frequency table for repeated measure

original df:

ID <- c(1,1,1,1,2,2,2,2,3,3,3,3,3)
DX <- c("A","A","B","B","C","C","A","B","A","A","A","B","B")
df <- data.frame(ID,DX)

   ID DX
1   1  A
2   1  A
3   1  B
4   1  B
5   2  C
6   2  C
7   2  A
8   2  B
9   3  A
10  3  A
11  3  A
12  3  B
13  3  B

I try to make a frequency table for DX.

tblFun <- function(x){
  tbl <- table(x)
  res <- cbind(tbl,round(prop.table(tbl)*100,2))
  colnames(res) <- c('Count','Percentage')
  res
}

do.call(rbind,lapply(df[2],tblFun))

  Count Percentage
A     6      46.15
B     5      38.46
C     2      15.38

The calculation above has the denominator 13 (which is the number of observations), but since there are only 3 distinct IDs, the denominator should be 3.
i.e: 3 people had A, 3 people had B, 1 person had C, so the calculations should be like the following:

  Count Percentage
A     3      100.00
B     3      100.00
C     1      33.33

How can I transform the data frame so the calculation could be done like the above?

I would appreciate all the help there is! Thanks!

>Solution :

After creating the table object, get the rowSums on rowMeans on a logical matrix

m1 <- table(df[2:1]) > 0
cbind(Count = rowSums(m1), Percentage = round(rowMeans(m1)* 100, 2))

-output

  Count Percentage
A     3     100.00
B     3     100.00
C     1      33.33