R代码在我的Windows机器上工作但不在闪亮的服务器上，请帮忙。
ui.r
library(shiny)
shinyUI(
fluidPage(
# Application title
titlePanel("Word Cloud"),
sidebarLayout(
sidebarPanel("Sidebar with a slider and selection inputs"
),
# Show Word Cloud
mainPanel(
plotOutput("plot")
)
)
)
)
**server.r**
library(shiny)
shinyServer(
function(input, output) {
output$plot <- renderPlot({
comparison.cloud(o_g_m,colors=c("orange", "blue"),max.words=50)
})
}
)
global.r
library(plyr)
library(rjson)
library(RCurl)
library(bitops)
library(qdapDictionaries)
library(qdapRegex)
library(validate)
library(qdapTools)
library(ggplot2)
library(base)
library(RDSTK)
library(readr)
library(twitteR)
library(syuzhet)
library(ggplot2)
library(NLP)
library(tm)
library(RWeka)
library(RColorBrewer)
library(wordcloud)
library(qdap)
library(stringr)
library(plyr)
#uplaoding file
pos <- readLines("PML.txt")
# All lowercase
pos<-tolower(pos)
#uplaoding file
neg <- readLines("OPPS.txt")
# All lowercase
neg<-tolower(neg)
#uplaoding file
editorials <- readLines("news.csv")
#uplaoding file
custom_stop_words <- readLines("customStopWords.txt")
new_stop_words<- c(custom_stop_words, stopwords("en"))
score.sentiment_p = function(sentences, pos.words, .progress='none')
{
# Parameters
# sentences: vector of text to score
# pos.words: vector of words of postive sentiment
#progress: passed to laply() to control of progress bar
#create simple array of scores w1th laply
scores = laply(sentences,
function(sentence, pos.words)
{
#remove punctuation using global substitute
sentence = gsub("[[ :punct:]]", "" ,sentence)
# remove control characters
sentence = gsub("[[ :cntrl:]]", "" ,sentence)
#remove digits
sentence = gsub('\\d+' , '', sentence)
#define error handling function when trying tolower
tryTolower = function(x)
{
#create miissing value
y = NA
#trycatch error
try_error = tryCatch(tolower (x) , error= function(e) e)
# if not an error
if (!inherits(try_error, "error"))
y = tolower (x)
# result
return(y)
}
#use tryTolower with sapply
sentence = sapply(sentence, tryTolower)
# split sentence into ngrams with BigramTokenizer (RWeka package)
ngram.list = BigramTokenizer(sentence)
words = unlist(ngram.list)
#sum up all positive terms
pos.matches = match(words, pos.words)
#get the position of the matched term or NA
#just want a TRUE /FALSE
pos.matches = !is.na(pos.matches)
#final score
score = sum(pos.matches)
return(score)
} , pos.words, .progress=.progress)
#data frame with scores for each sentence
scores.df = data.frame(text=sentences, score=scores)
return(scores.df)
}
score.sentiment_n = function(sentences, neg.words, .progress='none')
{
# Parameters
# sentences: vector of text to score
# neg.words: vector of worrds of negative sentiment
#progress: passed to laply() to control of progress bar
#create simple array of scores w1th laply
scores = laply(sentences,
function(sentence, neg.words)
{
#remove punctuation using global substitute
sentence = gsub("[[ :punct:]]", "" ,sentence)
# remove control characters
sentence = gsub("[[ :cntrl:]]", "" ,sentence)
#remove digits
sentence = gsub('\\d+' , '', sentence)
#define error handling function when trying tolower
tryTolower = function(x)
{
#create miissing value
y = NA
#trycatch error
try_error = tryCatch(tolower (x) , error= function(e) e)
# if not an error
if (!inherits(try_error, "error"))
y = tolower (x)
# result
return(y)
}
#use tryTolower with sapply
sentence = sapply(sentence, tryTolower)
# split sentence into ngrams with BigramTokenizer (RWeka package)
ngram.list = BigramTokenizer(sentence)
words = unlist(ngram.list)
#sum up all negative terms
neg.matches = match(words, neg.words)
#get the position of the matched term or NA
#just want a TRUE /FALSE
neg.matches = !is.na(neg.matches)
#final score
score = sum(neg.matches)
return(score)
} , neg.words, .progress=.progress)
#data frame with scores for each sentence
scores.df = data.frame(text=sentences, score=scores)
return(scores.df)
}
#-----------------------------------------------------------------------------
#Function to create n-gram
BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min=2,max=4))
#score.sentiment function is self written (getting positive score)
gov_sentiment= score.sentiment_p(editorials, pos)
gov_not.blank<-subset(gov_sentiment, score!=0)
gov_articles<-gov_not.blank
gov_senti_score <-sum(gov_articles$score)
gov_articles<-gov_articles$text
#score.sentiment function is self written (getting negative score)
opp_sentiment= score.sentiment_n(editorials, neg)
opp_not.blank<-subset(opp_sentiment, score!=0)
opp_articles<-opp_not.blank
opp_senti_score <-sum(opp_articles$score)
opp_senti_score
opp_articles<-opp_articles$text
clean.text <- function(x, lowercase=TRUE, numbers=TRUE, punctuation=TRUE, spaces=TRUE)
{
# x: character string
# lower case
if (lowercase)
x = tolower(x)
# remove numbers
if (numbers)
x = gsub("[[:digit:]]", "", x)
# remove punctuation symbols
if (punctuation)
x = gsub("[[:punct:]]", "", x)
# remove extra white spaces
if (spaces) {
x = gsub("[ \t]{2,}", " ", x)
x = gsub("^\\s+|\\s+$", "", x)
}
# return
x
}
# Remove stop words from text
gov_articles<-clean.text(gov_articles)
opp_articles<-clean.text(opp_articles)
gov_articles<-removeWords(gov_articles,new_stop_words)
opp_articles<-removeWords(opp_articles,new_stop_words)
#-------------------------------------------------------------------
# Make a volatile corpus
gov_articles_c<-VCorpus(VectorSource(gov_articles))
opp_articles_c<-VCorpus(VectorSource(opp_articles))
# clean corpus Function
clean_corpus <- function(corpus){
corpus <- tm_map(corpus, stripWhitespace)
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, removeWords, c(stopwords("en")))
return(corpus)
}
# Apply your customized function to clean corp: clean_corp
clean_corp_g <- clean_corpus(gov_articles_c)
clean_corp_o <- clean_corpus(opp_articles_c)
# Make tokenizer function
tokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 4))
# Create bigram_dtm
gov_articles_bigram_dtm <- DocumentTermMatrix(
clean_corp_g,
control = list(tokenize = tokenizer)
)
opp_articles_bigram_dtm <- DocumentTermMatrix(
clean_corp_o,
control = list(tokenize = tokenizer)
)
# Create bigram_dtm_m
gov_articles_bigram_dtm_m<-as.matrix(gov_articles_bigram_dtm)
opp_articles_bigram_dtm_m<-as.matrix(opp_articles_bigram_dtm)
# Create freq
gov_articles_freq<-colSums(gov_articles_bigram_dtm_m)
opp_articles_freq<-colSums(opp_articles_bigram_dtm_m)
#--------------------------------------------------------------------
# Sort term_frequency in descending order
gov_articles_term_freq<-sort(gov_articles_freq, decreasing=TRUE)
opp_articles_term_freq<-sort(opp_articles_freq, decreasing=TRUE)
# View the top 10 most common words
gov_articles_term_freq[1:10]
opp_articles_term_freq[1:10]
# Plot a barchart of the 10 most common words
barplot(gov_articles_term_freq[1:10], col = "red" , las = 2)
barplot(opp_articles_term_freq[1:10], col = "red" , las = 2)
#---------------------------------------------------------------------
# #Sentiments
govt<-as.vector(gov_articles)
opps<-as.vector(opp_articles)
g_scores<-get_nrc_sentiment(govt)
o_scores<-get_nrc_sentiment(opps)
g_scores
o_scores
g_polarity<-g_scores[c(9:10)]
o_polarity<-o_scores[c(9:10)]
g_sentiment<-g_scores[c(1:8)]
o_sentiment<-o_scores[c(1:8)]
g_polarity
o_polarity
g_sentiment
o_sentiment
sum_g_polarity <-colSums(g_polarity)
sum_o_polarity <-colSums(o_polarity)
sum_g_polarity
sum_o_polarity
barplot(sum_g_polarity)
barplot(sum_o_polarity)
g_polarity_m<-as.matrix(g_polarity, rownames.force = TRUE)
o_polarity_m<-as.matrix(o_polarity, rownames.force = TRUE)
barplot(g_polarity_m)
barplot(o_polarity_m)
#-------------------------------------------------------------------
###Visualize common words
# Create all_coffee (as a single document )
all_gov_articles<-paste(gov_articles, collapse=" ")
# Create all_chardonnay(as a single document )
all_opp_articles<-paste(opp_articles,collapse=" ")
# Create all_tweets
o_g_articles<-c(all_gov_articles,all_opp_articles)
# Convert to a vector source
o_g_articles<-VectorSource(o_g_articles)
# Create all_corpus
o_g_corpus<-VCorpus(o_g_articles)
