R support

Ploomber officially supports R. The same concepts that apply to Python scripts apply to R scripts; this implies that R scripts can render as notebooks in Jupyter and the cell injection works. The only difference is how to declare upstream dependencies:

For the R Markdown format (.Rmd):

```{r, tags=c("parameters")}
upstream = list('one_task', 'another_task')

If you prefer, you can also use plain R scripts:

# %% tags=["parameters"]
upstream = list('one_task', 'another_task')

If your script doesn’t have dependencies: upstream = NULL

To read more about how Ploomber executes scripts and integrates with Jupyter, check the Jupyter Integration guide.

Configuring R environment

To run R scripts as Jupyter notebooks, you need to install Jupyter first, have an existing R installation and install the IRkernel package.

If you are using conda and a environment.yml file to manage dependencies, keep on reading. Otherwise, read the IRkernel installation instructions.

Setting up R and IRkernel via conda

Even if you already have R installed, it is good to isolate your environments from one project to another. conda can install R inside your project’s environment.

Add the following lines to your environment.yaml:

name: some_project

  # ...
  # existing conda dependencies...
  - r-base
  - r-irkernel
  # optionally add r-essentials to install commonly used R packages

  - pip:
    # ...
    # existing pip dependencies...
    - ploomber

For more information on installing R via conda click here.

Once you update your environment.yml, re-create or update your environment.

Finally, activate the R kernel for Jupyter. If you’re using Linux or macOS:

echo "IRkernel::installspec()" | Rscript -

If using Windows, start an R session and run IRkernel::installspec() on it.

Interactive example

Click the button above to see an interactive example (no installation needed, but takes about a minute to be ready):

Example source code