Tskit and R

Tskit and R#

To interface with tskit in R, we can use the reticulate package, which lets you call tskit’s Python API from an R session. In this tutorial, we walk through a few simple examples to help you get started.

First, install reticulate in R with install.packages("reticulate"). Then, install the required Python packages with reticulate::py_require(c("tskit", "msprime")).

By default, this uses reticulate’s isolated Python virtual environment, but you can also use another Python environment as described at https://rstudio.github.io/reticulate/.

install.packages("reticulate")
reticulate::py_require(c("tskit", "msprime"))

We’ll begin by simulating a small tree sequence by calling msprime$sim_ancestry() from R.

msprime <- reticulate::import("msprime")
ts <- msprime$sim_ancestry(80, sequence_length=1e4, recombination_rate=1e-4, random_seed=42)
ts  # See "Jupyter notebook tips" below for nicer rendering

<tskit.trees.TreeSequence object at 0x7f99442aa0d0>

Attributes and methods#

reticulate lets us access a Python object’s attributes with the $ operator. For example, we can access the number of samples in the tree sequence and store it into an R object:

n <- ts$num_samples
n
is(n)  # Show the type of the object
160
  1. 'integer'
  2. 'double'
  3. 'numeric'
  4. 'vector'
  5. 'data.frameRowLabels'
  6. 'index'
  7. 'replValue'
  8. 'numLike'
  9. 'number'
  10. 'atomicVector'
  11. 'replValueSp'

The $ operator can also be used to call a Python object’s methods, for example the simplify() method for a tree sequence. Method arguments are given as native R objects and then passed to reticulate Python (but note that object IDs still use tskit’s 0-based indexing system).

reduced_ts <- ts$simplify(0:7)  # only keep samples with IDs 0, 1, 2, 3, 4, 5, 6, 7
reduced_ts <- reduced_ts$delete_intervals(list(c(6000, 10000)))  # delete data after 6 kb
reduced_ts <- reduced_ts$trim()  # remove the deleted region
paste(
    "Reduced from", ts$num_trees, "trees over", ts$sequence_length/1e3, "kb to",
    reduced_ts$num_trees, "trees over", reduced_ts$sequence_length/1e3, "kb.")
'Reduced from 26 trees over 10 kb to 6 trees over 6 kb.'

IDs and indexing#

If you pass a bare number to one of these methods, R treats it as a floating point value (the default numeric type in R). This matters when calling tskit methods that require integers (such as object IDs). For example, the following raises a TypeError because 0 is passed as a float:

try(ts$node(0))  # Will raise a TypeError
is(0)

Error in py_call_impl(callable, call_args$unnamed, call_args$named) : 
  TypeError: Index must be of integer type, not 'float'
Run `reticulate::py_last_error()` for details.
  1. 'numeric'
  2. 'vector'
  3. 'index'
  4. 'replValue'
  5. 'numLike'
  6. 'number'
  7. 'atomicVector'
  8. 'replValueSp'

To pass 0 as an integer, either coerce it with as.integer or append L:

is(as.integer(0))
ts$node(as.integer(0))
# or
is(0L)
ts$node(0L)
  1. 'integer'
  2. 'double'
  3. 'numeric'
  4. 'vector'
  5. 'data.frameRowLabels'
  6. 'index'
  7. 'replValue'
  8. 'numLike'
  9. 'number'
  10. 'atomicVector'
  11. 'replValueSp'
Node(id=0, flags=1, time=0.0, population=0, individual=0, metadata=b'')
  1. 'integer'
  2. 'double'
  3. 'numeric'
  4. 'vector'
  5. 'data.frameRowLabels'
  6. 'index'
  7. 'replValue'
  8. 'numLike'
  9. 'number'
  10. 'atomicVector'
  11. 'replValueSp'
Node(id=0, flags=1, time=0.0, population=0, individual=0, metadata=b'')

