这是一个使用Twitter API分析推文的应用程序 有一个textInput，当前每次输入更改时，服务器都会提取数据，清理数据并更新绘图。 我只希望在用户键入搜索词然后按“ enter”（输入）后才发生这种情况。
我相信可以使用isolate（）完成此操作，但是我很茫然。 感谢您的任何帮助，谢谢！
我当前有一个tag $ script，它在最后一次按下用户键时读取。经过测试，初始图显示了当我按Enter键时显示的内容，但是，每当以后更新文本字段时，绘图都会更新，而无需按Enter键。不知道为什么，因为我将代码包含在observe（）中，但是我必须了解一些错误。下面是代码，但是如果没有您自己的Twitter API密钥，您将无法完全运行它
library(shiny)
library(ggplot2)
library(readr)
library(rsconnect) # Deploy Shiny App
library(data.table)
library(plotly)
library(tm) # Text mining
library(dplyr)
library(twitteR) # Pull from Twitter API
library(sentimentr) # Sentiment Analysis
library(tidytext)
library(Unicode)
library(RColorBrewer) # Color palletes
library(base64enc)
library(shinyWidgets)
library(shinycssloaders) # Loading animatiions
library(wordcloud) # Create wordclouds
library(shinyjs) # Calls shinyjs functions
ui <- fluidPage(
tags$script(' $(document).on("keydown", function (e) {
Shiny.onInputChange("lastkeypresscode", e.keyCode);
});'),
fluidRow(
column(12,
h2("Show me analysis on")),
textInput("search","", value = "cats")),
)
),
fluidRow(
column(6, plotOutput("wordcloud") %>% withSpinner()),
column(6, plotlyOutput("sentiment") %>% withSpinner()
)
)
)
server <- function(input, output,session) {
# Authentication keys to access TWitter's API
consumerKey <- 'k1WaBd'
consumerSecret <- 'XFQMKfh'
accessToken <- '14554'
accessSecret <- 'ZHDvhGmdh'
setup_twitter_oauth(consumerKey, consumerSecret, accessToken, accessSecret)
# Get Tweet Data
rawTweets <- reactive({rawTweets <- searchTwitter(req(input$search), n = 200) })
# Clean raw Twitter data, returns just the body of tweets w/o links and emojis
cleanTweets <- function(rawTweets){
df<- do.call("rbind",lapply(rawTweets,as.data.frame))
# Remove emojis
df$text <- sapply(df$text,function(row) iconv(row, "latin1", "ASCII", sub=""))
df[, c('isRetweet','id', 'longitude','latitude', 'replyToUID', 'replyToSID', 'replyToSN')] <- NULL
df <- df[!duplicated(df$text), ] # Remove duplicate tweets
df <- df[!duplicated(df$screenName), ] # Remove duplicate users
df$text[df$text == ""] <- NA
df$letterCount <- nchar(gsub(" ","",df$text))
df$text[df$letterCount == '0'] <- NA
df <- na.omit(df)
return (df$text)
}
# Holds list of cleaned tweets
words <- reactive({words <- cleanTweets(rawTweets())
})
createCorpus <-function(words){
# Create a corpus to store word data, from package tm
corpus <- Corpus(VectorSource(words))
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus,function(x)removeWords(x,stopwords()))
return(corpus)
}
corpus <- reactive({corpus <- createCorpus(words() )})
observe({
if(!is.null(input$lastkeypresscode)) {
if(input$lastkeypresscode == 13){
if (is.null(input$search) || input$search == "")
return()
output$wordcloud <- renderPlot({
wordcloud(corpus(), min.freq = 3, scale = c(7, 2), random.order = F, colors = brewer.pal(8,'Dark2'))
})
# Sentiment Scoring
sentiment <- reactive({sentiment <- sentiment_by(words()) })
emotion <- reactive({emotion <- emotion_by(words() )})
output$sentiment <- renderPlotly({
# Count # of tweets in each category
neutral <- sum(sentiment()$ave_sentiment == "0")
positive <- sum(sentiment()$ave_sentiment > "0")
negative <- sum(sentiment()$ave_sentiment < "0")
feeling <- c('positive', 'negative', 'neutral')
count <- c(positive,negative,neutral)
df <- data.frame(feeling,count, stringsAsFactors=FALSE)
plot_ly(df,labels = ~feeling, values = ~count, type = 'pie',
marker = list(colors = c('#c2fa87', '#ffb39c', '#d1d1c9'))
})
}
}
})
}
shinyApp(ui = ui, server = server)
答案 0 :(得分：1)
我尝试使用搜索操作按钮
ui <- fluidPage(
fluidRow(column(6,
