centroid_fusion calculates the centroid (mean location) of each
timestep in fusion events. The function accepts an edge list of fusion events
identified by fusion_id from edge lists generated with
edge_dist and a data.table with relocation data appended with a
timegroup column from group_times. It is recommended to use the
argument fillNA = FALSE for edge_dist when using
centroid_fusion to avoid unnecessarily merging additional rows.
Relocation data should be in two columns representing the X and Y
coordinates.
Usage
centroid_fusion(
edges = NULL,
DT = NULL,
id = NULL,
coords = NULL,
timegroup = "timegroup",
na.rm = FALSE
)Arguments
- edges
edge list generated generated by
edge_distoredge_nn, with fusionID column generated byfusion_id- DT
input data.table with timegroup column generated with
group_timesmatching the input data.table used to generate the edge list withedge_nnoredge_dist- id
Character string of ID column name
- coords
Character vector of X coordinate and Y coordinate column names. Note: the order is assumed X followed by Y column names.
- timegroup
timegroup field in the DT within which the grouping will be calculated
- na.rm
if NAs should be removed in calculating mean location, see
rowMeans
Value
centroid_fusion returns the input edges appended with
centroid columns for the X and Y coordinate columns.
These columns represents the centroid coordinate columns for each timestep in a fusion event. The naming of these columns will correspond to the provided coordinate column names prefixed with "centroid_".
Note: due to the merge required within this function, the output needs to
be reassigned unlike some other spatsoc functions like
fusion_id and group_pts.
A message is returned when centroid columns are already exists in the input
edges, because they will be overwritten.
Details
The edges and DT must be 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 edges and DT are internally merged in this function using
the columns timegroup (from group_times) and ID1 and
ID2 (in edges, from dyad_id) and id (in
DT). This function expects a fusionID present, generated with
the fusion_id function. The timegroup argument expects the
names of a column in edges which correspond to the timegroup column.
The id, coords and timegroup arguments expect the names
of a column in DT which correspond to the id, X and Y coordinates and
timegroup columns. The na.rm argument is passed to the rowMeans
function to control if NA values are removed before calculation.
See also
Other Centroid functions:
centroid_dyad(),
centroid_group()
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
# Edge list generation
edges <- edge_dist(
DT,
threshold = 100,
id = 'ID',
coords = c('X', 'Y'),
timegroup = 'timegroup',
returnDist = TRUE,
fillNA = FALSE
)
# Generate dyad id
dyad_id(edges, id1 = 'ID1', id2 = 'ID2')
#> timegroup ID1 ID2 distance dyadID
#> <int> <char> <char> <num> <char>
#> 1: 1 G B 5.782904 B-G
#> 2: 1 H E 65.061671 E-H
#> 3: 1 B G 5.782904 B-G
#> 4: 1 E H 65.061671 E-H
#> 5: 2 H E 79.659918 E-H
#> ---
#> 17174: 1440 I C 2.831071 C-I
#> 17175: 1440 C F 9.372972 C-F
#> 17176: 1440 I F 7.512922 F-I
#> 17177: 1440 C I 2.831071 C-I
#> 17178: 1440 F I 7.512922 F-I
# Generate fusion id
fusion_id(edges, threshold = 100)
#> timegroup ID1 ID2 distance dyadID fusionID
#> <int> <char> <char> <num> <char> <int>
#> 1: 1 G B 5.782904 B-G 1
#> 2: 1 H E 65.061671 E-H 2
#> 3: 1 B G 5.782904 B-G 1
#> 4: 1 E H 65.061671 E-H 2
#> 5: 2 H E 79.659918 E-H 2
#> ---
#> 17174: 1440 I C 2.831071 C-I 2846
#> 17175: 1440 C F 9.372972 C-F 2845
#> 17176: 1440 I F 7.512922 F-I 2847
#> 17177: 1440 C I 2.831071 C-I 2846
#> 17178: 1440 F I 7.512922 F-I 2847
# Calculate fusion centroid
centroids <- centroid_fusion(
edges,
DT,
id = 'ID',
coords = c('X', 'Y'),
timegroup = 'timegroup', na.rm = TRUE
)
print(centroids)
#> timegroup ID1 ID2 distance dyadID fusionID centroid_X centroid_Y
#> <int> <char> <char> <num> <char> <int> <num> <num>
#> 1: 1 G B 5.782904 B-G 1 699637.9 5509637
#> 2: 1 H E 65.061671 E-H 2 701698.0 5504306
#> 3: 1 B G 5.782904 B-G 1 699637.9 5509637
#> 4: 1 E H 65.061671 E-H 2 701698.0 5504306
#> 5: 2 H E 79.659918 E-H 2 701652.4 5504236
#> ---
#> 17174: 1440 I C 2.831071 C-I 2846 702960.6 5509447
#> 17175: 1440 C F 9.372972 C-F 2845 702960.7 5509451
#> 17176: 1440 I F 7.512922 F-I 2847 702959.5 5509452
#> 17177: 1440 C I 2.831071 C-I 2846 702960.6 5509447
#> 17178: 1440 F I 7.512922 F-I 2847 702959.5 5509452