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.
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)
data.table::setDTthreads(1)
# 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
#> <char> <num> <num> <POSc> <int>
#> 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
#> <char> <num> <num> <POSc> <int> <int>
#> 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
# EPSG code for example data
utm <- 'EPSG:32736'
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
#> <char> <num> <num> <POSc> <int> <int> <int>
#> 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')