sequential_variation.R

sequential_variation.R

library(plyr)

# turn the data into event-graphs
graph_transformation <- function(dat){
  dat2 <- ddply(dat, .(id), function(d){
    data.frame(
      event = d$event[-1],
      from = d$event[-NROW(d)],
      to = d$event[-1],
      time = paste(d$time[-NROW(d)], d$time[-1], sep = "-")
    )
  })
  rc1_edge_list <- cbind(dat2["from"], dat2["to"])
  network_data_object <- try_default(as.network(rc1_edge_list[, 1:2], hyper=FALSE, loops=TRUE, multiple=TRUE), default = NULL)
  (network_data_object)
}

# This function extracts the degrees for each event (i.e. the number of sequential transitions it has with other event types)
extract_degrees <- function(x){
  list_rc <- split(x, x$id)
  sna_list <- lapply(list_rc, graph_transformation)
  
  edgecount_list <- list()
  for (i in 1:length(sna_list)){
    if(is.null(sna_list[[i]])) { 
      edgecount_list[i] <- list(NULL) 
    } else {
      edgecount_list[i] <- network.edgecount(sna_list[[i]])
    }
  }
  for (i in 1:length(edgecount_list)){
    if(is.null(edgecount_list[[i]])) { 
      edgecount_list[[i]] <- 0 
    } else {
      edgecount_list[[i]] <- edgecount_list[[i]]
    }
  }
  
  (unlist(edgecount_list))
}

sequential_variation <- extract_degrees(data)

sequential_variation_normalized <- sequential_variation/seqlength(data.seq)