rgee: R Bindings for Calling the 'Earth Engine' API

Documented in ee_extract

#' Extract values from EE Images or ImageCollections objects
#'
#' Extract values from an \code{ee$Image} based on the locations of a geometry object.
#' Users can utilize  \code{ee$Geometry$*}, \code{ee$Feature}, \code{ee$FeatureCollection},
#' sf or sfc object for spatial filter.
#' This function emulates the functionality of the existing \link[raster]{extract} method.
#'
#' @param x ee$Image.
#' @param y ee$Geometry$*, ee$Feature, ee$FeatureCollection, sfc or sf objects.
#' @param fun ee$Reducer object. Function to summarize the values. The function
#' must take a single numeric value as an argument and return a single value.
#' See details.
#' @param scale A nominal scale given in meters of the Image projection to work in.
#' By default 1000.
#' @param sf Logical. Should the function return an sf object?
#' @param lazy Logical. If TRUE, a \code{\link[future:sequential]{
#' future::sequential}} object is created to evaluate the task in the future.
#' Ignore if \code{via} is set as "getInfo". See details.
#' @param via Character. Method for exporting the image.
#' Three methods are available: "getInfo", "drive", "gcs".
#' @param container Character. Name of the folder ('drive') or bucket ('gcs')
#' to export the image into (ignore if \code{via} is not defined as "drive" or
#' "gcs").
#' @param quiet Logical. Suppress info message.
#' @param ... ee$Image$reduceRegions additional parameters. See
#' \code{ee_help(ee$Image$reduceRegions)} for more details.
#'
#' @return A data.frame or an sf object depending on the sf argument.
#' Column names are extracted from band names. Use \code{ee$Image$rename} to
#' rename the bands of an \code{ee$Image}. See \code{ee_help(ee$Image$rename)}.
#'
#' @details
#' The reducer functions that return one value are:
#' \itemize{
#' \item  \strong{allNonZero}: Returns a Reducer that returns 1 if all of its
#' inputs are non-zero, 0 otherwise. \cr
#' \item \strong{anyNonZero}: Returns a Reducer that returns 1 if any of its
#' inputs are non-zero, 0 otherwise. \cr
#' \item \strong{bitwiseAnd}: Returns a Reducer that computes the bitwise-and
#' summation of its inputs.
#' \item \strong{bitwiseOr}: Returns a Reducer that computes the bitwise-or
#' summation of its inputs.
#' \item \strong{count}: Returns a Reducer that computes the number of
#' non-null inputs.
#' \item \strong{first}: Returns a Reducer that returns the first of its inputs.
#' \item \strong{firstNonNull}: Returns a Reducer that returns the first of
#' its non-null inputs.
#' \item \strong{kurtosis}: Returns a Reducer that Computes the kurtosis of
#' its inputs.
#' \item \strong{last}: Returns a Reducer that returns the last of its inputs.
#' \item \strong{lastNonNull}: Returns a Reducer that returns the last of its
#' non-null inputs.
#' \item \strong{max}: Creates a reducer that outputs the maximum value of
#' its (first) input. If numInputs is greater than one, also outputs the
#' corresponding values of the additional inputs.
#' \item \strong{mean}: Returns a Reducer that computes the (weighted)
#' arithmetic mean of its inputs.
