slimr: An R package for tailor‐made integrations of data in population genomic simulations over space and time

Software for realistically simulating complex population genomic processes is rev-olutionizing our understanding of evolutionary processes, and providing novel op-portunities for integrating empirical data with simulations. However, the integrationbetween standalone simulation software and R is currently not well developed. Here,we present slimr, an R package designed to create a seamless link between standalonesoftware SLiM >3.0, one of the most powerful population genomic simulation frame-works, and the R development environment, with its powerful data manipulation andanalysis tools. We show how slimr facilitates smooth integration between geneticdata, ecological data and simulation in a single environment. The package enablespipelines that begin with data reading, cleaning and manipulation, proceed to con-structing empirically based parameters and initial conditions for simulations, then torunning numerical simulations and finally to retrieving simulation results in a formatsuitable for comparisons with empirical data – aided by advanced analysis and visu-alization tools provided by R. We demonstrate the use of slimr with an example fromour own work on the landscape population genomics of desert mammals, highlightingthe advantage of having a single integrated tool for both data analysis and simulation.slimr makes the powerful simulation ability of SLiM directly accessible to R users,allowing integrated simulation projects that incorporate empirical data without theneed to switch between software environments. This should provide more opportu-nities for evolutionary biologists and ecologists to use realistic simulations to betterunderstand the interplay between ecological and evolutionary processes.