I'm thinking of notebooks as more granular reporting. I am trying to create an automated report in rmarkdown for business partners. one that builds a consistent, coherent and correct report from an arbitrary set of data, through an arbitrary model to inferences is a TALL order for rmarkdown reporting. R Markdown and Latex: Change font family and size for table of contents. Writing a fully reproducible, fully automated, fully parameterised report i.e. I had a recent discussion with a colleague who was about to embark on some analysis and simulations for clinical trial design using Bayesian inference, and Notebooks seem an ideal way to capture prior choice (what is the prior based on?), model assumptions (where does it predict well, what are the limitations), simulation scenarios (null model, realistic, optimistic cases), and sensitivity analyses. Too often the "WHY" isn't addressed in code comments. and to "leave a trail" that they can follow later. It encourages the analyst to be explicit about assumptions etc. I'm an advocate for using rmarkdown notebooks for analysis. Preparing an idealised example for presentation at conference is one thing, doing it in anger with a real-world project can sometimes be slightly more squirrely! I'll be happy to answer any questions you might have. Without R Markdown, the user would need to compute the mean and median, and then report it manually.Hi - Thanks for you kind comments, and for sharing the link. We may want to explain in words, that the mean of the length of the petal is a certain value, while the median is another value. For instance, suppose we work on the iris dataset (preloaded in R). It is often the case that, when writing interpretations or detailing an analysis, we would like to refer to a result directly in our text. Ordered list, item 2īefore going further, I would like to introduce an important feature of R Markdown.
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