将相关矩阵转换为具有每个行列对记录的数据框 [英] Transform Correlation Matrix into dataframe with records for each row column pair

问题描述

我有一个很大的相关矩阵(1093 x 1093).我正在尝试将矩阵插入一个数据框，该数据框的每一行和每一列对都具有一列，因此它将是(1093)^ 2条记录.

I have a large matrix of correlations (1093 x 1093). I'm trying my matrix into a dataframe that has a column for every row and column pair, so it would (1093)^2 records.

这是我的矩阵的一个片段

Here's a snippet of my matrix

            60516        45264        02117
60516  1.00000000 -0.370793012 -0.082897941
45264 -0.37079301  1.000000000  0.005145601
02117 -0.08289794  0.005145601  1.000000000

从这里开始的目标是拥有一个看起来像这样的数据框:

The goal from here would be to have a dataframe that looks like this:

row column correlation
60516 60516 1.000000000
60516 45264 -0.370793012

........等等.

........ and so on.

任何人都有提示吗?让我知道我是否可以澄清

Anyone have any tips? Let me know if I can clarify anything

谢谢， 本

推荐答案

对于矩阵m，您可以执行以下操作:

For matrix m, you could do:

data.frame(row=rownames(m)[row(m)], col=colnames(m)[col(m)], corr=c(m))

#     row   col         corr
# 1 60516 60516  1.000000000
# 2 45264 60516 -0.370793010
# 3 02117 60516 -0.082897940
# 4 60516 45264 -0.370793012
# 5 45264 45264  1.000000000
# 6 02117 45264  0.005145601
# 7 60516 02117 -0.082897941
# 8 45264 02117  0.005145601
# 9 02117 02117  1.000000000

但是，如果您的矩阵是对称的，并且您对对角线不感兴趣，则可以将其简化为:

But if your matrix is symmetrical and if you are not interested in the diagonal, then you can simplify it to:

data.frame(row=rownames(m)[row(m)[upper.tri(m)]], 
           col=colnames(m)[col(m)[upper.tri(m)]], 
           corr=m[upper.tri(m)])

#     row   col         corr
# 1 60516 45264 -0.370793012
# 2 60516 02117 -0.082897941
# 3 45264 02117  0.005145601

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