Once I switched to reproducible presentations for these sorts of projects I saved a lot of time overall (and made a lot less copy-paste mistakes).
#Rmarkdown presentation guide update
However, when I was working on a long project with large tables and many figures, with several presentations along the way at various stages in the analysis I realized how much time I was wasting copying and pasting figures and tables from my re-run analysis into Power Point/Keynote and I got very sad and thought there has to be a better way… Through the Software Carpentry community I learned about using reproducible presentation tools, which like reproducible reports automatically update tables and figures when you render the presentation. In my career, I too started out creating presentations using Power Point and Keynote. But if it is a presentation you might re-use, or your analysis might change (and hence your figures and tables in the presentation might change) then in the long run this actually saves you time. This blog post aims to motivate those currently not using reproducible presentation tools to try one out, and guide them in their choice of tool based on their intended use case.īefore I dig into the tools, I will first try to answer why I recommend reproducible presentations? At first glance they seem like much more work… And yes, sometimes they are, for example if it is just a one off presentation that you will never re-use ever again. Many Data Scientists and researchers that value reprodcuibility are still using the more commonly known what you see is what you get (WYSWYG) presentation tools (e.g., Power Point, Keynote, Google Slides, etc), perhaps because until vert recently the reproducible presentation tools were less mature in terms of visual appeal and the ability to be used in collaboration with others who are not familar with things like Markdown or LaTeX syntax. Reproducible presentation tools, however, are less well known.
#Rmarkdown presentation guide code
Reproducible reports, a single document contains both the code needed to create figures and tables as well as the written narrative that tells the story of the analysis (e.g., Jupyter notebooks and R Markdown reports), are gaining buy-in traction in the Data Science community due to fact that they allow for re-creation an analysis from beginning to end, and because they help make an analysis tranparent or auditable.
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