Is there an R package to calculate 1st order transition matrix from a frequency table?

I have a frequency table aggregated from 800 millions of records and am wondering if I can use a package to calculate 1st order transition matrix from the frequency table, which is not symmetric because some state just never happened again. A sample of the frequency table is:

library(data.table)
model.data <- data.table(state1 = c(3, 1, 2, 3), state2 = c(1, 2, 1, 2), Freq = c(1,2,3,4))

model.data looks like this:

Using the package pollster, I can compute the proportion table:

library(pollster)
crosstab(model.data, state1, state2, Freq)

However, the symmetric transition matrix I am looking for is:

That is, I still want to include the state 3 even though no one transitioned to it, and the code should be able to automatically find out 3 needs to be appended with a column of 0s.

I am not sure if the markovchain package with the markovchainFit function is going to handle my 800 million rows of data that I need to transform into a list of millions of sequences, due to memory constraints and slow computing speed.

Does anyone know?

An option with igraph

model.data %>%
  setorder(state1) %>%
  graph_from_data_frame() %>%
  as_adjacency_matrix(attr = "Freq", sparse = FALSE) %>%
  proportions(1)  # 1 sets rows as the margin, similar to `prop.table`

gives

    1   2 3
1 0.0 1.0 0
2 1.0 0.0 0
3 0.2 0.8 0

Or with base R

> proportions(xtabs(Freq ~ ., model.data), 1)
      state2
state1   1   2
     1 0.0 1.0
     2 1.0 0.0
     3 0.2 0.8