0

我正在尝试做一个小的闪亮的 Kmeans 练习，我下载一个 csv 文件并在其上运行 kmeans（忽略任何必需的预处理步骤）---获得集群后，我想将这些集群编号附加到原始数据并输出在交互式数据表中（来自 DT 包）......但我遇到了一个错误......下面的代码......

library(shiny)

 # Loading the required packages


pacman::p_load(Amelia,broom,caret,cluster,clustertend,clValid,corrplot,dbscan,dplyr,DT,data.table,forecast,fpc,FPDclustering,fpp,GGally,ggfortify,ggraph,ggplot2,ggrepel,ggthemes,gmodels,googleVis,gridExtra,igraph,knitr,mice,missForest,NbClust,optCluster,pacman,plyr,purrr,qcc,randomForest,rCharts,reshape2,tibble,tidyr,tidyverse,TSA,tseries,vegan,VIM,zoo) # add 'caret',`IIPR`,'ggthemes','ggraph',igraph,VIM,missForest to the list when using the script in spark envir
#compareGroups

library(markdown)
library(imputeTS)

# Define UI for application 
ui <- navbarPage(

  # Application title
  titlePanel("ShinyApp "),

  # Sidebar layout with input and output definitions ----
  sidebarLayout(

    # Sidebar panel for inputs ----
    sidebarPanel(

      # Input: Select a file ----
      fileInput("dataset", "Choose CSV File",
                multiple = TRUE,
                accept = c("text/csv",
                           "text/comma-separated-values,text/plain",
                           ".csv")),
      # Include clarifying text ----
      helpText("Note: First select the dataset of csv format only for the App to give any insight!!"),
      # Horizontal line ----
      tags$hr(),

      # Input: Checkbox if file has header ----
      checkboxInput("header", "Header", TRUE),

      # Input: Select separator ----
      radioButtons("sep", "Separator",
                   choices = c(Comma = ",",
                               Semicolon = ";",
                               Tab = "\t"),
                   selected = ","),

      # Horizontal line ----
      tags$hr(),


      # Input: actionButton() to defer the rendering of output ----
      # until the user explicitly clicks the button (rather than
      # doing it immediately when inputs change). This is useful if
      # the computations required to render output are inordinately
      # time-consuming.
      actionButton("update", "Update button", class = "btn-primary"),
      tags$hr()
      ),

      mainPanel(
        tabsetPanel(
          navbarMenu("Kmeans",
                     tabPanel("Raw data with cluster membership",
                              # Output: Interactive DT table ----
                              h4("Cluster Table"),
                              DT::dataTableOutput("cluster_table")
                     )
          ),
          tabPanel("Random Forest", "This panel is intentionally left blank")
         )
   )
)

)

# Define server logic 
server <- function(input, output) {

  datasetInput <- eventReactive(input$update, {
    read.csv(input$dataset$datapath,
             header = input$header,
             sep = input$sep)
  }, ignoreNULL = FALSE)

  #Selecting only numeric variables
  MS.num<- reactive({sapply(datasetInput(), is.numeric)})
  MS.DATA.IN.NUM <- reactive({datasetInput()[ , MS.num()]})
  # imputing NAs by zeros
  df<- reactive({imputeTS::na.replace(MS.DATA.IN.NUM(), 0)})
  # Keeping a sample of 10k for modeling
  sample_data <-reactive({df()[1:10000,]})

  #### Kmeans

  opt.cluster=9
  set.seed(115)
  MS.DATA.KMEANS.Mdl <- reactive({kmeans(scale(sample_data()),opt.cluster,nstart=25)})

  # appending clusters to the raw sample data
  MS.DATA_KMEANS<-reactive({
    x<-MS.DATA.KMEANS.Mdl()$cluster
    sample_data()$cluster.kmeans <-x 
  })

  output$cluster_table <- renderDataTable({
    DT::datatable(MS.DATA_KMEANS())
  })   
}

# Run the application 
shinyApp(ui = ui, server = server)

我收到以下错误：

Error in <-: invalid (NULL) left side of assignment
Stack trace (innermost first):
    96: <reactive:MS.DATA_KMEANS> [C:\Users\ADMIN\Documents\shiny_test/app.R#124]
    85: MS.DATA_KMEANS
    84: base::rownames
    83: DT::datatable
    82: exprFunc [C:\Users\ADMIN\Documents\shiny_test/app.R#128]
    81: widgetFunc
    80: func
    79: origRenderFunc
    78: renderFunc
    77: origRenderFunc
    76: output$cluster_table
     1: runApp

不知道我做错了什么？？

4

1 回答 1

0

找到了解决方案...

将簇附加到原始样本数据

x<-reactive({
    cluster<-MS.DATA.KMEANS.Mdl()$cluster
    cluster
  })

  output$x1 <- renderPrint({
    dataset <- x()
    table(dataset)
  })

  add_to_df <- reactive({
    sample_data1<-cbind(sample_data(),x())
    sample_data1

  })

  output$cluster_table <- renderDataTable({
    DT::datatable(add_to_df())
  })

只需要在这里使用 cbind() ....

于 2017-08-20T13:32:49.310 回答