assigning new values based on the location in the sequence

Question

Working in R. The data tracks changes in brain activity over time. Column "mark" contains information when a particular treatment begins and ends. For examples, the first condition (mark==1) begins in row 3 and ends in row 6. The second experimental condition (mark==2) starts in row 9 and ends in 12. Another batch of treatment one is repeated between rows 15 and 18.

ob.id <- c(1:20)
mark <- c(0,0,1,0,0,1,0,0,2,0,0,2,0,0,1,0,0,1,0,0)
condition<-c(0,0,1,1,1,1,0,0,2,2,2,2,0,0,1, 1,1,1,0,0)
start <- data.frame(ob.id,mark)
result<-data.frame(ob.id,mark,condition)
print (start)
> print (start)
   ob.id mark
1      1    0
2      2    0
3      3    1
4      4    0
5      5    0
6      6    1
7      7    0
8      8    0
9      9    2
10    10    0
11    11    0
12    12    2
13    13    0
14    14    0
15    15    1
16    16    0
17    17    0
18    18    1
19    19    0
20    20    0

I need to create a column that would have a dummy variable indicating the membership of an observation in corresponding experimental condition, like this:

> print(result)
   ob.id mark condition
1      1    0         0
2      2    0         0
3      3    1         1
4      4    0         1
5      5    0         1
6      6    1         1
7      7    0         0
8      8    0         0
9      9    2         2
10    10    0         2
11    11    0         2
12    12    2         2
13    13    0         0
14    14    0         0
15    15    1         1
16    16    0         1
17    17    0         1
18    18    1         1
19    19    0         0
20    20    0         0

Thanks for your help!

Answer 1

This is a fun little problem. The trick I use below is to first calculate the rle of the mark vector, which makes the problem simpler, as the resulting values vector will always have just one 0 that may or may not need to be replaced (depending on the surrounding values).

# example vector with some edge cases
v = c(0,0,1,0,0,0,1,2,0,0,2,0,0,1,0,0,0,0,1,2,0,2)

v.rle = rle(v)
v.rle
#Run Length Encoding
#  lengths: int [1:14] 2 1 3 1 1 2 1 2 1 4 ...
#  values : num [1:14] 0 1 0 1 2 0 2 0 1 0 ...

vals = rle(v)$values

# find the 0's that need to be replaced and replace by the previous value
idx = which(tail(head(vals,-1),-1) == 0 & (head(vals,-2) == tail(vals,-2)))
vals[idx + 1] <- vals[idx]

# finally go back to the original vector
v.rle$values = vals
inverse.rle(v.rle)
# [1] 0 0 1 1 1 1 1 2 2 2 2 0 0 1 1 1 1 1 1 2 2 2

Probably the least cumbersome thing to do is to put the above in a function and then apply that to your data.frame vector (as opposed to manipulating the vector explicitly).

---

Another approach, based on @SimonO101's observation, involves constructing the right groups from the starting data (run the by part separately, piece by piece, to see how it works):

library(data.table)
dt = data.table(start)

dt[, result := mark[1],
     by = {tmp = rep(0, length(mark));
           tmp[which(mark != 0)[c(F,T)]] = 1;
           cumsum(mark != 0) - tmp}]
dt
#    ob.id mark result
# 1:     1    0      0
# 2:     2    0      0
# 3:     3    1      1
# 4:     4    0      1
# 5:     5    0      1
# 6:     6    1      1
# 7:     7    0      0
# 8:     8    0      0
# 9:     9    2      2
#10:    10    0      2
#11:    11    0      2
#12:    12    2      2
#13:    13    0      0
#14:    14    0      0
#15:    15    1      1
#16:    16    0      1
#17:    17    0      1
#18:    18    1      1
#19:    19    0      0
#20:    20    0      0

The latter approach will probably be more flexible.