Documentation changes

Removal of sp and raster

DESCRIPTION

@@ -48,9 +48,7 @@ Suggests:
     knitr,
     magick,
     matrixStats,
-    raster,
     rmarkdown,
-    sp,
     spThin,
     stars,
     testthat (>= 3.0.0),

R/data.R

@@ -127,30 +127,6 @@
 "sample_filt_data"
 
 
-#' @name sample_biome_data
-#' @title Biome classification raster for southern Africa.
-#' @description
-#' Categorical RasterLayer cropped to southern Africa at 0.08 degree resolution from OpenLandMap
-#' Potential Distribution of Biomes (Hengl et al., 2018).
-#' @format A RasterLayer
-#' \describe{
-#'   \item{class}{RasterLayer}
-#'   \item{dimensions}{4839, 3243, 15692877  (nrow, ncol, ncell)}
-#'   \item{resolution}{0.008983153, 0.008983153  (x, y)}
-#'   \item{extent}{11.71845, 40.85081, -47.89832, -4.428839  (xmin, xmax, ymin, ymax)}
-#'   \item{crs}{"+proj=longlat +datum=WGS84 +no_defs"}
-#'   \item{source}{biome_type_2001-01-01.tif }
-#'   \item{names}{biome_type_2001-01-01.tif }
-#'   \item{values}{Numerical categorical value ranging from 0, 255  (min, max).}
-#' }
-#' @docType data
-#' @references
-#' Hengl T, Walsh MG, Sanderman J, Wheeler I, Harrison SP, Prentice IC. (2018) Global Mapping of
-#' Potential Natural Vegetation: An Assessment of Machine Learning Algorithms for Estimating Land
-#' Potential. PeerJ Preprints.
-"sample_biome_data"
-
-
 
 #' @name sample_events_data
 #' @title  Sample e-Bird sampling event records
@@ -176,7 +152,6 @@
 #'  \doi{10.2173/ebirdst.2020}
 #'
 #' GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}
-
 "sample_events_data"
 
 
@@ -212,25 +187,3 @@
 #' hazards infrared precipitation with stations-a new environmental record for monitoring extremes".
 #' Scientific Data 2, 150066.
 "sample_cov_data"
-
-#' @name sample_proj_rast
-#' @title Distribution suitability projections for red-billed quelea in southern Africa
-#' @description
-#' RasterBrick of distribution suitability projections for red-billed quelea across southern at one
-#' degree resolution for the dates: "2018-01-01", "2018-04-01", "2018-07-01" and "2018-10-01". A
-#' value of 1 represents high suitability and a value of 0 low suitability.
-#' @format A RasterBrick
-#' \describe{
-#'   \item{class}{RasterBrick}
-#'   \item{dimensions}{31, 30, 930, 4  (nrow, ncol, ncell, nlayers)}
-#'   \item{resolution}{1, 1  (x, y)}
-#'   \item{extent}{11.6901, 41.6901, -35.43802, -4.438023  (xmin, xmax, ymin, ymax)}
-#'   \item{crs}{"+proj=longlat +datum=WGS84 +no_defs"}
-#'   \item{source}{memory} \item{names}{ X2018.01.01_proportional, X2018.04.01_proportional,
-#'   X2018.07.01_proportional, X2018.10.01_proportional }
-#'   \item{min values}{Numerical minimum distribution suitability projected in each layer.}
-#'   \item{max values}{Numerical maximum distribution suitability projected in each layer.}
-#' }
-#' @docType data
-"sample_proj_rast"
-

