Make dataframe of top N frequent terms for multiple corpora using tm package in R

Question

I have several TermDocumentMatrixs created with the tm package in R.

I want to find the 10 most frequent terms in each set of documents to ultimately end up with an output table like:

corpus1   corpus2
"beach"   "city"
"sand"    "sidewalk"
...        ...
[10th most frequent word]

By definition, findFreqTerms(corpus1,N) returns all of the terms which appear N times or more. To do this by hand I could change N until I got 10 or so terms returned, but the output for findFreqTerms is listed alphabetically so unless I picked exactly the right N, I wouldn't actually know which were the top 10. I suspect that this involves manipulating the internal structure of the TDM that you can see with str(corpus1) as in R tm package create matrix of Nmost frequent terms but the answer here was very opaque to me so I wanted to rephrase the question.

Thanks!

Solution

Here's one way to find the top N terms in a document term matrix. Briefly, you convert the dtm to a matrix, then sort by row sums:

# load text mining library    
library(tm)

# make corpus for text mining (data comes from package, for reproducibility) 
data("crude")
corpus <- Corpus(VectorSource(crude))

# process text (your methods may differ)
skipWords <- function(x) removeWords(x, stopwords("english"))
funcs <- list(tolower, removePunctuation, removeNumbers, stripWhitespace, skipWords)
a <- tm_map(corpus, FUN = tm_reduce, tmFuns = funcs)
a.dtm1 <- TermDocumentMatrix(a, control = list(wordLengths = c(3,10))) 

Here's the method in your Q, which returns words in alpha order, not always very useful, as you note...

N <- 10
findFreqTerms(a.dtm1, N)

[1] "barrel"     "barrels"    "bpd"        "crude"      "dlrs"       "government" "industry"   "kuwait"    
[9] "market"     "meeting"    "minister"   "mln"        "month"      "official"   "oil"        "opec"      
[17] "pct"        "price"      "prices"     "production" "reuter"     "saudi"      "sheikh"     "the"       
[25] "world"

And here's what you can do to get the top N words in order of their abundance:

m <- as.matrix(a.dtm1)
v <- sort(rowSums(m), decreasing=TRUE)
head(v, N)

oil prices   opec    mln    the    bpd   dlrs  crude market reuter 
86     48     47     31     26     23     23     21     21     20 

For several document term matrices, you could do something like this:

# make a list of the dtms
dtm_list <- list(a.dtm1, b.dtm1, c.dtm1, d.dtm1)
# apply the rowsums function to each item of the list
lapply(dtm_list, function(x)  sort(rowSums(as.matrix(x)), decreasing=TRUE))

Is that what you want to do?

Hat-tip to Ian Fellows' wordcloud package where I first saw this method.

UPDATE: following the comment below, here's some more detail...

Here's some data to make a reproducible example with multiple corpora:

examp1 <- "When discussing performance with colleagues, teaching, sending a bug report or searching for guidance on mailing lists and here on SO, a reproducible example is often asked and always helpful. What are your tips for creating an excellent example? How do you paste data structures from r in a text format? What other information should you include? Are there other tricks in addition to using dput(), dump() or structure()? When should you include library() or require() statements? Which reserved words should one avoid, in addition to c, df, data, etc? How does one make a great r reproducible example?"

examp2 <- "Sometimes the problem really isn't reproducible with a smaller piece of data, no matter how hard you try, and doesn't happen with synthetic data (although it's useful to show how you produced synthetic data sets that did not reproduce the problem, because it rules out some hypotheses). Posting the data to the web somewhere and providing a URL may be necessary. If the data can't be released to the public at large but could be shared at all, then you may be able to offer to e-mail it to interested parties (although this will cut down the number of people who will bother to work on it). I haven't actually seen this done, because people who can't release their data are sensitive about releasing it any form, but it would seem plausible that in some cases one could still post data if it were sufficiently anonymized/scrambled/corrupted slightly in some way. If you can't do either of these then you probably need to hire a consultant to solve your problem" 

examp3 <- "You are most likely to get good help with your R problem if you provide a reproducible example. A reproducible example allows someone else to recreate your problem by just copying and pasting R code. There are four things you need to include to make your example reproducible: required packages, data, code, and a description of your R environment. Packages should be loaded at the top of the script, so it's easy to see which ones the example needs. The easiest way to include data in an email is to use dput() to generate the R code to recreate it. For example, to recreate the mtcars dataset in R, I'd perform the following steps: Run dput(mtcars) in R Copy the output In my reproducible script, type mtcars <- then paste. Spend a little bit of time ensuring that your code is easy for others to read: make sure you've used spaces and your variable names are concise, but informative, use comments to indicate where your problem lies, do your best to remove everything that is not related to the problem. The shorter your code is, the easier it is to understand. Include the output of sessionInfo() as a comment. This summarises your R environment and makes it easy to check if you're using an out-of-date package. You can check you have actually made a reproducible example by starting up a fresh R session and pasting your script in. Before putting all of your code in an email, consider putting it on http://gist.github.com/. It will give your code nice syntax highlighting, and you don't have to worry about anything getting mangled by the email system."