#' \item \strong{median}: Create a reducer that will compute the median of
#' the inputs. For small numbers of inputs (up to maxRaw) the median will be
#' computed directly; for larger numbers of inputs the median will be derived
#' from a histogram.
#' \item \strong{min}: Creates a reducer that outputs the minimum value
#' of its (first) input.  If numInputs is greater than one, also outputs
#' additional inputs.
#' \item \strong{mode}: Create a reducer that will compute the mode of the
#' inputs.  For small numbers of inputs (up to maxRaw) the mode will be
#' computed directly; for larger numbers of inputs the mode will be derived
#' from a histogram.
#' \item \strong{product}: Returns a Reducer that computes the product of
#' its inputs.
#' \item \strong{sampleStdDev}: Returns a Reducer that computes the sample
#' standard deviation of its inputs.
#' \item \strong{sampleVariance}: Returns a Reducer that computes the sample
#' variance of its inputs.
#' \item \strong{stdDev}: Returns a Reducer that computes the standard
#' deviation of its inputs.
#' \item \strong{sum}: Returns a Reducer that computes the (weighted) sum
#' of its inputs.
#' \item \strong{variance}: Returns a Reducer that computes the variance
#' of its inputs.
#' }
#' @examples
#' \dontrun{
#' library(rgee)
#' library(sf)
#'
#' ee_Initialize(gcs = TRUE, drive = TRUE)
#'
#' # Define a Image or ImageCollection: Terraclimate
#' terraclimate <- ee$ImageCollection("IDAHO_EPSCOR/TERRACLIMATE") %>%
#'  ee$ImageCollection$filterDate("2001-01-01", "2002-01-01") %>%
#' ee$ImageCollection$map(
#'    function(x) {
#'      date <- ee$Date(x$get("system:time_start"))$format('YYYY_MM_dd')
#'      name <- ee$String$cat("Terraclimate_pp_", date)
#'      x$select("pr")$rename(name)
#'    }
#'  )
#'
#' # Define a geometry
#' nc <- st_read(
#'  dsn = system.file("shape/nc.shp", package = "sf"),
#'  stringsAsFactors = FALSE,
#'  quiet = TRUE
#' )
#'
#'
#' #Extract values - getInfo
#' ee_nc_rain <- ee_extract(
#'  x = terraclimate,
#'  y = nc["NAME"],
#'  scale = 250,
#'  fun = ee$Reducer$mean(),
#'  sf = TRUE
#' )
#'
#' # Extract values - drive (lazy = TRUE)
#' ee_nc_rain <- ee_extract(
#'  x = terraclimate,
#'  y = nc["NAME"],
#'  scale = 250,
#'  fun = ee$Reducer$mean(),
#'  via = "drive",
#'  lazy = TRUE,
#'  sf = TRUE
#' )
#' ee_nc_rain <- ee_nc_rain %>% ee_utils_future_value()
#'
#' # Extract values - gcs (lazy = FALSE)
#' ee_nc_rain <- ee_extract(
#'  x = terraclimate,
#'  y = nc["NAME"],
#'  scale = 250,
#'  fun = ee$Reducer$mean(),
#'  via = "gcs",
#'  container = "rgee_dev",
#'  sf = TRUE
#' )
#'
#' # Spatial plot
#' plot(
#'  ee_nc_rain["X200101_Terraclimate_pp_2001_01_01"],
#'  main = "2001 Jan Precipitation - Terraclimate",
#'  reset = FALSE
#' )
#' }
#' @export
ee_extract <- function(x,
                       y,
                       fun = ee$Reducer$mean(),
                       scale = NULL,
                       sf = FALSE,
                       via = "getInfo",
                       container = "rgee_backup",
                       lazy = FALSE,
                       quiet = FALSE,
                       ...) {
  ee_check_packages("ee_extract", c("geojsonio", "sf"))

