This repository contains resources of the Berlin Time Series Analysis Meetup.

contact: [email protected]

Important: Make sure you read the Code of Conduct.

You can find a list of references on the resources section (which we will continuously update).

As we want to make the code reproducible, here are some options to manage dependencies (choose R or Python as preferred):

• Create conda environment:

  conda env create -f environment.yml

• Activate conda environment:

  conda activate btsa

• Run Jupyter Lab:

  jupyter lab

  ---

• Build Docker image:

  docker build -t docker-btsa .

• Run container

  docker run -p 10000:8888 docker-btsa

Visiting https://<hostname>:10000/?token=<token> in a browser loads the Jupyter Notebook dashboard page, where hostname is the name of the computer running docker and token is the secret token printed in the console.

You can find the python folder under the work directory.

• From the root (btsa) directory, with R installed, run the following command: Rscript renv/activate.R.

This command will activate the environment and the renv package manager. From there you have two options depending on how you like to edit your projects.

Note: This configuration has been tested on R version 3.6.3.

From the root directory type R and hit enter. Then type renv::restore() and respond with 'y' when prompted to install the packages. Your environment is now set up. You can continue writing commands or quit the console, your folder is now set up with the required packages.

Open an Rstudio session. Go to RStudio > 'Open Project in New Session'. Navigate to the btsa folder and click open. In the console, type renv::restore() to install the necessary packages. Your RStudio session is now configured.

We highly encourage to contribute to this repository!

• If you find and error or have a specific request you can create an Issue.
• Add an analysis/experiment using a Pull Request.