在使用来自“reshape2”的melt 之后,这是一种使用基础R 中的split 的方法:
library(reshape2)
x <- melt(lst)
split(x$L1, x$value)
# $`1`
# [1] "c"
#
# $`2`
# [1] "a" "c"
#
# $`3`
# [1] "a" "b" "c" "d"
#
# $`4`
# [1] "b" "b" "c"
#
# $`6`
# [1] "a" "b" "b" "d"
#
# $`7`
# [1] "b"
#
# $`9`
# [1] "b" "c"
#
# $`10`
# [1] "a" "b"
#
# $`15`
# [1] "a"
#
# $`17`
# [1] "a" "d"
同样,在带有stack 的基础 R 中:
x <- stack(lapply(lst, c))
split(as.character(x$ind), x$values)
如果您使用的是“lst”而不是“lst”,甚至更直接:
x <- stack(lst)
split(as.character(x$ind), x$values)
为了详细说明我的评论,我描述的更有效的方式是:
split(rep(names(lst), lapply(lst, nrow)), unlist(lst, use.names = FALSE))
应用于更大的lst,我们得到以下结果:
fun1 <- function() split(rep(names(lst), lapply(lst, nrow)), unlist(lst, use.names = FALSE))
fun2 <- function() { x <- stack(lapply(lst, c)) ; split(as.character(x$ind), x$values) }
fun3 <- function() { x <- melt(lst) ; split(x$L1, x$value) }
fun4 <- function() unstack(stack(lapply(lst, as.vector)), ind ~ values)
## Make lst much bigger
lst <- unlist(replicate(10000, lst, simplify = FALSE), recursive=FALSE)
names(lst) <- make.unique(names(lst))
library(microbenchmark)
system.time(fun3())
# user system elapsed
# 48.338 0.000 47.643
microbenchmark(fun1(), fun2(), fun4(), times = 5)
# Unit: milliseconds
# expr min lq median uq max neval
# fun1() 454.5913 456.6793 473.901 555.8954 574.4394 5
# fun2() 922.1282 1028.4972 1034.872 1068.4761 1150.8072 5
# fun4() 1222.5296 1300.0643 1323.253 1339.2037 1421.1546 5