R 从指定日期运行应用程序时出错。警告：错误：结果的长度必须为121728，而不是0

我正在尝试创建一个闪亮的应用程序，它使用一个数据集Shinny_clean，并创建一个ggplot线条图，显示x轴上的模型功率预测与时间的关系。这是我的数据

Timestamp Turbine_ID Grd_Prod_Pwr_Avg Amb_WindSpeed_Max Amb_WindSpeed_Min Amb_WindSpeed_Avg Amb_WindSpeed_Std
1 2017-06-02 00:00:00        T07            891.6              14.2               2.8               7.8               1.4
2 2017-06-02 00:00:00        T06           1338.1              17.2               3.8               9.3               1.5
3 2017-06-02 00:00:00        T11            677.6              16.3               3.5               7.5               1.2
4 2017-06-02 00:00:00        T01            656.1              17.5               1.2               7.2               1.7
5 2017-06-02 00:10:00        T01            759.6              16.4               2.1               7.6               1.4
6 2017-06-02 00:10:00        T06           1145.0              13.9               3.3               8.6               1.3
  Amb_WindDir_Abs_Avg Nac_Direction_Avg Min_Windspeed1 Max_Windspeed1 Avg_Windspeed1 Var_Windspeed1 Min_Windspeed2
1               106.2              93.6            7.1           12.0            9.7           0.63            6.8
2                84.3              98.4            7.1           12.0            9.7           0.63            6.8
3                90.5              99.6            7.1           12.0            9.7           0.63            6.8
4               106.8             116.2            7.1           12.0            9.7           0.63            6.8
5                98.8             116.2            7.9           12.1           10.1           0.73            7.4
6                96.2              98.4            7.9           12.1           10.1           0.73            7.4
  Max_Windspeed2 Avg_Windspeed2 Var_Windspeed2 Min_AmbientTemp Max_AmbientTemp Avg_AmbientTemp Min_Pressure Max_Pressure
1           10.6            8.6           0.40              21              22              22         1008         1009
2           10.6            8.6           0.40              21              22              22         1008         1009
3           10.6            8.6           0.40              21              22              22         1008         1009
4           10.6            8.6           0.40              21              22              22         1008         1009
5           10.5            8.9           0.47              21              22              22         1008         1009
6           10.5            8.9           0.47              21              22              22         1008         1009
  Avg_Pressure Min_Humidity Max_Humidity Avg_Humidity   results     model
1         1009           68           69           68  788.8035 WGT_RF_NN
2         1009           68           69           68 1283.3277 WGT_RF_NN
3         1009           68           69           68  712.6971 WGT_RF_NN
4         1009           68           69           68  651.6901 WGT_RF_NN
5         1009           68           69           68  734.4558 WGT_RF_NN
6         1009           68           69           68  940.1862 WGT_RF_NN

我的ui工作得很好。但当我运行应用程序时，我在中得到一个警告错误：结果的长度必须为121728，而不是0。我怀疑这与过滤有关，但我无法解决。这是我运行应用程序时看到的

这是我的密码：

library(shiny)
library(ggplot2)
library(tidyverse)
library(lubridate)

shiny_clean <- read.csv("shiny_clean.csv", stringsAsFactors = FALSE)
shiny_clean <- filter(shiny_clean, shiny_clean$Timestamp > as.Date("2017-06-01"))

ui <- fluidPage(
  titlePanel("Machine learning ensemble model to predict wind farm power output"),
    sidebarPanel(
      dateInput("dateInput", "Date", min = min(shiny_clean$Timestamp), max =max(shiny_clean$Timestamp), value = min(shiny_clean$Timestamp)),
      radioButtons("typeInput", "Turbine",
                   choices = c("T01", "T06", "T07", "T11"),
                   selected = "T01"),
      uiOutput("modelOutput")
    ),
  mainPanel(
    plotOutput("coolplot"),
    br(), br(),
    tableOutput("results")
  )
  )

server <- function(input, output) {

  output$modelOutput <- renderUI({
    selectInput("modelInput", "Model",
                sort(unique(shiny_clean$model)),
                selected = "WGT_RF_NN")
  })  

filtered <- reactive({
  if (is.null(input$modelInput)) {
    return(NULL)
  }    

  shiny_clean %>%
    filter(Timestamp == input$dateInput,
           Turbine_ID == input$typeInput,
           model == input$modelinput
    )
})

output$coolplot <- renderPlot({
  if (is.null(filtered())) {
    return()
  }
  ggplot()+
    geom_line(data = filtered, aes(x=Timestamp, y=results))
})

output$results <- renderTable({
  filtered()
})
}

shinyApp(ui = ui, server = server)

