Uploaded November 2020 | Updated September 2026, 2 weeks ago
This event was organized by Drs Tabea Schoeler and Jessie Baldwin, with the support of Nadia Soliman and Dr Samuel Westwood and with financial support from The Crick Institute
About this Event
For data analysis to be reproducible, the data and code should be assembled in a way such that results (e.g. tables and figures) can be re-created. While the scientific community is by and large in agreement that reproducibility is a minimal standard by which data analyses should be evaluated, and a myriad of software tools for reproducible computing exist, it is still not trivial to reproduce someone's (sometimes your own!) results without fiddling with unavailable analysis data, external dependencies, missing packages, out of date software, etc. In this workshop we will demonstrate a workflow for reproducible data science with R, R Markdown, Git, and GitHub. Experience with R is expected but familiarity with the other tools is not required. The workshop will consist of demonstrations and hands-on exercises.
About the speaker
Dr Mine Çetinkaya-Rundel is Senior Lecturer in the School of Mathematics at University of Edinburgh and Data Scientist and Professional Educator at RStudio. Her work focuses on innovation in statistics pedagogy, with an emphasis on computation, reproducible research, open-source education, and student-centered learning. She is the author of three open-source introductory statistics textbooks as part of the OpenIntro project and she has been developing and teaching numerous open online courses. Mine also work on research projects that aim to assess the effectiveness of these approaches with respect to learning, retention, and self-efficacy.
This event was organized by Drs Tabea Schoeler and Jessie Baldwin, with the support of Nadia Soliman and Dr Samuel Westwood and with financial support from The Crick Institute
About this Event
For data analysis to be reproducible, the data and code should be assembled in a way such that results (e.g. tables and figures) can be re-created. While the scientific community is by and large in agreement that reproducibility is a minimal standard by which data analyses should be evaluated, and a myriad of software tools for reproducible computing exist, it is still not trivial to reproduce someone's (sometimes your own!) results without fiddling with unavailable analysis data, external dependencies, missing packages, out of date software, etc. In this workshop we will demonstrate a workflow for reproducible data science with R, R Markdown, Git, and GitHub. Experience with R is expected but familiarity with the other tools is not required. The workshop will consist of demonstrations and hands-on exercises.
About the speaker
Dr Mine Çetinkaya-Rundel is Senior Lecturer in the School of Mathematics at University of Edinburgh and Data Scientist and Professional Educator at RStudio. Her work focuses on innovation in statistics pedagogy, with an emphasis on computation, reproducible research, open-source education, and student-centered learning. She is the author of three open-source introductory statistics textbooks as part of the OpenIntro project and she has been developing and teaching numerous open online courses. Mine also work on research projects that aim to assess the effectiveness of these approaches with respect to learning, retention, and self-efficacy.
