I have a table of survey with answers that might be "right" or "wrong":
| Participant | Answer 1 | Answer 2 | Answer 3 | ... | Answer n |
|---|---|---|---|---|---|
| 1 | Right | Wrong | Wrong | ... | Right |
| 2 | Right | Wrong | Right | .... | Right |
| 3 | Wrong | Right | Right | ... | Wrong |
I want to calculate the number and percentage of "right" questions for every participant and make result that will look like this:
| Participant | Number of right answers | Percentage of right answers |
|---|---|---|
| 1 | p1 | p1, % |
| 2 | p2 | p2, % |
| 3 | p3 | p3, % |
| ... | ... | .... |
| n | pn | pn, % |
I think it is possible to do through loop, however, I don' know how to do this.
CodePudding user response：
You can use rowSums and rowMeans from base R. Since you don't provide a reproducible example, I created one, with 10 questions and participants:
df <- data.frame(participant = 1:10,
as.data.frame(matrix(sample(c("Right", "Wrong"), 100, replace = TRUE), nrow = 10)))
# participant V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
# 1 1 Right Right Right Wrong Wrong Right Wrong Wrong Wrong Right
# 2 2 Wrong Right Right Right Wrong Right Wrong Wrong Right Right
# 3 3 Right Right Wrong Right Right Wrong Right Wrong Right Right
# 4 4 Right Wrong Right Right Right Right Right Right Wrong Wrong
# 5 5 Right Right Right Wrong Right Right Wrong Right Wrong Wrong
# 6 6 Wrong Wrong Right Wrong Wrong Wrong Wrong Wrong Wrong Right
# 7 7 Wrong Right Wrong Wrong Right Right Wrong Wrong Wrong Wrong
# 8 8 Right Wrong Wrong Right Right Wrong Wrong Right Right Wrong
# 9 9 Right Wrong Wrong Right Right Right Wrong Wrong Right Right
# 10 10 Wrong Wrong Wrong Wrong Wrong Wrong Right Wrong Wrong Right
data.frame(df[1],
right_answers = rowSums(df[-1] == "Right"),
p_right_answers = rowMeans(df[-1] == "Right"))
# participant right_answers p_right_answers
# 1 1 5 0.5
# 2 2 6 0.6
# 3 3 7 0.7
# 4 4 7 0.7
# 5 5 6 0.6
# 6 6 2 0.2
# 7 7 3 0.3
# 8 8 5 0.5
# 9 9 6 0.6
# 10 10 2 0.2