Deploying cross-language in high impact projects

The materials here support a talk at the Spatial Data Science across Languages (SDSL) conference 2024.

See the slides here and in the dropdown menu above.

The quickest way to reproduce the code is probably with GitHub codespaces, by opening the following link.

Open in GitHub Codespaces

See the source code at github.com/robinlovelace.

See the README for more information.

Based on https://robinlovelace.github.io/reproducible-slides-repo-template/slides

To create your own “reproducible slides repo” follow the instructions in the link above, starting by cloning this repo or creating a template from this one:

gh repo clone robinlovelace/cross_language_projects
code cross_language_projects

Or go to https://github.com/Robinlovelace/reproducible-slides-repo-template and click “Use this template”.

Setup

To setup the repo the following commands were used

gh repo create # create the repo on github

# Install R dependencies:
remotes::install_github("robinlovelace/cross_language_projects")

Install Python and necessary Python packages if not already installed with:

reticulate::install_python()

reticulate::py_install("geopandas")

Using virtual environment '/Users/runner/.virtualenvs/r-reticulate' ...

+ /Users/runner/.virtualenvs/r-reticulate/bin/python -m pip install --upgrade --no-user geopandas

reticulate::py_install("matplotlib")

Using virtual environment '/Users/runner/.virtualenvs/r-reticulate' ...

+ /Users/runner/.virtualenvs/r-reticulate/bin/python -m pip install --upgrade --no-user matplotlib

Install Julia if not already installed:

curl -fsSL https://install.julialang.org | sh

Activate the Julia environment:

using Pkg
Pkg.activate(".")
Pkg.status()