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These are the materials for the "Reproducible reports" Brown Bag. At the bottom of this document you will find some additional resources to learn more about R Markdown. The simple-report/ folder contains the example that was used during the talk.

R Markdown and reproducible reports

R Markdown is a "low-overhead way of writing reports which includes R code and the code's automatically-generated output." In other words, it is a system that allows you to write documents that intercalate code and the output of the code. The documents themselves can also include rich text features like mathematical notation, hyperlinks, or images in addition to some formatting.

Think for instance about some of the work on propensity modeling that we are currently doing. For each individual in a sample we want to estimate their probability of responding to the survey. It is a research problem in which we want to try different data cleaning options, different modeling strategies, and see how they perform. When we meet, we want to see where things stand, the code that has been used for data cleaning, how the data looks like at each step, and the intermediate output of some of the models together with some tests. But just seeing the results is not very useful. I personally like to have comments to interpret the output and to explain why some decisions have been taken.

We could run our code in whatever language we prefer, copy the output and paste it into a Word document or an Excel sheet, and then add comments around. If the statistician wants to see the code that generated the output, we could attach a log file or maybe copy the significant portions into our final document. But there is a strong inefficiency in this approach: if anything changes in the code or in the data, we would need to do the process all over again. There is a disconnect between the solution we use to run the analysis and produce the statistical output and the file that documents and describes it.

This is where literate programming, reproducible reports and R Markdown in particular come to help. They allow you to use a single document to include all the information and also enough flexibility to make it look good for public sharing.

A basic R Markdown report

There are three components: a header, the body, and chunks of code.

The header contains metainformation about the report, like the title or the author but also some instructions about the type of output, extensions, ... It is written in the YAML format:

title: "This is my report"
author: "Gonzalo Rivero"
date: October 16

The body of the document, which is written in the markdown language. We will talk about the markdown format in a bit, but by now think of plain text.

Chunks with the code. They are used to include the actual code that you will be using. These chunks are delimited by three backticks.

In the simple-report/ folder we will see a more elaborated example and some of the other options that R Markdown allows us.


  1. The official site of the packge contains a really good introduction that also discusses more advanced topics. It also includes an introduction to the pandoc dialect of Markdown.

  2. More information about the pandoc engine for format conversion can be found here.

  3. An alternative to the R Markdown reports is Jupyter.

  4. To learn more about LaTeX.

  5. R Markdown can be used to create simple dashboards using flexdashboard. A more complete (R-based) solution is Shiny..


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