[{"_1":2,"_152":-5,"_153":-5},"loaderData",{"_3":4,"_13":14,"_20":21,"_131":132},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","routes/_layout.r.$namespace.$reponame",{"_22":23,"_24":-5,"_25":11,"_26":11,"_27":28,"_29":30,"_31":-5,"_32":11,"_33":34,"_83":-5,"_84":85,"_124":-5,"_125":11,"_126":17,"_127":11,"_128":-5,"_129":130},"canonicalUrl","https://hub.docker.com/r/csaybar/rgee/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","csaybar","reponame","rgee","cloudBuildSource","isAutobuildsAvailable","repo",{"_35":36,"_37":38,"_40":41,"_42":43,"_44":11,"_45":46,"_47":48,"_49":50,"_52":30,"_27":28,"_53":54,"_55":39,"_56":57,"_58":59,"_60":61,"_62":-5,"_63":57,"_64":65,"_66":11,"_67":28,"_68":69,"_74":11,"_75":76,"_80":28,"_81":82},"categories",[],"content_types",[39],"image","date_registered","2019-09-15T15:46:37.607481Z","description","Official Docker Image for rgee","is_private","last_updated","2021-06-17T12:39:49.894411Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[51],"application/vnd.docker.container.image.v1+json","name","pull_count",1873,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","[![R build status](https://github.com/csaybar/rgee/workflows/R-CMD-check/badge.svg)](https://github.com/csaybar/rgee/actions)\n[![Project Status: Active – The project has reached a stable, usable\nstate and is being actively\ndeveloped.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n[![codecov](https://codecov.io/gh/csaybar/rgee/branch/master/graph/badge.svg)](https://codecov.io/gh/csaybar/rgee)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)\n[![CRAN\nstatus](https://www.r-pkg.org/badges/version/rgee)](https://cran.r-project.org/package=rgee)\n\n\n# Google Earth Engine for R\n

\n\n`rgee` is a binding package for calling [Google Earth Engine\nAPI](https://developers.google.com/earth-engine/) from within R.\nAdditionally, several functions have been implemented to make simple the connection with the R spatial ecosystem. The current version of rgee has been built considering the [earthengine-api 0.1.217](https://pypi.org/project/earthengine-api/0.1.217/). **Note that access to Google Earth Engine is only available to [registered users](https://earthengine.google.com/)**.\n\n### More than 250+ examples using Google Earth Engine with R are available [here](https://csaybar.github.io/rgee-examples/README.html)\n\n

