Berlin Time Series Analysis (BTSA) Repository
This repository contains resources of the Berlin Time Series Analysis Meetup. We encourage everyone to contribute to this repository! Here you can find the schedule and details of the meetup.
contact: [email protected]
You can also visit us on Medium.
Code of Conduct
Important: Make sure you read the Code of Conduct.
Resources
You can find a list of references on the resources section (which we will continuously update).
Environment
As we want to make the code reproducible, here are some options to manage dependencies (choose R or Python as preferred):
Conda Environment (Python)
-
Create conda environment (you can also update the environment from as described here):
conda env create -f environment.yml
-
Activate conda environment:
conda activate btsa
-
Run Jupyter Lab:
jupyter lab
jupyter-docker-stacks)
Docker (see-
Build Docker image:
docker build -t docker-btsa .
-
Run container
docker run -p 10000:8888 docker-btsa
Visiting
http://<hostname>:10000/?token=<token>
in a browser loads the Jupyter Notebook dashboard page, wherehostname
is the name of the computer running docker andtoken
is the secret token printed in the console.
You can find the python
folder under the work
directory.
R Environment
- 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.
Option 1. Command Line:
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.
RStudio
Option 2: FromOpen 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.