The paradigm shift of literate analysis comes in the switch to RMarkdown, where instead of assuming you are writing code, Rmarkdown assumes that you are writing prose unless you specify that you are writing code. In R, the language assumes that you are writing R code, unless you specify that you are writing prose (using a comment, designated by #). RMarkdown is a combination of two things - R, the programming language, and markdown, a set of text formatting directives. RMarkdown is an excellent way to generate literate analysis, and a reproducible workflow. As Knuth describes, in the literate analysis model, the author is an “essayist” who chooses variable names carefully, explains what they mean, and introduces concepts in the analysis in a way that facilitates understanding. By switching to a literate analysis model, you help enable human understanding of what the computer is doing. All too often, computational methods are written in such a way as to be borderline incomprehensible - even to the person who originally wrote the code! The reason for this is obvious, computers interpret information very differently than people do. If our aim is to make scientific research more transparent, the appeal of this paradigm reversal is immediately apparent. Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do. In this article, Knuth proposes a reversal of the programming paradigm. The concept of literate analysis dates to a 1984 article by Donald Knuth.
15.2 Full source code for the final application.15.1.7 Finishing touches: data citation.15.1.6 Extending the user interface with dynamic plots.15.1.3 Create a sample shiny application.14 Session 14: Reproducibility and Provenance.13.1.6 Visualize sf objects with leaflet.13 Session 13: Geospatial Analysis in R.12.1 Hands On: Clean and Integrate Datasets.12 Session 12: Exercise - Cleaning and Manipulating Data.10.2.9 Sharing and releasing your package.10.2.8 Checking and installing your package.10.1.4 Examples: Minimizing work with functions.10 Session 10: Writing Functions and Packages.9.2.4 Interactive visualization using leaflet and DT.9 Session 9: Data Visualisation and Publishing to the Web.8 Session 8: Cleaning and Manipulating Data.6.2 Additional Resources: Collaboration, authorship and data policies.6.1.4 Bonus Activity: Your Complex Self.6.1.3 About the Whole Brain Thinking System.6 Session 6: Social Aspects of Collaboration.5.5.1 Producing and resolving merge conflicts.5.3.5 Step 5: Owner edits, commit, and push.5.3.3 Step 3: Collaborator commit and push.5.3 Collaborating with a trusted colleague without conflicts.5 Session 5: Git Collaboration and Conflict Management.4.5 Setting up git on an existing project.
2 Session 2: Documenting and Publishing Data.1.1.4 Setting up the R environment on your local computer.1.1.2 Introduction to reproducible research.1.1 Reproducible Research, RStudio and Git/GitHub Setup.