Installation instructions

Quick installation

slendr is currently in the process of the first CRAN submission which is why it can’t be installed with simple install.packages("slendr") just yet.

For the moment, the easiest way to install it is to get its development version directly from GitHub by executing devtools::install_github("bodkan/slendr") via the R package devtools ( install.packages("devtools")). In fact, if you decide to try slendr, please make sure to update it regularly and keep an eye on the changelog on a regular basis!

Once you install slendr, calling library(slendr) will check that all software dependencies are available. If they are not, the R package will provide a brief helpful guide on how to resolve potential issues. The rest of this vignette talks about the necessary software dependencies in a bit more detail.

Please note that slendr is only supported on macOS and Linux at the moment. However, because slendr’s software dependencies are already available on Windows, I hope to make the R package fully portable as soon as I get access to a Windows machine to do proper testing on. If you are a Windows user and would like to help, please get in touch!

Software dependencies

slendr relies on three main software dependencies:

geospatial data analysis R package sf (for encoding spatial slendr models and analysing spatial tree-sequence data),
forward population genetic simulator SLiM (for forward simulations),
Python modules tskit, msprime, and pyslim (for coalescent simulations and tree-sequence analysis).

All three are widely used in their respective fields and, as such, are easily obtainable on all major operating systems (see below for more information on how to troubleshoot potential problems).

Note that depending on your use case, not all three sets of dependencies will be necessarily needed. If you’re not going to be running forward spatial simulations, you don’t need SLiM. If you don’t want to analyze tree sequences in R, you won’t need slendr’s Python dependencies.

In this vignette, I will briefly explain how to get all slendr’s software dependencies installed. That said, note that under normal circumstances (with the exception of SLiM), no manual installation of individual dependencies is required.

sf

The R package sf is at the heart of geospatial data analysis in R. It is available on CRAN and can be installed for all major platforms by executing install.packages("sf") in your R session.

When you install slendr (see the very top of this page), the installation procedure will automatically install sf for you. Under normal circumstances, you should not have to worry about sf at all.

That said, sf itself depends on a number of geospatial libraries and depending on the exact setup of your Linux or macOS machine, some of those libraries could be missing. Luckily, all of them are very easy to install via Homebrew (on macOS) or via the appropriate package manager of your Linux distribution (Ubuntu, Fedora, etc.). Detailed instructions on how to do this for your operating system can be found here.

If you’re having trouble installing slendr in your R session, it’s worth testing whether you can run install.packages("sf") and successfully load library(sf). In nearly 100% cases of my test cases, if a slendr installation failed, it was due to a problem with some missing dependency of sf. If you’re having these problems, look for help here.

macOS

One user who recently installed slendr on a fresh macOS system reported that they needed to install libgit2 in order to be able to install the package devtools for the devtools::install_github("bodkan/slendr") step described on top of this page. Additionally, they had to install a couple of C/C++ libraries as well (all dependencies of the sf package). In the end, they were able to successfully install slendr after running:

brew install libgit2 udunits gdal proj

Note that this assumes that you have the Homebrew package manager already setup on your Mac. If you’re a beginning computational scientist using a Mac, I strongly encourage you to install Homebrew. Sooner or later you will need some specific Linux/unix program anyway, and Homebrew is the way to get it (Mac is a unix machine, but without Homebrew a very poor one by default).

SLiM

The forward population genetic software SLiM is available on all major software platforms. Its complete installation instructions can be found here. On a Mac, I recommend installing SLiM via the pkg installer available for direct download from its website. On Linux, you can either install SLiM via the appropriate package manager for your Linux distribution (see SLiM manual here for more information), or you can easily compile your own.

Note that slendr requires SLiM 3.7.1 and will not work with an earlier version. Again, running library(slendr) will inform you of any potential issues with your SLiM installation.

Python

In order to be able to run coalescent simulations and process tree-sequence files, slendr needs Python modules tskit, msprime, and pyslim.

Setting up an isolated Python environment with specific version of Python packages (which is very important to avoid clashes among different Python programs needed by your system) can be a bit of a hassle for some users. This is especially true for R users who might not use Python in their daily work, such as yours truly.

In order to make sure that the R package has the most appropriate version of Python available, with the correct versions of all of its Python module dependencies, slendr provides a dedicated function setup_env() which automatically downloads a completely separate Python distribution and installs the required versions of tskit, msprime, and pyslim modules into a dedicated virtual environment. Moreover, this Python installation and virtual environment are entirely isolated from other Python configurations that are already present on the user’s system, avoiding potential conflicts with the versions of Python and Python modules required by slendr.

Not only that, after this dedicated Python environment is created, calling library(slendr) at any later point will activate this environment automatically. Therefore, despite using Python for internal handling of tree-sequence data and coalescent simulation, no interaction with Python is necessary for working with slendr in R.

You only have to call setup_env() once! After that, everything will be taken care of by library(slendr) automatically.

Information for Python experts

In case you are wondering how does slendr accomplish the above: slendr’s Python interface is not a hack that would simply call Python from the command-line in the background, but is implemented using the R package reticulate. This embeds a Python session within an R session, enabling high-performance interoperability between both languages without any need for user intervention.