https://github.com/datacarpentry/r-intro-geospatial

Introduction to R for Geospatial Data
https://github.com/datacarpentry/r-intro-geospatial

carpentries data-carpentry data-visualisation data-visualization data-wrangling english geospatial geospatial-data lesson r stable

Last synced: about 2 months ago
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Introduction to R for Geospatial Data

• Host: GitHub
• URL: https://github.com/datacarpentry/r-intro-geospatial
• Owner: datacarpentry
• License: other
• Created: 2018-04-05T15:28:24.000Z (almost 7 years ago)
• Default Branch: main
• Last Pushed: 2024-11-19T00:31:59.000Z (about 2 months ago)
• Last Synced: 2024-11-19T01:31:37.299Z (about 2 months ago)
• Topics: carpentries, data-carpentry, data-visualisation, data-visualization, data-wrangling, english, geospatial, geospatial-data, lesson, r, stable
• Language: R
• Homepage: https://datacarpentry.org/r-intro-geospatial/
• Size: 127 MB
• Stars: 47
• Watchers: 13
• Forks: 72
• Open Issues: 20
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION

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# Intro to R for Geospatial data

An introduction to R for non-programmers using the [Gapminder][gapminder] data.

Please see [https://datacarpentry.org/r-intro-geospatial](https://datacarpentry.org/r-intro-geospatial) for a rendered

version of this material,

[the lesson template documentation][lesson-example]

for instructions on formatting, building, and submitting material,

or run `make` in this directory for a list of helpful commands.

The goal of this lesson is to revise best practices for using R in data

analysis. This lesson is a modification of the [Software Carpentry: Programming with R](https://swcarpentry.github.io/r-novice-gapminder), and is part of the [Data Carpentry Geospatial Curriculum](https://datacarpentry.org/geospatial-workshop/). It introduces the R skills needed in the [Introduction to Raster and Vector Geospatial Data lesson](https://datacarpentry.org/r-raster-vector-geospatial).

R is commonly used in many scientific disciplines for statistical analysis and

its array of third-party packages. These materials are designed to provide

attendees with a concise introduction in the fundamentals of R, and to introdue

best practices for scientific computing: breaking down analyses into modular

units, task automation, and encapsulation, before getting started with working

with geospatial data.

Note that this workshop focuses on the fundamentals of the programming

language R, and not on statistical analysis.

The lesson contains material than can be taught in about 4 hours. The

[instructor notes

page](https://datacarpentry.org/r-intro-geospatial/guide/index.html) has some

suggested lesson plans suitable for a one or half day workshop.

#### Maintainers:

- Johanna Bayer

- Mike Mahoney

- Alber Sánchez

#### Previous maintainers:

- Leah Wasser

- Jemma Stachelek

- Tyson Swetnam

- Lauren O'Brien

- Janani Selvaraj

- Lachlan Deer

- Chris Prener

- Juan Fung

[gapminder]: https://www.gapminder.org/

[lesson-example]: https://carpentries.github.io/lesson-example