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

projectInputs(
  x,
  targetCRS,
  verbose = getOption("reproducible.verbose", 1),
  ...
)

# S3 method for default
projectInputs(x, targetCRS, ...)

# S3 method for Raster
projectInputs(
  x,
  targetCRS = NULL,
  verbose = getOption("reproducible.verbose", 1),
  rasterToMatch = NULL,
  cores = NULL,
  useGDAL = getOption("reproducible.useGDAL", TRUE),
  ...
)

# S3 method for sf
projectInputs(
  x,
  targetCRS,
  verbose = getOption("reproducible.verbose", 1),
  ...
)

# S3 method for Spatial
projectInputs(
  x,
  targetCRS,
  verbose = getOption("reproducible.verbose", 1),
  ...
)

Arguments

x

A Raster*, Spatial* or sf object

targetCRS

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

verbose

Numeric, -1 silent (where possible), 0 being very quiet, 1 showing more messaging, 2 being more messaging, etc. Default is 1. Above 3 will output much more information about the internals of Caching, which may help diagnose Caching challenges. Can set globally with an option, e.g., options('reproducible.verbose' = 0) to reduce to minimal

...

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

# 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)
#> No cacheRepo supplied and getOption('reproducible.cachePath') is inside a temporary directory; #> this will not persist across R sessions.
#> ...(Object to retrieve (1ca251f991c47008.rds))
#> loaded cached result from previous cropInputs call,
#> Checking for errors in SpatialPolygon
#> Found no errors.
#> No cacheRepo supplied and getOption('reproducible.cachePath') is inside a temporary directory; #> this will not persist across R sessions.
#> ...(Object to retrieve (b8053bfec9665f45.rds))
#> loaded cached result from previous projectInputs call,
#> Checking for errors in SpatialPolygon
#> Found no errors.
#> No cacheRepo supplied and getOption('reproducible.cachePath') is inside a temporary directory; #> this will not persist across R sessions.
#> ...(Object to retrieve (51007a91ef41d8db.rds))
#> loaded cached result from previous maskInputs call,
#> Skipping writeOutputs; filename2 is NULL
# Try manually, individual pieces shpEcozoneReprojected <- projectInputs(shpEcozone, StudyArea)
#> reprojecting ...
shpEcozoneCropped <- cropInputs(shpEcozone, StudyArea)
#> cropping ...
#> useGDAL is TRUE, but problem is small enough for RAM; skipping GDAL; useGDAL = 'force' to override
#> Checking for errors in SpatialPolygon
#> Found no errors.
#> although coordinates are longitude/latitude, st_intersection assumes that they are planar
shpEcozoneClean <- fixErrors(shpEcozone)
#> Checking for errors in SpatialPolygon
#> Found no errors.
shpEcozoneMasked <- maskInputs(shpEcozone, StudyArea)
#> Checking for errors in SimpleFeature
#> Found no errors.
#> maskInputs with sf class objects is still experimental
#> intersecting ...
#> Checking for errors in SimpleFeature
#> Found no errors.
#> although coordinates are longitude/latitude, st_intersection assumes that they are planar
#> dist is assumed to be in decimal degrees (arc_degrees).
setwd(ow)