reproducible (version 1.1.1)

cropInputs: Crop a Spatial* or Raster* object

Description

This function can be used to crop or reproject module inputs from raw data.

Usage

cropInputs(x, studyArea, rasterToMatch, ...)
# S3 method for default
cropInputs(x, studyArea, rasterToMatch, ...)
# S3 method for spatialObjects
cropInputs(
  x,
  studyArea = NULL,
  rasterToMatch = NULL,
  extentToMatch = NULL,
  extentCRS = NULL,
  useGDAL = getOption("reproducible.useGDAL", TRUE),
  ...
)
# S3 method for sf
cropInputs(
  x,
  studyArea = NULL,
  rasterToMatch = NULL,
  extentToMatch = NULL,
  extentCRS = NULL,
  ...
)

Arguments

x

A Spatial*, sf, or Raster* object.

studyArea

SpatialPolygons* object used for masking and possibly cropping if no rasterToMatch is provided. If not in same CRS, then it will be spTransformed to CRS of x before masking. Currently, this function will not reproject the x. Optional in postProcess.

rasterToMatch

Template Raster* object used for cropping (so extent should be the extent of desired outcome) and reprojecting (including changing the resolution and projection). See details in postProcess.

...

Passed to raster::crop

extentToMatch

Optional. Can pass an extent here and a crs to extentCRS instead of rasterToMatch. These will override rasterToMatch, with a warning if both passed.

extentCRS

Optional. Can pass a crs here with an extent to extentTomatch instead of rasterToMatch

useGDAL

Logical or "force". Defaults to getOption("reproducible.useGDAL" = TRUE). If TRUE, then this function will use gdalwarp only when not small enough to fit in memory (i.e., if the operation fails the raster::canProcessInMemory(x, 3) test). Using gdalwarp will usually be faster than raster::projectRaster, the function used if this is FALSE. Since since the two options use different algorithms, there may be different projection results. "force" will cause it to use GDAL regardless of the memory test described here.

Examples

# NOT RUN {
# Add a study area to Crop and Mask to
# Create a "study area"
library(sp)
library(raster)
ow <- setwd(tempdir())

# make a SpatialPolygon
coords1 <- structure(c(-123.98, -117.1, -80.2, -100, -123.98, 60.9, 67.73, 65.58, 51.79, 60.9),
                     .Dim = c(5L, 2L))
Sr1 <- Polygon(coords1)
Srs1 <- Polygons(list(Sr1), "s1")
shpEcozone <- SpatialPolygons(list(Srs1), 1L)
crs(shpEcozone) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

# make a "study area" that is subset of larger dataset
coords <- structure(c(-118.98, -116.1, -99.2, -106, -118.98, 59.9, 65.73, 63.58, 54.79, 59.9),
                    .Dim = c(5L, 2L))
Sr1 <- Polygon(coords)
Srs1 <- Polygons(list(Sr1), "s1")
StudyArea <- SpatialPolygons(list(Srs1), 1L)
crs(StudyArea) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
#'
#'
##########
shpEcozonePostProcessed <- postProcess(shpEcozone, studyArea = StudyArea)
#'
# Try manually, individual pieces
shpEcozoneReprojected <- projectInputs(shpEcozone, StudyArea)
shpEcozoneCropped <- cropInputs(shpEcozone, StudyArea)
shpEcozoneClean <- fixErrors(shpEcozone)
shpEcozoneMasked <- maskInputs(shpEcozone, StudyArea)

setwd(ow)
# }