I am working with some large text files (up to 16 GBytes). I am interested
in extracting the words and counting each time each word appears in the
text. I have written a very simple R program by following some suggestions
and examples I found online.
If my input file is 1 GByte, I see that R uses up to 11 GBytes of memory
when executing the program on
a 64-bit system running CentOS 6.3. Why is R using so much memory? Is there
a better way to do this that will
minimize memory usage.
I am very new to R, so I would appreciate some tips on how to improve my
program or a better way to do it.
R program:
# Read in the entire file and convert all words in text to lower case
words.txt<-tolower(scan("text_file","character",sep="\n"))
# Extract words
pattern <- "(\\b[A-Za-z]+\\b)"
match <- gregexpr(pattern,words.txt)
words.txt <- regmatches(words.txt,match)
# Create a vector from the list of words
words.txt<-unlist(words.txt)
# Calculate word frequencies
words.txt<-table(words.txt,dnn="words")
# Sort by frequency, not alphabetically
words.txt<-sort(words.txt,decreasing=TRUE)
# Put into some readable form, "Name of word" and "Number of times it
occurs"
words.txt<-paste(names(words.txt),words.txt,sep="\t")
# Results to a file
cat("Word\tFREQ",words.txt,file="frequencies",sep="\n")
--
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