Examples of RMarkdown Analysis Reports

Can anyone point me in the right direction of some Github repositories that show rmarkdown analysis projects/reports? I'm trying to review some examples of other people's work in order to improve my own. Looking at things like workflow, object naming, etc.

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This is not a direct example but your question make me think of this great blog post about worflows with Rmarkdown

You may (or not) find this interesting!


This looks great! While it doesn't answer my specific question, it gives me a TON to think about. Thank you!

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One way to discover great GitHub content is by scrolling through #rstats on twitter, which has plenty of links to Rmd files which people promote. Once you stumble across a good one there are likely to be many others from the same author.

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I found Bookdown: Authoring Books and Technical Documents with R Markdown by Yihui Xie and the R Markdown Gallery useful.

Thanks to everyone who has responded. No one has any links to actual data analysis projects that I can review as an example?

I love your question! I learned a lot from @MikeKSmith 's 2019 rstudio::conf talk for using parameterized reports to adjust the level of detail of your report. I used this in my own group to capture my entire analysis, background data, assumptions, and recommendations in one file but parameterized for the audience. If your analysis reports follow a common structure, then you can create your own R Markdown template to start each project as well with child documents for Scope, Assumptions, Data Sources, etc.

Video of the talk is here: https://resources.rstudio.com/rstudio-conf-2019/the-lazy-and-easily-distracted-report-writer-using-rmarkdown-and-parameterised-reports

Github materials here: https://github.com/MikeKSmith/RStudioConf2019

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Thanks @katie ! I'll have to check it out and report back!

Hi there.

@Katie - Thanks for you kind comments, and for sharing the link. I'll be happy to answer any questions you might have... 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'm an advocate for using rmarkdown notebooks for analysis. It encourages the analyst to be explicit about assumptions etc. and to "leave a trail" that they can follow later. Too often the "WHY" isn't addressed in code comments... 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.

Writing a fully reproducible, fully automated, fully parameterised report i.e. 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. I'm thinking of notebooks as more granular reporting... i.e. results for THIS model...

Happy to discuss further, if that helps.

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