\n\n\n\n## What is Google Earth Engine?\n\n[Google Earth Engine](https://earthengine.google.com/) is a cloud-based platform that allows users to have an easy access to a petabyte-scale archive of remote sensing data and run geospatial analysis on Google’s infrastructure. Currently, Google offers support only for Python and JavaScript. `rgee` will fill the gap **starting to provide support to R\\!**. Below you will find the comparison between the syntax of `rgee` and the two Google-supported client libraries.\n\n**Earth Engine Javascript API**:\n\n``` javascript\nimage = ee.Image('CGIAR/SRTM90_V4')\nprint(image.bandNames())\n#> 'elevation'\n```\n\n**Earth Engine Python API**:\n\n``` python\nimport ee\nee.Initialize()\nimage = ee.Image('CGIAR/SRTM90_V4')\nimage.bandNames().getInfo()\n#> [u'elevation']\n```\n\n**rgee:**\n\n``` r\nlibrary(rgee)\nee_Initialize()\n\n# Python Style\nimage <- ee$Image('CGIAR/SRTM90_V4')\nimage$bandNames()$getInfo()\n#> [1] \"elevation\"\n\n# Or use Pipes instead!!\nimage <- ee$Image('CGIAR/SRTM90_V4') %>%\n ee$Image$bandNames() %>% \n ee$List$getInfo()\n#> [1] \"elevation\"\n```\n\n**Quite similar, isn’t it?**. However, there are additional smaller changes that you must consider when you use Google Earth Engine with R. Please check the [consideration section](https://csaybar.github.io/rgee/articles/considerations.html) before start coding\\!\n\n## Installation\n\nInstall the `rgee` package from GitHub is quite simple, you just have to run in your R console as follows:\n\n``` r\nremotes::install_github(\"csaybar/rgee\")\n```\n\n**`rgee` depends on [sf](https://github.com/r-spatial/sf)**. Therefore, it is necessary to install its external libraries, follow the installation steps specified [here](https://github.com/r-spatial/sf#installing).\n\n#### Docker image\n \n docker pull csaybar/rgee\n docker run -d -p 8787:8787 -e USER=rgee -e PASSWORD=rgee --name rgee-dev csaybar/rgee\n\nAfter that, in your preferred browser, run:\n\n 127.0.0.1:8787\n\n## Requirements\n\nPrior to using `rgee` you will need to install a **Python version higher than 3.5** in your system. `rgee` counts with a installation module, use it to quickly set up the external dependencies of `rgee`:\n\n```r\nlibrary(rgee)\n\n# 1. Initialize rgee with ee_Initialize(). If there is no any Python environment, miniconda\n# will be installed by default.\nee_Initialize()\n\n# 2. Create a Python environment, e.g. ee.\nee_create_pyenv(python_env = \"ee\")\n\n# 3. Find all Python environments in the system.\nee_discover_pyenvs()\n\n# 4. Set a Python environment (e.g. ee) and restart R to see changes. e.g\nee_set_pyenv(\n python_path = '/home/MY_USER_HERE/.virtualenvs/ee/bin/python',\n python_env = 'ee'\n)\n\n# 5. Install Python package dependencies\nee_install_python_packages()\n\n# 6. Initialize rgee again!\nee_Initialize()\n```\n\nAdditionally, use the functions below, as many times as you want, for checking user info, check sanity of credentials and Python packages, and remove credentials.\n\n```r\nee_check() # Check non-R dependencies\nee_user_info() # Display credentials information\nee_users() # Display credentials information of all users\nee_remove_credentials() # Remove credentials of a specific user\nee_clean_pyenv() # Remove reticulate system variables\n```\n\nAlso, consider checking the [setup section](https://csaybar.github.io/rgee/articles/setup.html) for major information to customizing Python installation.\n\n\n## Package Conventions\n\n - All `rgee` functions have the prefix ee\\_. Auto-completion is\n your friend :).\n - Full access to the Earth Engine API with the prefix\n [**ee$…:**](https://developers.google.com/earth-engine/).\n - Authenticate and Initialize the Earth Engine R API with\n [**ee\\_Initialize:**](https://csaybar.github.io/rgee/reference/ee_Initialize.html), you just will need to do it once by session!.\n - `rgee` is “pipe-friendly”, we re-exports %\\>%, but `rgee` does\n not require its use.\n - Wrap your R function using `ee_pyfunc` before passing them to the\n Earth Engine Web REST API. This is not compulsory, but it will help\n reduce possible [bugs](https://csaybar.github.io/rgee/articles/considerations.html#the-map-message-error) :bug:.\n\n## Quick Demo\n\n### 1. Compute the trend of night-time lights ([JS version](https://github.com/google/earthengine-api))\n\nAuthenticate and Initialize the Earth Engine R API.\n\n``` r\nlibrary(rgee)\nee_Initialize()\n#ee_reattach() # reattach ee as a reserve word\n```\n\nAdds a band containing image date as years since 1991.\n\n``` r\ncreateTimeBand <-function(img) {\n year <- ee$Date(img$get('system:time_start'))$get('year')$subtract(1991L)\n ee$Image(year)$byte()$addBands(img)\n}\n```\n\nMap the time band creation helper over the [night-time lights collection](https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS).\n\n``` r\ncollection = ee$\n ImageCollection('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS')$\n select('stable_lights')$\n map(createTimeBand)\n```\n\nCompute a linear fit over the series of values at each pixel, visualizing the y-intercept in green, and positive/negative slopes as red/blue.\n\n``` r\ncol_reduce <- collection$reduce(ee$Reducer$linearFit())\ncol_reduce <- col_reduce$addBands(\n col_reduce$select('scale'))\nee_print(col_reduce)\n```\n\nCreate a interactive visualization\\! \n\n``` r\nMap$setCenter(9.08203, 47.39835, 3)\nMap$addLayer(\n eeObject = col_reduce,\n visParams = list(\n bands = c(\"scale\", \"offset\", \"scale\"),\n min = 0,\n max = c(0.18, 20, -0.18)\n ),\n name = \"stable lights trend\"\n)\n\n```\n\n![rgee\\_01](https://user-images.githubusercontent.com/16768318/71565699-51e4a500-2aa9-11ea-83c3-9e1d32c82ba6.png)\n\n### 2. Extract precipitation values\n\nLoad `sf` and authenticate and initialize the Earth Engine R API.\n\n``` r\nlibrary(rgee)\nlibrary(sf)\nee_Initialize()\n# ee_reattach() # reattach ee as a reserve word\n```\n\nRead the `nc` shapefile.\n\n``` r\nnc <- st_read(system.file(\"shape/nc.shp\", package = \"sf\"), quiet = TRUE) %>%\n st_transform(4326) # Transform coordinates\n```\n\nMap each image from 2001 to extract the monthly precipitation (Pr) from the [Terraclimate\ndataset](https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE)\n\n``` r\nterraclimate <- ee$ImageCollection(\"IDAHO_EPSCOR/TERRACLIMATE\")$\n filterDate(\"2000-01-01\", \"2001-01-01\")$\n map(ee_pyfunc(function(x) x$select(\"pr\")))\n```\n\nExtract monthly precipitation values from the Terraclimate ImageCollection through `ee_extract`. `ee_extract` works\nsimilar to `raster::extract` you just need to define: the\nImageCollection object (x), the geometry (y), and a function to\nsummarize the values (fun).\n\n``` r\nee_nc_rain <- ee_extract(x = terraclimate, y = nc, fun = ee$Reducer$max(), id = \"FIPS\")\ncolnames(ee_nc_rain) <- c(\"FIPS\", month.abb)\nhead(ee_nc_rain)\n```\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\n\nFIPS\n\n	\n\nJan\n\n	\n\nFeb\n\n	\n\nMar\n\n	\n\nApr\n\n	\n\nMay\n\n	\n\nJun\n\n	\n\nJul\n\n	\n\nAug\n\n	\n\nSep\n\n	\n\nOct\n\n	\n\nNov\n\n	\n\nDec\n\n
\n\n37009\n\n	\n\n93\n\n	\n\n68\n\n	\n\n106\n\n	\n\n168\n\n	\n\n73\n\n	\n\n97\n\n	\n\n117\n\n	\n\n107\n\n	\n\n166\n\n	\n\n4\n\n	\n\n89\n\n	\n\n56\n\n
\n\n37005\n\n	\n\n85\n\n	\n\n64\n\n	\n\n99\n\n	\n\n165\n\n	\n\n66\n\n	\n\n96\n\n	\n\n107\n\n	\n\n106\n\n	\n\n163\n\n	\n\n4\n\n	\n\n83\n\n	\n\n53\n\n
\n\n37171\n\n	\n\n95\n\n	\n\n54\n\n	\n\n87\n\n	\n\n143\n\n	\n\n59\n\n	\n\n114\n\n	\n\n101\n\n	\n\n119\n\n	\n\n162\n\n	\n\n2\n\n	\n\n67\n\n	\n\n48\n\n
\n\n37053\n\n	\n\n122\n\n	\n\n50\n\n	\n\n67\n\n	\n\n118\n\n	\n\n135\n\n	\n\n183\n\n	\n\n142\n\n	\n\n213\n\n	\n\n174\n\n	\n\n7\n\n	\n\n72\n\n	\n\n49\n\n
\n\n37131\n\n	\n\n115\n\n	\n\n49\n\n	\n\n63\n\n	\n\n108\n\n	\n\n115\n\n	\n\n163\n\n	\n\n152\n\n	\n\n195\n\n	\n\n132\n\n	\n\n0\n\n	\n\n57\n\n	\n\n44\n\n
\n\n37091\n\n	\n\n122\n\n	\n\n43\n\n	\n\n64\n\n	\n\n109\n\n	\n\n121\n\n	\n\n169\n\n	\n\n146\n\n	\n\n200\n\n	\n\n143\n\n	\n\n1\n\n	\n\n57\n\n	\n\n43\n\n

