Iterate through raster in R and apply conditional statements

Question

I have a raster data set in R with numeric values ranging from 0 to 61.5, including some random NAs throughout. I would provide a reproducible data set, but I don't know how to create a raster with random NAs and I cannot share my actual data online. I simply need to iterate through each raster value and apply the following logic:

> for (i in 1:length(raster){  
>  if (value(i) >= 5.1) {value(i) = 1}  
>  else if (value(i) < 5.1) {value(i) = value(i)/5.1}  
>  else (is.na(value(i)) == TRUE) {value(i) = 0.000001} 
> }

I often do loop on data frames in R but I'm new to the raster format and can't quite get this up and running. I'm not sure how to access each raster value in a for loop and apply my conditional statements. If something isn't clear let me know in the comments and I will fix my question as needed.

Answer 1

If you can do this on a vector you can do this on a raster.

To do this on a vector, first set up a test vector so you can see if it works.

x = c(0, 1, 2, 3, NA, 5, 5.1, NA, 6, 7)

if I understand the first part, anything >= 5.1 becomes 1, anything less than is scaled by 5.1. That's this:

ifelse(x>=5.1, 1, x/5.1)

which looks like this:

[1] 0.0000000 0.1960784 0.3921569 0.5882353        NA 0.9803922 1.0000000
[8]        NA 1.0000000 1.0000000

and now we ifelse on the NAs to set them to 0.000001:

> ifelse(is.na(x),.000001, ifelse(x>=5.1, 1, x/5.1))
 [1] 0.0000000 0.1960784 0.3921569 0.5882353 0.0000010 0.9803922 1.0000000
 [8] 0.0000010 1.0000000 1.0000000

bit hard to tell how everything has mapped there so let's combine it with the original:

> cbind(x, ifelse(is.na(x),.000001, ifelse(x>=5.1, 1, x/5.1)))
        x          
 [1,] 0.0 0.0000000
 [2,] 1.0 0.1960784
 [3,] 2.0 0.3921569
 [4,] 3.0 0.5882353
 [5,]  NA 0.0000010
 [6,] 5.0 0.9803922
 [7,] 5.1 1.0000000
 [8,]  NA 0.0000010
 [9,] 6.0 1.0000000
[10,] 7.0 1.0000000

that all looks good.

To work this on a raster you can treat r[] as the vector of values in the raster. Let's make an example from x:

> r = raster(matrix(x, ncol=2))

so thats a 2x5 raster:

> as.matrix(r)
     [,1] [,2]
[1,]    0  5.0
[2,]    1  5.1
[3,]    2   NA
[4,]    3  6.0
[5,]   NA  7.0

then use r[] everywhere we had x and replace the values in r:

> r[] =  ifelse(is.na(r[]),.000001, ifelse(r[]>=5.1, 1, r[]/5.1))

giving:

> as.matrix(r)
          [,1]      [,2]
[1,] 0.0000000 0.9803922
[2,] 0.1960784 1.0000000
[3,] 0.3921569 0.0000010
[4,] 0.5882353 1.0000000
[5,] 0.0000010 1.0000000

Learning outcomes: R can work on entire vectors and is a zillion times faster when you do that instead of for loops; you can make a reproducible example even if you can't dish out the data; make small examples so you can verify by eye that your code works; rasters are vectors - if you can do something on a vector in R you can do it on a raster.