Creating origin-destination matrices with R

Question

My data frame consists of individuals and the city they live at a point in time. I would like to generate one origin-destination matrix for each year, which records the numb

Answer 1

You could use reshape2's dcast and a loop to do this.

library(reshape2)

# write function
write_matrices <- function(year){
  mat <- dcast(subset(df, df$year_move == year), origin ~ destination)
  print(year)  
  print(mat)
}

# get unique list of years (there was an NA in there, so that's why this is longer than it needs to be
years <- unique(subset(df, is.na(df$year_move) == FALSE)$year_move)

# loop though and get results
for (year in years){
  write_matrices(year)
}

The only thing this doesn't address is the requirement for each matrix to have 5*5, because if some years do not have all the 5 cities only cities in that year are shown.

You could fix this by adding a step in that turns your observations into a frequency table first, so they are included but as zeros.

Answer 2

You can split your data from by id, perform the necessary computations on the id-specific data frame to grab all the moves from that person, and then re-combine:

spl <- split(df, df$id)
move.spl <- lapply(spl, function(x) {
  ret <- data.frame(from=head(x$city, -1), to=tail(x$city, -1),
                    year=ceiling((head(x$year, -1)+tail(x$year, -1))/2),
                    stringsAsFactors=FALSE)
  ret[ret$from != ret$to,]
})
(moves <- do.call(rbind, move.spl))
#       from    to year
# 1.1  City4 City2 2007
# 1.2  City2 City1 2008
# 1.3  City1 City5 2009
# 1.4  City5 City4 2009
# 1.5  City4 City2 2009
# ...

Because this code uses vectorized computations for each id, it should be a good deal quicker than looping through each row of your data frame as you did in the provided code.

Now you could grab the year-specific 5x5 move matrices using split and table:

moves$from <- factor(moves$from)
moves$to <- factor(moves$to)
lapply(split(moves, moves$year), function(x) table(x$from, x$to))
# $`2005`
#        
#         City1 City2 City3 City4 City5
#   City1     0     0     0     0     1
#   City2     0     0     0     0     0
#   City3     0     0     0     0     0
#   City4     0     0     0     0     0
#   City5     0     0     1     0     0
# 
# $`2006`
#        
#         City1 City2 City3 City4 City5
#   City1     0     0     0     1     0
#   City2     0     0     0     0     0
#   City3     1     0     0     1     0
#   City4     0     0     0     0     0
#   City5     2     0     0     0     0
# ...