从R中的向量中提取单词的总频率
Extract total frequency of words from vector in R
这是我的向量：
posts = c("originally by: cearainmy only concern with csm is they seem a bit insulated from players. they have private message boards where it appears most of their work goes on. i would bet they are posting more there than in jita speakers corner. i think that is unfortunate because its hard to know who to vote for if you never really see what positions they hold. its sort of like ccp used to post here on the forums then they stopped. so they got a csm to represent players and use jita park forum to interact. now the csm no longer posts there as they have their internal forums where they hash things out. perhaps we need a csm to the csm to find out what they are up to.i don't think you need to worry too much. the csm has had an internal forum for over 2 years, although it is getting used a lot more now than it was. a lot of what goes on in there is nda stuff that we couldn't discuss anyway.i am quite happy to give my opinion on any topic, to the extent that the nda allows, and i" , "fot those of you bleating about imagined nda scandals as you attempt to cast yourselves as the julian assange of eve, here's a quote from the winter summit thread:originally by: sokrateszday 3post dominion 0.0 (3hrs!)if i had to fly to iceland only for this session i would have done it. we had gathered a list of items and prepared it a bit. important things we went over were supercaps, force projection, empire building, profitability of 0.0, objectives for small gangs and of course sovereingty.the csm spent 3 hours talking to ccp about how dominion had changed 0.0, and the first thing on sokratesz's list is supercaps. its not hard to figure out the nature of the discussion.on the other hand, maybe you're right, and the csm's priority for this discussion was to talk about how underpowered and useless supercarriers are and how they needed triple the ehp and dps from their current levels?(it wasn't)"
我想要一个 data frame 结果，其中包含单词及其出现的频率。
所以结果应该类似于：
word count
a 300
and 260
be 200
... ...
... ...
我尝试做的是使用 tm
corpus <- VCorpus(VectorSource(posts))
corpus <-tm_map(corpus, removeNumbers)
corpus <-tm_map(corpus, removePunctuation)
m <- DocumentTermMatrix(corpus)
运行 findFreqTerms(m, lowfreq =0, highfreq =Inf ) 只是给了我单词，所以我理解它是一个稀疏矩阵，我如何提取单词及其频率？
有没有更简单的方法，也许根本不使用 tm？
普通 R 解决方案，假设所有单词都由 space:
分隔
words <- strsplit(posts, " ", fixed = T)
words <- unlist(words)
counts <- table(words)
names(counts)保存单词，值是计数。
您可能希望使用 gsub 删除 (),.?: 和 's、't 或 're，如您的示例所示。如：
posts <- gsub("'t|'s|'t|'re", "", posts)
posts <- gsub("[(),.?:]", " ", posts)
posts = c("originally by: cearainmy only concern with csm is they seem a bit insulated from players. they have private message boards where it appears most of their work goes on. i would bet they are posting more there than in jita speakers corner. i think that is unfortunate because its hard to know who to vote for if you never really see what positions they hold. its sort of like ccp used to post here on the forums then they stopped. so they got a csm to represent players and use jita park forum to interact. now the csm no longer posts there as they have their internal forums where they hash things out. perhaps we need a csm to the csm to find out what they are up to.i don't think you need to worry too much. the csm has had an internal forum for over 2 years, although it is getting used a lot more now than it was. a lot of what goes on in there is nda stuff that we couldn't discuss anyway.i am quite happy to give my opinion on any topic, to the extent that the nda allows, and i" , "fot those of you bleating about imagined nda scandals as you attempt to cast yourselves as the julian assange of eve, here's a quote from the winter summit thread:originally by: sokrateszday 3post dominion 0.0 (3hrs!)if i had to fly to iceland only for this session i would have done it. we had gathered a list of items and prepared it a bit. important things we went over were supercaps, force projection, empire building, profitability of 0.0, objectives for small gangs and of course sovereingty.the csm spent 3 hours talking to ccp about how dominion had changed 0.0, and the first thing on sokratesz's list is supercaps. its not hard to figure out the nature of the discussion.on the other hand, maybe you're right, and the csm's priority for this discussion was to talk about how underpowered and useless supercarriers are and how they needed triple the ehp and dps from their current levels?(it wasn't)")
