优化运行时：更改igraph中边的权重需要很长时间。有没有办法优化它？

我正在从osmar对象构建的igraph中搜索一组边，并希望更改这些边的权重。 因为我的图表很大，所以这个任务需要很长时间。 因为我在循环中运行这个函数，所以运行时变得更大

有什么方法可以优化它吗

代码如下：

library(osmar)
library(igraph)
library(tidyr)
library(dplyr)

### Get data ----
src <- osmsource_api(url = "https://api.openstreetmap.org/api/0.6/")
muc_bbox <- center_bbox(11.575278, 48.137222, 1000, 1000)
muc <- get_osm(muc_bbox, src)

### Reduce to highways: ----
hways <- subset(muc, way_ids = find(muc, way(tags(k == "highway"))))
hways <- find(hways, way(tags(k == "name")))
hways <- find_down(muc, way(hways))
hways <- subset(muc, ids = hways)

#### Plot data ----
## Plot complete data and highways on top:
plot(muc)
plot_ways(muc, col = "lightgrey")
plot_ways(hways, col = "coral", add = TRUE)

### Define route start and end nodes: ----
id<-find(muc, node(tags(v %agrep% "Sendlinger Tor")))[1]
hway_start_node <-find_nearest_node(muc, id, way(tags(k == "highway"))) 
hway_start <- subset(muc, node(hway_start_node))

id <- find(muc, node(attrs(lon > 11.58 & lat > 48.15)))[1]
hway_end_node <- find_nearest_node(muc, id, way(tags(k == "highway")))
hway_end <- subset(muc, node(hway_end_node))

## Add the route start and and nodes to the plot:
plot_nodes(hway_start, add = TRUE, col = "red", pch = 19, cex = 2)
plot_nodes(hway_end, add = TRUE, col = "red", pch = 19, cex = 2)

### Create street graph ----
gr <- as.undirected(as_igraph(hways))

### Compute shortest route: ----
# Calculate route
route <- function(start_node,end_node) {
  get.shortest.paths(gr,
                     from = as.character(start_node),
                     to = as.character(end_node), 
                     mode = "all")[[1]][[1]]}
# Plot route
plot.route <- function(r,color) {
  r.nodes.names <- as.numeric(V(gr)[r]$name)
  r.ways <- subset(hways, ids = osmar::find_up(hways, node(r.nodes.names)))
  plot_ways(r.ways, add = TRUE, col = color, lwd = 2)
}
nways <-  1
numway <- 1
r <- route(hway_start_node,hway_end_node)

# Plot route

color <- colorRampPalette(c("springgreen","royalblue"))(nways)[numway]
plot.route(r,color)


## Route details ----
# Construct a new osmar object containing only elements 
# related to the nodes defining the route:
route_nodes <- as.numeric(V(gr)[r]$name)
route_ids <- find_up(hways, node(route_nodes))

osmar.route <- subset(hways, ids = route_ids)
osmar.nodes.ids <- osmar.route$nodes$attrs$id

# Extract the nodes’ coordinates,
osmar.nodes.coords <- osmar.route$nodes$attrs[, c("lon", "lat")]
osmar.nodes <- cbind(data.frame(ids = osmar.nodes.ids),
                     data.frame(ids_igraph = as.numeric(V(gr)[r]) ),
                     osmar.nodes.coords) 


## Find edges ids containing points of interest ----
wished.coords <- data.frame(wlon = c(11.57631),
                            wlat = c(48.14016)) 


# Calculate all distances
distances <- crossing(osmar.nodes,wished.coords) %>%
             mutate(dist = geosphere::distHaversine(cbind(lon,lat),cbind(wlon,wlat)))


# Select nodes below maximum distance :
mindist <- 50 #m

wished.nodes <- distances %>% filter(dist < mindist)

# Select edges incident to these nodes :
selected.edges <- unlist(incident_edges(gr,V(gr)[wished.nodes$ids_igraph]))

This is where the slowdown occurs: Weight of selected edges, change it by multiplying it with 10
E(gr)[selected.edges]$weight<-E(gr)[selected.edges]$weight*10

库（osmar）
图书馆（igraph）
图书馆（tidyr）
图书馆（dplyr）
###获取数据----
src如中所示，R中的实现性能可能会因语法的不同而有很大差异

E(gr)[selected.edges]$weight<-E(gr)[selected.edges]$weight*10

因此，让我们比较两种解决方案：

microbenchmark::microbenchmark(
  ref={E(gr)[selected.edges]$weight<-E(gr)[selected.edges]$weight*10},
  new={set.edge.attribute(graph=gr,name="weight",index=selected.edges,value=10*get.edge.attribute(graph=gr,name="weight",index=selected.edges))})

Unit: microseconds
 expr       min        lq       mean    median        uq       max neval cld
  ref 15920.404 16567.788 17793.4412 17111.583 18491.685 25867.477   100   b
  new   246.974   266.462   296.5088   278.769   292.718   662.974   100  a 

microbenchmark:：microbenchmark(
ref={E（gr）[selected.edges]$weight很抱歉，我刚才没有使用此解决方案…；）我确实在数据集上检查了它，它将运行时间缩短了一半。Waldi没问题！我非常感谢您和您已经做出的所有努力！Waldi您是如何得出答案的？此调用指定在哪里？（抱歉，可能是一个愚蠢的问题）没有愚蠢的问题：
no_cores <- detectCores(logical = FALSE) 
 data <- split(selected.edges,factor(sort(rank(selected.edges)%%no_cores)))
 c_result <- mclapply(1:no_cores, function(x) {
 E(gr)[unlist(data[[x]])]$weight * 1000 / mean_value }, mc.cores = no_cores) 
   
     E(gr)[unlist(data)]$weight<-unlist(c_result)

E(gr)[selected.edges]$weight<-E(gr)[selected.edges]$weight*10

set.edge.attribute(graph=gr,name="weight",index=selected.edges,value=10*get.edge.attribute(graph=gr,name="weight",index=selected.edges))

microbenchmark::microbenchmark(
  ref={E(gr)[selected.edges]$weight<-E(gr)[selected.edges]$weight*10},
  new={set.edge.attribute(graph=gr,name="weight",index=selected.edges,value=10*get.edge.attribute(graph=gr,name="weight",index=selected.edges))})

Unit: microseconds
 expr       min        lq       mean    median        uq       max neval cld
  ref 15920.404 16567.788 17793.4412 17111.583 18491.685 25867.477   100   b
  new   246.974   266.462   296.5088   278.769   292.718   662.974   100  a