Today we’re excited to announce Radix, a new R Markdown format optimized for scientific and technical communication. Features of Radix include:
Reader-friendly typography that adapts well to mobile devices.
Flexible figure layout options (e.g. displaying figures at a larger width than the article text).
The ability to create a blog composed of a collection of Radix articles.
Below we’ll demonstrate some of the key features of Radix. To learn more about installing and using Radix, check out the Radix for R Markdown website.
Radix provides many flexible options for laying out figures. While the main text column in Radix articles is relatively narrow (optimized for comfortable reading), figures can occupy a larger region. For example:
For figures you want to emphasize or that require lots of visual space, you can also create layouts that occupy the entire width of the screen:
Of course, some figures and notes are only ancillary and are therefore better placed in the margin:
Radix articles support including citations and a corresponding bibliography using standard R Markdown citation syntax.
In addition, when you provide a
citation_url metadata field for your article, a citation appendix that makes it easy for others to cite your article is automatically generated:
You can publish a series of Radix articles as either a website or a blog. For example, the TensorFlow for R blog is implemented using Radix:
To learn more, see the article on creating a blog with Radix.
To create an R Markdown document that uses the Radix format, first install the radix R package:
Using Radix requires Pandoc v2.0 or higher. If you are using RStudio then you should use RStudio v1.2.718 or higher (which comes bundled with Pandoc v2.0). You can download the preview release of RStudio v1.2 at https://www.rstudio.com/products/rstudio/download/preview/.
Next, use the New R Markdown dialog within RStudio to create a new Radix article:
This will give you a minimal new Radix document.
Then, check out the Radix for R Markdown website to learn more about what’s possible. Happy authoring!
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We’d like to highlight and answer some of the great audience questions asked during the "MLOps with vetiver in Python and R" webinar.