Package index • animovement

Read

These functions are the links between R and your data.

read_animalta(): Read AnimalTA data

read_bonsai(): Read centroid tracking data from Bonsai

read_deeplabcut(): Read DeepLabCut data

read_idtracker(): Read idtracker.ai data

read_lightningpose(): Read LightningPose data

read_movement() experimental: Read movement data

read_sleap(): Read SLEAP data

read_trackball(): Read trackball data

read_treadmill() experimental: Read treadmill data

read_trex(): Read TRex Movement Tracking Data

Clean

These functions ensure that your data is ready for analysis.

check_na_timing(): Visualize the timing of missing values in the data

check_na_gapsize(): Visualize the occurrence of gap sizes in the data

check_confidence(): Visualize the distribution of confidence values for each keypoint

check_pose(): Analyze the distribution of distances from keypoints to the centroid

filter_na_confidence(): Filter low-confidence values in a dataset

filter_na_speed(): Filter values by speed threshold

filter_na_roi(): Filter coordinates outside a region of interest (ROI)

replace_na(): Replace Missing Values Using Various Methods

replace_na_linear(): Replace Missing Values Using Linear Interpolation

replace_na_locf(): Replace Missing Values Using Last Observation Carried Forward

replace_na_spline(): Replace Missing Values Using Spline Interpolation

replace_na_stine(): Replace Missing Values Using Stineman Interpolation

replace_na_value(): Replace Missing Values with a Constant Value

filter_movement() experimental: Smooth Movement Data

filter_lowpass(): Apply Butterworth Lowpass Filter to Signal

filter_highpass(): Apply Butterworth Highpass Filter to Signal

filter_lowpass_fft(): Apply FFT-based Lowpass Filter to Signal

filter_highpass_fft(): Apply FFT-based Highpass Filter to Signal

filter_kalman(): Kalman Filter for Regular Time Series

filter_kalman_irregular(): Kalman Filter for Irregular Time Series with Optional Resampling

filter_rollmean() experimental: Apply Rolling Mean Filter

filter_rollmedian() experimental: Apply Rolling Median Filter

filter_sgolay(): Apply Savitzky-Golay Filter to Movement Data

Transformations

These functions allow you to make tranformations to your coordinate system, such as translations, rotations or conversion to polar coordinates.

transform_to_egocentric() experimental: Transform coordinates to egocentric reference frame

translate_coords() experimental: Translate coordinates (Cartesian)

rotate_coords() experimental: Rotate coordinates in Cartesian space

map_to_cartesian(): Map from polar to Cartesian coordinates

map_to_polar(): Map from Cartesian to polar coordinates

Calculate

These functions are where your results begin to emerge.

add_centroid(): Add Centroid to Movement Data

calculate_kinematics() experimental: Calculate kinematics from position data

calculate_statistics() experimental: Calculate summary statistics

calculate_derivative(): Calculate the derivative (dx/dt) Calculate the derivative (dx/dt) with four arguments

calculate_direction(): Calculate direction Calculate direction (angle) from x and y distance using the (two-argument) arc-tangent. Converts to circular.

calculate_distance(): Calculate distance (Pythagoras) Calculate distance from an x and y distance, using Pythagoras theorem.

calculate_speed(): Calculate Speed from Position Data

calculate_straightness(): Calculate straightness measures

Metadata

These functions allow you to interact with your metadata.

init_metadata() experimental: Initiate movement metadata

get_metadata() experimental: Get/extract metadata

set_uuid() experimental: Set UUID

set_start_datetime() experimental: Set starting datetime

set_framerate(): Adjust time values to reflect a new framerate

set_individual(): Assign a new individual identifier to all rows in a dataset

Helpers

These functions help you along the way.

get_example_data(): Download example tracking data

group_every() experimental: Group every N observations together

align_timeseries(): Align a time series with a reference series using cross-correlation

find_lag(): Find optimal time lag between two time series using cross-correlation

find_peaks(): Find Peaks in Time Series Data

find_troughs(): Find Troughs in Time Series Data

plot_position_timeseries(): Plot Time Series of Keypoint Position

plot_speed_timeseries(): Plot Time Series of Keypoint Speed

classify_by_stability(): Classify Movement States Based on Stability Analysis

classify_by_threshold(): Classify Values Into Sequences with Minimum Run Length Constraints

classify_high_periods(): Classifies Periods of High Activity in Time Series Using Peaks and Troughs

classify_low_periods(): Classifies Periods of Low Activity in Time Series Using Peaks and Troughs