randomizations performs data-stream social network randomization. The function accepts a data.table with relocation data, individual identifiers and a randomization type. The data.table is randomized either using step or daily between-individual methods, or within-individual daily trajectory method described by Spiegel et al. (2016).

randomizations(DT = NULL, type = NULL, id = NULL, group = NULL,
  coords = NULL, datetime = NULL, splitBy = NULL,
  iterations = NULL)

Arguments

DT

input data.table

type

one of 'daily', 'step' or 'trajectory' - see details

id

Character string of ID column name

group

generated from spatial grouping functions - see details

coords

Character vector of X coordinate and Y coordinate column names

datetime

field used for providing date time or time group - see details

splitBy

List of fields in DT to split the randomization process by

iterations

The number of iterations to randomize

Value

randomizations returns the random date time or random id along with the original DT, depending on the randomization type. The length of the returned data.table is the original number of rows multiplied by the number of iterations + 1. For example, 3 iterations will return 4x - one observed and three randomized.

Two columns are always returned:

  • observed - if the rows represent the observed (TRUE/FALSE)

  • iteration - iteration of rows (where 0 is the observed)

In addition, depending on the randomization type, random ID or random date time columns are returned:

  • step - randomID each time step

  • daily - randomID for each day and jul indicating julian day

  • trajectory - a random date time ("random" prefixed to datetime argument), observed jul and randomJul indicating the random day relocations are swapped to.

Details

The DT must be a data.table. If your data is a data.frame, you can convert it by reference using data.table::setDT.

Three randomization types are provided:

  1. step - randomizes identities of relocations between individuals within each time step.

  2. daily - randomizes identities of relocations between individuals within each day.

  3. trajectory - randomizes daily trajectories within individuals (Spiegel et al. 2016).

Depending on the type, the datetime must be a certain format:

  • step - datetime is integer group created by group_times

  • daily - datetime is POSIXct format

  • trajectory - datetime is POSIXct format

The id, datetime, (and optional splitBy) arguments expect the names of respective columns in DT which correspond to the individual identifier, date time, and additional grouping columns. The coords argument is only required when the type is "trajectory", since the coordinates are required for recalculating spatial groups with group_pts, group_lines or group_polys.

Please note that if the data extends over multiple years, a column indicating the year should be provided to the splitBy argument. This will ensure randomizations only occur within each year.

The group argument is expected only when type is 'step' or 'daily'.

For example, using data.table::year:

DT[, yr := year(datetime)]
randomizations(DT, type = 'step', id = 'ID', datetime = 'timegroup', splitBy = 'yr')

iterations is set to 1 if not provided. Take caution with a large value for iterations with large input DT.

References

http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12553/full

See also

Other Social network tools: get_gbi

Examples

# Load data.table library(data.table) # Read example data DT <- fread(system.file("extdata", "DT.csv", package = "spatsoc")) # Date time columns DT[, datetime := as.POSIXct(datetime)]
#> ID X Y datetime population #> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 #> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 #> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 #> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 #> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 #> --- #> 14293: J 700616.5 5509069 2017-02-28 14:00:54 1 #> 14294: J 700622.6 5509065 2017-02-28 16:00:11 1 #> 14295: J 700657.5 5509277 2017-02-28 18:00:55 1 #> 14296: J 700610.3 5509269 2017-02-28 20:00:48 1 #> 14297: J 700744.0 5508782 2017-02-28 22:00:39 1
DT[, yr := year(datetime)]
#> ID X Y datetime population yr #> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 2016 #> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 2016 #> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 2016 #> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 2016 #> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 2016 #> --- #> 14293: J 700616.5 5509069 2017-02-28 14:00:54 1 2017 #> 14294: J 700622.6 5509065 2017-02-28 16:00:11 1 2017 #> 14295: J 700657.5 5509277 2017-02-28 18:00:55 1 2017 #> 14296: J 700610.3 5509269 2017-02-28 20:00:48 1 2017 #> 14297: J 700744.0 5508782 2017-02-28 22:00:39 1 2017
# Temporal grouping group_times(DT, datetime = 'datetime', threshold = '5 minutes')
#> ID X Y datetime population yr minutes #> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 2016 0 #> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 2016 0 #> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 2016 0 #> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 2016 0 #> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 2016 0 #> --- #> 14293: J 700616.5 5509069 2017-02-28 14:00:54 1 2017 0 #> 14294: J 700622.6 5509065 2017-02-28 16:00:11 1 2017 0 #> 14295: J 700657.5 5509277 2017-02-28 18:00:55 1 2017 0 #> 14296: J 700610.3 5509269 2017-02-28 20:00:48 1 2017 0 #> 14297: J 700744.0 5508782 2017-02-28 22:00:39 1 2017 0 #> timegroup #> 1: 1 #> 2: 2 #> 3: 3 #> 4: 4 #> 5: 5 #> --- #> 14293: 1393 #> 14294: 1394 #> 14295: 1449 #> 14296: 1395 #> 14297: 1396
# Spatial grouping with timegroup group_pts(DT, threshold = 5, id = 'ID', coords = c('X', 'Y'), timegroup = 'timegroup')
#> ID X Y datetime population yr minutes #> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 2016 0 #> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 2016 0 #> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 2016 0 #> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 2016 0 #> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 2016 0 #> --- #> 14293: J 700616.5 5509069 2017-02-28 14:00:54 1 2017 0 #> 14294: J 700622.6 5509065 2017-02-28 16:00:11 1 2017 0 #> 14295: J 700657.5 5509277 2017-02-28 18:00:55 1 2017 0 #> 14296: J 700610.3 5509269 2017-02-28 20:00:48 1 2017 0 #> 14297: J 700744.0 5508782 2017-02-28 22:00:39 1 2017 0 #> timegroup group #> 1: 1 1 #> 2: 2 2 #> 3: 3 3 #> 4: 4 4 #> 5: 5 5 #> --- #> 14293: 1393 13883 #> 14294: 1394 13884 #> 14295: 1449 13885 #> 14296: 1395 13886 #> 14297: 1396 13887
# Randomization: step randStep <- randomizations( DT, type = 'step', id = 'ID', group = 'group', datetime = 'timegroup', splitBy = 'yr', iterations = 2 ) # Randomization: daily randDaily <- randomizations( DT, type = 'daily', id = 'ID', group = 'group', datetime = 'datetime', splitBy = 'yr', iterations = 2 ) # Randomization: trajectory randTraj <- randomizations( DT, type = 'trajectory', id = 'ID', group = NULL, coords = c('X', 'Y'), datetime = 'datetime', splitBy = 'yr', iterations = 2 )