Final project

General information

See the information given on Blackboard.

R project

R Projects can either be delivered as:

Option A. Quarto reproducible workflow

• A spatial analysis reproducible workflow.
• Be sure to use the Quarto functionalities to add documentation and explanations of your workflow alongside code and output
• You can choose the topic you want to work on, including other projects you have implemented in other courses and programming languages and “translating” them to R
• You should include at least one visual element (interactive map, static map, graphs and plots, tables)

Submission on Blackboard

1. Quarto document (.qmd) file
   • Make sure to include any data not publicly accessible
   • The .qmd file should be fully reproducible
2. Report including:
   • two line description of your work
   • a link to the published Quarto document on a website (hosted on GitHub)
   • an AI use disclaimer
   • a contribution section when working in groups:
     • specify for all group members: 1) How much they contributed (every group member has to contribute at least 40% of the entire work) and 2) What they contributed (programming, documentation, …)

Option B. R package

• Automate a spatial analysis workflow. Think of a task that you often have to do an automate it by organising your R code in a package
• The package should be released on GitHub and should have at least one vignette explaining the main functionalities

Submission on Blackboard

1. Report including:
   • two line description of your work
   • a link to the GitHub repository with your package and at least one vignette
     • the package should be possible to install locally using remotes::install_github()
   • an AI use disclaimer
   • a contribution section when working in groups:
     • specify for all group members: 1) How much they contributed (every group member has to contribute at least 40% of the entire work) and 2) What they contributed (programming, documentation, …)

About the use of AI

Using AI in this course is not discouraged if it helps you understand code or discover new ways of doing a task. Think of AI as an extended version of googling your issues or a Stack Overflow entry. However, beware to always test the code that you receive as an output, and also think about the logic that the AI tool follows. I expect that you learn from this course as much as possible, and copy pasting solutions generated by a LLM without critical thinking is unfortunately not going to meet those expectations.

Warning

If you select a final project using R, please add a disclaimer section explaining if you used AI for the completion of the project and how you used it.

Ideas with an extra twist:

Quarto report ideas

• Multi-language Quarto file to compare and benchmark spatial analyses in R and Python.
  • See an example of a R and Python combined workflow here (raw .qmd file here)
• Quarto dashboard with interactive maps to visualise the results of spatial analysis or an interesting dataset.
  • See an example of a R spatial dashboard here

R package ideas

R packages should have at least one vignette explaining main functionalities

• An R package to automate a workflow normally performed in QGIS/GRASS/SAGA-GIS/etc.
  • Calculating terrain derivatives
  • Computing zonal statistics
• An R package to interface the sen2cube.at API.
  • See a first attempt here: https://github.com/Sen2Cube-at/sen2cubeR
• An R package to run the NetAScore toolbox
  • Calling docker within R with the CLI
  • https://github.com/plus-mobilitylab/netascore

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