R - 数据帧 - 转换为稀疏矩阵

Question

我有一个数据框,大部分是零(稀疏数据帧？)类似于

name,factor_1,factor_2,factor_3
ABC,1,0,0
DEF,0,1,0
GHI,0,0,1

实际数据大约是90,000行,包含10,000个功能.我可以将其转换为稀疏矩阵吗？我期望通过利用稀疏矩阵而不是数据帧来获得时间和空间效率.

任何帮助,将不胜感激

更新#1:这是生成数据帧的一些代码.谢谢理查德提供这个

x <- structure(list(name = structure(1:3, .Label = c("ABC", "DEF", "GHI"),
                    class = "factor"), 
               factor_1 = c(1L, 0L, 0L), 
               factor_2 = c(0L,1L, 0L), 
               factor_3 = c(0L, 0L, 1L)), 
               .Names = c("name", "factor_1","factor_2", "factor_3"), 
               class = "data.frame",
               row.names = c(NA,-3L))

Answer 1

为了避免将所有数据复制到密集矩阵中,可能会有更高的内存效率(但速度更慢):

y <- Reduce(cbind2, lapply(x[,-1], Matrix, sparse = TRUE))
rownames(y) <- x[,1]

#3 x 3 sparse Matrix of class "dgCMatrix"
#         
#ABC 1 . .
#DEF . 1 .
#GHI . . 1

如果你有足够的内存,你应该使用理查德的答案,即将你的data.frame变成密集矩阵而不是使用Matrix.

Answer 2

我一直这样做,这是一个痛苦的屁股,所以我在我的R包中写了一个名为sparsify()的方法- mltools.它运行data.table的只是花哨的data.frames.

解决你的具体问题......

安装mltools(或者只是将sparsify()方法复制到您的环境中)

加载包

library(data.table)
library(Matrix)
library(mltools)

Sparsify

x <- data.table(x)  # convert x to a data.table
sparseM <- sparsify(x[, !"name"])  # sparsify everything except the name column
rownames(sparseM) <- x$name  # set the rownames

> sparseM
3 x 3 sparse Matrix of class "dgCMatrix"
    factor_1 factor_2 factor_3
ABC        1        .        .
DEF        .        1        .
GHI        .        .        1

通常,sparsify()方法非常灵活.以下是一些如何使用它的示例:

制作一些数据.注意数据类型和未使用的因子级别

dt <- data.table(
  intCol=c(1L, NA_integer_, 3L, 0L),
  realCol=c(NA, 2, NA, NA),
  logCol=c(TRUE, FALSE, TRUE, FALSE),
  ofCol=factor(c("a", "b", NA, "b"), levels=c("a", "b", "c"), ordered=TRUE),
  ufCol=factor(c("a", NA, "c", "b"), ordered=FALSE)
)
> dt
   intCol realCol logCol ofCol ufCol
1:      1      NA   TRUE     a     a
2:     NA       2  FALSE     b    NA
3:      3      NA   TRUE    NA     c
4:      0      NA  FALSE     b     b

开箱即用

> sparsify(dt)
4 x 7 sparse Matrix of class "dgCMatrix"
     intCol realCol logCol ofCol ufCol_a ufCol_b ufCol_c
[1,]      1      NA      1     1       1       .       .
[2,]     NA       2      .     2      NA      NA      NA
[3,]      3      NA      1    NA       .       .       1
[4,]      .      NA      .     2       .       1       .

将NAs转换为0并将其稀疏化

> sparsify(dt, sparsifyNAs=TRUE)
4 x 7 sparse Matrix of class "dgCMatrix"
     intCol realCol logCol ofCol ufCol_a ufCol_b ufCol_c
[1,]      1       .      1     1       1       .       .
[2,]      .       2      .     2       .       .       .
[3,]      3       .      1     .       .       .       1
[4,]      .       .      .     2       .       1       .

生成标识NA值的列

> sparsify(dt[, list(realCol)], naCols="identify")
4 x 2 sparse Matrix of class "dgCMatrix"
     realCol_NA realCol
[1,]          1      NA
[2,]          .       2
[3,]          1      NA
[4,]          1      NA

生成以最具记忆效率的方式识别NA值的列

> sparsify(dt[, list(realCol)], naCols="efficient")
4 x 2 sparse Matrix of class "dgCMatrix"
     realCol_NotNA realCol
[1,]             .      NA
[2,]             1       2
[3,]             .      NA
[4,]             .      NA

Answer 3

您可以将第一列设置为行名称，然后Matrix从Matrix包中使用。

rownames(x) <- x$name
x <- x[-1]
library(Matrix)
Matrix(as.matrix(x), sparse = TRUE)
# 3 x 3 sparse Matrix of class "dtCMatrix"
#     factor_1 factor_2 factor_3
# ABC        1        .        .
# DEF        .        1        .
# GHI        .        .        1

原始x数据框在哪里

x <- structure(list(name = structure(1:3, .Label = c("ABC", "DEF", 
"GHI"), class = "factor"), factor_1 = c(1L, 0L, 0L), factor_2 = c(0L, 
1L, 0L), factor_3 = c(0L, 0L, 1L)), .Names = c("name", "factor_1", 
"factor_2", "factor_3"), class = "data.frame", row.names = c(NA, 
-3L))