posts <- gsub("[[:punct:]]", '', posts) # remove punctuations
posts <- gsub("[[:digit:]]", '', posts) # remove numbers
word_counts <- as.data.frame(table(unlist( strsplit(posts, "\ ") ))) # split vector by space
word_counts <- with(word_counts, word_counts[ Var1 != "", ] ) # remove empty characters
head(word_counts)
# Var1 Freq
# 2 a 8
# 3 about 3
# 4 allows 1
# 5 although 1
# 6 am 1
# 7 an 1
tidytext 包允许相当直观的文本挖掘，包括标记化。它旨在在 tidyverse 管道中工作，因此它提供了一个停用词列表（"a"、"the"、"to" 等）以使用 dplyr::anti_join 排除。在这里，你可能会做
library(dplyr) # or if you want it all, `library(tidyverse)`
library(tidytext)
data_frame(posts) %>%
unnest_tokens(word, posts) %>%
anti_join(stop_words) %>%
count(word, sort = TRUE)
## # A tibble: 101 × 2
## word n
## <chr> <int>
## 1 csm 7
## 2 0.0 3
## 3 nda 3
## 4 bit 2
## 5 ccp 2
## 6 dominion 2
## 7 forum 2
## 8 forums 2
## 9 hard 2
## 10 internal 2
## # ... with 91 more rows
你有两个选择。取决于您是要对每个文档还是对所有文档进行字数统计。
所有文件
library(dplyr)
count <- as.data.frame(t(inspect(m)))
sel_cols <- colnames(count)
count$word <- rownames(count)
rownames(count) <- seq(length = nrow(count))
count$count <- rowSums(count[,sel_cols])
count <- count %>% select(word,count)
count <- count[order(count$count, decreasing=TRUE), ]
### RESULT of head(count)
# word count
# 140 the 14
# 144 they 10
# 4 and 9
# 25 csm 7
# 43 for 5
# 55 had 4
这应该捕获所有文档中的事件（通过使用 rowSums）。
每个文档
如果你想要每个文档的词频，我建议使用 tidytext 包。
library(tidytext)
m_td <- tidy(m)
这是我的向量：
posts = c("originally by: cearainmy only concern with csm is they seem a bit insulated from players. they have private message boards where it appears most of their work goes on. i would bet they are posting more there than in jita speakers corner. i think that is unfortunate because its hard to know who to vote for if you never really see what positions they hold. its sort of like ccp used to post here on the forums then they stopped. so they got a csm to represent players and use jita park forum to interact. now the csm no longer posts there as they have their internal forums where they hash things out. perhaps we need a csm to the csm to find out what they are up to.i don't think you need to worry too much. the csm has had an internal forum for over 2 years, although it is getting used a lot more now than it was. a lot of what goes on in there is nda stuff that we couldn't discuss anyway.i am quite happy to give my opinion on any topic, to the extent that the nda allows, and i" , "fot those of you bleating about imagined nda scandals as you attempt to cast yourselves as the julian assange of eve, here's a quote from the winter summit thread:originally by: sokrateszday 3post dominion 0.0 (3hrs!)if i had to fly to iceland only for this session i would have done it. we had gathered a list of items and prepared it a bit. important things we went over were supercaps, force projection, empire building, profitability of 0.0, objectives for small gangs and of course sovereingty.the csm spent 3 hours talking to ccp about how dominion had changed 0.0, and the first thing on sokratesz's list is supercaps. its not hard to figure out the nature of the discussion.on the other hand, maybe you're right, and the csm's priority for this discussion was to talk about how underpowered and useless supercarriers are and how they needed triple the ehp and dps from their current levels?(it wasn't)"
我想要一个 data frame 结果，其中包含单词及其出现的频率。
所以结果应该类似于：
word count
a 300
and 260
be 200
... ...
... ...