You only need this coercion when calling underlying tskit methods that expect integer arguments. You do not need it for array indexing itself. However, when indexing tskit arrays in R, remember that tskit IDs start at zero, whereas R indexes start at one:

root_id_int <- ts$first()$root
is(root_id_int)
root_id_num <- as.numeric(root_id_int)

# Using integer ID will work
paste("Root time via tskit method using integer ID:", ts$node(root_id_int)$time)
# Using numeric ID will raise TypeError
try(paste("Root time via tskit method using float ID:", ts$node(root_id_num)$time))

# When indexing into tskit arrays in R, add 1 to the ID (integer or numeric)
paste("Root time via integer array:", ts$nodes_time[root_id_int + 1])
paste("Root time via float   array:", ts$nodes_time[root_id_num + 1])
  1. 'integer'
  2. 'double'
  3. 'numeric'
  4. 'vector'
  5. 'data.frameRowLabels'
  6. 'index'
  7. 'replValue'
  8. 'numLike'
  9. 'number'
  10. 'atomicVector'
  11. 'replValueSp'
'Root time via tskit method using integer ID: 3.4634393266485'
Error in py_call_impl(callable, call_args$unnamed, call_args$named) : 
  TypeError: Index must be of integer type, not 'float'
Run `reticulate::py_last_error()` for details.
'Root time via integer array: 3.4634393266485'
'Root time via float array: 3.4634393266485'

Analysis#

From within R, we can use tskit’s powerful Statistics framework to efficiently compute many summary statistics from a tree sequence. To illustrate this, we’ll first add mutations with the msprime.sim_mutations() function and then compute genetic diversity:

ts_mut <- msprime$sim_mutations(reduced_ts, rate=1e-4, random_seed=321)
paste(ts_mut$num_mutations, "mutations, genetic diversity is", ts_mut$diversity())
'13 mutations, genetic diversity is 0.000720238095238095'

Numerical arrays and matrices work as expected: they are converted to equivalent R objects. For instance, we can use the tree sequence genotype_matrix() method to return the genotypes of the tree sequence as a matrix object in R.

G <- ts_mut$genotype_matrix()
G
is(G)
A matrix: 13 × 8 of type int
00010000
00000010
10000000
00100000
00110110
01110110
01111111
01111111
10000000
00001001
10000000
01110110
00001000
  1. 'matrix'
  2. 'array'
  3. 'replValueSp'
  4. 'structure'
  5. 'vector'

We can then use R functions directly on the genotype matrix:

allele_frequency <- rowMeans(G)
allele_frequency
  1. 0.125
  2. 0.125
  3. 0.125
  4. 0.125
  5. 0.5
  6. 0.625
  7. 0.875
  8. 0.875
  9. 0.125
  10. 0.25
  11. 0.125
  12. 0.625
  13. 0.125

Jupyter notebook tips#

When running R in a Jupyter notebook, you can define a few functions so that tskit objects render nicely in notebook output:

# Define some magic functions to allow objects to be displayed in R Jupyter notebooks
repr_html.tskit.trees.TreeSequence <- function(obj, ...){obj$`_repr_html_`()}
repr_html.tskit.trees.Tree <- function(obj, ...){obj$`_repr_html_`()}
repr_svg.tskit.drawing.SVGString <- function(obj, ...){obj$`__str__`()}

This leads to much nicer tabular summaries:

ts_mut
Tree Sequence
Trees6
Sequence Length6 000
Time Unitsgenerations
Sample Nodes8
Total Size6.1 KiB
MetadataNo Metadata
Table Rows Size Has Metadata
Edges 32 1.0 KiB
Individuals 4 136 Bytes
Migrations 0 8 Bytes
Mutations 13 497 Bytes
Nodes 20 568 Bytes
Populations 1 224 Bytes ✅
Provenances 5 3.1 KiB
Sites 13 341 Bytes
Provenance Timestamp Software Name Version Command Full record
02 March, 2026 at 07:52:33 AM msprime 1.4.0 sim_mutations
Details
dict schema_version: 1.0.0
software:
dict name: msprime
version: 1.4.0

