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DOI 01 Build and Deploy Site Create a Slack Account with us Slack Status

R for Raster and Vector Data

This is a fork of the canonical lesson for geo4Lib

Summer, 2023

Possibilities

At Geo4lib Camp on July 24, the group will have 90 minutes to look at this geospatial Data Carpentry lesson. In the spirit of Un-Conference, we are prepared to take this discussion in 1 of these 4 directions:

  • Re-outline an introduction to geospatial data from scratch. This lesson is a giant monster-- more than 11 hours! Can we outline a better way to provide an introduction to scripting-based GIS? This would be a largely white-board and sticky-note session.
  • Closely read 3-4 episodes and put together detailed issues and start forks in small groups
    • Brief overview of the Carpentries Workbench / Issues
    • Small groups create issues and start writing markdown in forks of the lesson--with pull requests to happen after the conference.
    • Report back on issues
  • An open discussion on the learning objectives for this lesson. Each episode has stated objectives at the top. Are they accurate? Are the goals achievable based on the content?
  • A brief hands-on overview of using the Carpentries WorkBench demonstrating the Carpentries teaching method.

Contributing to lesson development

  • The lesson files to be edited are in the _episodes folder. This repository uses the main branch for development.
  • You can visualize the changes locally with the sandpaper R package by executing either the sandpaper::serve() or sandpaper::build_lesson() commands. In the former case, the site will be rendered at http://localhost:4321
  • Each time you push a change to GitHub, Github Actions rebuilds the lesson, and when it's successful (look for the green badge at the top of the README file), it publishes the result at http://www.datacarpentry.org/r-raster-vector-geospatial/
  • Note: any manual commit to gh-pages will be erased and lost during the automated build and deploy cycle operated by Github Actions.

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Introduction to Geospatial Raster and Vector Data with R

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