Package index • tidytracks

Tibble of tracks

Functions for loading, saving and displaying tibbles of tracks.

tt_read_data(): Read data from a CSV file into a move2 object
tt_write_data(): Write a tibble of tracks to CSV files
as.data.frame(<move2>): Convert a move2 object to a data frame
show_meta() `show_meta<-`(): Show or set the track metadata of a move2 object
as_meta_column(): Move one (or more) variable(s) from the event data to the metadata as a new column(s).
as_event_column(): Move one (or more) variable(s) from the metadata to the event data as a new column(s).
filter_by_meta(): Filter the tracks based on variables from the metadata
mt_as_move2(<trip>): Method to convert trip objects to move2 objects
sf_point_col(): Create a simple feature POINT geometry column
tidytracks_printing(): Enable or disable custom tidytracks printing

Functions operating on events (return an object of length # of events).

event_azimuth(): Measure the azimuth between two events
event_distance(): Measure the distance between pairs of consecutive events within each track
event_flag_mcconnell(): Clean data based on speed
event_interval(): Measure the time interval between pairs of consecutive events within each track
event_speed(): Return the speed of each segment between events
event_time(): Return the time of each event
event_track_id(): Return the track id of each event

Functions operating on tracks (return an object of length # of tracks).

Functions operating on the full tibble (return a full tibble of tracks).

tt_split_trips(): Split tracks into trips for central place foragers
tt_regular_time(): Resample a move2 track to regular time intervals by geometric interpolation
tt_order_time(): Order a move2 object by time within each track_id
tt_clean_mcconnell(): Clean a move2 object using McConnell's algorithm
tt_drop_units(): Drop units from a tibble of tracks

Functions to visualise movement.

Estimate home range and habitat use from movement data.

hr_tt_kde() hr_kde(): Quantify the home range using kernel density estimation
hr_tt_mcp() hr_mcp(): Quantify the home range using minimum convex polygon (MCP)
hr_tt_ud_iso() hr_ud_iso(): Create isopleths from utilisation distributions
hr_tt_ud_overlap() hr_ud_overlap(): Compute overlap for utilisation distributions
hr_tt_ud_saveRDS() hr_ud_saveRDS(): Save utilisation distributions as an RDS file
hr_tt_ud_sum() hr_ud_sum(): Compute the normalised sum of multiple UDs
hr_tt_ud_unwrap() hr_ud_unwrap(): Unwrap utilisation distributions after loading
hr_tt_ud_wrap() hr_ud_wrap(): Wrap utilisation distributions for storage

Process lists of rasters (used to represent UDs for home ranges).

PackedSpatRaster_list(): Create a PackedSpatRaster_list
as.list(<PackedSpatRaster_list>): Unpack all rasters into a plain list of live SpatRasters
as_PackedSpatRaster_list(): Coerce a plain list of SpatRasters to PackedSpatRaster_list
`$`(<PackedSpatRaster_list>): Returns SpatRaster by name from PackedSpatRaster_list
`$<-`(<PackedSpatRaster_list>): Add SpatRaster by name to PackedSpatRaster_list
print(<PackedSpatRaster_list>): Print a summary of the PackedSpatRaster_list
`[`(<PackedSpatRaster_list>): Subset a PackedSpatRaster_list by position or name
`[[`(<PackedSpatRaster_list>): Returns SpatRaster by position or name from PackedSpatRaster_list
`[[<-`(<PackedSpatRaster_list>): Add SpatRaster by position or name to PackedSpatRaster_list

Helper functions to manipulate and convert data.

Example datasets for testing and demonstration.