r - 计算 R 中矩阵中每个唯一列出现次数的最快方法

Question

我是 R(以及 stackoverflow)的新手，非常感谢您的帮助。我想计算矩阵中每个唯一列的出现次数。我编写了以下代码，但速度非常慢:

frequencyofequalcolumnsinmatrix = function(matrixM){

# returns a matrix columnswithfrequencyofmtxM that contains each distinct column and the frequency of each distinct columns on the last row. Hence  if the last row is c(3,5,3,2), then matrixM has 3+5+3+2=13 columns; there are 4 distinct columns; and the first distinct column appears 3 times, the second distinct column appears 5 times, etc.


n = nrow(matrixM)

columnswithfrequencyofmtxM = c()

while (ncol(matrixM)>0){

  indexzero = which(apply(matrixM-matrixM[,1], 2, function(x) identical(as.vector(x),rep(0,n))));

  indexnotzero = setdiff(seq(1:ncol(matrixM)),indexzero);

  frequencyofgivencolumn = c(matrixM[,1], length(indexzero)); #vector of length n. Coordinates 1 to nrow(matrixM) contains the coordinates of the given distinct column while coordinate nrow(matrixM)+1 contains the frequency of appearance of that column

  columnswithfrequencyofmtxM = cbind(columnswithfrequencyofmtxM,frequencyofgivencolumn, deparse.level=0);

  matrixM=matrixM[,indexnotzero];

  matrixM = as.matrix(matrixM);

  }

return(columnswithfrequencyofmtxM)


} 

如果我们应用矩阵'testmtx'，我们得到:

> testmtx = matrix(c(1,2,4,0,1,1,1,2,1,1,2,4,0,1,1,0,1,1), nrow=3, ncol=6)
> frequencyofequalcolumnsinmatrix(testmtx)
     [,1] [,2] [,3]
[1,]    1    0    1
[2,]    2    1    2
[3,]    4    1    1
[4,]    2    3    1

其中最后一行包含上面列的出现次数。

对我的代码不满意，我浏览了 stackoverflow。我发现了以下问题:

Fastest way to count occurrences of each unique element

结果表明，计算向量中每个唯一元素出现次数的最快方法是使用 data.table() 包。这是代码:

f6 <- function(x){
data.table(x)[, .N, keyby = x]
}

当我们运行它时，我们得到:

> vtr = c(1,2,3,1,1,2,4,2,4)
> f6(vtr)
   x N
1: 1 3
2: 2 3
3: 3 1
4: 4 2

我试图修改此代码以便在我的情况下使用它。这需要能够将 vtr 创建为一个向量，其中每个元素都是一个向量。但我没能做到。(很可能是因为在 R 中，c(c(1,2),c(3,4)) 与 c(1,2,3,4) 相同)。

我应该尝试修改函数 f6 吗？如果是这样，如何？
或者我应该采取完全不同的方法？如果有，是哪一个？

谢谢!

Answer 1

一种简单的方法是将您的行一起粘贴到一个向量中，然后使用该函数。

mat <- matrix(c(1,2,4,0,1,1,1,2,1,1,2,4,0,1,1,0,1,1), nrow=3, ncol=6)

vec <- apply(mat, 2, paste, collapse=" ")

f6(vec)
     x N
1: 011 3
2: 121 1
3: 124 2

编辑

@RohitDas 的回答让我想到，在考虑性能时，最好检查一下。如果我采用问题中先前显示的所有功能，则 OP 链接 here并添加
f7 <- table
还添加了@DavidArenburg 的 f10 建议

f10 <- function(x){ 
  table(unlist(data.table(x)[, lapply(.SD, paste, collapse = "")])) 
}

结果如下:

添加@MaratTalipov 的解决方案后，它显然是赢家。直接应用于矩阵它比所有向量解决方案都快。

set.seed(1)
testmx <- matrix(sample(1:10, 3 * 1e3, rep=T), nrow=1000)

microbenchmark(
   f1(apply(testmx, 2, paste, collapse=" ")),
   f2(apply(testmx, 2, paste, collapse=" ")),
   f3(apply(testmx, 2, paste, collapse=" ")),
   f4(apply(testmx, 2, paste, collapse=" ")),
   f5(apply(testmx, 2, paste, collapse=" ")),
   f6(apply(testmx, 2, paste, collapse=" ")),
   f7(apply(testmx, 2, paste, collapse=" ")),
   f8(apply(testmx, 2, paste, collapse=" ")),
   f9(apply(testmx, 2, paste, collapse=" ")),
   f10(testmx),
   f11(testmx),
   f12(testmx)
   )
Unit: microseconds
                                       expr      min        lq      mean   median        uq       max neval
 f1(apply(testmx, 2, paste, collapse = " ")) 3311.770 3511.5620 3901.0020 3612.035 3849.3600  9569.987   100
 f2(apply(testmx, 2, paste, collapse = " ")) 3044.997 3263.6515 3667.9232 3430.914 3847.2430  6721.318   100
 f3(apply(testmx, 2, paste, collapse = " ")) 2032.179 2118.0245 2371.8638 2213.301 2430.4155  6631.624   100
 f4(apply(testmx, 2, paste, collapse = " ")) 2119.949 2218.3050 2497.1513 2286.442 2425.0260  6258.987   100
 f5(apply(testmx, 2, paste, collapse = " ")) 2131.498 2221.5775 2459.9300 2309.925 2530.3115  4222.575   100
 f6(apply(testmx, 2, paste, collapse = " ")) 3121.217 3367.7815 3738.3239 3486.155 3835.1175  7979.352   100
 f7(apply(testmx, 2, paste, collapse = " ")) 1766.175 1832.9650 2040.5483 1889.169 2032.1795  3784.110   100
 f8(apply(testmx, 2, paste, collapse = " ")) 2085.303 2169.2240 2435.6932 2237.168 2404.2380  5002.109   100
 f9(apply(testmx, 2, paste, collapse = " ")) 2802.090 2988.0230 3449.0685 3056.930 3373.1710 17640.957   100
                                f10(testmx) 4027.017 4251.6385 4865.7036 4399.461 4848.7035 11811.581   100
                                f11(testmx)  500.058  549.1395  624.9526  576.279  636.1395  1176.809   100
                                f12(testmx) 1827.769 1886.4740 1957.0555 1902.834 1964.4270  3600.487   100