# Jupyter

## Table of Contents

- Additional Jupyter resources or tricks
  - Tips and tricks
  - Running R and Python in the same notebook
  - Exporting a notebook
- Installing Jupyter on your own machine
  - Installing Jupyter
  - Side note about Jupyter, JupyterLab, JupyterHub, …
  - Exporting your notebooks with latex
  - Jupyter extensions/plugins
    - Improving notebook readability
    - Interacting with GitLab and GitHub

## Additional Jupyter resources or tricks

### Tips and tricks

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).

### Running R and Python in the same notebook

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:

1. Loading `rpy2`:

       %load_ext rpy2.ipython

2. 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.

### Exporting a notebook

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.

## Installing Jupyter on your own machine

### Installing Jupyter

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

### Side note about Jupyter, JupyterLab, JupyterHub, …

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.

### Exporting your notebooks with latex

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

### Jupyter extensions/plugins

#### Improving notebook readability

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

#### Interacting with GitLab and GitHub

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.