h2("Show me analysis on")),
textInput("keySearch", NULL)),
column(
width = 1,
actionButton("search", "Search"),
tags$style(
"#search { color: #fff; background-color: #00557F; margin-top:24px; font-family: 'Candara'; }"
)
),
fluidRow(
column(6, plotOutput("wordcloud") %>% withSpinner()),
column(6, plotlyOutput("sentiment") %>% withSpinner())
)
)
server <- function(input, output, session) {
# Authentication keys to access TWitter's API
consumerKey <- 'your key'
consumerSecret <- 'your secret'
accessToken <- 'your token'
accessSecret <- 'your secret'
setup_twitter_oauth(consumerKey, consumerSecret, accessToken, accessSecret)
# Get Tweet Data
rawTweets <-
eventReactive(input$search, {
searchTwitter(req(input$keySearch), n = 200)
})
# Clean raw Twitter data, returns just the body of tweets w/o links and emojis
cleanTweets <- function(rawTweet) {
df <- do.call("rbind", lapply(rawTweet, as.data.frame))
# Remove emojis
df$text <-
sapply(df$text, function(row)
iconv(row, "latin1", "ASCII", sub = ""))
df[, c(
'isRetweet',
'id',
'longitude',
'latitude',
'replyToUID',
'replyToSID',
'replyToSN'
)] <- NULL
df <- df[!duplicated(df$text),] # Remove duplicate tweets
df <- df[!duplicated(df$screenName),] # Remove duplicate users
df$text[df$text == ""] <- NA
df$letterCount <- nchar(gsub(" ", "", df$text))
df$text[df$letterCount == '0'] <- NA
df <- na.omit(df)
return (df$text)
}
# Holds list of cleaned tweets
words <- reactive({
words <- cleanTweets(rawTweets())
})
createCorpus <- function(words) {
# Create a corpus to store word data, from package tm
corpus <- Corpus(VectorSource(words))
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, function(x)
removeWords(x, stopwords()))
return(corpus)
}
corpus <- reactive({
corpus <- createCorpus(words())
})
observeEvent(input$search, {
output$wordcloud <- renderPlot({
wordcloud(
corpus(),
min.freq = 3,
scale = c(7, 2),
random.order = F,
colors = brewer.pal(8, 'Dark2')
)
})
# Sentiment Scoring
sentiment <- reactive({
sentiment <- sentiment_by(words())
})
emotion <- reactive({
emotion <- emotion_by(words())
})
output$sentiment <- renderPlotly({
# Count # of tweets in each category
neutral <- sum(sentiment()$ave_sentiment == "0")
positive <- sum(sentiment()$ave_sentiment > "0")
negative <- sum(sentiment()$ave_sentiment < "0")
feeling <- c('positive', 'negative', 'neutral')
count <- c(positive, negative, neutral)
df <- data.frame(feeling, count, stringsAsFactors = FALSE)
plot_ly(
df,
labels = ~ feeling,
values = ~ count,
type = 'pie',
marker = list(colors = c('#c2fa87', '#ffb39c', '#d1d1c9'))
)
})
})
}
shinyApp(ui = ui, server = server)
答案 1 :(得分：0)
我尚未进行正确的测试，但是您可以尝试使用observeEvent代替如下所示的observer：
observeEvent(req(input$lastkeypresscode==13), {
if (is.null(input$search) || input$search == "")
return()
output$wordcloud <- renderPlot({
wordcloud(corpus(), min.freq = 3, scale = c(7, 2), random.order = F, colors = brewer.pal(8,'Dark2'))
})
# Sentiment Scoring
sentiment <- reactive({sentiment <- sentiment_by(words()) })
emotion <- reactive({emotion <- emotion_by(words() )})
output$sentiment <- renderPlotly({
# Count # of tweets in each category
neutral <- sum(sentiment()$ave_sentiment == "0")
positive <- sum(sentiment()$ave_sentiment > "0")
negative <- sum(sentiment()$ave_sentiment < "0")
feeling <- c('positive', 'negative', 'neutral')
count <- c(positive,negative,neutral)
df <- data.frame(feeling,count, stringsAsFactors=FALSE)
plot_ly(df,labels = ~feeling, values = ~count, type = 'pie',
marker = list(colors = c('#c2fa87', '#ffb39c', '#d1d1c9'))
})
})