Awesome

Why rgeeExtra is needed? 🤔

The goal of rgeeExtra is to enhance the user experience ⚡ of Google Earth Engine (GEE) 🌐 for R enthusiasts by simplifying its syntax. While GEE's primary language is JavaScript, which diverges from R's structure, rgeeExtra serves as a bridge 🌉, offering R users a suite of tools and functions to navigate GEE's complexity with ease. This extension elevates R interaction with GEE through high-level 📈 abstractions and tailored functions, all adapted from the JavaScript API.

import ee
ee.Initialize()
db = 'CGIAR/SRTM90_V4'
image = ee.Image(db)
image.bandNames().getInfo()
#> [u'elevation']

library(rgee)
ee_Initialize()
db <- 'CGIAR/SRTM90_V4'
image <- ee$Image(db)
image$bandNames()$getInfo()
#> [1] "elevation"

library(rgee)
library(rgeeExtra)

ee_Initialize()
extra_Initialize()

image <- ee$Image$Dataset$CGIAR_SRTM90_V4
names(image)
#> [1] "elevation"

Installation 🚀

You can install rgeeExtra from GitHub with:

remotes::install_github("r-earthengine/rgeeExtra")

For additional details on connecting GEE and R, refer to the documentation provided here. To set up an account, click here.

How does it work?️ 🛠

The rgeeExtra extends the following Earth Engine classes:

• ee$Geometry
• ee$Feature
• ee$FeatureCollection
• ee$Image
• ee$ImageCollection

rgeeExtra develops and maintains new methods and constructors that extend the most popular GEE classes (e.g., ee$Feature$Extra\_...). All third-party methods implemented by rgeeExtra start with Extra\_. To learn more about all the functionalities that rgeeExtra offers, please refer to the article Features for additional information.

Example 💡

Look at this simple example to estimate the NDVI from a Landsat-8 Surface Reflectance image.

With rgee:

# Load 'rgee' and initialize GEE
library(rgee)
ee_Initialize()

# Compute squared NDVI from Landsat 8 image
img <- ee$Image("LANDSAT/LC08/C02/T1_L2/LC08_007067_20140822")$
  normalizedDifference(c("SR_B5", "SR_B4"))$
  pow(2)

# Visualize squared NDVI on map
Map$centerObject(img)
Map$addLayer(
  eeObject = img, 
  visParams = list(
    min = 0, 
    max = 0.2, 
    palette = c("brown", "yellow", "green")
    ),
  name = "Squared NDVI")

With rgeeExtra:

# Load 'rgee' and initialize GEE
library(rgee)
library(rgeeExtra)

ee_Initialize()
extra_Initialize()

# Compute squared NDVI from Landsat 8 image
img <- ee$Image("LANDSAT/LC08/C02/T1_L2/LC08_007067_20140822")
ndvi <- ((img[["SR_B5"]] - img[["SR_B4"]]) / (img[["SR_B5"]] + img[["SR_B4"]])) ** 2
names(ndvi) <- "NDVI"

# Visualize squared NDVI on map
Map$centerObject(ndvi)
Map$addLayer(
  eeObject = ndvi, 
  visParams = list(
    min = 0, 
    max = 0.2, 
    palette = c("brown", "yellow", "green")
    ),
  name = "Squared NDVI"
  )

Code of Conduct 📜

Please note that the rgeeExtra project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Contributing Guide 🤝

👍 Thanks for taking the time to contribute! 🎉👍 Please review our Contributing Guide.

Share the love ❤️

Think rgeeExtra is useful? Let others discover it, by telling them in person via Twitter or a blog post.

Using rgeeExtra for a paper you are writing? Consider citing it

citation("rgeeExtra")
To cite rgee in publications use:
  
  C Aybar, Q Wu, L Bautista, R Yali and A Barja (2020) rgee: An R
  package for interacting with Google Earth Engine Journal of Open
  Source Software URL https://github.com/r-spatial/rgee/.

A BibTeX entry for LaTeX users is

@Article{,
  title = {rgee: An R package for interacting with Google Earth Engine},
  author = {Cesar Aybar and Quisheng Wu and Lesly Bautista and Roy Yali and Antony Barja},
  journal = {Journal of Open Source Software},
  year = {2020},
}

Credits 👏

We would like to mention the following third-party R/Python packages for contributing indirectly to the improvement of rgeeExtra:

• eemont - David Montero Loaiza