mean,GRaster-method | R Documentation |
These functions can be applied to a "stack" of GRaster
s with two or more layers. They return a single-layered GRaster
. If you want to summarize across cells in a raster (e.g., calculate the mean value of all cells on a raster), use global()
. Options include:
Numeration: count()
(number of non-NA
cells), sum()
.
Central tendency: mean()
, mmode()
(mode), median()
.
Extremes: min()
, max()
, which.min()
(index of raster with the minimum value), which.max()
(index of the raster with the maximum value)
Dispersion: range()
, stdev()
(standard deviation), var()
(sample variance), varpop()
(population variance), nunique()
(number of unique values), quantile()
(use argument probs
), skewness()
, and kurtosis()
.
Regression: Assuming we calculate a linear regression for each set of cells through all values of the cells, we can calculate its slope()
, intercept()
, r2()
, and tvalue()
.
## S4 method for signature 'GRaster'
mean(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
mmode(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
median(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
count(x)
## S4 method for signature 'GRaster'
sum(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
min(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
max(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
which.min(x)
## S4 method for signature 'GRaster'
which.max(x)
## S4 method for signature 'numeric'
sdpop(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
varpop(x, na.rm = FALSE)
## S4 method for signature 'numeric'
varpop(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
stdev(x, pop = TRUE, na.rm = FALSE)
## S4 method for signature 'GRaster'
var(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
nunique(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
skewness(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
kurtosis(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
slope(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
intercept(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
r2(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
tvalue(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
range(x, na.rm = FALSE)
## S4 method for signature 'GRaster'
quantile(x, prob, na.rm = FALSE)
x |
A |
na.rm |
Logical: If |
pop |
Logical (for |
prob |
Numeric: Quantile to calculate. Used for |
A GRaster
.
if (grassStarted()) {
# Setup
library(sf)
library(terra)
# Example data
madElev <- fastData("madElev")
# Convert a SpatRaster to a GRaster
elev <- fast(madElev)
elevs <- c(elev, elev, log10(elev) - 1, sqrt(elev))
names(elevs) <- c("elev1", "elev2", "log_elev", "sqrt_elev")
elev
elevs
# do some math
elev + 100
elev - 100
elev * 100
elev / 100
elev ^ 2
elev %/% 100 # divide then round down
elev %% 100 # modulus
100 + elev
100 %/% elev
100 %% elev
elevs + 100
100 + elevs
# math with logicals
elev + TRUE
elev - TRUE
elev * TRUE
elev / TRUE
elev ^ TRUE
elev %/% TRUE # divide then round down
elev %% TRUE # modulus
elevs + TRUE
TRUE + elevs
# Raster interacting with raster(s):
elev + elev
elev - elev
elev * elev
elev / elev
elev ^ log(elev)
elev %/% sqrt(elev) # divide then round down
elev %% sqrt(elev) # modulus
elevs + elev
elev * elevs
# sign
abs(-1 * elev)
abs(elevs)
# powers
sqrt(elevs)
# trigonometry
sin(elev)
cos(elev)
tan(elev)
asin(elev)
acos(elev)
atan(elev)
atan(elevs)
atan2(elev, elev^1.2)
atan2(elevs, elev^1.2)
atan2(elev, elevs^1.2)
atan2(elevs, elevs^1.2)
# logarithms
exp(elev)
log(elev)
ln(elev)
log2(elev)
log1p(elev)
log10(elev)
log10p(elev)
log(elev, 3)
log(elevs)
# rounding
round(elev + 0.5)
floor(elev + 0.5)
ceiling(elev + 0.5)
trunc(elev + 0.5)
# comparison
elev < 100
elev <= 100
elev == 100
elev != 100
elev > 100
elev >= 100
elev + 100 < 2 * elev
elevs > 10
10 > elevs
# logic
elev < 10 | elev > 200
elev < 10 | cos(elev) > 0.9
elev < 10 | TRUE
TRUE | elev > 200
elev < 10 | FALSE
FALSE | elev > 200
elev < 10 & cos(elev) > 0.9
elev < 10 & TRUE
TRUE & elev > 200
elev < 10 & FALSE
FALSE & elev > 200
# Mathematical functions on GRasters with >= 2 layers:
mean(elevs)
mmode(elevs)
median(elevs)
nunique(elevs)
sum(elevs)
count(elevs)
min(elevs)
max(elevs)
range(elevs)
skewness(elevs)
kurtosis(elevs)
which.min(elevs)
which.max(elevs)
slope(elevs)
intercept(elevs)
r2(elevs)
tvalue(elevs)
stdev(elevs)
stdev(elevs, pop = FALSE)
var(elevs)
varpop(elevs)
# Note: To get quantiles for each layer, use
# global(x, "quantile", probs = 0.2).
quantile(elevs, 0.1)
}
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