Native R kernel for Jupyter b-CI b-CRAN

For detailed requirements and install instructions see



This package is available on CRAN:

IRkernel::installspec()  # to register the kernel in the current R installation
jupyter labextension install @techrah/text-shortcuts  # for RStudio’s shortcuts

Per default IRkernel::installspec() will install a kernel with the name “ir” and a display name of “R”. Multiple calls will overwrite the kernel with a kernel spec pointing to the last R interpreter you called that commands from. You can install kernels for multiple versions of R by supplying a name and displayname argument to the installspec() call (You still need to install these packages in all interpreters you want to run as a jupyter kernel!):

# in R 3.3
IRkernel::installspec(name = 'ir33', displayname = 'R 3.3')
# in R 3.2
IRkernel::installspec(name = 'ir32', displayname = 'R 3.2')

By default, it installs the kernel per-user. To install system-wide, use user = FALSE. To install in the sys.prefix of the currently detected jupyter command line utility, use sys_prefix = TRUE.

Now both R versions are available as an R kernel in the notebook.

If you encounter problems during installation

  1. Have a look at the full installation instructions!
  2. Search the existing open and closed issues.
  3. If you are sure that this is a new problem, file an issue.

Running the notebook

If you have Jupyter installed, you can create a notebook using IRkernel from the dropdown menu.

You can also start other interfaces with an R kernel:

# “ir” is the kernel name installed by the above `IRkernel::installspec()`
# change if you used a different name!
jupyter qtconsole --kernel=ir
jupyter console --kernel=ir

Run a stable release in a Docker container

Refer to the jupyter/docker-stacks r-notebook repository

If you have a Docker daemon running, e.g. reachable on localhost, start a container with:

docker run -d -p 8888:8888 jupyter/r-notebook

Open localhost:8888 in your browser. All notebooks from your session will be saved in the current directory.

On other platforms without docker, this can be started using docker-machine by replacing “localhost” with an IP from docker-machine ip <MACHINE>. With the deprecated boot2docker, this IP will be boot2docker ip.

Develop and run from source in a Docker container

make docker_dev_image #builds dev image and installs IRkernel dependencies from github
make docker_dev #mounts source, installs, and runs Jupyter notebook; docker_dev_image is a prerequisite
make docker_test #builds the package from source then runs the tests via R CMD check; docker_dev_image is a prerequisite

How does it know where to install?

The IRKernel does not have any Python dependencies whatsoever, and does not know anything about any other Jupyter/Python installations you may have. It only requires the jupyter command to be available on $PATH. To install the kernel, it prepares a kernelspec directory (containing kernel.json and so on), and passes it to the command line jupyter kernelspec install [options] prepared_kernel_dir/, where options such as --name, --user, --prefix, and --sys-prefix are given based on the options.