\n\nCreate a simple plot\\!\n\n``` r\nee_nc_rain <- merge(nc, ee_nc_rain, by = \"FIPS\")\nplot(ee_nc_rain[\"Jan\"], main = \"2001 Jan Precipitation - Terraclimate\", reset = FALSE)\n```\n\n

\n \n

\n \n### 3. Create an NDVI-animation ([JS version](https://developers.google.com/earth-engine/tutorials/community/modis-ndvi-time-series-animation))\n\n\nLoad sf and authenticate and initialize the Earth Engine R API.\n\n``` r\nlibrary(rgee)\nlibrary(sf)\nee_Initialize()\n# ee_reattach() # reattach ee as a reserve word\n```\n\nDefine the regional bounds of animation frames and a mask to clip the NDVI data by.\n\n``` r\nmask <- system.file(\"shp/arequipa.shp\", package = \"rgee\") %>% \n st_read(quiet = TRUE) %>% \n sf_as_ee()\nregion <- mask$geometry()$bounds()\n```\n\nRetrieve the MODIS Terra Vegetation Indices 16-Day Global 1km dataset as an `ee.ImageCollection`\nand select the NDVI band.\n\n``` r\ncol <- ee$ImageCollection('MODIS/006/MOD13A2')$select('NDVI')\n```\n\nGroup images by composite date\n\n``` r\ncol <- col$map(function(img) {\n doy <- ee$Date(img$get('system:time_start'))$getRelative('day', 'year')\n img$set('doy', doy)\n})\ndistinctDOY <- col$filterDate('2013-01-01', '2014-01-01')\n```\n\nDefine a filter that identifies which images from the complete collection match the DOY\nfrom the distinct DOY collection.\n\n``` r\nfilter <- ee$Filter$equals(leftField = 'doy', rightField = 'doy');\n```\n\nDefine and Apply the join; convert the resulting FeatureCollection to an ImageCollection.\n\n``` r\njoin <- ee$Join$saveAll('doy_matches')\njoinCol <- ee$ImageCollection(join$apply(distinctDOY, col, filter))\n```\n\nApply median reduction among matching DOY collections.\n\n``` r\ncomp <- joinCol$map(function(img) {\n doyCol = ee$ImageCollection$fromImages(\n img$get('doy_matches')\n )\n doyCol$reduce(ee$Reducer$median())\n})\n```\n\nDefine RGB visualization parameters.\n\n``` r\nvisParams = list(\n min = 0.0,\n max = 9000.0,\n bands = \"NDVI_median\",\n palette = c(\n 'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',\n '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',\n '012E01', '011D01', '011301'\n )\n)\n```\n\nCreate RGB visualization images for use as animation frames.\n\n```r\nrgbVis <- comp$map(function(img) {\n do.call(img$visualize, visParams) %>% \n ee$Image$clip(mask)\n})\n```\n\nDefine GIF visualization parameters.\n\n```r\ngifParams <- list(\n region = region,\n dimensions = 600,\n crs = 'EPSG:3857',\n framesPerSecond = 10\n)\n```\n\nRender the GIF animation in the console.\n\n```r\nprint(rgbVis$getVideoThumbURL(gifParams))\nbrowseURL(rgbVis$getVideoThumbURL(gifParams))\n```\n\n

\n \n

\n\n## How does rgee work?\n\n`rgee` is **not** a native Earth Engine API like the Javascript or Python client, to do this would be extremely hard, especially considering that the API is in [active development](https://github.com/google/earthengine-api). So, how is it possible to run Earth Engine using R? the answer is [reticulate](https://rstudio.github.io/reticulate/). `reticulate` is an R package designed to allow a seamless interoperability between R and Python. When an Earth Engine **request** is created in R, `reticulate` will transform this piece of code to Python. Once the Python code is obtained, the `Earth Engine Python API` transform the request to a `JSON` format. Finally, the query (in JSON) is received by the Google Earth Engine Platform thanks to a Web REST API. The **response** will follow the same path. If you are searching a way to interact with the Earth Engine Asset (EEA), `rgee` offers also functions to batch [upload](https://csaybar.github.io/rgee/reference/sf_as_ee.html)([download](https://csaybar.github.io/rgee/reference/ee_as_sf.html)) spatial objects. Additionally, you could easily manage EEA through the [ee\\_manage\\_\\*](https://csaybar.github.io/rgee/reference/ee_manage-tools.html) interface.\n\n

\n\n## Code of Conduct\n\nPlease note that the `rgee` project is released with a [Contributor Code\nof Conduct](CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.\n\n## Contributing Guide\n\n👍🎉 First off, thanks for taking the time to contribute\\! 🎉👍 Please review our [Contributing Guide](CONTRIBUTING.md).\n\n## Share the love ❤️\n\nThink **rgee** is useful? Let others discover it, by telling them in\nper on, via Twitter or a blog post.\n\nUsing **rgee** for a paper you are writing? Consider citing it\n\n``` r\ncitation(\"rgee\")\n#> \n#> WORKING ON THIS :)\n``` \n\n## Credits :bow:\n\nMost of the `rgee` functionalities were based in the following third-party R/Python packages:\n\n - **[gee\\_asset\\_manager - Lukasz Tracewski](https://github.com/tracek/gee_asset_manager)** \n - **[geeup - Samapriya Roy](https://github.com/samapriya/geeup)**\n - **[geeadd - Samapriya Roy](https://github.com/samapriya/gee_asset_manager_addon)**\n - **[cartoee - Kel Markert](https://github.com/KMarkert/cartoee)**\n - **[geetools - Rodrigo E. Principe](https://github.com/gee-community/gee_tools)**\n - **[landsat-extract-gee - Loïc Dutrieux](https://github.com/loicdtx/landsat-extract-gee)**\n - **[earthEngineGrabR - JesJehle](https://github.com/JesJehle/earthEngineGrabR)**\n - **[sf - Edzer Pebesma](https://github.com/r-spatial/sf)**\n - **[stars - Edzer Pebesma](https://github.com/r-spatial/stars)**\n - **[gdalcubes - Marius Appel](https://github.com/appelmar/gdalcubes)**\n\n#### Readme template obtained from [dbparser](https://github.com/Dainanahan/dbparser)\n","has_starred","hub_user","immutable_tags_settings",{"_70":11,"_71":72},"enabled","rules",[73],".*","is_automated","permissions",{"_77":17,"_78":11,"_79":11},"read","write","admin","user","storage_size",11835692715,"orgData","recentTags",[86],{"_87":39,"_88":89,"_90":91,"_92":93,"_94":95,"_45":113,"_114":89,"_115":28,"_116":51,"_52":117,"_118":119,"_120":104,"_121":113,"_122":61,"_123":17},"content_type","creator",7634022,"full_size",1749127865,"id",83381446,"images",[96],{"_97":98,"_99":100,"_101":102,"_103":104,"_105":106,"_107":108,"_109":102,"_110":-5,"_111":91,"_58":61,"_112":-5},"architecture","amd64","digest","sha256:039f352dd19e490ae429fb73a7d66f4e11c55c3b17c53d8209b36f27a0d2a336","features","","last_pulled","2026-09-15T10:52:28.084848484Z","last_pushed","2021-06-17T12:39:50Z","os","linux","os_features","os_version","size","variant","2021-06-17T12:39:49.592034Z","last_updater","last_updater_username","media_type","latest","repository",7702263,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_33":133,"_140":141,"_129":150,"_151":11},{"_35":134,"_37":135,"_40":41,"_42":43,"_44":11,"_45":46,"_47":48,"_49":136,"_52":30,"_27":28,"_53":54,"_55":39,"_56":57,"_58":59,"_60":61,"_62":-5,"_63":57,"_64":65,"_66":11,"_67":28,"_68":137,"_74":11,"_75":139,"_80":28,"_81":82},[],[39],[51],{"_70":11,"_71":138},[73],{"_77":17,"_78":11,"_79":11},"tags",{"_142":59,"_143":-5,"_144":-5,"_145":146},"count","next","previous","results",[147],{"_87":39,"_88":89,"_90":91,"_92":93,"_94":148,"_45":113,"_114":89,"_115":28,"_116":51,"_52":117,"_118":119,"_120":104,"_121":113,"_122":61,"_123":17},[149],{"_97":98,"_99":100,"_101":102,"_103":104,"_105":106,"_107":108,"_109":102,"_110":-5,"_111":91,"_58":61,"_112":-5},[],"canDeleteTags","actionData","errors"]

csaybar/rgee

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