https://github.com/btmoyers/r-intro-geospatial
Introduction to R for Geospatial Data for COBALT
Science Score: 10.0%
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Low similarity (11.0%) to scientific vocabulary
Last synced: 10 months ago
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Repository
Introduction to R for Geospatial Data for COBALT
Basic Info
- Host: GitHub
- Owner: btmoyers
- License: other
- Language: R
- Default Branch: main
- Homepage: https://cobalt-casco.github.io/r-intro-geospatial/
- Size: 121 MB
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Fork of cobalt-casco/r-intro-geospatial
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· Last pushed over 2 years ago
https://github.com/btmoyers/r-intro-geospatial/blob/main/
[](https://zenodo.org/badge/latestdoi/128225991) [](https://github.com/datacarpentry/r-intro-geospatial/actions/workflows/website.yml) [](https://slack-invite.carpentries.org/) [](https://carpentries.slack.com/messages/C9ME7G5RD) # 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: - 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
Owner
- Name: Brook Moyers
- Login: btmoyers
- Kind: user
- Location: Boston, MA, USA
- Company: University of Massachusetts Boston
- Website: http://www.brookmoyers.com
- Repositories: 1
- Profile: https://github.com/btmoyers
Assistant Professor of Ecological Genomics --- I study how and why individuals vary within crop species wild relatives.