将数据导入 shiny-application 时创建变量，管理接收到的数据

Question

同志们！问候。请帮帮我...有一些重大的误解。

假设我这样创建 data.frame:

df<-data.frame(num = c(1:250),
           app_num =  sample(1:100, 250, replace=T),
           entrance=sample(1:4, 250, replace=T),
           gender=sample(c('m','f'), 250,replace=T),
           age= sample(1:100, 250, replace=T))

我将其保存为“*csv”格式，使用命令：

write.csv2(data_file,file = file.choose(new = T), row.names = FALSE, quote = FALSE)

O.K。现在我想创建一个闪亮的应用程序来显示和使用他的数据：

    library("shiny")
    #to work with extra string functions
    library("stringr") 
    library("data.table") 
    library("readr")

    # Define UI for application that draws a histogram
    ui <- fluidPage(
      titlePanel(h2(strong("Analysis of the composition and structure of residents"),
                 align = "center")),
      fileInput(
        inputId="fileInput",
        label="Choose file",
        multiple = FALSE,
        accept = ".csv",
        width = '100%',
        buttonLabel = "Choosing ...",
        placeholder = "No files selected yet"
      ),
      sidebarPanel(
        checkboxGroupInput(inputId="gender", label = "Choosing a gender feature:",
          choices = c("Men" = "m",
                      "Women" = "f"),
          selected= c("Men" = "m",
                      "Women" = "f")),
        sliderInput(inputId = "age", label = "Indicate the age group:",
                    min = 1, max = 100, value = c(1, 100)),
        selectInput(
          inputId = "group",
          label="Indicate the entrance",
          choices=c(1:4),
          selected = c(1:4),
          multiple = TRUE,
          selectize = TRUE,
          width = NULL,
          size = NULL
        )
      ),
      mainPanel(
        navbarPage("",
          tabPanel("Сommon data",
            textOutput(outputId = "text1"),
            ),
          tabPanel("Results table",
                   dataTableOutput(outputId = "content")
            ),
          tabPanel("Graphic data")
        )
      )
    )

    # Define server logic required to draw a histogram
    server <- function(input, output) {

      fileinfor <- reactiveValues(file=NULL,
                                 ext=NULL,
                                 datapath=NULL)
      
      output$content <- renderDataTable({
        fileinfor$file <- input$fileInput
        fileinfor$datapath<-fileinfor$file$datapath
        fileinfor.datapath <- fileinfor$file$datapath
        fileinfor$ext <- tools::file_ext(fileinfor$datapath)
        req(fileinfor$file)
        validate(need(fileinfor$ext== "csv", "Please upload a csv file"))
        fread(fileinfor$datapath,
              showProgress = FALSE,
              sep=";", quote="",header=TRUE)
      })

      output$text1 <- renderUI(renderText({ 
        paste("Check ", fileinfor$datapath)
        }))
      
    }

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

在服务器端，我有几个问题：

如何正确获取数据，以便您可以基于它创建一个变量并多次使用它。在我的代码示例中，您可以看到下面的服务器端代码块不再看到创建的变量：

输出 $ text1 <- renderUI (renderText ({ 粘贴（“检查”，文件信息$数据路径） }))
能否通过我的例子说明操纵变量的创建及其应用？不知道移动到哪里以及如何移动？

Answer 1

也许您正在寻找这个。

server <- function(input, output) {
  
  mydf <- reactive({
    req(input$fileInput)
    inData <- input$fileInput
    if (is.null(inData)){ return(NULL) }
    mydata <- read.csv(inData$datapath, header = TRUE, sep=",")
  })
  
  output$content <- renderDT(mydf())
  
  output$text1 <- renderText({
    req(input$fileInput)
    paste("Check ", input$fileInput$datapath)
  })
  
}

Answer 2

首先感谢@YBS的教导。多亏了这些小技巧，我成功解决了一半的问题。

解决方案的本质在于Shainiy如何处理变量。事实上，没有办法像编写常规代码时那样存储变量。但是，您可以编写一个反应函数，该函数将接收数据并在调用时将其发送到另一个函数框架内的变量。

需要注意的是，在教程“Mastering Shiny”

中明确提到了这种方法

因此，获得了一个版本的工作代码。如果你想尝试最终结果，那么依次进行以下步骤：

为我们的实验创建一个 CSV 文件：

df<-data.frame(num = c(1:250), app_num = 样本(1:100, 250, replace=T), 入口=样本(1:4, 250, replace=T), 性别=样本（c（'m'，'f'），250，替换=T），年龄=样本(1:100, 250, replace=T))
保存为"*csv"格式，使用命令：

write.csv2(data_file,file = file.choose(new = T), row.names = FALSE, quote = FALSE)

使用下面提到的代码创建 Shiny 应用程序：

        library("shiny")
        library("stringr")
        library("data.table")
        library("readr")
        library("DT")
        library("readr")
        library("here")
        library("ggplot2")
        library("dplyr")
        library("tidyr")

        # Define UI for application that draws a histogram
        ui <- fluidPage(
          titlePanel(h2(strong("Analysis of the composition and structure of residents"),
                     align = "center")),
          fileInput(
            inputId="fileInput",
            label="Choose file",
            multiple = FALSE,
            accept = ".csv",
            width = '100%',
            buttonLabel = "Choosing ...",
            placeholder = "No files selected yet"
          ),
          sidebarPanel(
            checkboxGroupInput(inputId="gender", label = "Choosing a gender feature:",
              choices = c("Men" = "M",
                          "Women" = "F"),
              selected= c("Men" = "M",
                          "Women" = "F")),
            sliderInput(inputId = "age", label = "Indicate the age group:",
                        min = 1, max = 100, value = c(1, 100)),
            selectInput(
              inputId = "group",
              label="Indicate the entrance",
              choices=c(1:4),
              selected = c(1:4),
              multiple = TRUE,
              selectize = TRUE,
              width = NULL,
              size = NULL
            )
          ),
          mainPanel(
            navbarPage("",
              tabPanel("РЎommon data",
                textOutput(outputId = "text1")
                ),
              tabPanel("Results table",
                dataTableOutput(outputId = "content")
                ),
              tabPanel("Graphic data",
                plotOutput(outputId = "my_plot")
              )
            )
          )
        )

        # Define server logic required to draw a histogram
        server <- function(input, output) {

          fileinfor <- reactiveValues(file=NULL,
                                      ext=NULL,
                                      datapath=NULL)


          gender = reactive({
            gender <- input$gender
            gender
            })
          age = reactive({
            cbind(input$age[1],input$age[2])
            })
          group = reactive({
            input$group
            })

          import_data <- reactive({
            req(input$fileInput)
            fileinfor$file <- input$fileInput
            if (is.null(input$fileInput)){ return(NULL) }
            fileinfor$datapath<-fileinfor$file$datapath
            fileinfor$ext <- tools::file_ext(fileinfor$datapath)
            validate(need(fileinfor$ext== "csv", "Please upload a csv file"))
            import_data <- fread(fileinfor$datapath,
                  showProgress = FALSE,
                  sep=";", quote="",header=TRUE)
          })


          output$content <- renderDT({
            GENDER = gender()
            GROUP = group()
            AGE = age()
            req(import_data())
            data_file <- import_data()
            names(data_file) <- c("ID", "App", "Entrance", "Gender", "Age")
            data_file <- mutate_at(data_file, vars(Gender), as.factor)
            data_file<- mutate(data_file, Gender = factor(Gender, labels = c("F", "M")))
            data_file <- subset(data_file,data_file$Age>=AGE[1]
                                & data_file$Age<=AGE[2]
                                & data_file$Entrance %in% GROUP
                                & data_file$Gender %in% GENDER)
                                     })

          output$text1 <- renderText({
            req(input$fileInput)
            gender <- gender()
            paste(length(gender))
          })

          output$my_plot= reactivePlot(function(){
            GENDER = gender()
            GROUP = group()
            AGE = age()
            req(import_data())
            data_file <- import_data()
            names(data_file) <- c("ID", "App", "Entrance", "Gender", "Age")
            data_file <- mutate_at(data_file, vars(Gender), as.factor)
            data_file<- mutate(data_file, Gender = factor(Gender, labels = c("F", "M")))
            data_file <- subset(data_file,data_file$Age>=AGE[1]
                                & data_file$Age<=AGE[2]
                                & data_file$Entrance %in% GROUP
                                & data_file$Gender %in% GENDER)

            df <- group_by(data_file, data_file$Entrance, data_file$Gender)
            df <- summarise(df, N = n())
            names(df) <- c("Entrance", "Gender", "Quantity")
            df <- mutate_at(df, vars(Gender), as.factor)
            print(data_file$Gender)
            #df <- mutate(df, Gender = factor(Gender, levels  = c("f", "m")))
            df <- complete(df, Gender, fill = list(M = 0, F = 0))

            baseR.sbst.rssgn <- function(x) {
              x[is.na(x)] <- 0
              x
            }

            df$Quantity <- baseR.sbst.rssgn(df$Quantity)

            ggplot(data = df, aes(x = factor(df$Gender), y = df$Quantity, fill = df$Gender)) +
              geom_bar(stat = "identity", position = position_dodge2(0.9)) +
              geom_text(data = df, aes(label = df$Quantity, y = 0), vjust = -0.5, position = position_dodge2(0.9)) +
              scale_fill_discrete(name = "Title", labels = c("F", "M")) +
              facet_wrap(~ df$Entrance, nrow = 1, strip.position = "bottom") +
              xlab("Distribution of residents by entrances, taking into account gender") +
              ylab("Number of residents") +
              theme(
                strip.placement = "outside",
                strip.background = element_blank(),
                axis.text.x = element_blank(),
                axis.ticks.x = element_blank()
              )


            #?(ZMlength ~ Month, data = dat[dat$Lake == LAKE, ],
            #        main = "", xlab = "Month", ylab = "Shell length (mm)")
          })


        }

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

有什么问题我没有解决：

我想在打开文件时立即计算“年龄”列中的最大值和最小值，并更改 sliderInput 的设置。我想对 selectInput 做同样的事情。
我想用赛尼应用程序不仅可以分析下载的数据，还可以补充CSV文件。这部分，我什么都不懂

将数据导入 shiny-application 时创建变量，管理接收到的数据

Creating variables when importing data into the shiny-application, managing the received data

r

shiny

data-science