In this blog post, we'll delve into R Markdown, R Markdown Automated Reports, AND an easier alternative to R Markdown report automation (spoiler alert — It's Wrk).

What is R Markdown?

R Markdown is an authoring framework for data science. It allows you to create documents that serve as a comprehensive record of your work. These documents can contain your code, the outputs produced by that code (like figures, tables, etc.), and narrative text that provides context for the code and its outputs.

Importance and use of R Markdown

R Markdown is invaluable in promoting reproducible research, a cornerstone of robust data science. It enables you to reproduce the exact computation and output at any time quickly. Furthermore, You can render R Markdown documents in numerous formats, including HTML, PDF, Word, slides, notebooks, dashboards, and more.

R Markdown vs. Traditional Markdown

Traditional Markdown is a lightweight markup language with plain-text formatting syntax. Its primary purpose is to simplify the writing of HTML. In contrast, R Markdown extends the traditional Markdown syntax to include chunks of executable R code within the document, making it a dynamic document generation tool.

Components of an R Markdown file

An R Markdown file has three main components. The first is a YAML header that includes metadata and options for rendering the document. The second component is the narrative text written in Markdown format. Finally, there are chunks of executable R code.

Getting Started with R Markdown

Required Software and Packages

To start with R Markdown, you must have R and RStudio installed on your machine. Additionally, you need to install the markdown package using the command install.packages("markdown") in R.

Creating Your First R Markdown Document

To create an R Markdown document in RStudio, go to File > New File > R Markdown..., then choose your desired document type and output format.

Basic Syntax for Formatting Text in R Markdown

R Markdown uses the Markdown language for text formatting. For example, you can make text italic by wrapping it with * or _and bold with ** or __. You can create headers by using #, with the level of heading determined by the number of # used.

Including Code Chunks in R Markdown

You must enclose the code within triple backticks and {r} to include R code chunks in your document. For example,

r markdown code


print("Hello, world!")


will print "Hello, world!" when the document is knit.

Markdown Advanced Usage

Parameterizing Reports

R Markdown supports parameterized reports, which allow you to run the same report with different input parameters. Having parameters is useful for creating similar reports for other datasets or different subsets of a dataset.

Incorporating R Scripts

R scripts can be sourced in an R Markdown document using the source() function. R scripts allow you to separate code into multiple files for better organization and reuse.

Output Formatting Options

R Markdown supports various output formats, including HTML, PDF, Word, slides, and more. You can control the output format and its options using the YAML header.

Integration with Knitr and Pandoc

R Markdown integrates with the Knitr and Pandoc packages to convert your document into the final output format. The knit package executes the code chunks and embeds the output in the document, while Pandoc converts the Markdown text into the desired output format.

Using Citations in R Markdown

You can easily include citations in your R Markdown documents. To do this, you specify a bibliography file in the YAML header, then use the @ syntax to cite references in the text.

Customizing R Markdown Reports with YAML Header

The YAML header in an R Markdown document provides a range of customization options, including document metadata (title, author, date), output format and opportunities, and more.

R Markdown Automated Reports

What is R Markdown Automation?

R Markdown automated reports involves is the process of automatically generating reports on a scheduled basis. This type of automation uses a scheduler like cron on Unix-based systems or Task Scheduler on Windows.

Benefits and Applications of R Markdown Automated Reporting

Automated reporting with R Markdown has benefits. It allows for up-to-date reports to be generated regularly without manual intervention. Automating your reports is particularly useful for tracking metrics over time, such as website analytics or business performance metrics.

Common Tools and Techniques for Automation

There are several tools and techniques for automating R Markdown reports. These include the cronR and task scheduler packages for scheduling report generation and the mail and blastula packages for email delivery of reports.

Building Automated Reports with R Markdown

Preparing Your R Markdown Document for Automation

To prepare your R Markdown document for automation, you first need to ensure that it can be rendered without manual intervention. This includes removing any interactive elements and ensuring that all data sources can be accessed automatically.

Scheduling and Running R Markdown Automated Reports

Once your document is ready, you can schedule it to be rendered regularly using a tool like cron or Task Scheduler. The specific steps for doing this will depend on your operating system.

Error Handling and Debugging in Automated Reports

Error handling is an essential part of automated reporting. You should include measures to handle potential errors in your code and log any errors that occur during the rendering of the report. You can use the try-catch () function in R for error handling.

