2
To get the distribution of NA/non-NA, use table on the values of the raster.
Test raster:
> r = raster()
Fill with 1 to N:
> r[] = 1:ncell(r)
Set some cells to NA:
> r[sample(ncell(r),ncell(r)/5)]=NA
To count NA/non-NA, use table:
> table(is.na(r[]))
FALSE TRUE
51840 12960
To plot this with NA in red, do:
plot(r, colNA="red")
2
A RAT is just a table that crosswalks the numeric values contained in an integer raster to descriptive attributes. It is not realistic to have a RAT for a true floating-point raster (eg., elevation) and really does not make much sense to. If one wants to represent a
process as nominal than you just reclassify it into the desired ranges.
A good way to ...
2
Are you looking for something like st_relate which implements the DE9-IM relation scheme?
https://en.wikipedia.org/wiki/DE-9IM
This tells you how the exterior, boundary, and interior of two features relate to each other. For example two polygons that only touch at a point have no interior-interior relation (which is coded as F) and a point dimension ...
2
It looks like your problem is that the polygon data is multipart geometry. This means that you have multiple features (polygons) associated with single rows (attributes). Even if raster::extract works, this makes very little sense from a results standpoint. For your data to match, you need to explode your geometry into single part.
Here is an example, you ...
1
You can build legends with the legend function, for example with your data this looks okay:
legend(1, 1.5,
legend=c("Houses","Inner","Outer"),
col=c("black","green","red"),
lwd=2,horiz=TRUE)
check the help and adjust parameters to suit. It is quite flexible.
1
First, you are using a double bracket where you do not need to and second, it is often better to use a numeric index.
library(modifiedmk)
x <- c(Nile)
Here, if you use a single bracket it returns the desired statistic.
pwmk(x)["P-value"]
Now, if you use a numeric index you can simplify things a bit. This returns the: "P-value", "Z-value", "Sen's ...
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