get_gbi generates a group by individual matrix. The function accepts a data.table with individual identifiers and a group column. The group by individual matrix can then be used to build a network using asnipe::get_network.

get_gbi(DT = NULL, group = "group", id = NULL)

Arguments

DT

input data.table

group

Character string of group column (generated from one of spatsoc's spatial grouping functions)

id

Character string of ID column name

Value

get_gbi returns a group by individual matrix (columns represent individuals and rows represent groups).

Note that get_gbi is identical in function for turning the outputs of spatsoc into social networks as asnipe::get_group_by_individual but is more efficient thanks to data.table::dcast.

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.

The group argument expects the name of a column which corresponds to an integer group identifier (generated by spatsoc's grouping functions).

The id argument expects the name of a column which corresponds to the individual identifier.

See also

group_pts group_lines group_polys

Other Social network tools: randomizations

Examples

# Load data.table library(data.table) # Read example data DT <- fread(system.file("extdata", "DT.csv", package = "spatsoc")) # Cast the character column to POSIXct DT[, datetime := as.POSIXct(datetime, tz = 'UTC')]
#> 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
utm <- '+proj=utm +zone=36 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs' group_polys(DT, area = FALSE, hrType = 'mcp', hrParams = list(percent = 95), projection = utm, id = 'ID', coords = c('X', 'Y'), splitBy = 'yr')
#> ID X Y datetime population yr group #> 1: A 715851.4 5505340 2016-11-01 00:00:54 1 2016 1 #> 2: A 715822.8 5505289 2016-11-01 02:01:22 1 2016 1 #> 3: A 715872.9 5505252 2016-11-01 04:01:24 1 2016 1 #> 4: A 715820.5 5505231 2016-11-01 06:01:05 1 2016 1 #> 5: A 715830.6 5505227 2016-11-01 08:01:11 1 2016 1 #> --- #> 14293: J 700616.5 5509069 2017-02-28 14:00:54 1 2017 2 #> 14294: J 700622.6 5509065 2017-02-28 16:00:11 1 2017 2 #> 14295: J 700657.5 5509277 2017-02-28 18:00:55 1 2017 2 #> 14296: J 700610.3 5509269 2017-02-28 20:00:48 1 2017 2 #> 14297: J 700744.0 5508782 2017-02-28 22:00:39 1 2017 2
gbiMtrx <- get_gbi(DT = DT, group = 'group', id = 'ID')