Delivering Automated Reports (Email, Dashboards, Webpages)

After your report is generated, you can deliver it in various ways. This could be as simple as writing it to a file on your system, or you could email it to stakeholders, publish it as a webpage, or integrate it into a dashboard.

Step-by-Step Guide to Building an Automated R Markdown Report

Step 1: Install Necessary Packages

The first step is to install the necessary packages. You'll need the markdown package for creating the report, and depending on your needs, you might also need packages for data access, visualization, scheduling, email delivery, and more.

Step 2: Create and Structure Your R Markdown Document

Next, create your R Markdown document. This will include writing the narrative text, adding code chunks, and specifying the output format and options in the YAML header.

Step 3: Add Dynamic Elements to Your R Markdown Document

To make your report dynamic, you can include elements that change based on the data or the time of rendering. This could consist of parameterized code chunks, dynamic text, or plots that show the most recent data.

Step 4: Set Up the YAML Header for Automation

The YAML header in your R Markdown document can include options that are specifically for automation. For example, you might include an eval option to specify that specific code chunks should only be run during automation.

Step 5: Test Your R Markdown Document

Before automating your document, make sure to thoroughly test it. This includes checking that it renders correctly, that all dynamic elements work as expected, and that all data sources can be accessed automatically.

Step 6: Schedule Your Automated Report

Once your document is ready and tested, you can schedule it to be rendered automatically. This can be done using a scheduler like cron or Task Scheduler.

Step 7: Implement Error Handling Measures

Ensure that your R Markdown document includes measures to handle and log errors that might occur during rendering. This can help you identify and fix issues that might prevent your automated report from being generated correctly.

Step 8: Decide on a Delivery Method for Your Automated Report

Decide how to deliver your automated reports. The delivery method could be as a file on your system, an email to stakeholders, a webpage, or part of a dashboard.

Step 9: Monitor Your Automated Report and Make Adjustments as Needed

While your report is being generated and delivered automatically, continue to monitor it to ensure it works correctly. Make adjustments as needed, such as tweaking the code to handle changes in the data or changing the delivery method to better meet the needs of your stakeholders.

R Markdown Automated Reports vs Workflow Automation Tools Like Wrk

While R Markdown and workflow automation platforms like Wrk are all powerful tools for reporting and automation, they differ significantly in their functions and use cases. Here's a comparison:

  1. Functionality and Purpose:
  • R Markdown Automated Reports: R Markdown is specifically designed for creating dynamic, reproducible documents that combine code (primarily R, but also Python and SQL), results, and narrative text. Automated R Markdown reports enable you to schedule these dynamic documents to be generated and updated regularly, making them excellent for monitoring metrics over time or creating up-to-date reports without manual intervention.
  • Workflow Automation Tools Like Wrk: These tools are designed to automate entire business processes and workflows, which can involve a wide range of tasks, not just report generation. Wrk has a range of existing Wrk actions and integrations making it an excellent project collaboration tool. Team members to manage projects, deadlines, and other Wrkflows while also automating repetitive tasks to improve efficiency
  • User Base:
  • R Markdown Automated Reports: R Markdown is typically used by data scientists, statisticians, and researchers who need to create reproducible research and reports that blend code execution with narrative text and visualizations.
  • Wrk: Wrk caters to a broader audience, including project managers, marketers, sales teams, customer support, and other non-technical roles that benefit from automating repetitive tasks and integrating various software applications.
  1. Language and Environment:
  • R Markdown Automated Reports: R Markdown is used within the R or RStudio environment. Users must have familiarity with R and the Markdown language to use it effectively.
  • Wrk: Wrk provides a graphical user interface (GUI) and does not require knowledge of a specific programming language. However, it may require understanding of different applications to set up effective Wrkflows (but our Automation Experts can also help with that!)
  1. Flexibility and Control:
  • R Markdown Automated Reports: R Markdown offers a high degree of flexibility and control over the contents and format of reports, with options to include sophisticated analyses and custom visualizations. However, its automation capabilities are relatively basic.
  • Wrk: Wrk offers powerful automation capabilities, with options to control the flow of data between different applications and perform complex sequences of tasks. What's more is Wrknhas automated reporting capabilities that require far less effort than R Markdown

If your goal is to automate complex workflows that involve multiple different applications and do not primarily revolve around creating Wrk might be the right tool for you.