Note: This book has been published by Chapman & Hall/CRC. The online version of this book is free to read here (thanks to Chapman & Hall/CRC), and licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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The document format “R Markdown” was first introduced in the knitr package (Xie 2015, 2019c) in early 2012. The idea was to embed code chunks (of R or other languages) in Markdown documents. In fact, knitr supported several authoring languages from the beginning in addition to Markdown, including LaTeX, HTML, AsciiDoc, reStructuredText, and Textile. Looking back over the five years, it seems to be fair to say that Markdown has become the most popular document format, which is what we expected. The simplicity of Markdown clearly stands out among these document formats.

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However, the original version of Markdown invented by John Gruber was often found overly simple and not suitable to write highly technical documents. For example, there was no syntax for tables, footnotes, math expressions, or citations. Fortunately, John MacFarlane created a wonderful package named Pandoc (http://pandoc.org) to convert Markdown documents (and many other types of documents) to a large variety of output formats. More importantly, the Markdown syntax was significantly enriched. Now we can write more types of elements with Markdown while still enjoying its simplicity.

In a nutshell, R Markdown stands on the shoulders of knitr and Pandoc. The former executes the computer code embedded in Markdown, and converts R Markdown to Markdown. The latter renders Markdown to the output format you want (such as PDF, HTML, Word, and so on).

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The rmarkdown package (J. Allaire, Xie, McPherson, et al. 2019) was first created in early 2014. During the past four years, it has steadily evolved into a relatively complete ecosystem for authoring documents, so it is a good time for us to provide a definitive guide to this ecosystem now. At this point, there are a large number of tasks that you could do with R Markdown:

• Compile a single R Markdown document to a report in different formats, such as PDF, HTML, or Word.

• Create notebooks in which you can directly run code chunks interactively.

• Make slides for presentations (HTML5, LaTeX Beamer, or PowerPoint).

• Produce dashboards with flexible, interactive, and attractive layouts.

• Build interactive applications based on Shiny.

• Write journal articles.

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• Author books of multiple chapters.

• Generate websites and blogs.

There is a fundamental assumption underneath R Markdown that users should be aware of: we assume it suffices that only a limited number of features are supported in Markdown. By “features”, we mean the types of elements you can create with native Markdown. The limitation is a great feature, not a bug. R Markdown may not be the right format for you if you find these elements not enough for your writing: paragraphs, (section) headers, block quotations, code blocks, (numbered and unnumbered) lists, horizontal rules, tables, inline formatting (emphasis, strikeout, superscripts, subscripts, verbatim, and small caps text), LaTeX math expressions, equations, links, images, footnotes, citations, theorems, proofs, and examples. We believe this list of elements suffice for most technical and non-technical documents. It may not be impossible to support other types of elements in R Markdown, but you may start to lose the simplicity of Markdown if you wish to go that far.

Epictetus once said, “Wealth consists not in having great possessions, but in having few wants.” The spirit is also reflected in Markdown. If you can control your preoccupation with pursuing typesetting features, you should be much more efficient in writing the content and can become a prolific author. It is entirely possible to succeed with simplicity. Jung Jae-sung was a legendary badminton player with a remarkably simple playing style: he did not look like a talented player and was very short compared to other players, so most of the time you would just see him jump three feet off the ground and smash like thunder over and over again in the back court until he beats his opponents.

Please do not underestimate the customizability of R Markdown because of the simplicity of its syntax. In particular, Pandoc templates can be surprisingly powerful, as long as you understand the underlying technologies such as LaTeX and CSS, and are willing to invest time in the appearance of your output documents (reports, books, presentations, and/or websites). As one example, you may check out the PDF report of the 2017 Employer Health Benefits Survey. It looks fairly sophisticated, but was actually produced via bookdown(Xie 2016), which is an R Markdown extension. A custom LaTeX template and a lot of LaTeX tricks were used to generate this report. Not surprisingly, this very book that you are reading right now was also written in R Markdown, and its full source is publicly available in the GitHub repository https://github.com/rstudio/rmarkdown-book.

R Markdown documents are often portable in the sense that they can be compiled to multiple types of output formats. Again, this is mainly due to the simplified syntax of the authoring language, Markdown. The simpler the elements in your document are, the more likely that the document can be converted to different formats. Similarly, if you heavily tailor R Markdown to a specific output format (e.g., LaTeX), you are likely to lose the portability, because not all features in one format work in another format.

Last but not least, your computing results will be more likely to be reproducible if you use R Markdown (or other knitr-based source documents), compared to the manual cut-and-paste approach. This is because the results are dynamically generated from computer source code. If anything goes wrong or needs to be updated, you can simply fix or update the source code, compile the document again, and the results will automatically updated. You can enjoy reproducibility and convenience at the same time.

This is the central repository for all materials related to Spark: The Definitive Guide by Bill Chambers and Matei Zaharia.

This repository is currently a work in progress and new material will be added over time.

You can find the code from the book in the code subfolder where it is broken down by language and chapter.

Run on your local machine

To run the example on your local machine, either pull all data in the data subfolder to /data on your computer or specify the path to that particular dataset on your local machine.

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Run on Databricks

To run these modules on Databricks, you're going to need to do two things.

1. Sign up for an account. You can do that here.
2. Import individual Notebooks to run on the platform

Databricks is a zero-management cloud platform that provides:

• Fully managed Spark clusters
• An interactive workspace for exploration and visualization
• A production pipeline scheduler
• A platform for powering your favorite Spark-based applications

Instructions for importing

1. Navigate to the notebook you would like to import

For instance, you might go to this page. Once you do that, you're going to need to navigate to the RAW version of the file and save that to your Desktop. You can do that by clicking the Raw button. Alternatively, you could just clone the entire repository to your local desktop and navigate to the file on your computer.

2. Upload that to Databricks

Read the instructions here. Simply open the Databricks workspace and go to import in a given directory. From there, navigate to the file on your computer to upload it. Unfortunately due to a recent security upgrade, notebooks cannot be imported from external URLs. Therefore you must upload it from your computer.

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3. You're almost ready to go!

Now you just need to simply run the notebooks! All the examples run on Databricks Runtime 3.1 and above so just be sure to create a cluster with a version equal to or greater than that. Once you've created your cluster, attach the notebook.

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4. Replacing the data path in each notebook

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Rather than you having to upload all of the data yourself, you simply have to change the path in each chapter from /data to /databricks-datasets/definitive-guide/data. Once you've done that, all examples should run without issue. You can use find and replace to do this very efficiently.