在 R 中将矩阵按行划分为子矩阵列表

Question

I have a matrix where each row is either all 1's or any number between 0 and 1. I need to partition this into list of matrices such that

1. Each sub matrix is either all 1's or all numbers between 0 and 1
2. Combining all the sub matrices of the list using rbind yields the original matrix

Example:

m <- 
structure(c(1, 1, 1, 0.84, 0.27, 0.24, 0.48, 0.28, 0.62, 0.55, 
1, 1, 0.26, 0.93, 0.87, 0.76, 1, 1, 1, 1, 1, 0.31, 0.32, 0.96, 
0.25, 0.96, 0.43, 0.66, 1, 1, 0.22, 0.88, 0.01, 0.14, 1, 1, 1, 
1, 1, 0.71, 0.99, 0.6, 0.22, 0.73, 0.54, 0.17, 1, 1, 0.59, 0.67, 
0.07, 0.4, 1, 1, 1, 1, 1, 0.27, 0.62, 0.52, 0.67, 0.69, 0.06, 
0.63, 1, 1, 0.27, 0.95, 0.16, 0.22, 1, 1, 1, 1, 1, 0.59, 0.94, 
0.4, 0.05, 0.05, 0.26, 0.31, 1, 1, 0.53, 0.52, 0.77, 0.06, 1, 
1, 1, 1, 1, 0.48, 0.47, 0.88, 0.7, 0.4, 0.4, 0.72, 1, 1, 0.79, 
0.58, 0.74, 0.4, 1, 1, 1, 1, 1, 0.27, 0.41, 0.36, 0.35, 0.48, 
0.2, 0.4, 1, 1, 0.17, 0.34, 0.97, 0.06, 1, 1, 1, 1, 1, 0.56, 
0.66, 0.29, 0.41, 0.56, 0.83, 0.97, 1, 1, 0.4, 0.35, 0.47, 0.23, 
1, 1, 1, 1, 1, 0.91, 0.15, 0.17, 0.82, 0.7, 0.15, 0.97, 1, 1, 
0.47, 0.02, 0.07, 0.05, 1, 1, 1, 1, 1, 0.9, 0.57, 0.17, 0.92, 
0.92, 0.8, 0.73, 1, 1, 0.87, 0.5, 0.65, 0.67, 1, 1), .Dim = c(18L, 
10L))

L <- 
list(structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), .Dim = c(3L, 10L
)), structure(c(0.84, 0.27, 0.24, 0.48, 0.28, 0.62, 0.55, 0.31, 
0.32, 0.96, 0.25, 0.96, 0.43, 0.66, 0.71, 0.99, 0.6, 0.22, 0.73, 
0.54, 0.17, 0.27, 0.62, 0.52, 0.67, 0.69, 0.06, 0.63, 0.59, 0.94, 
0.4, 0.05, 0.05, 0.26, 0.31, 0.48, 0.47, 0.88, 0.7, 0.4, 0.4, 
0.72, 0.27, 0.41, 0.36, 0.35, 0.48, 0.2, 0.4, 0.56, 0.66, 0.29, 
0.41, 0.56, 0.83, 0.97, 0.91, 0.15, 0.17, 0.82, 0.7, 0.15, 0.97, 
0.9, 0.57, 0.17, 0.92, 0.92, 0.8, 0.73), .Dim = c(7L, 10L)), 
    structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1), .Dim = c(2L, 10L)), structure(c(0.26, 0.93, 
    0.87, 0.76, 0.22, 0.88, 0.01, 0.14, 0.59, 0.67, 0.07, 0.4, 
    0.27, 0.95, 0.16, 0.22, 0.53, 0.52, 0.77, 0.06, 0.79, 0.58, 
    0.74, 0.4, 0.17, 0.34, 0.97, 0.06, 0.4, 0.35, 0.47, 0.23, 
    0.47, 0.02, 0.07, 0.05, 0.87, 0.5, 0.65, 0.67), .Dim = c(4L, 
    10L)), structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1), .Dim = c(2L, 10L)))

Answer 1

Here is a solution for you. It uses data.table's rleid for convenience.

