I have a matrix with some documents and all the words in them. The number indicates the number of occurrences of this word in a document.
| 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 |
|...
I want to compute and visualize their correlations. So say I would like to see if the document about music and more similar to the document about movies, or polities, etc.
When doing:
csv <- read.csv("documents.csv")
matrix <- data.matrix(csv)
cor(matrix)
I get:
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
Actually I'm not sure if I got the right results and how to interpret them.
Update:
> 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"
)))
You probably want to remove the first column and work on the transposed matrix:
csv <- read.csv("documents.csv")
row.names(csv) <- csv[,1]
csv <- csv[,-1]
matrix <- as.matrix(csv)
cor(t(matrix))