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I have a data set with the following structure:

dat<- data.frame(ID= c("A","A","A", "A", "B", "B", "B", "B"), 
             test= rep(c("pre","post"),4),
             item= c(rep("item1",2), rep("item2",2)),
             answer= c(0,5,4,-3,1,1,5,6))

For each level of ID, test, and item, I want to measure the amount of change and create a categorical variable that identifies the amount as a positive, negative or no change (none).

The result data frame for this example would look like:

res<- data.frame(ID= c("A","A", "B", "B"), 
             item= c(rep(c("item1","item2"),2)),
             diff= c(5, -7, 0,1), 
             change_type=c("positive","negative", "none", "positive"))
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1 Answer 1

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First of all, I believe the second line in dat should be as follows: test = rep(c("pre", "post"), 4)?

This way, you'll have a following table.

ID test item   answer
A  pre  item1  0
A  post item1  5
A  pre  item2  4
A  post item2 -3
B  pre  item1  1
B  post item1  1
B  pre  item2  5
B  post item2  6

If this is a case, then this little piece of code should help you.

result <- dat %>%
  group_by(ID, item) %>%
  mutate(diff = answer - lag(answer, 1)) %>%
  mutate(change_type = ifelse(answer - lag(answer, 1) > 0, "positive", 
                              ifelse(answer - lag(answer, 1) < 0, "negative", "none"))) %>%
  select(-test, -answer) %>%
  drop_na()
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    $\begingroup$ Thank you and I edited the post fromtest= rep("pre","post",4) to test= rep(c("pre","post"),4) $\endgroup$
    – WabiSabi
    May 4, 2022 at 21:06

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