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).
The best solution to this is to install rpy2. On my machine, I have
installed the python3-rpy2 debian package with apt-get install. E.g.,
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
Then you'll be able 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 allows you to use R for the whole cell but
an other possibility is to use %R to have a single line of R within a
python cell.
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.
Follow these instructions if you wish to have a Jupyter environment on your own machine 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 jupyter notebook
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.
Here is what we had to install on our recent debian machine to make sure the notebook export via latex works:
sudo apt-get install texlive-xetex wkhtmltopdf
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.