examp4 <- "Do your homework before posting: If it is clear that you have done basic background research, you are far more likely to get an informative response. See also Further Resources further down this page. Do help.search(keyword) and apropos(keyword) with different keywords (type this at the R prompt). Do RSiteSearch(keyword) with different keywords (at the R prompt) to search R functions, contributed packages and R-Help postings. See ?RSiteSearch for further options and to restrict searches. Read the online help for relevant functions (type ?functionname, e.g., ?prod, at the R prompt) If something seems to have changed in R, look in the latest NEWS file on CRAN for information about it. Search the R-faq and the R-windows-faq if it might be relevant (http://cran.r-project.org/faqs.html) Read at least the relevant section in An Introduction to R If the function is from a package accompanying a book, e.g., the MASS package, consult the book before posting. The R Wiki has a section on finding functions and documentation"

examp5 <- "Before asking a technical question by e-mail, or in a newsgroup, or on a website chat board, do the following:  Try to find an answer by searching the archives of the forum you plan to post to. Try to find an answer by searching the Web. Try to find an answer by reading the manual. Try to find an answer by reading a FAQ. Try to find an answer by inspection or experimentation. Try to find an answer by asking a skilled friend. If you're a programmer, try to find an answer by reading the source code. When you ask your question, display the fact that you have done these things first; this will help establish that you're not being a lazy sponge and wasting people's time. Better yet, display what you have learned from doing these things. We like answering questions for people who have demonstrated they can learn from the answers. Use tactics like doing a Google search on the text of whatever error message you get (searching Google groups as well as Web pages). This might well take you straight to fix documentation or a mailing list thread answering your question. Even if it doesn't, saying “I googled on the following phrase but didn't get anything that looked promising” is a good thing to do in e-mail or news postings requesting help, if only because it records what searches won't help. It will also help to direct other people with similar problems to your thread by linking the search terms to what will hopefully be your problem and resolution thread. Take your time. Do not expect to be able to solve a complicated problem with a few seconds of Googling. Read and understand the FAQs, sit back, relax and give the problem some thought before approaching experts. Trust us, they will be able to tell from your questions how much reading and thinking you did, and will be more willing to help if you come prepared. Don't instantly fire your whole arsenal of questions just because your first search turned up no answers (or too many). Prepare your question. Think it through. Hasty-sounding questions get hasty answers, or none at all. The more you do to demonstrate that having put thought and effort into solving your problem before seeking help, the more likely you are to actually get help. Beware of asking the wrong question. If you ask one that is based on faulty assumptions, J. Random Hacker is quite likely to reply with a uselessly literal answer while thinking Stupid question..., and hoping the experience of getting what you asked for rather than what you needed will teach you a lesson."

Now let's process the example text a little, in the usual way. First convert the character vectors to corpora.

library(tm)
list_examps <- lapply(1:5, function(i) eval(parse(text=paste0("examp",i))))
list_corpora <- lapply(1:length(list_examps), function(i) Corpus(VectorSource(list_examps[[i]])))

Now remove stopwords, numbers, punctuation, etc.

skipWords <- function(x) removeWords(x, stopwords("english"))
funcs <- list(tolower, removePunctuation, removeNumbers, stripWhitespace, skipWords)
list_corpora1 <- lapply(1:length(list_corpora), function(i) tm_map(list_corpora[[i]], FUN = tm_reduce, tmFuns = funcs))

Convert processed corpora to term document matrix:

list_dtms <- lapply(1:length(list_corpora1), function(i) TermDocumentMatrix(list_corpora1[[i]], control = list(wordLengths = c(3,10))))

Get the most frequently occuring words in each corpus:

top_words <- lapply(1:length(list_dtms), function(x)  sort(rowSums(as.matrix(list_dtms[[x]])), decreasing=TRUE))

And reshape it into a dataframe according to the specified form:

library(plyr)
top_words_df <- t(ldply(1:length(top_words), function(i)  head(names(top_words[[i]]),10)))
colnames(top_words_df) <- lapply(1:length(list_dtms), function(i) paste0("corpus",i))
top_words_df

    corpus1    corpus2      corpus3    corpus4     corpus5    
V1  "example"  "data"       "code"     "functions" "answer"   
V2  "addition" "people"     "example"  "prompt"    "help"     
V3  "data"     "synthetic"  "easy"     "relevant"  "try"      
V4  "how"      "able"       "email"    "book"      "question" 
V5  "include"  "actually"   "include"  "keywords"  "questions"
V6  "what"     "bother"     "recreate" "package"   "reading"  
V7  "when"     "consultant" "script"   "posting"   "answers"  
V8  "are"      "cut"        "check"    "read"      "people"   
V9  "avoid"    "form"       "data"     "search"    "search"   
V10 "bug"      "happen"     "mtcars"   "section"   "searching"

Can you adapt that to work with your data? If not, please edit your question to more accurately show what your data look like.

Comments

[elfs]

thanks! this is great except for the final step doesn't quite get me there -- the ultimate goal is to have a data frame with the top N words in each of the dtms -- let's say, a long df with a column for the document_id, a column for the term, and a column for frequency. if I do data.frame(unlist(lapply...))[1:N] then I get a data frame with the top N terms of the first dtm on the list, but the row names are the terms and the frequencies are what's in the table. I haven't done a lot of work with lists so I'm not sure how else to move forward.

[Ben]

Yes lists can be a bit tricky to get used to, but once you're comfortable with them you can do all sort of handy stuff with lapply and the plyr functions. I've edited my answer to show how you might go from multiple corpora to the dataframe you want. The key is to get your corpora into a list. Without knowing more about your specific data I can't be sure it will work for you. Give it a try and let me know.

[elfs]

thanks for the thorough response, this is exactly what I needed.