Shiny中二进制运算符错误的非数字参数

Question

我在我的虚拟机上运行Rstudio IDE。我正在使用Shiny开发一个Twitter情绪分析应用程序。我使用Github处的代码。

我已经更改了函数TwitterOAuth（）的代码。这是server.R的代码：

library(twitteR)
library(stringr)
library(ROAuth)
library(RCurl)
library(ggplot2)
library(reshape)
library(tm)
library(RJSONIO)
library(wordcloud)
library(gridExtra)
library(plyr)
library(shiny)
library(base64enc)
library(devtools)

#source("twitter_credentials.txt")

TwitterOAuth<-function(){
reqURL <- "https://api.twitter.com/oauth/request_token"
accessURL <- "https://api.twitter.com/oauth/access_token"
authURL <- "https://api.twitter.com/oauth/authorize"

consumerKey <- "Ls2EIqOFtJqRDsWbHpVusbPNr"
consumerSecret <- "Pzku2xDSQsSgHR1sFMV7uGivhZ7O6YoxJ1DTVzKVuNZimoI0Ha"

twitCred <- OAuthFactory$new(consumerKey = consumerKey,
                         consumerSecret = consumerSecret,
                         requestURL = reqURL,
                         accessURL = accessURL,
                         authURL = authURL)

options(RCurlOptions = list(cainfo = system.file("CurlSSL", "cacert.pem",     package =  "RCurl")))
twitCred$handshake()
registerTwitterOAuth(twitCred)
}

# Function to create a data frame from tweets
shinyServer(function(input, output,session) {

  # Function to clean tweets, Stanton 2013
  CleanTweets<-function(tweets)
  {
    # Remove redundant spaces
    tweets <- str_replace_all(tweets," "," ")
    # Get rid of URLs
    tweets <- str_replace_all(tweets, "http://t.co/[a-z,A-Z,0-9]*{8}","")
    # Take out retweet header, there is only one
    tweets <- str_replace(tweets,"RT @[a-z,A-Z]*: ","")
    # Get rid of hashtags
    tweets <- str_replace_all(tweets,"#[a-z,A-Z]*","")
    # Get rid of references to other screennames
    tweets <- str_replace_all(tweets,"@[a-z,A-Z]*","")
    return(tweets)

  }

  #Search tweets and create a data frame
  TweetFrame<-function(searchTerm, maxTweets)
  {
    twtList<-searchTwitter(searchTerm,n=maxTweets,cainfo="cacert.pem",lang="en")
    twtList1<- do.call("rbind",lapply(twtList,as.data.frame))
    twtList1$text<-iconv(twtList1$text, 'UTF-8', 'ASCII') #WILL THIS SOLVE THE UTF ENCODING PROBLEM: http://lists.hexdump.org/pipermail/twitter-users-hexdump.org/2013-May/000335.html
    return(twtList1)

  }

  # function to calculate number of tweets (input is text column, if the entire data frame was submitted,
  #could've used nrow(), as done at a different place below)

  numoftweets<-function(entity1,entity2,entity1entry,entity2entry){
    ent1numtweets<-nrow(entity1)
    ent2numtweets<-nrow(entity2)
    notweets<-c(ent1numtweets,ent2numtweets)
    names(notweets)<-c(entity1entry,entity2entry)
    notweets
  }


  # function for word cloud

  wordcloudentity<-function(entitycleantext)
  {
    tweetCorpus<-Corpus(VectorSource(CleanTweets(entitycleantext)))
    tweetTDM<-TermDocumentMatrix(tweetCorpus,control=list(removePunctuation=TRUE,
                                                      stopwords=c(stopwords('english')),
                                                      removeNumbers=TRUE,tolower=TRUE))
    tdMatrix <- as.matrix(tweetTDM) # creating a data matrix
    sortedMatrix<-sort(rowSums(tdMatrix),decreasing=TRUE) # calculate row sum of each term and sort in descending order (high freq to low)
    cloudFrame<-data.frame(word=names(sortedMatrix),freq=sortedMatrix)#extracting names from named list in prev command and binding together into a dataframe with frequencies - called cloudFrame, names in separate columns

    wcloudentity<-wordcloud(cloudFrame$word,cloudFrame$freq,max.words=100, colors=brewer.pal(8,"Dark2"),scale=c(8,1), random.order=TRUE)
    print(wcloudentity)
  }

