The goal of tidypopgen is to provide a tidy grammar of population genetics, facilitating the manipulation and analysis of genetic data. Currently, it is focussed on biallelic single nucleotide polymorphisms (SNPs).
We are making available a preview version of the package. Everything should work, but be vigilant as not all functions have been tested extensively. Also, the syntax of functions is still slightly in flux, so the names and order of some parameters might change. Make sure you don’t rely on position when giving multiple parameters to a function, so that any change will trigger an informative error in the code that will allow you to update.
Installation
You can install the latest version of tidypopgen directly from r-universe (reccomended):
install.packages('tidypopgen', repos = "https://evolecolgroup.r-universe.dev")Alternatively, you can install tidypopgenusing devtools (but you might need to set up your development environment, which can be a bit more complex):
install.packages("devtools")
devtools::install_github("EvolEcolGroup/tidypopgen")Examples
There are a several vignettes designed to teach you how to use tidypopgen. The ‘overview’ vignette explains how the data structures are designed, and provides a illustration of the grammar used to manipulate individuals and loci.
The ‘example workflow’ vignette provides a fully annotated example of how to run population genetics analysis with tidypopgen.
The ‘quality control’ vignette illustrates the tidypopgen functions that help running a fully QC of a dataset before analysis.
Finally, we provide a ‘PLINK cheatsheet’ aimed at translating common tasks performed in PLINK into tidypopgen commands.
When something does not work
This is a preview version of tidypopgen. Everything should work, but not all elements have been extensively tested. If something does not work, check the issues on GitHub to see whether the problem has already been reported. If not, feel free to create an new issue. Please make sure you have updated to the latest version of tidypopgen on Github, as well as updating all other packages on your system, and provide a reproducible example for the developers to investigate the problem. Ideally, try to create a minimalistic dataset that reproduces the error, as it will be much easier (and thus faster!) for the developers to track down the problem.