---

只是过滤部分的一些小的修正。您忘记了$modelInput中的大写字母i。需要将时间戳调整为日期。在绘图中，您需要使用筛选而不是仅使用筛选

以下是完整的代码：

library(shiny)
library(ggplot2)
library(tidyverse)
library(lubridate)

shiny_clean <- read.csv("shiny_clean.csv", stringsAsFactors = FALSE)
shiny_clean <- filter(shiny_clean, shiny_clean$Timestamp > as.Date("2017-06-01"))

ui <- fluidPage(
  titlePanel("Machine learning ensemble model to predict wind farm power output"),
  sidebarPanel(
    dateInput("dateInput", "Date", min = min(shiny_clean$Timestamp), max =max(shiny_clean$Timestamp), value = min(shiny_clean$Timestamp)),
    radioButtons("typeInput", "Turbine",
                 choices = c("T01", "T06", "T07", "T11"),
                 selected = "T01"),
    uiOutput("modelOutput")
  ),
  mainPanel(
    plotOutput("coolplot"),
    br(), br(),
    tableOutput("results")
  )
)

server <- function(input, output) {

  output$modelOutput <- renderUI({
    selectInput("modelInput", "Model",
                sort(unique(shiny_clean$model)),
                selected = "WGT_RF_NN")
  })  

  filtered <- reactive({
    if (is.null(input$modelInput)) {
      return(NULL)
    }    

    shiny_clean %>%
      filter(as.Date(Timestamp) == as.Date(input$dateInput),
             Turbine_ID == input$typeInput,
             model == input$modelInput
      )
  })

  output$coolplot <- renderPlot({
    if (is.null(filtered())) {
      return()
    }
    ggplot()+
      geom_line(data = filtered(), aes(x=Timestamp, y=results))
  })

  output$results <- renderTable({
    filtered()
  })
}

shinyApp(ui = ui, server = server)

library(shiny)
library(ggplot2)
library(tidyverse)
library(lubridate)

shiny_clean <- read.csv("shiny_clean.csv", stringsAsFactors = FALSE)
shiny_clean <- filter(shiny_clean, shiny_clean$Timestamp > as.Date("2017-06-01"))

ui <- fluidPage(
  titlePanel("Machine learning ensemble model to predict wind farm power output"),
  sidebarPanel(
    dateInput("dateInput", "Date", min = min(shiny_clean$Timestamp), max =max(shiny_clean$Timestamp), value = min(shiny_clean$Timestamp)),
    radioButtons("typeInput", "Turbine",
                 choices = c("T01", "T06", "T07", "T11"),
                 selected = "T01"),
    uiOutput("modelOutput")
  ),
  mainPanel(
    plotOutput("coolplot"),
    br(), br(),
    tableOutput("results")
  )
)

server <- function(input, output) {

  output$modelOutput <- renderUI({
    selectInput("modelInput", "Model",
                sort(unique(shiny_clean$model)),
                selected = "WGT_RF_NN")
  })  

  filtered <- reactive({
    if (is.null(input$modelInput)) {
      return(NULL)
    }    

    shiny_clean %>%
      filter(as.Date(Timestamp) == as.Date(input$dateInput),
             Turbine_ID == input$typeInput,
             model == input$modelInput
      )
  })

  output$coolplot <- renderPlot({
    if (is.null(filtered())) {
      return()
    }
    ggplot()+
      geom_line(data = filtered(), aes(x=Timestamp, y=results))
  })

  output$results <- renderTable({
    filtered()
  })
}

shinyApp(ui = ui, server = server)