  # print scale
  if (!quiet & is.null(scale)) {
    scale <- 1000
    message(sprintf("The image scale is set to %s.", scale))
  }

  # Is x a Image or ImageCollection?
  if (!any(class(x) %in% ee_get_spatial_objects("i+ic"))) {
    stop("x is neither an ee$Image nor ee$ImageCollection")
  }

  # Is x a ImageCollection?
  if (any(class(x) %in%  "ee.imagecollection.ImageCollection")) {
    # if (!quiet) {
    #   message("x is an ImageCollection, running 'x$toBands()' to ",
    #           "convert it into an Image")
    # }
    x <- ee$ImageCollection$toBands(x)
  }

  # Load Python module
  oauth_func_path <- system.file("python/ee_extract.py", package = "rgee")
  extract_py <- ee_source_python(oauth_func_path)

  # spatial classes
  sp_objects <- ee_get_spatial_objects('Table')

  # Is y a Spatial object?
  if (!any(class(y) %in% c("sf", "sfc", sp_objects))) {
    stop("y is not a sf, sfc, ee$Geometry, ee$Feature or ee$FeatureCollection object.")
  }

  # If y is a sf object convert into a ee$FeatureCollection object
  if (any("sf" %in% class(y))) {
    sf_y <- y
    # if (!quiet) {
    #   message("NOTE: y is an sf object, running 'sf_as_ee(y$geometry)' to ",
    #           "convert in an ee$FeatureCollection object.")
    # }
    ee_y <- sf_as_ee(y[[attr(y, "sf_column")]], quiet = TRUE)
  } else if(any("sfc" %in%  class(y))) {
    sf_y <- sf::st_sf(id = seq_along(y), geometry = y)
    # if (!quiet) {
    #   message("y is an sfc object, running 'sf_as_ee(y)' to ",
    #           "convert it into an ee$FeatureCollection object.")
    # }
    ee_y <- sf_as_ee(y, quiet = TRUE)
    # If y is a ee$FeatureCollection object and 'sf' arg is TRUE convert it to an
    # sf object
  } else if(any(ee_get_spatial_objects('Table') %in%  class(y))) {
    ee_y <- ee$FeatureCollection(y)
    sf_y <- tryCatch(
      expr = ee_as_sf(y, quiet = FALSE, maxFeatures = 10000),
      error = function(e) {
          stop(
            "The ee$FeatureCollection (y) must be not higher than 10 000."
          )
      }
    )
  }

  #set ee_ID for identify rows in the data.frame
  ee_add_rows <- function(f) {
    f_prop <- ee$Feature$get(f, "system:index")
    ee$Feature(ee$Feature$set(f, "ee_ID", f_prop))
  }
  ee_y <- ee$FeatureCollection(ee_y) %>%
    ee$FeatureCollection$map(ee_add_rows)

  # Get the funname
  fun_name <- gsub("Reducer.", "", (ee$Reducer$getInfo(fun))[["type"]])

  # Convert Image into ImageCollection
  x_ic <- bands_to_image_collection(x)

  # triplets save info about the value, the row_id (ee_ID) and col_id (imageId)
  create_tripplets <- function(img) {
    img_reduce_regions <- ee$Image$reduceRegions(
      image = img,
      collection = ee_y,
      reducer = fun,
      scale = scale,
      ...
    )
    ee$FeatureCollection$map(
      img_reduce_regions,
      function(f) {
        ee$Feature$set(f, "imageId", ee$Image$get(img, "system:index"))
      }
    )
  }

  triplets <- x_ic %>%
    ee$ImageCollection$map(create_tripplets) %>%
    ee$ImageCollection$flatten()

  # From ee$Dict format to a table
  table <- extract_py$
    table_format(triplets, "ee_ID", "imageId", fun_name)$
    map(function(feature) {
      ee$Feature$setGeometry(feature, NULL)
    })

  # Extracting data and passing to sf
  if (via == "drive") {

    # Getting image ID if it is exist
    # table_id is the name of the table in the container
    table_id <- basename(tempfile("rgee_file_"))

    # Have you loaded the necessary credentials?
    # Only important for drive or gcs.
    ee_user <- ee_exist_credentials()
    dsn <- sprintf("%s/%s.csv", tempdir(), table_id)
    # From Earth Engine to drive
    table_task <- ee_init_task_drive_fc(
      x_fc = table,
      dsn = dsn,
      container = container,
      table_id = table_id,
      ee_user = ee_user,
      selectors =  NULL,
      timePrefix = TRUE,
      quiet = quiet
    )

    if(lazy) {
      prev_plan <- future::plan(future::sequential, .skip = TRUE)
      on.exit(future::plan(prev_plan, .skip = TRUE), add = TRUE)
      future::future({
        ee_extract_to_lazy_exp_drive(table_task, dsn, quiet, sf, sf_y)
      }, lazy = TRUE)
    } else {
      ee_extract_to_lazy_exp_drive(table_task, dsn, quiet, sf, sf_y)
    }
  } else if(via == "gcs") {

    # Getting image ID if it is exist
    # table_id is the name of the table in the container
    table_id <- basename(tempfile("rgee_file_"))

