Assume we have the following matrix :
M=structure(c(1, 2, 2, 2, 1, 3, 3, 4, 4, 6, 5, 5, 5, 6, 5, 5, 2,
2, 4, 1, 3, 1, 6, 4, 2, 5, 1, 3, 4, 6, 0.849542987113752, 0.849542987113752,
0.788371730579003, 0.788371730579003, 0.788371730579003, 0.788371730579003
), .Dim = c(6L, 6L), .Dimnames = list(NULL, c("", "", "", "",
"index", "cum_detection")))
The matrix M is sorted according to its last row :
index cum_detection
[1,] 1 3 5 4 2 0.8495430
[2,] 2 4 6 1 5 0.8495430
[3,] 2 4 5 3 1 0.7883717
[4,] 2 6 5 1 3 0.7883717
[5,] 1 5 2 6 4 0.7883717
[6,] 3 5 2 4 6 0.7883717
I’m searching to obtain this following output :
index cum_detection
[1,] 1 3 5 4 2 0.8495430
[2,] 0 4 6 1 5 0.8495430
[3,] 0 4 0 3 1 0.7883717
[4,] 0 6 0 0 3 0.7883717
[5,] 0 0 0 6 4 0.7883717
[6,] 0 0 0 0 6 0.7883717
For each row , we search through columns from 1 to 4.
If we found a value that is equal to any of previous visited rows index values , we replace it by 0.
For example , consider the third row M[3,1:4] :
> M[3,1:4]
2 4 5 3
The values 2 and 5 are already present in previous rows index column ( M[1:2,"index"]).
I believe the solution is something like :
t(apply(M,1,function(x) replace(x,M[1:current_row-1,"index"],0)))
Where current_row is the current row of apply loop. The problem here is that i don’t know how to get apply current row index.
>Solution :
We can loop over the sequence of rows from ‘M’ (starting from 2), then find if the columns 1:4 for that row is found %in% sequence of rows till the previous row (1:(i-1)) for ‘index’, then assign the values in those columns to 0
for(i in 2:nrow(M)) M[i, 1:4][M[i, 1:4] %in% M[1:(i-1), "index"]] <- 0
-output
> M
index cum_detection
[1,] 1 3 5 4 2 0.8495430
[2,] 0 4 6 1 5 0.8495430
[3,] 0 4 0 3 1 0.7883717
[4,] 0 6 0 0 3 0.7883717
[5,] 0 0 0 6 4 0.7883717
[6,] 0 0 0 0 6 0.7883717
Or using dplyr
library(dplyr)
library(tibble)
M %>%
as_tibble %>%
mutate(rn = row_number()) %>%
rowwise %>%
mutate(across(1:4, ~ replace(.x, .x %in% M[, "index"][
seq_len(rn - 1)], 0)), rn = NULL) %>%
ungroup
# A tibble: 6 × 6
V1 V2 V3 V4 index cum_detection
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 3 5 4 2 0.850
2 0 4 6 1 5 0.850
3 0 4 0 3 1 0.788
4 0 6 0 0 3 0.788
5 0 0 0 6 4 0.788
6 0 0 0 0 6 0.788