First, we generate some data:

set.seed(123)
input <- matrix(runif(180),ncol=10)
input[c(1:3,5,9:10),]<-1

Then we identify which rows have only ones

is_one <- apply(input,1,function(x){all(x==1)})

We want to split our rows by groups of is one/is not one, so we generate row id's and split id's

row_ids <- 1:nrow(input)
split_ids <- data.table::rleid(is_one)

The we generate our output, by splitting the row id's by groups of is one/is not one and returning our input data for each set of row ids.

output <- lapply(split(row_ids,split_ids),function(x){input[x,]})

Answer 2

You can also use split.data.frame with a matrix:

> m=structure(c(1,1,1,0.84,0.27,0.24,0.48,0.28,0.62,0.55,1,1,0.26,0.93,0.87,0.76,1,1,1,1,1,0.31,0.32,0.96,0.25,0.96,0.43,0.66,1,1,0.22,0.88,0.01,0.14,1,1,1,1,1,0.71,0.99,0.6,0.22,0.73,0.54,0.17,1,1,0.59,0.67,0.07,0.4,1,1,1,1,1,0.27,0.62,0.52,0.67,0.69,0.06,0.63,1,1,0.27,0.95,0.16,0.22,1,1,1,1,1,0.59,0.94,0.4,0.05,0.05,0.26,0.31,1,1,0.53,0.52,0.77,0.06,1,1,1,1,1,0.48,0.47,0.88,0.7,0.4,0.4,0.72,1,1,0.79,0.58,0.74,0.4,1,1,1,1,1,0.27,0.41,0.36,0.35,0.48,0.2,0.4,1,1,0.17,0.34,0.97,0.06,1,1,1,1,1,0.56,0.66,0.29,0.41,0.56,0.83,0.97,1,1,0.4,0.35,0.47,0.23,1,1,1,1,1,0.91,0.15,0.17,0.82,0.7,0.15,0.97,1,1,0.47,0.02,0.07,0.05,1,1,1,1,1,0.9,0.57,0.17,0.92,0.92,0.8,0.73,1,1,0.87,0.5,0.65,0.67,1,1),.Dim=c(18L,10L))
> r=rle(rowSums(m==1)==ncol(m))$lengths
> split.data.frame(m,rep(1:length(r),r))
$`1`
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    1    1    1    1    1    1    1    1    1     1
[2,]    1    1    1    1    1    1    1    1    1     1
[3,]    1    1    1    1    1    1    1    1    1     1

$`2`
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 0.84 0.31 0.71 0.27 0.59 0.48 0.27 0.56 0.91  0.90
[2,] 0.27 0.32 0.99 0.62 0.94 0.47 0.41 0.66 0.15  0.57
[3,] 0.24 0.96 0.60 0.52 0.40 0.88 0.36 0.29 0.17  0.17
[4,] 0.48 0.25 0.22 0.67 0.05 0.70 0.35 0.41 0.82  0.92
[5,] 0.28 0.96 0.73 0.69 0.05 0.40 0.48 0.56 0.70  0.92
[6,] 0.62 0.43 0.54 0.06 0.26 0.40 0.20 0.83 0.15  0.80
[7,] 0.55 0.66 0.17 0.63 0.31 0.72 0.40 0.97 0.97  0.73

$`3`
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    1    1    1    1    1    1    1    1    1     1
[2,]    1    1    1    1    1    1    1    1    1     1

$`4`
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 0.26 0.22 0.59 0.27 0.53 0.79 0.17 0.40 0.47  0.87
[2,] 0.93 0.88 0.67 0.95 0.52 0.58 0.34 0.35 0.02  0.50
[3,] 0.87 0.01 0.07 0.16 0.77 0.74 0.97 0.47 0.07  0.65
[4,] 0.76 0.14 0.40 0.22 0.06 0.40 0.06 0.23 0.05  0.67

$`5`
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,]    1    1    1    1    1    1    1    1    1     1
[2,]    1    1    1    1    1    1    1    1    1     1