Your collaborators don’t even need RStudio to edit them, since notebooks can be rendered in the R console using the open source rmarkdown package. Because they’re plain-text files, they work well with version control systems like Git. R Notebooks are easy to share with collaborators. The same document can be used as a notebook when you’re quickly iterating on ideas and later rendered to a wholly different format for publication – no duplication of code, data, or output required. You can use any existing R Markdown document as a notebook, or render (knit) a notebook to any R Markdown output type. Since they’re built on R Markdown, R Notebooks work seamlessly with other R Markdown output types. There’s a built-in command to do this, too, so it’s easy to test your notebooks for reproducibility. While you can run chunks (and even individual lines of R code!) in any order you like, a fully reproducible document must be able to be re-executed start-to-finish in a clean environment. This makes an R Notebook an excellent tool for orchestrating a reproducible, end-to-end data analysis workflow you can easily ingest data using your tool of choice, and share data among languages by using packages like feather, or ordinary CSV files. You can run chunks written in other languages, like Python, Bash, or C++ (Rcpp). R Notebooks can run more than just R code. When you’re done, you’ll have a formatted, reproducible record of what you’ve accomplished, with plenty of context, perfect for your own records or sharing with others. Everything you need is at your fingertips in the editor, reducing distractions and helping you concentrate on your analysis. This focused mode of interaction doesn’t require you to keep the console, viewer, or output panes open. You can preview the results of individual inline expressions, too:Įven your LaTeX equations render in real-time as you type:
You can see the progress of the code as it runs: They can include just about any kind of content R produces, including console output, plots, data frames, and interactive HTML widgets. R Notebooks, however, let you run code and see the results in the document immediately. In a typical R Markdown document, you must re-knit the document to see your changes, which can take some time if it contains non-trivial computations. This video provides a bit more background and a demonstration of notebooks in action: R Notebooks bring the interactive model of execution to your R Markdown documents, giving you the capability to work quickly and iteratively in a notebook interface without leaving behind the plain-text tools and production-quality output you’ve come to rely on from R Markdown.
However, code in notebooks is typically executed interactively, one cell at a time, whereas code in R Markdown documents is typically executed in batch.
Interactive R MarkdownĪs an authoring format, R Markdown bears many similarities to traditional notebooks like Jupyter and Beaker. You can try out R Notebooks today in the RStudio Preview Release. Notebooks are also an excellent tool for teaching and a convenient way to share analyses. Notebook interfaces for data analysis have compelling advantages including the close association of code and output and the ability to intersperse narrative with computation. Today we’re excited to announce R Notebooks, which add a powerful notebook authoring engine to R Markdown.