我有一个矩阵，里面有一些文件和所有文字。该数字表示文档中该单词的出现次数。
| topic | word1 | word2 | word3 | word4 | word5 |...
|----------|-------|-------|-------|-------|-------|
| politics | 5 | 2 | 4 | 0 | 1 |
| sports | 2 | 0 | 1 | 1 | 6 |
| music | 2 | 3 | 1 | 3 | 6 |
| movies | 0 | 3 | 2 | 6 | 1 |
| history | 4 | 6 | 2 | 3 | 3 |
|...
我想计算和可视化他们的相关性。所以说我想知道关于音乐的文件是否与关于电影或政体等的文件更相似。
做的时候：
csv <- read.csv("documents.csv")
matrix <- data.matrix(csv)
cor(matrix)
我明白了：
topic word1 word2 word3 word4 word5
topic 1.00000000 0.08111071 -0.94812244 0.00000000 -0.6868028 0.3779645
word1 0.08111071 1.00000000 0.21296184 0.62828086 -0.7687575 -0.1635038
word2 -0.94812244 0.21296184 1.00000000 0.09415545 0.4307761 -0.3032248
word3 0.00000000 0.62828086 0.09415545 1.00000000 -0.3546635 -0.8132501
word4 -0.68680282 -0.76875749 0.43077610 -0.35466345 1.0000000 -0.2249755
word5 0.37796447 -0.16350382 -0.30322482 -0.81325006 -0.2249755 1.0000000
实际上我不确定我是否得到了正确的结果以及如何解释它们。
更新
> dput(csv)
structure(list(topic = structure(c(4L, 5L, 3L, 2L, 1L), .Label = c("history",
"movies", "music", "politics", "sports"), class = "factor"),
word1 = c(5L, 2L, 2L, 0L, 4L), word2 = c(2L, 0L, 3L, 3L,
6L), word3 = c(4L, 1L, 1L, 2L, 2L), word4 = c(0L, 1L, 3L,
6L, 3L), word5 = c(1, 6, 6, 1, 3)), .Names = c("topic", "word1",
"word2", "word3", "word4", "word5"), class = "data.frame", row.names = c(NA,
-5L))
> dput(matrix)
structure(c(4, 5, 3, 2, 1, 5, 2, 2, 0, 4, 2, 0, 3, 3, 6, 4, 1,
1, 2, 2, 0, 1, 3, 6, 3, 1, 6, 6, 1, 3), .Dim = 5:6, .Dimnames = list(
NULL, c("topic", "word1", "word2", "word3", "word4", "word5"
)))
答案 0 :(得分：1)
您可能想要删除第一列并处理转置矩阵：
csv <- read.csv("documents.csv")
row.names(csv) <- csv[,1]
csv <- csv[,-1]
matrix <- as.matrix(csv)
cor(t(matrix))