Newbie trouble building script for a report - r

Question

I am a newbie to coding and to R. I have been trying to solve a problem for a report that I am drawing and have hit a wall.
I have spent the last two days trying to find a workable answer and am now at my wit's end.
I have a data frame of student results. The columns are as follows
Student Number
Academic Year eg 2014, 2015 etc
Academic Semester eg Jan or June
Qualification eg Qual1, Qual2 etc
Modules eg Subject1, subject2 etc. the issue here is that subject1 may be in Qual1 and Qual2 but suject2 may only be in Qual1
Result. This is either "Passed" or "FAILED"
I am trying to create a summary/list showing the percentage passed for each module where students were active. Something like this
Year Semester Qualification Module PassRate
2014 Jan Qual1 Subject1 62.54%
2014 Jan Qual1 Subject2 72.81%
.
.
.
2014 July Qual1 Subject1 69.51%
.
.
2014 Jan Qual2 Subject1 42.86%
2014 Jan Qual2 Subject3 55.95%
etc.
I thought that perhaps an IF statement might work but that seems way too cumbersome. I also looked at For each but I can't seem to figure how to get it to work or a combination of the above. I have tried aggregate, count =, cbind and anything that i could find from my good friend Google.
I have the following code
AcademicYears <- as.character(unique(unlist(HE_Stats$Year)))
AcademicYears_count <- NROW(AcademicYears)
AcademicSemesters <- as.character(unique(unlist(HE_Stats$ActualSemester)))
AcademicSemesters_count <- NROW(AcademicSemesters)
Qualifications <- as.character(unique(unlist(HE_Stats$Qualification)))
Qualifications_count <- NROW(Qualifications)
Modules <- as.character(unique(unlist(HE_Stats$ModuleCode)))
Modules_count <- NROW(Modules)
df <- HE_Stats %>%
group_by(Year,ActualSemester,Qualification, ModuleCode) %>%
aggregate(cbind(count = AcademicSemesters) ~ AcademicYears,
data = HE_Stats,
FUN = function(AcademicSemesters){NROW(AcademicSemesters)})
the result of this is that it shows me one semester per year. My latest plan is to build the matrix column by column.

Answer 1

If you could supply sample data would be able to give you a better answer. But say that your data looked something like (this solution uses the dplyr package:
library(dplyr)
data <- tibble(student_number = c(1, 2, 3, 4, 5, 6),
academic_year = c(2014, 2014, 2014, 2015, 2015, 2015),
semester = c("jan", "jan", "jan","jan", "june", "june"),
qualification = c("qual1", "qual2", "qual1", "qual1", "qual2",
"qual2"),
module = c("subject1", "subject1", "subject1", "subject1",
"subject2", "subject2"),
result = c("passed", "failed", "passed", "passed", "passed",
"failed"))
# A tibble: 6 x 6
student_number academic_year semester qualification module result
<dbl> <dbl> <chr> <chr> <chr> <chr>
1 1 2014 jan qual1 subject1 passed
2 2 2014 jan qual2 subject1 failed
3 3 2014 jan qual1 subject1 passed
4 4 2015 jan qual1 subject1 passed
5 5 2015 june qual2 subject2 passed
6 6 2015 june qual2 subject2 failed
First I would make a logical vector of whether the subject had passed:
data <- data %>%
mutate(pass = ifelse(result == "passed", TRUE, FALSE))
Then summarise the grouped data:
data %>%
group_by(academic_year, semester, qualification, module) %>%
summarise(
pass_rate = (sum(pass)/n())*100
)
To produce:
academic_year semester qualification module pass_rate
<dbl> <chr> <chr> <chr> <dbl>
1 2014 jan qual1 subject1 100
2 2014 jan qual2 subject1 0
3 2015 jan qual1 subject1 100
4 2015 june qual2 subject2 50