Konvertálja az azonos sorrendű mátrixok listáját tömbbé

Íme egy benchmark teszt a javasolt különböző stratégiákhoz. Nyugodtan frissítse, ha új ötletei / stratégiái vannak.

# packages
require(data.table)
require(tidyr)
require(microbenchmark)

# data
lst <- replicate(100, matrix(rnorm(16), ncol = 4), simplify = FALSE)
# benchmark test
microbenchmark(
  do.call(rbind, lst), 
  Reduce(rbind, lst), 
  apply(simplify2array(lst), 2, rbind), 
  rbindlist(lapply(lst, data.frame)), 
  unnest(lapply(lst, data.frame))
  )

És az eredmények:

Unit: microseconds
                                 expr       min         lq        mean     median        uq       max neval  
                  do.call(rbind, lst)    43.290    47.9760    55.63858    52.8845    62.703   101.307   100  
                   Reduce(rbind, lst)   542.236   570.7985   620.99652   585.3020   610.518  1871.272   100  
 apply(simplify2array(lst), 2, rbind)   311.061   345.2010   382.22978   368.6315   388.268  1563.782   100  
   rbindlist(lapply(lst, data.frame)) 11827.884 12472.3190 13092.57937 12823.0995 13595.841 15833.736   100   
      unnest(lapply(lst, data.frame)) 12371.905 12927.9765 13514.24261 13236.1360 14008.655 16121.143   100  

Csak kíváncsiságból elvégeztem ezeket a benchmark teszteket a data.frame bemenetekre is, és ott nagyon más a kép:

# packages
require(data.table)
require(tidyr)
require(microbenchmark)
# data
lst <- replicate(100, as.data.frame(matrix(rnorm(16), ncol = 4)), simplify=FALSE)
# benchmark test
microbenchmark(
  do.call(rbind, lst), 
  Reduce(rbind, lst), 
  apply(simplify2array(lapply(lst, as.matrix)), 2, rbind), 
  rbindlist(lst), 
  unnest(lst)
)

eredményekkel:

Unit: microseconds
                                                    expr       min         lq       mean    median        uq        max neval 
                                     do.call(rbind, lst) 12406.716 12944.2660 13746.8552 13571.966 14564.056  16333.128   100    
                                      Reduce(rbind, lst) 36316.866 38450.7765 39894.9806 39299.610 40325.395 100949.158   100    
 apply(simplify2array(lapply(lst, as.matrix)), 2, rbind)  9577.717  9940.9930 10273.8674 10065.059 10291.996  12114.846   100    
                                          rbindlist(lst)   324.896   369.0770   397.7828   402.995   426.202    500.732   100  
                                             unnest(lst)   926.487   974.9095  1011.7322  1010.834  1033.596   1171.051   100 

Használhatja a Reduce:

> lst=list(matrix(1:16, ncol=4), matrix(4:23, ncol=4))
[[1]]
     [,1] [,2] [,3] [,4]
[1,]    1    5    9   13
[2,]    2    6   10   14
[3,]    3    7   11   15
[4,]    4    8   12   16

[[2]]
     [,1] [,2] [,3] [,4]
[1,]    4    9   14   19
[2,]    5   10   15   20
[3,]    6   11   16   21
[4,]    7   12   17   22
[5,]    8   13   18   23

> Reduce(rbind, lst)
      [,1] [,2] [,3] [,4]
 [1,]    1    5    9   13
 [2,]    2    6   10   14
 [3,]    3    7   11   15
 [4,]    4    8   12   16
 [5,]    4    9   14   19
 [6,]    5   10   15   20
 [7,]    6   11   16   21
 [8,]    7   12   17   22
 [9,]    8   13   18   23

@Roland javaslata szerint kerüld a Reduce-t :)

#> lst=lapply(1:100000, function(u) m+u)
#> system.time(do.call(rbind, lst))
#   user  system elapsed 
#   0.37    0.01    0.39 
#> system.time(Reduce(rbind, lst))
#   user  system elapsed 
# 704.94   38.66  743.96 

#A legegyszerűbb megoldás

lst <- replicate(100, matrix(rnorm(16), ncol=4), simplify=FALSE)

mat_array <- do.call(rbind, lst) #this is the answer

ra<-simplify2array(lst) #collection of matrices in an array

dim(ra)

#[1]   4   4 100

Súgó a simplify2array-hez