fast_extract: Fast extraction of raster values
In Pakillo/rgis: Some utilities to perform GIS operations in R

Description Usage Arguments Value Examples

View source: R/fast_extract.R

Extract values from raster layers for a given set of points or polygons. Using velox package in parallel for fast extraction.

fast_extract(
  sf = NULL,
  ras = NULL,
  funct = "mean.na",
  small.algo = FALSE,
  col.names = NULL,
  parallel = TRUE,
  n.cores = NULL
)

`sf`	sf data frame containing point or polygon data.
`ras`	A Raster* object (RasterLayer, RasterStack, or RasterBrick), a named list of Raster objects, or a character vector or list of paths to Raster files on disk (e.g. as obtained through `list.files`). Thus, raster files can be stored on disk, without having to load them on memory first.
`funct`	The name of a function to summarise raster values within polygons. Default is 'mean.na' (simple average, excluding NA), but other functions can be used (in that case, provide function name without quotes, e.g. funct = median). See `velox::VeloxRaster_extract()`. No function is used when `sf` are points.
`small.algo`	Logical. Use 'small' algorithm to detect overlap between polygons and raster cells? See `velox::VeloxRaster_extract()`. Default is FALSE.
`col.names`	Optional. Character vector with names for extracted columns in the output dataframe. If not provided, the function will use the Raster* layer names or, if `ras` is a list of paths, the file name followed by layer names.
`parallel`	Logical. Run function in parallel (using `future.apply`)? Default is TRUE.
`n.cores`	Number of cores to use when parallel = TRUE. If not specified, using all available cores (see `future::multiprocess()`).

A sf data frame with the same number of rows as the original, and new columns containing the extracted raster values.

library(raster)
library(rgis)

## Example taken from raster::extract

# Create polygons
poly1 <- rbind(c(-180,-20), c(-160,5), c(-60, 0), c(-160,-60), c(-180,-20))
poly2 <- rbind(c(80,0), c(100,60), c(120,0), c(120,-55), c(80,0))
polys <- spPolygons(poly1, poly2)
polys.sf <- sf::st_as_sf(polys)
sf::st_crs(polys.sf) <- "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"

# Create rasters
r1 <- raster(ncol = 36, nrow = 18, vals = 1:(18*36))
r2 <- raster(ncol = 36, nrow = 18, vals = (1:(18*36))*2)
ras <- stack(r1, r2)

plot(ras, 1)
plot(polys, add = TRUE)

# Extract values
extract.parallel <- fast_extract(sf = polys.sf, ras = ras, parallel = TRUE)
head(extract.parallel)

extract.noparallel <- fast_extract(sf = polys.sf, ras = ras, parallel = FALSE)

# Compare with raster::extract
raster.extract <- raster::extract(ras, polys, fun = mean, df = TRUE)
head(raster.extract)



### Providing named list of rasters
ras.list <- list(r1 = r1, r2 = r2)
rgis.out <- fast_extract(sf = polys.sf, ras = ras.list, parallel = FALSE)
head(rgis.out)