Skip to contents

Compute summary statistics for cells, either across layers or between layers (parallel summary).

The following summary methods are available for SpatRaster: any, anyNA, all, allNA, max, min, mean, median, prod, range, stdev, sum, which.min, which.max. See modal to compute the mode and app to compute summary statistics that are not included here.

Because generic functions are used, the method applied is chosen based on the first argument: "x". This means that if r is a SpatRaster, mean(r, 5) will work, but mean(5, r) will not work.

The mean method has an argument "trim" that is ignored.

If pop=TRUE stdev computes the population standard deviation, computed as:

f <- function(x) sqrt(sum((x-mean(x))^2) / length(x))

This is different than the sample standard deviation returned by sd (which uses n-1 as denominator).

## Usage

# S4 method for SpatRaster
min(x, ..., na.rm=FALSE)

# S4 method for SpatRaster
max(x, ..., na.rm=FALSE)

# S4 method for SpatRaster
range(x, ..., na.rm=FALSE)

# S4 method for SpatRaster
prod(x, ..., na.rm=FALSE)

# S4 method for SpatRaster
sum(x, ..., na.rm=FALSE)

# S4 method for SpatRaster
any(x, ..., na.rm=FALSE)

# S4 method for SpatRaster
all(x, ..., na.rm=FALSE)

# S4 method for SpatRaster
range(x, ..., na.rm=FALSE)

# S4 method for SpatRaster
which.min(x)

# S4 method for SpatRaster
which.max(x)

# S4 method for SpatRaster
stdev(x, ..., pop=TRUE, na.rm=FALSE)

# S4 method for SpatRaster
mean(x, ..., trim=NA, na.rm=FALSE)

# S4 method for SpatRaster
median(x, na.rm=FALSE, ...)

# S4 method for SpatRaster
anyNA(x)

# S4 method for SpatRaster
countNA(x, n=0)

# S4 method for SpatRaster
noNA(x, falseNA=FALSE)

# S4 method for SpatRaster
allNA(x, falseNA=FALSE)

## Arguments

x

SpatRaster

...

additional SpatRasters or numeric values; and arguments par for parallel summarization (see Details), and filename, overwrite and wopt as for writeRaster

na.rm

logical. If TRUE, NA values are ignored. If FALSE, NA is returned if x has any NA values

trim

ignored

pop

logical. If TRUE, the population standard deviation is computed. Otherwise the sample standard deviation is computed

falseNA

logical. If TRUE, cells that would otherwise be FALSE are set to NA

n

integer. If n > 0, cell values are TRUE if at least n of its layers are NA

SpatRaster

## Details

Additional argument par can be used for "parallel" summarizing a SpatRaster and a numeric or logical value. If a SpatRaster x has three layers, max(x, 5) will return a single layer (the number five is treated as a layer in which all cells have value five). In contrast max(x, 5, par=TRUE) returns three layers (the number five is treated as another SpatRaster with a single layer with all cells having the value five.

## See also

app, Math-methods, modal, which.lyr

## Examples

set.seed(0)
r <- rast(nrows=10, ncols=10, nlyrs=3)
values(r) <- runif(ncell(r) * nlyr(r))

x <- mean(r)
# note how this returns one layer
x <- sum(c(r, r[[2]]), 5)

# and this returns three layers
y <- sum(r, r[[2]], 5)

max(r)
#> class       : SpatRaster
#> dimensions  : 10, 10, 1  (nrow, ncol, nlyr)
#> resolution  : 36, 18  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84)
#> source(s)   : memory
#> name        :       max
#> min value   : 0.1808664
#> max value   : 0.9926841

## when adding a number, do you want 1 layer or all layers?
# 1 layer
max(r, 0.5)
#> class       : SpatRaster
#> dimensions  : 10, 10, 1  (nrow, ncol, nlyr)
#> resolution  : 36, 18  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84)
#> source(s)   : memory
#> name        :       max
#> min value   : 0.5000000
#> max value   : 0.9926841

# all layers
max(r, 0.5, par=TRUE)
#> class       : SpatRaster
#> dimensions  : 10, 10, 3  (nrow, ncol, nlyr)
#> resolution  : 36, 18  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84)
#> source(s)   : memory
#> names       :     lyr.1,     lyr.2,     lyr.3
#> min values  : 0.5000000, 0.5000000, 0.5000000
#> max values  : 0.9919061, 0.9926841, 0.9815635

y <- stdev(r)
# not the same as
yy <- app(r, sd)

z <- stdev(r, r*2)

x <- mean(r, filename=paste0(tempfile(), ".tif"))

v <- values(r)
set.seed(3)
v[sample(length(v), 50)] <- NA
values(r) <- v
is.na(r)
#> class       : SpatRaster
#> dimensions  : 10, 10, 3  (nrow, ncol, nlyr)
#> resolution  : 36, 18  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84)
#> source(s)   : memory
#> names       : lyr.1, lyr.2, lyr.3
#> min values  : FALSE, FALSE, FALSE
#> max values  :  TRUE,  TRUE,  TRUE
anyNA(r)
#> class       : SpatRaster
#> dimensions  : 10, 10, 1  (nrow, ncol, nlyr)
#> resolution  : 36, 18  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84)
#> source(s)   : memory
#> name        :  lyr1
#> min value   : FALSE
#> max value   :  TRUE
allNA(r)
#> class       : SpatRaster
#> dimensions  : 10, 10, 1  (nrow, ncol, nlyr)
#> resolution  : 36, 18  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84)
#> source(s)   : memory
#> name        :  lyr1
#> min value   : FALSE
#> max value   :  TRUE
countNA(r)
#> class       : SpatRaster
#> dimensions  : 10, 10, 1  (nrow, ncol, nlyr)
#> resolution  : 36, 18  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84)
#> source(s)   : memory
#> name        : lyr1
#> min value   :    0
#> max value   :    3
countNA(r, 2)
#> class       : SpatRaster
#> dimensions  : 10, 10, 1  (nrow, ncol, nlyr)
#> resolution  : 36, 18  (x, y)
#> extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (CRS84) (OGC:CRS84)
#> source(s)   : memory
#> name        :  lyr1
#> min value   : FALSE
#> max value   :  TRUE