8  

It might be a bit more memory efficient (but slower) to avoid copying all the data into a dense matrix:

它可能会提高内存的效率(但速度较慢)，以避免将所有数据复制到一个稠密的矩阵中:

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

If you have sufficient memory you should use Richard's answer, i.e., turn your data.frame into a dense matrix and than use Matrix.

如果你有足够的记忆，你应该用理查德的答案，即。把你的数据框变成一个密集矩阵，而不是使用矩阵。

4  

I do this all the time and it's a pain in the butt, so I wrote a method for it called sparsify() in my R package - mltools. It operates on data.tables which are just fancy data.frames.

我一直这样做，这是很痛苦的，所以我写了一种方法叫sparsify()在我的R包- mltools中。它操作的数据。这些表格只是花哨的数据。

To solve your specific problem...

解决你的具体问题……

Install mltools (or just copy the sparsify() method into your environment)

安装mltools(或将sparsify()方法复制到您的环境中)

Load packages

加载包

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

Sparsify

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

In general, the sparsify() method is pretty flexible. Here's some examples of how you can use it:

一般来说，sparsify()方法非常灵活。下面是一些如何使用的例子:

Make some data. Notice data types and unused factor levels

做一些数据。注意数据类型和未使用的因素级别。

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

Out-Of-The-Box Use

开箱即用的使用

> 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       .

Convert NAs to 0s and Sparsify Them

将NAs转换为0并使其稀疏。

> 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       .

Generate Columns That Identify NA Values

生成识别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

Generate Columns That Identify NA Values In the Most Memory Efficient Manner

生成以最高效的方式识别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

3  

You could make the first column into row names, then use Matrix from the Matrix package.

您可以将第一个列变成行名称，然后使用矩阵包中的矩阵。

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

where the original x data frame is

原始x数据帧在哪里?

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))

3  

Just how sparse is your matrix? That determines how how to improve it's size.

你的矩阵有多稀疏?这决定了如何改进它的大小。

Your example matrix has 3 1s and 6 0s. With that ratio, there's little space savings by naively using Matrix.

你的例子矩阵有3个1和6个0。有了这个比例，天真地使用矩阵就节省了很少的空间。

> library('pryr') # for object_size
> library('Matrix')
> m <- matrix(rbinom(9e4*1e4, 1, 1/3), ncol = 1e4)
> object_size(m)
3.6 GB
> object_size(Matrix(m, sparse = T))
3.6 GB