Extract rows from a single column to form two new columns - r

Question

Update:
I realized that the dummy data frame I created originally does not reflect the structure of the data frame that I am working with. Allow me to rephrase my question here.
Data frame that I'm starting with:
StudentAndClass <- c("Anthropology College_Name","x","y",
"Geology College_Name","z","History College_Name", "x","y","z")
df <- data.frame(StudentAndClass)
Students ("x","y","z") are enrolled in classes that they are listed under. e.g. "x" and "y" are in Anthropology, while "x", "y", "z" are in History.
How can I create the desired data frame below?
Student <- c("x", "y", "z", "x", "y","z")
Class <- c("Anthropology College_Name", "Anthropology College_Name",
"Geology College_Name", "History College_Name",
"History College_Name", "History College_Name")
df_tidy <- data.frame(Student, Class)
Original post:
I have a data frame with observations of two variables merged in a single column like so:
StudentAndClass <- c("A","x","y","A","B","z","B","C","x","y","z","C")
df <- data.frame(StudentAndClass)
where "A", "B", "C" represent classes, and "x", "y", "z" students who are taking these classes. Notice that observations of students are wedged between observations of classes.
I'm wondering how I can create a new data frame with the following format:
Student <- c("x", "y", "z", "x", "y","z")
Class <- c("A", "A", "B", "C", "C", "C")
df_tidy <- data.frame(Student, Class)
I want to extract the rows containing observations of students and put them in a new column, while making sure that each Student observation is paired with the corresponding Class observation in the Class column.

Answer 1

One option is to create a vector
v1 <- c('x', 'y', 'z')
Then split the data based on logical vector and rbind
setNames(do.call(cbind, split(df, !df[,1] %in% v1)), c('Student', 'Class'))
# Student Class
#2 x A
#3 y A
#6 z B
#9 x B
#10 y C
#11 z C
Or with tidyverse
library(tidyverse)
df %>%
group_by(grp = c('Class', 'Student')[(StudentAndClass %in% v1) + 1]) %>%
mutate(n = row_number()) %>%
spread(grp, StudentAndClass) %>%
select(-n)
# A tibble: 6 x 2
# Class Student
#* <fctr> <fctr>
#1 A x
#2 A y
#3 B z
#4 B x
#5 C y
#6 C z
Update
If we need this based on elements between each pair of same 'LETTERS'
grp <- with(df, cummax(match(StudentAndClass, LETTERS[1:3], nomatch = 0)))
do.call(rbind, lapply(split(df, grp), function(x)
data.frame(Class = x[,1][2:(nrow(x)-1)], Student = x[[1]][1], stringsAsFactors=FALSE)))

Answer 2

Updated
In essence, you just need to find which indexes have college names, use those to get the range of students in each college, then subset the main vector by those ranges. Since students aren't guaranteed to be nested between two similar values, you have to be careful about any "empty" colleges.
college_indices <- which(endsWith(StudentAndClass, 'College_Name'))
colleges <- StudentAndClass[college_indices]
bounds_mat <- rbind(
start = college_indices,
end = c(college_indices[-1], length(StudentAndClass))
)
colnames(bounds_mat) <- colleges
bounds_mat['start', ] <- bounds_mat['start', ] + 1
bounds_mat['end', ] <- bounds_mat['end', ] - 1
# This prevents any problems if a college has no listed students
empty_college <- bounds_mat['start', ] > bounds_mat['end', ]
bounds_mat <- bounds_mat[, !empty_college]
class_listing <- apply(
bounds_mat,
2,
function(bounds) {
StudentAndClass[bounds[1]:bounds[2]]
}
)
df_tidy <- data.frame(
Student = unlist(class_listing),
Class = rep(names(class_listing), lengths(class_listing)),
row.names = NULL
)