The results in Table 1 don’t seem to correspond to those in Figure 2
Associate Professor of the Practice, Duke University Data Scientist + Professional Educator, RStudio
Abstract: For a 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 results without fiddling with unavailable analysis data, external dependencies, missing packages, out of date software, etc.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 talk we present good, better, and best workflows for reproducibility that touch on everything from data storage, cleaning, analysis, to communication of final results.
Bio: Mine Çetinkaya-Rundel is Associate Professor of the Practice in the at Duke University as well as a Data Scientist and Professional Educator at RStudio. Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computation, reproducible research, student-centered learning, and open-source education. Mine also works on the OpenIntro project and teaches the popular Statistics with R MOOC on Coursera. In 2016 Mine received the ASA Waller Education Award and in 2018 the Harvard Pickard Lecture Award.