R/dynamic_proj.R

@@ -34,7 +34,7 @@
 #'  Default is "+proj=longlat +datum=WGS84".
 #'@param proj.prj a character string, the coordinate reference system desired for output projection
 #'  rasters. Default is assumed to be the same as prj.
-#'@param spatial.mask an object of class `Raster`, `sf` or `Spatial`, representing a mask in which
+#'@param spatial.mask an object of class `SpatRaster` or`sf` polygon, representing a mask in which
 #'  NA cells in the mask layer are removed from the projection covariates.
 #'@details Function projects a model object or list of model objects onto projection covariate data
 #'  frames for each projection date given.
@@ -190,8 +190,13 @@ dynamic_proj <-  function(dates,
   # Convert mask to SPDF for use with raster::mask
 
   if (!missing(spatial.mask)) {
-    spatial.mask<-convert_to_sf(spatial.mask,prj)
-   # spatial.mask<-sf::as_Spatial(spatial.mask)
+
+    spatial.mask <- convert_to_sf(spatial.mask, prj)
+
+    if(inherits(spatial.mask, "sfc_POLYGON")){
+      spatial.mask <- terra::vect(spatial.mask)
+    }
+
   }
 
 
@@ -499,7 +504,7 @@ dynamic_proj <-  function(dates,
 
           binaryrast <- terra::rast(as.matrix(cbind(projection_df[, "x"],
                                                     projection_df[, "y"],
-                                                    SDMbinary), crs = prj),
+                                                    SDMbinary)), crs = prj,
                                     type="xyz")
 
         }
@@ -528,7 +533,7 @@ dynamic_proj <-  function(dates,
         if (cov.file.type == "csv") {
           abundancerast <-  terra::rast(as.matrix(cbind(projection_df[, "x"],
                                                         projection_df[, "y"],
-                                                        SAMpred), crs = prj),
+                                                        SAMpred)), crs = prj,
                                         type="xyz")
 
         }
@@ -554,8 +559,8 @@ dynamic_proj <-  function(dates,
         if (cov.file.type == "csv") {
           proportionalrast <- terra::rast(as.matrix(cbind(projection_df[, "x"],
                                                           projection_df[, "y"],
-                                                          SDMpred), crs = prj),
-                                          type="xyz")
+                                                          SDMpred)), crs = prj,
+                                          type = "xyz")
         }
 
         if (!missing(spatial.mask)) {
@@ -579,7 +584,7 @@ dynamic_proj <-  function(dates,
         if (cov.file.type == "csv") {
           stackedrast <- terra::rast(as.matrix(cbind(projection_df[, "x"],
                                                     projection_df[, "y"],
-                                                    stacked), crs = prj),
+                                                    stacked)), crs = prj,
                                     type="xyz")
         }
 

