disp: Displacements

In steffilazerte/feedr: Transforming Raw RFID Data

Displacements

Description

For an entire visits data frame, identifies displacement events. Displacements are events when one animal leaves the logger right before the arrival of another.

Usage

disp(v, bw = 2, pass = TRUE)

Arguments

v	Dataframe. A visits data frame containing all visits from all animals. From the output of visits. Must contain columns animal_id, logger_id, start, and end.
bw	Numeric. The maximum interval in seconds between visits by two different animals for the interaction to be considered a displacement.
pass	Logical. Pass 'extra' columns through the function and append them to the output.

Details

The first and last visits on the record are automatically assumed to be non-displacer and non-displacee, respectively.

In some species displacements can be used to infer dominance. Displacements The interactions data frame returned by the disp() function can be passed directly to the Perc::as.conflictmat() function of the Perc package to be transformed into a conflict matrix, ready for analysis of dominance using percolation and conductance. Finally, the displacements data frame can also be converted using the convert_anidom() function to a data frame for use by the aniDom package's elo_scores function.

Value

A list with the following named items:

1. displacements: A data frame of individual displacement events, including the following columns:

   • logger_id: ID of the logger at which the event occurred

   • ID of the animal being displaced (displacee)

   • ID of the animal doing the displacing (displacer)

   • Time of the departure of the displacee (left)

   • Time of the arrival of the displacer (arrived)

2. summaries: A data frame of overall wins/lossess per individual, containing the following columns:

   • ID of the animal (animal_id)

   • No. of times the animal was displaced (displacee)

   • No. of times the animal was a displacer (displacer)

   • Proportion of wins (p_win)

3. interactions: A data frame of interaction summaries, containing the following columns:

   • ID of the displacee (displacee)

   • ID of the displacer (displacer)

   • No. of times this interaction occurred (n)

Examples

# Look at displacements for chickadees in experiment 2
 v <- visits(chickadees[chickadees$experiment == "exp2",])
 d <- disp(v)

 # Look at displacement events:
 d[['displacements']] #or
 d$displacements

 # Look at summaries (identical methods):
 d[['summaries']] #or
 d$summaries

 # Look at interactions (identical methods):
 d[['interactions']] #or
 d$interactions

 # Calculate across different experiments (expect warnings about unequal factor levels):
library(dplyr)

v <- chickadees %>%
  group_by(experiment) %>%
  do(visits(.))

d <- v %>%
  group_by(experiment) %>%
  do(data = disp(.))

# Look at the data stored in the 2nd experiment:
d$data[d$experiment == "exp2"][[1]] #or
d[["data"]][[1]] #or
d$data[[1]]

# Access the displacements from the 3rd experiment:
d$data[d$experiment == "exp3"][[1]]$displacements #or
d[["data"]][[2]]$displacements #or
d$data[[2]]$displacements

steffilazerte/feedr documentation built on Jan. 27, 2023, 3:46 a.m.