reproducible (version 1.1.1)

projectInputs: Project Raster* or Spatial* or sf objects

Description

A simple wrapper around the various different tools for these GIS types.

Usage

projectInputs(x, targetCRS, ...)
# S3 method for default
projectInputs(x, targetCRS, ...)
# S3 method for Raster
projectInputs(
  x,
  targetCRS = NULL,
  rasterToMatch = NULL,
  cores = NULL,
  useGDAL = getOption("reproducible.useGDAL", TRUE),
  ...
)
# S3 method for sf
projectInputs(x, targetCRS, ...)
# S3 method for Spatial
projectInputs(x, targetCRS, ...)

Arguments

x

A Raster*, Spatial* or sf object

targetCRS

The CRS of x at the end of this function (i.e., the goal)

...

Passed to projectRaster.

rasterToMatch

Template Raster* object passed to the to argument of projectRaster, thus will changing the resolution and projection of x. See details in postProcess.

cores

An integer* or 'AUTO'. This will be used if gdalwarp is triggered. 'AUTO'* will calculate 90 number of cores in the system, while an integer or rounded float will be passed as the exact number of cores to be used.

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.

Value

A file of the same type as starting, but with projection (and possibly other characteristics, including resolution, origin, extent if changed).

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)
# }