Question

我想用Shiny来演示我的文本挖掘分析过程

但我很困惑，闪亮不能在不同的tabPanel中使用完成的函数（或数据，数据帧，矩阵等）。

构建下一个分析过程对我来说真的很麻烦。

有什么方法可以解决这个问题吗？

显示错误消息：

这是我的测试分析文件下载链接： https://drive.google.com/uc?export=download&id=1A9BtTplgy42etAh42YwLiZJAwl_RSNF-

ui.r

library(shiny)

# Define UI for data upload app ----
navbarPage( 
  title = 'Patent Analysis System',        
  #Table1----------------------------------------------------           
  tabPanel("Upload Patent File", fluidPage(

    # App title ----
    titlePanel("Uploading Files"),

    # Sidebar layout with input and output definitions ----
    sidebarLayout(
      # Sidebar panel for inputs ----
      sidebarPanel(

        # Input: Select a Patent file ----
        fileInput("patent_file", "Choose Patent File",
                  multiple = TRUE,
                  accept = c("text/csv",
                             "text/comma-separated-values,text/plain",
                             "csv")),

        #submit button
        submitButton("Update View", icon("refresh"))

      ),

      # Main panel for displaying outputs ----
      mainPanel(
        tabsetPanel(
          tabPanel("Load Files",tableOutput("contents"),id="load_files"),

          tabPanel("Patents Preprocess",fluidPage(
            sidebarPanel(
              selectizeInput("choose_patent", "Choose Patent",
                             choices = 1:10 ,selected = 1),
              submitButton("Update", icon("refresh"))
            )
          ),
          verbatimTextOutput("patent_pre_process")),
          tabPanel("Convert martix(TF)", 
                   tableOutput("mat_tf"), id="matrix_tf")
        )
      )
    )
  )),
  tabPanel('Process2 ',  DT::dataTableOutput('ex5'))
)

server.r

library(ontologyIndex)
library(magrittr)
library(tidyr) #separate_rows
library(quanteda) #tolower
library(SnowballC) #wordstem
library(gtools)
library(tm)
library(wordcloud)
library(textreg)

server <- function(input, output, session) {
  options(shiny.maxRequestSize = 30*1024^2)
  #1. File load------------------------------------------------------------------------
  output$contents <- renderTable({
    req(input$patent_file)
    patent_df <- read.csv(input$patent_file$datapath)
    patent_df

  })

  #2. Patent preprocess----------------------------------------------------------------
  output$patent_pre_process <- renderPrint({
    req(input$patent_file)
    df_cmp <-  read.csv(input$patent_file$datapath, stringsAsFactors = F)

    #Build Corpus (Title)--------------------------------------------------------------
    df_title <- data.frame(Title=df_cmp$English.title)
    corpus_title <- Corpus(DataframeSource(df_title))

    #Pre-processing and tranforming the Corpus
    corpus_tm_title <- tm_map(corpus_title, content_transformer(tolower)) %>%
      tm_map(removeNumbers)

    #Build Corpus (Abstract)------------------------------------------------------------
    df_abstract <- data.frame(abstract=df_cmp$English.abstract)
    corpus_abstract <- Corpus(DataframeSource(df_abstract))

    #Pre-processing and tranforming the Corpus
    corpus_tm_abstract <- tm_map(corpus_abstract, content_transformer(tolower)) %>%
      tm_map(removeNumbers) %>%
      tm_map(stripWhitespace)

    #Patent result output---------------------------------------------------------------
    pat_num <- 1:length(df_cmp$Publication.numbers)
    updateSelectInput(session, "choose_patent", "Choose Patent :", choices = pat_num)
    choose_pat_num <- as.integer(input$choose_patent)

    list(Title=content(corpus_tm_title[[choose_pat_num]]),
         Abstract= content(corpus_tm_abstract[[choose_pat_num]]))

  })

  #3. Matix-----------------------------------------------------------------------------

  output$mat_tf <- renderTable({

    #Title
    title_convert <- convert.tm.to.character(corpus_tm_title)
    mat_title <- dfm(title_convert, what = "word", remove_punct = FALSE, 
                     ngrams = 1:3, concatenator = " ") %>%
      as.matrix()*3

    #Combine title/ads/claim dtm--------------------------------------------------------
    tf_mat <- cbind(mat_title, mat_abstract) 
    tf_mat
  })
}

Answer 1

您的问题是您已将所有计算放在渲染函数中。这些设计是为了将输出呈现给单个源而不是其他任何东西。你真的想把这样的所有计算都放在反应式表达式中并用它们构建链。下面我已经以我认为应该运行良好的方式重新格式化了代码（没有测试）。

library(ontologyIndex)
library(magrittr)
library(tidyr) #separate_rows
library(quanteda) #tolower
library(SnowballC) #wordstem
library(gtools)
library(tm)
library(wordcloud)
library(textreg)

server <- function(input, output, session) {
  options(shiny.maxRequestSize = 30*1024^2)
  #1. File load------------------------------------------------------------------------
  patent_df <- reactive({
    read.csv(input$patent_file$datapath, stringsAsFactors = F)
  })

  output$contents <- renderTable({
    req(input$patent_file)
    patent_df()

  })

  #2. Patent preprocess----------------------------------------------------------------
  preProcess <- reactive({
    req(input$patent_file)
    df_cmp <-  patent_df()

    #Build Corpus (Title)--------------------------------------------------------------
    df_title <- data.frame(Title=df_cmp$English.title)
    corpus_title <- Corpus(DataframeSource(df_title))

    #Pre-processing and tranforming the Corpus
    corpus_tm_title <- tm_map(corpus_title, content_transformer(tolower)) %>%
      tm_map(removeNumbers)

    corpus_tm_title
  })

  dtaCorpus <- reactive({
    #Build Corpus (Abstract)------------------------------------------------------------
    df_cmp <-  patent_df()
    corpus_tm_title <- preProcess()
    df_abstract <- data.frame(abstract=df_cmp$English.abstract)
    corpus_abstract <- Corpus(DataframeSource(df_abstract))

    #Pre-processing and tranforming the Corpus
    corpus_tm_abstract <- tm_map(corpus_abstract, content_transformer(tolower)) %>%
      tm_map(removeNumbers) %>%
      tm_map(stripWhitespace)

    #Patent result output---------------------------------------------------------------
    pat_num <- 1:length(df_cmp$Publication.numbers)
    updateSelectInput(session, "choose_patent", "Choose Patent :", choices = pat_num)
    choose_pat_num <- as.integer(input$choose_patent)

    list(Title=content(corpus_tm_title[[choose_pat_num]]),
         Abstract= content(corpus_tm_abstract[[choose_pat_num]]))

  })
  output$patent_pre_process <- renderPrint({
    dtaCorpus()
  })

  #3. Matix-----------------------------------------------------------------------------

  output$mat_tf <- renderTable({

    corpus_tm_title <- preProcess()
    #Title
    title_convert <- convert.tm.to.character(corpus_tm_title)
    mat_title <- dfm(title_convert, what = "word", remove_punct = FALSE, 
                     ngrams = 1:3, concatenator = " ") %>%
      as.matrix()*3

    #Combine title/ads/claim dtm--------------------------------------------------------
    tf_mat <- cbind(mat_title, mat_abstract) 
    tf_mat
  })
}

希望这有帮助

R Shiny：不能在不同的tabPanel中使用功能

1 个答案: