r - Distance Matrix from second variable using get.shortest.paths() -


i go 1 step further question (find total of second variable related distance of route get.shortest.paths()). how 1 matrix of distances between nodes, when 'shortest' path found using newcost variable?

(my experience igraph limited). 

      df2 = rbind(c(234,235,21.6,75),       c(234,326,11.0,35),       c(235,241,14.5,78),       c(326,241,8.2,98),       c(241,245,15.3,75),       c(234,245,38.46,65))        df2 = as.data.frame(df2)       names(df2) = c("start_id","end_id","newcost","distance")        require(igraph)       g2 <- graph.data.frame(df2, directed=false)       tmp2 = shortest.paths(g2,weights=e(g2)$newcost)       tmp2 #this gives matrix of newcost-weighted shortest distances

where use how find paths, using optimal.path <- get.shortest.paths, , using sum(e(g2, path = optimal.path)$distance) create matrix of distances

what edgelist of distances node pairs, like:

      startid endid shortestdist       234     235     75       234     245     208

what tricky problem newcost used find shortest paths, want sum of variable - distance variable on each shortest path between node pairs.

ok, first let me make clear not igraph user myself. nevertheless thought question interesting thought i'd take look. , find no easy solution problem having. ended making helper functions make process possible. there chance i've re-coded functionality inside igraph not find it.

let me first define get.shortest.paths.for.all not return shortest path lengths given attribute, return shortest path vertices in graph. here's code

get.shortest.paths.for.all<-function(graph, attr) {     paths<-lapply(1:(vcount(graph)-1), function(i) {         get.all.shortest.paths(             graph,              weights=get.edge.attribute(g2,attr),             = v(graph)[i],             = v(graph)[(i+1):vcount(graph)]         )$res     })     unsplit(paths, rep.int(seq_along(paths), sapply(paths, length))) }

now let me define get.path.dist.matrix takes graph , list of paths (like 1 returned get.shortest.paths.for.all) , calculate distance given attribute between each of paths

get.path.dist.matrix<-function(graph, path, attr) {     dd<-get.adjacency(graph, attr=attr)     uniqs <- numeric(0)     if (is.list(path)) {         starts<-sapply(path, head, 1)         ends<-sapply(path, tail, 1)         uniqs <- unique(sort(c(starts,ends)))     } else {         uniqs <- c(head(path,1), tail(path,1))     }     m<-matrix(0, nrow=length(uniqs), ncol=length(uniqs),         dimnames=list(v(graph)$name[uniqs],v(graph)$name[uniqs]))     for(pp in path) {         m[pp[1], pp[length(pp)]]<-sum(dd[embed(pp,2)])     }     m+t(m) }

using sample data, use them this

paths <- get.shortest.paths.for.all(g2, "newcost") get.path.dist.matrix(g2, paths,"distance")  #     234 235 326 241 245 # 234   0  75  35 133 208 # 235  75   0 176  78 153 # 326  35 176   0  98 173 # 241 133  78  98   0  75 # 245 208 153 173  75   0

which seems reasonable , distinct shortest.paths(g2,weights=e(g2)$distance). try test functions, see 

all(tmp2==get.path.dist.matrix(g2, paths,"newcost"))

so feel free try these out , let me know if see problems or possible improvements.


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