summarizeContacts: Summarize Contact Events
In contact: Creating Contact and Social Networks

Description Usage Arguments Details Value Examples

This function takes the output from contactDur.all or contactDur.area and reports the number of durations when tracked individuals are in "contact" with one another (contactDur.all) or with specified fixed points/polygons (contactDur.area).

summarizeContacts(
  x,
  importBlocks = FALSE,
  avg = FALSE,
  parallel = FALSE,
  nCores = (parallel::detectCores()/2)
)

`x`	Output from the contactDur.all or contactDur.area functions. Can be either a data frame or list of data frames.
`importBlocks`	Logical. If true, each block in x will be analyzed separately. Defaults to FALSE. Note that the "block" column must exist in x.
`avg`	Logical. If TRUE, summary output from all data frames contained in x will be averaged together. Output will produce an extra data frame containing the mean column values for each id (per block if importBlocks == TRUE). Defaults to FALSE.
`parallel`	Logical. If TRUE, sub-functions within the summarizeContacts wrapper will be parallelized. Note that the only sub-function parallelized here is called ONLY when importBlocks == TRUE.
`nCores`	Integer. Describes the number of cores to be dedicated to parallel processes. Defaults to half of the maximum number of cores available (i.e., (parallel::detectCores()/2)).

If x is a list, and avg == TRUE, this function will produce an extra data frame containing the mean column values for each id (per block if importBlocks == TRUE).

This is a sub-function found within the contactTest and ntwrkEdges function.

Returns a data frame (or list of data frames if x is a list of data frames) with the following columns:

`id`	The unique ID of a tracked individual for which we will summarize to all other individuals/fixed locations observed in `x`.
`id`	Sum number of individuals/fixed locations observed in contact specific individuals.
`id`	Sum number of contacts associated with specific individuals.
`contactDuration_...`	Number of contacts between specific dyads.

If importBlocks == TRUE, the following columns are appended to the output data frame described above:

`block`	Integer ID describing unique blocks of time during which contacts occur.
`block.start`	The timepoint in `x` at which the `block` begins.
`block.end`	The timepoint in `x` at which the `block` ends.
`numBlocks`	Integer describing the total number of time blocks observed within `x` at which the `block`

data(calves)

calves.dateTime<-datetime.append(calves, date = calves$date, 
   time = calves$time) #create a dataframe with dateTime identifiers for location fixes

calves.agg<-tempAggregate(calves.dateTime, id = calves.dateTime$calftag, 
   dateTime = calves.dateTime$dateTime, point.x = calves.dateTime$x, 
   point.y = calves.dateTime$y, secondAgg = 300, extrapolate.left = FALSE, 
   extrapolate.right = FALSE, resolutionLevel = "reduced", parallel = FALSE, 
   na.rm = TRUE, smooth.type = 1) #smooth to 5-min fix intervals.

calves.dist<-dist2All_df(x = calves.agg, parallel = FALSE, 
   dataType = "Point", lonlat = FALSE) 
calves.contact.block<-contactDur.all(x = calves.dist, dist.threshold=1, 
   sec.threshold=10, blocking = TRUE, blockUnit = "hours", blockLength = 1, 
   equidistant.time = FALSE, parallel = FALSE, reportParameters = TRUE) 
   
calves.contactSumm.NOblock <- summarizeContacts(calves.contact.block)
head(calves.contactSumm.NOblock)

calves.contactSumm.block <- summarizeContacts(calves.contact.block, 
   importBlocks = TRUE)
head(calves.contactSumm.block)