trying to get slot "className" from an object of a basic class ("function") with no slots

[frhamira19]

EnsurePackage<-function(x)
{x<-as.character(x)
if(!require(x,character.only = TRUE))
{
  install.packages(pkgs=x,repos="http://cran.r-project.org")
  require(x,character.only=TRUE)
  
}}

#Identifying packages required
PrepareTwitter<-function()
{
  EnsurePackage("twitteR")
  EnsurePackage("stringr")
  EnsurePackage("ROAuth")
  EnsurePackage("RCurl")
  EnsurePackage("ggplot2")
  EnsurePackage("reshape")
  EnsurePackage("tm")
  EnsurePackage("RJSONIO")
  EnsurePackage("wordcloud")
  EnsurePackage("gridExtra")
  EnsurePackage("plyr")
  
}

PrepareTwitter()

shinyServer(function(input,output){
    
  #TABLE
  #Search tweets and create a data frame
  #Clean the tweets
  TweetFrame<-function(twtList)
  {
    #for(i in 2:length(dates)){
    #tweets <- c(tweets, searchTwitter(searchTerm, since=dates[i-1], until=dates[i], n=maxTweets))
    #}
    
    df<-do.call("rbind",lapply(twtList,as,data.frame))
    #removes emoticons
    
      df$text <- sapply(df$text,function(row) iconv(row, "latin1", "ASCII", sub=""))
      df$text = gsub("f|ht)tp(s?)://(.*)[.][a-z]+","",df$text)
    return(df$text)
  }
  
  #Function to create a data frame from tweets
    pos.words=scan('C:/Users/User/Desktop/Sentiment UKM/pos.txt', what='character', comment.char=';')
    neg.words=scan('C:/Users/User/Desktop/Sentiment UKM/neg.txt', what='character', comment.char=';')
  
    wordDatabase<-function()
    {
      pos.words<<-c(pos.words, 'Congrats', 'prizes', 'prize', 'thanks', 'thnx', 'Grt', 'gr8', 'plz', 'trending', 'recovering', 'brainstorm', 'leader')
      neg.words<<-c(neg.words, 'Fight', 'fighting', 'wtf', 'arrest', 'no', 'not')
    }
  
  score.sentiment <- function(sentences, pos.words, neg.words, .progress='none')
  {
    require(plyr)
    require(stringr)
    list=lapply(sentences,function(sentence,pos.words,neg.words)
      {
        sentence = gsub('[[:punct:]]','',sentence)
        sentence = gsub('[[:cntrl:]]','',sentence)
        sentence = gsub('\\d+','',sentence)
        sentence = gsub('\n','',sentence)
        
        sentence = tolower(sentence)
        word.list = str.split(sentence,'\\s+')
        words = unlist(word.list)
        pos.matches = match(words,pos.words)
        neg.matches = match(words,neg.words)
        pos.matches = !is.na(pos.matches)
        neg.matches = !is.na(neg.matches)
        pp = sum(pos.matches)
        nn = sum(neg.matches)
        score = sum(pos.matches)-sum(neg.matches)
        list1 = c(score,pp,nn)
        return(list1)
    },pos.words,neg.words)
    score_new=lapply(list, `[[`, 1)
    pp1=score=lapply(list, `[[`, 2)
    nn1=score=lapply(list, `[[`, 3)
    
    scores.df = data.frame(score=score_new,text=sentences)
    positive.df=data.frame(Positive=pp1,text=sentences)
    negative.df=data.frame(Negative=nn1,text=sentences)
    
    list_df=list(scores.df,positive.df,negative.df)
    return(list_df)
  }
  
  library(reshape)
  sentimentAnalyser<-function(result)
  {
    #Creating a copy of result data frame
    test1=result[[1]]
    test2=result[[2]]
    test3=result[[3]]
    
    #Creating three different data frames for Score, Positive and Negative
    #Removing text column from data frame
    test1$text=NULL
    test2$text=NULL
    test3$text=NULL
    #Storing the first row(Containing the sentiment scores) in variable q
    q1=test1[1,]
    q2=test2[1,]
    q3=test3[1,]
    qq1=melt(q1, ,var='Score')
    qq2=melt(q2, ,var='Positive')
    qq3=melt(q3, ,var='Negative') 
    qq1['Score'] = NULL
    qq2['Positive'] = NULL
    qq3['Negative'] = NULL
    #Creating data frame
    table1 = data.frame(Text=result[[1]]$text, Score=qq1)
    table2 = data.frame(Text=result[[2]]$text, Score=qq2)
    table3 = data.frame(Text=result[[3]]$text, Score=qq3)
    
    #Merging three data frames into one
    table_final=data.frame(Text=table1$Text, Positive=table2$value, Negative=table3$value, Score=table1$value)
    return(table_final)
  }
  
  percentage<-function(table_final)
  {
    #Positive Percentage
    
    #Renaming
    posSc=table_final$Positive
    negSc=table_final$Negative
    
    #Adding column
    table_final$PosPercent = posSc/ (posSc+negSc)
    
    #Replacing Nan with zero
    pp = table_final$PosPercent
    pp[is.nan(pp)] <- 0
    table_final$PosPercent = pp*100
    
    #Negative Percentage
    
    #Adding column
    table_final$NegPercent = negSc/ (posSc+negSc)
    
    #Replacing Nan with zero
    nn = table_final$NegPercent
    nn[is.nan(nn)] <- 0
    table_final$NegPercent = nn*100
    
    return(table_final)
  }
  
  wordDatabase()
  
  #dates <- reactive({ as.Date(as.Date(Sys.Date()-input$noOfDays):as.Date(Sys.Date()), origin="1970-01-01") }) #SUGGESTION: Modify these dates of upto 7 days before running this program 
  #twtList<-reactive({twtList<-searchTwitter(input$searchTerm, n=input$maxTweets, since=dates()[1], until=dates()[2], lang="en") })
  
  #for (i in 3:length(dates())) {
  #	twtList()<- c(twtList(), searchTwitter(input$searchTerm, n=input$maxTweets, since=dates()[i-1], until=dates()[i], lang="en"))
  #		}
  
  twtList<-reactive({twtList<-searchTwitter(input$searchTerm, n=input$maxTweets, lang="en") })
  tweets<-reactive({tweets<-TweetFrame(twtList() )})
  
  result<-reactive({result<-score.sentiment(tweets(), pos.words, neg.words, .progress='none')})
  
  table_final<-reactive({table_final<-sentimentAnalyser(  result() )})
  table_final_percentage<-reactive({table_final_percentage<-percentage(  table_final() )})
  
  output$tabledata<-renderTable(table_final_percentage())
  
  
  #HISTOGRAM
  
  output$histPos<- renderPlot({hist(table_final()$Positive, col=rainbow(10), main = "Histogram of Positive Sentiment", xlab = "Positive Score") })
  output$histNeg<- renderPlot({hist(table_final()$Negative, col=rainbow(10), main = "Histogram of Negative Sentiment", xlab = "Negative Score") })
  output$histScore<- renderPlot({hist(table_final()$Score, col=rainbow(10), main = "Histogram of Score", xlab = "Overall Score")})
  
  #PIE CHART
  
  slices <- reactive({c(sum(table_final()$Positive), sum(table_final()$Negative)) })
  labels <- c("Positive", "Negative")
  library(plotrix)
  #pie(slices(), labels = labels, col=rainbow(length(labels)), main="Sentiment Analysis")
  output$piechart<-renderPlot({pie3D(slices(), labels = labels, col=rainbow(length(labels)),explode=0.00, main="Sentiment Analysis") })
  }
)

[EconomiCurtis]

Could you format this into a reproducible example? IE something folks can easily get up and running to replicate your issue? Currently, this is only part of a shiny app.

IF you aren't familiar with best practices for shiny reprexes, check out

Shiny debugging and reprex guide

Shiny issues can be challenging to resolve relative to other problems with your code or statistical methods. Shiny apps are often large, complex projects with interacting files. When seeking help from others it is considered polite to: First, do your best to work through RStudio's debugging tools to diagnose your issue on your own. Often those shiny logs and tracebacks are useful to others trying to help out. Second, strive to minimize the effort required to replicate your issue. You can do this with a reproducible example ("reprex"). Shiny Debugging Errors in Shiny code can be difficult to track down. If you don't know where your problem is coming from, you can track it down with some o…

This will make it easier for folks to repliate your issue and offer suggestions to solve it.