问题不是rbind，问题是Reduce.不幸的是，R中的函数调用非常昂贵，尤其是当您继续创建新对象时.在这种情况下，您调用rbind 65999次，每次创建一个新的R对象并添加一行.相反，您只能使用66000个参数调用一次rbind，这将更快，因为内部rbind将在C中进行绑定，而不必调用R函数66000次并仅分配一次内存.在这里，我们将您的Reduce使用与Zheyuan的矩阵/未列表进行比较，最后将rbind与使用do.call调用一次的rbind(do.call允许您将所有参数指定为列表的函数)进行比较:

The problem is not rbind, the problem is Reduce. Unfortunately, function calls in R are expensive, and particularly so when you keep creating new objects. In this case, you call rbind 65999 times, and each time you do you create a new R object with one row added. Instead, you can just call rbind once with 66000 arguments, which will be much faster since internally rbind will do the binding in C without having to call R functions 66000 times and allocating the memory just once. Here we compare your Reduce use with Zheyuan's matrix/unlist and finally with rbind called once with do.call (do.call allows you to call a function with all arguments specified as a list):

out1 <- replicate(1000, 1:20, simplify=FALSE)  # use 1000 elements for illustrative purposes

library(microbenchmark)    
microbenchmark(times=10,
  a <- do.call(rbind, out1),
  b <- matrix(unlist(out1), ncol=20, byrow=TRUE),
  c <- Reduce(rbind, out1)
)
# Unit: microseconds
#                                                expr        min         lq
#                           a <- do.call(rbind, out1)    469.873    479.815
#  b <- matrix(unlist(out1), ncol = 20, byrow = TRUE)    257.263    260.479
#                            c <- Reduce(rbind, out1) 110764.898 113976.376
all.equal(a, b, check.attributes=FALSE)
# [1] TRUE
all.equal(b, c, check.attributes=FALSE)
# [1] TRUE

浙源是最快的，但是无论从什么目的和目的来看，do.call(rbind())方法都非常相似.

Zheyuan is the fastest, but for all intents and purposes the do.call(rbind()) method is pretty similar.