About this resource

    Type of resources
    Module
    Education level(s)
    Graduate / Professional
    Primary user(s)
    student, teacher
    Subject area(s)
    Computer Science, Information Science, Measurement and Data
    Language(s)
    English
    Cluster
    Transparency and Reproducibility in Computation and Analysis, FAIR Data and Materials
    Last updated
    8 November 2026
    Module

    Introduction to R for Geospatial Data

    By Anne Fouilloux, butterflyskip, Chris Prener, Claudia Engel, David Mawdsley, Erin Becker, François Michonneau, Ido Bar, Jeffrey Oliver, Juan Fung, Katrin Leinweber, Kevin Weitemier, Kok Ben Toh, Lachlan Deer, Marieke Frassl, Matt Clark, Miles McBain, Naupaka Zimmerman, Paula Andrea Martinez, Preethy Nair, Raniere Silva, Rayna Harris, Richard McCosh, Vicken Hillis

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    Level Graduate / Professional · For student, teacher · Language English

    Abstract

    The goal of this lesson is to provide an introduction to R for learners working with geospatial data. It is intended as a pre-requisite for the R for Raster and Vector Data lesson for learners who have no prior experience using R. This lesson can be taught in approximately 4 hours and covers the following topics: Working with R in the RStudio GUI Project management and file organization Importing data into R Introduction to R’s core data types and data structures Manipulation of data frames (tabular data) in R Introduction to visualization Writing data to a file The R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data.

    Keywords Analysis · Data · Education · Geospatial · Open Scholarship Tools and Technologies · R · Reproducibility · Research Data Management Tools · Researchers