在 Shiny 中按日期范围计算平均值和中位数

Question

希望仅为数据表而不是传单数据计算按选定日期范围分组的数值变量的平均值和中位数。传单地图有效（只需缩小以查看假的长/纬度图，但现在不用担心）。

我创建了第二个数据框df10 用于数据表的中位数/平均值总和。

到目前为止，我尝试更改输入函数来为均值创建单独的变量，但发现它很麻烦，而且对于我的需要来说不是必需的。

尝试在此处使用colMeans(dataset()[,which(sapply(dataset(), class) != "Date")]) Shiny calculate the mean of columns in dataframe

错误是"invalid 'x' type in 'x && y"。它与colmeans有关

### Generate a dataset ###
start_date <- as.Date('2018-01-01')  
end_date <- as.Date('2019-05-10')   
set.seed(1984)
date1 <- as.Date(sample( as.numeric(start_date): as.numeric(end_date), 988, 
                         replace = T), origin = '1970-01-01')
group <- rep(letters[1:26], each = 38)
x1 <- runif(n = 988, min = 3.26, max = 10)
x2 <- runif(n = 988, min = 3.26, max = 10)
x3 <- runif(n = 988, min = 3.26, max = 10)
x4 <- runif(n = 988, min = 3.26, max = 10)
x5 <- runif(n = 988, min = 3.26, max = 10)
latitude <- runif(988,40.75042,50.75042)
longitude <- runif(988,-73.98928,-63.98928)

dataframe <- cbind(data.frame(date1,group,x1,x2,x3,x4,x5,latitude,longitude))

df10 <- cbind(data.frame(date1,group,x1,x2,x3,x4,x5))
library(lubridate)
dataframe$date <- ymd(dataframe$date1)
df10$date <- ymd(df10$date1)

library(shiny)
library(leaflet)
library(DT)
dataframe$defectrateLvl <- cut(dataframe$x1, 
                               c(3.26,6,100), include.lowest = T,
                               labels = c('3.26-6x','6x+')) 
beatCol <- colorFactor(palette = c('yellow', 'red'), dataframe$defectrateLvl)


ui <- fluidPage(
  dateInput(inputId = "date", label="Select a date", value = "2019-03-01", min = "2018-01-01", max = "2019-05-10",
            format = "yyyy-mm-dd", startview = "month",
            language = "en", width = NULL),
  leafletOutput("map"),
  fluidRow(
    dateRangeInput("daterange","Date range:",start=Sys.Date()-10, end=Sys.Date() -1),
    DT::dataTableOutput("tbl")
  )
)

server <- shinyServer(function (input, output,session) {
  dailyData <- reactive(dataframe[dataframe$date == format(input$date, '%Y/%m/%d'), ] )
  output$map <- renderLeaflet({
    dataframe <- dailyData()  # Added this in attempt to integrate
    dataframe %>% leaflet() %>% 
      setView(lng = -73.98928, lat = 40.75042, zoom = 10) %>%
      addProviderTiles("CartoDB.Positron", options = providerTileOptions(noWrap = TRUE)) %>%
      addCircleMarkers(
        lng=~dataframe$longitude, # Longitude coordinates
        lat=~dataframe$latitude, # Latitude coordinates
        #radius=~defectrateLvl, # Total count
        popup =~ dataframe$group,
        color = ~beatCol(dataframe$defectrateLvl),
        fillOpacity=0.5 # Circle Fill Opacity
      )
  })  
  output$tbl<-DT::renderDataTable({
    dataset <- reactive({df10 })
    dataset() %>% group_by(group) %>% 
      filter(date > input$daterange[1],
             date < input$daterange[2])
    #sapply(Filter(is.numeric, df6), mean)
    colMeans(dataset()[,which(sapply(dataset(), class) !="date","date1","group")])
  })

})


shinyApp(ui, server)

