Question

我已转换为以下格式：

Date         price Industry  stock
29/10/2018   3      Airline   A
28/10/2018   4      Airline   A
27/10/2018   2      Airline   A
29/10/2018   5      Bank      B
29/10/2018   3      Food      C
28/10/2018   4      Bank      B
27/10/2018   2      Bank      B
27/10/2018   6      Food      C

我还输入了开始日期，结束日期，行业和库存。我已经根据用户输入使用以下代码创建了一个子集：

desc_filtered <- reactive({
c<-  dailyprice_gather %>%
  group_by(stocks) %>%
  mutate(
price_at_date = price[Date == selected_date2],
new_price = price - price_at_date)
c <- subset(c, Date>=input$dateRange[1] )
c <- subset(c, Date<=input$dateRange[2] )
c <- subset(c, Industry == input$industry2)
c <- subset(c, stocks == input$equities)
 })

我想显示用户在指定时间段内选择的行业和股票的均值和标准差。行业和股票是多选下拉列表我可能需要使用rowMeans，但不确定如何将rowMean用于响应函数。

Answer 1

从软件包 dplyr 中，您可以使用函数filter()和group_by()来按股票计算价格均值（和sd）。

假设您的数据集为df：

df %>%
   filter(Date >= input$dateRange[1] & Date <= input$dateRange[2]) %>%
   filter(Industry %in% input$industry_choices) %>%
   group_by(Industry, stock) %>%
   summarise(
       price_mean = mean(price),
       price_sd   = mean(sd)
   )

在Shiny中，输入被视为字符串，有时您需要使用group_by_()。例如，如果用户可以选择变量分组依据：group_by_(input$grouping_choice)

Answer 2

这是一个根据您在问题中所描述内容的有效示例。我认为您的方向正确。关键是为数据帧的子集创建反应性对象。在我的示例中，它称为sub_dat。然后，我们可以基于mean计算sd和sub_dat并用textOutput打印。

由于您正在使用dplyr，因此我认为没有必要使用基本R子集函数。我们可以使用filter完成所有子集任务。另一件事是，我认为您不需要任何group_by操作。但是，如果这样做，可以很容易地修改我的示例以包含group_by操作。

# Load packages
library(tidyverse)
library(lubridate)
library(shiny)

# Create example data frame
dailyprice_gather <- tribble(
  ~Date,   ~price, ~Industry,  ~stock,
'29/10/2018',   3,      'Airline',   'A',
'28/10/2018',   4,      'Airline',   'A',
'27/10/2018',   2,      'Airline',   'A',
'29/10/2018',   5,      'Bank',      'B',
'29/10/2018',   3,      'Food',      'C',
'28/10/2018',   4,      'Bank',      'B',
'27/10/2018',   2,      'Bank',      'B',
'27/10/2018',   6,      'Food',      'C')

# Convert to date class
dailyprice_gather <- dailyprice_gather %>% mutate(Date = dmy(Date))

# A vector to show the choices for industry
ind_choices <- sort(unique(dailyprice_gather$Industry))

# A vector to show the choices for the stock
stock_choices <- sort(unique(dailyprice_gather$stock))

# Create the UI
ui <- fluidPage(
  # Select the date range
  dateRangeInput(inputId = "DateRange", label = "Select Date Range", 
                 start = min(dailyprice_gather$Date), 
                 end = max(dailyprice_gather$Date),
                 min = min(dailyprice_gather$Date),
                 max = max(dailyprice_gather$Date)),
  # Select the Industry
  selectInput(inputId = "Industry", label = "Select the Industry",
              choices = ind_choices, selected = ind_choices[1]),
  # Select the stock
  selectInput(inputId = "Stock", label = "Select the Stock",
              choices = stock_choices, selected = stock_choices[1]),
  # Show the mean
  h3("The Mean of Price"),
  textOutput(outputId = "MEAN"),
  # Show the standard deviation
  h3("The SD of Price"),
  textOutput(outputId = "SD")
)

# Create SERVER
server <- function(input, output) {
  # # Create a reactive object for subset data frame
  sub_dat <- reactive({
    dailyprice_gather %>%
      filter(Date >= input$DateRange[1], 
             Date <= input$DateRange[2],
             Industry %in% input$Industry,
             stock %in% input$Stock)
  })
  # Calculate the mean and sd based on sub_dat
  output$MEAN <- renderText({
    as.character(mean(sub_dat()$price))
  })
  output$SD <- renderText({
    as.character(sd(sub_dat()$price))
  })
}

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

用户指定条件下的均值和标准差

2 个答案: