This function combines information from the table of individuals and table of nodes into a single data frame which can be used in downstream analyses.
ts_nodes(x, sf = TRUE)Tree sequence object of the class slendr_ts or a phylo
object extracted by ts_phylo
Should spatial data be returned in an sf format? If FALSE,
spatial geometries will be returned simply as x and y columns, instead of
the standard POINT data type.
Data frame with processed information from the tree sequence object.
If the model which generated this data was spatial, result will be returned
as a spatial object of the class sf.
The source of data (tables of individuals and nodes recorded in the tree
sequence generated by SLiM) are combined into a single data frame. If the
model which generated the data was spatial, coordinates of nodes (which are
pixel-based by default because SLiM spatial simulations occur on a raster),
the coordinates are automatically converted to an explicit spatial object of
the sf class unless spatial = FALSE. See
https://r-spatial.github.io/sf/ for an extensive introduction to the sf
package and the ways in which spatial data can be processed, analysed, and
visualised.
ts_table for accessing raw tree sequence tables
without added metadata annotation. See also ts_ancestors to
learn how to extract information about relationship beteween nodes in the
tree sequence, and how to analysed data about distances between nodes in
the spatial context.
check_dependencies(python = TRUE) # make sure dependencies are present
# load an example model with an already simulated tree sequence
slendr_ts <- system.file("extdata/models/introgression.trees", package = "slendr")
model <- read_model(path = system.file("extdata/models/introgression", package = "slendr"))
# load the tree-sequence object from disk
ts <- ts_load(slendr_ts, model, simplify = TRUE)
# extract an annotated table with (spatio-)temporal node information
ts_nodes(ts)
#> slendr 'nodes' object
#> ---------------------
#> times are expressed in a backward time direction
#>
#> summary of the table data contents:
#> CH - 1 'sampled', 1 'remembered', 1 'retained', 1 'alive' individuals
#> AFR - 5 'sampled', 5 'remembered', 5 'retained', 5 'alive' individuals
#> EUR - 5 'sampled', 5 'remembered', 5 'retained', 5 'alive' individuals
#> NEA - 2 'sampled', 2 'remembered', NA 'retained', 0 'alive' individuals
#>
#> total:
#> - 13 'sampled' individuals
#> - 13 'remembered' individuals
#> - 42 'retained' individuals
#> - 11 'alive' individuals
#> ---------------------
#> oldest sampled individual: 70000 time units 'before present'
#> youngest sampled individual: 0 time units 'before present'
#>
#> oldest node: 6000170 time units 'before present'
#> youngest node: 0 time units 'before present'
#> ---------------------
#> overview of the underlying table object:
#>
#> # A tibble: 68 × 12
#> name pop node_id time time_tskit sampled remembered retai…¹ alive pedig…²
#> <chr> <fct> <int> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl> <dbl>
#> 1 CH_1 CH 14 0 0 TRUE TRUE TRUE TRUE 5520343
#> 2 CH_1 CH 15 0 0 TRUE TRUE TRUE TRUE 5520343
#> 3 AFR_1 AFR 4 0 0 TRUE TRUE TRUE TRUE 5520350
#> 4 AFR_1 AFR 5 0 0 TRUE TRUE TRUE TRUE 5520350
#> 5 AFR_2 AFR 6 0 0 TRUE TRUE TRUE TRUE 5520351
#> 6 AFR_2 AFR 7 0 0 TRUE TRUE TRUE TRUE 5520351
#> 7 AFR_3 AFR 8 0 0 TRUE TRUE TRUE TRUE 5520352
#> 8 AFR_3 AFR 9 0 0 TRUE TRUE TRUE TRUE 5520352
#> 9 AFR_4 AFR 10 0 0 TRUE TRUE TRUE TRUE 5520357
#> 10 AFR_4 AFR 11 0 0 TRUE TRUE TRUE TRUE 5520357
#> # … with 58 more rows, 2 more variables: ind_id <dbl>, pop_id <int>, and
#> # abbreviated variable names ¹retained, ²pedigree_id