Description Usage Arguments Details Value See Also Examples

Calculate focal ("moving window") values for the neighborhood of focal cells using a matrix of weights, perhaps in combination with a function.

1 2 |

`x` |
RasterLayer |

`w` |
matrix of weights (the moving window), e.g. a 3 by 3 matrix with values 1; see Details. The matrix does not need to be square, but the sides must be odd numbers. If you need even sides, you can add a column or row with weights of zero |

`fun` |
function (optional). The function fun should take multiple numbers, and return a single number. For example mean, modal, min or max. It should also accept a |

`filename` |
character. Filename for a new raster (optional) |

`na.rm` |
logical. If |

`pad` |
logical. If |

`padValue` |
numeric. The value of the cells of the padded rows and columns |

`NAonly` |
logical. If |

`...` |
Additional arguments as for |

`focal`

uses a matrix of weights for the neighborhood of the focal cells. The default function is `sum`

. It is computationally much more efficient to adjust the weights-matrix than to use another function through the `fun`

argument. Thus while the following two statements are equivalent (if there are no `NA`

values), the first one is faster than the second one:

`a <- focal(x, w=matrix(1/9, nc=3, nr=3))`

`b <- focal(x, w=matrix(1,3,3), fun=mean)`

There is, however, a difference if `NA`

values are considered. One can use the `na.rm=TRUE`

option which may make sense when using a function like `mean`

. However, the results would be wrong when using a weights matrix.

Laplacian filter: `filter=matrix(c(0,1,0,1,-4,1,0,1,0), nrow=3)`

Sobel filter: `filter=matrix(c(1,2,1,0,0,0,-1,-2,-1) / 4, nrow=3)`

see the `focalWeight`

function to create distance based circular, rectangular, or Gaussian filters.

RasterLayer

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | ```
r <- raster(ncols=36, nrows=18, xmn=0)
r[] <- runif(ncell(r))
# 3x3 mean filter
r3 <- focal(r, w=matrix(1/9,nrow=3,ncol=3))
# 5x5 mean filter
r5 <- focal(r, w=matrix(1/25,nrow=5,ncol=5))
# Gaussian filter
gf <- focalWeight(r, 2, "Gauss")
rg <- focal(r, w=gf)
# The max value for the lower-rigth corner of a 3x3 matrix around a focal cell
f = matrix(c(0,0,0,0,1,1,0,1,1), nrow=3)
f
rm <- focal(r, w=f, fun=max)
# global lon/lat data: no 'edge effect' for the columns
xmin(r) <- -180
r3g <- focal(r, w=matrix(1/9,nrow=3,ncol=3))
## Not run:
## focal can be used to create a cellular automaton
# Conway's Game of Life
w <- matrix(c(1,1,1,1,0,1,1,1,1), nr=3,nc=3)
gameOfLife <- function(x) {
f <- focal(x, w=w, pad=TRUE, padValue=0)
# cells with less than two or more than three live neighbours die
x[f<2 | f>3] <- 0
# cells with three live neighbours become alive
x[f==3] <- 1
x
}
# simulation function
sim <- function(x, fun, n=100, pause=0.25) {
for (i in 1:n) {
x <- fun(x)
plot(x, legend=FALSE, asp=NA, main=i)
dev.flush()
Sys.sleep(pause)
}
invisible(x)
}
# Gosper glider gun
m <- matrix(0, nc=48, nr=34)
m[c(40, 41, 74, 75, 380, 381, 382, 413, 417, 446, 452, 480,
486, 517, 549, 553, 584, 585, 586, 619, 718, 719, 720, 752,
753, 754, 785, 789, 852, 853, 857, 858, 1194, 1195, 1228, 1229)] <- 1
init <- raster(m)
# run the model
sim(init, gameOfLife, n=150, pause=0.05)
## End(Not run)
``` |

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