R - data frame - convert to sparse matrix

问题

I have a data frame which is mostly zeros (sparse data frame?) something similar to

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

The actual data is about 90,000 rows with 10,000 features. Can I convert this to a sparse matrix? I am expecting to gain time and space efficiencies by utilizing a sparse matrix instead of a data frame.

Any help would be appreciated

Update #1: Here is some code to generate the data frame. Thanks Richard for providing this

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))

回答1:

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

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.

回答2:

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.

To solve your specific problem...

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

Load packages

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

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

Make some data. Notice data types and unused factor levels

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

> 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

> 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

> 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

> 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

回答3:

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

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

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))

回答4:

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

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

> 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

来源：https://stackoverflow.com/questions/27008633/r-data-frame-convert-to-sparse-matrix

标签

dataframe

sparse-matrix