define_events: Define Events of Collective Motion

Description

This function adds a keep TRUE/FALSE column in the input dataframe based on whether the average speed and polarization of the group is larger than the input thresholds, reflecting whether a timestep is considered part of a collective event or not.

Usage

define_events(df, sp_lim, pol_lim, step2time, noise_thresh = 0)

Value

the dataframe that was given as input with an extra keep

column. The function also prints the number and duration of the defined events.

Arguments

df: A dataframe with a pol_av and speed_av columns for polarization and speed, respectively (as calculated by the group_metrics_per_set function).
sp_lim: The (lower) threshold of speed to use for defining which timesteps are part of an events of collective motion. In other words, during an event the group should have an average speed of at least sp_lim.
pol_lim: The (lower) threshold of polarization to use for defining which timesteps are part of an events of collective motion. In other words, during an event the group's polarization should be at least pol_lim.
step2time: Sampling frequency, i.e. the relation between time steps (rows) in the input dataframe and real time (in seconds).
noise_thresh: The limit of time difference between consecutive events to be considered the same event. The default value is 0 (no event merging).

Author

Marina Papadopoulou m.papadopoulou.rug@gmail.com

Examples

Run this code

data <- data.frame(
set = rep('1', 50),
pol_av = rnorm(50, mean = 0.5, sd = 0.2),
speed_av = rnorm(50, mean = 5)
)
data <- define_events(data, sp_lim = 5, pol_lim = 0.4, step2time = 1)