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如何将两个因子转换为R中的邻接矩阵?

[英]How to convert two factors to adjacency matrix in R?

I have a data frame with two columns (key and value) where each column is a factor: 我有一个包含两列(键和值)的数据框,其中每一列都是一个因素:

df = data.frame(gl(3,4,labels=c('a','b','c')), gl(6,2))
colnames(df) = c("key", "value")
   key value
1    a     1
2    a     1
3    a     2
4    a     2
5    b     3
6    b     3
7    b     4
8    b     4
9    c     5
10   c     5
11   c     6
12   c     6

I want to convert it to adjacency matrix (in this case 3x6 size) like: 我想将其转换为邻接矩阵(在这种情况下为3x6大小),例如:

  1 2 3 4 5 6
a 1 1 0 0 0 0
b 0 0 1 1 0 0
c 0 0 0 0 1 1

So that I can run clustering on it (group keys that have similar values together) with either kmeans or hclust. 这样我就可以使用kmeans或hclust在其上运行集群(具有相似值的组键)。

Closest that I was able to get was using model.matrix( ~ value, df) which results in: 我能够获得的最接近模型是使用model.matrix( ~ value, df)

   (Intercept) value2 value3 value4 value5 value6
1            1      0      0      0      0      0
2            1      0      0      0      0      0
3            1      1      0      0      0      0
4            1      1      0      0      0      0
5            1      0      1      0      0      0
6            1      0      1      0      0      0
7            1      0      0      1      0      0
8            1      0      0      1      0      0
9            1      0      0      0      1      0
10           1      0      0      0      1      0
11           1      0      0      0      0      1
12           1      0      0      0      0      1

but results aren't grouped by key yet. 但结果尚未按关键字分组。

From another side I can collapse this dataset into groups using: 另一方面,我可以使用以下方法将此数据集折叠成组:

aggregate(df$value, by=list(df$key), unique)
  Group.1 x.1 x.2
1       a   1   2
2       b   3   4
3       c   5   6

But I don't know what to do next... 但是我不知道下一步该怎么做...

Can someone help to solve this? 有人可以帮助解决这个问题吗?

An easy way to do it in base R: base R中执行此操作的简单方法:

res <-table(df)
res[res>0] <-1
res
   value
#key 1 2 3 4 5 6
#  a 1 1 0 0 0 0
#  b 0 0 1 1 0 0
#  c 0 0 0 0 1 1

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