1/1/2024 0 Comments Function raster in rtxt files as in the first few exercises below. I include these exercises only for those that may perhaps only have alternate rasters available (e.g., ASCII). #Note: Most of the exercises that follow are exploratory in nature and not the recommended way to bring raster data into R. All of the raster layers we are going to use will be located here Create an Ascii file from your raster grid in ArcMap 10.X if experimenting with your own raster using the following Toolbox in ArcMap 10.X:ĪrcToolbox - Conversion Tools - From Raster - Raster to AsciiĪlternatively, you can set your working directory by opening a previously saved workspace by double clicking it in that folder which will also serve to set that folder as the working directory. Now open the script "RasterScript.R" in that folder and run code directly from the script. Install.packages(c("adehabitat","adehabitatHR","maptools","raster", "rgdal")) First we need to load packages to work with raster datasets.Set working directory to the extracted folder in R under File - Change dir.Exercise 1.7 - Download and extract zip folder into your preferred location. ![]() # simulation function sim <- function (x, fun, n= 100, pause= 0. R 3 ] <- 0 # cells with three live neighbours become alive x <- 1 x This does not matter for "sum", nor for "mean" (zeros are removed), but it affects many other functions such as "var" as you could be adding a lot of zeros that should not be there. ![]() A zero weight cell is included in the computation, whereas a NA weight cell is excluded. Note that there is a difference between 0 and NA in the weights matrix. See the focalWeight function to create distance based circular, rectangular, or Gaussian filters. However, the results would be wrong when using a weights matrix. One can use the na.rm=TRUE option which may make sense when using a function like mean. There is, however, a difference if NA values are considered. Thus while the following two statements are equivalent (if there are no NA values), the first one is faster than the second one: It is computationally much more efficient to adjust the weights-matrix than to use another function through the fun argument. If TRUE, only cell values that are NA are replaced with the computed focal valuesįocal uses a matrix of weights for the neighborhood of the focal cells. The value of the cells of the padded rows and columns This can be useful when a function needs to have access to the central cell of the filter If TRUE, additional 'virtual' rows and columns are padded to x such that there are no edge effects. Except for some special cases (weights of 1, functions like min, max, mean), using na.rm=TRUE may not be a good idea in this function because it can unbalance the effect of the weights The result will only be NA if all focal cells are NA. If TRUE, NA will be removed from focal computations. For example, length will fail, but function(x. It should also accept a na.rm argument (or ignore it, e.g. ![]() The function fun should take multiple numbers, and return a single number. If you need even sides, you can add a column or row with weights of zero or NAįunction (optional). The matrix does not need to be square, but the sides must be odd numbers. a 3 by 3 matrix with values 1 see Details. Matrix of weights (the moving window), e.g.
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