试试这个

data("crude")
myTdm <- as.matrix(TermDocumentMatrix(crude))
FreqMat <- data.frame(ST = rownames(myTdm), 
                      Freq = rowSums(myTdm), 
                      row.names = NULL)
head(FreqMat, 10)
#            ST Freq
# 1       "(it)    1
# 2     "demand    1
# 3  "expansion    1
# 4        "for    1
# 5     "growth    1
# 6         "if    1
# 7         "is    2
# 8        "may    1
# 9       "none    2
# 10      "opec    2

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我在R中有以下几行，可以帮助创建单词频率并将它们放在一个表中，它读取.txt格式的文本文件并创建单词的频率，我希望这可以帮助任何感兴趣的人。

avisos<- scan("anuncio.txt", what="character", sep="\n")
avisos1 <- tolower(avisos)
avisos2 <- strsplit(avisos1, "\\W")
avisos3 <- unlist(avisos2)
freq<-table(avisos3)
freq1<-sort(freq, decreasing=TRUE)
temple.sorted.table<-paste(names(freq1), freq1, sep="\\t")
cat("Word\tFREQ", temple.sorted.table, file="anuncio.txt", sep="\n")

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查看findFreqTerms的源代码，似乎函数slam::row_sums在对术语-文档矩阵调用时会发挥作用。例如，尝试：

data(crude)
slam::row_sums(TermDocumentMatrix(crude))

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根据您的需要，使用一些tidyverse函数可能是一个粗略的解决方案，在如何处理大写，标点符号和停用词方面提供了一些灵活性：

text_string <- 'I have been using the tm package to run some text analysis. My problem is with creating a list with words and their frequencies associated with the same. I typically use the following code for generating list of words in a frequency range. Is there any way to automate this such that we get a dataframe with all words and their frequency?
The other problem that i face is with converting the term document matrix into a data frame. As i am working on large samples of data, I run into memory errors. Is there a simple solution for this?'

stop_words <- c('a', 'and', 'for', 'the') # just a sample list of words I don't care about

library(tidyverse)
data_frame(text = text_string) %>% 
  mutate(text = tolower(text)) %>% 
  mutate(text = str_remove_all(text, '[[:punct:]]')) %>% 
  mutate(tokens = str_split(text, "\\s+")) %>%
  unnest() %>% 
  count(tokens) %>% 
  filter(!tokens %in% stop_words) %>% 
  mutate(freq = n / sum(n)) %>% 
  arrange(desc(n))

# A tibble: 64 x 3
  tokens      n   freq
  <chr>   <int>  <dbl>
1 i           5 0.0581
2 with        5 0.0581
3 is          4 0.0465
4 words       3 0.0349
5 into        2 0.0233
6 list        2 0.0233
7 of          2 0.0233
8 problem     2 0.0233
9 run         2 0.0233
10 that       2 0.0233
# ... with 54 more rows

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a = scan(file='~/Desktop//test.txt',what="list")
a1 = data.frame(lst=a)
count(a1,vars="lst")

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似乎工作，以获得简单的频率。我用扫描，因为我有一个txt文件，但它应该与read.csv太。

apply(myTdm, 1, sum)或rowSums(as.matrix(myTdm))是否给予您想要的ngram计数？

使用qdap包：

text_string <- 'I have been using the tm package to run some text analysis. My problem is with creating a list with words and their frequencies associated with the same. I typically use the following code for generating list of words in a frequency range. Is there any way to automate this such that we get a dataframe with all words and their frequency?
The other problem that i face is with converting the term document matrix into a data frame. As i am working on large samples of data, I run into memory errors. Is there a simple solution for this?'

qdap::freq_terms(text_string, stopwords = c("of", "a", "i", "with", "is"), top = Inf)
#    WORD        FREQ
# 1  the            5
# 2  words          3
# 3  and            2
# 4  data           2
# 5  for            2
# 6  frequency      2
# 7  into           2
# 8  list           2
# 9  problem        2
# 10 run            2
# ...

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默认情况下，该函数只显示前20名，因此我们设置top = Inf。您可以将任何字符向量传递给stopwords参数：c(tm::stopwords(), "other", "stop", "words")。