在R中将CSV转换为稀疏矩阵的有效方法

I have a very large csv file (about 91 million rows so a for loop takes too long in R) of similarities between keywords that when I read into a data.frame looks like:

我有一个非常大的csv文件（大约9100万行，因此for循环在R中花费太长时间）关键字之间的相似性，当我读入data.frame看起来像：

> df   
kwd1 kwd2 similarity  
a  b  1  
b  a  1  
c  a  2  
a  c  2

It is a sparse list and I would like to convert it into a sparse matrix:

它是一个稀疏列表，我想将其转换为稀疏矩阵：

> myMatrix 
  a b c  
a . 1 2
b 1 . .
c 2 . .

I tried using sparseMatrix(), but converting the keyword names to integer indexes takes too much time.

我尝试使用sparseMatrix（），但将关键字名称转换为整数索引需要花费太多时间。

Thanks for any help!

谢谢你的帮助！

1 个解决方案

#1

acast from the reshape2 package will do this nicely. There are base R solutions but I find the syntax much more difficult.

来自reshape2包的acast会做得很好。有基本R解决方案，但我发现语法更难。

library(reshape2)
df <- structure(list(kwd1 = structure(c(1L, 2L, 3L, 1L), .Label = c("a", 
"b", "c"), class = "factor"), kwd2 = structure(c(2L, 1L, 1L, 
3L), .Label = c("a", "b", "c"), class = "factor"), similarity = c(1L, 
1L, 2L, 2L)), .Names = c("kwd1", "kwd2", "similarity"), class = "data.frame", row.names = c(NA, 
-4L))

acast(df, kwd1 ~ kwd2, value.var='similarity', fill=0)

  a b c
a 0 1 2
b 1 0 0
c 2 0 0
>

using sparseMatrix from the Matrix package:

使用Matrix包中的sparseMatrix：

library(Matrix)
df$kwd1 <- factor(df$kwd1)
df$kwd2 <- factor(df$kwd2)

foo <- sparseMatrix(as.integer(df$kwd1), as.integer(df$kwd2), x=df$similarity)

> foo
3 x 3 sparse Matrix of class "dgCMatrix"


foo <- sparseMatrix(as.integer(df$kwd1), as.integer(df$kwd2), x=df$similarity, dimnames=list(levels(df$kwd1), levels(df$kwd2)))

> foo 

3 x 3 sparse Matrix of class "dgCMatrix"
  a b c
a . 1 2
b 1 . .
c 2 . .

#1