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Since its first official release back in 1995, R has outgrown its statistician-tool origin, spreading out to different fields. A key factor in R’s popularity is without a doubt its approachability for people without a software engineering background. As a result, R is often considered more as a scripting / prototyping / data analytics tool than a proper software development language. Thanks to its low barrier and accessibility, however, people with a wide variety of (non-technical) backgrounds can quickly become active and effective users. This in turn can set the ground for exploring and building up programming and development skills, transitioning towards what is normally associated with software engineering profiles. This raises some questions: what does it mean to be an R user? Is there such a thing as an R developer, and (how) does it differ from being an R user? In this time where IT skills are required across virtually every domain, can an R user afford not to be a software engineer as well? What is (and does it even exist) the R equivalent of the Python stack developer? What type of background and expertise should an R user have to fit what companies are looking for? And what about academia? What is the current trend? We will discuss these points with: → Martin Mächler https://stat.ethz.ch/~maechler/ → Alexis Iglauger PartnerRe / aiglauer → Nic Crane / nicolacrane → Sandrine Dudoit https://www.stat.berkeley.edu/~sandrine/ → Rafael Irizarry https://www.hsph.harvard.edu/rafael-i... Moderation: Francesca Vitalini / francesca-vitalini-phd-csm-61896034