R/dynamic_proj_covariates.R

@@ -4,9 +4,9 @@
 #'stacked and then exported as a covariate data frame or raster stack for each projection date.
 #'
 #'@param dates a character string, vector of dates in format "YYYY-MM-DD".
-#'@param spatial.ext optional; the spatial extent to crop explanatory variable rasters to. Object of
-#'  class `Extent`, `RasterLayer`, `sf`, `SpatialPolygonsDataFrame`, `polygon` or numeric vector
-#'  listing xmin, xmax, ymin and ymax in order.
+#'@param spatial.ext optional; the spatial extent to crop explanatory variable
+#'  rasters to. Object of class `SpatExtent`, `SpatRaster`, an `sf` polygon or
+#'  numeric vector listing xmin, xmax, ymin and ymax in order.
 #'@param varnames a character string, the unique names for each explanatory variable.
 #'@param spatial.res.degrees optional; a numeric value, the spatial resolution in degrees for
 #'  projection rasters to be resampled to. Required if `spatial.ext` given.
@@ -24,7 +24,7 @@
 #'@param save.drive.folder optional; a character string, Google Drive folder to save projection
 #'  covariates to. Folder must be uniquely named within Google Drive. Do not provide path.
 #'@param cov.file.type a character string, the type of file to export projection covariates as. One
-#'  of: `tif` (raster stack) or `csv`(data frame).
+#'  of: `tif` (SpatRaster with multiple layers) or `csv`(data frame).
 #'@param prj a character string, the coordinate reference system desired for projection covariates.
 #'  Default is "+proj=longlat +datum=WGS84".
 #'@param cov.prj a character string, the coordinate reference system desired for output projection
@@ -54,9 +54,10 @@
 #'date and explanatory variable name, then the function will error.
 #'
 #'# Processing rasters
-#'If required, rasters are cropped and resampled to the same spatial extent and resolution. If
-#'`spatial.mask` is given, then cells with NA in this mask layer are removed from the returned
-#'projection covariates. See `raster::mask()` in R package `raster` for details.
+#'If required, rasters are cropped and resampled to the same spatial extent and
+#'resolution. If `spatial.mask` is given, then cells with NA in this mask layer
+#'are removed from the returned projection covariates. See `terra::mask()` in R
+#'package `terra` for details (Hijmans et al., 2022).
 #'
 #'Rasters are then stacked and reprojected if `cov.prj` is different to `prj`.
 #'
@@ -105,7 +106,7 @@
 #'
 #'#' # Spatial buffering of static rasters (optional)
 #'
-#'  Using the `focal` function from `raster` R package (Hijmans et al., 2015),
+#'  Using the `focal` function from `terra` R package (Hijmans et al., 2022),
 #'  `GEE.math.fun` is calculated across the spatial buffer area from the record
 #'  co-ordinate. The spatial buffer area used is specified by the argument
 #'  `moving.window.matrix`, which dictates the neighbourhood of cells
@@ -129,6 +130,9 @@
 #'@returns Exports combined covariates in "csv" or "tif" file for each projection date to the local
 #'directory or Google Drive folder.
 #'@export
+#'@references
+#'Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+#'2022. Package 'terra'. Maintainer: Vienna, Austria.
 #'@examplesIf googledrive::drive_has_token()
 #'
 #'data("sample_extent_data")
@@ -238,33 +242,13 @@ dynamic_proj_covariates <- function(dates,
     stop("Provide save.directory or save.drive.folder to export data.frame.")
   }
 
-  # If mask if sf polygon, convert to spatial polygons dataframe for raster:: mask
-  #if (!missing(spatial.mask)) {
- ##   if (any(class(spatial.mask) == "sf")) {
-  #    spatial.mask<-sf::as_Spatial(spatial.mask)
-  #  }
- # }
 
   # Process spatial extent given for cropping rasters before stacking into covariate data frame
 
   # Check spatial.ext appropriate object class.
 
   if (!missing(spatial.ext)) {
 
-    if (!any(class(spatial.ext) %in% c("numeric",
-                                       "SpatialPolygonsDataFrame",
-                                       "Extent",
-                                       "RasterLayer",
-                                       "Polygon",
-                                       "SpatRaster",
-                                       "SpatExtent",
-                                       "sf"))) {
-
-      stop("Please check spatial.ext is of the correct class")
-
-    }
-
-
 
     # Numeric extent to co-ords
     if (is.numeric(spatial.ext) && length(spatial.ext) != 4) {
@@ -436,9 +420,18 @@ dynamic_proj_covariates <- function(dates,
       r <- terra::setValues(r, 1:terra::ncell(r))
 
       if(!missing(spatial.mask)) {
+
+        if(inherits(spatial.mask, "sfc_POLYGON")){
+          spatial.mask <- terra::vect(spatial.mask)
+        }
+
+        if("sf" %in% class(spatial.mask)){
+          spatial.mask <- terra::vect(spatial.mask)
+        }
+
         # Mask to original spatial.ext, if fails keep original. Depending on spatial.ext type.
         tryCatch({
-          r <- terra::mask(r, spatial.ext)
+          r <- terra::mask(r, spatial.mask)
         }, error = function(error_message) {
           r <- r
           message("spatial.mask could not be used. Check valid input type.")
@@ -453,6 +446,16 @@ dynamic_proj_covariates <- function(dates,
       if (!length(static.resample.method) == 1) {
         raster <- terra::resample(raster, r, method = static.resample.method[sr])
       }
+
+      if(!missing(spatial.mask)) {
+        tryCatch({
+          raster <- terra::mask(raster, spatial.mask)
+        }, error = function(error_message) {
+          raster <- raster
+          message("spatial.mask could not be used. Check valid input type.")
+        })}
+
+
     }
 
     terra::crs(raster) <- prj
@@ -522,10 +525,15 @@ dynamic_proj_covariates <- function(dates,
         terra::res(r) <- spatial.res.degrees
         r <- terra::setValues(r, 1:terra::ncell(r))
 
