Get the table of individuals/nodes/edges/mutations from the tree sequence
Source:R/tree-sequences.R
ts_table.RdThis function extracts data from a given tree sequence table. All times are converted to model-specific time units from tskit's "generations backwards" time direction.
Usage
ts_table(ts, table = c("individuals", "edges", "nodes", "mutations"))Value
Data frame with the information from the give tree-sequence table (can be either a table of individuals, edges, nodes, or mutations).
Details
For further processing and analyses, the output of the function
ts_nodes might be more useful, as it merges the information in
node and individual tables into one table and further annotates it with
useful information from the model configuration data.
Examples
# load an example model with an already simulated tree sequence
slendr_ts <- system.file("extdata/models/introgression_slim.trees", package = "slendr")
model <- read_model(path = system.file("extdata/models/introgression", package = "slendr"))
# load the tree-sequence object from disk and add mutations to it
ts <- ts_load(slendr_ts, model) %>% ts_mutate(mutation_rate = 1e-8, random_seed = 42)
# get the 'raw' tskit table of individuals
ts_table(ts, "individuals")
#> # A tibble: 73 × 9
#> ind_id time pedigree_id pop_id alive remembered retained sampled time_tskit
#> <dbl> <dbl> <dbl> <int[1> <lgl> <lgl> <lgl> <lgl> <dbl[1d]>
#> 1 0 -10 16023346 0 TRUE TRUE TRUE TRUE 0
#> 2 1 -10 16023350 1 TRUE TRUE TRUE TRUE 0
#> 3 2 -10 16023352 1 TRUE TRUE TRUE TRUE 0
#> 4 3 -10 16023353 1 TRUE TRUE TRUE TRUE 0
#> 5 4 -10 16023355 1 TRUE TRUE TRUE TRUE 0
#> 6 5 -10 16023359 1 TRUE TRUE TRUE TRUE 0
#> 7 6 -10 16024625 3 TRUE TRUE TRUE TRUE 0
#> 8 7 -10 16025081 3 TRUE TRUE TRUE TRUE 0
#> 9 8 -10 16025520 3 TRUE TRUE TRUE TRUE 0
#> 10 9 -10 16026684 3 TRUE TRUE TRUE TRUE 0
#> # ℹ 63 more rows
# get the 'raw' tskit table of edges
ts_table(ts, "edges")
#> # A tibble: 298 × 5
#> id child parent left right
#> <dbl> <int> <int> <dbl> <dbl>
#> 1 0 6 26 0 500000
#> 2 1 13 26 0 500000
#> 3 2 7 27 0 500000
#> 4 3 8 27 0 500000
#> 5 4 12 27 0 500000
#> 6 5 5 28 0 500000
#> 7 6 10 28 0 500000
#> 8 7 11 29 306056 500000
#> 9 8 26 29 306056 500000
#> 10 9 27 30 0 500000
#> # ℹ 288 more rows
# get the 'raw' tskit table of nodes
ts_table(ts, "nodes")
#> # A tibble: 86 × 5
#> node_id ind_id pop_id time time_tskit
#> <int> <int[1d]> <int> <dbl> <dbl>
#> 1 0 25 2 70010 2334
#> 2 1 25 2 70010 2334
#> 3 2 43 2 40010 1334
#> 4 3 43 2 40010 1334
#> 5 4 1 1 -10 0
#> 6 5 1 1 -10 0
#> 7 6 2 1 -10 0
#> 8 7 2 1 -10 0
#> 9 8 3 1 -10 0
#> 10 9 3 1 -10 0
#> # ℹ 76 more rows
# get the 'raw' tskit table of mutations
ts_table(ts, "mutations")
#> # A tibble: 2,246 × 5
#> id site node time time_tskit
#> <dbl> <int> <int> <dbl> <dbl>
#> 1 0 0 83 3626483. 120883.
#> 2 1 1 83 1766659. 58889.
#> 3 2 2 83 2476958. 82566.
#> 4 3 3 83 4356462. 145216.
#> 5 4 4 83 876159. 29206.
#> 6 5 5 83 3848814. 128294.
#> 7 6 6 83 2554081. 85136.
#> 8 7 7 83 976093. 32537.
#> 9 8 8 32 3622571. 120753.
#> 10 9 9 32 104626. 3488.
#> # ℹ 2,236 more rows