Efficient Way To Find Neighboring Coordinates In R - r

Question

I am working with a matrix data set that has X-Y coordinates, and rest of the columns have logical values containing different parameters. I want to find the neighboring coordinates of X-Y given at least one of the corresponding parameters is true, and then append it to new matrix as rows. Below is the sample matrix data.
Data_1
X Y P1 P2 P3 P4
-52 32 1 0 0 1
-50 34 0 0 0 0
-50 26 0 0 0 1
-52 31 0 1 1 1
To solve this, I am planning to use following algorithm:
Algorithm
# Find row wise sum
newCol <- rowSums(Data_1)
# Bind as first column with Data_1
newData <- cbind(newCol, Data_1)
# Not R code, pseduo code
if (newData[,1] != 0{
store newData[,2] and newData[,3].
Data_2 <- find neighboring coordinates to newData[,2] and newData[,3].
}
finalData <- cbind(Data_1, Data_2)
Output
X Y P1 P2 P3 P4 N1.x N1.y N2.x N2.y N3.x N3.y N4.x N4.y N5.x N5.y N6.x N6.y N7.x N7.y N8.x N8.y
-52 32 1 0 0 1 <Neighboring Coordinates---->
-50 34 0 0 0 0 <NULL>
-50 26 0 0 0 1 <Neighboring Coordinates---->
-52 31 0 1 1 1 <Neighboring Coordinates---->
The problem with this approach is scalability when the matrix will have millions of rows and columns.
Following image shows neighbor coordinates for (x,y).
Please suggest better approach if possible, thanks.

Answer 1

How about a data frame approach--does it need to be a matrix?
# Create one data frame with the starting points
points <- data.frame(x = c(-52, -50, -50, -52),
y = c( 32, 34, 26, 31))
# Create a second data frame with the desired combinations of distances
distances <- expand.grid(xd = 1:4,
yd = 1:4)
# Repeat the distances for each point (cartesian product/outer join)
neighbors <- merge(points, distances)
# Compute neighbor coordinates
neighbors$nx <- neighbors$x + neighbors$xd
neighbors$ny <- neighbors$y + neighbors$yd
# sort
neighbors <- neighbors[order(neighbors$x, neighbors$y), ]
# display
head(neighbors)
Result
x y xd yd nx ny
4 -52 31 1 1 -51 32
8 -52 31 2 1 -50 32
12 -52 31 3 1 -49 32
16 -52 31 4 1 -48 32
20 -52 31 1 2 -51 33
24 -52 31 2 2 -50 33