+
         if(!missing(spatial.mask)) {
+
+          if(inherits(spatial.mask, "sfc_POLYGON")){
+            spatial.mask <- terra::vect(spatial.mask)
+          }
           # Mask to original spatial.ext, if fails keep original. Depending on spatial.ext type.
           tryCatch({
-            r <- terra::mask(r, spatial.ext)
+            r <- terra::mask(r, spatial.mask)
           }, error = function(error_message) {
             r <- r
             message("spatial.mask could not be used. Check valid input type.")
@@ -540,6 +548,17 @@ dynamic_proj_covariates <- function(dates,
         if (!length(resample.method) == 1) {
           raster <- terra::resample(raster, r, method = resample.method[v])
         }
+
+        if(!missing(spatial.mask)) {
+        # Mask to original spatial.ext, if fails keep original. Depending on spatial.ext type.
+        tryCatch({
+          raster <- terra::mask(raster, spatial.mask)
+        }, error = function(error_message) {
+          raster <- raster
+          message("spatial.mask could not be used. Check valid input type.")
+        })}
+
+
       }
 
       terra::crs(raster) <- prj
@@ -557,7 +576,7 @@ dynamic_proj_covariates <- function(dates,
     names(stack) <- varnames_all # Label each layer in stack as variable
 
     if (!prj == cov.prj) {
-      stack <- terra::project(stack, crs = cov.prj)
+      stack <- terra::project(stack, cov.prj)
     }
 
     cov.file.type <- match.arg(cov.file.type, choices = c("tif", "csv"))

R/extract_buffered_coords.R

@@ -52,7 +52,7 @@
 #'
 #'  # Spatial dimension
 #'
-#'  Using the `focal` function from `raster` R package (Hijmans et al., 2015), `GEE.math.fun` is
+#'  Using the `focal` function from `terra` R package (Hijmans et al., 2022), `GEE.math.fun` is
 #'  calculated across the spatial buffer area from the record co-ordinate. The spatial buffer area
 #'  used is specified by the argument `moving.window.matrix`, which dictates the neighbourhood of
 #'  cells surrounding the cell containing the occurrence record to include in this calculation.
@@ -101,7 +101,7 @@
 #'  # Aggregation factor
 #'
 #'  `agg.factor` given represents the factor to aggregate `RasterLayer` data with function
-#'  `aggregate` in `raster` R package (Hijmans et al., 2015). Aggregation uses the `GEE.math.fun` as
+#'  `aggregate` in `terra` R package (Hijmans et al., 2022). Aggregation uses the `GEE.math.fun` as
 #'  the function. Following aggregation spatial buffering using the moving window matrix occurs.
 #'  This is included to minimise computing time if data are of high spatial resolution and a large
 #'  spatial buffer is needed. Ensure to calculate `get_moving_window()` with the spatial resolution
@@ -151,9 +151,8 @@
 #'  D'Agostino McGowan L., and Bryan J., 2022. googledrive: An Interface to Google Drive.
 #'  <https://googledrive.tidyverse.org>, <https://github.com/tidyverse/googledrive>.
 #'
-#'  Hijmans, R. J., Van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J. A.,
-#'  Lamigueiro, O. P., Bevan, A., Racine, E. B. & Shortridge, A. 2015. Package ‘raster’. R package,
-#'  734.
+#'Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+#'2022. Package 'terra'. Maintainer: Vienna, Austria.
 #'@return Returns details of successful explanatory variable extractions.
 #'@export
 #'@examplesIf googledrive::drive_has_token()

