Reproducibility using R

#rmarkdown #data science #reproducibility

Beyond the availability of data and methods, reproducible science requires the traceability of analyses. Whether it is for yourself or for collaborators, as series of tools and good practices can facilitate your work flow, simplify analyses, and prevent the loss of data and results. This lecture provides an introduction to reproducibility using R. Slides Click on the image below to access the slides: Related packages knitr knitr provides excellent resources for literate programming mixing R with LaTeX or markdown. ...

Tools for outbreak analytics infrastructures

#rmarkdown #data science #reproducibility #outbreak #response

Beyond the availability of data and methods, and the use of good practices for reproducible science, the outbreak response context poses a number of practical challenges for data analysis. In this lecture, we introduce tools which can help address some of these challenges, and create robust, efficient, and more easily deployable data analytics pipelines using R. Slides Click on the image below to access the slides: Alternatively, you can view these slides directly on google slides. ...