Keep Document Id with R Corpus

Keep document ID with R corpus

In newer versions of tm this is a lot easier with the DataframeSource() function.

"A data frame source interprets each row of the data frame x as a document. The first column must be named "doc_id" and contain a unique string identifier for each document. The second column must be named "text" and contain a "UTF-8" encoded string representing the document's content. Optional additional columns are used as document level metadata."

So in this case:

dd <-data.frame(
    doc_id=10:13,
    text=c("No wonder, then, that ever gathering volume from the mere transit ",
      "So that in many cases such a panic did he finally strike, that few ",
      "But there were still other and more vital practical influences at work",
      "Not even at the present day has the original prestige of the Sperm Whale")
    ,stringsAsFactors=F
 )

Corpus = VCorpus(DataframeSource(dd))

Summarizing R corpus with doc ID

If I look at your expected output, you don't need to use this line of code word_freqs=sort(rowSums(tdm_m), decreasing = TRUE). Because this creates a total sum of the word, like Apple = 3 instead of 2 and 1 over multiple documents.

To get to the output you want, instead of using TermDocumentMatrix, using DocumentTermMatrix is slightly easier. No need in switching columns around. I'm showing you two examples on how to get the result. One with melt from the reshape2 package and one with the tidy function from the tidytext package.

# example 1
dtm <- DocumentTermMatrix(df_corpus)
dtm_df <- reshape2::melt(as.matrix(dtm))
# remove 0 values and order the data.frame
dtm_df <- dtm_df[dtm_df$value > 0, ]
dtm_df <- dtm_df[order(dtm_df$value, decreasing = TRUE), ]

or using tidytext::tidy to get the data into a tidy format. No need to remove the 0 values as tidytext doesn't transform it into a matrix before casting it into a data.frame

# example 2
dtm_tidy <- tidytext::tidy(dtm)
# order the data.frame or start using dplyr syntax if needed
dtm_tidy <- dtm_tidy[order(dtm_tidy$count, decreasing = TRUE), ] 

In my tests tidytext is a lot faster and uses less memory as there is no need to first create a dense matrix.

How can I manually set the document id in a corpus?

Well, one simple but not very elegant way to assign your ids to your documents afterward could be the following :

for (i in 1:length(corpus)) {
   attr(corpus[[i]], "ID") <- df$ids[i]
}

tm: read in data frame, keep text id's, construct DTM and join to other dataset

First, some example data from https://stackoverflow.com/a/15506875/1036500

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."

Put example data in a data frame...

df <- data.frame(ID = sapply(1:5, function(i) paste0(sample(letters, 5), collapse = "")),
                 txt = sapply(1:5, function(i) eval(parse(text=paste0("examp",i))))
                 )

Here is the answer to "Question 1: How do I convert this data frame into a corpus and get to keep ID information?"

Use DataframeSource and readerControl to convert data frame to corpus (from https://stackoverflow.com/a/15693766/1036500)...

require(tm)
m <- list(ID = "ID", Content = "txt")
myReader <- readTabular(mapping = m)
mycorpus <- Corpus(DataframeSource(df), readerControl = list(reader = myReader))

# Manually keep ID information from https://stackoverflow.com/a/14852502/1036500
for (i in 1:length(mycorpus)) {
  attr(mycorpus[[i]], "ID") <- df$ID[i]
}

Now some example data for your second question...

Make Document Term Matrix from https://stackoverflow.com/a/15506875/1036500...

skipWords <- function(x) removeWords(x, stopwords("english"))
funcs <- list(content_transformer(tolower), removePunctuation, removeNumbers, stripWhitespace, skipWords)
a <- tm_map(mycorpus, FUN = tm_reduce, tmFuns = funcs)
mydtm <- DocumentTermMatrix(a, control = list(wordLengths = c(3,10)))
inspect(mydtm)

Make another example dataset to join to...

df2 <- data.frame(ID = df$ID,
                  date =  seq(Sys.Date(), length.out=5, by="1 week"),
                  topic =   sapply(1:5, function(i) paste0(sample(LETTERS, 3), collapse = "")) ,
                  sentiment = sample(c("+ve", "-ve"), 5, replace = TRUE)
                  )

Here is the answer to "Question 2: After getting a dtm, how can I join it with another data set by ID?"

