This repository contains material for the course Methods for Unstructured Data (57366-01) at the University of Basel. The lab material is set up to run on Binder.

Jupyter Notebook
Jupyter Lab
Rstudio

1. Install R

   You can install R by downloading the installer from the website (on Windows), or via your system's package manager (e.g. homebrew on MacOS or apt on Ubuntu/Debian Linux). On Windows, you will also need to install the Rtools toolchain.

   Alternatively, you can install R through conda after installing the Anaconda distribution (or its smaller Miniconda version). Anaconda provides Python, R, and a repository hosting most of the libraries for both languages. Download links for different operating systems are found here, documentation and instructions here.

2. Install an R IDE or text editor with R support

   Plugins are available for most editors (VS Code, Emacs, Vim, Atom, ...). I recommend using one of the following if you are starting out.

   • RStudio
   • VS Code with R plugin

   All code is provided as simple text files (suffix .r) and as Jupyter notebooks using the R kernel (suffix .ipynb). I am using Jupyter for didactic purposes only. You do not need to use the notebooks to follow the course.

   However, if you want access to Jupyter notebooks, you need to install Anaconda (see above), or install Python and then the jupyter package using the pip package manager (instructions here). If you are using Windows and are unsure what a package manager is, I recommend installing Anaconda.

3. Install required R libraries

   Installation files are provided in the folder Setup. If you use a native R installation (e.g., from the R project website), just run the contents of the install.r file provided. On MacOS and Linux, you may need to install additional dependencies on your system (I recommend using Homebrew for this on MacOS). The error messages during the installation will typically point you towards the solution.

   If you use Anaconda, import the file environment.yml using the GUI. Alternatively, run the following commands in a terminal or the Anaconda console (on Windows) to create the environment and to activate it.

   conda env create --file Setup/environment.yml 
   conda activate course-text-analysis
   

4. Troubleshooting

   If you run into trouble during installation, please contact me. Supporting all possible edge cases on different operating systems is difficult. If all else fails, simply run the lab material in your browser using the links below.

   Jupyter Notebook
   Jupyter Lab
   Rstudio