The following webpage lists several Jupyter tricks (in particular, it
illustrates many Ipython magic commands) that should improve your
efficiency (note that this blog post is about two years old so some of
the tricks may have been integrated in the default behavior of Jupyter
now).
rpy2 package allows to use both languages in the same notebook by:
Loading rpy2:
%load_ext rpy2.ipython
Using the %R Ipython magic:
%%R summary(cars)
Python objects can then even be passed to R as follows (assuming df
is a pandas dataframe):
%%R -i df plot(df)
Note that this %%R notation indicates that R should be used for the whole cell but
an other possibility is to use %R to have a single line of R within a
python cell.
For any reason, you may be unsatisfied with the use of R or of Python. Many other languages are available: https://github.com/jupyter/jupyter/wiki/Jupyter-kernels, including non-free languages like SAS, Mathematica, Matlab…
None of these other languages have been deployed in the context of our MOOC but you may want to read the next sections to know more about how to set up your own Jupyter notebooks on your computer and benefit from these extensions.
In this Section, we provide information on how to set up on your own computer a Jupyter environment similar to the one deployed for this MOOC.
Note that Jupyter notebooks are only a small part of the picture and that Jupyter is now part of a bigger project: JupyterLab, which allows you to mix various components (including notebooks) in your browser. In the context of this MOOC, our time frame was too short to benefit from JupyterLab which was still under active development but this is probably the best option now if you want to benefit from cutting-edge Jupyter notebooks.
Follow these instructions if you wish to have a Jupyter environment on your own computer similar to the one we set up for this MOOC.
First, download and install the latest version of Miniconda. We use
Miniconda version 4.5.4 and Python version 3.6 on our server.
Miniconda is a light version of Anaconda, which includes Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science.
Then download the moocrr environment file and create the environment using conda:
conda env create -f environment.yml # Windows activate the environment activate mooc_rr # Linux and MacOS activate the environment source activate mooc_rr # Linux, MacOS and Windows: launch the notebook jupyter notebook
The previous environment should ship with R but if you proceeded otherwise and only have python available in Jupyter, you may want to read the following section.
Do the following in R console:
Install the devtools package:
install.packages('devtools',dep=TRUE)
Define a proxy if needed:
library(httr) set_config(use_proxy(url="proxy", port=80, username="username", password="password"))
Install the IRkernel package:
devtools::install_github('IRkernel/IRkernel') IRkernel::installspec() # to register the kernel in the current R installation
On Linux, the rpy2 package is available in standard distributions
sudo apt-get install python3-rpy2 python3-tzlocal
An other (not really recommended if the first one is available) alternative consists in going through the python package manager with
pip3 install rpy2
Windows
Download rpy2 binary file by choosing the right operating system.
Open a DOS console and type the following command:
python -m pip install rpy2‑2.9.4‑cp37‑cp37m‑win_amd64.whl # adapt filename
Install also tzlocal:
python -m pip install tzlocal
Here is what we had to install on our recent debian computer to make sure the notebook export via LaTeX works:
sudo apt-get install texlive-xetex wkhtmltopdf
Obviously, you can convert to html or pdf using the using the File > Download as > HTML (or PDF) menu option. This can also be done from
the command line with the following command:
ipython3 nbconvert --to pdf Untitled.ipynb
If you want to use a specific style, then the nbconvert exporter
should be customized. This is discussed and demoed here. We encourage
you to simply read the doc of nbconvert.
Instead of going directly through LaTeX and playing too much with the
nbconvert exporter, an other option consists in exporting to Markdown
and playing with pandoc. Both approaches work, it's rather a matter of
taste.
Windows
Download and install MiKTeX from the MiKTeX webpage by choosing the right operating system. You will be prompted to install some specific packages when exporting to pdf.
Here are a few extensions that can ease your life:
Code folding to improve readability when browsing the notebook.
pip3 install jupyter_contrib_nbextensions # jupyter contrib nbextension install --user # not done yet
Hiding code to improve readability when exporting.
sudo pip3 install hide_code sudo jupyter-nbextension install --py hide_code jupyter-nbextension enable --py hide_code jupyter-serverextension enable --py hide_code
To ease your experience, we added some pull/push buttons that allow you to commit and sync with GitLab. This development was specific to the MOOC but inspired from a previous proof of concept. We have recently discovered that someone else developed about at the same time a rather generic version of this Jupyter plugin. Otherwise, remember that it is very easy to insert a shell cell in Jupyter in which you can easily issue git commands. This is how we work most of the time.
This being said, you may have noticed that Jupyter keeps a perfect track of the sequence in which cells have been run by updating the "output index". This is a very good property from the reproducibility point of view but depending on your usage, you may find it a bit painful when committing. Some people have thus developed specific git hooks to ignore these numbers when committing Jupyter notebooks. There is a long an interesting discussion about various options on StackOverflow.
Last but not least, remember that Jupyter notebooks are only a small part of the picture and that Jupyter is now part of a bigger project: JupyterLab, which allows you to mix various components (including notebooks) in your browser. A specific JupyterLab git plugin has been developed to offer a nice version control experience.
For any reason, you may be unsatisfied with the use of R or of Python. Many other languages are available: https://github.com/jupyter/jupyter/wiki/Jupyter-kernels, including non-free languages like SAS, Mathematica, Matlab…
Since the question was asked several times, if you really need to stay with SAS, you should know that SAS can be used within Jupyter using either the Python SASKernel or the Python SASPy package (step by step explanations about this are given here).
Since such software cannot easily be opened for inspection not widely used, we discourage this approach as it hinders reproducibility by essence. But perfection does not exist anyway and using Jupyter literate programming approach allied with systematic control version and environment control will certainly help anyway.