子选择闪亮的ggplots的项目

时间：2019-07-30 10:23:52

标签： r ggplot2 shiny

我正在与闪亮的人一起绘制国家次区域的人口金字塔以及国家一级的摘要图。我有一个由其ISO代码（iso）和区域标识符（id）标识的国家/地区列表。 输入数据的输入在底部。 我如何才能在区域中可视化，仅向下滚动所选国家/地区的区域，而不向下滚动所有国家/地区的所有区域？有没有办法只选择那些？

预期输出将是一个页面，其中包含3个下拉菜单，其中一个包含用于国家级别图形的国家/地区选择，一个包含用于国家/地区选择的菜单，以及一个包含基于该国家/地区的区域选择的菜单。

这是我尝试过的：

ui <- fluidPage(
  fluidRow(column(2, "region")),
  fluidRow(column(width=10, "country")),

  # Application title
  titlePanel("Population Graphics"),

  # Sidebar with a slider input for number of bins 
  sidebarLayout(
    sidebarPanel(
      selectizeInput("country",
                     label = "Country",
                     choices = unique(newdf1$iso),
                     selected = "GIN"),
      selectizeInput("region",
                     label = "Country",
                     choices = unique(newdf1$iso),
                     selected = "AFG"),
      selectizeInput("region",
                     label = "Region",
                     choices = unique(newdf1$id), multiple = T, options = list(maxItems = 16, placeholder = 'Select one'), selected = "5")
    ),
    # Show a plot of the generated distribution
    mainPanel(
      uiOutput("region", height="600px"),
      uiOutput("country", width= "300px", height="300px")
    )))

server = shinyServer(function(input,output){

    mydata <- reactive({
      iso <- input$iso
      newdf1 <- subset(newdf1, newdf1$iso==iso)
      return(c("Choose One" = "", unique(as.character(newdf1$iso))))
    })
    observe({
      updateSelectInput(session,"country",choices = mydata())
    })

output$country <- renderUI({
    selectInput(inputId = "iso", "Select iso", choices = iso(), selected = "GIN")
    ggplot(data = mydata %>% group_by(iso, year, sex, age, age2, ageno) %>% mutate(national=sum(pop)), aes(x = age, y = national, fill = year)) +
      geom_bar(data = mydata %>% filter(sex == "female") %>% arrange(rev(year)),
               stat = "identity",
               position = "identity", width = 4.5) +
      geom_bar(data = mydata %>% filter(sex == "male") %>% arrange(rev(year)),
               stat = "identity",
               position = "identity",
               mapping = aes(y = -national)) +
      coord_flip() +
      ggtitle("National")+
      geom_hline(yintercept = 0) +
      theme_economist_white(horizontal = FALSE) +
      scale_fill_wsj() +
      labs(fill = "", x = "", y = "")

  })

output$region <- renderUI({
    selectInput(inputId = "id", "Select Admin",choices = var_country(), selected = "3")
     ggplot(data = file2, aes(x = age, y = pop, fill = year, group=id)) +
      geom_bar(data = file2 %>% filter(sex == "female") %>% arrange(rev(year)),
               stat = "identity",
               position = "identity", width = 4.5) +
      geom_bar(data = file2 %>% filter(sex == "male") %>% arrange(rev(year)),
               stat = "identity",
               position = "identity",
               mapping = aes(y = -pop/1000)) +
      coord_flip() +
      geom_hline(yintercept = 0) +
      theme_economist_white(horizontal = FALSE) +
      scale_fill_economist() +
      labs(fill = "", x = "", y = "")+
      facet_wrap(~id)
  })

var_country <- reactive({
    file1 <- data()
    if(is.null(data())){return()}
    as.list(unique(file1$iso))
  })

  # Creating reactive function to subset data
  country_function <- reactive({
    file1 <- data()
    country <- input$iso
    file2 <- sqldf(sprintf("select * from file1 where ISO = '%s' ", country))
    return (file2)
  })

  var_region <- reactive({
    file1 <- country_function()
    if(is.null(file1)){return()}
    as.list(unique(file1$id))
  })

  region_function <- reactive({
    file1 <- country_function()
    region <- input$id
    file2 <- sqldf(sprintf("select * from file1 where ID = '%s' ", region))
    return (file2)
  })
 })

shinyApp(ui = ui, server = server)

输入数据：

"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", "GIN", 
"GIN", "GIN", "GIN"), id = c(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3), year = c(2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 
2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 
2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 
2000, 2000, 2000, 2000, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
2010, 2010, 2010, 2010, 2010, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2000, 2000, 2000, 2000, 2000, 
2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 
2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 
2000, 2000, 2000, 2000, 2000, 2000, 2000, 2010, 2010, 2010, 2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2000, 2000, 
2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 
2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 
2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
2020), sex = c("female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "male", "male", "male", "male", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"male", "male", "male", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "male", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "male", "male", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"male", "male", "male", "male", "male", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"male", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "male", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"male", "male", "male", "male", "male", "male", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "male", "male", "male", "male", "male", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"male", "male", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "female", "female", "female", "female", "female", 
"female", "female", "male", "male", "male", "male", "male", "male", 
"male", "male", "male", "male", "male", "male", "male", "male", 
"male", "male", "male"), age = c(0, 5, 10, 15, 20, 25, 30, 35, 
40, 45, 50, 55, 60, 65, 70, 75, 80, 0, 5, 10, 15, 20, 25, 30, 
35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 0, 5, 10, 15, 20, 25, 
30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 0, 5, 10, 15, 20, 
25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 0, 5, 10, 15, 
20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 0, 5, 10, 
15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 0, 5, 
10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 0, 
5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 
0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 
80, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 
75, 80, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 
70, 75, 80, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 
65, 70, 75, 80, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 
60, 65, 70, 75, 80, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 
55, 60, 65, 70, 75, 80, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 
50, 55, 60, 65, 70, 75, 80, 0, 5, 10, 15, 20, 25, 30, 35, 40, 
45, 50, 55, 60, 65, 70, 75, 80, 0, 5, 10, 15, 20, 25, 30, 35, 
40, 45, 50, 55, 60, 65, 70, 75, 80, 0, 5, 10, 15, 20, 25, 30, 
35, 40, 45, 50, 55, 60, 65, 70, 75, 80), age2 = c("0-4", "05-Sep", 
"Oct-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-44", 
"45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", 
"80+", "0-4", "05-Sep", "Oct-14", "15-19", "20-24", "25-29", 
"30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", 
