блестящее приложение для твиттера не работает. Нужна помощь в отладке

Question

Я постараюсь быть очень сложным в вопросе, с которым я столкнулся.

Мое приложение должно

• Возьмите ввод от пользователя

• Получить твиты, очистить твиты, найти настроения на твитов, вернуть настроения оценки и другие ценности.

Это ссылка на набор слов pos и neg, которые вы должны хранить в своем рабочем каталоге перед анализом. https://github.com/williamgunn/SciSentiment

Это server.R

 library(twitteR) 
library(stringr) 
library(ROAuth) 
library(RCurl) 
library(ggplot2) 
library(reshape) 
library(tm) 
library(RJSONIO) 
library(wordcloud) 
library(gridExtra) 
library(plyr) 
library(bitops) 
library(NLP) 
library(shiny) 
library(shinyIncubator) 

#library(shinyIncubator) 
library(shiny) 
#the below 2 lines of code is not required as this is obsolete and is used only with twitter cred handshake 
#load("twitteR_credentials") 
#registerTwitterOAuth(twitCred) 

#use setup_twitter_oauth(consumer_key = "your key", consumer_secret = "your secret") 

# 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,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) 
    { 

    # we got a vector of sentences. plyr will handle a list 
    # or a vector as an "l" for us 
    # we want a simple array ("a") of scores back, so we use 
    # "l" + "a" + "ply" = "laply": 
    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, nomatch=NA_integer_) 
     neg.matches = match(words, neg.words, nomatch=NA_integer_) 

     # 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){ 

    # A compiled list of words expressing positive and negative sentiments ---- 
    #http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html 
    # List of words and additional information on the original source from Jeffrey Breen's github site at: 
    #https://github.com/jeffreybreen/twitter-sentiment-analysis-tutorial-201107/tree/master/data/opinion-lexicon-English 

    positivewords=readLines("positive_words.txt") 
    negativewords=readLines("negative_words.txt") 

    #Applying score.sentiment algorithm to cleaned tweets and getting data frames of tweets, net sentiment score for a tweet 
    #(number of positive sentiments minus negative sentiments) 

    entity1score = score.sentiment(CleanTweets(entity1text),positivewords,negativewords) 
    entity2score = score.sentiment(CleanTweets(entity2text),positivewords,negativewords) 

    # Adding a dummy variable useful for a ggplot 
    entity1score$entity = entity1entry 
    entity2score$entity = entity2entry 

    #combine all of this 
    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)} 
) 
    #entity 2 
    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)}) 

    #Preparing the output in a series of tabs 

    #tab 1 - number of tweets for the two entities and also plotting the probability of arrival of a new tweet 
    #within a particular time t 

    #number of tweets 
    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]}) 


}) 

Это ui.R

#install.packages("shinyIncubator") 
library(shiny) 
#install shiny incubator using dev tools 
library(shinyIncubator) 

shinyUI(fluidPage(


    headerPanel("Twitter Sentiment Analysis"), 

    # Getting User Inputs 

    sidebarPanel(

    wellPanel(
     textInput("entity1", "Handle 1: ","#apple"), 
     textInput ("entity2","Handle 2: ","#google"), 
     HTML 
     ("<div style='font-size: 10px;font-weight: bold'> Enter the tweet tags that you want '#'</div>") 
    ) , 
    wellPanel(
     sliderInput("maxTweets","Number of recent tweets to use for analysis:",min=10,max=15000,value=20,step=1), # The max can, of course, be increased 
     actionButton(inputId='actb',icon =icon("twitter"), label="Analyze!") 
    ) 

    ), 

    mainPanel(

    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")) 
    ) 
) 

)) 

Это снимок экрана моего UI (функциональность которых не функционирует, как ожидалось)

Я не смог отладить эти две ошибки.

• Ошибка в значении - частный $ min: нечисловой аргумент для двоичного оператора.
• Предупреждение при запуске (timeoutMs): is.na() применяется к не (списку или вектору) типа «среда».

Может кто-нибудь помочь мне с любыми входами или предложениями, чтобы приветствовать это блестящее приложение. Прежде чем запускать код, вы должны иметь свою учетную запись разработчика в настройке Twitter.

Answer 1

Я выяснил, что вызывало эти ошибки. Первоначально нужно было использовать shinyincubator и индикаторы прогресса, которые пришли с ним. Теперь блестящий был обновлен, и прогресс-бары теперь являются его частью.

withProgress() Функция вызывала ошибки. Изменяя его на текущий синтаксис в блестящем, ошибки исчезают.