Use merge to join the dtm to example dataset of dates, topics, sentiment...

mydtm_df <- data.frame(as.matrix(mydtm))
# merge by row.names from https://stackoverflow.com/a/7739757/1036500
merged <- merge(df2, mydtm_df, by.x = "ID", by.y = "row.names" )
head(merged)

      ID     date.x topic sentiment able actually addition allows also although
1 cpjmn 2013-11-07   XRT       -ve    0        0        2      0    0        0
2 jkdaf 2013-11-28   TYJ       -ve    0        0        0      0    1        0
3 jstpa 2013-12-05   SVB       -ve    2        1        0      0    1        0
4 sfywr 2013-11-14   OMG       -ve    1        1        0      0    0        2
5 ylaqr 2013-11-21   KDY       +ve    0        1        0      1    0        0
always answer answering answers anything archives are arsenal ask asked asking
1      1      0         0       0        0        0   1       0   0     1      0
2      0      0         0       0        0        0   0       0   0     0      0
3      0      8         2       3        1        1   0       1   2     1      3
4      0      0         0       0        0        0   0       0   0     0      0
5      0      0         0       0        1        0   0       0   0     0      0

There, now you have:

1. Answers to your two questions (normally this site is just one question per... question)
2. Several kinds of example data that you can use when you ask your next question (makes your question a lot more engaging for folks who might want to answer)
3. Hopefully a sense that the answers to your questions can already be found elsewhere on the stackoverflow r tag, if you can think of how to break your questions down into smaller steps.

If this doesn't answer your questions, ask another question and include code to reproduce your use-case as exactly as you can. If it does answer your question, then you should mark it as accepted (at least until a better one comes along, eg. Tyler might pop in with a one-liner from his impressive qdap package...)

How to keep the text id of removed text in lda

The problem here is that LDA() removes the rownames from your document-term matrix and replaces them with a simple serial number. This no longer corresponds to your original dtext$id. But you can replace the LDA id with the document name, and then link this back to your input text.

To make this more clear, we are first going to replace your dtext$id with something that can be more clearly distinguished from the serial number that LDA() returns.

# to distinguish your id from those from LDA()
dtext$id <- paste0("doc_", dtext$id)

# this takes the document name from "id"
toks <- corpus(dtext, docid_field = "id") %>%
  tokens()

Then run your other steps exactly as above.

We can see that the first document is empty (has zero feature counts). This is the one that is dropped in the conversion of the dfm to the "topicmodels" format.

ntoken(myDfm)
## text1 text2 text3 text4 
##     0    49    63   201

as.matrix(dtm[, 1:3])
##        Terms
## Docs    dataset_contain contain_movi movi_review
##   text2               1            1           1
##   text3               1            0           0
##   text4               0            0           0

These document names are obliterated by LDA(), however.

toptopics
##   document topic
## 1        1    V2
## 2        2    V2
## 3        3    V1

But we can (re)assign them from the rownames of dtm, which will correspond 1:1 to the documents returned by LDA().

toptopics$docname <- rownames(dtm)
toptopics
##   document topic docname
## 1        1    V2   text2
## 2        2    V2   text3
## 3        3    V1   text4

And now, toptopics$docname can be merged with dtext$id, solving your problem.

R: find corpus document by ID-tag and set an additional tag

According to ?meta

meta(crude, type="local", tag="someID") <- someID

will assign the meta data tag someID at the individual document level. What you want is to create a tagging at the collection level. For this, you want to manipulate the DMetaData attribute of the corpus crude. You can do this as:

meta(crude, type="indexed", tag="someID") <- someID

but I find it much easier to use the access

DMetaData(crude)$someID  <- someID

(this at least works for corpora of type VCorpus). With this adjustment:

library("tm")
someID <- paste(letters[1:15], 16:30, sep="")
someTag <- sample(c("a","x","g","h","e"), 15, replace=TRUE)

data(crude) # a corpus with 20 docs
# Need to be sure to allocate full tag and id set.
DMetaData(crude)$someID <- c(someID,rep(NA,5))
DMetaData(crude)$someTag <- rep(NA,20)

mydf <- data.frame(cbind(someTag, someID), stringsAsFactors=FALSE)  # Creating a dataframe with similar IDs
mydf <- mydf[sample(nrow(mydf)),]  # permutation of elements (rows)
rownames(mydf) <- 1:15  # overwriting the rownames

for (i in 1:nrow(mydf)){
      DMetaData(crude)$someTag[DMetaData(crude)$someID==mydf$someID[i]]<- mydf$someTag[i]
    }

Result:

> DMetaData(crude)
   MetaID someID someTag  
1       0    a16       a
2       0    b17       h
3       0    c18       g
4       0    d19       a
5       0    e20       e
6       0    f21       a
7       0    g22       x
8       0    h23       g
9       0    i24       h
10      0    j25       e
11      0    k26       x
12      0    l27       a
13      0    m28       a
14      0    n29       h
15      0    o30       a
16      0   <NA>    <NA>
17      0   <NA>    <NA>
18      0   <NA>    <NA>
19      0   <NA>    <NA>
20      0   <NA>    <NA>

Keep EXACT words from R corpus

Switching grammars to tidytext, your current transformation would be

library(tidyverse)
library(tidytext)
library(stringr)

dd %>% unnest_tokens(word, text) %>% 
    mutate(word = str_replace_all(word, setNames(keep, paste0('.*', keep, '.*')))) %>% 
    inner_join(data_frame(word = keep))

##   id   word
## 1 10 wonder
## 2 10    the
## 3 10   that
## 4 11   that
## 5 12    the
## 6 12    the
## 7 13    the

Keeping exact matches is easier, as you can use joins (which use ==) instead of regex:

dd %>% unnest_tokens(word, text) %>% 
    inner_join(data_frame(word = keep))

##   id word
## 1 10 then
## 2 10 that
## 3 11 that
## 4 13  the

To take it back to a document-term matrix,

library(tm)

dd %>% mutate(id = factor(id)) %>%    # to keep empty rows of DTM
    unnest_tokens(word, text) %>% 
    inner_join(data_frame(word = keep)) %>% 
    mutate(i = 1) %>% 
    cast_dtm(id, word, i) %>% 
    inspect()

## <<DocumentTermMatrix (documents: 4, terms: 3)>>
## Non-/sparse entries: 4/8
## Sparsity           : 67%
## Maximal term length: 4
## Weighting          : term frequency (tf)
## 
##     Terms
## Docs then that the
##   10    1    1   0
##   11    0    1   0
##   12    0    0   0
##   13    0    0   1

---

Currently, your function is matching words with a boundary before or after. To change it to before and after, change the collapse parameter to include boundaries:

tm <- VCorpus(DataframeSource(dd), readerControl = list(reader = myReader))

keepOnlyWords<-content_transformer(function(x,words) {
        regmatches(x, 
            gregexpr(paste0("(\\b",  paste(words, collapse = "\\b|\\b"), "\\b)"), x)
        , invert = T) <- " "
        x
    })

tm <- tm_map(tm, content_transformer(tolower))
tm <- tm_map(tm, keepOnlyWords, keep)
tm <- tm_map(tm, stripWhitespace)

inspect(DocumentTermMatrix(tm))

## <<DocumentTermMatrix (documents: 4, terms: 3)>>
## Non-/sparse entries: 4/8
## Sparsity           : 67%
## Maximal term length: 4
## Weighting          : term frequency (tf)
## 
##     Terms
## Docs that the then
##   10    1   0    1
##   11    1   0    0
##   12    0   0    0
##   13    0   1    0

R - Text Mining - Importing a Corpus and keeping the file names in document term matrix

Here's a debugging session to identify / correct the loss of file name. The tolower line was modified, and the plaintext line was commented-out since these lines remove the file information. Also, if you check ds$reader, you can see the baseline reader creates a plain text document.

library("tm")
library("SnowballC")

# corpus <-Corpus(DirSource("blog"))

sf<-system.file("texts", "txt", package = "tm")
ds <-DirSource(sf)
your_corpus <-Corpus(ds)

# Check status with the following line
meta(your_corpus[[1]])

#pre_processing
myStopwords <- c(stopwords("english"))
# your_corpus <- tm_map(your_corpus, tolower)
your_corpus <- tm_map(your_corpus, content_transformer(tolower))
meta(your_corpus[[1]])
your_corpus <- tm_map(your_corpus, removeNumbers)
meta(your_corpus[[1]])
your_corpus <- tm_map(your_corpus, removeWords, myStopwords) 
meta(your_corpus[[1]])
your_corpus <- tm_map(your_corpus, stripWhitespace)
meta(your_corpus[[1]])
your_corpus <- tm_map(your_corpus, removePunctuation)
meta(your_corpus[[1]])
your_corpus <- tm_map(your_corpus, stemDocument)
meta(your_corpus[[1]])
#your_corpus <- tm_map(your_corpus, PlainTextDocument)
#meta(your_corpus[[1]])

#creating a doucment term matrix
myDtm <- DocumentTermMatrix(your_corpus, control=list(wordLengths=c(3,Inf)))

dim(myDtm)
inspect(myDtm)

---

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