"65-69", "70-74", "75-79", "80+", "0-4", "05-Sep", "Oct-14", 
"15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", 
"50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80+", 
"0-4", "05-Sep", "Oct-14", "15-19", "20-24", "25-29", "30-34", 
"35-39", "40-44", "45-49", "50-54", "55-59", "60-64", "65-69", 
"70-74", "75-79", "80+", "0-4", "05-Sep", "Oct-14", "15-19", 
"20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54", 
"55-59", "60-64", "65-69", "70-74", "75-79", "80+", "0-4", "05-Sep", 
"Oct-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-44", 
"45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", 
"80+", "0-4", "05-Sep", "Oct-14", "15-19", "20-24", "25-29", 
"30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", 
"65-69", "70-74", "75-79", "80+", "0-4", "05-Sep", "Oct-14", 
"15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", 
"50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80+", 
"0-4", "05-Sep", "Oct-14", "15-19", "20-24", "25-29", "30-34", 
"35-39", "40-44", "45-49", "50-54", "55-59", "60-64", "65-69", 
"70-74", "75-79", "80+", "0-4", "05-Sep", "Oct-14", "15-19", 
"20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54", 
"55-59", "60-64", "65-69", "70-74", "75-79", "80+", "0-4", "05-Sep", 
"Oct-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-44", 
"45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", 
"80+", "0-4", "05-Sep", "Oct-14", "15-19", "20-24", "25-29", 
"30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", 
"65-69", "70-74", "75-79", "80+", "0-4", "05-Sep", "Oct-14", 
"15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", 
"50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80+", 
"0-4", "05-Sep", "Oct-14", "15-19", "20-24", "25-29", "30-34", 
"35-39", "40-44", "45-49", "50-54", "55-59", "60-64", "65-69", 
"70-74", "75-79", "80+", "0-4", "05-Sep", "Oct-14", "15-19", 
"20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54", 
"55-59", "60-64", "65-69", "70-74", "75-79", "80+", "0-4", "05-Sep", 
"Oct-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-44", 
"45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", 
"80+", "0-4", "05-Sep", "Oct-14", "15-19", "20-24", "25-29", 
"30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", 
"65-69", "70-74", "75-79", "80+", "0-4", "05-Sep", "Oct-14", 
"15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", 
"50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80+"), 
    ageno = c(0.5, 5.5, 10.5, 15.5, 20.5, 25.5, 30.5, 35.5, 40.5, 
    45.5, 50.5, 55.5, 60.5, 65.5, 70.5, 75.5, 80.5, 0.5, 5.5, 
    10.5, 15.5, 20.5, 25.5, 30.5, 35.5, 40.5, 45.5, 50.5, 55.5, 
    60.5, 65.5, 70.5, 75.5, 80.5, 0.5, 5.5, 10.5, 15.5, 20.5, 
    25.5, 30.5, 35.5, 40.5, 45.5, 50.5, 55.5, 60.5, 65.5, 70.5, 
    75.5, 80.5, 0.5, 5.5, 10.5, 15.5, 20.5, 25.5, 30.5, 35.5, 
    40.5, 45.5, 50.5, 55.5, 60.5, 65.5, 70.5, 75.5, 80.5, 0.5, 
    5.5, 10.5, 15.5, 20.5, 25.5, 30.5, 35.5, 40.5, 45.5, 50.5, 
    55.5, 60.5, 65.5, 70.5, 75.5, 80.5, 0.5, 5.5, 10.5, 15.5, 
    20.5, 25.5, 30.5, 35.5, 40.5, 45.5, 50.5, 55.5, 60.5, 65.5, 
    70.5, 75.5, 80.5, 0.5, 5.5, 10.5, 15.5, 20.5, 25.5, 30.5, 
    35.5, 40.5, 45.5, 50.5, 55.5, 60.5, 65.5, 70.5, 75.5, 80.5, 
    0.5, 5.5, 10.5, 15.5, 20.5, 25.5, 30.5, 35.5, 40.5, 45.5, 
    50.5, 55.5, 60.5, 65.5, 70.5, 75.5, 80.5, 0.5, 5.5, 10.5, 
    15.5, 20.5, 25.5, 30.5, 35.5, 40.5, 45.5, 50.5, 55.5, 60.5, 
    65.5, 70.5, 75.5, 80.5, 0.5, 5.5, 10.5, 15.5, 20.5, 25.5, 
    30.5, 35.5, 40.5, 45.5, 50.5, 55.5, 60.5, 65.5, 70.5, 75.5, 
    80.5, 0.5, 5.5, 10.5, 15.5, 20.5, 25.5, 30.5, 35.5, 40.5, 