parameters:
dict command: sim_mutations
tree_sequence:
dict __constant__: __current_ts__

rate: 0.0001
model: None
start_time: None
end_time: None
discrete_genome: None
keep: None
random_seed: 321

environment:
dict
os:
dict system: Linux
node: runnervmnay03
release: 6.14.0-1017-azure
version: #17~24.04.1-Ubuntu SMP Mon Dec
1 20:10:50 UTC 2025
machine: x86_64

python:
dict implementation: CPython
version: 3.11.14

libraries:
dict
kastore:
dict version: 2.1.1

tskit:
dict version: 1.0.0

gsl:
dict version: 2.6
02 March, 2026 at 07:52:33 AM tskit 1.0.0 trim
Details
dict schema_version: 1.0.0
software:
dict name: tskit
version: 1.0.0

parameters:
dict command: trim

environment:
dict
os:
dict system: Linux
node: runnervmnay03
release: 6.14.0-1017-azure
version: #17~24.04.1-Ubuntu SMP Mon Dec
1 20:10:50 UTC 2025
machine: x86_64

python:
dict implementation: CPython
version: 3.11.14

libraries:
dict
kastore:
dict version: 2.1.1
02 March, 2026 at 07:52:33 AM tskit 1.0.0 delete_intervals
Details
dict schema_version: 1.0.0
software:
dict name: tskit
version: 1.0.0

parameters:
dict command: delete_intervals
TODO: add parameters

environment:
dict
os:
dict system: Linux
node: runnervmnay03
release: 6.14.0-1017-azure
version: #17~24.04.1-Ubuntu SMP Mon Dec
1 20:10:50 UTC 2025
machine: x86_64

python:
dict implementation: CPython
version: 3.11.14

libraries:
dict
kastore:
dict version: 2.1.1
02 March, 2026 at 07:52:33 AM tskit 1.0.0 simplify
Details
dict schema_version: 1.0.0
software:
dict name: tskit
version: 1.0.0

parameters:
dict command: simplify
TODO: add simplify parameters

environment:
dict
os:
dict system: Linux
node: runnervmnay03
release: 6.14.0-1017-azure
version: #17~24.04.1-Ubuntu SMP Mon Dec
1 20:10:50 UTC 2025
machine: x86_64

python:
dict implementation: CPython
version: 3.11.14

libraries:
dict
kastore:
dict version: 2.1.1
02 March, 2026 at 07:52:33 AM msprime 1.4.0 sim_ancestry
Details
dict schema_version: 1.0.0
software:
dict name: msprime
version: 1.4.0

parameters:
dict command: sim_ancestry
samples: 80.0
demography: None
sequence_length: 10000.0
discrete_genome: None
recombination_rate: 0.0001
gene_conversion_rate: None
gene_conversion_tract_length: None
population_size: None
ploidy: None
model: None
initial_state: None
start_time: None
end_time: None
record_migrations: None
record_full_arg: None
additional_nodes: None
coalescing_segments_only: None
num_labels: None
random_seed: 42
stop_at_local_mrca: None
replicate_index: 0

environment:
dict
os:
dict system: Linux
node: runnervmnay03
release: 6.14.0-1017-azure
version: #17~24.04.1-Ubuntu SMP Mon Dec
1 20:10:50 UTC 2025
machine: x86_64

python:
dict implementation: CPython
version: 3.11.14

libraries:
dict
kastore:
dict version: 2.1.1

tskit:
dict version: 1.0.0

gsl:
dict version: 2.6
To cite this software, please consult the citation manual: https://tskit.dev/citation/

