How to categorize multi-index data in R?
Question
I have a multi-index data set with 100 cases, and each case has 10 questions. Each question was scored by 3 raters.
Case Question A B C
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 1 0
1 2 1 1 1
1 3 1 1 0
1 4 1 1 1
1 5 1 1 1
1 6 1 1 1
1 7 1 1 1
1 8 1 1 1
1 9 1 1 0
1 10 1 1 0
2 1 1 1 1
2 2 1 0.5 1
2 3 1 1 1
2 4 1 1 1
2 5 1 1 1
2 6 1 1 1
2 7 1 1 1
2 8 1 1 1
2 9 1 0 0
2 10 1 0 0
3 1 1 1 1
3 2 1 1 1
3 3 1 1 1
3 4 1 1 1
3 5 1 1 1
3 6 1 1 1
3 7 1 1 1
3 8 1 1 1
3 9 1 1 0
3 10 1 0 0
......
I have used the following function (thanks to @Limey) to get the sum of each cases
addSummaryRow <- function(data, qFilter, newIndex) {
data %>%
bind_rows(
data %>%
pivot_longer(cols = c(colnames(df)[3:5])) %>%
filter(Question %in% qFilter) %>%
group_by(Case, name) %>%
summarise(value=sum(value), .groups="drop") %>%
pivot_wider(id_cols=c(Case), names_from=name, values_from=value) %>%
mutate(Question=newIndex)
) %>%
arrange(Case, Question)
}
df %>%
addSummaryRow(1:10, 11)
Case Question A B C
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 1 0
1 2 1 1 1
1 3 1 1 0
1 4 1 1 1
1 5 1 1 1
1 6 1 1 1
1 7 1 1 1
1 8 1 1 1
1 9 1 1 0
1 10 1 1 0
1 11 10 10 6
2 1 1 1 1
2 2 1 0.5 1
2 3 1 1 1
2 4 1 1 1
2 5 1 1 1
2 6 1 1 1
2 7 1 1 1
2 8 1 1 1
2 9 1 0 0
2 10 1 0 0
2 11 10 7.5 8
3 1 1 1 1
3 2 1 1 1
3 3 1 1 1
3 4 1 1 1
3 5 1 1 1
3 6 1 1 1
3 7 1 1 1
3 8 1 1 1
3 9 1 1 0
3 10 1 0 0
3 11 10 9 8
......
Now I want to add question 12 at the end of each case by converting the results of question 11 if <Q11 = 0-4.0, Q12 = 1>; <Q11 = 4.1-7.0, Q12 = 2>; <Q11 = 7.1-10, Q12 = 3>, so it would be:
Case Question A B C
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 1 0
1 2 1 1 1
1 3 1 1 0
1 4 1 1 1
1 5 1 1 1
1 6 1 1 1
1 7 1 1 1
1 8 1 1 1
1 9 1 1 0
1 10 1 1 0
1 11 10 10 6
1 12 3 3 2
2 1 1 1 1
2 2 1 0.5 1
2 3 1 1 1
2 4 1 1 1
2 5 1 1 1
2 6 1 1 1
2 7 1 1 1
2 8 1 1 1
2 9 1 0 0
2 10 1 0 0
2 11 10 7.5 8
2 12 3 3 3
3 1 1 1 1
3 2 1 1 1
3 3 1 1 1
3 4 1 1 1
3 5 1 1 1
3 6 1 1 1
3 7 1 1 1
3 8 1 1 1
3 9 1 1 0
3 10 1 0 0
3 11 10 9 8
3 12 3 3 3
......
I wonder how to achieve this conversion.
1 answers
solution1
0 ACCPTED 2022-12-02 11:29:44
As already stated in comments, I think it's a bad practice to add summary rows into data. (Probably this comes from "spreadsheet-thinking".)
Instead, consider this approach: Use a summary data frame a ,
a <- aggregate(cbind(A, B, C) ~ Case, dat, sum)
a
# Case A B C
# 1 1 10 10.0 6
# 2 2 10 7.5 8
# 3 3 10 9.0 8
cut the values at the desired breakpoints, and assign labels= .
a[2:4] <- lapply(a[2:4], cut, breaks=c(.4, 4.1, 7.1, 10), labels=1:3)
a
# Case A B C
# 1 1 3 3 2
# 2 2 3 3 3
# 3 3 3 3 3
Edit
If you really need to add the results as rows to your data, you can rbind them, and order the results. Using this method you can easily add more of such rows.
a1 <- cbind(a <- aggregate(cbind(A, B, C) ~ Case, dat, sum), Question=11)
a[2:4] <- lapply(a[2:4], cut, c(.4, 4.1, 7.1, 10), labels=1:3)
a2 <- cbind(a, Question=12)
dat <- rbind(dat, a1, a2)
res <- dat[with(dat, order(Case, Question)), ]
Note, that cut s below .4 in this case become NA , try sq <- seq.int(0, 10, .1); cut(sq, c(.4, 4.1, 7.1, 10)) sq <- seq.int(0, 10, .1); cut(sq, c(.4, 4.1, 7.1, 10)) to see how cut works.
Gives
res
# Case Question A B C
# 1 1 1 1 1 0
# 2 1 2 1 1 1
# 3 1 3 1 1 0
# 4 1 4 1 1 1
# 5 1 5 1 1 1
# 6 1 6 1 1 1
# 7 1 7 1 1 1
# 8 1 8 1 1 1
# 9 1 9 1 1 0
# 10 1 10 1 1 0
# 31 1 11 10 10 6
# 34 1 12 3 3 2
# 11 2 1 1 1 1
# 12 2 2 1 0.5 1
# 13 2 3 1 1 1
# 14 2 4 1 1 1
# 15 2 5 1 1 1
# 16 2 6 1 1 1
# 17 2 7 1 1 1
# 18 2 8 1 1 1
# 19 2 9 1 0 0
# 20 2 10 1 0 0
# 32 2 11 10 7.5 8
# 35 2 12 3 3 3
# 21 3 1 1 1 1
# 22 3 2 1 1 1
# 23 3 3 1 1 1
# 24 3 4 1 1 1
# 25 3 5 1 1 1
# 26 3 6 1 1 1
# 27 3 7 1 1 1
# 28 3 8 1 1 1
# 29 3 9 1 1 0
# 30 3 10 1 0 0
# 33 3 11 10 9 8
# 36 3 12 3 3 3
Data:
dat <- structure(list(Case = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L), Question = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L), A = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), B = c(1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0.5, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0), C = c(0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L)), class = "data.frame", row.names = c(NA, -30L))
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