Aggregating sequential and grouped data in R

Question

I have a dataset that looks like this toy example. The data describes the location a person has moved to and the time since this relocation happened. For example, person 1 started out in a rural area, but moved to a city 463 days ago (2nd row), and 415 days ago he moved from this city to a town (3rd row), etc.

set.seed(123)
df <- as.data.frame(sample.int(1000, 10))
colnames(df) <- "time"
df$destination <- as.factor(sample(c("city", "town", "rural"), size = 10, replace = TRUE, prob = c(.50, .25, .25)))
df$user <- sample.int(3, 10, replace = TRUE)
df[order(df[,"user"], -df[,"time"]), ]

The data:

time destination user
 526       rural    1
 463        city    1
 415        town    1
 299        city    1
 179       rural    1
 938        town    2
 229        town    2
 118        city    2
 818        city    3
 195        city    3

I wish to aggregate this data to the format below. That is, to count the types of relocations for each user, and sum it up to one matrix. How do I achieve this (preferably without writing loops)?

from  to     count
city  city   1
city  town   1
city  rural  1
town  city   2
town  town   1
town  rural  0
rural city   1
rural town   0
rural rural  0

Answer 1

One possible way based on data.table package:

library(data.table)

cases <- unique(df$destination)

setDT(df)[, .(from=destination, to=shift(destination, -1)), by=user
          ][CJ(from=cases, to=cases), .(count=.N), by=.EACHI, on=c("from", "to")]


#      from     to count
#    <char> <char> <int>
# 1:   city   city     1
# 2:   city  rural     1
# 3:   city   town     1
# 4:  rural   city     1
# 5:  rural  rural     0
# 6:  rural   town     0
# 7:   town   city     2
# 8:   town  rural     0
# 9:   town   town     1