R: Get proportions of values in nested list and store in data frame

Question

I'm fairly new to R and trying to build a data frame that includes the frequency for each unique observation for each element of my nested list:

lst <- list(
  c('A', 'A', 'A'), 
  c("A", "B"), 
  c("A", "A", "B", "B", "B", "B"), 
  c("A", "C", "C"), 
  c("B", "A")
)

I could figure out how to do that disregarding the elements of the list or for just one of the elements:

prop <- prop.table(table(unlist(lapply(lst, unique))))
as.data.frame(prop)

# or

as.data.frame(prop.table(table(lst[[1]]))

But not how to effectively combine the two.

My desired output is along the lines of:

type  1  2   3  4   5
======================
A     1 .5 .33 .33 .5 
B     0 .5 .67  0  .5
C     0  0  0  .67  0

Additionally, I would like the output to have more digits than they do when just using prop.table(). Any advice is much appreciated

Answer 1

We may do this in a single line - the trick is to create a two column data.frame with stack, get the table and use proportions with MARGIN as 2

proportions(table(stack(setNames(lst, seq_along(lst)))), 2)

-output

    ind
values         1         2         3         4         5
     A 1.0000000 0.5000000 0.3333333 0.3333333 0.5000000
     B 0.0000000 0.5000000 0.6666667 0.0000000 0.5000000
     C 0.0000000 0.0000000 0.0000000 0.6666667 0.0000000

Answer 2

An approach using prop.table

First get the unique entries un_lst, then simply compare them with each list entry.

un_lst <- unique(unlist(lst))

data.frame(
  sapply(lst, function(x) 
    prop.table(setNames(rowSums(sapply(x, function(y) 
      y == un_lst)), un_lst))))
  X1  X2        X3        X4  X5
A  1 0.5 0.3333333 0.3333333 0.5
B  0 0.5 0.6666667 0.0000000 0.5
C  0 0.0 0.0000000 0.6666667 0.0