How to isolate non-0 answers from survey data - r

Question

I have survey data in which the same people are asked the same question during 6 different periods. Sometimes they answer (in which case we get a score from 1 to 10), sometimes they don’t (in which case the answer is 0).
In the end, I got a data frame that looks like this (the only difference being that in this example the answers are from 1 to 2, that’s just because it was easier to generate an adequate number of 0s that way for me):
period_1 <- sample(0:2, 100, replace=T)
period_2 <- sample(0:2, 100, replace=T)
period_3 <- sample(0:2, 100, replace=T)
period_4 <- sample(0:2, 100, replace=T)
period_5 <- sample(0:2, 100, replace=T)
period_6 <- sample(0:2, 100, replace=T)
df <- cbind(period_1, period_2, period_3, period_4, period_5, period_6)
head(df)
period_1 period_2 period_3 period_4 period_5 period_6
[1,] 0 2 1 1 0 1
[2,] 2 1 1 2 0 0
[3,] 1 0 2 0 1 1
[4,] 1 2 2 1 0 2
[5,] 1 1 2 2 0 2
[6,] 1 0 1 2 2 0
Now, I want to see the evolution of their answer over time. But with the current structure of the data frame, it is a bit awkward: I can’t just compare period 1 to period 2, for instance, because they didn’t all answer at period 1 (or 2).
Instead, what I would like would be a data frame which shows their first answer in one vector, no matter from which period that answer came from, and then the second answer, and so on…
In others words, get the first non-0 answer in survey_1, the second non-0 answer in survey_2, etc…
This is probably not the best solution, but it's the most simple one and it would work just fine for me.
This would allow me to turn this:
period_1 period_2 period_3 period_4 period_5 period_6
[1,] 0 2 1 1 0 1
[2,] 2 1 1 2 1 0
[3,] 1 0 2 0 1 1
Into this:
survey_1 survey_2 survey_3 survey_4 survey_5 survey_6
[1,] 2 1 1 1 0 0
[2,] 2 1 1 2 1 0
[3,] 1 2 1 1 0 0
But to be honest, I'm still a big newbie in R and programming in general and I don't even know where to begin with achieving this, and I've been stuck on this for some time now without making any progress toward a solution.
Can anyone offer me tips, or even a sample code, that would allow me to get to the desired result, please ?
Thank you !

Answer 1

We can use apply and order by whether an element is 0 or not for each row:
df[] <- t(apply(df, 1, function(x) x[order(x == 0)]))
Result:
period_1 period_2 period_3 period_4 period_5 period_6
[1,] 1 2 2 1 0 0
[2,] 2 2 1 0 0 0
[3,] 1 1 1 2 2 0
[4,] 2 2 1 2 1 0
[5,] 2 1 1 1 1 1
[6,] 2 2 1 1 0 0
Data:
df <- structure(c(0L, 2L, 1L, 2L, 2L, 0L, 1L, 0L, 1L, 2L, 1L, 2L, 0L,
2L, 1L, 1L, 1L, 2L, 2L, 0L, 2L, 2L, 1L, 1L, 2L, 0L, 2L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 1L, 0L), .Dim = c(6L, 6L), .Dimnames = list(
NULL, c("period_1", "period_2", "period_3", "period_4", "period_5",
"period_6")))