R tm package create matrix of Nmost frequent terms

Question

I have a `termDocumentMatrix` created using the `tm` package in R. I'm trying to create a matrix/dataframe that has the 50 most frequently occurring terms. When I try to convert to a matrix I get this error: ``` > ap.m <- as.matrix(mydata.dtm) Error: cannot allocate vector of size 2.0 Gb ``` So I tried converting to sparse matrices using Matrix package: ``` > A <- as(mydata.dtm, "sparseMatrix") Error in as(from, "CsparseMatrix") : no method or default for coercing "TermDocumentMatrix" to "CsparseMatrix" > B <- Matrix(mydata.dtm, sparse = TRUE) Error in asMethod(object) : invalid class 'NA' to dup_mMatrix_as_geMatrix ``` I've tried accessing the different parts of the tdm using: ``` > freqy1 <- data.frame(term1 = findFreqTerms(mydata.dtm, lowfreq=165)) > mydata.dtm[mydata.dtm$ Terms %in% freqy1$term1,] Error in seq_len(nr) : argument must be coercible to non-negative integer ``` Here's some other info: ``` > str(mydata.dtm) List of 6 $ i : int [1:430206] 377 468 725 3067 3906 4150 4393…

Accepted Answer

The term-document matrices in tm are already created as sparse matrices. Here, `mydata.tdm$i` and `mydata.tdm$j` are the vectors of indexes of the matrix and `mydata.tdm$v` is the related vector of frequencies. So that you can create a sparse matrix writing :

```
sparseMatrix(i=mydata.tdm$i, j=mydata.tdm$j, x=mydata.tdm$v)
```

Then you can use `rowSums` and link the rows, you're interested in, to the terms, they stand for, with `$Terms`.

R tm package create matrix of Nmost frequent terms

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