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centroid_group calculates the centroid (mean location) of all individuals in each spatiotemporal group identified by group_pts. The function accepts a data.table with relocation data appended with a group column from group_pts. Relocation data should be in two columns representing the X and Y coordinates.

Usage

centroid_group(DT = NULL, coords = NULL, group = "group", na.rm = FALSE)

Arguments

DT

input data.table with group column generated with group_pts

coords

character vector of X coordinate and Y coordinate column names. Note: the order is assumed X followed by Y column names.

group

Character string of group column

na.rm

if NAs should be removed in calculating mean location, see mean

Value

centroid_group returns the input DT appended with centroid columns for the X and Y coordinate columns.

These columns represents the centroid coordinate columns. The naming of these columns will correspond to the provided coordinate column names prefixed with "centroid_".

A message is returned when centroid columns are already exists in the input DT, because they will be overwritten.

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 or by reassigning using data.table::data.table.

The coords and group arguments expect the names of a column in DT which correspond to the X and Y coordinates and group columns. The na.rm argument is passed to the mean function to control if NA values are removed before calculation.

See also

group_pts

Other Centroid functions: centroid_dyad(), centroid_fusion()

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
#>        <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

# Temporal grouping
group_times(DT, datetime = 'datetime', threshold = '20 minutes')
#>            ID        X       Y            datetime population minutes timegroup
#>        <char>    <num>   <num>              <POSc>      <int>   <int>     <int>
#>     1:      A 715851.4 5505340 2016-11-01 00:00:54          1       0         1
#>     2:      A 715822.8 5505289 2016-11-01 02:01:22          1       0         2
#>     3:      A 715872.9 5505252 2016-11-01 04:01:24          1       0         3
#>     4:      A 715820.5 5505231 2016-11-01 06:01:05          1       0         4
#>     5:      A 715830.6 5505227 2016-11-01 08:01:11          1       0         5
#>    ---                                                                         
#> 14293:      J 700616.5 5509069 2017-02-28 14:00:54          1       0      1393
#> 14294:      J 700622.6 5509065 2017-02-28 16:00:11          1       0      1394
#> 14295:      J 700657.5 5509277 2017-02-28 18:00:55          1       0      1440
#> 14296:      J 700610.3 5509269 2017-02-28 20:00:48          1       0      1395
#> 14297:      J 700744.0 5508782 2017-02-28 22:00:39          1       0      1396

# Spatial grouping with timegroup
group_pts(DT, threshold = 5, id = 'ID',
          coords = c('X', 'Y'), timegroup = 'timegroup')
#>            ID        X       Y            datetime population minutes timegroup
#>        <char>    <num>   <num>              <POSc>      <int>   <int>     <int>
#>     1:      A 715851.4 5505340 2016-11-01 00:00:54          1       0         1
#>     2:      A 715822.8 5505289 2016-11-01 02:01:22          1       0         2
#>     3:      A 715872.9 5505252 2016-11-01 04:01:24          1       0         3
#>     4:      A 715820.5 5505231 2016-11-01 06:01:05          1       0         4
#>     5:      A 715830.6 5505227 2016-11-01 08:01:11          1       0         5
#>    ---                                                                         
#> 14293:      J 700616.5 5509069 2017-02-28 14:00:54          1       0      1393
#> 14294:      J 700622.6 5509065 2017-02-28 16:00:11          1       0      1394
#> 14295:      J 700657.5 5509277 2017-02-28 18:00:55          1       0      1440
#> 14296:      J 700610.3 5509269 2017-02-28 20:00:48          1       0      1395
#> 14297:      J 700744.0 5508782 2017-02-28 22:00:39          1       0      1396
#>        group
#>        <int>
#>     1:     1
#>     2:     2
#>     3:     3
#>     4:     4
#>     5:     5
#>    ---      
#> 14293: 13882
#> 14294: 13883
#> 14295: 13884
#> 14296: 13885
#> 14297: 13886

# Calculate group centroid
centroid_group(DT, coords = c('X', 'Y'), group = 'group', na.rm = TRUE)
#>            ID        X       Y            datetime population minutes timegroup
#>        <char>    <num>   <num>              <POSc>      <int>   <int>     <int>
#>     1:      A 715851.4 5505340 2016-11-01 00:00:54          1       0         1
#>     2:      A 715822.8 5505289 2016-11-01 02:01:22          1       0         2
#>     3:      A 715872.9 5505252 2016-11-01 04:01:24          1       0         3
#>     4:      A 715820.5 5505231 2016-11-01 06:01:05          1       0         4
#>     5:      A 715830.6 5505227 2016-11-01 08:01:11          1       0         5
#>    ---                                                                         
#> 14293:      J 700616.5 5509069 2017-02-28 14:00:54          1       0      1393
#> 14294:      J 700622.6 5509065 2017-02-28 16:00:11          1       0      1394
#> 14295:      J 700657.5 5509277 2017-02-28 18:00:55          1       0      1440
#> 14296:      J 700610.3 5509269 2017-02-28 20:00:48          1       0      1395
#> 14297:      J 700744.0 5508782 2017-02-28 22:00:39          1       0      1396
#>        group centroid_X centroid_Y
#>        <int>      <num>      <num>
#>     1:     1   715851.4    5505340
#>     2:     2   715822.8    5505289
#>     3:     3   715872.9    5505252
#>     4:     4   715820.5    5505231
#>     5:     5   715830.6    5505227
#>    ---                            
#> 14293: 13882   700616.5    5509069
#> 14294: 13883   700622.6    5509065
#> 14295: 13884   700657.5    5509277
#> 14296: 13885   700610.3    5509269
#> 14297: 13886   700744.0    5508782