    45.5, 50.5, 55.5, 60.5, 65.5, 70.5, 75.5, 80.5, 0.5, 5.5, 
    10.5, 15.5, 20.5, 25.5, 30.5, 35.5, 40.5, 45.5, 50.5, 55.5, 
    60.5, 65.5, 70.5, 75.5, 80.5, 0.5, 5.5, 10.5, 15.5, 20.5, 
    25.5, 30.5, 35.5, 40.5, 45.5, 50.5, 55.5, 60.5, 65.5, 70.5, 
    75.5, 80.5, 0.5, 5.5, 10.5, 15.5, 20.5, 25.5, 30.5, 35.5, 
    40.5, 45.5, 50.5, 55.5, 60.5, 65.5, 70.5, 75.5, 80.5, 0.5, 
    5.5, 10.5, 15.5, 20.5, 25.5, 30.5, 35.5, 40.5, 45.5, 50.5, 
    55.5, 60.5, 65.5, 70.5, 75.5, 80.5, 0.5, 5.5, 10.5, 15.5, 
    20.5, 25.5, 30.5, 35.5, 40.5, 45.5, 50.5, 55.5, 60.5, 65.5, 
    70.5, 75.5, 80.5, 0.5, 5.5, 10.5, 15.5, 20.5, 25.5, 30.5, 
    35.5, 40.5, 45.5, 50.5, 55.5, 60.5, 65.5, 70.5, 75.5, 80.5, 
    0.5, 5.5, 10.5, 15.5, 20.5, 25.5, 30.5, 35.5, 40.5, 45.5, 
    50.5, 55.5, 60.5, 65.5, 70.5, 75.5, 80.5), pop = c(48.8, 
    42.9, 40.1, 35.4, 19.6, 13.8, 12.7, 11.7, 11.6, 10.5, 12.7, 
    9.5, 8, 8.1, 7.5, 6.5, 5.6, 52.2, 40.6, 38.1, 33.6, 33.4, 
    22.1, 20, 18, 17.8, 12.1, 14.9, 10.8, 8.9, 9, 8.2, 7, 5.8, 
    61.7, 60.8, 58, 48.4, 25.8, 18.4, 16.8, 14.9, 14.5, 12.7, 
    15.2, 11.5, 9.8, 9.7, 8.5, 7.1, 5.9, 66, 57.6, 54.9, 45.9, 
    45.5, 31, 27.8, 24.3, 23.4, 14.9, 18.1, 13.4, 11.2, 11.1, 
    9.5, 7.7, 6.2, 64.5, 66.8, 74.3, 70.5, 37.4, 24.2, 21.6, 
    20, 19.4, 16.4, 19.8, 14.2, 11.4, 12, 10.7, 8.3, 6.4, 69, 
    63.2, 70.3, 66.6, 68, 42.3, 37.3, 34.1, 33.1, 19.7, 24.1, 
    16.8, 13.3, 14.1, 12.4, 9.2, 6.8, 48.5, 48.6, 42.4, 33.4, 
    20.1, 15.4, 14.1, 13.8, 12.6, 10.6, 13.6, 10.2, 9.7, 7.9, 
    8.1, 6.1, 6.3, 52.9, 47.3, 41.3, 32.6, 25.5, 19.1, 17.4, 
    17, 15.3, 11.7, 15.2, 11.1, 10.6, 8.5, 8.7, 6.3, 6.6, 61.1, 
    69.2, 61.5, 45.6, 26.5, 20.7, 18.9, 18, 15.9, 12.9, 16.4, 
    12.4, 12.5, 9.5, 9.4, 6.5, 7.1, 66.8, 67.4, 59.8, 44.4, 34.2, 
    26.4, 23.9, 22.7, 19.8, 14.3, 18.6, 13.8, 13.9, 10.3, 10.2, 
    6.7, 7.4, 63.9, 76, 78.8, 66.2, 38.5, 27.6, 24.7, 24.6, 21.6, 
    16.7, 21.6, 15.5, 15.1, 11.6, 12.1, 7.3, 8.1, 69.9, 74, 76.7, 
    64.4, 50.5, 35.7, 31.7, 31.7, 27.6, 18.9, 24.7, 17.5, 17, 
    12.9, 13.5, 7.7, 8.7, 131.2, 113, 94.2, 73.5, 74.5, 58.6, 
    52.5, 46.4, 37.9, 32.9, 23.6, 21.5, 20.3, 15, 12.2, 7, 6.7, 
    133, 110.8, 92.4, 72.1, 50.4, 40, 36, 32, 26.5, 32.7, 23.5, 
    21.4, 20.2, 15, 12.2, 6.9, 6.7, 185.9, 158, 138, 110, 93, 
    82.2, 77.6, 63.4, 58, 50.7, 35.8, 29.2, 22.9, 17, 11.8, 8.1, 
    6.7, 192.5, 160.1, 139.9, 111.5, 64.6, 57.3, 54.2, 44.6, 
    40.9, 45.8, 32.5, 26.6, 21, 15.7, 11, 7.8, 6.5, 254.1, 225.3, 
    198.7, 160, 134.4, 123, 119, 99.4, 89.1, 72.7, 48.8, 39.4, 
    35.4, 25.6, 15, 9.9, 7.7, 263.2, 228.3, 201.4, 162.2, 92.7, 
    85, 82.3, 69, 62, 65.4, 44.1, 35.7, 32.1, 23.4, 13.9, 9.4, 
    7.4)), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -306L), spec = structure(list(cols = list(
    iso = structure(list(), class = c("collector_character", 
    "collector")), id = structure(list(), class = c("collector_double", 
    "collector")), year = structure(list(), class = c("collector_double", 
    "collector")), sex = structure(list(), class = c("collector_character", 
    "collector")), age = structure(list(), class = c("collector_double", 
    "collector")), age2 = structure(list(), class = c("collector_character", 
    "collector")), ageno = structure(list(), class = c("collector_double", 
    "collector")), pop = structure(list(), class = c("collector_double", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 1), class = "col_spec"))

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