# Clean the corpus
o_g_clean_corpus<-clean_corpus(o_g_corpus)
# Create all_tdm
o_g_tdm<-TermDocumentMatrix(o_g_clean_corpus)
# Create all_m
o_g_m<-as.matrix(o_g_tdm)
#--------------------------------------------------------------
# Give the columns distinct names
colnames(o_g_m) <- c("Government","Opposition")
日志
2017-07-30T17:51:16.404481+00:00 shinyapps[198754]: The following object is masked from ‘package:qdapRegex’:
2017-07-30T17:51:16.404482+00:00 shinyapps[198754]:
2017-07-30T17:51:16.404483+00:00 shinyapps[198754]: %+%
2017-07-30T17:51:16.404484+00:00 shinyapps[198754]:
2017-07-30T17:51:16.567970+00:00 shinyapps[198754]:
2017-07-30T17:51:16.567972+00:00 shinyapps[198754]: Attaching package: ‘twitteR’
2017-07-30T17:51:16.567972+00:00 shinyapps[198754]:
2017-07-30T17:51:16.568624+00:00 shinyapps[198754]: The following objects are masked from ‘package:validate’:
2017-07-30T17:51:16.568258+00:00 shinyapps[198754]: The following object is masked from ‘package:qdapTools’:
2017-07-30T17:51:16.568624+00:00 shinyapps[198754]:
2017-07-30T17:51:16.568625+00:00 shinyapps[198754]:
2017-07-30T17:51:16.568625+00:00 shinyapps[198754]: created, description
2017-07-30T17:51:16.569012+00:00 shinyapps[198754]: The following object is masked from ‘package:plyr’:
2017-07-30T17:51:16.568258+00:00 shinyapps[198754]:
2017-07-30T17:51:16.568259+00:00 shinyapps[198754]: id
2017-07-30T17:51:16.568259+00:00 shinyapps[198754]:
2017-07-30T17:51:16.569013+00:00 shinyapps[198754]:
2017-07-30T17:51:16.569013+00:00 shinyapps[198754]: id
2017-07-30T17:51:16.569013+00:00 shinyapps[198754]:
2017-07-30T17:51:16.646722+00:00 shinyapps[198754]:
2017-07-30T17:51:16.646726+00:00 shinyapps[198754]: Attaching package: ‘NLP’
2017-07-30T17:51:16.646727+00:00 shinyapps[198754]:
2017-07-30T17:51:16.646983+00:00 shinyapps[198754]: The following object is masked from ‘package:ggplot2’:
2017-07-30T17:51:16.646984+00:00 shinyapps[198754]:
2017-07-30T17:51:16.646985+00:00 shinyapps[198754]: annotate
2017-07-30T17:51:16.646985+00:00 shinyapps[198754]:
2017-07-30T17:51:17.786148+00:00 shinyapps[198754]:
2017-07-30T17:51:17.786151+00:00 shinyapps[198754]: Attaching package: ‘qdap’
2017-07-30T17:51:17.786152+00:00 shinyapps[198754]:
2017-07-30T17:51:17.786445+00:00 shinyapps[198754]: The following objects are masked from ‘package:tm’:
2017-07-30T17:51:17.786446+00:00 shinyapps[198754]:
2017-07-30T17:51:17.786447+00:00 shinyapps[198754]: as.DocumentTermMatrix, as.TermDocumentMatrix
2017-07-30T17:51:17.786447+00:00 shinyapps[198754]:
2017-07-30T17:51:17.786889+00:00 shinyapps[198754]: The following object is masked from ‘package:NLP’:
2017-07-30T17:51:17.786890+00:00 shinyapps[198754]: ngrams
2017-07-30T17:51:17.786890+00:00 shinyapps[198754]:
2017-07-30T17:51:17.786891+00:00 shinyapps[198754]:
2017-07-30T17:51:17.788601+00:00 shinyapps[198754]: The following object is masked from ‘package:base’:
2017-07-30T17:51:17.788602+00:00 shinyapps[198754]:
2017-07-30T17:51:17.788603+00:00 shinyapps[198754]: Filter
2017-07-30T17:51:17.793538+00:00 shinyapps[198754]: Attaching package: ‘stringr’
2017-07-30T17:51:17.793537+00:00 shinyapps[198754]:
2017-07-30T17:51:17.793538+00:00 shinyapps[198754]:
2017-07-30T17:51:17.793805+00:00 shinyapps[198754]: %>%
2017-07-30T17:51:17.788603+00:00 shinyapps[198754]:
2017-07-30T17:51:17.793803+00:00 shinyapps[198754]: The following object is masked from ‘package:qdap’:
2017-07-30T17:51:17.793804+00:00 shinyapps[198754]:
2017-07-30T17:51:17.793805+00:00 shinyapps[198754]:
2017-07-30T17:51:18.496556+00:00 shinyapps[198754]: Error in value[[3L]](cond) : java.lang.NullPointerException
2017-07-30T17:51:18.496580+00:00 shinyapps[198754]: Calls: local ... tryCatch -> tryCatchList -> tryCatchOne -> <Anonymous>
2017-07-30T17:51:18.496584+00:00 shinyapps[198754]: Execution halted
我尝试了很多解决方案但都徒劳无功。 R代码适用于我的Windows机器，但不适用于Shiny Server。请帮忙，谢谢。
我被困在这里。我想在shinyapps.io
上看到我的文字云（发生错误之前连接尝试未成功 / [3L]中的cancel./Error：java.lang.NullPointerException 电话：本地... tryCatch - ＆gt; tryCatchList - ＆gt; tryCatchOne - ＆gt; 执行暂停）