  # Scoring sentiment expressed - Breen's algorithm
  #Jeffrey Breen: http://jeffreybreen.wordpress.com/2011/07/04/twitter-text-mining-r-slides/
  #via Gaston Sanchez's twitter mining project: https://sites.google.com/site/miningtwitter/questions/sentiment/analysis

  score.sentiment = function(sentences, pos.words, neg.words)
  {


    scores = laply(sentences, function(sentence, pos.words, neg.words) {

      # clean up sentences with R's regex-driven global substitute, gsub():
      sentence = gsub('[[:punct:]]', '', sentence)
      sentence = gsub('[[:cntrl:]]', '', sentence)
      sentence = gsub('\\d+', '', sentence)
      # and convert to lower case:
      sentence = tolower(sentence)

      # split into words. str_split is in the stringr package
      word.list = str_split(sentence, '\\s+')
      # sometimes a list() is one level of hierarchy too much
      words = unlist(word.list)

      # compare our words to the dictionaries of positive & negative terms
      pos.matches = match(words, pos.words)
      neg.matches = match(words, neg.words)

      # match() returns the position of the matched term or NA
      # we just want a TRUE/FALSE:
      pos.matches = !is.na(pos.matches)
      neg.matches = !is.na(neg.matches)

      # and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():
      score = sum(pos.matches) - sum(neg.matches)

      return(score)
    }, pos.words, neg.words)

    scores.df = data.frame(score=scores, text=sentences, size=seq(length(scores)))
    return(scores.df)
  }

  #calling the above sentiment scoring function, the text of tweets serve as inputs

  sentimentalanalysis<-function(entity1text,entity2text,entity1entry,entity2entry){

    positivewords=readLines("positive_words.txt")
    negativewords=readLines("negative_words.txt")
    entity1score = score.sentiment(CleanTweets(entity1text),positivewords,negativewords)
    entity2score = score.sentiment(CleanTweets(entity2text),positivewords,negativewords)

    entity1score$entity = entity1entry
    entity2score$entity = entity2entry
    entityscores<-rbind(entity1score,entity2score)

  }

  # Time for execution

  # Reading in values for the two entities
  entity1<-reactive({
    if(input$actb>=0 ){
      withProgress(session, min=1, max=15, expr={
        for(i in 1:15) {
          setProgress(message = 'Calculation in progress',
                  detail = 'This may take a while...',
                  value=i)

          Sys.sleep(0.1)
        }
      })}
    entity1<-TweetFrame(input$entity1, input$maxTweets)}
  )
  entity2<-reactive({
    if(input$actb>=0 ){
      withProgress(session, min=1, max=15, expr={
        for(i in 1:15) {
          setProgress(message = 'Calculation in progress',
                  detail = 'This may take a while...',
                  value=i)

          Sys.sleep(0.1)
        }
      })}
    entity2<-TweetFrame(input$entity2, input$maxTweets)}
  )


  #Creating sentiment scores
  entityscores<-reactive({
    if(input$actb>=0 ){
      withProgress(session, min=1, max=15, expr={
        for(i in 1:15) {
          setProgress(message = 'Calculation in progress',
                  detail = 'This may take a while...',
                  value=i)

          Sys.sleep(0.1)
        }
      })}
    entityscores<-sentimentalanalysis(entity1()$text,entity2()$text,input$entity1,input$entity2)})
  output$notweets<-renderPrint({
    if(input$actb>=0 ){
      withProgress(session, min=1, max=15, expr={
        for(i in 1:15) {
          setProgress(message = 'Calculation in progress',
                  detail = 'This may take a while...',
                  value=i)

          Sys.sleep(0.1)
        }
      })}
    numoftweets(entity1(),entity2(),input$entity1,input$entity2)})


  #tab 1: Not all chatter may be good. So a box plot to see the distribution of scores of sentiments

  output$sentiboxplot<-renderPlot({
    if(input$actb>=0 ){
      withProgress(session, min=1, max=15, expr={
        for(i in 1:15) {
          setProgress(message = 'Calculation in progress',
                  detail = 'This may take a while...',
                  value=i)