R/extract_buffered_raster.R

@@ -3,8 +3,8 @@
 #'Extract rasters for spatially buffered and temporally dynamic explanatory variables at each
 #'projection date using Google Earth Engine.
 #'@param dates a character string, vector of dates in format "YYYY-MM-DD".
-#'@param spatial.ext the spatial extent for the extracted raster. Object from which extent can be
-#'  extracted of class `Extent`, `RasterLayer`,`SpatialPolygonsDataFrame`, `sf` or `polygon` or
+#'@param spatial.ext the spatial extent for the extracted raster. Object from which extent
+#'  can be extracted of class `SpatExtent`, `SpatRaster`, `sf` polygon or
 #'  numeric vector listing xmin, xmax, ymin and ymax in order.
 #'@param datasetname a character string, the Google Earth Engine dataset to extract data from.
 #'@param bandname a character string, the Google Earth Engine dataset bandname to extract data for.
@@ -62,7 +62,7 @@
 #'
 #'  # Spatial dimension
 #'
-#'  Using the `focal` function in `raster` R package (Hijmans et al., 2015), `GEE.math.fun` is
+#'  Using the `focal` function in `terra` R package (Hijmans et al., 2022), `GEE.math.fun` is
 #'  calculated across the spatial buffer area from each cell in `spatial.ext`. The spatial buffer
 #'  area used is defined by `moving.window matrix`, which dictates the neighbourhood of cells
 #'  surrounding each cell in `spatial.ext` to include in the calculation.
@@ -90,8 +90,8 @@
 #'
 #'  # Aggregation factor
 #'
-#'  `agg.factor` given represents the factor to aggregate `RasterLayer` data with function
-#'  `aggregate` in `raster` R package (Hijmans et al., 2015). Aggregation uses the `GEE.math.fun` as
+#'  `agg.factor` given represents the factor to aggregate `SpatRaster` data with function
+#'  `aggregate` in `terra` R package (Hijmans et al., 2022). Aggregation uses the `GEE.math.fun` as
 #'  the function. Following aggregation spatial buffering using the moving window matrix occurs.
 #'  This is included to minimise computing time if data are of high spatial resolution and a large
 #'  spatial buffer is needed. Ensure to calculate `get_moving_window()` with the spatial resolution
@@ -158,9 +158,8 @@
 #'  D'Agostino McGowan L., and Bryan J., 2022. googledrive: An Interface to Google Drive.
 #'  <https://googledrive.tidyverse.org>, <https://github.com/tidyverse/googledrive>.
 #'
-#'  Hijmans, R. J., Van Etten, J., Cheng, J., Mattiuzzi, M., Sumner, M., Greenberg, J. A.,
-#'  Lamigueiro, O. P., Bevan, A., Racine, E. B. & Shortridge, A. 2015. Package ‘raster’. R package,
-#'  734.
+#'Hijmans, R.J., Bivand, R., Forner, K., Ooms, J., Pebesma, E. and Sumner, M.D.,
+#'2022. Package 'terra'. Maintainer: Vienna, Austria.
 #'@return Returns details of successful explanatory variable raster extractions for each projection
 #'  date.
 #'@export

R/extract_dynamic_raster.R

@@ -4,7 +4,7 @@
 #'Earth Engine.
 #'@param dates a character string, vector of dates in format "YYYY-MM-DD".
 #'@param spatial.ext the spatial extent for the extracted raster. Object from which extent can be
-#'  extracted of class `Extent`, `RasterLayer`, `SpatialPolygonsDataFrame`, `sf` or `polygon` or
+#'  extracted of class `SpatExtent`, `SpatRaster`, an `sf` polygon or a
 #'  numeric vector listing xmin, xmax, ymin and ymax in order.
 #'@param datasetname a character string, the Google Earth Engine dataset to extract data from.
 #'@param bandname a character string, the Google Earth Engine dataset bandname to extract data for.