R Intro Geospatial
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Deck.gl | 10,947 |  | 280 | 131 | 17 hours ago | 553 | September 16, 2022 | 222 | mit | JavaScript |
| WebGL2 powered visualization framework | ||||||||||
| Kepler.gl | 9,428 |  | 3 | 15 | 16 hours ago | 15 | September 16, 2021 | 552 | mit | TypeScript |
| Kepler.gl is a powerful open source geospatial analysis tool for large-scale data sets. | ||||||||||
| L7 | 3,078 |  | 2 | 42 | 4 days ago | 475 | September 20, 2022 | 101 | mit | TypeScript |
| 🌎 Large-scale WebGL-powered Geospatial Data Visualization analysis engine | ||||||||||
| Geemap | 2,733 | 2 days ago | 23 | mit | Python | |||||
| A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets. | ||||||||||
| Leafmap | 1,663 | a day ago | 4 | mit | Python | |||||
| A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment | ||||||||||
| Lets Plot | 879 |  | 1 | 15 hours ago | 44 | June 20, 2022 | 95 | mit | Kotlin | |
| An open-source plotting library for statistical data. | ||||||||||
| Leaflet Dvf | 651 | 4 | 5 years ago | 4 | February 16, 2017 | 58 | mit | JavaScript | ||
| Leaflet Data Visualization Framework | ||||||||||
| Streamlit Geospatial | 617 | 2 months ago | 2 | mit | Python | |||||
| A multi-page streamlit app for geospatial | ||||||||||
| Greppo | 332 | 4 months ago | 23 | June 09, 2022 | 33 | apache-2.0 | Python | |||
| Build & deploy geospatial applications quick and easy. | ||||||||||
| Kaggle Talkingdata Visualization | 326 | 6 years ago | 2 | JavaScript | ||||||
| Source code for blog post: Interactive Data Visualization of Geospatial Data using D3.js, DC.js, Leaflet.js and Python | ||||||||||
Intro to R for Geospatial data
An introduction to R for non-programmers using the Gapminder data.
Please see https://datacarpentry.org/r-intro-geospatial for a rendered
version of this material,
the lesson template documentation
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, and is part of the Data Carpentry Geospatial Curriculum. It introduces the R skills needed in the Introduction to Raster and Vector Geospatial Data lesson.
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 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
