creating a transitions matrix for markov Model in R

Question

I need help on a topic related to markov chains and preprocessing of data. Suppose I have the following matrix relating individuals to states over time:

     ID Time1 Time2
1 14021     A     A
2 15031     B     A
3 16452     A     C

I would like to obtain, for this matrix, the state transition matrix: Hence, what is required is

  A  B  C
A 1  0  1
B 1  0  0
C 0  0  0

and the same thing, but now weighted by the toal number of transitions from that state, i.e,

  A    B   C
A 0.5  0  0.5
B 1    0   0
C 0    0   0

(as there are two transitions leaving from state A). I know that the markovchain package has a functionality for doing this if one has a sequence, say AAABBAAABBCC, but not if data is set up like I have. Ideally a direct procedure would be great, but if there is some way of turning the data into a set of sequences that would work as well.

Any ideas?

Thanks in advance

Answer 1

An igraph approach, so using df from Joseph's answer:

library(igraph)

g <- graph_from_data_frame(df)

E(g)$weight = 1/degree(g, mode="out")[df$Time1] # get counts

as_adj(g, attr = "weight", sparse=FALSE) # output weighted adjacency matrix

    A B   C
A 0.5 0 0.5
B 1.0 0 0.0
C 0.0 0 0.0