r recode by a splitting rule - r

Question

I have a student dataset including student information, question id (5 questions), the sequence of each trial to answer the questions. I would like to create a variable to distinguish where exactly student starts reviewing questions after finishing all questions.
Here is a sample dataset:
data <- data.frame(
person = c(1,1,1,1,1,1,1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2),
question = c(1,2,2,3,3,3,4,3,5,1,2, 1,1,1,2,3,4,4,4,5,5,4,3,4,4,5,4,5),
sequence = c(1,1,2,1,2,3,1,4,1,2,3, 1,2,3,1,1,1,2,3,1,2,4,2,5,6,3,7,4))
data
person question sequence
1 1 1 1
2 1 2 1
3 1 2 2
4 1 3 1
5 1 3 2
6 1 3 3
7 1 4 1
8 1 3 4
9 1 5 1
10 1 1 2
11 1 2 3
12 2 1 1
13 2 1 2
14 2 1 3
15 2 2 1
16 2 3 1
17 2 4 1
18 2 4 2
19 2 4 3
20 2 5 1
21 2 5 2
22 2 4 4
23 2 3 2
24 2 4 5
25 2 4 6
26 2 5 3
27 2 4 7
28 2 5 4
sequence variables record each visit by giving a sequence number. Generally revisits could be before seeing all questions. However, the attempt variable should only record after the student sees all 5 questions. With the new variable, I target this dataset.
> data
person question sequence attempt
1 1 1 1 initial
2 1 2 1 initial
3 1 2 2 initial
4 1 3 1 initial
5 1 3 2 initial
6 1 3 3 initial
7 1 4 1 initial
8 1 3 4 initial
9 1 5 1 initial
10 1 1 2 review
11 1 2 3 review
12 2 1 1 initial
13 2 1 2 initial
14 2 1 3 initial
15 2 2 1 initial
16 2 3 1 initial
17 2 4 1 initial
18 2 4 2 initial
19 2 4 3 initial
20 2 5 1 initial
21 2 5 2 initial
22 2 4 4 review
23 2 3 2 review
24 2 4 5 review
25 2 4 6 review
26 2 5 3 review
27 2 4 7 review
28 2 5 4 review
Any ideas?
Thanks!

Answer 1

What a challenging question. Took almost 2 hours to find the solution.
Try this
library(dplyr)
dist_cum <- function(var)
sapply(seq_along(var), function(x) length(unique(head(var, x))))
data %>%
mutate(var0 = n_distinct(question)) %>%
group_by(person) %>%
mutate(var1 = dist_cum(question),
var2 = cumsum(c(1, diff(question) != 0))) %>%
ungroup() %>%
mutate(var3 = if_else(sequence == 1 | var1 < var0, 0, 1)) %>%
group_by(person, var2) %>%
mutate(var4 = min(var3)) %>%
ungroup() %>%
mutate(attemp = if_else(var4 == 0, "initial", "review")) %>%
select(-starts_with("var")) %>%
as.data.frame
Result
person question sequence attemp
1 1 1 1 initial
2 1 2 1 initial
3 1 2 2 initial
4 1 3 1 initial
5 1 3 2 initial
6 1 3 3 initial
7 1 4 1 initial
8 1 3 4 initial
9 1 5 1 initial
10 1 1 2 review
11 1 2 3 review
12 2 1 1 initial
13 2 1 2 initial
14 2 1 3 initial
15 2 2 1 initial
16 2 3 1 initial
17 2 4 1 initial
18 2 4 2 initial
19 2 4 3 initial
20 2 5 1 initial
21 2 5 2 initial
22 2 4 4 review
23 2 3 2 review
24 2 4 5 review
25 2 4 6 review
26 2 5 3 review
27 2 4 7 review
28 2 5 4 review
dist_cum is a function to calculate rolling distinct (Source). var0...var4 are helpers

Answer 2

One way to do it is by finding where the reviewing starts (i.e. the next entry after the fifth question has been seen) and where the sequence is 2. See v1 and v2. Then by means of subsetting for every individual person and looping by each subset, you can update the missing entries for the attempt variable since it is now known where the reviewing starts.
v1 <- c(FALSE, (data$question == 5)[-(nrow(data))])
v2 <- data$sequence == 2
data$attempt <- ifelse(v1 * v2 == 1, "review", NA)
persons <- unique(data$person)
persons.list <- vector(mode = "list", length = length(persons))
for(i in 1:length(persons)){
person.i <- subset(data, person == persons[i])
n <- which(person.i$attempt == "review")
m <- nrow(person.i)
person.i$attempt[(n+1):m] <- "review"
person.i$attempt[which(is.na(person.i$attempt))] <- "initial"
persons.list[[i]] <- person.i
}
do.call(rbind, persons.list)
person question sequence attempt
1 1 1 1 initial
2 1 2 1 initial
3 1 2 2 initial
4 1 3 1 initial
5 1 3 2 initial
6 1 3 3 initial
7 1 4 1 initial
8 1 3 4 initial
9 1 5 1 initial
10 1 1 2 review
11 1 2 3 review
12 2 1 1 initial
13 2 1 2 initial
14 2 1 3 initial
15 2 2 1 initial
16 2 3 1 initial
17 2 4 1 initial
18 2 4 2 initial
19 2 4 3 initial
20 2 5 1 initial
21 2 5 2 review
22 2 4 4 review
23 2 3 2 review
24 2 4 5 review
25 2 4 6 review
26 2 5 3 review
27 2 4 7 review
28 2 5 4 review
Alternatively, you can also use lapply:
do.call(rbind,
lapply(persons, function(x){
person.x <- subset(data, person == x)
n <- which(person.x$attempt == "review")
m <- nrow(person.x)
person.x$attempt[(n+1):m] <- "review"
person.x$attempt[which(is.na(person.x$attempt))] <- "initial"
person.x
}))