    # Have you loaded the necessary credentials?
    # Only important for drive or gcs.
    ee_user <- ee_exist_credentials()
    dsn <- sprintf("%s/%s.csv", tempdir(), table_id)
    # From Earth Engine to drive
    table_task <- ee_init_task_gcs_fc(
      x_fc = table,
      dsn = dsn,
      container = container,
      table_id = table_id,
      ee_user = ee_user,
      selectors =  NULL,
      timePrefix = TRUE,
      quiet = quiet
    )

    if(lazy) {
      prev_plan <- future::plan(future::sequential, .skip = TRUE)
      on.exit(future::plan(prev_plan, .skip = TRUE), add = TRUE)
      future::future({
        ee_extract_to_lazy_exp_gcs(table_task, dsn, quiet, sf, sf_y)
      }, lazy = TRUE)
    } else {
      ee_extract_to_lazy_exp_gcs(table_task, dsn, quiet, sf, sf_y)
    }
  } else {
    table_geojson <- table %>%
      ee$FeatureCollection$getInfo() %>%
      ee_utils_py_to_r()
    class(table_geojson) <- "geo_list"
    table_sf <- geojsonio::geojson_sf(table_geojson)
    sf::st_geometry(table_sf) <- NULL
    table_sf <- table_sf[, order(names(table_sf))]

    # Removing helper index's
    table_sf["id"] <- NULL
    table_sf["ee_ID"] <- NULL
    if (isTRUE(sf)) {
      table_geometry  <- sf::st_geometry(sf_y)
      table_sf <- sf_y %>%
        sf::st_drop_geometry() %>%
        cbind(table_sf) %>%
        sf::st_sf(geometry = table_geometry)
    } else {
      table_sf <- sf_y %>%
        sf::st_drop_geometry() %>%
        cbind(table_sf)
    }
    table_sf
  }
}

#' Converts all bands in an image to an image collection.
#' @param img ee$Image. The image to convert.
#' @return ee$ImageCollection
#' @noRd
bands_to_image_collection <- function(img) {
  bname_to_image <- function(band) {
    img %>%
      ee$Image$select(ee$String(band)) %>%
      ee$Image$set(
        list("system:index" = ee$String(band))
      )
  }
  img %>%
    ee$Image$bandNames() %>%
    ee$List$map(ee_utils_pyfunc(bname_to_image)) %>%
    ee$ImageCollection()
}


#' From drive to local (sf). Function to be evaluated in future::plan
#' @noRd
ee_extract_to_lazy_exp_drive <- function(table_task, dsn, quiet, sf, sf_y) {
  # From drive to local
  table_csv <- ee_sf_drive_local(
    table_task = table_task,
    dsn = dsn,
    metadata = FALSE,
    public = FALSE,
    overwrite = TRUE,
    quiet = quiet
  )

  # Removing helper index's
  table_sf <- read.csv(table_csv$dsn)
  table_sf["system.index"] <- NULL
  table_sf["ee_ID"] <- NULL
  table_sf[".geo"] <- NULL

  if (isTRUE(sf)) {
    table_geometry  <- sf::st_geometry(sf_y)
    table_sf <- sf_y %>%
      sf::st_drop_geometry() %>%
      cbind(table_sf) %>%
      sf::st_sf(geometry = table_geometry)
  } else {
    table_sf <- sf_y %>%
      sf::st_drop_geometry() %>%
      cbind(table_sf)
  }
  table_sf
}



#' From GCS to local (sf). Function to be evaluated in future::plan
#' @noRd
ee_extract_to_lazy_exp_gcs <- function(table_task, dsn, quiet, sf, sf_y) {
  # From GCS to local
  table_csv <- ee_sf_gcs_local(
    table_task = table_task,
    dsn = dsn,
    metadata = FALSE,
    public = FALSE,
    overwrite = TRUE,
    quiet = quiet
  )

  # Removing helper index's
  table_sf <- read.csv(table_csv$dsn)
  table_sf["system.index"] <- NULL
  table_sf["ee_ID"] <- NULL
  table_sf[".geo"] <- NULL

  if (isTRUE(sf)) {
    table_geometry  <- sf::st_geometry(sf_y)
    table_sf <- sf_y %>%
      sf::st_drop_geometry() %>%
      cbind(table_sf) %>%
      sf::st_sf(geometry = table_geometry)
  } else {
    table_sf <- sf_y %>%
      sf::st_drop_geometry() %>%
      cbind(table_sf)
  }
  table_sf
}