mc.cores> 1不支持在Windows上

如果你所要做的只是让这个代码不能并行运行，那么你只需要告诉它使用1个内核，那么它​​就会在lapply使用lapply 。

 ll <- parallel::mclapply(waydf$geometry$coordinates, st_linestring, mc.cores = 1) 

或者只是将mclapply lapply 。

 ll <- lapply(waydf$geometry$coordinates, st_linestring) 

你必须弄清楚st_linestring是什么或者做了什么，因为你试图将waydf$geometry$coordinates的内容传递给它，但是没有指定任何参数，比如st_linestring(waydf$geometry$coordinates[i])

在Windows中，您将使用parLapply而不是mclapply 。

 # original ll <- parallel::mclapply( waydf$geometry$coordinates , st_linestring, mc.cores =parallel::detectCores() - 1 ) # replace the above with all of the below library(parallel) cl <- makeCluster(detectCores()) cl <- clusterEvalQ(cl, { library(sf) }) # you need to export packages as well # cl <- clusterExport(cl, "st_linestring") # each worker is a new environment, you will need to export variables/functions to ll <- parallel::parLapply(cl, waydf$geometry$coordinates, function(i) st_linestring) # if st_linestring takes arguments then st_linestring(x) stopCluster(cl) 

由于st_linestring是从包sf中的函数编辑 ，因此足以导出sf

第二编辑

 rpl <- unlist( lapply( waydf$geometry$coordinates , nrow ) ) # row per line waydf <- waydf[ rpl > 1 , ] library(parallel) cl <- makeCluster(detectCores()) cl <- clusterEvalQ(cl, { library(sf) }) # you need to export packages as well # cl <- clusterExport(cl, "st_linestring") # each worker is a new environment, you will need to export variables/functions to ll <- parallel::parLapply(cl, waydf$geometry$coordinates, function(i) st_linestring) # if st_linestring takes arguments then st_linestring(x) stopCluster(cl) outdf <- sf::st_sf( line_geometry = sf::st_sfc( ll , crs = epsg ) , osm_id = waydf$id ) 

问题是，你在所有这些行中做cl <- ... ; 你继续重新定义这个变量是别的东西。 你应该只分配一次，然后重用它。

 library("parallel") cl <- makeCluster(detectCores()) clusterEvalQ(cl, { library("sf") }) clusterExport(cl, "st_linestring") res <- parallel::parLapply(cl, X = waydf$geometry$coordinates, fun = function(i) st_linestring) stopCluster(cl) 

消息Error in checkCluster(cl): not a valid cluster使用代码获得Error in checkCluster(cl): not a valid cluster ，因为在执行cl <- clusterEvalQ(cl, { library("sf") })它不再是cluster对象。