trend. It should work smoothly with trajectories of more then a million records. We have successfully loaded up to 30 million events into R, however at some stage memory limitations of the host computer start being a concern. This can to some extent be alleviated by omitting unnecessary columns from the data set, either at download or when reading the data. An alternative approach would be to facilitate working with trajectories on disk or within a database (alike
dbplyr). However since many functions and packages we rely on do not support this, we opt not to do this. Therefore, if reducing the data loaded does not solve the problem, it can be advisable to use a computer with more memory or when possible split up analysis per track.