我希望将数值变量按均值进行汇总，如果可能的话按中位数进行汇总，但目前这不太重要。任何帮助将不胜感激。

Answer 1

错误是由最后一个函数引起的。

colMeans(df[,which(sapply(df, class) !="date","date1","group")])

此代码会将函数应用于所有非 xy 类的列。 "date" 或 "group" 是列名。

ColMeans 也会产生一个数字向量，这会导致错误，因为DT 只能显示一个矩阵或一个data.frame。我为您提供了一个创建数据框的代码。但总的来说，我会考虑使用dplyr 来创建您的结果。这要容易得多。

这是一个可行的解决方案，但是您必须更改日期输入，因为预定义的选择会创建一个包含 0 行的 data.frame。

library(lubridate)
library(shiny)
library(leaflet)
library(DT)
library(dplyr)

### Generate a dataset ###
start_date <- as.Date('2018-01-01')  
end_date <- as.Date('2019-05-10')   
set.seed(1984)
date1 <- as.Date(sample( as.numeric(start_date): as.numeric(end_date), 988, 
                         replace = T), origin = '1970-01-01')
group <- rep(letters[1:26], each = 38)
x1 <- runif(n = 988, min = 3.26, max = 10)
x2 <- runif(n = 988, min = 3.26, max = 10)
x3 <- runif(n = 988, min = 3.26, max = 10)
x4 <- runif(n = 988, min = 3.26, max = 10)
x5 <- runif(n = 988, min = 3.26, max = 10)
latitude <- runif(988,40.75042,50.75042)
longitude <- runif(988,-73.98928,-63.98928)

dataframe <- cbind(data.frame(date1,group,x1,x2,x3,x4,x5,latitude,longitude))

df10 <- cbind(data.frame(date1,group,x1,x2,x3,x4,x5))
dataframe$date <- ymd(dataframe$date1)
df10$date <- ymd(df10$date1)


dataframe$defectrateLvl <- cut(dataframe$x1, 
                               c(3.26,6,100), include.lowest = T,
                               labels = c('3.26-6x','6x+')) 
beatCol <- colorFactor(palette = c('yellow', 'red'), dataframe$defectrateLvl)


ui <- fluidPage(
    dateInput(inputId = "date", label="Select a date", value = "2019-03-01", min = "2018-01-01", max = "2019-05-10",
              format = "yyyy-mm-dd", startview = "month",
              language = "en", width = NULL),
    leafletOutput("map"),
    fluidRow(
        dateRangeInput("daterange","Date range:",start=Sys.Date()-10, end=Sys.Date() -1),
        DT::dataTableOutput("tbl")
    )
)

server <- shinyServer(function (input, output,session) {
    dailyData <- reactive(dataframe[dataframe$date == format(input$date, '%Y/%m/%d'), ] )
    output$map <- renderLeaflet({
        dataframe <- dailyData()  # Added this in attempt to integrate
        dataframe %>% leaflet() %>% 
            setView(lng = -73.98928, lat = 40.75042, zoom = 10) %>%
            addProviderTiles("CartoDB.Positron", options = providerTileOptions(noWrap = TRUE)) %>%
            addCircleMarkers(
                lng=~dataframe$longitude, # Longitude coordinates
                lat=~dataframe$latitude, # Latitude coordinates
                #radius=~defectrateLvl, # Total count
                popup =~ dataframe$group,
                color = ~beatCol(dataframe$defectrateLvl),
                fillOpacity=0.5 # Circle Fill Opacity
            )
    })  

    dataset <- reactive({df10 })

    output$tbl <-DT::renderDataTable({
        df <- dataset()

        df <- df %>% 
            group_by(group) %>% 
            filter(date > input$daterange[1],
                   date < input$daterange[2])
        #sapply(Filter(is.numeric, df6), mean)
        result <- data.frame(colMeans(df[which(sapply(df, class)=="numeric")]))
        colnames(result)[1] <- "Result"
        result
        #colMeans(df[,which(sapply(df, class) !="date","date1","group")])
    })

})


shinyApp(ui, server)