Package: rabbit 0.0.0.9000

Courntey Adams

rabbit: Rabbitly Process Data From Triaxial Accelerometers

Rabbitly Process Data From Triaxial Accelerometers.

Authors:Courntey Adams [aut, cre], Jack Bilby [aut, ctb], Daniel Falster [aut, ctb], Will Cornwell [aut, ctb]

# Install 'rabbit' in R:

install.packages('rabbit', repos = c('https://traitecoevo.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Last updated from:1705756640. Checks:7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	ERROR	138
source / vignettes	OK	223
linux-release	ERROR	139
macos-release	ERROR	123
macos-oldrel	ERROR	85
windows-devel	ERROR	452
windows-release	ERROR	176
windows-oldrel	ERROR	183
wasm-release	OK	104

Help page	Topics
Benchmark rolling methods used by movement dynamics	benchmark_movement_dynamics
Crop data to specified start and end times, to remove records at the beginning and end of each file. This is useful for removing records collected before the device was attached or after it was removed. crop_diurnal defaults to cropping dataframe to the first instance of 01:00:00 and the last instance of 01:00:00. crop_nocturnal defaults to cropping dataframe to the first instance of 12:00:00 and the last instance of 12:00:00. crop_ends is a more general function that allows users to specify any start and end times. crop_diurnal	crop_ends crop_ends_diurnal crop_ends_nocturnal
This function takes standardised accelerometer data and caluclates a set of metrics of movement dynamics across a sliding window of specified number of datapoints. Caluclate metrics include including means, variances, covariances, of xyz, as well as overall dynamic body acceleration (ODBA) and vectorial dynamic body acceleration (VDBA). The function is designed to be fast, using the RcppRoll package to calculate rolling means and sums in C++.	extract_movement_dynamics
Generate fake ample accelerometer data	generate_fake_data
Standardise accelerometer data	standardise_data standardize_data