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Contributing

Contributions are welcome at a later stage.

Rendering Documentation

To render the .qmd file in the data folder to the .md files in the docs folder, run the Render_Quarto.R script located in the R/data_gen directory.

Building the Package

Building in R

To build the R package, simply check in the files. If another user wants to build the package, they can simply check it out. However, using devtools::install_github("tensorchiefs/data/R/edudat") is slow since it involves cloning, see also #2

Installation of the R Package (current version w/o building)**:

-   Install the `devtools` package if you haven't already:
    ``` r
    install.packages("devtools")
    ```

-   Install the `edudat` package directly from GitHub:

    ``` r
    devtools::install_github("tensorchiefs/data/R/edudat")
    ```

Build the source in R:

data % R CMD build R/edudat

The from the github side upload the tar.gz file and do a new release (Create a new release in https://github.com/tensorchiefs/data). This can be downloaded via:

install.packages("https://github.com/tensorchiefs/data/releases/download/testrelease/edudat_0.1.tar.gz", repos = NULL, type = "source")
Local Building

To check the package, run build_and_check.R in the R directory. This also change the NAMESPACE file to include all the functions in the package which should be exposed. Hint: If you sourced the functions in development, this a good idea to restart R.

Building in Python

The project is hosted on PyPI. To build the Python package, run the following commands:

cd data/python
python setup.py sdist bdist_wheel
twine upload dist/*


#### Local installation

Local installation of the python module:

``` bash
pip install -e edudat