tm:读入数据框，保留文本 ID，构建 DTM 并加入其他数据集 [英] tm: read in data frame, keep text id's, construct DTM and join to other dataset

问题描述

我正在使用包 tm.

假设我有一个 2 列 500 行的数据框.第一列是随机生成的ID，里面有字符和数字:txF87uyK"第二列是实际文本:今天天气很好.约翰去慢跑了.等等，等等，......"

Say I have a data frame of 2 columns, 500 rows. The first column is ID which is randomly generated and has both character and number in it: "txF87uyK" The second column is actual text : "Today's weather is good. John went jogging. blah, blah,..."

现在我想从这个数据框创建一个文档术语矩阵.

Now I want to create a document-term matrix from this data frame.

我的问题是我想保留 ID 信息，以便在获得文档术语矩阵后，我可以将此矩阵与另一个矩阵连接起来，该矩阵的每一行都是每个文档的其他信息(日期、主题、情绪)和每行由文档 ID 标识.

My problem is I want to keep the ID information so that after I got the document-term matrix, I can join this matrix with another matrix that has each row being other information (date, topic, sentiment) of each document and each row is identified by document ID.

我该怎么做?

问题 1:如何将这个数据框转换成语料库并保留 ID 信息?

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

问题 2:得到一个 dtm 后，如何将它与另一个通过 ID 设置的数据连接起来?

Question 2: After getting a dtm, how can I join it with another data set by ID?

推荐答案

首先，一些来自 https://stackoverflow.com 的示例数据/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."

将示例数据放入数据框中...

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))))
                 )

这是对问题 1:如何将这个数据框转换为语料库并保留 ID 信息?"的答案

使用 DataframeSource 和 readerControl 将数据框转换为语料库(来自 https://stackoverflow.com/a/15693766/1036500)...

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]
}

现在为您的第二个问题提供一些示例数据...

Now some example data for your second question...

从 https://stackoverflow.com/a/15506875/1036500...制作文档术语矩阵>

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)

制作另一个示例数据集以加入...

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)
                  )

这是对问题 2:获得 dtm 后，如何将其与另一个通过 ID 设置的数据集连接起来?"的答案

使用 merge 将 dtm 加入到日期、主题、情绪的示例数据集...

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

好了，现在你有:

1. 回答你的两个问题(通常这个网站每个...问题只有一个问题)
2. 在您提出下一个问题时可以使用的几种示例数据(使您的问题对可能想要回答的人更具吸引力)
3. 希望你的问题的答案已经可以在 stackoverflow 的其他地方找到 r 标签，如果你能想到如何将你的问题分解成更小的步骤.

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.

如果这不能回答您的问题，请提出另一个问题并包含尽可能准确地重现您的用例的代码.如果它确实回答了您的问题，那么您应该将其标记为已接受(至少直到更好的一个出现，例如.泰勒可能会从他令人印象深刻的 qdap 包中弹出一个单线...)

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...)

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