我尝试做的是使用 tm
corpus <- VCorpus(VectorSource(posts))
corpus <-tm_map(corpus, removeNumbers)
corpus <-tm_map(corpus, removePunctuation)
m <- DocumentTermMatrix(corpus)
运行 findFreqTerms(m, lowfreq =0, highfreq =Inf ) 只是给了我单词，所以我理解它是一个稀疏矩阵，我如何提取单词及其频率？
有没有更简单的方法，也许根本不使用 tm？
普通 R 解决方案，假设所有单词都由 space:
分隔words <- strsplit(posts, " ", fixed = T)
words <- unlist(words)
counts <- table(words)
names(counts)保存单词，值是计数。
您可能希望使用 gsub 删除 (),.?: 和 's、't 或 're，如您的示例所示。如：
posts <- gsub("'t|'s|'t|'re", "", posts)
posts <- gsub("[(),.?:]", " ", posts)
posts = c("originally by: cearainmy only concern with csm is they seem a bit insulated from players. they have private message boards where it appears most of their work goes on. i would bet they are posting more there than in jita speakers corner. i think that is unfortunate because its hard to know who to vote for if you never really see what positions they hold. its sort of like ccp used to post here on the forums then they stopped. so they got a csm to represent players and use jita park forum to interact. now the csm no longer posts there as they have their internal forums where they hash things out. perhaps we need a csm to the csm to find out what they are up to.i don't think you need to worry too much. the csm has had an internal forum for over 2 years, although it is getting used a lot more now than it was. a lot of what goes on in there is nda stuff that we couldn't discuss anyway.i am quite happy to give my opinion on any topic, to the extent that the nda allows, and i" , "fot those of you bleating about imagined nda scandals as you attempt to cast yourselves as the julian assange of eve, here's a quote from the winter summit thread:originally by: sokrateszday 3post dominion 0.0 (3hrs!)if i had to fly to iceland only for this session i would have done it. we had gathered a list of items and prepared it a bit. important things we went over were supercaps, force projection, empire building, profitability of 0.0, objectives for small gangs and of course sovereingty.the csm spent 3 hours talking to ccp about how dominion had changed 0.0, and the first thing on sokratesz's list is supercaps. its not hard to figure out the nature of the discussion.on the other hand, maybe you're right, and the csm's priority for this discussion was to talk about how underpowered and useless supercarriers are and how they needed triple the ehp and dps from their current levels?(it wasn't)")
posts <- gsub("[[:punct:]]", '', posts) # remove punctuations
posts <- gsub("[[:digit:]]", '', posts) # remove numbers
word_counts <- as.data.frame(table(unlist( strsplit(posts, "\ ") ))) # split vector by space
word_counts <- with(word_counts, word_counts[ Var1 != "", ] ) # remove empty characters
head(word_counts)
# Var1 Freq
# 2 a 8
# 3 about 3
# 4 allows 1
# 5 although 1
# 6 am 1
# 7 an 1
tidytext 包允许相当直观的文本挖掘，包括标记化。它旨在在 tidyverse 管道中工作，因此它提供了一个停用词列表（"a"、"the"、"to" 等）以使用 dplyr::anti_join 排除。在这里，你可能会做
library(dplyr) # or if you want it all, `library(tidyverse)`
library(tidytext)
data_frame(posts) %>%
unnest_tokens(word, posts) %>%
anti_join(stop_words) %>%
count(word, sort = TRUE)
## # A tibble: 101 × 2
## word n
## <chr> <int>
## 1 csm 7
## 2 0.0 3
## 3 nda 3
## 4 bit 2
## 5 ccp 2
## 6 dominion 2
## 7 forum 2
## 8 forums 2
## 9 hard 2
## 10 internal 2
## # ... with 91 more rows
你有两个选择。取决于您是要对每个文档还是对所有文档进行字数统计。
所有文件
library(dplyr)
count <- as.data.frame(t(inspect(m)))
sel_cols <- colnames(count)
count$word <- rownames(count)
rownames(count) <- seq(length = nrow(count))
count$count <- rowSums(count[,sel_cols])
count <- count %>% select(word,count)
count <- count[order(count$count, decreasing=TRUE), ]
### RESULT of head(count)
# word count
# 140 the 14
# 144 they 10
# 4 and 9
# 25 csm 7
# 43 for 5
# 55 had 4
这应该捕获所有文档中的事件（通过使用 rowSums）。
每个文档
如果你想要每个文档的词频，我建议使用 tidytext 包。
library(tidytext)
m_td <- tidy(m)