How to convert a data set to a logic data frame in R?

Question

I have a series of dataset with repeating scores, the data frame is as follows

ID,Variable,Category
1,6,A
2,4,C
3,3,D
4,4,C
5,5,B
6,3,D
7,6,A
8,4,C
9,5,B
10,3,D

I want it to create a logic like this

ID,A,B,C,D
1,1,0,0,0
2,0,0,1,0
3,0,0,0,1
4,0,0,1,0
5,0,1,0,0
6,0,0,0,1
7,1,0,0,0
8,0,0,1,0
9,0,1,0,0
10,0,0,0,1

Answer 1

Three options.

1. This doesn't technically return a data.frame, it returns a "xtabs","table" class object, whose conversion to a data.frame is not necessarily what one might expect.

   xtabs(~ID + Category, data=dat)
   #     Category
   # ID   A B C D
   #   1  1 0 0 0
   #   2  0 0 1 0
   #   3  0 0 0 1
   #   4  0 0 1 0
   #   5  0 1 0 0
   #   6  0 0 0 1
   #   7  1 0 0 0
   #   8  0 0 1 0
   #   9  0 1 0 0
   #   10 0 0 0 1
   class(xtabs(~ID + Category, data=dat))
   # [1] "xtabs" "table"
   head(as.data.frame(xtabs(~ID + Category, data=dat)))
   #   ID Category Freq
   # 1  1        A    1
   # 2  2        A    0
   # 3  3        A    0
   # 4  4        A    0
   # 5  5        A    0
   # 6  6        A    0
   

2. Using tidyr::pivot_wider:

   tidyr::pivot_wider(dat, ID, names_from = Category, values_from = Variable, values_fill = list(Variable = 0))
   # # A tibble: 10 x 5
   #       ID     A     C     D     B
   #    <int> <int> <int> <int> <int>
   #  1     1     6     0     0     0
   #  2     2     0     4     0     0
   #  3     3     0     0     3     0
   #  4     4     0     4     0     0
   #  5     5     0     0     0     5
   #  6     6     0     0     3     0
   #  7     7     6     0     0     0
   #  8     8     0     4     0     0
   #  9     9     0     0     0     5
   # 10    10     0     0     3     0
   

3. data.table::dcast:

   library(data.table)
   dcast(as.data.table(dat), ID~Category, value.var = "Variable", fill = 0)
   #     ID A B C D
   #  1:  1 6 0 0 0
   #  2:  2 0 0 4 0
   #  3:  3 0 0 0 3
   #  4:  4 0 0 4 0
   #  5:  5 0 5 0 0
   #  6:  6 0 0 0 3
   #  7:  7 6 0 0 0
   #  8:  8 0 0 4 0
   #  9:  9 0 5 0 0
   # 10: 10 0 0 0 3
   

While options 2 and 3 do not produce your literal output, it shows their flexibility: you can adjust them to be all 0s and 1s by preemptively converting dat$Variable <- 1L.