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I am using a multibeam echosounder to create a raster stack in R with layers all in the same resolution, which I then convert to a data frame so I can create additive models to describe the distribution of fish around bathymetry variables (depth, aspect, slope, roughness etc.).

The issue I have is that I would like to keep my resonse variable (fish school volume) fine and my predictive variables (bathymetry) coarse, such that I have say 1 x 1m cells representing the distribution of fish schools and 10 x 10m cells representing bathymetry (so the coarse cell is divisible by the fine cell with no remainder).

I can easily create these rasters individually but relating them is the problem. As each coarser cell would contain 10 x 10 = 100 finer cells, I am not sure how to program this into R so that the values are in the right location relative to an x and a y column (for cell addresses). But I realise in this case, I would need each coarse cell value to be repeated 100 times in the data frame.

Any advice would be greatly appreciated! Thanks!

Matthew Holland
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  • You should try to provide a [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example). But if I understood correctly what you're trying to do, then you need to get the rasters to a common resolution. You can use `disaggregate` to your 10m cells into 100 1m cells – Val Jan 18 '19 at 09:25
  • Amazing thanks so much! It worked perfectly. – Matthew Holland Jan 22 '19 at 04:14

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