I have three numerical vectors containing position values (pos
), a category (type
), and an index (ind
), in these general forms:
pos =
2 4 5 11 1 5 8 11 12 20
type =
1 2 1 2 1 1 2 1 2 3
ind =
1 1 1 1 2 2 2 2 2 2
I want to calculate the difference between values held within pos but only between the same types, and confined to each index. Using the above example:
When ind = 1
The difference(s) between type 1 positions = 3 (5-2).
The difference(s) between type 2 positions = 7 (11-4).
In the case where more than two instances of any given type exist within any index, the differences are calculate sequentially from left to right as shown here:
When ind = 2
The difference(s) between type 1 positions = 4 (5-1), 6 (11-5).
The difference(s) between type 2 positions = 4 (12-8).
Even though index 2 contains type '3', no difference is calculated as only 1 instance of this type is present.
Types are not always only 1, 2 or 3.
Ideally, the desired output would be matrix containing the same number of columns as length(unique(type))
with rows containing all differences calculated for that type. The output does not need to separate by index, only the actual calculation needs to. In this case there are three unique types, so the output would be (labels added for clarity only):
Type 1 Type 2 Type 3
3 7 0
4 4 0
6 0 0
Any empty entries can be padded with zeroes.
Is there a concise or fast manner to do this?
EDIT:
EDIT 2:
Additional input/output example.
pos = [1 15 89 120 204 209 8 43 190 304]
type = [1 1 1 2 2 1 2 3 2 3]
ind = [1 1 1 1 1 1 2 2 2 2]
Desired output:
Type 1 Type 2 Type 3
14 84 261
74 182 0
120 0 0
In this case, the script works perfectly: