cannot coerce type 'closure' to vector of type 'character' while building shiny web interface

Question

Am trying sentiment analysis where my shiny web interface would display pie chart with positive and negative slices. But I also want to display the percentage of both the slices. Which gives me this error: cannot coerce type 'closure' to vector of type 'character'. Please help me solve this. Note: Its a reactive web interface. Here is my full code for the server.r part.

    shinyServer(function(input, output) {
    #Search tweets and Clean the tweets
    TweetFrame<-function(twtList)
    {
    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\\Rahul\\Desktop\\Converge\\Positive-words.txt', what='character', comment.char=';')
   neg.words = scan('C:\\Users\\Rahul\\Desktop\\Converge\\Negative-words.txt', what='character', comment.char=';')

   wordDatabase<-function()
   {
   pos.words<<-c(pos.words, 'Congrats', 'prizes', 'prize', 'thanks', 'thnx', 'Grt', 'gr8', 'plz', 'trending', 'recovering', 'brainstorm', 'leader', 'power', 'powerful', 'latest')
   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)
}

#TABLE DATA 

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()

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() )})


#Pie
slices <- reactive ({ slices <- c(sum(table_final()$Positive), sum(table_final()$Negative)) 
pct <- round(slices/sum(slices)*100)
})
labels <- c("Positive", "Negative")
labels <- paste(labels, pct()) # add percents to labels 
labels <- paste(labels,"%",sep="")

library(plotrix)
output$piechart <- renderPlot({
pie3D(slices(), labels = labels, col=rainbow(length(labels)),explode=0.00, main="Sentiment Analysis") })

})