starsExtra
R package starsExtra provides several miscellaneous functions for
working with stars objects, mainly single-band rasters. Currently
includes functions for:
- Focal filtering
- Detrending of Digital Elevation Models
- Calculating flow length
- Calculating the Convergence Index
- Calculating topographic aspect
Installation
CRAN version:
install.packages("starsExtra")GitHub version:
install.packages("remotes")
remotes::install_github("michaeldorman/starsExtra")Usage
Once installed, the library can be loaded as follows.
library(starsExtra)
#> Loading required package: sf
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
#> Loading required package: stars
#> Loading required package: abindDocumentation
The complete documentation can be found at https://michaeldorman.github.io/starsExtra/.
Example
The following code applied a 15*15 mean focal filter on a 533*627
stars Digital Elevation Model (DEM):
data(carmel)
carmel_mean15 = focal2(
x = carmel, # Input 'stars' raster
w = matrix(1, 15, 15), # Weights
fun = "mean", # Aggregation function
na.rm = TRUE, # 'NA' in neighborhood are removed
mask = TRUE # Areas that were 'NA' in 'x' are masked from result
)The calculation takes: 0.5631919 secs.
The original DEM and the filtered DEM can be combined and plotted with the following expressions:
r = c(carmel, carmel_mean15, along = 3)
r = st_set_dimensions(r, 3, values = c("input", "15*15 mean filter"))
plot(r, breaks = "equal", col = terrain.colors(10), key.pos = 4)Timing
The following code section compares the calculation time of focal2 in
the above example with raster::focal (both using C/C++) and the
reference method starsExtra:::focal2r (using R code only).
library(microbenchmark)
library(starsExtra)
library(raster)
#> Loading required package: sp
data(carmel)
carmelr = as(carmel, "Raster")
res = microbenchmark(
focal2 = focal2(carmel, w = matrix(1, 15, 15), fun = "mean", na.rm = FALSE),
focal = focal(carmelr, w = matrix(1, 15, 15), fun = mean, na.rm = FALSE),
focal2r = starsExtra:::focal2r(carmel, k = 15, mean),
times = 10
)
res
#> Unit: milliseconds
#> expr min lq mean median uq max
#> focal2 490.8400 497.2334 507.1330 500.9711 505.5926 543.1901
#> focal 115.7206 119.2707 153.4027 121.3225 124.4334 413.7475
#> focal2r 14946.2629 15031.5734 15172.0134 15111.5967 15311.7349 15582.1324
#> neval
#> 10
#> 10
#> 10boxplot(res)
