ECSSN and NZSA Joint Webinar: Setting up a reproducible data analysis project in R – featuring Github, renv, targets and more

This talk is a follow up to last year’s presentation by  Olivia Angelin-Bonnet on the relevance of software engineering best practices in statistics and data science. Here, Olivia will go through how she setup a new data analysis project in R, following some of these best practices to ensure reproducibility and reusability. In particular, she will demonstrate how she use GitHub to version control the code, and how she organise a typical analysis directory. Olivia will also showcase some very useful R packages, such as renv to document the computational environment and targets to turn the scripts into a reproducible analytical pipeline.

Biography:

Olivia completed her PhD in Statistics at Massey University, where she worked on unravelling genotype-to-phenotype relationships from multi-omics data, with a focus on polyploid organisms. After a year as a lecturer in Statistics at Massey University, she is now a statistical Scientist at Plant & Food Research. Her research interests include Systems Biology, multi-omics data integration, the study of biological networks from a statistical and computational perspective, and the development of visualisation tools for omics data.