问r:闪亮的App不显示结果
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Stack Overflow用户

提问于 2018-05-07 10:41:57

回答 2查看 1.2K关注 0票数 0

这里的新手..。

我正在努力创建一个字云的r闪亮的应用程序。

这是我在戒毒所里的情况：

tabPanel("Word Cloud", sidebarLayout( # Sidebar with a slider and selection inputs sidebarPanel( selectInput("Subcategory", "Choose a Subcategory:", choices = makeup$SubCategory), sliderInput("freq", "Maximum Number of Words:", min = 1, max = 7, value = 5), sliderInput("max", "Minimum Frequency:", min = 1, max = 6000, value = 1000) ), mainPanel( plotOutput("plot") ))

这是我在我的服务器上所拥有的。

shinyServer(输入、输出、会话){ word =反应性({ myCorpus =Corpus(VectorSource( select，SubCategory == >input$==，select=ChemicalName)) myCorpus = tm_map( myCorpus，content_transformer(Tolower) myCorpus = tm_map(myCorpus，removePunctuation) myCorpus= tm_map(myCorpus，removeNumbers) myDTM = TermDocumentMatrix( myCorpus ) m= as.matrix(myDTM) v= sort(rowSums(m)，递减=真) data.frame(word=names(v)，freq=v)$word }) freq =反应性({ myCorpus =Corpus(子集(化妆，SubCategory == >输入$$子类别，select =ChemicalName))myCorpus=ChemicalName( content_transformer(tolower)) myCorpus = tm_map( myCorpus，removePunctuation) myCorpus= tm_map(myCorpus，removeNumbers) myDTM = TermDocumentMatrix(myCorpus) m= as.matrix(myDTM) v= sort(rowSums(m)，递减= TRUE) data.frame(word=names(v)，myDTM })输出$plot <- (字数= word，random.order=FALSE，rot.per=0.3，scale=c(4，.5)，max.words=15，colors=brewer.pal(8，“Dark2”) })

我不能让我的闪亮的应用程序显示字云..。

我还收到了一个警告信息：

。警告:最大值中的错误:参数的类型(闭包)无效

有什么主意吗？！

谢谢!

word-cloud

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回答 2

Stack Overflow用户

发布于 2018-05-08 06:43:22

反应是函数而不是变量。试试这个-应该管用的。

output$plot <- renderPlot({
     wordcloud(words = word(), 
                freq = freq(), 
                random.order=FALSE, rot.per=0.3,   
                scale=c(4,.5),max.words=15, 
                colors=brewer.pal(8,"Dark2"))
      })

票数 1

Stack Overflow用户

发布于 2018-05-08 06:56:02

@Bertil男爵对你最紧迫的问题有正确的答案。我想我可以提供一些关于精简应用程序/代码的建议。

shinyServer(function(input, output, session) {

     word = reactive({
     myCorpus = Corpus(VectorSource(subset(makeup, 
                       SubCategory == > input$Subcategory, 
                       select = ChemicalName)))
     myCorpus = tm_map(myCorpus, content_transformer(tolower))
     myCorpus = tm_map(myCorpus, removePunctuation)
     myCorpus = tm_map(myCorpus, removeNumbers)
     myDTM = TermDocumentMatrix(myCorpus)
     #m = as.matrix(myDTM) ##this can cause an memory error if your corpus is even moderately sized.
     #row_sums from the slam package is much more efficient as it is designed to operate on sparse matrices and TDM/DTMs from the tm package are sparse matrices which are constructed using slam
     v = sort(slam::row_sums(myDTM),decreasing = TRUE)
     data.frame(word=names(v),freq=v) #Since you've already calculated frequency here there is no need for the freq reactive. We can just access the freq from this data.frame
     })

 #this is a redundant reprocessing of your corpus (which can be time consuming if it is big. 
 #freq = reactive({
 #    myCorpus = Corpus(VectorSource(subset(makeup, SubCategory == >              input$Subcategory, select = ChemicalName)))
 #    myCorpus = tm_map(myCorpus, content_transformer(tolower))
 #    myCorpus = tm_map(myCorpus, removePunctuation)
 #    myCorpus = tm_map(myCorpus, removeNumbers)
 #    myDTM = TermDocumentMatrix(myCorpus)
 #    m = as.matrix(myDTM)
 #    v = sort(rowSums(m),decreasing = TRUE)
 #    data.frame(word=names(v),freq=v)$freq
 #    })

  output$plot <- renderPlot({
     wordcloud(words = word()$word,
                freq = word()$freq, 
                random.order=FALSE, rot.per=0.3,   
                scale=c(4,.5),max.words=15, 
                colors=brewer.pal(8,"Dark2"))
      })