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
library(tm)
library(RWeka)
txt <- read.csv("HW.csv",header=T)
df <- do.call("rbind", lapply(txt, as.data.frame))
names(df) <- "text"
myCorpus <- Corpus(VectorSource(df$text))
myStopwords <- c(stopwords('english'),"originally", "posted")
myCorpus <- tm_map(myCorpus, removeWords, myStopwords)
#building the TDM
btm <- function(x) NGramTokenizer(x, Weka_control(min = 3, max = 3))
myTdm <- TermDocumentMatrix(myCorpus, control = list(tokenize = btm))
I typically use the following code for generating list of words in a frequency range
frq1 <- findFreqTerms(myTdm, lowfreq=50)
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?
Try this
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
Looking at the source of findFreqTerms, it appears that the function slam::row_sums does the trick when called on a term-document matrix. Try, for instance:
data(crude)
slam::row_sums(TermDocumentMatrix(crude))
I have the following lines in R that can help to create word frequencies and put them in a table, it reads the file of text in .txt format and create the frequencies of words, I hope that this can help to anyone interested.
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")
Depending on your needs, using some tidyverse functions might be a rough solution that offers some flexibility in terms of how you handle capitalization, punctuation, and stop words:
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
a = scan(file='~/Desktop//test.txt',what="list")
a1 = data.frame(lst=a)
count(a1,vars="lst")
seems to work to get simple frequencies. I've used scan because I had a txt file, but it should work with read.csv too.
Does apply(myTdm, 1, sum) or rowSums(as.matrix(myTdm)) give the ngram counts you're after?
来源：https://stackoverflow.com/questions/18101047/list-of-word-frequencies-using-r