The R kernel for the 'Jupyter' environment executes R code which the front-end ('Jupyter Notebook' or other front-ends) submits to the kernel via the network.
For detailed requirements and install instructions see irkernel.github.io
This package is available on CRAN:
install.packages('IRkernel')IRkernel::installspec() # to register the kernel in the current R installation
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
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 3.3IRkernel::installspec(name = 'ir33', displayname = 'R 3.3')# in R 3.2IRkernel::installspec(name = 'ir32', displayname = 'R 3.2')
Now both R versions are available as an R kernel in 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=irjupyter console --kernel=ir
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
make docker_dev_image #builds dev image and installs IRkernel dependencies from githubmake docker_dev #mounts source, installs, and runs Jupyter notebook; docker_dev_image is a prerequisitemake docker_test #builds the package from source then runs the tests via R CMD check; docker_dev_image is a prerequisite