With an increasing number of academic journals requiring authors to submit code, an increasing number of PhD students developing R packages, and more open source packages requiring maintenance, the list of R programming skills required of new quantitative PhD students is ever growing. Many of these PhD students don’t have backgrounds in computer science, but find themselves writing code and developing software on a daily basis. They don’t always have supervisors with backgrounds in computer science either. So how do we help students go from writing spaghetti code, to working with good software development practices? In this talk, I’ll outline what training is currently offered to PhD students, gaps that have been identified (often by students themselves), and a suggestion of how we can better prepare PhD students for quantitative research so that none of them say “If I knew then what I know now, I would have done things entirely differently.” Nicola Rennie, Lecturer in Health Data Science Nicola Rennie is a Lecturer in Health Data Science based within the Centre for Health Informatics, Computing, and Statistics at Lancaster Medical School. Her research interests include applications of statistics and machine learning to healthcare and medicine, communicating data through visualisation, and understanding how we teach statistical concepts. Nicola can often be found at data science meetups, presenting at conferences, and is the R-Ladies Lancaster chapter organiser.