It also allows trees and tree sequences to be plotted inline:

ts_mut$draw_svg(y_axis=TRUE, y_ticks=0:10)
'<svg baseProfile="full" height="200" version="1.1" width="1200" xmlns="http://www.w3.org/2000/svg" xmlns:ev="http://www.w3.org/2001/xml-events" xmlns:xlink="http://www.w3.org/1999/xlink"><defs><style type="text/css"><![CDATA[.background path {fill: #808080; fill-opacity: 0}.background path:nth-child(odd) {fill-opacity: .1}.x-regions rect {fill: yellow; stroke: black; opacity: 0.5}.axes {font-size: 14px}.x-axis .tick .lab {font-weight: bold; dominant-baseline: hanging}.axes, .tree {font-size: 14px; text-anchor: middle}.axes line, .edge {stroke: black; fill: none}.axes .ax-skip {stroke-dasharray: 4}.y-axis .grid {stroke: #FAFAFA}.node > .sym {fill: black; stroke: none}.site > .sym {stroke: black}.mut text {fill: red; font-style: italic}.mut.extra text {fill: hotpink}.mut line {fill: none; stroke: none}.mut .sym {fill: none; stroke: red}.mut.extra .sym {stroke: hotpink}.node .mut .sym {stroke-width: 1.5px}.tree text, .tree-sequence text {dominant-baseline: central}.plotbox .lab.lft {text-anchor: end}.plotbox .lab.rgt {text-anchor: start}.polytomy line {stroke: black; stroke-dasharray: 1px, 1px}.polytomy text {paint-order:stroke;stroke-width:0.3em;stroke:white}]]></style></defs><g class="tree-sequence"><g class="background"><path d="M56.8,0 l187.2,0 l0,138.2 l-144.144,25 l0,5 l-43.056,0 l0,-5 l0,-25 l0,-138.2z"/><path d="M244,0 l187.2,0 l0,138.2 l123.739,25 l0,5 l-455.083,0 l0,-5 l144.144,-25 l0,-138.2z"/><path d="M431.2,0 l187.2,0 l0,138.2 l217.714,25 l0,5 l-281.174,0 l0,-5 l-123.739,-25 l0,-138.2z"/><path d="M618.4,0 l187.2,0 l0,138.2 l184.954,25 l0,5 l-154.44,0 l0,-5 l-217.714,-25 l0,-138.2z"/><path d="M805.6,0 l187.2,0 l0,138.2 l129.168,25 l0,5 l-131.414,0 l0,-5 l-184.954,-25 l0,-138.2z"/><path d="M992.8,0 l187.2,0 l0,138.2 l0,25 l0,5 l-58.032,0 l0,-5 l-129.17,-25 l0,-138.2z"/></g><g class="axes"><g class="x-axis"><g class="title" transform="translate(618.4 200)"><text class="lab" text-anchor="middle" transform="translate(0 -11)">Genome 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Interaction with R libraries#

R has a number of libraries for genomic data and trees. Below we show the phylogenetic tree representation from the popular ape package, using all trees exported in Nexus format, or individual trees exported in Newick format:

file <- tempfile()
ts_mut$write_nexus(file)
# Warning: ape trees are stored independently, so this uses much more memory than tskit
trees <- ape::read.nexus(file, force.multi=TRUE)  # return a set of trees

# Or simply read in a single tree
tree <- ape::read.tree(text=ts_mut$first()$as_newick())

# Now we can plot the tree in tskit style, but using the ape library
plot(tree, direction="downward", srt=90, adj=0.5)  # or equivalently use trees[[1]]
_images/ba4e21555374a0a3002f3cbfaba7386fcc049d206857d1b97cda5bae4dc2c46e.png

Note that nodes are labelled with the prefix n, so nodes 0, 1, 2, … become n0, n1, n2, … This helps avoidinng confusion between the zero-based counting system used natively by tskit and the one-based counting system used in R.

Further information#

Be sure to check out the reticulate documentation, in particular on Calling Python from R, which includes important information on how R data types are converted to their equivalent Python types.

Advanced usage through tskit’s C API#

While the reticulate approach is powerful and useful for most analyses, it does add overhead from Python calls and object conversion. This overhead may be minimal for some use cases but significant for others, such as repeatedly calling Python from an R loop. An alternative is to use RcppTskit, which provides access to the tskit C API (see https://cran.r-project.org/package=RcppTskit/), but this is an advanced topic beyond the scope of this tutorial.

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