raster (version 2.1-41)

calc: Calculate

Description

Calculate values for a new Raster* object from another Raster* object, using a formula. If x is a RasterLayer, fun is typically a function that can take a single vector as input, and return a vector of values of the same length (e.g. sqrt). If x is a RasterStack or RasterBrick, fun should operate on a vector of values (one vector for each cell). calc returns a RasterLayer if fun returns a single value (e.g. sum) and it returns a RasterBrick if fun returns more than one number, e.g., fun=quantile. In many cases, what can be achieved with calc, can also be accomplished with a more intuitive 'raster-algebra' notation (see Arith-methods). For example, r <- r * 2 instead of r <- calc(r, fun=function(x){x * 2}, or r <- sum(s) instead of r <- calc(s, fun=sum). However, calc should be faster when using complex formulas on large datasets. With calc it is possible to set an output filename and file type preferences. See (overlay) to use functions that refer to specific layers, like (function(a,b,c){a + sqrt(b) / c})

Usage

## S3 method for class 'Raster,function':
calc(x, fun, filename='', na.rm, forcefun=FALSE, forceapply=FALSE, ...)

Arguments

x

Raster* object

fun

function

filename

character. Output filename (optional)

na.rm

Remove NA values, if supported by 'fun' (only relevant when summarizing a multilayer Raster object into a RasterLayer)

forcefun

logical. For debugging. Force calc to not use fun with apply

forceapply

logical. For debugging. Force calc to use fun with apply

...

Additional arguments as for writeRaster

Value

• a Raster* object

See Also

overlay , reclassify, Arith-methods, Math-methods

Examples

r <- raster(ncols=36, nrows=18)
r[] <- 1:ncell(r)

# multiply values with 10
fun <- function(x) { x * 10 }
rc1 <- calc(r, fun)

# set values below 100 to NA. 
fun <- function(x) { x[x<100] <- NA; return(x) }
rc2 <- calc(r, fun)

# set NA values to -9999
fun <- function(x) { x[is.na(x)] <- -9999; return(x)} 
rc3 <- calc(rc2, fun)

# using a RasterStack as input
s <- stack(r, r*2, sqrt(r))
# return a RasterLayer
rs1 <- calc(s, sum)

# return a RasterBrick
rs2 <- calc(s, fun=function(x){x * 10})
# recycling by layer
rs3 <- calc(s, fun=function(x){x * c(1, 5, 10)})

# use overlay when you want to refer to indiviudal layer in the function
# but it can be done with calc: 
rs4 <- calc(s, fun=function(x){x[1]+x[2]*x[3]})

## 
# Some regression examples
## 

# create data
r <- raster(nrow=10, ncol=10)
s1 <- s2<- list()
for (i in 1:12) {
	s1[i] <- setValues(r, rnorm(ncell(r), i, 3) )
	s2[i] <- setValues(r, rnorm(ncell(r), i, 3) )
}
s1 <- stack(s1)
s2 <- stack(s2)

# regression of values in one brick (or stack) with another
s <- stack(s1, s2)
# s1 and s2 have 12 layers; coefficients[2] is the slope
fun <- function(x) { lm(x[1:12] ~ x[13:24])$coefficients[2] }
x1 <- calc(s, fun)

# regression of values in one brick (or stack) with 'time'
time <- 1:nlayers(s)
fun <- function(x) { lm(x ~ time)$coefficients[2] }
x2 <- calc(s, fun)

# get multiple layers, e.g. the slope _and_ intercept
fun <- function(x) { lm(x ~ time)$coefficients }
x3 <- calc(s, fun)

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