dup: Identify and Remove Duplicated Data Points
In contact: Creating Contact and Social Networks

Description Usage Arguments Details Value Examples

dup (a.k.a. Multiple instance filter) identifies and removes timepoints when tracked individuals were observed in >1 place concurrently. If avg == TRUE, duplicates are replaced by a single row describing an individuals' average location (e.g., planar xy coordinates) during the duplicated time point. If avg == FALSE, all duplicated timepoints will be removed, as there is no way for the function to determine which instance among the duplicates should stay. If users are not actually interested in filtering datasets, but rather, determining what observations should be filtered, they may set filterOutput == FALSE. By doing so, this function will append a "duplicated" column to the dataset, which reports values that describe if any timepoints in a given individual's path are duplicated. Values are: 0: timepoint is not duplicated, 1: timepoint is duplicated.

dup(
  x,
  id = NULL,
  point.x = NULL,
  point.y = NULL,
  dateTime = NULL,
  avg = TRUE,
  parallel = FALSE,
  nCores = (parallel::detectCores()/2),
  filterOutput = TRUE
)

`x`	Data frame containing real-time-location data that will be filtered.
`id`	Vector of length nrow(data.frame(x)) or singular character data, detailing the relevant colname in x, that denotes what unique ids for tracked individuals will be used. If argument == NULL, the function assumes a column with the colname "id" exists in x. Defaults to NULL.
`point.x`	Vector of length nrow(data.frame(x)) or singular character data, detailing the relevant colname in x, that denotes what planar-x or longitude coordinate information will be used. If argument == NULL, the function assumes a column with the colname "x" exists in x. Defaults to NULL.
`point.y`	Vector of length nrow(data.frame(x)) or singular character data, detailing the relevant colname in x, that denotes what planar-y or lattitude coordinate information will be used. If argument == NULL, the function assumes a column with the colname "y" exists in x. Defaults to NULL.
`dateTime`	Vector of length nrow(data.frame(x)) or singular character data, detailing the relevant colname in x, that denotes what dateTime information will be used. If argument == NULL, the function assumes a column with the colname "dateTime" exists in x. Defaults to NULL.
`avg`	Logical. If TRUE, point.x and point.y values for duplicated time steps will be averaged, producing a singular point for all time steps in individuals' movement paths. If FALSE, all duplicated time steps wherein individuals were observed in different locations concurrently are removed from the data set.
`parallel`	Logical. If TRUE, sub-functions within the dup wrapper will be parallelized. This is only relevant if avg == TRUE. Defaults to FALSE.
`nCores`	Integer. Describes the number of cores to be dedicated to parallel processes. Defaults to the maximum number of cores available (i.e., (parallel::detectCores()/2)).
`filterOutput`	Logical. If TRUE, output will be a data frame containing only movement paths with non-duplicated timesteps. If FALSE, no observations are removed and a "duplicated" column is appended to x, detailing if time steps are duplicated (column value == 1), or not (column value == 0). Defaults to TRUE.

If users want to remove specific duplicated observations, we suggest setting filterOutput == FALSE, reviewing what duplicated timepoints exist in individuals' paths, and manually removing observations of interest.

If filterOutput == TRUE, returns x less observations at duplicated timepoints.

If filterOutput == FALSE, returns x appended with a "duplicated" column which reports timepoints are duplicated (column value == 1), or not (column value == 0).

data(calves2018) #load the data set

calves_dup<- dup(calves2018, id = calves2018$calftag, 
   point.x = calves2018$x, point.y = calves2018$y, 
   dateTime = calves2018$dateTime, avg = FALSE, parallel = FALSE, 
   filterOutput = TRUE) #there were no duplicates to remove in the first place.