r语言wordcloud2 导出图片怎么导出图

wordcloud2安装问题_r语言吧_百度贴吧
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wordcloud2安装问题收藏
安装出现这个提示,请问是什么问题?
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为兴趣而生,贴吧更懂你。或wordcloud_r语言吧_百度贴吧
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wordcloud收藏
想做个词云,但是导入数据的时候,为什么不能全部显示,只能从九百多行显示,如图
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关于词云图Wordcloud,能实现输出的图片个性化吗收藏
近期学习R语言,看到课后作业是将QQ聊天记录做成词云图,现在已经实现,但怎么样实现输出的图形个性化,例如人脸、或者爱心的形状呢,求解答
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请问QQ聊天记录导出后,还应该怎么处理,剔除其中的时间之类没用的,怎么做成关键词列表,词频列表???
请问在wordcloud()函数时为什么有的文字显示不了,老报错。例如In
wordcloud(word,freq)××× cloud not
你看看这篇博客吧,看了就估计懂来
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R语言wordcloud学习
下面的例子来自于twitteR,用R从twitter中挖掘信息, 其实中文也有
,有时间可以研究一下。
共有三篇详细介绍,可能需要越过GFW
下面是第一篇的例子:
A wordcloud can be one of the best tools that allows us to visualize most of the words and terms contained in tweets. Although its main use is for exploratory&
purposes, they have the advantage to be understandable by most users, and to be visually attractive to the human eyes (if done adequately).
How to create a wordcloud?
Wordclouds are relatively simple to make.&Here's the main recipe steps
Download some tweets (via twitteR or XML)
Extract the text from the tweets
Do some text manipulation (for cleaning & formatting)
Create a lexical Corpus and a TermDocumentMatrix (via tm)
Obtain words and their frequencies
Plot the wordcloud
Example 1: tweets via twitteR
Load all the required packages
library(twitteR)
library(tm)
library(wordcloud)
library(RColorBrewer)
Let's get some tweets in english containing the words &machine learning&
mach_tweets = searchTwitter(&machine learning&, n=500, lang=&en&)
Extract the text from the tweets in a vector
mach_text = sapply(mach_tweets, function(x) x$getText())
Construct the lexical Corpus and the Term Document Matrix
We use the function&
to create the corpus, and the function&
VectorSource
to indicate that the text is in the character vector&
. In order to create the term-document matrix we apply different transformation such as removing numbers, punctuation symbols, lower case, etc.
# create a corpus
mach_corpus = Corpus(VectorSource(mach_text))
# create document term matrix applying some transformations
tdm = TermDocumentMatrix(mach_corpus,
control = list(removePunctuation = TRUE,
stopwords = c(&machine&, &learning&, stopwords(&english&)),
removeNumbers = TRUE, tolower = TRUE))
Obtain words and their frequencies
# define tdm as matrix
m = as.matrix(tdm)
# get word counts in decreasing order
word_freqs = sort(rowSums(m), decreasing=TRUE)
# create a data frame with words and their frequencies
dm = data.frame(word=names(word_freqs), freq=word_freqs)
Let's plot the wordcloud
# plot wordcloud
wordcloud(dm$word, dm$freq, random.order=FALSE, colors=brewer.pal(8, &Dark2&))
# save the image in png format
png(&MachineLearningCloud.png&, width=12, height=8, units=&in&, res=300)
wordcloud(dm$word, dm$freq, random.order=FALSE, colors=brewer.pal(8, &Dark2&))
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