如何根据sliderInput创建输出？

深入挖掘光明的可能性，我再一次面临无法克服的困难。所以寻求帮助：）

我有一个数据集，其中包含许多

国家

，每个国家或多或少都有一组不同的

合作伙伴

国家。对于

国家

和

合作伙伴

的每一种组合，我都有一个

数量

分配给

年数

以下是一个示例：

data <- data.frame(country = c("Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde"), 
                   partner = c("France", "France", "France", "France", "France", "France", "France", "France", "Ireland", "Ireland", "Ireland", "Ireland", "Netherlands", "Netherlands", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain"),
                   year = c(1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2000, 2001, 2002, 2003, 2002, 2003, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 1997, 1998, 1999, 2001, 2002, 2003, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 1997, 1998, 1999, 2001, 2002, 2003, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 1997, 1998, 1999, 2001, 2002, 2003, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017),
                   quantity = c(9, 9, 9, 7, 14, 7, 6, 6, 4, 2, 1, 1, 1, 1, 2, 2, 2, 5, 10, 5, 4, 4, 10, 10, 10, 31, 62, 31, 23, 23, 27, 27, 27, 25, 25, 25, 17, 17, 17, 17, 17, 16, 16, 16, 16, 16, 16, 16, 11, 11, 11, 12, 12, 12, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 9, 9, 38, 38, 38, 80, 80, 80, 60, 60, 60, 60, 60, 49, 49, 49, 46, 46, 46, 46))

库（闪亮）
图书馆（GG2）
图书馆（dplyr）
数据%
按（国家、合作伙伴）分组%>%
总结（最小值=最小值（年），最大值=最大值（年））
每年
#为绘制时间序列的应用程序定义UI
ui
库（闪亮）
图书馆（GG2）
图书馆（dplyr）
数据%
按（国家、合作伙伴）分组%>%
总结（最小值=最小值（年），最大值=最大值（年））
每年
#为绘制时间序列的应用程序定义UI
哦，哇！太好了，非常感谢。这非常有效，我看到的唯一问题是，
partnerInput
列表中包含了所有这些，而不仅仅是所选的
国家/地区可能包含的。是否可以根据实际输出同时进行
选择
和
选择
？是的，使用相同的技术，为该数据帧创建一个数据帧、一个条件过滤器，并使用renderUI在服务器端创建输入。服务器端渲染对于处理依赖的内容非常好：-）谢谢！我会试试：）祝你好运，如果你需要帮助，请毫不犹豫地提出一个新问题。我经常来这里帮忙哦，哇！太好了，非常感谢。这非常有效，我看到的唯一问题是，
partnerInput
列表中包含了所有这些，而不仅仅是所选的
国家/地区可能包含的。是否可以根据实际输出同时进行
选择
和
选择
？是的，使用相同的技术，为该数据帧创建一个数据帧、一个条件过滤器，并使用renderUI在服务器端创建输入。服务器端渲染对于处理依赖的内容非常好：-）谢谢！我会试试：）祝你好运，如果你需要帮助，请毫不犹豫地提出一个新问题。我经常来这里帮忙
library(shiny)
library(ggplot2)
library(dplyr)


# Define UI for application that draws time-series
ui <- fluidPage(

  # Application title
titlePanel("Dummy shiny"),

# Create filters 
fluidRow(

  column(3,
         selectInput("countryInput", label = h4("Select country:"), 
                     as.character(unique(data$country)))),
  column(3,
         uiOutput("partnerOutput")),
  column(6,
         sliderInput("dateInput", label = h4("Select time range:"),
                     min = min(data$year), 
                     max = max(data$year), 
                     value = c(min(data$year), max(data$year), step = 1),
                     sep = "")
  )
),
plotOutput("distPlot")
)

# Define server logic required to draw the wanted time-series
server <- function(input, output) {
output$partnerOutput <- renderUI({
  selectInput("partnerInput", label = h4("Pick partner:"), choices = as.character(data[data$country==input$countryInput,"partner"]))
})

filtered <- reactive({
  data %>%
    filter(country == input$countryInput,
           partner == input$partnerInput,
           year >= input$dateInput[1],
           year <= input$dateInput[2]
    )
})

  output$distPlot <- renderPlot({
  ggplot(filtered(), aes(x = year, y = quantity)) +
    geom_point() +
    geom_smooth() +
    labs(x = "", y = "") +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0))
})
}

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

library(shiny)
library(ggplot2)
library(dplyr)


data <- data.frame(country = c("Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Angola", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde", "Cabo Verde"), 
                   partner = c("France", "France", "France", "France", "France", "France", "France", "France", "Ireland", "Ireland", "Ireland", "Ireland", "Netherlands", "Netherlands", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "France", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Portugal", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain", "Spain"),
                   year = c(1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2000, 2001, 2002, 2003, 2002, 2003, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 1997, 1998, 1999, 2001, 2002, 2003, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 1997, 1998, 1999, 2001, 2002, 2003, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 1997, 1998, 1999, 2001, 2002, 2003, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017),
                   quantity = c(9, 9, 9, 7, 14, 7, 6, 6, 4, 2, 1, 1, 1, 1, 2, 2, 2, 5, 10, 5, 4, 4, 10, 10, 10, 31, 62, 31, 23, 23, 27, 27, 27, 25, 25, 25, 17, 17, 17, 17, 17, 16, 16, 16, 16, 16, 16, 16, 11, 11, 11, 12, 12, 12, 7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 9, 9, 38, 38, 38, 80, 80, 80, 60, 60, 60, 60, 60, 49, 49, 49, 46, 46, 46, 46))

peryear = data %>%
    group_by(country, partner) %>%
    summarise(min = min(year), max = max(year))
peryear

# Define UI for application that draws time-series
ui <- fluidPage(

    # Application title
    titlePanel("Dummy shiny"),

    # Create filters 
    fluidRow(

        column(3,
               selectInput("countryInput", label = h4("Select country:"), 
                           as.character(unique(data$country)))),
        column(3,
               uiOutput("partnerOutput")),
        column(6,
               uiOutput("dynamicdates")
        )
    ),
    plotOutput("distPlot")
)

# Define server logic required to draw the wanted time-series
server <- function(input, output) {
    output$partnerOutput <- renderUI({
        print(as.character(data[data$country==input$countryInput,"partner"]))
        selectInput("partnerInput", label = h4("Pick partner:"), choices = unique(data$partner), selected = unique(data$partner)[1])
    })

    filtered <- reactive({
        data %>%
            filter(country == input$countryInput,
                   partner == input$partnerInput,
                   year >= input$dateInput[1],
                   year <= input$dateInput[2]
            )
    })

    output$dynamicdates <- renderUI({

        if(is.null(input$partnerInput)) {
            return(NULL)
        }

        filterdf <- peryear %>%
            filter(country == input$countryInput) %>%
            filter(partner == input$partnerInput)

        sliderInput("dateInput", label = h4("Select time range:"),
                    min = filterdf$min, 
                    max = filterdf$max,
                    value = c(filterdf$min, filterdf$max, step = 1),
                    sep = "")
    })

    output$distPlot <- renderPlot({
        ggplot(filtered(), aes(x = year, y = quantity)) +
            geom_point() +
            geom_smooth() +
            labs(x = "", y = "") +
            scale_x_continuous(expand = c(0, 0)) +
            scale_y_continuous(expand = c(0, 0))
    })
}

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