Clean

R/create_rgee_environment.R

@@ -6,18 +6,20 @@
 #' @export
 #' @importFrom reticulate condaenv_exists
 #' @importFrom kwb.python conda_py_install
-create_ad4gd_env <- function(force = FALSE, debug = FALSE) {
+create_ad4gd_env <- function(force = FALSE, debug = FALSE)
+{
+  if (!reticulate::condaenv_exists("ad4gd") || force) {
 
-  if(!reticulate::condaenv_exists("ad4gd") | force) {
-  kwb.python::conda_py_install(env_name = "ad4gd",
-                               pkgs = list(conda = c("python=3.12.2",
-                                                     "numpy"),
-                                           py = "earthengine-api==0.1.370"))
-  } else {
-    if(debug) {
-      message(paste0("Conda environment 'ad4gd' already exists. Use ",
-                             "'force' = TRUE, to reinstall if required"))
-    }
+    kwb.python::conda_py_install(env_name = "ad4gd", pkgs = list(
+      conda = c("python=3.12.2", "numpy"),
+      py = "earthengine-api==0.1.370"
+    ))
+
+  } else if (debug) {
+
+    message(
+      "Conda environment 'ad4gd' already exists. ",
+      "Use 'force' = TRUE to reinstall, if required."
+    )
   }
 }
-

R/flatten_results.R

@@ -10,17 +10,17 @@
 #' @importFrom dplyr bind_rows
 #' @importFrom tidyr unnest
 #' @importFrom sf st_polygon st_sfc st_set_crs
-flatten_results <- function(sat_data_list,
-                            cols_unnest = "satellite_data_metadata") {
-
-  if(sum(c("satellite_data", "satellite_metadata") %in% cols_unnest) == 2) {
-    names_sep <- "."
-  } else {
-    names_sep <- NULL
-  }
+flatten_results <- function(
+    sat_data_list,
+    cols_unnest = "satellite_data_metadata"
+)
+{
+  satellite_columns <- c("satellite_data", "satellite_metadata")
 
   sat_data_list %>%
     dplyr::bind_rows() %>%
-    tidyr::unnest(tidyselect::all_of(cols_unnest),
-                  names_sep = names_sep)
+    tidyr::unnest(
+      tidyselect::all_of(cols_unnest),
+      names_sep = if (all(satellite_columns %in% cols_unnest)) "." # else NULL
+    )
 }

R/get_metadata_gee.R

@@ -1,78 +1,72 @@
 #' Helper function: convert to list
 #'
-#' @param coords column with satellite metadata coordinates (system:footprint`$coordinates)
-#'
+#' @param coords column with satellite metadata coordinates
+#'   (system:footprint`$coordinates)
 #' @return list of coordinates
 #' @keywords internal
 #'
-convert_to_list <- function(coords) {
-
-  tmp_mat <- lapply(seq_along(coords),
-                    FUN = function(idx) {
-
-                      t(as.matrix(coords[[idx]]))
-                    })
-
-  list(do.call(rbind, tmp_mat))
+convert_to_list <- function(coords)
+{
+  lapply(coords, function(x) t(as.matrix(x))) %>%
+    do.call(what = rbind) %>%
+    list()
 }
 
-
 #' Google Earth Engine: get metadata for collection
 #'
 #' @param collection collection
-#'
 #' @return tibble with metadata. In addition also the complex column
-#' "geometry_meta_org" is simplified and stored in the new column "geometry_meta_cleaned"
+#'   "geometry_meta_org" is simplified and stored in the new column
+#'   "geometry_meta_cleaned"
 #' @export
 #' @importFrom tibble as_tibble tibble
 #' @importFrom dplyr bind_cols bind_rows
+#' @importFrom kwb.utils removeColumns
 #' @importFrom tidyr nest
-#' @importFrom tidyselect matches all_of
-gee_get_metadata <- function(collection) {
-
-  nImages <- collection$size()$getInfo()
+#' @importFrom tidyselect matches
+gee_get_metadata <- function(collection)
+{
+  metadata_list <- collection$size()$getInfo() %>%
+    collection$toList() %>%
+    collection$fromImages()
 
-  collectionList <- collection$toList(nImages)
-
-  metadata_list <- collection$fromImages(collectionList)
   metadata_list <- metadata_list$getInfo()
 
-
-  metadata_features_df <- lapply(seq_along(metadata_list$features), function(idx) {
-    tibble::as_tibble(metadata_list$features[[idx]]$properties)
-  }) %>% dplyr::bind_rows() %>%
+  metadata_features_df <- metadata_list$features %>%
+    lapply(function(x) tibble::as_tibble(x$properties)) %>%
+    dplyr::bind_rows() %>%
     tidyr::nest(geometry_meta_org = tidyselect::matches("system:footprint"))
 
+  geometries <- metadata_features_df$geometry_meta_org
 
-  coords <- lapply(seq_len(nrow(metadata_features_df)), function(idx) {
-    convert_to_list(metadata_features_df$geometry_meta_org[[idx]]$`system:footprint`$coordinates) %>%
+  coords <- lapply(seq_len(nrow(metadata_features_df)), function(i) {
+    geometries[[i]]$`system:footprint`$coordinates %>%
+      convert_to_list() %>%
       sf::st_polygon() %>%
       sf::st_sfc() %>%
       sf::st_set_crs(value = 4326)
   })
 
   metadata_features_df$geometry_meta_cleaned <- coords
 
-  metadata_ids <- tibble::tibble(
-    id = sapply(seq_along(metadata_list$features), function(idx) metadata_list$features[[idx]]$id)
-  )
-
-  dplyr::bind_cols(metadata_ids, metadata_features_df) %>%
-    tidyr::separate(col = "id",
-                    into = c("provider_name", "provider_collection", "id_short"),
-                    sep = "/",
-                    remove = FALSE) %>%
+  tibble::tibble(
+    id = sapply(metadata_list$features, function(x) x$id)
+  ) %>%
+    dplyr::bind_cols(metadata_features_df) %>%
+    tidyr::separate(
+      col = "id",
+      into = c("provider_name", "provider_collection", "id_short"),
+      sep = "/",
+      remove = FALSE
+    ) %>%
     tidyr::separate(
       col = "id_short",
-      into = c("datetime_start",
-               "datetime_end",
-               "tile_id"),
+      into = c("datetime_start", "datetime_end", "tile_id"),
       sep = "_"
     ) %>%
     dplyr::mutate(
       datetime_start = lubridate::ymd_hms(datetime_start),
       datetime_end = lubridate::ymd_hms(datetime_end)
     ) %>%
-    dplyr::select(- tidyselect::all_of(c("provider_name","provider_collection")))
-
+    kwb.utils::removeColumns(c("provider_name", "provider_collection"))
 }