          Sys.sleep(0.1)
        }
      })}
    cutoff <- data.frame(yintercept=0, cutoff=factor(0))
    sentiboxplot<-ggplot(entityscores(),aes(x=size,y=score))+
      facet_grid(entity ~ .)+
      geom_point(color = "black",size = 2, alpha = 1/2)+
      geom_smooth(method = "loess",se=FALSE,col='red',size=1.5, alpha = 0.7)+
      geom_hline(aes(yintercept=yintercept, linetype=cutoff), data=cutoff)+
      xlab('Tweet number')+
      ylab('Sentiment Score')+
      theme_bw()
    print(sentiboxplot)})

  # getting a feel for how sentiments were scored by scanning 4 tweets per entity and sentiment scores - data frame entity scores shown
  output$sentiheadtable<-renderTable({tab<-head(entityscores(),4)})
  output$sentitailtable<-renderTable({tab<-tail(entityscores(),4)})

  #tab 2 - Word Clouds to highlight terms used in tweets associated with the two entities
  output$entity1wc<-renderText({

    input$entity1})
  output$entity1wcplot<-renderPlot({
    if(input$actb>=0 ){
      withProgress(session, min=1, max=15, expr={
        for(i in 1:15) {
          setProgress(message = 'Calculation in progress',
                  detail = 'This may take a while...',
                  value=i)

          Sys.sleep(0.1)
        }
      })}
    wordcloudentity(entity1()$text)})

  output$entity2wc<-renderText({input$entity2})
  output$entity2wcplot<-renderPlot({
    if(input$actb>=0 ){
      withProgress(session, min=1, max=15, expr={
        for(i in 1:15) {
          setProgress(message = 'Calculation in progress',
                  detail = 'This may take a while...',
                  value=i)

          Sys.sleep(0.1)
        }
      })}
    wordcloudentity(entity2()$text)})


  #tab  3: Raw tweets of entity 1
  output$tableentity1 <- renderTable({tab<-entity1()[1]})

  #tab 4: Raw tweets of entity 2

  output$tableentity2<-renderTable({tab<-entity2()[1]})


})

这是ui.R的代码：

library(shiny)
library(shinyIncubator)

shinyUI(fluidPage(



  headerPanel("Twitter Analytics - Sentiment Analysis and more"),

  # Getting User Inputs

  sidebarPanel(

    wellPanel(
      textInput("entity1", "Handle 1: ","#thrilled"),
      textInput ("entity2","Handle 2: ","#frustrated"),
      HTML
      ("<div style='font-size: 10px;font-weight: bold'> Feel free to replace above text with your own twitter handle name 
          starting with '@' or hashtag starting with '#'</div>")
    )  ,
    wellPanel(
      sliderInput("maxTweets","Number of recent tweets to use for analysis:",min=10,max=300,value=30,step=1), # The max can, of course, be increased
  actionButton(inputId='actb',icon =icon("twitter"), label="Hit it!")
    )

  ),

  mainPanel(
    progressInit(),
    tabsetPanel(

      #Output from tab 4 ----So a box plot to see the distribution of scores of sentiments 
      tabPanel("Sentiment Analysis", plotOutput("sentiboxplot"), HTML
           ("<div> This plot shows the distribution of positive/negative sentiments about each entity. Note that tweets were cleaned before this analysis was performed. For each tweet, a net score of positive and negative sentiments are computed and this plot shows the distribution of scores.A higher sentiment score suggests more positive (or a less negative) discussion of that entity than the other.</div>"),
               tableOutput("sentiheadtable"),tableOutput("sentitailtable"),id="test"),

      #Output from tab 5 - Word clouds - with some html tags

      tabPanel("Word Clouds",h2(textOutput("entity1wc")),plotOutput("entity1wcplot"),h2(textOutput("entity2wc")),plotOutput("entity2wcplot")),

      #Output from tabs 6 and 7, the raw tweets
      tabPanel("Entity 1 Raw tweets",tableOutput("tableentity1")),
      tabPanel("Entity 2 Raw tweets",tableOutput("tableentity2"))
    )
  )

))

在Rstudio上运行代码后，我收到以下错误：

Error: non-numeric argument to binary operator

这是快照：

我在SO上遇到了很多关于上述错误的问题，但所有情况都不同。请为我提供解决此特定错误的解决方案。任何形式的帮助将不胜感激。